GOVERNMENT SPENDING PRIORITIES: A CROSS-NATIONAL PERSPECTIVE

By

Niccole M. Pamphilis

A DISSERTATION

Submitted to

Michigan State University

in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

Political Science

2012

ABSTRACT

GOVERNMENT SPENDING PRIORITIES: A CROSS-NATIONAL PERSPECTIVE

By

Niccole M. Pamphilis

In the work to follow, I examine government spending priorities across 25 democratic

nations from 1990-2009. The goal of this research is to provide a better understanding of how

and why governments spend different amounts of money on similar types of public policies.

Specifically, I look at how expenditures are divided across a range of policy and how this

translates into interpreting government spending patterns. I further explore how commonly

found influences on government expenditures relate to spending priorities. Finally, I consider

how the number of institutional constraints present in a nation interacts with both mass and elite

preferences to decrease the responsiveness of democratic governments.

Using expenditure data on ten different policy areas, I construct a single measure of

government spending. This measure is more encompassing than prior measures that use fewer

policy areas or combine items that represent different aspects of the policy process. To do this, I

apply a unidimensional, metric, least-squares unfolding technique to the data. I find that policies

group into two distinct clusters. The results show a simple-to-interpret dimension of spending

where governments trade-off between particularized benefits that target specific groups within a

society, like the elderly, and collective goods that are intended to benefit society in more general

terms through areas such as education or economic development. The measure also captures

compromise by governments on its outputs as it expresses how governments spend scarce

resources across a range of policy domains.

After establishing how governments spend, I show why governments allocate their

resources to different policies. I argue that previous works use a combination of misspecified

models and measures of government outputs to explain government spending. The spending

priorities variable offers an improvement for examining the public policy outputs of

governments. I merge several arguments regarding spending patterns and find that the available

resources, what citizens want and need, as well as the individual institutions present in a nation

shape spending priorities. The results show how aspects from each separate theory influence

spending when analyzed in a more fully specified model.

The final section of this dissertation examines how the separate components of the

political system in a nation have a cumulative influence on government spending, expanding on

the individual effects explored in the literature to date. Institutions in a nation that increase the

number of actors involved in the decision making process, referred to as institutional constraints,

decrease the ability of governments to spend on policy areas that target particular groups, like the

unemployed. Instead, these attributes shift spending in a direction that favors society in broader

terms with spending on areas such as defense or environmental protection. The constraints in a

nation also mitigate the influences elite and mass preferences play in shaping government

spending, thereby making governments less responsive to demands. This finding suggests that

the exclusion of the institutional constraints from models may overstate the role citizens play in

shaping government outputs.

iv

To my husband, Steven, your constant encouragement and support gave me the strength to press

on to the end.

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ACKNOWLEDGEMENTS

I would sincerely like to thank Saundra K. Schneider for her invaluable assistance and guidance

that has helped me to grow as a researcher, a political scientist, and an instructor. I would like to

thank William G. Jacoby, whose comments and suggestions, over the years have helped me to

become a better researcher. I would also like to thank the other members of my dissertation

committee, Ani Sarkissian and Christopher Maxwell, for their time and assistance with my work.

The chapters comprising this dissertation also benefited greatly from feedback from fellow

graduate students at Michigan State University, including Kurt Pyle, Robert N. Lupton, Seo

Youn Choi, Petra Hendrickson, Dominique Lewis, and Daniel Thaler.

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TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................... viii LIST OF FIGURES .......................................................................................................... ix CHAPTER 1 INTRODUCTION ............................................................................................................. 1 OVERVIEW .......................................................................................................... 6 CHAPTER 2 A REVIEW OF THE LITERATURE ............................................................................... 10

EXPENDITURES AS GOVERNMENT OUTPUTS ............................................. 10 SOCIO-ECONOMIC INFLUENCES ................................................................... 17

POLITICAL PREFERENCES AS INFLUENCES ................................................ 22 INSTITUTIONS AS INFLUENCES ..................................................................... 30 INSTITUTIONAL CONSTRAINTS AND POLICY RESPONSIVENESS ............. 35 CONCLUSION .................................................................................................... 39 CHAPTER 3 GOVERNMENT SPENDING PRIORITIES .................................................................... 41 DATA SELECTION ............................................................................................. 42 UNFOLDING ...................................................................................................... 53 Details of the Unfolding Procedure .......................................................... 54 WHY UNFOLDING? ........................................................................................... 56 Data Reduction ........................................................................................ 56 Original Data ............................................................................................ 57 Single Dimension ..................................................................................... 58 No A Priori Assumptions .......................................................................... 61 Reliability ................................................................................................. 61 RESULTS OF THE UNFOLDED EXPENDITURE DATA ................................... 62 Differences between Nations’ Spending Priorities and Policy Points ...... 68 Differences between Nations’ Spending Priorities .................................. 70 Close Examination of Spending Priority Scores ................... ……………..73 CONCLUSION .................................................................................................... 75 CHAPTER 4 DATA AND HYPOTHESES ........................................................................................... 77 FACTORS INFLUENCING GOVERNMENT SPENDING ................................... 77 POLICY RESPONSIVENESS HYPOTHESES ................................................... 95

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CHAPTER 5 TRADITIONAL INFLUENCES AND SPENDING PRIORITIES ................................... 100

SPENDING PRIORITIES MODEL .................................................................... 101 Socio-Economic Factors and Spending Priorities .................................. 101

Group Preferences and Spending Priorities ........................................... 106 Institutions and Spending Priorities ........................................................ 110

Country Examples.................................................................................. 113 OLD MODELS, NEW MEASURE ..................................................................... 115 CONCLUSION .................................................................................................. 124 CHAPTER 6 INSTITUTIONAL CONSTRAINTS AND POLICY RESPONSIVENESS ...................... 125

THE ROLE OF INSTITUTIONAL CONSTRAINTS ........................................... 128 MODEL ............................................................................................................. 130

Fixed and Random Effects ..................................................................... 135 RESULTS ......................................................................................................... 136

Controls ................................................................................................. 138 Institutional Constraints.......................................................................... 140

IMPLICATIONS FOR NATIONAL SPENDING PRIORITIES ............................ 150 CONCLUSION .................................................................................................. 154

CHAPTER 7 CONCLUSION ............................................................................................................ 156 GENERAL FINDINGS ...................................................................................... 156 EXTENSIONS AND IMPLICATIONS ................................................................ 159 CONCLUSION .................................................................................................. 164 APPENDICES ............................................................................................................. 167

APPENDIX A: DISTRIBUTION OF SPENDING BY POLICY AREA ................ 168 APPENDIX B: SEPARATE SPENDING MODELS BY SET OF INFLUENCES ......................................................................................................................... 175 APPENDIX C: DIAGNOSTIC TESTS AND MODEL SELECTION ................... 178

Time Dummies ....................................................................................... 178 Lags ....................................................................................................... 178 Transformation ....................................................................................... 180 Multicollinearity ...................................................................................... 183 Residuals ............................................................................................... 183 Influential Observations ......................................................................... 184

REFERENCES ............................................................................................................ 198

viii

LIST OF TABLES

Table 3.1 Democratic Nations and Time Periods .......................................................... 44

Table 3.2 Examples of Expenditures by Policy Area ..................................................... 45

Table 3.3 Average Error in Capturing Actual Spending with Unfolding ......................... 58

Table 3.4 Policy Typology using Lowi’s Categories....................................................... 59

Table 3.5 Exploratory Factor Analysis of Policy Areas .................................................. 60

Table 4.1 Results of Factor Analysis for Interest Groups .............................................. 89

Table 4.2 Summary Statistics ........................................................................................ 98

Table 4.3 Summary of Hypotheses ............................................................................... 99

Table 5.1 Spending Priorities Model............................................................................ 102

Table 5.2 Replication of Milesi-Ferretti et al. Model using Spending Priorities ............ 117

Table 5.3 Replication of Huber and Stephens Model using Spending Priorities .......... 120

Table 6.1 Traditional Influences of Government Spending Priorities ........................... 131

Table 6.2 The Effect of Institutional Constraints on Policy Responsiveness ............... 137

Table 6.3 Government Composition and Spending Priorities in the United Kingdom .. 142

Table 6.4 Observations for the United Kingdom from 1990-2009 used in the Interaction Model ......................................................................................... 150

Table B.1 Influences of Socio-Economic Factors on Spending Priorities .................... 175

Table B.2 Influences of Group Preferences on Spending Priorities ............................ 176

Table B.3 Influences of Institutions on Spending Priorities .......................................... 177

Table C.1 Variance Inflation Factor Scores ................................................................. 186

Table C.2 Correlations Matrix for Independent Variables in Interaction Model ............ 187

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LIST OF FIGURES

Figure 3.1 Distribution for the Proportion of Spending on Health ................................. 46

Figure 3.2 Distribution for the Proportion of Spending on Social Protection ................. 47

Figure 3.3 Distribution for the Proportion of Spending on Public Order and Safety ........................................................................................................... 48

Figure 3.4 Location of Unfolded Policy Points ............................................................... 63

Figure 3.5 Distribution of Spending Priorities within Nations over Time ........................ 69

Figure 3.6 Distribution of Spending Priorities over Time ............................................... 72

Figure 3.7 Distribution of Spending Priorities across Nations ........................................ 74

Figure 6.1 Distribution of Spending Priorities by Nation over Time ............................. 127

Figure 6.2 Distribution of Spending Priorities over Time for Nations with Three or Four Institutional Constraints .............................................................................. 128

Figure 6.3 Number of Years by Nation in the Panel Data ............................................ 133

Figure 6.4 Predicted Spending Priorities for Government Composition ...................... 144

Figure 6.5 Predicted Spending Priorities for Role of Government ............................... 146

Figure 6.6 Predicted Spending Priorities for Public Opinion ........................................ 147

Figure 6.7 Predicted Spending Priorities for Interest Groups ...................................... 149

Figure 6.8 Predicted Spending Priorities for the United Kingdom with Zero and Three Institutional Constraints .............................................................................. 152

Figure 7.1: Nation Gini Coefficients against Spending Priorities ................................. 162

Figure A.1 Distribution for the Proportion of Spending on Defense ............................. 168

Figure A.2 Distribution for the Proportion of Spending on Economic Development .... 169

Figure A.3 Distribution for the Proportion of Spending on Education .......................... 170

Figure A.4 Distribution for the Proportion of Spending on Environmental Protection .. 171

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Figure A.5 Distribution for the Proportion of Spending on Government Operations .... 172

Figure A.6 Distribution for the Proportion of Spending on Community Development .. 173

Figure A.7 Distribution for the Proportion of Spending on Recreation ......................... 174

Figure C.1 Component plus Residual Plot for the Natural Log of GDP/Capita ............ 188

Figure C.2 Component plus Residual Plot for the Natural Log of Unemployment ....... 189

Figure C.3 Component plus Residual Plot for the Aged Population ............................ 191

Figure C.4 Component plus Residual Plot for Government Composition .................... 192

Figure C.5 Component plus Residual Plot for Role of Government ............................ 193

Figure C.6 Component plus Residual Plot for Public Opinion ..................................... 194

Figure C.7 Component plus Residual Plot for Interest Groups .................................... 195

Figure C.8 Scatter Plot of Residuals against Fitted Values………………… ... ……...…196

Figure C.9 Plot of Leverage and Residuals by Observation ........................................ 197

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CHAPTER 1 INTRODUCTION

All democratic societies are faced with a multitude of demands from citizens, ranging

from building a strong and growing economy, to providing emergency services, to helping those

in need. In order to address these expectations, governments spend on a variety of policies to

alleviate or prevent the cause of societal strife including spending on economic development, on

fire and police services, and on areas of social protection. While nations face similar issues, not

all governments prioritize the problems in the same manner nor do they always respond to the

same issue in the same manner.

Looking at program expenditure data from democratic nations provides initial evidence

that there is a great deal of variation in how governments address societal problems. Variation is

evident in both the proportion of total spending across policy areas and in the level of spending

within similarly ranked policy areas. Twenty-two of the twenty-five nations examined in this

analysis spend the most on social protection in terms of total spending, which includes programs

such as survivor benefits, old-age pensions, and unemployment insurance than any other

alternative policy areas (i.e., defense or education).1

Even among nations that dedicate the majority of their total l expenditures to social

protection, there is a sizable degree of variation. For example, Austria and Iceland both spent the

most on social protection relative to other policy areas in 2002, but they differ in the percentage

1 The three exceptions include South Korea, Canada, and the United States. South Korea spends

more on economic development, which focuses on aspects such as fuel and energy,

transportation, and communication, with social protection typically ranking sixth in terms of the

percentage of total expenditures. Canada spends more on government operations, which

includes administrative costs and foreign economic aid. And since 2004, health expenditures

have replaced social protection as the number one spending area in the United States at

approximately 20% of total expenditures.

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of spending devoted to social protection relative to their overall spending profiles. In 2002,

Austria dedicated approximately 40% of its total expenditures to the area of social protection,

while Iceland spent only about 20% of its total expenditures on the area of social protection.2

Furthermore, Iceland appears to have a relatively balanced spending profile with the top

three expenditures of social protection, health, and education each receiving roughly 18-20% of

total expenditures. Meanwhile in Austria, social protection received a much larger share

compared to the second highest spending area government operations, which received

approximately 14% of total expenditures. This example demonstrates that although nations may

share similar rank-order spending preferences across various policy areas, there are noticeable

differences in their resource allocations to particular policy areas.

Prior work examining the variation in government spending has resulted in a variety of

incorrect measures of government outputs. Previous studies have used measures on government

transfer payments, changes in spending on policies, spending on sets of policy areas in isolation

from one another, and on specific expenditures within policy areas (such as pension plans or

unemployment insurance). Research that focuses on an individual policy domain ignores the

possible connections that might exist between policy areas, where increasing spending on any

one policy area, like defense, reduces the resources available to spend on alternative policies,

such as education.

Studies that use composite measures of governmental activity do increase the number of

policy areas examined, but they also make implicit assumptions about what policy areas can be

grouped together. For example, Huber and Stephens (2001) place old-age pensions and health

2 For the 25 OECD nations examined in this analysis from 1990-2009, the range of spending on

social protection for nations, where it was the number one spending area, ranged from roughly

18% of expenditures to 47% with a mean value of 36% and a standard deviation of 6%.

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care expenditures in the same category. Such categorizations, however, may mask important

differences between program in terms of their intended beneficiaries or the scope of their efforts.

In order to address these issues, I create a measure of government spending priorities that

encompasses a number of policy areas without making a priori assumptions about how the policy

areas should be categorized. To produce a measure of government spending priorities, I apply an

unfolding technique, developed by Jacoby and Schneider (2001) for use on the American states,

to create a single measure of government spending priorities that captures expenditures over ten

policy areas for 25 democratic nations. The priorities variable results in any easy-to-interpret

dimension that distinguishes between policies that favor specific groups in society like the

unemployed, referred to as particularized benefits, versus policies that provide broader collective

goods across society, such as defense and economic development.

The measure of spending priorities provides an answer to the first question my

dissertation addresses: Can government activities be captured in a more parsimonious, reliable,

and encompassing manner than in previous works? The findings not only produce a single

variable that is capable of expressing expenditures across a range of policy areas in a

parsimonious and easy-to-interpret manner, it also expands the work by Jacoby and Schneider

(2001, 2009) and Schneider and Jacoby (2006) on government spending priorities in the

American states and the theoretical work on spending trade-offs (Banks and Duggan 2000, 2005;

Lizzeri and Persico 2001; Volden and Wiseman, 2007).

Using this new measure of spending priorities for democratic nations, the second

question I address is: Do factors traditionally found to shape government spending patterns still

influence an encompassing measure of government spending? The evaluation of prior work in

relation to the new dependent variable includes measures from functionalist arguments that focus

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on economic wealth, market openness, and the size of dependent populations; indicators

involving the preferences of different groups in society, such as political parties, the general

public, and organized interests; and the role of institutions in shaping the behavior of political

actors and general citizenry. These separate approaches, however, result in misspecified models

as researchers typically omit the influence of concepts presented in alternative arguments. I

combine these three sets of factors to create a better specified model that controls for a number

of variables that are argued to influence spending. Through this approach I am able to determine

the correct influence of each variable in relation to spending without having to question how

omitted variables are biasing the estimates of the coefficients in the model. Additionally, I am

better able to explain the variation in government spending priorities than any one set of factors

tested separately.

I further expand the understanding of variation in expenditure patterns by exploring how

institutions that constrain governments’ abilities to act can impede democracy. Institutions that

can constrain the ability of governments to act include presidential systems, electoral formulas

such as proportional representation and high district, bicameral legislatures, and federal systems

(Tsebelis 1995, 2002; Huber and Stephens 1993, 2000, 2001; Cox and McCubbins 2001). All of

these constraints increase the number of actors who have preferences over policy outputs in the

decision making process, making agreement on any issue more difficult.

Tsebelis (1995, 2002) argues that as the number of institutions constraining government

actions increase in a nation, the range of policy decisions over which agreement can be reached

can only get smaller. The decrease in viable policy solutions that can be agreed upon makes it

more difficult to enact policies that diverge from the status quo. Similar findings have been

found in the work of Cox and McCubbins (2001) where, as decision making power is separated

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amongst multiple actors, it become more difficult for actors in the policy making process to

agree and the status quo becomes more resolute.

The prior research regarding the effect of institutions on legislation has generally been

restricted to the ability to pass legislation and has not focused on its relationship with the

resulting outputs of government. In an attempt to push the understanding of institutional

constraints further, I argue that constraints also shape the ability of different actors to obtain their

preferred spending priorities based on the number of institutional constraints present in a nation.

This leads into the third question I address: Do constraints alter the role of preferences in shaping

spending priorities in democratic systems?

Institutions that are labeled as constraints are those that increase the number of actors

with ideal policy outcomes present in the decision making process. As the number of constraints

increase, the number of preferences in the decision making process increase, and so does the

difficulty of reaching agreement on policy. In order for any policy to be enacted as the number

of constraints increases, compromises and bartering will have to occur in order to reach

agreement. I argue that the act of bartering and compromise prevents governments from

spending more on policy areas that target particular groups within the population and may

provide little to no benefit for some actors present in the decision making process. Instead,

government with more constraints will focus spending on policy areas that target society in more

general terms, and provide benefits for all the actors with preferences for government spending.

Further, through this process, no particular group is in a position to obtain its ideal policy outputs

from government. Therefore, as the number of constraints increases, any groups’ preferences

should matter less for what governments do, in terms of matching spending priorities to different

groups’ expectations. These findings are important for understanding why governments in

6

similar situations can have drastically different spending patterns and why governments with

citizens who demand one policy output, such as more spending on unemployment benefits, may

end up doing something entirely different, such as focusing on promoting economic growth

through tax cuts and stimulus packages.

OVERVIEW

The analyses presented in this work covers 25 democratic nations that are members of

the Organization for Economic Co-operation and Development from 1990-2009. In order to

answer the three questions set out above, I use a measure of government spending priorities

based on expenditure data for ten policy areas: government operations, social protection, health,

education, economic development, community development, defense, public order and safety,

environmental protection, and recreation. Expenditure data are useful to capture what

governments do in a given year as expenditures represent the end product of how scarce

resources are allocated across a variety of policy areas (Garand 1985). For example, Obinger

and Wagscahl (2010) use social expenditures as a means of understanding the mix of social

policies in place in different nations. I use government expenditures across a range of policy

areas as a means of understanding the overall policy mix present in nations. Taken as a whole,

expenditures show the pattern of behavior by a government and how it prioritizes its actions

(Dean 2006). Additionally, money represents a government’s commitment to a policy area

(Jacoby and Schneider 2001), and as Klingemann et al. (1994) aptly state, “money is not all there

is to policy, but there is precious little without it” (41).

Chapter 2 provides a review of prior work regarding why expenditures are an appropriate

avenue for evaluating government actions and compares this approach to alternative measures

that are used. This chapter also discusses the factors that are found to influence the variation in

7

spending priorities within and across nations, including socio-economic conditions, mass and

elite preferences, and institutional arrangements. I also examine the literature on institutional

constraints and explain my argument for how and why constraints should influence the spending

priorities of democratic nations. Chapter 2 also includes a discussion of the theoretical

justifications for the measures that are used to examine potential influences on governmental

actions.

In Chapter 3, I present the dependent variable used to capture government spending

priorities. This builds on the work done on spending priorities in the American states by Jacoby

and Schneider (2001, 2009) by extending their work to government spending across a number of

democratic nations. Chapter 3 explains the unidimensional, least-squares, unfolding technique

that is used to produce the measure of government spending priorities and provides an

explanation of how to interpret the values of the variable. The chapter concludes with a

discussion of the additional findings from the unfolding and answers the first question of the

dissertation: Can government activities be captured in a more parsimonious, reliable, and

encompassing manner than in previous works?

Chapter 4 translates the expectations from the prior findings presented in Chapter 2 into

testable hypotheses that are used in the subsequent analyses sections. The two sets of hypotheses

focus on the expectations for how influential factors should affect the new dependent variable,

and how institutional constraints affect government outputs and the role of preferences in

shaping policy expenditures. Additionally, following each hypothesis, the chapter presents how

each factor is operationalized, including the sources of the data and a discussion of any

additional calculations that are applied to the data to create the variables used in the following

chapters.

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Chapter 5 provides an answer to the second question of the dissertation: Do factors

traditionally found to shape government spending patterns still influence an encompassing

measure of government spending? Chapter 5 provides empirical analyses on how socio-

economic factors, mass and elite preferences, and institutions previously found in the literature

affect the new measures of government spending priorities created in Chapter 4. Prior research

examines a number of variables but tends to focus on more limited measures of government

activities, usually expenditures on individual policy areas such as welfare, health, education, or

transportation. The new government spending priorities variable captures a range of government

expenditures across ten policy areas, providing a more parsimonious measure that can be used to

examine the potential influential factors shaping the variation in government spending

allocations.

In Chapter 6, I explore how institutional constraints might alter the spending allocations

of democratic governments. The analysis reveals that as the number of institutional constraints

increases, nations spend more on collective goods that are intended to provide benefits to all

members of society. Further, institutional constraints reduce the ability of democratic

governments to respond to the demands from both elites and masses. The interaction between the

number of institutional constraints and preferences reduces the cumulative influence of both

mass and elite expectations.

Chapter 7 concludes the work presented throughout the dissertation, provides

implications for past findings, and gives points to consider in future analyses. I discuss how the

measure of government spending priorities can be used in future empirical analyses, particularly

as an explanatory factor of policy outcomes. I explain how factors typically argued to affect

government spending patterns work when policy areas are examined simultaneously instead of in

9

isolation of one another. The findings suggest that certain variables representing influences like

globalization, may not alter government activities as prior studies suggests, highlighting the

importance of reliable, encompassing measures when attempting to understand government

spending patterns. I end with a discussion on how the omission of the interaction between

institutional constraints and preferences can actually overstate the role citizens’ preferences play

in democratic nations.

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CHAPTER 2 A REVIEW OF THE LITERATURE

What causes the variation in spending patterns across democratic nations? A number of

scholars examine the variation in policy outputs in nations around the world and over time. The

different studies approach the examination of government activities in a variety of manners,

including the types and volume of legislation enacted, expenditures on individual policy areas,

and changes in spending. Regardless of how government actions are measured, a number of

factors are continually argued to shape the differences that exist both within and across nations.

Some of the most frequently used indicators include the socio-economic climate, the distribution

of power among members of society, and the institutional arrangements that exist within

particular nations.

EXPENDITURES AS GOVERNMENT OUTPUTS

Drawing on both Easton (1953) and Salisbury (1968), public policy represents the end,

aggregate product of what a government does with its time in office. As I wish to understand

the variation in government spending and what causes the differences seen in government

activities, examining public policy as an output in relation to traits in a nation presents itself as a

starting point. Salisbury notes that public policy “is patterns of behavior, rather than separate,

discrete acts” (153). As public policy represents an array of actions in relation to one another, I

need a means of capturing the variation in actions of democratic governments in a concise

manner.

One approach that is used to examine and capture the variation in government outputs is

through the use of typologies. Typologies are used to breakdown the different policy decisions a

government makes, from the groups in society who have access to services, who pays for the

policy, and what level of government is responsible for particular policy areas (Lowi 1964, 1972;

11

Salisbury 1968; Peterson, 1995; Savas 2000; and Wilson 1989). Based on historical patterns,

Lowi (1964) originally argued that governments create three types of policies. These policy

types are categorized according to their “impact on society” in the form of distributive policies

where no group is deprived of benefit (like national defense), regulatory policies that determine

what can and cannot be done and by whom (like labor laws), and redistributive policies that

focus on the divide between the “haves” and “have-nots” (like housing vouchers). When,

however, Salisbury (1968) re-examined the different policy decisions, he concluded that there

are four types of policies based upon the fragmentation of the demand pattern and the decision

system. Even Lowi (1972) later expanded his typology to include a fourth category referred to

as constituent policies.

While typologies may be useful to present a common understanding and simplify

complex topics, individuals may understand the issues presented in the typologies differently

(Baumgartner and Jones 1993). Depending on the dimensions used to create the typology,

different researchers can arrive at contradictory conclusions. For example, contrast Lowi’s

(1964) typology that focuses on who is affected in society, to Salisbury’s typology (1968) that

looks at how fragmented demands are versus the decision making body, and finally Peterson

(1995) who emphasizes the role of spending to either promote the economy or to address

divisions between the haves and have-nots.

Beyond the conflict over what the correct dimensions are, typologies can lose some of the

more intricate details of specific policies. For instance, take the example of Austria and Iceland

presented in Chapter 1. Spending on social protection would be categorized as a redistributive

policy area using Lowi’s typology. However, the variation in the level of resources dedicated by

12

each nation to social protection, as well as the variation in spending profiles for each nation is

missed (or disguised) if Lowi’s typology is used.

Even when researchers are able to create a typology that they believe represents the

events they are attempting to classify, the ability to place real world, complex events into

simplified typologies rarely results in clean classifications. Salisbury (1968) argues that it does

not matter what categories a researcher uses as the real world is fluid. Further, the use of

typologies decreases the level of measurement for expenditures from interval level variables to

nominal variables, reducing the information available for testing. Once the spending data are

classified into categories, variations over time and within policy areas are lost. For example, the

change in spending priorities for the United States in 2004 when health spending surpassed

social protection would not be evident.

An alternative to fitting policies within the confines of a typology has been to examine

the activities of the legislature in terms of time spent handling particular policy issues and

changes to legislation (Page and Shapiro 1983; Baumgartner and Jones 1993; Kingdon 1984;

Heller and McCubbins 2001; Haggard and Noble 2001). Examining the amount of time a

government spends talking about a policy area or issue can provide insights into what factors

brought the topic to the forefront of discussion; however, attention to a policy area or issue does

not necessarily imply a change in terms of how a government responds to these issues. For

example, in the United States from 1993-94, health care received a high level of attention;

however, in the end, attempts at reform were unsuccessful at changing how the United States

handled health care.

Additionally, a number of the studies that examine media, public, and government

attention to policy areas and resulting changes in specific legislation do not present predictive

13

models to understand how government is acting now or will act in the future given a set of

circumstances (Baumgartner and Jones 1993; Kingdon 1984). Typically, models of this form are

retroactive. For example, Kingdon’s (1984) three streams involving problems, solutions, and

policy entrepreneurs and windows of opportunity can help explain why policies change at one

point in time versus another, but do not offer the ability to predict when future policies or

changes to current policies will occur and what the final policies will contain.

Government expenditures lend themselves as an avenue to capturing what governments

do in a given year. Expenditures tell us how governments allocate scarce resources across a

variety of policy areas (Garand, 1985). As such, a growing consensus has emerged in the

literature that, “expenditures across substantive areas provide accurate representations of

governmental commitments to address various problems” (Jacoby and Schneider 2009, 3).

Government activity has been measured using individual expenditures like health, welfare,

education, defense, and transportation area (Obinger and Wagschal 2010; Chang 2008; Ringquist

1999; Budge and Keman 1990; Huber and Stephens 2001; Bräuninger 2005; Garand and

Hendrick 1991; Shelton 2007; Garand 1985; Soroka and Wlezien 2005; Penner, Blidook, and

Soroka 2006) and changes in expenditures (Klingemann et al. 1994; Haggard and McCubbins

2001; Garand 1985; Baumgartner and Jones 1993; Hofferbert and Budge 1992; Breunig et al.

2009; Soroka and Wlezien 2005; Persson and Tabellini 2003; and Jones et al. 2009). The use of

a single indicator to measure policy priorities can help determine what factors affect a given

policy area in isolation of the other policy decisions. On the other hand, a single indicator is not

capable of showing the effect of factors on a system of policy priorities. By using a single

indicator, the researcher makes the assumption that policy areas are not linked together.

However looking at real governments and decisions they must make, systems produce a full

14

range of policy expenditures which co-exist, and increasing spending on one policy area deprives

alternative policy areas of monetary resources.

The use of composite measures, such as additive scales and factor analysis, is another

approach that is used to study government spending priorities (Hofferbert 1974; Erikson, Wright,

and McIver 1989; Klingman and Lammers 1984). Hofferbert (1974) uses a factor analysis

combining both a variety of policy expenditures and policy outputs resulting in two dimensions

of choices in the American states composed of a welfare-education dimension and a highways-

natural resources dimension. Klingman and Lammers (1984) use a principal components

analysis on six policy areas that mix expenditure data and non-fiscal measures involving policy

outputs over time. Though not related to expenditures, Erikson, Wright, and McIver (1989) use

an additive scale based on legislation representing policy liberalism to create a composite

measure of policy in the American states.

While the development of composite measures moves in the right direction by creating

more encompassing measures of government activity, the use of factor analysis and additive

scales to construct such measures has several limitations. Though factor analysis can reduce the

number of variables needed to capture information, it tends to produce at least two dimensions to

represent the underlying structure of policy areas. For example, in Hofferbert’s research (1974)

factor analysis was used to reduce four policy areas to only two dimensions.

At the same time, factor analysis is based around observed correlations being a product of

unobservable variables. The correlations between the observed variables are used to create the

underlying dimension(s). The variables that are seen to load on the same factor in the analysis

are then assumed to move together as change occurs, which is not an intuitive finding (Jacoby

and Schneider 2001). In the case of Hofferbert, (1974) highways and natural resources load

15

together on the same factor, implying that as expenditures increase for highways so do

government expenditures on natural resources, which is not necessarily true. Nor is it intuitively

logical that as spending on highways increases or decreases spending on natural resources will

mirror those changes in spending.

When using a factor analysis or additive scale, the researcher is responsible for selecting

the variables that will represent the underlying dimension and typically involves the combination

of data representing different parts of the policy process including inputs, outputs, and outcomes.

The range of indicators representing different stages of the policy making process makes it

difficult to determine what part of the policy process is being influenced in any analysis (Jacoby

and Schneider 2009). Not only do researchers combine a variety of measures, the variables may

come from different time periods. Using data from different time points, like combining data

from different parts of the policy process, can increase the difficulty of determining how factors

that influence policy work across time. In the case of Hofferbert (1974) and Klingman and

Lammers (1984), the analyses combine variables that represent both outputs in regards to

expenditure levels and outcomes in regards to quality of the policy areas such as high school

completion rates, infant mortality rates, and policy innovations in the form of enacted policies. If

these measures are used in empirical models it would be impossible to tell if the independent

variables are influencing the outputs of expenditures or the outcomes of the policy decisions.

The selection of variables in the composite measure can also omit categories that are of

importance in the policy process. For example, Hofferbert (1974) excludes health policy issues

and general spending because the variables do not load onto the two factors solution. However,

expenditures on health in the United States include Medicaid which is traditionally seen as part

of welfare and would therefore be expected to be a part of the welfare-education dimension.

16

Additionally, Klingman and Lammers (1984) omit social services, and Erikson, Wright and

McIver (1989) omit policy areas that are not believed to have a partisan interest, such as

highways.

If a democracy is a system which translates preferences into policy, and if expenditures

are linked to government actions, spending priorities should reflect preferences for government

actions. Examining the percentage share of total expenditures a policy area receives; its relative

importance compared to other areas can be found and used to determine a government’s

spending priorities. Spending priorities are the rank order of spending on policy areas. A policy

area receiving the largest portion of total expenditures in a given year will be ranked higher in

terms of priorities than alternative policy areas (Garand 1985; Hofferbert and Budge 1992;

Ringquist and Garand 1999; Jacoby and Schneider 2001, 2009).

A better way of capturing governmental outputs involves combining expenditure data,

which are argued to provide a measure of what governments do, over a variety a of policy areas

in a manner that is reliable, parsimonious, and substantively meaningful based on the data. This

is exactly what is done in the case of the American states to examine government spending

priorities (Jacoby and Schneider 2001, 2009; Schneider and Jacoby 2006). Jacoby and Schneider

use a spatial proximity model referred to as a unidimensional, metric, least-squares unfolding

analysis on government policy expenditures to create a measure of government policy priorities

that represents a continuum of policy packages. This approach allows for testing across multiple

policy areas at one time in a cohesive manner that is not based on correlations and retains the

uniqueness of the individual observations that are used to create the measure of government

policy priorities, compared to individual policy areas. Further, the data are not pre-selected

based on how the policies should group together or what the policies should represent. Instead,

17

the unfolding allows the data itself to determine what the underlying dimension of spending

involves, opposed to composite measures resulting from factor analysis, additive scales, or

principal components analysis.

When studying the pattern of government behavior, researchers apply a variety of labels

to different groups of policy areas including: targeted goods, public goods, distributive policy,

redistributive policy, rents, purchases, transfers, particularized benefits, and collective goods.3

Moving forward throughout the dissertation, policy groupings are referred to as either

particularized benefits or collective goods. The labels I apply throughout this work are based on

a policy dichotomy that has emerged in the literature (Volden and Wiseman 2007; Jacoby and

Schneider 2001, 2009). Particularized benefits are defined as policies intended to benefit

specific subgroups within a population and include items such as old-age pensions and

unemployment insurance. Collective goods are policies intended to benefit the more general

population and include areas such as defense and environmental protection.

SOCIO-ECONOMIC INFLUENCES

There are a number of indicators that are repeatedly argued and found to influence the

variation in government activities. One set of factors falls under the functionalist argument, that

it is the socio-economic climate that shapes what governments do. Under a functionalist

argument, policies of any type are the product of economic resources and the demands for those

resources. In particular, “social policies are the unmediated response to social and economic

pressures. . .” and “…intervening forces such as the political organization of social demands or

3 While these labels have been repeatedly used, there is wide variation in what is included under

the heading from research to research. While at times there is overlap, policy areas have also

been seen to switch sides; take, for example the use of the term public goods. Public goods have

been shown to include healthcare, welfare, and education in one instance (Edwards and Thames

2006) and spending on bridges in another (Milesi-Ferretti et al. 2002), where welfare fell under

transfers.

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governmental institutions are assumed to be either neutral towards or fully determined by the

socio-economic change” (Zutavern and Kohli 2010, 173). A range of socio-economic factors are

said to influence how governments spend money and prioritize policy areas in relation to one

another including: wealth, inflation, unemployment, the size of the dependent population, female

labor force participation rates, and the openness of a nation’s economy.

Wagner’s Law suggests that as a nation’s wealth increases so too will its role in the

public sector. Changes and growth in the economy result in governments taking on new

functions beyond traditional roles of providing defense and public order. The expansion of the

role of the governments includes providing educational services and welfare assistance to

address new and growing issues within nations due to economic development and growth

(Peacock and Scott 2000). Changes in the economy are a result of shifts from agrarian to

manufacturing societies with industrialization, and the growth of the service sector. Greater

levels of wealth are then associated with increases in spending on particularized benefits such as

unemployment insurance, pensions, and daycare services to accommodate the needs of the public

due to changing economic conditions. Evidence suggests wealthier nations are associated with

governments that spend more on particularized benefits, typically in the form of welfare

spending (Huber and Stephens 1993, 2001, 2003; Crepaz, 1998; Milesi-Ferretti et al. 2002;

Bräuninger 2005; Iversen and Soskice 2006; Shelton 2007; Brook and Manza 2007).

As the wealth of a nation rises, government can accommodate the needs of multiple

subgroups, as it has more resources at its disposal to spend on particularized policy areas;

however, inflation is found to limit a government’s resource pool. The higher the level of the

inflation rate, the more money is required to obtain the same level of goods and services than

before. In such situations, when inflation is high, a government has less money to spend on

19

particularized policy areas and can please fewer subgroups within the population, resulting in a

decrease in spending on particularized policy areas relative to collective goods that benefits the

broader community. General support has been found for inflation limiting government spending

on particularized policy areas (Huber and Stephens 1993, 2000; Crepaz 1998; Chang 2008).

Government actions are also shaped by the social pressures that determine what demands

governments face, including unemployment and the size of the dependent population comprising

young children and the elderly. As the level of unemployment in a nation rises, the proportion of

individuals who are in need of assistance to maintain a minimum standard of living also

increases. The rise in unemployment then challenges governments to provide goods and services

in the form of particularized benefits like unemployment insurance. Studies examining the effect

of unemployment find that government spending patterns are influenced by the levels of

unemployment in the nation (Crepaz 1998; Huber and Stephens 2000, 2001; Bräuninger 2005;

Iversen and Soskice 2006; Shelton 2007).

Similar to the argument in place for unemployment, the size of the dependent population,

consisting of the elderly and the young, is found to shape government spending patterns. Larger

elderly populations are related to more people in a nation that are typically no longer working

and are in need of aid from the government. The increase in a subgroup of the population

requiring government assistances shifts government priorities towards particularized spending

such as old age pensions. Additionally, as the proportion of the elderly increases, so should their

influence over policy outputs that favor their group, including increasing pension benefits (Huber

and Stephens 2001). Indeed, increases to the proportion of the aged population are shown to

increase government spending on particularized policy areas such as spending on pensions

20

(Huber and Stephens 1993, 2001; Scartascini and Crain 2002; Milesi-Ferretti et al. 2002;

Bräuninger 2005; Hay 2006; Shelton 2007; Chang 2008).

Increases in the size of the youth population are also suggested to increase government

spending on particularized policy areas. As the number of children increase, household incomes

are less able to provide for basic needs. As families are less able to provide themselves with

basic goods, government services are necessary in the form of items like daycare so parents can

work, after school services, food, and housing assistance. Studies examining the influence of the

proportion of the youth population in a nation have found it to be associated with greater

spending on particularized policies (Huber and Stephens 2000, 2001; Chang 2008).

Prior work shows that female participation in the workforce increases the level of

spending in different policy areas including welfare services (Huber and Stephens 2000, 2001;

Iversen and Soskice 2006). However, there are different arguments put forward on why female

labor force participation increases spending on welfare policy issues. One argument is based on

the notion that as women enter the workforce they require assistance to replace their traditional

care-giving duties in the form of particularized spending on such services as day care (Huber and

Stephens 2000). Another theory revolves around the increase to the number of workers. As the

size of the workforce increases, so does the number of workers who are entitled to benefits. If

women do not enter the workforce, they would not have access to certain particularized benefits

such as unemployment compensation (Iversen and Soskice 2006). Regardless of the different

theories, female participation in the workforce is shown to affect government spending patterns

(Huber and Stephens 2000, 2001; Iversen and Soskice 2006).

Risks posed by external factors through economic openness from trade and globalization

are suggested to shape government spending patterns and behavior. There are two opposing

21

arguments for the effect of economic openness in terms of trade and globalization on government

spending patterns. The first argument has openness resulting in governments that spend more on

social protection (Shelton 2007). As nations’ economies become more open, their domestic

economies are at increased risk for shocks posed by external factors and external economic

crises. In order to compensate for the increased risks that are posed by opening domestic

economies, governments increase the social safety nets in place and spend more on particularized

policy areas (Cameron 1978; Rodrik 1998; Shelton 2007; Swank 2010).

An alternative argument revolves around the idea of a “race to the bottom.” In this

context governments are unable to “sustain generous systems of public social protection” (Swank

2010, 319) because manufacturers rush to produce in the least costly nation. As a result, nations

that have generous welfare systems and strict laws in place, find businesses shifting production

to less costly nations and domestic unemployment rising. Therefore, governments make cuts to

welfare provisions to maintain competitive environments for producers. While more open

economies are suggested to influence government activity, the findings are far from conclusive.

Both arguments have found support, while other works produce null findings (Swank 2010;

Shelton 2007; Huber and Stephens 1993, 2000, 2001; Castles 2006; Scharpf 2000).

Another consideration regarding the openness of a nation’s economy involves

membership in the European Union. Countries that belong to the European Union have made

efforts toward economic integration among fellow member nations with the goal to prevent

future conflict (Europa.eu). This interconnectedness relating to the economies and the mix of

binding/ non-binding policies should influence the spending priorities of European Union

members. Connections in terms of policy outputs can be expected where supra-national

agreements in 2009 total roughly “eighty binding norms…in the three main fields of European

22

Union social policy regulation: health and safety, other working conditions, and equality at the

workplace and beyond…approximately ninety amendments [and]…approximately 120 non-

binding policy outputs” (Falkner 2010, p 293).

As the economies of the member nations of the European Union are connected through

policy agreements and a shared currency, a crisis in one economy can and does affect all

members. Consider the case of Greece in terms of its inability to finance its own government

operations and pay back loans. In order to continue operations, the European Union and the

International Monetary Fund have provided a series of bailouts to Greece in order to prevent the

Euro and the European Market from collapsing. Similar threats to European Union members’

economic security have come from Portugal, Ireland, and Italy. Economic consequences for

other nations that have dedicated money to help alleviate the debt of the aforementioned nations

include new austerity measures such as reduced salaries, higher taxes, and fewer employment

benefits (Associate Press: Austerity in Europe).

POLITICAL PREFERENCES AS INFLUENCES

Moving beyond the consequences of the socio-economic influences within nations, mass

and elite preferences are shown to alter a government’s policy priorities. Preferences linked to

spending priorities include the parties that comprise the government (Garand 1985; Hofferbert

and Budge 1992; Huber and Stephens 1993, 2000; Klingemann et al. 1994; Bräuninger 2005;

Breunig 2006), citizen mobilization (Baumgartner and Jones 1993; Huber and Stephens 1993,

2000, 2001; Hill and Hinton-Anderson 1995; Ringquist and Garand 1999; Lijphart 1997;

Jackman 1987), the general culture of expectations for the role of government (Almond and

Verba 1965; Inglehart 1990; Inglehart and Abramson 1995; Goren 2004; Norris 2004), public

opinion (Page and Shapiro 1983; Kingdon 1984; Erikson, Wright and McIver 1989;

23

Baumgartner and Jones 1993; Raimondo 1996; Ringquist and Garand 1999; Jacoby and

Schneider 2001, 2004; Soroka and Wlezien 2004, 2005), and interest groups (Schattschneider

1975; McConnell 1970; Lehmbruch and Schmitter 1982; Wilson 1982; Kingdon 1984;

Baumgartner and Jones 1993; Gray and Lowery 1996; Ringquist and Garand 1999; Jacoby and

Schneider 2001; Schneider and Jacoby 2006).

The composition of the government denotes what political parties hold office in

government. The parties that hold office are able to transfer their policy preferences into

governmental spending priorities. Research shows that political parties follow through on

implementing their party platforms as public policy once in office (Klingemann et al. 1994;

Hofferbert and Budge 1992). Political parties on the left tend to emphasize social services while

rightist parties have been found to emphasize areas of defense and order (Klingemann et al.

1994). Governments dominated by leftist parties are shown to spend more on particularized

benefits such as pensions and housing benefits for low-income groups, while those controlled by

rightist parties are shown to spend less on particularized benefits relative to collective goods

(Huber and Stephens 1993, 2000, 2001; Budge and Keman 1990).

The argument behind the effect of political parties finds support in both power resource

theory and partisan theory. Power resource theory argues that as the laborers organize and gain

strength more leftist party members will be elected to government (Korpi 1983). As leftist party

strength in government increases, there should be an increase in spending on policy areas

promoted by leftist parties including spending on particularized policy areas, such as retirement

benefits, unemployment insurance, and disability benefits. Partisan theory suggests that political

parties provide a number of policies to win elections and “implement policies favoring their core

constituencies” (Hibbs 1992). Leftist parties tend to be supported by the economically insecure

24

while rightist parties are supported by the more prosperous (Iversen and Soskice 2006). A

number of studies show that the divide between leftist and rightist parties in government shapes

spending patterns (Klingemann et al. 1994; Budge and Keman 1990; Huber and Stephens 2001;

Garand 1985; Hibbs 1992; Chang 2008; Korpi 1983, 1989).

Citizen mobilization is another potential factor that is suggested to influence government

priorities. Citizen mobilization can be measured in terms of voter turnout. In the end, voters are

responsible for electing officials to office, which, in turn, produces the makeup of government

parties in office. Citizen income within democratic nations tends to be right skewed with the

average worker earning less than the median income (Austen-Smith 2000). As a larger portion

of the population becomes active in voting, representation of lower income citizens increases

(Austen-Smith 2000) and as the lower income bracket has a high preference for redistribution

there will be an increase in parties elected who emphasize spending on particularized policy

areas (Hill and Hinton-Anderson 1995).

An alternative argument for the role of voter turnout suggests that as turnout increases

government will spend more on collective goods. Lijphart (1997) notes non-turnout rates are

higher among the poor. Therefore, as turnout increases, it is more likely to be voters from higher

income brackets voting for parties that favor less redistribution found in particularized policy

spending (Iversen and Soskice 2006). Mixed findings for the role of voter turnout exist

throughout the literature, supporting increases in spending for particularized policies, increases

in spending on collective goods, or no effect on government spending (Iversen and Soskice

2006; Chhibber and Nooruddin 2004; Crepaz 1998; Huber and Stephens 1993, 2000, 2001). The

mixed results may be a product of examining nations that have compulsory systems. Lijphart

(1997) notes that compulsory voting alters the probability of voting based on income.

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Preferences can also manifest through public expectations on the role of government.

The general culture of public expectations about the proper role of government represents the

underlying preferences of citizens within a nation, setting the acceptable boundaries of

governmental activity. Public expectations about government responsibilities are akin to

political culture and are based upon the argument that, “culture is a system of attitudes, values,

and knowledge that is widely shared within a society and transmitted from generation to

generation” (Inglehart 1990, 18). Who people are and what people do are contingent on the

culture that surrounds them and as a culture changes over time, so will the nature of people’s

preferences (Easton 1953). Inglehart (1990) argues that there are enduring differences across

cultures and that these differences are tied to political outcomes. Political culture involves “a set

of orientations towards a special set of social objects and processes” and “patterns of orientation

toward political objects among the members of the nation” (Almond and Verba 1965, 12-13).

As such, citizens’ expectations about governmental responsibilities represent a set of orientations

that should affect subsequent governmental policy activities.

Essentially, there is a general sentiment in a nation that orients the public in viewing

political issues encountered over time. For “cultures are theories; they organize experience. If

everything is possible without constraint, there is no need to choose and no way to think, because

no act interferes with any other. If nothing is possible, everything being constrained, there is

also no way to choose and no point in thinking” (Wildavsky 1998, 196). As the expectations for

government represent the public’s preferences for political involvement, they set the boundaries

of what should be possible for government to do and what is not. Beliefs about the role of

government should then also influence the allocation of government resources across policy

areas with higher allocations going towards issues the public feels the government should be

26

responsible for addressing. As an example, if the public wants the role of government limited to

economic stability and defense, there should be greater government attention to these policies as

opposed to other areas where the public feels government should not be involved in, such as

foreign aid. If cultural norms influence policy preferences in a democracy, then these

preferences should be transferred into government priorities.

Similarly, public opinion represents how citizens feel the government should handle

specific societal problems, as well as the priority that government gives to various governmental

actions to address these problems (Baumgartner and Jones 1993). Public opinion plays an

important role as it can have both positive and negative consequences for policy outputs: “(I)t

might thrust some items onto the governmental agenda because the vast number of people

interested in the issue would make it popular for vote-seeking politicians” (Kingdon 1984, 65);

alternatively, it could also prevent some issues from ever getting on the governmental agenda for

policy action. In democracies, politicians are rewarded for responding to public preferences; if

public opinion goes unheeded during a politician’s time in office it is likely the incumbent will

be voted out during the next election. Because of politicians’ need for public support to stay in

office, public opinion should affect government priorities. A number of scholars have found that

public opinion in different manifestations influence government spending. Jacoby and Schneider

(2001) show that public opinion measured through citizen ideology and partisanship affects

government spending priorities with more liberal public opinion increasing government spending

on particularized benefits in the United States. Other research has shown that governments do

respond, in more general terms, to public opinion in the form of policy outputs (Soroka and

Wlezien 2004, 2005; Page and Shapiro 1983; Hill and Hinton-Anderson 1995; Penner, Blidook,

and Soroka 2006).

27

The culture of expectations for governmental action and public opinion are two separate

components regarding policy preferences. Knowing or identifying the expectations for

government in a society therefore, does not guarantee the ability to predict public opinion on

issues. For example, while expectations may set the boundaries of government involvement to

include economic stability, public opinion may favor handling unemployment with greater

unemployment assistance, a particularized benefit, or alternatively through economic stimulus

packages and tax cuts, which are closer to collective goods. Alternatively, expectations may

favor the promotion of equality and an egalitarian society. To get there, public opinion may

prefer equality in outcomes, with greater spending on social protection like housing and food

benefits (particularized benefits), or equality of opportunity, with greater spending on economic

development to promote increased employment a collective good.

Prior research suggests that public opinion may also be influenced by governmental

policy outputs, such that; citizens are responding to what governments do, instead of

governments responding to what the public wants. Page and Shapiro (1983) find that public

opinion typically moves before policy change. In situations where change occurs first, Page and

Shapiro (1983) suggest that public opinion may still be the driving force and that policy makers

are acting on anticipation of changes in public opinion. Soroka and Wlezien (2004) find a

feedback loop in public opinion and policy change in Canada, where changes in spending result

in changes in public preferences for government spending. However, after the public re-

evaluates its preferences, governments are found to respond to the new preferences for

government actions, such that when citizens want less spending on a policy area governments are

found to decrease spending, after which governments respond to the new evaluations of the

28

spending level by reducing spending further if cuts were not enough or increasing spending if

cuts went too far.

Members of the public may also collectively express their preferences to the government

in the form of interest groups. Interest groups/organizations represent, “highly detailed, nuanced

signals about the specific problems citizens face, as well as potential solutions” (Gray and

Lowery 1999, 242).4 As opposed to beliefs on the role of government or public opinion that

pervade society, interest groups represent particular sets of individuals in society and typically

target specific policy areas and push for their preferred policy and influence government actions

(McConnell 1970; Ringquist 1999; Crepaz 1998; Gray and Lowery 1999). Interest groups have

also been noted to play a strong role in shaping policy in corporatist nations where interest

groups replace, “representation based on geographic units or units of approximately equal

number of voters” (Wilson 1982, 219-220; Lehmbruch and Schmitter 1982).

Although interest groups do represent particular sets of preferences within a nation, more

interest groups do not always correspond with increases in particularized policy spending.

Instead, increases in the number of interest groups, measured as the number of state government

employees in the American states, are associated with greater spending on collective goods

(Jacoby and Schneider 2001). When there are fewer interest groups pushing for particularized

interests, it is easier for governments to accommodate their demands. However, as the number

of interest groups increases it becomes difficult to appease all interests at the same time. Instead

governments are in a better position to move forward on collective goods that benefit many

groups within the population. As Schattschneider (1975) noted, “If there are twenty thousand

4 Gray and Lowery prefer the use of the term interest organizations in reference to the

composition of units whose interests are being represented as opposed to interest groups, which

are restricted to membership groups.

29

pressure groups and two parties, who has the favorable bargaining position? In the face of this

ratio it is unlikely that the pressure groups will be able to play off the parties against each other”

(56).

For example, “there is no one organization that can speak for employers in the United

States,” making it difficult to best serve the interest of employers in policy as there is no

agreement on what employers want (Wilson 1982, 222). Additional work has shown that as

more preferences are introduced into the decision making process by using proportional

representation, the role of interest groups in shaping government outputs is diminished (Crepaz

1998). A summary report by Kenworthy (2003) on measures of interest groups shows that

different measures of corporatism have yielded both increases and decreases in the level of

redistribution in a nation, thereby affecting spending on particularized policy areas.

The importance of interest groups in shaping government actions can be seen when

looking at the process of health care reform under both Presidents Clinton and Obama. Under

Clinton, interest groups were able to turn public opinion against health care reform by using their

resources to distribute information suggesting that health care reform would limit/restrict the

rights of individuals to choose their own medical care. Ultimately, interest groups were able to

mobilize public opinion against health care reform. Learning from the reform attempts of 1993-

94, Obama attempted to co-opt strong interest groups because “major economic-interests groups

with profits at stake would be much more vigilant, motivated, and organized than the diffuse

public” (Jacobs and Skocpol 2010, 69).

Unification of purpose allows organized interest groups to influence government more

effectively, with tangible consequences for policy outputs. Another example of organized

interests shaping policy can be found in Sweden during the 1990s. As the Swedish government

30

attempted to reduce spending on social protection, particularly pensions and unemployment

insurance, organized groups representing the interests of companies and union workers guided

the reform efforts (Anderson 2001). In this case, without a unified voice, it would be difficult for

employers and employees to shape policy in a manner that benefits their respective

memberships.

INSTITUTIONS AS INFLUENCES

As researchers seek to understand the causes of different policy outputs across

governments, another line of research examines institutional differences between nations. Here,

the argument focuses on how certain institutions create different incentives for politicians and

voters which, in turn, leads to varying policy outputs (Austen-Smith 2000). Influential political

institutions include majoritarian versus proportional representation (Persson and Tabellini 1999;

Ringquist and Garand 1999; Austen-Smith 2000; Tabellini 2000; Cox and McCubbins 2001;

Milesi-Ferretti et al. 2002; Iversen and Soskice 2006; Shelton 2007; Persson et al.. 2007),

presidential versus parliamentary systems (Huber and Stephens 1993; Lijphart 1999; Persson and

Tabellini 1999; Tabellini 2000; Tsebelis 2000; Haggard and McCubbins 2001; Shugart and

Haggard 2001; Scartascini and Crain 2002; Lienert 2005; Edwards and Thames 2006; Iversen

and Soskice 2006), district magnitude (Huber and Stephens 1993, 2000; Hill and Anderson 1995;

Ringquist and Garand 1999; Cox and McCubbins 2001; Milesi-Ferretti et al. 2002; Edwards and

Thames 2006; Persson et al.. 2007), bicameralism versus unicameral legislatures (Huber and

Stephens 1993, 2000; Immergut 2010), and federalism versus unitary governmental structures

(Obinger, Leibfried, and Castles 2005; Bednar 2009; and Immergut 2010). Each institutional

attribute affects how governments allocate their resources toward either particularized benefits or

collective goods.

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The electoral systems that are used to determine which candidate wins office are argued

and have been found to shape government spending. The overwhelming finding is that

proportional representation systems favor spending on particularized policy areas and

majoritarian systems spend more on collective goods (Scartascini and Crain 2002; Chhibber and

Nooruddin 2004; Chang 2008). One of the general arguments focuses on the campaign strategy

adopted by candidates to win elections. In a majoritarian system, the candidate with the most

votes wins, so it is the candidate’s goal to please a majority of voters (Tabellini 2000). In a

given district voters generally benefit from the same form of collective goods, but have different

preferences for particularized benefits. Therefore, it is in the interest of the candidate to promote

collective goods to gain a chance at the majority vote share. Therefore, candidates aim policy

platforms at more collective goods; like community or economic development, to avoid

alienating a subgroup of voters that could prevent them from winning the election.5

Under proportional representation systems, candidates have different incentives than in

majoritarian systems. In a proportional representation system, a candidate can win with just a

few votes as seats go to more than just the single candidate with the most votes (Tabellini

2000).6 Proportional representation creates districts where voters have similar preferences for

collective goods but different preferences for particularized benefits. Proportional representation

produces candidates then that promote more particularized benefits like pensions or family and

5 Similar to what Norris (2004) refers to as a bridging strategy. Bridging strategies involve

bringing together individuals with heterogeneous interests to form a broad coalition. 6 Unless the district magnitude is equal to one, in which case it is essentially a majoritarian

system.

32

children benefits, to target subgroups.7 Using proportional representation, if a candidate

promotes collective goods, voters opt for the candidate whose platform caters to their groups’

particular preferences.

In addition to changing the motives from the candidate’s, voters also have different

preferences for parties under the majoritarian/proportional representation divide (Lijphart 1999;

Iversen and Soskice 2006). Assuming there are three classes within a society, Iversen and

Soskice (2006) argue that the middle class has different motives for aligning with either the

upper or lower income brackets based on the electoral structure. In a system using proportional

representation, all three groups will have representation in office. In this situation, the middle

class will tend to form coalitions with the lower income bracket to tax the upper class and

redistribute the benefits. Here, if the lower income bracket attempts to tax the middle class as

well as the upper class, the middle class can leave the coalition and join with the upper class to

prevent taxation. Proportional representation then creates a government favoring particularized

benefits that provide redistributive goods.

Under a majoritarian system, typically only two parties hold office and the middle class is

faced with joining either the upper or lower income brackets’ parties. Unlike proportional

representation systems, once elected, the middle class cannot prevent the lower income group

from taxing the middle class through defections. In a majoritarian system, if the lower income

bracket’s party wins, the party controls government without requiring a coalition for support, and

can tax the middle class and the upper class and redistribute more benefits solely to the lower

income bracket. Under a majoritarian system, out of fear of taxation, the middle class will

support the upper class parties and at least avoid taxation at the loss of some redistributive

7 Similar to what Norris (2004) referred to as a bonding strategy. Bonding implies bringing

together individuals with homogenous preferences on certain issues (Norris 2004).

33

benefits. Lijphart (1999) finds similar evidence that proportional representation systems support

more leftist parties that favor more particularized benefits and majoritarian systems support more

rightist parties that favor more collective goods.

Presidential and parliamentary systems have a similar divide in outputs to that of

majoritarian and proportional representation. Under a presidential system, the executive is

elected at large by the nation and holds power independently of the nation’s legislature, and

while the executive cannot dissolve the legislature, the executive typically possesses some form

of veto power over legislation (Shugart and Haggard 2001). Under a parliamentary system, the

executive is appointed by the majority party or governing coalition in the legislature; both the

executive and the legislature have the power to dissolve government, and typically legislation

passes with a majority vote (Shugart and Haggard 2001).

These two systems are found to produce governments with different public policy

patterns. The underlying argument to these differences is based on the constituencies to which

the executives in the different contexts are accountable. Under a presidential system, the

executive is accountable to the public at large and must seek out policy areas that benefit the

largest number of voters. In this context, policies that are voted on by the legislature and favor

more particularized groups can be vetoed in some manner by the executive.

An executive in a parliamentary system is accountable to the legislature that he/she is

appointed by and typically represents more particularized interests depending on its party

composition. In a parliamentary system it is expected that more particularized policies favoring

the parties in office would dominate over collective goods policy areas. Prior works shows that

presidential systems do spend more on collective goods and parliamentary nations spend more

34

on particularized benefits, including spending on welfare issues (Lijphart 1999; Scartascini and

Crain 2002; Edwards and Thames 2007; Crepaz 1998).

Crepaz’s (1998) work on political institutions and welfare expenditures highlights an

example of how presidential systems behave relative to parliamentary system. Crepaz argues

that parliamentary systems are in a better position to incorporate the needs of groups that benefit

from increased spending on social protection and unemployment insurance than presidential

systems. However, in presidential systems, the need to win the majority of votes to obtain office

produces spending that benefits broad communities in the form of collective goods instead of

particular groups across regions. Looking at welfare expenditures as a proportion of gross

domestic product, that represents spending on particular groups of individuals in a society,

Crepaz (1998) finds parliamentary systems spend more on welfare than presidential systems.

Examining district magnitude and its effect on policy is similar to the comparison of

majoritarian and proportional representation. As district magnitude increases the number of

votes required to win offices decreases. As a candidate becomes less tied to pleasing as many

people as possible the candidate can go after subgroups in the population that are large enough to

ensure a successful election campaign with promises of particularized policies. High district

magnitudes can also constrain the ability of governments to enact or pass legislation. As the

number of candidates than can be elected in a particular district increase, so do the number of

viable candidates for office and the political parties they represent (Cox 1997). A variety of

works find that higher district magnitudes increase government spending on particularized policy

areas (Edwards and Thames 2007; Milesi-Ferretti et al. 2002).

When Milesi-Ferretti et al. (2002) look at proportional representation versus majoritarian

systems, they argue that spending that targets particular groups is more prevalent in nations that

35

use proportional representation. While majoritarian and proportional representation determine

the rules for elections, district magnitude sets the level of proportionality. As district magnitude

increases, candidates need fewer votes to win and will use spending that favors set groups within

the society to increase their likelihood of winning office. Higher district magnitudes are found to

be associated with greater spending on particularized benefits, such as social security payments

and less spending on collective goods like building bridges.

INSTITUTIONAL CONSTRAINTS AND POLICY RESPONSIVENESS

In addition to examining the role of institutions independently from one another,

researchers look at how institutions work together to constrain policy outputs (Tsebelis 1995,

2000; Cox and McCubbins 2001; Iversen and Soskice 2006). These studies focus on how

institutions create veto players (Tsebelis 1995), the role of the institutions in the form of veto

points (Immergut 1990), and the ability of institutions to promote policy decisiveness or

resoluteness (Cox and McCubbins 2001).8 Institutional constraints are features of the

governmental/political system that increase the number of preferences present in the decision

making process by separating decision making power across multiple actors. Researchers

examine a variety of different institutional characteristics in their examination of constraints

including: presidential systems, bicameralism, federalism, and proportional representation

systems (Tsebelis 1995; Huber and Stephens 1993, 2000, 2003; Cox and McCubbins 2001;

Immergut 2010).

Constraints are found to affect the ease with which policy agreement can occur. As the

number of constraints increases, the ability for policy agreement to be reached decreases and

governments become more resolute in their policy actions (Cox and McCubbins 2001). As the

8 Decisiveness is defined as the ability to reach and enact policies whereas resoluteness is

defined as the level of commitment to the current status quo (Cox and McCubbins 2001).

36

number of constraints increases, the ability for incumbents to reach agreement on particularized

policy areas decreases due to a greater number of actors with preferences over governments

actions present in the decision making process.

While majoritarian and proportional representation can shape the spending patterns of a

government, proportional representation also serves to constrain the ability of governments to

reach policy agreements (Immergut 2010; Lijphart 1999). Proportional representation tends to

promote multiparty systems and simple majoritarian systems support two party systems (Riker

1982). As the number of political parties increases so do the number of preferences over policy

outputs. The increase in the number of demands constrains a government from acting quickly as

bartering and negotiations on policy must first occur to appease the multiple actors involved in

the decision making process. Not only does the increase in the number of preferences decrease

the ability of government to reach agreements, it also leads to greater compromises among the

various groups involved in policymaking. Greater compromise means no particular group is able

to obtain its ideal policy outputs. Thus, in a two party system, however, one party will have

greater control over policy and is therefore less constrained in its ability to act with fewer

opponents to appease. Under a majoritarian system, political parties should be able to achieve

policy outputs closer to their desired expectations.

Presidential systems, as opposed to parliamentary systems, can serve to constrain a

government’s ability to act (Immergut 2010; Bradley et al 2008; Tsebelis 2000; Huber and

Stephens 2000, 2001). As noted earlier, in a presidential system the executive is elected

separately from the legislature and tends to have different expectations for policy outputs.

Additionally, presidents typically possess some form of veto power over legislation before it can

be enacted. Under this context, presidential systems can constrain government action by vetoing

37

legislation that does not meet with the executive’s preference for policy outputs. In a

parliamentary system, however, the executive’s fate is tied to the legislature which can end

government with a vote of no confidence, reining in the executive’s ability to prevent legislation

from passing and from constraining government’s ability to act.

Similar to the argument for proportional representation, as the number of parties with

demands present in the decision making process increases, so does the difficultly of enacting

legislation that appeases enough voters in the decision making process. Therefore, as district

magnitude increases, more parties enter government due to the lower threshold to win office, and

governments are more constrained in their ability to act. Prior research supports the argument

that increases in district magnitude makes it more difficult for government to reach an

agreement on policy outputs as it diffuses decision making power across more actors with

different preferences (Tsebelis 2000; Immergut 2010)

In unicameral systems, only one house is responsible for writing and passing legislations.

However, in a bicameral legislature, the ability to write legislation is divided between two houses

that both need to agree for a policy to be passed. The two houses then increase the difficulty of

reaching agreement. For example, each house may be able to reach compromise within itself on

legislation, and yet unable to reach a compromise with the other house. Therefore, bicameral

legislatures are found to constrain a government’s ability act compared to unicameral legislatures

(Cox and McCubbins 2001; Shugart and Haggard 2001; Huber and Stephens 2000, 2001;

Immergut 2010).

With a federal system, decision making power is dispersed across different levels of

government and is found to decrease a government’s ability to reach policy agreements (Huber

and Stephens 2000; Lijphart 1999; Cox and McCubbins 2001; Shugart and Haggard 2001;

38

Immergut 2010). This dispersion of power is shown to constrain governments from being able to

reach agreements over policy (Obinger, Leibfried, and Castles 2005; Immergut 2010). Examples

of how federal systems are constrained compared to unitary systems can be seen through looking

at the “old” and “new” politics of the welfare state (Obinger, Leibfried, and Castles 2005).

When welfare states were on the rise, nations with federal systems were unable to rapidly shift

spending towards particularized benefits like social protection. However, in the “new” politics

of the welfare state, federal systems are unable to cut funding to welfare policy issues compared

to unitary systems and have higher levels of spending on areas of social protection (Obinger,

Leibfried, and Castles 2005). Additionally, the dispersion of power can allow lower levels of

government to pre-empt action at the higher levels on policy areas, making it more difficult for

the national governments to create legislation as it infringes on the rights of the lower levels of

government (Obinger, Leibfried, and Castles 2005).

The argument I test in Chapter 6 is based on the institutional constraints in democratic

nations. As the number of constraints increase, the number of actors/groups with preferences

present in the decision making process increases, and so does the difficulty of reaching

agreement on policy outputs. In order to reach an agreement on policy, as the number of

constraints increase, compromises and bartering among the actors will have to occur. The

process of bartering will increase spending on collective goods that provide benefits to all

members involved in the decision making process. There should be a decrease in spending on

particularized policies as the number of actors present in the decision making process increases

because particularized spending will only benefit some members at the expense of others who

have the ability to block legislation. Through this process, no particular group is in a position to

implement its ideal policy from government. Additionally, as the number of constraints

39

increases, the ability of any preference to drive, or influence, policy outputs will decrease,

making government less responsive to actors in terms of matching policy outputs to preferences,

where responsiveness is defined as, “the degree to which policy choices follow public

preferences” (Roberts and Kim 2011, 819).

CONCLUSION

A number of approaches are used to explain government outputs including examining

specific acts of legislation and changes to particular policy areas; however, expenditure data are

suggested as the primary way to examine governmental commitments as it shows the “tangible

distribution of public resources and not merely the intention of politicians and office holders”

(Jacoby and Schneider 2001, 546). Scholars have shown that the socio-economic climate, the

preferences of different groups, and political institutions do influence the policy outputs of

governments. However, most of the factors that are studied in relation to government priorities

have taken place in separate analyses, looking only at the role of political institutions or the

affect of socio-economic conditions without also examining the institutional make-up of

governmental systems. The omission of critical explanatory factors needs to be addressed in

order to properly understand the relationship between the explanatory variables and government

spending. The priorities model in Chapter 5 presents a more fully specified model of

government spending. The fully specified model allows me to explain why factors like citizen

mobilization has mixed findings in prior models and why the elderly are the only dependent

population found to influence government spending.

In addition to looking at the individual factors discussed in this chapter, I will also

examine the relationship between political institutions in nations and citizen preferences.

Although prior research indicates that the combined institutional factors can make political

40

systems more resolute in terms of enacting policies different from the status quo, the interaction

between institutional design and citizens preferences has not been examined. In this research, I

show how institutional factors alter the role mass and elite preferences play in shaping policy

outputs in democratic governments.

41

CHAPTER 3 GOVERNMENT SPENDING PRIORITIES

Prior studies on government spending priorities tend to divide policies into two

categories: policy choices that favor subsets or particular group of the population referred to here

as particularized benefits and policy choices that favor the majority of citizens or the general

population within a nation that are referred to as collective goods (Persson and Tabellini 2003;

Hofferbert 1974; Klingemann et al. 1994; Huber and Stephens 2001; Iversen and Soskice 2006;

and Milesi-Ferretti et al. 2002; Penner, Blidook, and Soroka 2006; Jacoby and Schneider 2001;

2009). However, a number of studies do not show that governments actually make policy

decisions along a single dimension of choice or that spending is based on the groups of citizens

intended to be affected by the policies. Instead, the two types of policies are examined in

separate models using total spending on particular sets of policies relative to gross domestic

product (Huber and Stephens 1993, 2001; Chhibber and Nooruddin 2004), using the proportion

of total spending (Garand 1985; Garand and Hendrick 1991; Hofferbert and Budge 1992;),

change in spending (Jones et al. 2009; Soroka and Wlezein 2004) or using a composite measure

(Hofferbert 1974; Klingman and Lammers1984; Erikson Wright and McIver 1989).

In this research, I use an unfolding model to operationalize government spending

priorities. The unfolding model shows that the spending patterns of democratic government

across a number of policy areas can be represented using a single dimension. As governments

choose policy packages along a single dimension, the unfolding model depicts the variation in

program expenditures found in the original data by policy area. Further, I provide support to the

literature on the pattern of spending regarding the dichotomy between particularized benefits and

collective goods at the state level in the United States. The results lend support to my first

hypothesis:

42

H1: Government expenditures can be captured by a single policy dimension that

represents two types of policies: those that benefit particular groups (particularized

benefits) and expenditure that benefit society more generally (collective goods).

The spending priorities variable can then be used to properly test the effect of factors that

have been argued to influence the variation in spending priorities across and within nations and

over time. Importantly, the unfolding produces a more ideal measure of spending priorities than

alternative approaches using single indicators, typologies, and composite measures.

DATA SELECTION

In order to create a measure of spending priorities I use government expenditures across a

wide range of policy areas. Government expenditures represent spending by the general

government and include national, state, and local government expenditures where information is

relevant. Although it has been noted that expenditures do not cover all policy decisions, such as

regulations (Hofferbert 1974), it has also been pointed out that the majority of policy debates and

decisions focus on the distribution of funds (Hofferbert and Budge 1992). Government

expenditures also reflect a degree of commitment by the government to a policy (Schneider and

Jacoby 2006). Expenditures serve as a central component of what governments’ do, where

agencies and policies grow and contract based on how much money they are allotted (Kingdon

1984). As such, the variation in shares of spending over time provides a measure for tracking the

rise and fall of policy areas in importance on government agendas (Hofferbert and Budge 1992).

Government expenditures by policy areas act as tangible representations of what

governments actually do, versus the promises of what they will do or of what they would like to

do (Jacoby and Schneider 2001). The reality is that governments cannot freely increase spending

across all policy areas without increasing deficits. As such, governments must make

43

compromises on how to allocate the resources they have across the issues they face. If

governments do increase spending on all policy areas without making trade-offs, they run the

risk of economic collapse. For example, Greece’s choices in spending on social protection,

particularly its high levels of spending on pensions with eligibility at the age of 57, helped to

push the government into bankruptcy and forced it to seek financial assistance from other

members of the European Union (OECD-Pensions at a Glance). Limitations on spending force

governments to trade spending more or less on one policy area in relation to the other policy

options, reflecting the spending priorities of a government (Ringquist and Garand 1999).

Expenditures by policy area are measured as a percentage of the total spending across a

range of program areas. I use the percentage of spending because my interest is in the relative

rank ordering of expenditure allocations, and not specific spending levels. The use of specific

program expenditures would not control for the total size of the government across time or

across countries and would prevent comparisons between nations in the form of a pooled

analysis (Huber and Stephens 2001). The percentages are also based on total expenditures

because I am interested in the percentage of the money that is spent on policies and how it was

spent to different areas instead of the amount spent relative to what could have been spent. Other

works have examined spending as a percentage of gross domestic product or gross national

product, implying an examination of the generosity of government with the denominator

representing total resources at a government’s disposal.9 Additionally, changes in spending

measured this way can be misleading if the level of spending does not change but the economy

expands or contracts; it can appear as if the government is spending more or less on goods and

services when nothing has actually changed regarding spending patterns.

9 If an unfolding were performed based on the expenditures as a proportion of GNP or GDP a

similar policy dimension would be produced.

44

Table 3.1 Democratic Nations and Time Periods

The expenditure data by policy area are available from the Organization for Economic

Co-Operation and Development (OECD) starting as early as 1990 for some nations and running

up through 2008 for most nations with 2009 data available for a several others as of December

2010 (Table 3.1). The general government accounts are based on total government spending by

all levels of government and are divided into ten different expenditure areas by function:

government operations, defense, public order and safety, economic development, environmental

Country Time-Span

Austria 1995-2009

Belgium 1990-2008

Canada 1990-2006

Czech 1995-2008

Denmark 1990-2009

Finland 1990-2008

France 1995-2008

Germany 1991-2008

Greece 2000-2008

Hungary 1995-2008

Iceland 1998-2007

Ireland 1990-2008

Italy 1990-2008

Japan 1996-2007

Korea 2000-2008

Luxembourg 1990-2009

Netherlands 1995-2009

Norway 1990-2008

Poland 2002-2008

Slovakia 1995-2007

Slovenia 1999-2008

Spain 1995-2008

Sweden 1995-2008

UK 1990-2008

US 1990-2008

45

protection, health, community development, recreation, education, and social protection. An

overview of spending by policy area can be found in Table 3.2.

Table 3.2 Examples of Expenditures by Policy Area

Activities by Policy Area

Government Operations Education

Executive and legislative organs, financial Pre-primary and primary education

and fiscal affairs, external affairs Secondary education

Foreign economic aid Post-secondary non-tertiary education

General services Tertiary education

Basic research Education not definable by level

Public debt transactions Subsidiary services to education

Defense Health

Military defense

Medical products, appliances and

Equipment

Civil defense Outpatient services

Foreign military aid Hospital services

R&D Defense Public health services

Public order and safety Recreation, culture and religion

Police services Recreational and sporting services

Fire-protection services Cultural services

Law courts Broadcasting and publishing services

Prisons Religious and other community services

Economic Development Social protection

Economic, commercial and labor affairs Sickness and disability

Agriculture, forestry, fishing and hunting Old age

Fuel and energy Survivors

Mining, manufacturing and construction Family and children

Transport Unemployment

Communication Housing

Environmental Protection Community Development

Waste management Housing development

Wastewater management Community development

Pollution abatement Water supply

Protection of biodiversity and landscape Street lighting

46

Figure 3.1 Distribution for the Proportion of Spending on Health

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

A brief examination of a few of the policy areas serves to highlight the variation in

expenditures. Figure 3.1 shows the proportion of total spending on health across the 25 OECD

nations from 1990-2009. The unimodal distribution shows most nations spending roughly 14%

of total expenditures on health care services. The right tail of the distribution is slightly longer

and is capturing the United States spending on health care, a nation that spends more on health

47

care than any other in the dataset and is its top ranked expenditure area for five of the years

examined.

Figure 3.2 Distribution for the Proportion of Spending on Social Protection

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

A different form of variation can be observed by looking at the proportion of spending on

social protection in Figure 3.2. Here there are two clusters of spending. The low end shows the

variation in spending for the nations that did not have social protection as the number one ranked

policy area in terms of expenditures; including Canada, the United States and South Korea. The

48

high end of distribution expresses the variation for nations that spent more on social protection

than the alternative policy areas. Both groups show differences in expenditures for nations that

did and did not rank social protection as the top expenditure area.

Figure 3.3 Distribution for the Proportion of Spending on Public Order and Safety

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

The final example of variation in spending can be seen in the area of public order and

safety (Figure 3.3). Here the proportion of expenditures ranges from 1.5% to 6.5%. There is a

49

higher proportion of nations spending across the entire range of public order and safety, unlike

health (Figure 3.1) and social protection (Figure 3.2) that each had unimodal distributions of

spending.10

The distribution is indicative of the nations spending relatively consistently on

public order and safety during the time period examined.

The expenditure data taken from the OECD’s Annual National Accounts statistics

classify government spending into the ten different policy areas listed above. The expenditures

include goods and services that take the form of cash benefits such as pensions or unemployment

and in-kind benefits, for example housing vouchers or education services.

Similar to the literature already discussed, the OECD attempts to break down the policy

areas as either providing collective goods or individual goods. The OECD defines a collective

good as “a good that benefits society as a whole” and an individual good as “a good that

primarily benefits individual citizens” (OECD 2009, 61). The OECD’s definition for collective

goods is a close match for the definition I use here; however, the individual goods definition is

slightly broader than the one for particularized benefits. According to the OECD, individual

goods include spending on health and education, while I label these policy areas as collective

goods. Although individuals are deriving direct benefits from spending on these policy areas,

through universal health care coverage and public education programs all individuals in a society

benefit from these goods and services, while particularized policies focus spending only on

specific sets of individual who are intended to receive benefits such as disability or survivor

benefits.

10

To see the distributions for the remaining policy areas (defense, economic development,

education, environmental protection, government operations, community development, and

recreation) see Appendix A.

50

What spending is represented by the policy areas? Government operations includes day

to day financing for operating the executive and legislative branches of government, such as the

office of the executive, town councils, political staffs, libraries and other reference services and

ad hoc commissions and committees. The funding of external affairs, such as office abroad and

cultural services extending beyond a nation’s borders and economic aid to developing countries,

are other examples of government operations. I would argue that the direct benefits of salaries

and government expenditures go to a particular group of individuals who are employed by

government or are not members of the society in terms of foreign aid and should be classified as

particularized benefits. A similar argument is made in the case of the American states by

Schneider and Jacoby (2006) who argue government salaries represent a distinct group.

Spending on defense covers a range of services to protect a nation from external threats.

Expenditures include funding the military operations for land, sea, air, and space defense.

Defense spending also includes aspects of civil defense and foreign military aid. Where defense

spending focuses on threats from outside a nation’s borders, public order and safety expenditures

address domestic issues. Police services, fire protection, law courts, and prisons make up the

expenditures for public order and safety.

Economic development deals with general spending concerning the economy, commerce,

and labor. This policy domain includes aspects like equal opportunity employment and labor

mobility. Expenditures also address the conservation of arable land and flood control issues for

agriculture, production, resources, and the distribution of fuel and energy. Transportation

services like road maintenance, waterways, railways, and air transit that can affect economic

activities are included in economic development. These are services that benefit the entire

community.

51

Environmental protection handles spending on issues related to creating a clean society.

Spending from waste management such as local street cleanings and public parks to the disposal

of physical, chemical and biological waste is covered. Additionally, spending on waste-water

management and pollution abatement to ensure clean air, soil and ground water are part of

environmental protection. The benefits here are intended to benefit society in broader terms

versus particular groups of individuals.

Community development focuses on spending for public and neighborhood

improvements. Expenditures include the promotion and monitoring of housing developments

and slum clearances to rebuild communities. Issues that relate to clean water supplies and

streetlights are also covered. The benefits of community development are intended to benefit

society in general terms versus particular types of individuals.

Spending on health includes a number of services: pharmaceuticals; medical appliances

and equipment; outpatient services like specialized medical care and dental; hospital services and

public health services like vaccines, disease detection and blood banks. While individuals may

be the direct recipients, society as a whole generally has access to these services making this

domain a collective good. Furthermore, many citizens within the nations examined here find

health care to be a right of citizenship, particularly in European nations where “they [Europeans]

see universal access to health care as a social right, a crucial element of a decent society” (Okma

2011). Note that all OECD nations examined here except for the United States had universal or

near-universal health care coverage as of 1990 (Gurría 2008). 11

As such, in these nations health

care is provided to all citizens with a “core” set of services covered (OECD Health at a Glance

11

The OECD notes that three nations do not have forms of universal health care coverage in

place during the time period under study from 1990-2009 include the United States, Turkey, and

Mexico.

52

2009). Although coverage reaches almost all of the citizens within these nations, forms of

funding vary and include a mix of both public and private funding and insurance markets where

citizens may still be responsible for co-payments, deductibles, and supplementary insurance

coverage (Docteur and Oxley 2003).

Recreation as a policy area includes spending on sporting services like sports facilities,

fields, parks, and campgrounds. Further, spending covers cultural services such as libraries,

zoos, aquariums, and museums. Additionally, broadcast and publishing services, including

regulations, are covered by recreational expenditures. The use and access of the goods and

services provided under recreation are for societies in general terms.

Spending on education covers all levels of schooling. Primary, secondary, and college

educational expenditures are included in this area. Scholarships and grants for educational

purposes as well as vocational training make up part of the total spending. Primary education is

compulsory in the democratic nations where everyone in society benefits from spending

(UNESCO, 2009). Further, many nations offer assistance for higher education, such as the

United States that awards Pell Grants for students attending college.

The last policy area is social protection and includes a number of expenditures that are

aimed at particular groups in the society. Expenditures focus on disability and sickness, old age

pensions, unemployment insurance, and survivor benefits for those who need assistance

providing for themselves. Additionally, spending on families and children through housing

benefits, food vouchers, orphanages, foster families, and nursing care for children are included

under social protection. The spending areas in social protection are aimed at particular groups of

individuals in society. The direct nature of the spending for specific subsets of the population is

why social protection is labeled as a particularized benefit.

53

Based on the spending categories for each policy area, I expect defense, public order and

safety, environmental protection, economic affairs, education, health, community development,

and recreation to form a cluster at one end of the policy continuum representing collective goods,

and government operations and social protection to form a cluster at the opposite end

representing particularized benefits.12

UNFOLDING

The unfolding technique is a means of locating an underlying, latent dimension based on

rank order preferences. The unfolding analysis produces two sets of points, one set of points

representing the decision making actors, such as individuals, corporations, or as is the case here,

governments. The other set of points represents the outputs of the decisionmaking process. In

the unfolding analysis performed here, the outputs are the proportion of spending on particular

program areas. The unfolding analysis arranges the two sets of points along a continuum such

that the distances between an actor point and an output point correspond to the relative

preference by that actor for that particular output. Smaller distances represent a higher

preference for a certain output and larger distances represent a lower preference for that

particular policy output.

The unidimensional, metric, least-squares unfolding approach applied to the expenditure

data from 25 democratic governments provides more information than the rank order preferences

of spending on policy areas. The interval level nature of the expenditure data are maintained and

the distances between nation spending priorities and policy areas capture the percentage of

12

Based on the OECD coding, health, social protection, education and market subsidies that

comprise economic development are individual goods, while defense, public order and safety,

environment protection, government operations, community development, and recreation are

considered to be collective goods.

54

spending by a particular nation on each policy area. A discussion of how to obtain the original

spending by nations on policy areas is provided later in the chapter.

Details of the Unfolding Procedure

The premise of unfolding is based foremost on the assumption that there is an underlying

dimension of choice that exists and is consistent across actors. Although the unfolding process

itself produces a dimension of choice based solely on the data, it falls on the researcher to

determine what the substantive order and groupings of the output points represents. In this

context, I use nations that have been repeatedly classified as democratic over the time period

from 1990-2009 as the decision-making actor.

The unfolding process I use to determine the location of the nations’ spending priority

points and the policy area stimulus points is the metric, least-squares unfolding method used by

Jacoby and Schneider (2001, 2009) when examining government spending priorities across the

American states. The approach attempts to place the output points (representing policy areas)

and the actor points (representing a nation’s spending priorities) to minimize the squared errors

between the actual percentage of nation spending (xijt) for nation i, on policy j, at time t, and the

predicted percentage of spending based on the unfolding for nation i, on policy j, at time t (dijt*):

min ∑eijt 2=

(dijt*- xijt) (3.1)

The predicted level of spending based on the unfolding is found using the distance

between nation points (nit) for nation i, at time t, and the points for each policy area (pj).

However, distances between a nation’s spending priority point and a policy point are inversely

proportional to the amount of spending from the government that policy receives. The closer the

policy point is to the nation’s spending priority point, the more funds that nation spends on that

55

particular policy area. If for example the distance between France in 2008 and the point

representing social protection is zero then France would be predicted to spend 100% of its total

expenditures on social protection and 0% on the alternative policy areas in 2008. To convert the

distance into the corresponding percentage of spending on the policy area the distance between

nation points and policy points must be subtracted from the total proportion of spending:

dijt*

= 100 - |nit - pj| (3.2)

In order to locate the policy points and the nation spending priority points that provide us

with the minimum squared errors the process uses partial derivatives to find conditional global

minima.13

The process begins by holding the nation points fixed and placing all the policy points

to the left of the nation points. Next the first policy point has its error calculated when it is to the

left of the first nation point. The first policy point then proceeds through each interval between

nation points and has its error calculated. After the first policy point has moved through the

nation ideal points and had its error calculated, it is placed within the interval that produced the

smallest squared error. This process is then repeated for each of the remaining nine policy areas.

After the policy points have moved through the nation points, the new locations of the

policy points are fixed and the process begins moving each of the nation points. The first nation

point moves through the intervals created by the policy points with its squared error calculated in

each interval. After moving through the intervals, each nation point is placed in the interval that

minimizes the squared error of that nation point.

13

The approach uses conditional global minima to maximize the variance explained. It is a

minimum in that the squared error is the smallest, conditional on the fixed set of nation or policy

points at the time of the calculation. Once a fixed policy or nation point is moved, the points

locations do not represent the minimum squared error that is possible.

56

An iteration of this process involves moving all the policy points through the fixed nation

points and moving all the nation points through the fixed policy points. The entire process is

repeated until further iterations no longer increase the variance explained. Regardless of the

starting points for the policy and the nation points, the process is quick to converge, with each

additional iteration decreasing the amount of total squared error present (Poole 1984). The

process results in policy points that represent the location of the policy area stimuli and nation

points that represent the spending priorities for nations in given years.

WHY UNFOLDING?

Unlike the alternative measures discussed in Chapter 2, unfolding presents a number of

benefits that the other approaches cannot. The unfolding provides one variable that can capture

all of the policy areas. Further, the spending priorities variable combines information on all

available policy areas, measured in the same manner, and from the same time points to produce a

measure that can be represented in only one dimension. The unfolding also produces a variable

that shows change over time, across nations, and within policy areas.

Data Reduction

One of the benefits of this approach is its ability to compress a large amount of information into

a small number of values. The original dataset used here includes all available information from

1990-2009 and contains 3,790 data values. The original dataset requires 10 data values for each

of the 25 countries, for each year, to convey the spending information across the ten policy areas.

After performing the unfolding analysis, each nation’s spending by year can be represented by

one data value, reducing the number of required data values by 90% to 379 data values.

The data values that capture spending across the range of policy areas are referred to as

nation spending priorities. Individually each nation spending priority can indicate the relative

57

spending by a nation in a given year on the two main sets of spending priorities. The average

nation spending priority for the OECD data is 53.36 and can be used to provide insight into

general spending patterns across the nations examined. For example, a nation with values lower

than the mean, such as Belgium in 1990 which scores a 50.90, spends more on the particularized

policy areas like social protection than the average nation from 1990-2009. Whereas a nation

with values higher than the mean spending priority, such as Ireland in 1999, which has a

spending priority value of 54.29, spends more on collective goods such as education than the

average nation during this time period.

Original Data

Another benefit of the unfolding analysis is that the original data values that go into the

technique can be obtained after the analysis has been completed. The following formula takes

the output of the unfolding technique and recreates the original data values if so desired and

prevents a loss of data from the process.

c - xijt = |nit - pj| + eijt (3.3)

Because the spending values by policy areas were in percentages c=100, which is the

total percentage to be distributed across the spending areas, xijt represents the original percentage

of spending by nation i, on policy j, at time t; pj is the location of policy j; n is the nation

spending priority point for nation i, at time t; and eijt is an error term for nation i, on policy j, at

time t. This formula can be used on the nation points and policy points from the unfolding

technique to reproduce the original data, unlike factor analysis, additive scales, or typologies

where the original data cannot be reconstructed from the assigned values alone after the

techniques have been applied.

58

An example of how this process works is as follows. Austria spent about 9.71% of its

total expenditures on economic development in 2005. Using the equation above and the

information from the unfolding analysis that follows, I can recreate the original percentage of

spending. The above equation can be rearranged such that:

xijt = c - |nit - pj| + eijt (3.4)14

Where c=100 and based on the unfolding analysis nAustria,2005=52.57 and peconomic =142.77

which gives us an xAustria,Economic,2005=9.80. The unfolding predicts the actual spending for

Austria on economic development in 2005 to be 9.80% of total expenditures, resulting in an error

of only 0.09. The average difference between the re-derived expenditures for nations by policy

and the actual spending is zero (Table 3.2).

Table 3.3 Average Error in Capturing Actual Spending with Unfolding

Policy Area Average

Error

Government Operations 0.00018

Defense -0.00018

Order and Safety -0.00018

Economic Development -0.00019

Environmental Protection -0.00018

Community Development -0.00018

Health -0.00019

Recreation -0.00018

Education -0.00018

Social Protection 0.00018

Single Dimension

The unfolding analysis provides a single dimension to test theories across a set of

spending priorities. Prior research has tested theories on separate policy areas in isolation from

14

Equation 3.4 is the same as equation 3.2 with an error term.

59

each other. Using the original dataset here, this approach would require ten different models

where the ten separate variables would fail to captures the relationship between policy areas.

Alternative methods have attempted to combine policy areas that were hypothesized by the

researcher to go together creating a variety of dimensions including Hofferbert’s two dimensions

of education/welfare and highways/natural resources, and Erikson, Wright, and McIver’s

measure of policy liberalism, which fails to account for policy areas that do not have an obvious

partisan slant.

Another approach used to explain policy outputs from government actions is the use of

typologies. While typologies can provide a simplified approach to viewing complex topics, the

categories used vary based on the objective of the researcher, often focusing on aspects such as

accessibility by use, who pays, and public versus private goods. Applying a common typology

derived by Lowi (1964, 1972), the ten policy areas I study here would fall into two of Lowi’s

four categories: distributive and redistributive policies (Table 3.4). Distributive policies offer

benefits to a wider community while redistributive benefits provide services to groups with

particular needs.

Table 3.4 Policy Typology using Lowi’s Categories

Distributive Policy Redistributive

Policy

Health

Community Development

Economic Development

Environmental Protection

Education

Defense

Public order and Safety

Recreation

Government Operations

Social Protection

60

Applying a typology to the data however, forces the researcher to place policies into

categories that are not a perfect fit. For example, when fitting the ten policy areas into Lowi’s

typology (Table 3.4) environmental protection is categorized as a distributive good as

expenditures are used to promote clean air, clean water, and proper disposal of toxic waste have

benefits that spread throughout a society. However, environmental protection will also involve a

number of regulations, which means it carries characteristics of other categories in Lowi’s

typology. Furthermore, models that use typologies to produce a categorical dependent variable

will only be able to examine how the probability of spending on a set of policies is affected as a

set, and cannot predict the percentage of spending for each area. Once assigned to a category,

the resulting data points will be unable to identify the spending that belongs to each policy area.

Table 3.5 Exploratory Factor Analysis of Policy Areas

Policy Area Factor

1 Factor

2

Government Operations 0.9483 0.1298

Defense 0.9690 -0.1541

Public Order and Safety 0.9605 0.2389

Economic Development 0.9055 0.2544

Environmental Protection 0.9670 0.0840

Community Development 0.9338 -0.2800

Health 0.9920 -0.0087

Recreation 0.9460 -0.2154

Education 0.9933 -0.0900

Social Protection 0.9801 0.0494

Running an experimental factor analysis on the raw spending data produces a six factor solution

for the ten policy areas when unconstrained and a one factor solution when constrained (Table

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3.5).15

Once the factor variables are created the original spending data cannot be retrieved. The

one factor solution does not provide intuitive insight into the pattern of government spending,

and appears to represent a factor covering government expenditures. Additionally, when using a

factor as a dependent variable, the predictions in spending will be for the set of policies and the

actual levels of spending by policy cannot be determined.

No A Priori Assumptions

Instead of pre-selecting policy areas and a variety of indicators that I believe may

represent a predetermined dimension of choice, the unfolding analysis allows the data to speak

for themselves. The unfolding analysis uses data measuring all available policy areas in the

same manner instead of combining a range of indicators representing inputs and outputs of

government and presents a dimension without a pre-specified label. After the unfolding analysis

has been executed, substantive interpretations of the underlying dimension can be discussed

based on what the data reveal. Although the assignment of descriptive label for the underlying

dimension may be subjective, it is based on what the data shows after the method is applied and

cannot bias what the unfolding technique produces.

Reliability

The reliability of the unfolding process can be determined by calculating the R2 value

using the unfolded spending priorities, the policy points, and the original data (Jacoby and

Schneider 2009). This is because the original expenditures can be re-calculated with equation

3.3 and simplified using equation 3.2 such that:

15

As the spending data are in the currency for each nation, only those nations that belong to the

Euro zone are included in the exploratory factor analysis: Austria, Belgium, Finland, France,

Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Slovakia, and Spain (OECD

2009).

62

xijt = dijt* +eijt (3.5)

The resulting equation represents an OLS equation where the intercept equals zero and

the slope equals 1.

xijt = 0.00 + 1.00dijt*

+ eijt (3.6)

The R2 of this OLS model shows the amount of variance from the original expenditure

dataset that is explained by the unfolding technique, where “reliability is defined as the

proportion of a measure’s variance that corresponds to variance in the phenomenon being

measured” (Hand 2004). If xijt represents the phenomenon being measured (actual spending by

policy area), then the R2 value shows what percentage of the variance in the phenomenon is

explained. The unfolding analysis explains about 91% of the variance for the ten policy areas.

This high degree of reliability demonstrates that a single dimension of spending can capture

government spending patterns. The reliability of the measure supports the first part of the

hypothesis that a single dimension of policy can be used to represent government expenditures in

a parsimonious, encompassing, and reliable manner.

RESULTS OF THE UNFOLDED EXPENDITURE DATA

Applying the unfolding technique to the expenditure data from 1990-2009, establishes the

location of the policy points (Figure 3.4). The unfolding results show a clear and simple

distinction between the policies located at the two ends of the continuum. The results of the

unfolding show that government spending can be expressed as a trade-off in spending on policies

that target particular groups versus spending that targets the community in more general terms.

Increases in relative spending on one set of these policy areas will result in governments that

spend less on the other set of policy areas.

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Figure 3.4 Location of Unfolded Policy Points

The resulting spending priority measure for each nation can also be taken to represent

government compromise, where the priority values represent the final spending package

approved by government across a range of policy areas. This measure can then be used to test

what factors result in government spending patterns and the resulting compromises of

government outputs. Further, as the spending priority variable represents government

compromises it could be used to test the resulting policy outcomes of government, and how

successful a policy was at achieving its desired goal.

Policy Points

Government Operations

Social Protection

Health

Education

Economic Development

Defense

Public Order and Safety

Recreation

Community Development

Environmental Protection

0 50 100 150

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The values assigned to the policy areas serve to provide an interpretable meaning to the

distance between themselves and the spending priority points. Additionally, the relat ive

distances of the policy points in relation to one another can also be interpreted. The data

produced a dimension that has two main clusters with one at the far left and one at the far right.

On the left end of the continuum are a set of policy areas that affect or are intended to benefit

particular subsets of the population. At the other end of the continuum are a group of policy

areas that are intended to benefit more general groupings of the population.

The data suggest spending patterns are based on the targeted population that is intended

to be affected by the policy area. At the far left are policy areas that benefit particular groups

within a society referred to as particularized policy areas. At the right are policy areas that are

geared towards the population of a society more generally and are referred to as collective

goods.16

Starting at the left end of the policy dimension are government operations and social

protection. The citizens who directly benefit from the policy expenditures under government

operations represent a fairly limited subset of the population, including non-citizens in foreign

countries and government employees. Government operations include foreign aid for economic

issues in developing and transitioning countries of which the citizens of the lending government

receive no direct benefit. Additionally, salaries and expenditures for running different levels of

government administration are included in this category. Therefore, unless a citizen is employed

in a section of the government, policy expenditures on this area do not directly affect them.

16

The policy dimension found for the 25 democracies here is similar in nature to the latent

policy priority dimension found for the American states by Jacoby and Schneider, which they

labeled particularized benefits and collective goods (2001, 2009).

65

Next to government operations is social protection, which consists of expenditures

received by particular subsets of a nation’s population. Social protection is typically one of the

largest expenditure categories for the nations in the dataset across time and includes expenditures

for unemployment insurance, old-age pensions, family allowances, children services, disability

compensation, housing support, and survivor benefits. Importantly, not everyone in a nation

meets the criteria to receive benefits from social protection, particularly in terms of services that

are referred to as “welfare,” and as Bahle, Pfeifer and Wendt (2010) explain, “[it] does not

include the concept of an unconditional basic income for all” (348).17

Examples of types of

individuals who do qualify for benefits under social protection include the elderly who have

worked a certain number of years, war veterans, and families that fall below minimum income

levels established by the government.

At the right end of the continuum is a grouping of policies that represent more collective

goods. These are policies that are accessible to broader groups of a nation’s population,

especially in terms of programs like health and education that all citizens expect to make use of

at some point in their lives (Dean 2006). Health expenditures include spending on equipment,

hospital services, and public health services such as vaccines, blood banks, and disease detection,

which benefit the majority of citizens. As noted earlier, all of the nations during the time period

examined this research except for the United States have universal or near universal health care

coverage which provides access to at least a minimum set of health care services across most (if

not all) of the population.

Education expenditures include spending on primary through secondary and college

education, as well as vocational training. Primary education expenditures include expenditures

17

Emphasis added.

66

on literacy programs for individuals who have not met primary school literacy standards. All

levels of education include scholarships and grant funding expenditures. All citizens have access

to public education through at least high school and many individuals have access to funding

through the government for higher education.

Economic development expenditures focus on a variety of issues facing a nation’s

economy including trade, the prevention of discrimination in the workforce, agricultural

protection, obtaining and developing fuel and energy sources, and the development/ maintenance

of infrastructures. Economic development affects national populations in more general and

broad terms. Travel, goods and services for purchase, power and fuel, and employment are all

tied to growing economies.

Defense expenditures include spending on military protection by land, sea, air, or space,

civil protection for civilian institutions, and foreign military aid such as peacekeeping forces.

Everyone in a nation is expected to benefit from defense programs, not just certain social or

political groups, geographic regions, or policy interests.

Public order and safety is the next policy point on the continuum and encompass a

number of public programs. These expenditures include the provision and maintenance of police

and fire protection services, law courts, and prisons. All the expenditures under public order and

safety are provided to citizens within a nation. If citizens from one city travel into another city,

they are still able to receive emergency assistance. A police department will respond even if a

citizen is not a resident of their respective jurisdiction.

Recreational expenditures include spending on cultural activities, sports and recreation

services, and other community programs. Cultural expenditures go to support libraries,

museums, zoos, concert events, art galleries, and historic sites. Sports and recreation

67

expenditures are used to maintain playing fields, courts, tracks, golf courses, pools, and parks.

Recreation expenditures are collective goods because the expenditures are targeted at

communities in broad term to citizens, and not at particular groups of individuals within the area.

Community development expenditures are used for housing developments, to provide

sewage and water supplies to communities, and to develop/maintain public transportation

systems. All individuals within a community benefit from such services, like housing

developments improving to the quality of living in neighborhoods and raising property values

and those traveling through benefit from better lighting and the water supply. Note that

expenditures providing short and long term housing solutions for individuals unable to meet a

minimum standard of living are included in social protection. Unlike the housing expenditures

covered by social protection, the housing expenditure category itself focus on community

improvement and is not directed at specific individuals.

Finally, at the far right end of the unfolded policy dimension is the policy area of

environmental protection that deals with the quality of the environment. Expenditures focus on

waste management including the disposal of nuclear material, wastewater management, pollution

abatement directed at air and climate protection, and the protection of biodiversity and

landscapes that includes the protection of endangered flora and fauna. Environmental protection

and maintenance is benefits all citizens in a nation who breathe the air, drink the water, and

interact with the environment around them. As such these expenditures are the most collective

goods in the unfolded spending priority dimension.

The location of the policy points within the dimension also indicates which policy areas

are ranked higher in spending than others. The policy areas that are located closer to the center

of the policy continuum, represent policy areas that typically receive more spending than the

68

policy areas that are father away from the center. The policy areas of health, education, and

economic affairs represent policies that receive slightly higher proportions of spending than the

other collective goods, such as defense and environmental protection, located at the right of the

policy area continuum.

One more subtle note can be observed by examining the collective goods grouping at the

right end of the continuum. Within the cluster of collective goods, there are two separate

groupings, one containing health, education, and economic development and the other comprised

of defense, public order and safety, community development, and environmental protection. As

discussed earlier, the OECD labels health, education, and portions of economic development as

individual goods, while I contend that these areas are collective goods. The grouping of health,

education, and economic development is intended to benefit the community in broader terms

even though the expenditures targeted at specific individuals such as students in school or

individuals with medical needs. The spending on defense, public order and safety, and

environmental protection are closer to being pure collective goods where benefits are dispersed

across the society and not directed as specific individuals.

Difference between Nations’ Spending Priorities and Policy Points

The general position of a government spending priority within the cluster of spending

priority points indicates which nations have a preference for particularized policy areas over

collective goods relative to one another (Figure 3.5). Nations that are located towards the left,

have lower spending priority scores and are closer to the particularized benefits policy cluster,

representing higher spending on particularized policy over collective goods compared to nations

with higher scores. This includes Greece, Italy, and Belgium which have lower than the average

priority scores for the nations and time period examined. In 1990, Belgium had the lowest

69

spending priority score at 50.9, indicating it spent the most on particularized benefits compared

to any other nation-year in the dataset.

Figure 3.5 Distribution of Spending Priorities within Nations over Time

Moving towards the right, nations have higher spending priority scores, placing these

nations closer to the collective goods end representing greater expenditures on policy areas such

as defense, economic development, or education. This includes nations such as the United

States, Iceland, and South Korea, which have the highest priority scores for the nations and time

50 52 54 56 58Spending Priority

IcelandUnited States

Czech RepublicJapan

SlovakiaIrelandNowaySpain

CanadaUnited Kingdom

SloveniaHungary

LuxembourgPolandFrance

NetherlandsAustriaFinland

SwedenBelgium

ItalyDenmarkGermany

Greece

70

period examined. South Korea had the highest spending priority score at 59.2 in 2003,

representing the nation-year with the highest priority for spending on collective goods over

particularized benefits.

The interpretation of particularized benefits and collective goods assigned to this

unfolded dimension serves to support my hypothesis regarding how governments spend and

conforms to expectations found in prior research. Based on the number of citizens who are

affected by the policy areas and how the policy areas are arrayed it appears that governments do

select policy packages based on the grouping of citizens who are intended to receive direct

benefits by the policy expenditures.

Differences between Nations’ Spending Priorities

Figure 3.6 shows the variation in policy priorities from 1990 through 2009. The variation

within a given year indicates that there are factors unique to the nations in this study that cause

the democratic governments to differ from each other in spending priorities at the same point in

time. The centers of the boxes represent the mean spending priority point for that given year.

Over the course of the twenty years, variation in spending priorities occurs repeatedly and is

substantial in size. In 2009 there is a more concentrated degree of variation, which is a product

of the limited number of nations with data available at the time of the analysis. While the

average range of spending priorities in a year may seem small at 7% of total government

spending, this is roughly equal to a €73 billion shift in spending for a nation like Germany or a

£23 billion shift in spending for the United Kingdom.

Nations’ spending priority points can also be interpreted in relation to one another. The

difference between two nations’ spending priority points represents the percentage point

difference between those two nations for spending on the different clusters of policies. An

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example of this can be seen by examining the spending priority scores for Germany and Iceland

in 2001. In 2001, Germany has a spending priority score of 51.85 and Iceland has a spending

priority score of 56.74. What do these two values imply? Individually, the spending priorities of

these two nations can be compared to the mean value of spending priorities, which is 53.36, it

can be seen that Iceland spends more on particularized policy areas such as social protection than

the average nation while Germany spends more on collective goods. In relation to each other,

the difference between the spending priorities reflects that Germany spent 4.89% more of its

total spending on particularized benefits such as pensions and unemployment than Iceland.

Conversely, Iceland spent 4.89% more on collective goods including items involving

environmental protection than Germany did in 2001. The 4.89% difference in 2001, is

equivalent to a shift in spending between these two sets of policy areas of 16 billion Icelandic

Krona or €49 billion in Germany.

The differences help reveal which nations spend more on certain types of policy areas

compared to others. Nations with spending priority scores that are closer together, where there

are smaller distances between points, have similar spending patterns than nations that are further

apart and have greater differences in terms of spending on policy areas. Looking at the

distribution of spending priorities by nation (Figure 3.6) it is clear that nations like Germany and

Italy spend more on particularized benefits than nations like Iceland and Japan. Additionally,

nations like Germany and Italy which have priority values closer to each other, will spend on

policy areas in a similar manner, where as the United States, which has much higher spending

priority scores compared to Germany, will exhibit a much different spending profile in terms of

spending on the same set of policy areas. As the spending priorities variable captures these

72

differences, it is possible to test what influences the variation in policy expenditures across

nations.

Figure 3.6 Distribution of Spending Priorities over Time

The differences between spending priorities can also be used to measure changes in

spending patterns over time within nations. Again looking at the distribution in spending

priorities (Figure 3.6), nations’ spending patterns are not constant from one year to the next and

this measure captures and retains these differences. In the same manner that different nations’

spending priorities can be compared, the differences within a nation, over time, can be examined.

73

For example, the United Kingdom had a spending priority of 52.95 in 1998 and 54.88 in 2008.

The difference between these two time points suggests a 1.93 percentage point or roughly £7.8

billion shift in spending from particularized benefits, such as children and family benefits, to

collective goods, like education and economic development.

Close Examination of Policy Priority Scores

In order to gain a better understanding of what the spending priority scores look like,

three nations representing the lower, middle, and upper end of the spending priority spectrum are

selected (Figure 3.7). Based on the arguments presented in Chapter 2, it is suggested that the

differences in spending patterns across nations are a result of political institutions. For example,

Austria represents a nation that has spending priority scores at the lower end of the policy

continuum indicating that it has higher expenditures on particularized benefits like

unemployment or housing vouchers than other nations. Austria is a country that has a

parliamentary system and uses proportional representation to elect officials for office. Austria

also possesses a system that has a relatively high district magnitude of about 20 seats per district.

The initial examination here supports the findings in the literature that nations with

parliamentary systems, proportional representation, and larger district magnitudes have higher

expenditures on particularized benefits.

Canada, however, is a nation that represents a government whose policy packages come

close to representing the mean of spending priorities of the democratic nations. Canada, like

Austria, uses a parliamentary system. But, unlike Austria, Canada has a majoritarian system for

electing officials to office and has a district magnitude size of one. Compared to Austria, Canada

has larger spending priority scores, indicating that Canada spends more on collective goods than

74

Figure 3.7 Distribution of Spending Priorities across Nations

75

does Austria. The greater spending collective goods in Canada may be a product of it using a

majoritarian system with a district magnitude of one for electing officials to office.

At the far right end of the spending priority spectrum is South Korea. South Korea

represents a nation that has relatively high expenditures on collective goods. South Korea has a

presidential system and uses a mixed form of voting to elect officials to office. Additionally,

South Korea has a district magnitude of about 8.6. Unlike Austria and Canada, South Korea’s

use of a presidential system may be influencing its focus on collective goods. At the same time,

South Korea has a relatively smaller district magnitude than Austria but one larger than Canada

and uses a mixed system for elections.

While political institutions may help to understand differences across nations, these

systems cannot explain the variations within nations as political institutions do not change

frequently over time. The variation occurring within nations may be a product of other

conditions with nations like inflation, unemployment, or government composition. The potential

effect of different combinations of political institutions cannot be fully worked out with the three

examples presented here and requires a more detailed analysis that follows in Chapter 5.

CONCLUSION

While single indicators, composite measures, and typologies have been used previously

as a means of capturing policy decisions, these measures have limitations that prevent a full

understanding of the outputs the measures are used to test. The single indicator approach risks

omitting policy areas that are related to the policy under examination. While composite

measures tend to combine either variables that represent different aspects of policy making or

different views on what the dimension of choice regarding policies is, before the technique is

applied. The spending priorities variable is a better measure of government outputs. The

76

variable is capable of tracking changes over-time and across nations. Further, it has a readily

interpretable meaning; lower scores represent nations that spend more on particularized benefits

while larger values indicate greater spending on collective goods. More detailed information on

the proportion of expenditures dedicated to each area can be obtained through simple subtraction

as well, allowing for detailed predictions.

The new measure of democratic government spending priorities provides support for the

argument that policy priorities can be shown along a single dimension and that the choices made

by governments are a product of the group of citizens who are intended to benefit from these

policies. Having demonstrated how governments spend using spending priorities, I can examine

what factors influence these priorities. Compared to prior research that examines the influence

of different indicators, like the state of the economy, on policy areas separately or through

composite measures that encompass different aspects of the policy process, I can now analyze

the t the factors that influence the overall pattern of spending priorities in democratic nations

across time within a single model.

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CHAPTER 4 DATA AND HYPOTHESES

Prior literature suggests that three main types of variables influence government spending

priorities. The first set of factors includes the socio-economic conditions that indicate who in

society needs government assistance, like the elderly population and the unemployed, and the

resources available for government to spend. Further, as a democratic government is expected to

have policy outputs that correspond to the expectations of the people it represents, the second set

of factors addresses how the preferences of different groups, including political parties and

citizens, affect government spending. The final set of variables I test focus on different types of

political institutions measured separately and in combination with each other. The role of

institutions is suggested to shape the behavior of incumbents and as such alter policy outputs in

predictable ways.

FACTORS INFLUENCING GOVERNMENT SPENDING

The socio-economic make-up of the society in which governments operate is found to

influence policy expenditures; this includes the level of wealth in a nation and the composition of

different groups in society like the elderly, the youth, and the workforce. Based on the socio-

economic condition of a nation, governments have various resources to work with and problems

to solve. In a nation where unemployment is high, a government has a greater need to address

social welfare issues to appease its citizens. As unemployment increases, more individuals are

unable provide basic necessities for themselves and their families. The expectation then is that

when unemployment in a nation is high, a nation will spend more on particularized policy areas

such as housing subsidies to help alleviate issues resulting from unemployment. However, in a

nation where unemployment is lower, the government will have less pressure from society to

alleviate problems and will spend less on particularized policy areas relative to collective goods.

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H2a: Increases (decreases) in unemployment produce governments that spend more on

particularized benefits (collective goods).

In order to operationalize unemployment, I use data available from the World Bank based

on the percent of unemployed out of the total work force population. Unemployed individuals

are considered as those who are out of work but are actively seeking employment. This

information is available for all years of the analysis and for all nations.

Tied to employment and policy expenditures is the number of women in the workforce.

As more women enter the workforce the dynamics of society change because the needs of those

who are employed change. The more women who participate in the workforce, the more aid

governments provide to make it easier for women to enter and remain in the workforce. In order

to help support female participation in the labor force, nations are expected to increase spending

on particularized policy areas that help reduce the barriers of female participation, such as

providing day care services for children. Therefore, the expectation is that as the percentage of

women who are a part of the workforce increases, the more a nation will spend on particularized

policies.

H2b: Increases (decreases) in the percentage of women in the workforce produce

governments that spend more on particularized benefits (collective goods).

Information regarding the percentage of female participation in the workforce is obtained

from the World Bank. The World Bank provides data on the workforce participation rate for

females over the age of 15 as a percentage of the total female population in a nation. This

information is available for all nations in the analysis for all time periods.

Another characteristic of a nation found to affect government expenditures is the size of

the dependent population, which involves the percentage of youth and elderly populations in

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nations. As the percentage of elderly increases in a nation, fewer citizens are working and able

to provide a minimum standard of living for themselves. Based on the increase in aid demanded

of a government for elderly citizens, the expectation is that as the percentage of elderly increases

in a nation, so will the nation’s expenditures on particularized policy areas such as pensions.

However, as a nation’s elderly population decreases, there is less strain placed on the

government to provide for the elderly population and it is expected that the nation will have

greater relative expenditures on collective goods.

While prior work at times has grouped the youth population together with the elderly as a

measure of “dependent populations” (Huber and Stephens 2000, 2001), the needs of the two

groups differ. The elderly may require assistance to maintain a minimum standard of living,

whereas younger populations require a different set government services, such as educational

spending. Because the needs of the youth population focus on services that are collective goods,

larger proportions of the youth population are expected to increase spending on collective goods.

As the needs of the two dependent populations differ, I will examine the effects of the aged and

youth populations separately.

H2c: Increases (decreases) in the percentage of elderly in the population produce

governments that spend more on particularized benefits (collective goods).

H2d: Increases (decreases) in the percentage of youth in the population produce

governments that spend more on collective benefits (particularized benefits).

Variables representing the size of the youth and aged population are created with data

from the World Bank. The aged population variable is based on data from the World Bank on

the population above the age of 65 as a percentage of the total population. The youth population

80

variable is based on the percentage of the population under fifteen years of age. This

information is available for all nations in the analysis across all time periods.

Another trait in society that is expected to alter policy expenditures is the wealth of the

population in a nation. As wealth in a nation increases the demands placed on the government

by the people to provide more goods and services increases. The form of the goods and services

demanded by the people as development occurs take on the form of particularized spending such

as unemployment and retirement benefits. Wealth is expected to increase spending on

particularized benefits because governments are in a position to provide services to particular

groups in the community without depriving the general population of services through collective

goods. When governments have fewer resources, relative spending is expected to favor more

collective goods that serve the broader community at the expense of particularized benefits such

as housing or daycare.

H2e: Increases (decreases) in national wealth produce governments that spend more on

particularized benefits (collective goods).

I operationalize wealth following standard practices in the literature and use real gross

domestic product per capita (GDP/Capita). This variable is created using data from the World

Bank and the Bureau of Labor Statistics (BLS). Information regarding the current gross

domestic product per capita in nominal US dollars, is obtained from the World Bank. As the

gross domestic product per capita figures are in nominal dollars adjustments are made for

inflation to obtain real gross domestic product per capita figures. In order to obtain real gross

domestic product per capita figures information regarding the Consumer Price Index (CPI) for

the US is taken from the BLS. Using the nominal gross domestic product per capita and the CPI,

the real gross domestic product per capita figures are calculated for all the nations in the study

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were in real 2008 US dollars. The following formula is used to change the nominal gross

domestic product per capita figures into real gross domestic product per capita. The nominal

gross domestic product per capita value at year t, is multiplied by the 2008 CPI value divided by

the CPI value for year t:

Real GDP/Capitat = Nominal GDP / Capitat * ( CPI2008 / CPIt ) (4.1)

In regards to the economy, the level of inflation in a nation alters the resources a nation

has to work with when dealing with policy expenditures. As the level of inflation increases, it

costs a government more to provide the same level of goods and services. Based on the effect of

inflation, it is expected that as inflation increases, governments are less able to spend on

particularized policy areas due to increased costs. As inflation decreases, governments are able

to supply more goods and services with the same amount of resources and will spend more on

particularized policy areas without having to decrease the level of collective goods provided to

the general public.

H2f: Increases (decreases) in the inflation rate produce governments that spend more on

collective goods (particularized benefits).

Inflation is examined in the analysis with data from the World Bank. The inflation

variable is measured as the annual percent of inflation for each nation year in the analysis. The

data are available for each year of the analysis for all nations.

The effect of trade openness due to globalization in relation to government spending has

varied in the literature. Some work has argued that increases in trade and globalization will

increase government spending on particularized benefits to offset the costs and risks due to more

open economies involving issues like unemployment or housing benefits (Cameron 1978; Rodrik

1998). Alternatively, other research suggests that increased openness will decrease government

82

spending on particularized benefits because policies favoring particularized benefits make

nations less attractive to businesses, as it is more costly to produce in nations with higher taxes

and better protected workforces with generous unemployment and disability benefits (Scharpf

2000). Therefore, I test the effect of openness on government spending with no previously

determined directional expectation.

To test the effect of openness on government spending patterns I use data on imports and

exports from the OECD. Following the approach used by Huber and Stephens (2001), I

operationalize trade openness as the total imports and exports as a percentage of GDP. Higher

proportions are suggested to represent greater openness in terms of a nation’s economy.

A final socio-economic indicator expected to shape government actions is membership in

the European Union. Nations that belong to the European Union are intentionally integrating

their economies and creating binding and non-binding policies. As such, the nations that are

members of the European Union are expected to behave differently than the nations that are not

members. Nations that belong to the European Union have traditionally had higher levels of

spending on particularized policy areas that focus on welfare. Nations that belong to the

European Union have attempted to establish minimum standards involving elements of social

protection for workers; promoting attention to particularized policy spending that includes

spending on disability, sickness, survivor benefits, pensions, and unemployment. Therefore, the

expectation is for nations that belong to the European Union to spend more on particularized

benefits than non-European Union member nations.

H2g: Members of the European Union will spend more on particularized benefits than

non-member nations.

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In order to capture the effect of the European Union, I create a variable based on

membership in the European Union with information obtained from Europa.org. The EU

variable is a dummy variable that receives a one if a nation was a member of the European Union

in a given year. If a nation was not a member of the European Union in a given year it is coded

as a zero.

Different forms of political preferences are found to shape government actions. As

discussed in Chapter 2, the political parties in office, voter turnout, role of government, public

opinion, and interest groups all influence government actions. The composition of the

government in terms of which parties are in office affect the decisions a government makes.

Political parties located on the ideological left are typically associated with greater emphasis on

policies which benefit particular groups in society, generally those who may be less well off and

in need of assistance. As such, governments dominated by left parties are expected to have

greater spending on particularized policy areas like housing or food benefits. Conversely,

political parties on the ideological right are less likely to be associated with greater spending on

policies aimed at particular groups in the society. Instead, the expectation is that governments,

dominated by political parties on the right will spend less on particularized policy areas and

relatively more on policy that target the general public in the form of collective goods, such as

economic development or defense.

H2h: Ideologically left (right) dominated governments spend more on particularized

benefits (collective goods).

The composition of the government is based on information available from the Database

of Political Institutions (DPI), political parties’ websites, and the European Elections Database

(EED). The DPI provides information about the number of seats held by the top three parties in

84

control of the government and the number of seats held by the top three opposition parties, in

addition to the total number of seats in the lower house with its gov#me and gov#seat

variables.18

At the same time, the DPI provides Left, Right and Center designations for each of

the major parties in office with its gov#rlc variables. Because the focus here is on expenditures

and the DPI coded Center parties as those that are fiscally conservative; not based on overall

Left/Right policy positions, Center designations are reclassified here as Right. For political

parties where the DPI did not provide a Left, Center, or Right position, information available

from the EED was used to determine the political parties designation based on names in the cases

of Green and Communist parties or the EED’s classification of Left or Right. For the smaller

parties where data was not available from the DPI or the EED, the political parties’ websites are

used and the Left/Right leanings of the parties label are assigned.19

When coding is a result of

personal judgment, attempts are made to follow the DPI coding as closely as possible with

“Right: for parties that are defined as conservative, Christian democratic, or right-

wing…[and]…Left: for parties that are defined as communist, socialist, social democratic, or

left-wing” (Keefer 2009, 6).

The variable representing the composition of the government is calculated as the

percentage of seats held by leftist parties. The percentage of seats belonging to leftist parties is

calculated based on the number of seats held by leftist party members and the total number of

seats in the lower house for which information is available for each year.

18

The # sign takes the place of 1st

, 2nd

, and 3rd

largest party numbers. The average number of

seats captured by the top three parties is 94.5%. 19

In situations where the party websites are not posted in English, Google Chrome is used to

translate the websites into English.

85

In addition to the parties in government, who votes has been suggested to influence

government actions. Citizen mobilization has been argued to shape government activities as it

determines what political parties are in office. However, there is disagreement on what increased

voter turnout implies: more low income voters electing left party members into government or

more wealthy voters electing right party candidates. The mixed findings for voter turnout may

be a product of the previous measures used to capture government activities. As such, I test for

the effect of voter turnout in Chapter 5. Therefore, while I expect voter turnout to influence

government spending patterns, there is no directional expectation for its influence.

Citizen mobilization is captured using a measure of voter turnout. The voter turnout

variable is created using data available from Institute for Democratic Election Assistance

(IDEA). The variable is created using data on the total number of votes cast (valid or invalid)

divided by the number of names on the voters' register. The level of turnout is held constant

between election years based on the percentage at the time of the last election year.

As democratic governments are expected to be responsive to the preferences of their

citizens, the expectations for the role of government should affect a nation’s policy decisions.

Some nations have expectations that emphasize a more limited role for governments, such as

focusing greater efforts on ensuring a growing economic climate and protecting its citizens. In

nations that exhibit beliefs with more limited roles governments are predicted to spend more on

collective goods that include policy areas like defense and economic development. Some

nations, however, have expectations of a greater role for government, such as ensuring freedoms.

In nations with a broader view of government’s role, the prediction is for government spending

to promote basic levels of equality that would require spending on more particularized benefits.

86

H2i: Expectations of government dominated by views of a limited (expansive) role for

government produce governments that spend more on collective goods (particularized

benefits).

The role of government is measured here using Inglehart’s Post-Materialism index

available through the World Values Survey (WVS) and the European Values Survey (EVS). The

questions used to create the four-item index can be seen as representing the different

expectations for government involvement in more aspects of life or less. Of the four goals for

their country people are asked to select among, two are maintaining order in the nation and

fighting rising prices, which can be seen as more limited roles of government. Meanwhile,

giving the people more say in important government decisions and protecting freedom of speech

and can be taken as expanding the role of government. Based on respondents’ selections of the

top two priorities the variable for role of government is created based on the percentage point

difference between those who selected the two values of expanding government’s involvement

and those who selected the two values that focus on government having a more limited role. As

survey data are not available for every year of the analysis, interpolation is used between

available data points.20

In situations where the observations fall after the last available year of

data, the last known interpolated rate of change is calculated and is used going forward. In years

20

Data for the United Kingdom was not available in a United Kingdom aggregated format.

Therefore when the data are available for Northern Ireland and Great Britain the data are

recombined to create a United Kingdom data value. In 1998, data for Northern Ireland and 1999

data for Great Britain were combined and used as both 1998 and 1999 values for the United

Kingdom. In 2006, only Great Britain has available data which are used as the last known data

point for the United Kingdom.

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before the first known data point, the first known data point is used as a constant going back in

time as the rate of change is unknown. 21

Role of Government = (Post-Materialist Values%) – (Materialist Values %) (4.3)

Even though societies tend to have established boundaries on the role of the government,

public opinion on how governments should address particular issues can still vary. As such, the

expectation is that when liberal preferences dominate citizens’ public opinion in a nation, the

government will spend more on particularized benefits that include welfare spending. However,

when conservative opinions dominate, the expectation is to see government focus shift to greater

spending toward collective goods. For example, when expectations limit the role of government

actions to areas like the economy a more liberal public opinion may prefer government

addressing unemployment through increased spending on unemployment benefits or housing

subsidies (particularized benefits), while more conservative publics may prefer governments to

use tax cuts to stimulate business growth to reduce unemployment (collective goods).

H2j: Public opinion dominated by liberal (conservative) attitudes produces governments

that spend more on particularized benefits (collective goods).

In order to capture public opinion, information from the WVS, waves one through five,

regarding what political party a respondent would vote for if an election was held today are used.

Klingemann et al. (1994) argue that political parties represent packages of policies that they

present to the voters through their party manifestos. If parties represent packages of policies to

21

Interpolation is an approach that can be used to calculate new data points within a range of

known data points. The interpolations were calculated in the following linear manner:

yt+1=yt+(yb-ya)/(b-a)

Here, a and b represent the values at the first (a) and last (b) year in the range of interpolation

and t=a. This approach is applied between all known data points where there is no available

data. This method of interpolation is applied to all survey data questions used in the analysis

where change occurs between time points.

88

be implemented upon obtaining office, then the political party individuals would vote for at any

given time should correspond to the policy packages individuals prefer. This variable is used as

a proxy for public opinion as it represents the preferences of individuals across a number of

policy areas based on the party platform they would support.

The political party respondents state they would vote for are coded as left or right using

the same coding scheme used to classify the parties for the composition of the government

variable. After adjusting for respondents who did not have an answer for the question, the

difference is calculated between the sum of those who would vote for a left party and those who

would vote for a right political party using the following formula for each nation year:

Public Opinion =

(4.4)

Again, as information is not available for all years, values are interpolated between years of

available data.22

The last interpolated rate of change for observations is used to extrapolate past

the last known data point to calculate values going forward, while the first known data point is

used as a constant for years occurring before the first data point as the pattern of change is

unknown prior to available information.

An additional factor that affects government actions in a nation is interest groups.

Interest groups organize to promote specific agendas based on the aligned preferences of their

members. If there are a limited number of interest groups in a nation, the groups may be capable

of capturing the attention of government and swaying officials to increase spending on particular

policy areas favoring particular groups’ interests. However, as the number of interest groups in a

22

Data for South Korea were not available for any time periods. Additionally, parties receiving

less than 0.2 percent of the respondents vote in the survey were omitted from the study as a result

of obscure and difficult to locate information on party positions.

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nation increase, it becomes difficult for a government to respond to the demands of interest

groups. Therefore, the expectation is that as the density of interest groups in a nation increase,

the ability of the groups to influence a government to spend more on their particular interests’

decreases; and as such the expectation is for greater spending on collective goods.

H2k: Increases (decreases) in the density of interest groups in a nation produce

governments that spend more on collective goods (particularized benefits).

I use a factor analysis on two variables representing interest group strength to create the

interest group variable. The first variable included in the factor analysis is the number of

business associations present in a nation based on information from the World Guide to Trade

Associations. The second variable is the number of public sector employees in thousands based

on data from the International Labour Organization’s Labour Statistics Database. Government

employees are used as a measure for interest group strength because government employees can

act as advocates for interest groups and have previously been used as a measure of interest group

strength (Jacoby and Schneider 2001). The factor analysis produced a single factor representing

interest group strength (Table 4.1).23

Table 4.1 Results of Factor Analysis for Interest Groups

Variable Factor 1 Uniqueness

Business Associations 0.7527 0.4335

Government Employees 0.7527 0.4335

As an additional check on the influence of interest groups, I use Lehmbruch’s (1984)

measure of corporatism. The higher the degree of corporatism on the 1 to 5 scale, the more

23

Additional model specification runs captured interest groups as: count of business

associations, count of government employees, and GDP/business association to capture resources

available to groups. The alternative measures resulted in models with similar results in terms of

signs, magnitude, and statistical significance of coefficients.

90

influence interest groups have over government actions. However, the data on this variable are

more limited in terms of the number of coded countries and only overlap with 214 of the nation-

years in my dataset. Therefore, I use this to confirm the results of prior work using this measure,

but also run alternative models using the variable produced by the factor analysis discussed

above.

Prior research has led to the conclusion that institutions should matter for policy outputs

in a nation (Iversen and Soskice 2006; Persson et al. 2007; Huber and Stephens 1993; Immergut

2010; Lijphart 1999). Institutions found to influence government actions include presidential

versus parliamentary systems, majoritarian versus proportional representation systems, district

magnitude, bicameralism, and federalism.

Democratic nations typically use either a presidential or parliamentary system to

determine the executive. Both presidential and parliamentary systems are associated with

different expectations about the behavior patterns of the executive. In a presidential system, the

executive is elected separately from the legislature, and must obtain a majority of the votes in

order to win office. Therefore, a presidential system promotes an executive who attempts to run

on a policy platform to appeal to as many citizens as possible (using collective goods). If a

presidential candidate runs on a policy package that is targeted towards too specific of a group in

society the candidate risks pushing away potential supporters.

Unlike a presidential system where the executive is elected separately from the

legislature, under parliamentary systems the executive is appointed by the legislature. Typically,

the controlling party or coalition within the legislature in a parliamentary system selects the

executive. In this situation, the executive serves to promote the interests of the group that

appointed the executive. The dynamics of a parliamentary system promote an executive who

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represents the interests of the ruling party or coalition. The expectation is for nations with

parliamentary systems to favor spending on more particularized policy areas as compared to

presidential systems. Conversely, in a presidential system, the expectation is for greater emphasis

on spending towards collective policy areas such as education and economic development.

H2l: Presidential (parliamentary) systems produce governments that spend more on

collective goods (particularized benefits).

In order to capture whether a nation has a presidential or parliamentary system, data from

the DPI are used. The DPI notes if in a given year a nation has a presidential or parliamentary

system with its system variable that I use to create a dummy variable representing the presence of

a presidential system. A one for the presidential systems variable represents a nation with a

presidential system and a zero indicates a parliamentary system in the nation, in a given year.

In a nation using a majoritarian system for its electoral formula, the candidate with the

most votes will gain office. Majoritarian systems promote candidates that appeal to as many

citizens as possible in order to win elections. Candidates in this situation are expected to run on

platforms that appeal to citizens in general and not to particular groups of individuals. If

candidates in a majoritarian system opt for particularized policy platforms they risk alienating a

voter base of citizens who either do not receive benefits from the particularized policies or are

made worse off by policy platforms. As a result, it is expected that nations that use majoritarian

systems will have greater spending on collective goods.

In a proportional representation system, candidates do not necessarily need to win a

majority of votes to win office; instead, candidates can win by appealing to subgroups within

society. Candidates then use particularized policy packages to appeal to targeted groups that are

large enough to win office. The particularized policies may alienate other groups within the

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society, but as a candidate does not need a majority to win an election, particularized policy

packages, such as family or disability benefits are used to secure a reliable vote base. Based on

the expected behavior of candidates in nations with proportional representation systems, the

expectation is for a greater emphasis on spending in particularized policy areas.

Some nations do not use either a pure majoritarian or pure proportional representation

system, but instead opt for an electoral system that uses a combination of the two formulas.

Compared to a pure proportional representation system, in a mixed system some candidates are

elected under majoritarian rules and others through proportional representation. The mix

produces some candidates then whose policy platforms target particular subgroups within the

population and some who run on more collective policy platforms. In this situation it is expected

that mixed systems will have greater spending on collective goods as compared to proportional

representation systems as some candidates aim for majority votes with collective goods.

Relative to majoritarian systems, nations with mixed systems will have greater spending on

particularized policy areas with some candidates targeting subgroups within a population to win

elections.

H2m: Proportional representation, compared to majoritarian systems, produces

governments that spend more on particularized benefits.

H2n: Mixed electoral systems, compared to majoritarian (proportional) systems, produce

governments that spend more on particularized benefits (collective goods).

A dummy variable representing the voting structure in a nation in a given year is created

using information from the DPI variables plurality and proportional representation. Using the

information on the voting structures I create three dummy variables called majoritarian,

93

proportional representation, and mixed voting based on the DPI. A one represents the presence

of the attribute for which the variable is named and a zero represents the absence of the attribute.

While the electoral formula determines how votes result in winning candidates, district

magnitude determines how many candidates can win an election. As district magnitude

increases, the number of votes a candidate needs to win an election decreases. In a district with a

magnitude of one, only the candidate with the most votes will win the seat. When more seats are

available in a district, candidates can win a seat without obtaining the most votes. Increases in

district magnitude are expected to mirror a proportional representation system, where candidates

turn towards targeted groups within a population in the hopes of obtaining a secure vote base that

can be relied on at election time. In order to win the support of targeted groups within the

population, candidates focus on policy areas that are aimed at the unique characteristics of a

group towards which other candidates are not catering. Based on the argument regarding district

magnitude, the expectation is that increases in district magnitude move candidates towards

catering to particular groups within the population, producing a government that will spend more

on particularized policy areas than proportional representation policy areas.

H2o: Larger district magnitudes produce governments that spend more on particularized

benefits.

In order to capture district magnitude size I use information from the DPI regarding mean

district magnitude for the lower house. As not all nations have a bicameral legislature data for

the district magnitude size of the upper house do not always exist, unlike information regarding

the mean district magnitude size for the lower house. The DPI calculates mean district

magnitude based on available data involving the number of representatives for each

constituency.

94

While bicameralism and federalism are both argued to act to constrain the ability of

governments to act in terms of reaching policy agreements, there are no standing arguments for

how these institutions should or would individually shape expenditures in democratic nations.

Particularly for federal systems, as the division of policy responsibilities is not universal and

varies widely in terms of the unique policy domains. However, as bicameral systems increase

the number of preferences present in the decision making process by dividing power across two

houses, it increases the difficultly for any particular actor to move policy in a direction that

favors a particular group at the expense of other groups. Therefore, I expect bicameral systems

to spend more on collective goods that benefit groups more broadly compared to unicameral

systems where fewer actors need to agree over policy.

H2p: Bicameral (unicameral) systems produce governments that spend more on

collective goods (particularized benefits).

Bicameral legislatures are captured by creating a dummy variable with information from

the Inter-Parliamentary Union’s PARLINE database on national parliaments. The bicameralism

variable is coded as a one when a nation has a bicameral legislature and a zero when a nation has

a unicameral system

Additionally, federal systems have been unable to reduce spending on areas of social

protection as opposed to unitary systems during the era of welfare retrenchment during the time

period examined (Obinger, Leibfried, Castles 2005). Therefore, I expect federal systems to

spend more on particularized benefits compared to unitary systems in the time period examined.

This expectation is based on their inability to shift spending priorities as quickly as unitary

systems, and is not based on federal systems having a particular nature for spending on one

policy area over another.

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H2q: Governments with federal (unitary) systems spend more on particularized benefits

(collective goods).

Nations with federal systems are captured using information from Bednar’s work on

federalism. Bednar breaks down nations that have unitary, quasi-federal, and federal systems. I

create a federalism dummy variable where a one corresponds to a nation with a federal system

based on Bednar’s coding and a zero in all other situations. Bednar’s coding produces three

quasi-federal systems in my dataset that are coded as zeros: Italy, Spain, and the United

Kingdom. Each quasi-federal system lacked one of Bednar’s three defining criteria to be labeled

as federal. Italy lacked direct governance where “authority is shared between the nation and the

national governments: each governs its citizens directly, so that each citizen is governed by at

least two authorities. Each level of government is sovereign in at least one policy realm. This

policy sovereignty is constitutionally declared” (Bednar 2009, 18). The inability to have unique

policy domains prevents federalism from acting as a constraint as discussed earlier because the

national government can intervene on any policy area without overstepping its political limits.

Spain and the United Kingdom both lack geopolitical division, where the “territory is divided

into mutually exclusive nations (or provinces, Länder, etc). The existence of each state is

constitutionally recognized and may not be unilaterally abolished” (Bednar 2009, 18). Both

Spain and the United Kingdom do not divide their territories into fully autonomous regions that

can act to constrain the national government’s actions and as such are coded as zeros.

POLICY RESPONSIVENESS HYPOTHESES

The second set of expectations involves institutions and the interaction between

institutional constraints and preferences. This set of hypotheses is used to address the third

question set out at the beginning of this dissertation: Does the institutional design of a nation

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alter the role of preferences in shaping government spending? As the number of institutional

constraints increases there are two anticipated results. The first expectation is that an increase in

the number of constraints will shift spending towards collective goods that are intended to

benefit more general groups within the population. This is a product of constraints introducing

more actors, with preferences over policy outputs, into the decision making process. As the

number of groups present in the decision making process increases, it becomes harder for any

actor to increase spending on policy areas that benefit their particular interest at the expense of

others. The result then is greater spending on collective goods that benefit groups more broadly.

H3a: Nations with more institutional constraints spend more on collective goods, relative

to nations that have fewer institutional constraints.

In order to capture the number of constraints in a nation, information regarding the

previously generated institutional variables is used. The constraint variable is an additive index

based on the count of institutional constraints present in a nation in a given year (alpha=0.7291).

For the purpose of the count of constraints, the district magnitude variable discussed above is

converted into a dummy variable where a one represents average district magnitudes greater than

one and a zero represents district magnitudes equal to one. I also collapse the electoral

institutions variable into a majoritarian/non-majoritarian dummy variable where a one represents

the presence of a proportional representation or mixed voting system. Both proportional

representation and mixed voting systems increase the number of actors present in the decision

making process and are expected to serve as constraints.

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Constraints = President + Non-majoritarian + District Magnitude Dummy

+ Bicameralism + Federalism (4.5)24

Further, as the constraints increase in number in a nation, policy becomes more difficult

to change and is more resolute. The increased policy resoluteness is a product of the increase in

the number of actors with preferences that need to be accommodated for policies different than

the status quo to be enacted. The increased difficulty of agreement should decrease the ability of

any group/actor to obtain its ideal policy whether it is for greater spending on particularized

benefits or for collective goods. Building upon the analyses in Chapter 5 that test the second set

of hypotheses presented earlier in this chapter, I expand the study of influences on government

spending patterns in Chapter 6. I argue that as the number of constraints increase, governments

respond less to the preferences of different groups. I test this argument using the following

hypothesis:

H3b: Increasing the number of institutional constraints decreases the policy

responsiveness of governments to different groups’ preferences for government outputs.

To test the effect of institutional constraints on preferences in Chapter 6, I interact the

constraints variable with each of the four measures of preferences: government composition, role

of government, public opinion, and interest group density. I use interactions between the number

of constraints and measures of preferences because I hypothesize that the effect of each group

should not only be a product of its respective expectations, but that its effect will be contingent

upon the constraints present. Therefore, as the number of institutional constraints changes, the

ability of government spending to respond to each group should also change.

24

Creating an additive index of institutional constraints is also the approach used by Huber and

Stephens (1993, 2000, 2001) and Brooks and Manza (2007).

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Table 4.2 provides a brief look at the data used in Chapter 5 and 6 to test the hypotheses

presented in this chapter. The first variable in the table provides descriptive statistics for the

spending priorities variable that is created in Chapter 3. A brief summary of the expectations

regarding the variables from this chapter is available in Table 4.3.

Table 4.2 Summary Statistics

Variable N Mean Standard Deviation Minimum Maximum

Government Spending Priority 369 53.36 1.45 50.90 57.48

Presidential System 369 0.07 0.26 0.00 1.00

Majoritarian System 369 0.19 0.39 0.00 1.00

Proportional Representation 369 0.55 0.50 0.00 1.00

Mixed Voting 369 0.26 0.44 0.00 1.00

District Magnitude 369 13.37 26.85 1.00 150.00

Bicameralism 369 0.57 0.50 0.00 1.00

Federalism 369 0.24 0.43 0.00 1.00

Constraints 369 2.47 0.98 0.00 4.00

Government Composition 369 46.60 16.83 5.61 94.82

Voter Turnout 369 74.92 11.11 40.57 93.38

Role of Government 369 -1.57 16.45 -57.90 29.30

Public Opinion 369 0.96 26.94 -71.11 60.08

Business Associations 369 874.98 1291.71 1.00 5773.00

Government Employees 369 2674.36 4415.92 26.79 21974.00

Corporatism 214 3.07 1.43 1.00 5.00

Female Participation Rate 369 50.30 8.81 33.20 75.60

Unemployment Rate 367 7.88 4.14 1.50 23.90

Aged Population 369 14.71 2.05 10.68 19.92

Youth Population 369 18.23 2.54 13.81 28.02

GDP/Capita 369 33332.55 15576.12 4547.40 110933.00

Inflation Rate 369 3.41 3.76 -1.88 30.62

Openness 369 88.03 51.50 16.01 319.55

EU Members 369 0.79 0.41 0.00 1.00

99

Table 4.3 Summary of Hypotheses

Directional Tests Non-Directional

Test

Particularized

Benefits Collective

Goods

Presidential System +

Majoritarian System

+

Proportional

Representation +

Mixed Voting +

District Magnitude +

Bicameralism

+

Federalism +

Constraints

+

Government Composition +

Voter Turnout

+

Role of Government +

Public Opinion +

Business Associations

+

Government Employees

+

Corporatism +

Female Participation Rate +

Unemployment Rate +

Aged Population +

Youth Population

+

GDP/Capita +

Inflation Rate +

Openness

+

EU Members +

Note: For the directional tests, + represent an increase in spending on for either

particularized benefits or collective goods in relation to the presence of the

characteristic for categorical variables and increases for the interval level

variables.

100

CHAPTER 5 TRADITIONAL INFLUENCES AND SPENDING PRIORITIES

Government expenditures are divided between policies that target particular groups

within the population, like the elderly or the poor, or the society more broadly through spending

on areas such as economic development or health. Where some nations like Greece and

Denmark spend more on particularized benefits and others such as Japan and the Czech Republic

spend more on collective goods.

Prior research provides several different sets of influences that are expected to shape

government spending patterns and outputs; including socio-economic factors, mass and elite

preferences, and political institutions. However, the work on these various arguments generally

results in misspecified models. Previous studies typically analyze one or two sets of influences

such the role of socio-economic influence and institutions (Crepaz 1998; Scartascini and Crain

2002; Shelton 2007; Edwards and Thames 2007; Chang 2008) or preferences and the socio-

economic climate (Bradley et al. 2003; Bräuninger 2005; Soroka and Wlezein 2005); however,

these studies show, all three sets of factors influence government activity after controlling for

each other.25

To address the concern of misspecified models I run a spending priorities model

using a number of measures from all three perspectives on what shapes spending. This approach

helps confirm how expectations for factors work when controlling for other theoretically

influential variables.

Even research that uses more complete model specifications produces questionable

results based on the dependent variables that are used to measure government outputs. For

example, Huber and Stephens (2001) combine health care and pension spending into a single

25

To see model runs for the three sets of influences (socio-economic, mass and elite preferences

and political institutions) see Appendix B.

101

variable. However, this is directly at odds with the results of the unfolded spending priorities

variable that demonstrates these two items belong to policy areas that target two different types

of populations and increases in spending on one area (health/pensions) will decrease spending in

the other (pensions/health). By using the spending priorities variable I am able to test the

influence of multiple variables on a measure of government outputs that correctly captures the

relationship between policies. Through the priorities model I show what factors influence

government spending patterns and determine if findings from prior work are maintained in a

correctly specified model.

SPENDING PRIORITIES MODEL

Model 1, referred to as the spending priorities model, shows the results of the fully

specified spending model (Table 5.1).26

The findings of the priorities model are broken down by

the three types of influences shaping government spending: socio-economic factors, citizens’

preferences, and political institutions.

Socio-Economic Influences and Spending Priorities

Regarding the socio-economic variables, the expectations from Chapter 4 predict that high levels

of wealth, unemployment, female participation in the workforce, dependent populations

comprising of the elderly and youth populations, and membership in the European

26

The models run in this chapter are time-series, cross-sectional analyses with panel corrected

standard errors. Each model includes year dummies that are omitted from the table of results for

clarity of reading. Each independent variable is lagged by two years to present a more accurate

picture of the expenditure process. The models follow the same arguments as the ones

presented in more detail in the next chapter. For more details on the model specification please

see Chapter 6 and Appendix C.

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Table 5.1 Spending Priorities Model

Variable Coefficient

(s.e.) p-value

GDP/Capita a

-0.59 0.000

(0.129)

Unemployment a

-0.51 0.000

(0.104)

Female Participation 0.04 0.000

(0.007)

Inflation 0.02 0.027

(0.011)

Aged Population -0.13 0.000

(0.025)

Openness b

0.01 0.000

(0.001)

European Union -1.67 0.000

(0.099)

Government Composition 0.001 0.366

(0.002)

Voter Turnout b

-0.0003 0.467

(0.005)

Role of Government -0.009 0.001

(0.003)

Public Opinion -0.01 0.000

(0.003)

Interest Groups 0.61 0.000

(0.093)

President -0.14 0.283

(0.245)

PR -1.42 0.000

(0.161)

Mixed Voting -0.89 0.000

(0.157)

District Magnitude a

0.34 0.000

(0.042)

Bicameralism 0.80 0.000

(0.059)

Federalism -1.14 0.000

(0.096)

R2

0.7647

N 367

103

Table 5.1 (cont’d)

Union would increase government spending on particularized benefits. 27

Higher levels of

inflation are expected to increase spending on collective goods, while the levels of trade

openness do not have a predicted directional expectation as past works have shown mixed

findings. Overall, the variables in the priorities model conform to the expectations found in the

previous literature.

Economic resources are related to government spending priorities. Specifically, wealthier

nations (represented by higher values for gross Domestic Product per capita) allocate greater

resources toward particularized benefits ( i.e., family benefits and housing assistance) as

represented by the negative coefficient. Conversely, nations with less economic resource are

more likely to spend money on collective goods. The effect of gross domestic product per

capita is constantly negative and is statistically significant at the 0.05 level for directional tests.

This result indicates that economic does, indeed, affect the spending priorities of democratic

nations.

As the percentage of the population unemployed increases, the expectation is that nations

will need to spend more on programs that assist individuals who cannot provide for themselves

or their families. As expected, the findings show that higher levels of unemployment are re

associated with greater spending on particularized benefits. The unemployment rate variable has

27

Studies that use either exclusively or predominately socio-economic indicators include:

Rodrik 1998; Cameron 1978; Shelton 2007; Bradley et al. 2003; Huber and Stephens 1993; and

Chhibber and Nooruddin 2004.

Note: a Indicates that the natural log of the

original variable was used in the model.

b Indicates that p-value for the variable is

for a non-directional test, all other

variables based on directional tests.

104

a negative sign, indicating that as more individuals become unemployed governments do spend

more of their resources on particularized benefits like unemployment benefits.

When inflation in a nation increases, it becomes more expensive for nations to provide

the same level of goods and services. So, nations with higher inflation rates will spend less on

areas that benefit only certain groups of the population and promote spending of more limited

resources that are intended to provide broader benefits to society as a whole. The results of the

priorities model shows that higher levels of inflation are associated with greater spending on

collective goods. The coefficient for the inflation rate is positive, indicating that nations with

higher levels of inflation are more likely to spend more on collective goods cluster of policies.

The result is statistically significant for a directional test at the 0.05 level.

Female participation in the workforce are expected to increase government spending on

particularized benefits; however, the results suggest that greater female participation rates are

associated with higher spending priorities values which represent greater spending on collective

goods such as education and economic development. The increase in spending on collective

goods may imply that as more women enter the workforce, they are better able to provide for

themselves and their families, requiring less assistance from the government in the form

particularized benefits. The impact of female participation is statistically significant for a

directional test at the 0.05 level.

Larger shares of the population that are dependent are expected to increase spending on

particularized benefits to address the needs of vulnerable population groups. Several models

were initially examined to test the influence of the dependent population on government

spending patterns. The first model combined the affect of the youth and aged groups in society.

Here the results indicated that the dependent population did not have a statistically significant

105

affect on spending at the traditional levels of significance. Since the old and the young have

different types of needs, this is not a surprising result. The elderly would require greater

assistance in the form of particularized spending on pensions, while the youth would have a

greater demand for services like education, a collective good. These opposing needs may result

in the effect of these groups cancelling each other out when the two are combined into a single

variable.

The second model examines the influence of the youth and aged populations, separately,

to determine if either group has a unique affect on spending priorities. The results of this model

show that higher levels of the aged population are associated with greater spending on

particularized benefits, like pensions, with the negative sign of its coefficient. However, the

effect of the youth population failed to reach statistical significance at either the 0.05 or 0.10

levels for a directional test, indicating that the young in a population do not contribute to the

spending patterns. This finding may be a result of the youth population who are younger than 15

and unable to vote and influence politicians and spending, while the elderly can and do vote.

The specification of the dependent population used in the priorities model presented here

omits the youth population that was not found to shape government spending priorities and still

shows that higher percentages of the aged population increase government spending on

particularized benefits. The results in Table 5.1 show that nations with larger aged populations

spend more on particularized benefits than collective goods. The finding supports the

expectation in the literature that the larger this group is, governments spend more on items like

pensions to address the needs and demands of this particular segment of society.

Prior research suggests that the degree of trade openness should affect government

spending patterns. However, the expectation associated with trade openness has produced mixed

106

results. Several studies indicate that more open economies should produce governments with

higher levels of spending on particularized benefits to protect workers from external economic

shocks (Cameron 1978; Rodrik 1998; Crepaz 1998; Shelton 2007), while other studies suggest

that greater openness should decrease spending on particularized benefits in order to create more

desirable economic conditions for producers (Swank 2010; Hay and Rosamond 2002). The

priorities model shows greater trade openness is associated with greater spending on collective

goods, represented by the positive coefficient and statistically significant for a non-directional

test at the 0.05 level. This finding suggests that more open economies actually spend less on

areas of social protection and supports the “race to the bottom” argument.

The final socio-economic variable examined in Table 5.1 is the effect of membership in

the European Union on spending priorities. The effect of the European Union conforms to

expectations that nations that belong to the European Union have higher levels of spending on

particularized benefits represented by the negative coefficient. The effect of the European Union

is negative and statistically significant at the 0.05 level. The result implies that European Union

member nations do behave differently from non-member nations in terms of spending and more

specifically, these nations spend more on areas such as social protection than non-member

nations.

Group Preferences and Spending Priorities

While the socio-economic climate can determine the level of resources governments have

to work with and what groups within the population may need particular services, the

preferences of both elites and the masses are also believed to shape government actions.28

The

28

Prior studies that focused exclusively or predominately on different measure of preferences to

explain government outputs include: Erikson, Wright and McIver 1989; Hofferbert and Budge

107

expectations or demands of the masses include the role of government and public opinion, where

the beliefs in the role of government indicate the boundaries on issues the public feels the

government should be involved in and public opinion represents the desired actions by the public

on specific issues. Voter turnout and the resulting composition of the government produce

another set of preferences for government actions. The power resource literature argues that as a

larger portion of the working population mobilizes and votes the greater their power is in shaping

political actions. The resulting composition of government contains elite actors with their own

set of ideal political outputs.

A final set of demands can be found in organized interests. Two different approaches to

examining the effect of interest groups were tested. One approach looked at the level of

corporatism in a nation, where higher levels of corporatism correspond to nations that are more

directly influenced by pressure groups when making policy decisions. However, as the number

of pressure groups increase in a nation, it becomes more difficult for any group to speak on

behalf of the entire population it represents. The alternative approach I use to examine the effect

of interest groups is based on a factor analysis for the count of business associations and

government employees in a nation. More pressure groups should correspond to nations that are

less corporatist in nature and governments that are less influenced by pressure groups.29

Table

5.1 shows the results using the density of pressure groups in a nation as this operationalization

provides more observations.

The expectation for government composition is that as the percentage of seats held by

leftist parties increases, governments should spend more on particularized benefits associated

1992; Page and Shapiro 1983; Garand 1985; Burstein 2006; and Penner, Blidook and Stuart

2006. 29

The correlation between the corporatism variable and the interest group variable is -0.5103.

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with leftist party platforms like unemployment, pensions, and sickness benefits. The priorities

model shows that more leftist parties in office increase spending on collective goods that include

spending on education and health. However, the coefficient for government composition in the

priorities model examining fails to reach statistical significance for directional tests at the 0.05 or

0.10 level.

While voter turnout is predicted to influence spending priorities, no directional affect is

assigned. Prior literature indicates that increases in voter turnout may be associated with greater

portions of low-income individuals turning out who have expectations for greater particularized

benefit spending (Jackman 1987; Powell 1986; Crepaz 1998) or more high-income voters turning

out who prefer greater spending for collective goods spending compared to particularized

benefits (Lijphart 1997; Iversen and Soskice 2006). However, the mix in results may be a

product of the dependent variables that are used to study the effect of voter turnout. If a concept

was mislabeled to represent particular group spending like health or education it may have biased

the results, as the unfolding in Chapter 3 showed these to be collective goods. Using the

spending priorities variable that captures a range of policy areas and how expenditures are

connected between policies, shows that higher levels of voter turnout are associated with greater

spending on particularized benefits like disability benefits. This effect is represented by the

negative coefficients but fails to reach statistical significance at the 0.05 or 0.10 level for non-

directional test.

The lack of statistical significance suggests the mixed finding in the literature may be a

product of the dependent variable that are used and whether they represent particularized benefits

like social protection or collective goods like economic development. While voter turnout does

not appear to statistically influence the pattern of spending, it may influence the overall level of

109

total spending which is not captured by the priorities variable that I use. If this is the case testing

voter turnout in relation to separate policy areas would show similar changes in spending on

social protection and areas like economic development, producing the mixed findings on what

types of policies are favored as turnout increase.

Higher values of the expectations for government correspond to demands for a more

expanded role and are predicted to increase spending on particularized benefits as larger portions

of the population want greater government involvement in promoting aspects like equality.

Greater spending on collective goods is predicted when beliefs support a more limited view on

the role of government, like focusing on activities such as economic development and defense.

The priorities model in Table 5.1 show that preferences for more government action are

associated with higher levels of spending on particularized benefits like housing and food

subsidies which are represented by the negative coefficients and are statistically significant at the

0.05 level for directional tests.

More liberal preferences for government actions are predicted to increase government

spending on particularized benefits on areas like social protection. The results of the model

show more liberal opinions in society are associated with greater spending on particularized

benefits such a pensions, represented by the negative and statistically significant coefficient at

the 0.05 level for a directional test. This implies that as public opinion favors demand greater

government attention to issues such poverty and equality of outcomes governments spend more

on items to address these demands through areas like children benefits and housing vouchers.

Greater numbers of interest groups are expected to increase government spending on

collective goods, where larger numbers of interest groups would decrease the ability of any one

group to speak on the behalf of everyone else, making each group less influential. Higher values

110

of the interest group variable represent the presence of more pressure groups in a nation and

should produce a cacophony of demands that the government cannot accommodate. The interest

group variable should carry a positive sign indicating that more interest groups push government

spending towards collective goods that benefit all groups instead of spending that favors

particular groups.

The priorities model shows that as the density of interest groups increases, governments

spend more on collective goods represented by the positive signed coefficients. This finding

suggests that as more groups issue demands for spending, governments spend more on collective

goods that benefit broader communities like education and economic development than on

particularized policy areas. The effect of interest groups is statistically significant the 0.05 level

for a directional test.

Institutions and Spending Priorities

The final set of factors captures the political institutions present in a nation.30

Presidential systems, majoritarian systems, low district magnitudes, bicameral legislatures, and

unitary systems are expected to increase spending on collective goods, while parliamentary

systems, proportional representation, high district magnitudes, unicameral legislatures, and

federal systems are expected to increase spending on particularized benefits. Table 5.1 shows

the effect of institutions in combination with the socio-economic climate, and mass and elite

preferences.

Expectations predict that presidential systems should produce governments that spend

more on collective goods. In a presidential system the executive is accountable to the entire

30

Prior studies that focused exclusively or predominately on institutional variables in relation to

government outputs include: Jackman 1987; Edwards and Thames 2006; Immergut 1990; Chang

2008; and Scartascini and Crain 2002.

111

nation and needs to ensure spending benefits the nation in broad terms to avoid upsetting voters.

In a parliamentary system the executive is accountable to the controlling party or coalitions and

will approve legislation that supports the controlling group’s particular interests. Table 5.1

shows that presidential systems are not found to influence spending priorities in a statistically

meaningful manner. Here the result may be a product of having only two nations that have

presidential systems in place (Poland and the United States). However, in a separate model run

looking only at the political institutions in place, presidential systems are shown to behave as

expected, where presidential systems spend more on collective goods than parliamentary systems

(Appendix B). Alternatively, this finding suggests that the expected relationship may be a

product of model misspecification in the literature that looks at political institutions in isolation

from other influential variables.

Electoral formulas that use proportional representation and mixed voting should spend

more on particularized benefits than nations than use purely majoritarian systems. Under

proportional representation candidates appeal to specific vote bases with policy platforms

targeted at particular group needs as opposed to majoritarian system candidates who use

collective goods to appeal to broader vote bases. Mixed systems use a combination a

proportional representation and majoritarian rules producing some candidates that target

particular groups and some candidates that need to appeal to broader vote bases. The priorities

model supports the expected relationships between electoral formulas and spending priorities.

Nations that use proportional representation spend more on particularized benefits like sickness

benefits and maternity leave than nations that use majoritarian systems, represented by the

negative coefficient that is statistically significant at the 0.05 level for a directional test. Mixed

voting systems also spend more on particularized benefits than pure majoritarian nations as

112

represented by the negative coefficient that is statistically significant at the 0.05 level for a

directional test.

As district magnitude increases, more candidates can win office with a smaller

percentage of votes, allowing candidates to target particular groups in the population. As more

candidates can be elected in a district the expectation is for governments to increase spending on

particularized benefits to cater to subsets of the population representing their constituencies. The

spending priorities model shows that higher levels of district magnitude are associated with

greater spending on collective goods such as economic development, as represented by the

positive coefficient and is statistically significant at the 0.05 level for a directional test. The

increase in spending on collective goods may imply that district magnitude effects post-election

behavior, unlike proportional representation, which affects pre-election behavior. Proportional

representation determines how candidates are elected, but district magnitude determines how

many incumbents need to agree before legislation can be enacted. As district magnitude

increases, more incumbents need to agree, meaning each incumbent can obtain less for their

constituents in the form of particularized benefits.

Prior literature does not lend specific expectation about the separate influence of

bicameralism and federalism on government spending patterns; however, the required agreement

between two separate houses in a bicameral legislature forces two separate entities to reach an

agreement before spending can occur. That, as I hypothesized in Chapter 4, should decrease

spending on particularized benefits that may serve the needs of members of one house at the

expense of the other, producing governments that spend more collective goods. The results of

the priorities model support the hypothesis where nations with bicameral legislatures spend more

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on collective goods than nations with unicameral legislatures. The positive coefficient is

statistically significant at the 0.05 level for a directional test.

Federal systems are expected to spend more on particularized benefits than unitary

systems. The expectation is not based on behavioral patterns associated with incumbents in

federal systems but on the inability of federal systems to move quickly to change spending

allocations as found in the welfare state literature. As a result, federal systems lag behind unitary

systems in their effort to dismantle or decrease the scope of government programs and they end

up spending more on particularized policy areas. The findings of the spending priorities model

lends support to this hypothesis, showing that federal systems do spend more on particularized

benefits than unitary systems. The coefficient for the variable captures the organizational

structure of the governmental system (federal versus unitary) is positive and statistically

significant at the 0.05 level for a directional test.

Country Examples

What do the results of the model suggest for spending patterns within the nations

examined? The general finding is that the demands of the general public and groups in need like

the elderly or poor, the resources at the government’s disposal, and the institutions that

governments operate in all influence the resulting outputs of government spending. A more

specific example can be observed by looking at Greece’s current economic crisis and how

different factors could shape its future spending priorities. Greece has been faced with budget

cuts in order to receive its financial bailouts. Additional recommendations have been presented

by the OECD to address Greece’s debt. In order to reduce expenditures by the government,

among the many areas that could be changed, the OECD recommended reform of Greece’s old-

age pension system including an increase in the effective age of retirement to 65 (Greece at a

114

Glance 2009). Changes to the retirement age would help decrease the proportion of individuals

receiving already generous pensions, where retirees can expect old-age pensions equivalent to

96% of previous earning compared to the OECD average of 52% (Greece at a Glance 2009).

By encouraging individuals to remain in the workforce longer, there should be a decrease

in spending attributed to particularized benefits that include pension benefits as fewer adults

would be drawing pensions. This would have a strong influence on Greece’s spending priorities

as the nation spends more on pensions than other OECD governments; in 2005, Greece spent

roughly 11.5% of its GDP on pensions compared to the OECD average of 7.2%. This reduction

alone would shift Greece’s spending priority closer to collective goods as there would be a

decrease in the size of the dependent population requiring assistance from the government during

retirement.

An alternative example comes from the United States. Economic problems in the United

States may see expenditures shift to favor greater relative spending on particularized benefits.

Since 2008, the United States has seen an increasing level of unemployment peaking at 10% in

2009 and as of May 2012 had not yet dropped below 8% nationally (Bureau of Labor Statistics).

High levels of unemployment have resulted in increases in the number of individuals who

require assistance from the government (in the form of unemployment benefits, housing and food

vouchers, and family and children benefits) in order to help meet their every day, basic needs.

The higher unemployment rate then should result in an increase in government

expenditures on particularized benefits and shift the United States priority score closer to that

end of the spending priorities continuum. In the last year of the sample data examined here, the

United States had an unemployment level of 4.6% for 2006; in 2010 the unemployment rate had

115

increased to 10%.31

Holding all else constant, this change in unemployment would shift

government spending allocations in the United States by three percentage points towards

particularized benefits in. This would be the equivalent of a $1.6 billion shift in spending based

on 2008 total expenditures from collective goods (like education) in favor of greater spending on

social protection (such as unemployment benefits and housing vouchers). However, this output

is contingent on all other influential factors remaining constant which is not the case as

demonstrated by changes to public opinion with the emergence and rise of the Tea Party,

changes to the composition of the government with the election in 2010 and the approaching

2012 election, and shocks posed by external economies facing economic recessions.

OLD MODELS, NEW MEASURE

Having explored a range of indicators previously examined in the literature, I now turn to

how the results prior models compare to government spending priorities, assuming the old

model specification was valid, in order to look at how limited measures of prior dependent

variables altered findings. I examine the relationship between spending priorities and the factors

of influence used in two commonly cited works: one that focuses predominately on a limited set

of economic variables by Milesi-Ferretti et al. (2002) and one by Huber and Stephens (2001) that

uses a range of socio-economic, preference, and institutional variables.

In Electoral Systems and Public Spending, Milesi-Ferretti et al. (2002) examine the effect

of electoral systems in relation to the level of government transfers and spending on public

goods. Milesi-Ferretti et al. (2002) argue that proportional representation should produce

31

As will be discussed in more detail in Chapter 6, all independent variables in the model are

lagged by 2 years to ensure a more accurate representation of the expenditure process. As such,

2006 unemployment rates are used to predict the 2008 spending priorities variable and the 2010

unemployment rate would be used to predict the 2012 spending priorities value for the United

States.

116

governments that have higher levels of transfer spending and majoritarian systems should have

higher levels of spending on public goods. In order to capture government spending Milesi-

Ferretti et al. use two main dependent variables, one representing government transfers, “defined

as the sum of social security payments and other transfers to families, plus subsidies to firms”

and the other public goods, “defined as the sum of current and capital spending on goods and

services” (629).

Regardless of the measure of government activity, the same four influences are controlled

for: district magnitude, the aged population, gross domestic product per capita, and OECD

membership. Milesi-Ferretti et al. (2002) use three different measures to operationalize the

degree of proportionality in a nation: average district magnitude, standardized district magnitude,

and the average deviation from proportionality (the average difference between the proportions

of seats each party holds versus the proportion of votes won); however, the models using the

three different measures of proportionality yield similar results in regards to coefficients’ signs,

magnitudes, and statistical significance. As a result, I only use average district magnitude to

measure proportionality when comparing findings from Milesi-Ferretti et al. (2002) to a

replication using the spending priorities variable.

Across the two models, higher district magnitudes are found to increase government

spending on transfers and decrease spending on public goods. The aged population was

repeatedly found to increase spending on transfers but not to have a statistically significant effect

on public goods. Gross domestic product per capita and OECD nations also showed mixed

results across the variety of models. In Table 5.2 I present the results from Milesi-Ferretti et al.

(2002) Model 4, Table V on “Primary Spending, Transfers, Public Goods, and Electoral Systems

(Full-Sample)” for transfer spending. I then run a similar model using spending priorities as the

117

dependent variable in Model 8 the Milesi-Ferretti et al. replication. However, as all the nations

used in my analysis are members of the OECD, I include the EU dummy variable as an

alternative.

Table 5.2 Replication of Milesi-Ferretti et al. Model using Spending Priorities

Milesi-Ferretti et al. Replication

Coefficient (t-statistic) p-value

Coefficient (s.e.) p-value

District Magnitude a

1.70 ** -0.05 0.314

(3.49)

(0.046)

Aged Population 1.25 ** -0.27 0.000

(4.04)

(0.027)

GDP/Capitaa 2.19

-0.40 0.000

(1.37)

(0.091)

OECD 1.20

(0.37)

European Union

-1.91 0.000

(0.148)

R2 0.84

0.5490

N 40 369

Note: a Indicates that the natural log of the original variable was used in the

model.

** Indicates the coefficient is statistically significant at the 0.05 level;

based on

Model 8 uses OLS following Milesi-Ferretti et al. (2002).

The results from my replication of Milesi-Ferretti et al.’s (2002) model show similar

results to that of the model run by Milesi-Ferretti et al. where the transfer spending falls under

social protection and is associated with particularized spending in the spending priorities

variable. Higher district magnitudes in both models correspond to governments that spend more

on particularized benefits. In Milesi-Ferretti et al. (2002)’s model it is associated with greater

spending on transfers, and in my model it is associated with greater spending on items such as

pensions or unemployment benefits. In the replication, district magnitude fails to reach

118

statistical significance at the 0.05 level. The results of my replication imply that when examined

across a range of policies simultaneously, greater wealth, aged populations, and membership in

the European Union increase government spending on areas like social protection, whereas

looking solely at transfers produces inconclusive results.

Unlike the Milesi-Ferretti et al. (2002) model shown in Table 5.2, the socio-economic

variables included are all statistically significant at the 0.05 level, where only the proportion of

the aged population was found to affect the level of government transfers in the model from

Milesi-Ferretti et al. (2002). The larger proportion of the aged population, gross domestic

product per capita, and membership in the European Union are all found to produce government

that have higher levels of spending on particularized benefits as indicated by the negative

coefficients.

This work serves as an example of models that looks at a small set of influential factors

when examining government activities. The model only controls for three socio-economic

indicators and looks at a single institutional factor, proportionality. Correcting for the limited

dependent variable yields different results. Further, compared to the priorities model with the

appropriate model specification shows even more changes to the results. In the priorities model,

district magnitude shows nations spending more on collective goods which runs counter to

Milesi-Ferretti et al.’s argument. Additionally, all the socio-economic variables have a statically

significant effect on government spending patterns in the priorities model unlike Milesi-Ferretti

et al.

Another frequently referenced work regarding government spending patterns is Huber

and Stephens’s (2001) Development and Crisis of the Welfare State. Huber and Stephens (2001)

argue that the distribution of power affects both the creation and maintenance of strong welfare

119

states. Their work examines a number of measures to capture spending associated with welfare

states including: spending on pensions, health, mother’s employment and youth, a measure of

decommodification, poverty, inequality, and measures of redistribution.

Going beyond more simplified models, like those used by Milesi-Ferretti et al. (2002),

Huber and Stephens (2001) use more expanded specifications to capture welfare state activity.

Huber and Stephens examine a range of socio-economic influences, preferences of the electorate

and elites, historical ties, and institutional designs. As there are a number of models used by

Huber and Stephens (2001), here I look at their model for the effect of influential measures on

the public share of health expenditures and pension generosity (76).

Unlike Huber and Stephens (2001), I do not include a measure of military spending in my

replication, as defense spending is included in the measure of spending priorities. The number of

observations available for comparison to the work of Huber and Stephens is drastically limited

compared to other models I have run. The 90 observations are a product of using variables

coded by Huber and Stephens for authoritarian legacy and strikes. There are several countries

that do not overlap between the two samples of data as well as there being differences in time

points examined. In order to mirror the approach used by Huber and Stephens, my replication is

a time-series, cross-sectional analysis with panel-corrected standard errors (Table 5.3).

In the case of the spending priorities variable, spending on health and pensions are

divided between the two clusters of particularized benefits and collective goods. Spending on

pensions fall with particularized benefits as only a certain group within the population is eligible

to receive benefits from spending on this area. Spending on health services fall within collective

goods as the majority of nations that are examined have universal health care coverage. The

combination of these two policy areas into a single measure will result in increases in one area

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Table 5.3 Replication of Huber and Stephens Model using Spending Priorities

Huber and

Stephens Model Replication

Coefficient

(s.e.) p-value Coefficient

(s.e.) p-value

Government Composition 0.47 ** -0.17 0.012

(0.073)

Christian Democratic Cabinet 0.30

-0.01 0.001

(0.005)

Constitutional Structure -5.19 *** 0.32 0.000

(0.086)

Female Participation 0.25 * -0.14 0.042

(0.082)

Government Composition x

Female -0.04 *** 0.003 0.013

(0.001)

Voter turnout 0.09

0.003 0.374

(0.009)

Aged Population 1.22

-0.02 0.323

(0.048)

Strikes -0.01

0.001 0.256

(0.001)

Authoritarian Legacy 0.78

-1.04 0.000

(0.163)

GDP/Capita a

1.18 ** -0.61 0.069

(0.041)

Consumer Price Index -1.63 * 0.01 0.452

(0.041)

Unemployment 0.21

-0.17 0.000

(0.028)

Military Spending -0.58

FDI Out -0.36

0.02 0.001

(0.007)

Openness b

-0.03

-0.03 0.000

(0.003)

R2

0.9002

Adjusted R2 0.7

0.8806

N 416 90

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Table 5.3 (cont’d)

Note: a Indicates that the natural log of the original variable was used in the model.

b Indicates that p-value for the variable is for a non-directional test, all other

variables

*, **, *** Indicates the coefficient is statistically significant at the 0.10, 0.05,

or 0.001 level for a directional test based on information in the original

work by Huber and Stephens (2001).

canceling out the decreases in spending that are occurring in the other. Increases in pensions will

increase spending on social protection and will take away resources that could be spent on

collective goods including health. Likewise, increases in health care spending will take away

resources from social protection that includes pension spending. Therefore, the results using the

additive spending on these two policy areas will be misleading in terms of how factors influence

higher or lower levels of spending on these two opposing policy areas.

In my replication of Huber and Stephens model, higher percentage of seats held by leftist

and Christian democratic parties are found to increase spending on particularized benefits. This

implies an increase in spending on social protection which includes pensions; however, it would

indicate a decrease in spending on health, which is again a collective good. However, in Huber

and Stephens’s model, Christian Democrats do not have a statistically significant affect on

spending for health and pensions. The lack of statistical significance in Huber and Stephens

model may serve as an example of how the two policy areas that target different groups cancel

each other out in terms of differences in spending.

Constitutional structure is an additive measure used to capture the number of institutional

constraints present in a nation. Huber and Stephens’ measure is based on the presence of the

following institutions: bicameralism, presidentialism, federalism, and referenda. The measure of

constitutional structure I use was defined in Chapter 4 and includes: presidentialism, proportional

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representation, district magnitude, bicameralism, and federalism.32

In both models, more

institutional constraints decrease spending on pensions which fall under particularized benefits in

the replication, and at a statistically significant level. However, my results show an increase in

spending on health compared to Huber and Stephens’s model which shows a decrease in

spending on health care.

Female participation in the workforce has a statistically significant effect across both

Huber and Stephens’s model and my replication. Increases in the proportion of women in the

workforce increase spending on particularized benefits and is at a statistically significant level

for a directional test. This again, though, implies a decrease in spending on health counter to the

Huber and Stephens’s model, which shows an increase in both types of spending. The

interaction between women in the workforce and left parties shows increases in the percentage of

left seats and women in the workforce collectively increase spending on collective goods and are

statistically significant for directional tests resulting in an increase in health care spending and a

decrease in social protection.

Gross domestic product per capita shows a different effect across both models in Table

5.3. In Huber and Stephens’s models higher levels of gross domestic product per capita are

associated with greater spending on pensions and health at a statistically significant level for a

directional test. In my replication, higher level of gross domestic product per capita is associated

with greater spending on particularized benefits that includes pensions but less spending on

health, and is statistically significant for a directional test.

The effect of unemployment found in Huber and Stephens’ model is again partially

supported by my replication. In both models, higher levels of unemployment increase spending

32

I omitted referenda from the institutional constraint index as it was found to decrease the

reliability of the index.

123

in the dependent variable. In both cases, the effect of unemployment is statistically significant

for a directional test for Huber and Stephens this would be for both pension and health where I

find an increase in social protection but a decrease in health. Further, in both models, voter

turnout, the aged population, and strikes fail to reach statistical significance.

Increases in the consumer price index (CPI) are similar across the two models, where

increases in the CPI are associated with greater spending on collective goods or less spending on

pensions and health services. This finding is statistically significant across both models for a

directional test. Therefore, as goods and services become more expensive nations provide less in

terms of spending for particular groups in society capturing social protection but my model

would imply a relative increase in health expenditures.

However, unlike Huber and Stephens’s model, an authoritarian history was found to be

statistically significant. Nations that had an authoritarian legacy in the replication spend more on

particularized benefits like pensions than the nations that did not have an authoritarian legacy.

Another difference was in the effect of foreign direct investment outward (FDI out). In Huber

and Stephens’s model FDI out was not found to be statistically significant. However, in the

replication, higher levels of FDI out are associated with greater spending on collective goods

which include health, at a statistically significant level for a directional test. Trade openness is

also found to affect spending priorities in the replication, as opposed to Huber and Stephens’s

model. Higher degrees of economic openness are associated with greater spending on

particularized benefits which includes pensions, which is at odds with the priorities model run in

this chapter adding to the mixed findings in the literature on the role of globalization and its link

to government activity.

124

The results from my replication model are similar to Huber and Stephens only in terms of

how the variables affect pensions, as health is shown to target a different group in society and

moves in an opposite direction to social protection in terms of increases and decreases in

expenditures. In Huber and Stephens’s model, the researchers combined spending based on the a

priori assumption that spending on health and pensions were similar in nature and could serve as

a measure of welfare state spending. However, the unfolding in Chapter 3 shows that the two

policy areas are different in nature based on the clustering of policy areas. The use of the

spending priorities variable then allows for a better understanding of the actual spending

allocations of nations on a priori notions about what policy areas ought to belong together.

CONCLUSION

Compared to previous research, the findings in this chapter produce a richer explanation

of the factors that influence the policy outputs of democratic political systems. Studies that use

individual measures and composite measure produce conflicting and contradictory results

pertaining to the influence of globalization and voter turnout in relation to spending patterns.

The priorities model improves upon previous research that examines how and why

certain policy areas fit together. For example, Huber and Stephens (2001) combine social

protection and health expenditures to represent welfare spending, whereas the unfolding in

Chapter 3 shows the two policy areas represent two different forms of expenditures:

particularized benefits and collective goods. Instead of allowing the a priori assumptions to

determine which policy areas belong together, the unfolding model allows the data to determine

how the policy areas are actually related to one another. This approach produces a better

understanding of the factors that influence government spending patterns based on the empirical

data.

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CHAPTER 6 INSTITUTIONAL CONSTRAINTS AND POLICY RESPONSIVENESS

While individual political institutions are shown to influence government spending

behaviors, the literature also suggests that national political institutions affect the ability of

governments to reach policy agreement. If this is true, then national institutions exert a

cumulative effect on government spending priorities, representing policy compromise across a

range of public programs.

A number of political institutions are said to have separate affects on government

spending patterns. Presidential systems are argued to promote executives who pursue more

collective policies compared to parliamentary systems (Lijphart 1999; Tabellini 2000; Persson

and Tabellini 1999). Proportional representation increases spending on particularized policy

areas that benefit candidates’ constituent bases relative to majoritarian systems (Persson and

Tabellini 1999; Austen-Smith 2000; Cox and McCubbins 2001; Iversen and Soskice 2006;

Milesi-Ferretti et al. 2002). And district magnitude, produces candidates who represent the

interests of subsets of citizens and favor spending that serves these groups (Hill and Andersen

1995; Persson et al. 2007; Milesi-Ferretti et al. 2002). Previous research has shown that these

institutional features do influence the spending patterns of nations. But these features are also

part of a set of institutions within a nation, referred to as institutional constraints, that increase

(or decrease) the ability of governments to reach policy agreements, impeding or inhibiting the

process of government action.

Institutional constraints are described as systems or rules that separate decision making

power across different actors within a political jurisdiction. They include: presidential systems,

proportional representation, high district magnitudes, bicameral legislatures, and federal systems.

Briefly, presidential systems divide policy objectives between the executive and the legislature in

126

which the president is responsible to the entire nation and represents one set of interests, and

members of the legislature represent smaller constituencies with different policy preferences.

Proportional representation and high district magnitude increase the number of actors with a vote

in the legislature that can influence government actions. Bicameral systems separate decision

making power across two houses that need to reach agreement before legislation can be enacted.

Federal systems divide the decision making power on a range of issues across various levels of

government, increasing the difficulty of achieving coordinated government action.

However, the variation in spending priorities within nations over time shows that

regardless of the number of national institutional constraints, governments are able to reach

policy agreement and establish clear spending priorities, and also make changes to these

priorities over time. This point can be observed initially by looking at the range of spending

priorities over time across all nations in Figure 6.1, which includes nations that have as few as

zero constraints (e.g. the United Kingdom) and as many as four institutional constraints (e.g.

Germany). A more explicit illustration of the variation in spending priorities can be observed by

looking exclusively at the nations that have three or four institutional constraints, which are

shown in Figure 6.2. Even nations exhibiting the maximum number of constraints show

spending priorities that change over time. For example, Belgium, which has a proportional

electoral system, a district magnitude of 13.63, a bicameral legislature, and a federal system, not

only reaches policy outputs captured by the priorities measure, but also shows variation over

time, with a spending range equivalent to roughly €4.5 billion in 2008.

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Figure 6.1 Distribution of Spending Priorities by Nation over Time

While prior literature on institutions suggests how these constraints increase policy

resoluteness—the inability of governments to enact policies different that the current status quo

(Tsebelis 1995, 2000; Cox and McCubbins 2001; Shugart and Haggard 2001; Immergut 1990,

2010)—history and current events show that even nations with multiple institutional constraints

are able to reach legislative agreements. For example, Germany has four institutional

constraints: proportional representation to elect a number of its legislators, an average district

50 52 54 56 58Spending Priority

IcelandUnited States

Czech RepublicJapan

SlovakiaIrelandNowaySpain

CanadaUnited Kingdom

SloveniaHungary

LuxembourgPolandFrance

NetherlandsAustriaFinland

SwedenBelgium

ItalyDenmarkGermany

Greece

128

magnitude of 3.6 representatives per district, a bicameral legislature, and a federal system, and is

still in a position to react to problems domestically and internationally. This leads to my final

question: How do institutional constraints alter government outputs?

Figure 6.2 Distribution of Spending Priorities over Time for Nations with Three or Four Institutional Constraints

THE ROLE OF INSTITUTIONAL CONSTRAINTS

Prior research argues that as the number of institutional constraints increases, more actors

with preferences over policy outputs enter the decision making process (Tsebelis 1995, 2000;

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Cox and McCubbins 2001; Shugart and Haggard 2001; Immergut 1990, 2010). As more actors

have a vote on policy outputs, the area of consensus or area of overlapping policy preferences

gets smaller, making it more difficult to reach an agreement. This result is referred to as policy

resoluteness (Cox and McCubbins 2001). But the question left untested by previous work is how

policy decisions that are reached are altered by the institutional constraints.

My argument for the influence of institutional constraints arises from the policy

resoluteness framework. Each additional institutional constraint results in more actors—each

possessing often divergent ideal policy outputs they would like to see implemented by the

government—participating in the policy process. Each actor prefers policy outputs as close as

possible to own ideal results. As more actors enter the decision making process, it becomes

more challenging for any actor to shift policy in a direction that favors his or her ideal interests.

In order to reach agreement when multiple actors with preferences over policy outputs are

present, negotiations and bartering over policy choices will occur.

Actors involved in the decision making process want to maximize their gains and

minimize their losses for the groups they represent. Phrased slightly differently, each actor does

not want other groups to get more than his or her own group. Therefore, when there are multiple

preferences for policy outputs, negotiating and bartering should result in greater spending on

collective goods that provide at least a small benefit to all parties involved and less spending on

particularized policies that may give zero benefits, or even penalize certain groups.

H3a: Nations with more institutional constraints spend more on collective goods, relative

to nations with fewer institutional constraints.

An extension of the above argument involves the role preferences play in shaping the

spending priorities of government. I argue that constraints should not only have a direct effect

130

on government actions, but should also reduce the ability of governments to incorporate both

mass and elite preferences into policy. If constraints induce bartering and compromise over

policy outputs, everyone should get less of what they want. Each group will have to forgo some

portion of their ideal outcome in order to get any portion of the outputs they desire. Therefore,

no group gets everything it would like and policy is less responsive to all parties involved in

order for anyone to get anything. Responsiveness is defined here as “the degree to which policy

choices follow public preferences” (Roberts and Kim 2011).

H3b: Increasing the number of institutional constraints decreases the policy

responsiveness to different groups’ preferences for government outputs.

MODEL

In order to test these two hypotheses I begin with the spending priorities model from

Chapter 5 explaining levels of national spending across policy areas. I replace the individual

variables representing national political institutions with an additive index representing the total

number of institutional constraints within a nation. (Table 6.1) 33

This variable allow for testing

of the direct effect of constraints on spending priorities. In other words, rather than testing each

institution separately, this model conceptualizes institutional constraints as interchangeable in

order to test the hypothesis that more constraints of any type should produce greater spending on

collective goods relative to particularized benefits. Support for my first argument is found if the

coefficient for the institutional constraints variable is positively signed and statistically

33

Several variables that are statistically significant in the priorities model are not statistically

significant in the initial constraint model: trade openness, inflation, female participation rates,

and voter turnout. In order to ensure a parsimonious model, I tested the explained variance of

the constraint model against the priorities model. The empirical F-statistic calculated based on

priorities model and constraint model is 0.827 which is less than the critical value of 2.40 at the

95% level, meaning I fail to reject the null that the priorities model explains more variance in

than the constraints model.

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Table 6.1 Traditional Influences of Government Spending Priorities

Priorities Model (Model 1)

Constraint Model (Model 2)

Coefficient

(s.e.) p-value Coefficient

(s.e.) p-value

GDP/Capita a

-1.05 0.000 -0.99 0.000

(0.109)

(0.080)

Unemployment a

-0.72 0.000 -0.73 0.000

(0.110)

(0.086)

Aged Population -0.24 0.000 -0.23 0.000

(0.030)

(0.012)

European Union -1.61 0.000 -1.65 0.000

(0.124)

(0.079)

Openness b

-0.001 0.541

(0.001)

Inflation Rate -0.002 0.442

(0.013)

Female Participation 0.01 0.284

(0.009)

Government Composition -0.01 0.002 -0.01 0.005

(0.002)

(0.003)

Role of Government -0.01 0.005 -0.01 0.000

(0.003)

(0.002)

Public Opinion -0.01 0.000 -0.01 0.000

(0.002)

(0.002)

Interest Groups 0.56 0.000 0.53 0.000

(0.045)

(0.031)

Voter Turnout b

0.01 0.201

(0.006)

Institutional Constraints -0.20 0.000 -0.19 0.000

(0.04)

(0.023)

R2

0.7265

0.7238

N 367 367

Note: a Indicates that the natural log of the original variable was used in the

model.

b Indicates that p-value for the variable is for a non-directional test, all

other variables

132

significant, representing greater spending on collective goods, such as economic development

and education that benefit the society in broader terms, as the number of institutional constraints

increases.

Then, I examine whether or not institutional constraints reduce the ability of governments

to respond to mass and elite preferences. Here, I specify an interaction model that includes

interaction terms to determine the relationship between institutional constraint and the four

different measures of elite and mass preferences (government composition, role of government,

public opinion, and interest group density). If the number of national institutional constraints

decreases the policy responsiveness of governments to preferences, then the interaction terms

should carry the opposite sign of each preference thereby decreasing the cumulative effect of the

preference in influencing government spending priorities.

In the interaction model, I use a time-series, cross-sectional analysis with panel-corrected

standard errors. 34

I use a time-series approach in order to ensure that the relationships between

the variables hold over time and are not a reflection of a specific time point. It has been argued

that a period of at least ten years is preferred when examining patterns in policy areas (Kingdon

1984; Baumgartner and Jones 1993). The unbalanced panel dataset covers the time period from

1990 through 2009. The shortest time period in the analysis is for Poland, with seven years, and

the longest time period is for Luxembourg and Denmark, at twenty years each. The average

number of nation-years for a nation in the sample is fifteen years. Figure 6.3 shows the number

of years of data for each nation.

34

See Appendix C for an explanation of the diagnostic tests performed to assess non-linearity in

the independent variables, multicollinearity, heteroskedasticity and influential observations, and

corrections that were made in response to these conditions.

133

Figure 6.3 Number of Years by Nation in the Panel Data

Instead of looking at only one nation’s spending over time, I use a cross-sectional

approach. By examining multiple nations over time, I am able to study factors that are constant

over the time period within nations but vary across nations. The examination of the institutional

constraints variable requires a cross-sectional approach, as none of the nations in the sample

experience a change in the number of constraints for the time period in the analysis.

134

Due to the nature of the data, traditional OLS assumptions regarding the error process

may be problematic. The observations may not have constant error variance (heteroskedastic

errors). Furthermore, as the variables observed are at the nation level, there is the risk that the

interactions among groups of nations may influence data points within other nations; for

example, nations that frequently interact as members of the European Union may influence each

other on policy areas relating to unemployment (the problem of spatial correlation). In order to

address these two issues, I use panel-corrected standard errors. Panel-corrected standard errors

assume that the variance of the error term is not constant and that the variance of the error term

across nations may be related (Beck and Katz 1993).

Another concern to address at the outset of this analysis is serial correlation, where the

errors for each observation are correlated with each other over time. Situations where serial

correlation exist and are not addressed can produce inflated t-values and deflated standard errors,

leading to overly confident estimates and Type I errors in which the null hypotheses concerning

the coefficients are wrongly rejected. I use Wooldridge’s (2002) test for first order correlation in

panel data and reject the null hypothesis that there is no first order correlation in the constraints

model. In order to address this issue, I re-specify the interaction model assuming an AR1

process (Model 4). The results between the interaction model and the model with the AR1

process are similar for most variables, except the constraint variable by itself that fails to reach

statistical significance at the 0.05 level for a directional test; however, as the results between the

two models are similar in terms of signs, magnitude, and statistical significance, I discuss the

results from the interaction model that are more consistent with the approach used in the

literature to examine government spending patterns.

135

Fixed and Random Effects

When dealing with cross-sectional data there are factors unique to each case (i.e. nations)

that may influence the regression results. Under certain situations using fixed effects may help

address some of the issues of case uniqueness. Fixed effects assume that there are omitted

variables α, but that these variables are time-invariant (αi), or constant across time. Fixed effects

essentially create separate intercepts for each of the cases in the data, but slopes for each of the

independent variables are assumed to be constant across cases. The separate intercepts are used

to control for the factors unique to the cases across time.

While fixed effects may help address factors unique to the cases, I do not specify a fixed

effects model because the process creates an issue for the political institutions under study in the

analysis. The institutional constraints and the European Union variables included in the analysis

are time-invariant for all nations during the time period in the analysis. In this situation, when

estimating a fixed effects model, the institutional constraints and the European Union variables

would be omitted and the effects would be grouped with unobserved country specific factors.

Even if there were a few instances where the institutional constraints and the European Union

variables changed within nations, the estimates would be based on the few instances of change

and would result in imprecise estimates of the effect of the institutions under study. For

example, if the institutions were examined separately, small changes such as Italy’s switch from

proportional representation to a mixed voting system in 1994 would incorrectly drive the

estimates of the institutional constraint coefficient.

Furthermore, a number of the variables in the data that do vary over time mostly occur

across cases rather than over time. Cameron and Travedi (2005) note that when variation is

cross-sectional instead of over time, estimates using fixed effects will also be imprecise.

136

Because of the nature of the variables under study and the conflict posed by using fixed effects, I

do not use fixed effects in the model.

Another common modeling approach to examine panel data is random effects. Unlike

fixed effects, which assumes α is time-invariant and correlated with the variables in the model,

random effects assumes that factors unique to the country α, are not correlated with the observed

independent variables, x. Under random effects, α is assumed to be independent and identically

distributed with mean of zero and a variance of σ2. If however, α is correlated with x, then the

coefficient estimates will be inconsistent (Cameron and Travedi 2005).

I do not specify a random effects model because the variables included in the analysis

include aspects of societies that are highly likely to be correlated with factors omitted from the

model. For example, the estimates of public opinion are related salient political issues, such as

focusing events that are omitted from the model, which in turn will affect policy priorities.

Therefore, using random effects with the foreknowledge that measures omitted from the model

are correlated with included variables would be to produce inconsistent parameter estimates

intentionally.

RESULTS

Table 6.2 shows the results of the constraint model that include the interaction terms

between the number of national institutional constraints and the four different measures of elite

and mass preferences. I refer to this model as the interaction model. The inclusion of the

interactions between the institutional constraints variable with the preferences measures

increases the variance explained in the dependent variable to 79.71%, a statistically significant

increase from the constraint model omitting the interactions which explains 72.38% of the total

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Table 6.2 The Effect of Institutional Constraints on Policy Responsiveness

Interaction Model

(Model 3) AR 1 Model (Model 4)

Coefficient

(s.e.) p-value Coefficient

(s.e.) p-value

GDP/Capita a

-0.89 0.000 -0.67 0.000

(0.090)

(0.165)

Unemployment a

-0.52 0.000 -0.42 0.000

(0.062)

(0.116)

Aged Population -0.28 0.000 -0.29 0.000

(0.012)

(0.034)

European Union -1.64 0.000 -1.63 0.000

(0.067)

(0.163)

Institutional Constraints (IC) 0.53 0.000 0.23 0.079

(0.118)

(0.165)

Government Composition 0.02 0.000 0.01 0.011

(0.005)

(0.006)

IC x Government Composition -0.01 0.000 -0.01 0.018

(0.003)

(0.003)

Role of Government -0.03 0.000 -0.03 0.004

(0.008)

(0.012)

IC x Role of Government 0.01 0.026 0.01 0.050

(0.007)

(0.005)

Public Opinion -0.06 0.000 -0.04 0.000

(0.008)

(0.011)

IC x Public Opinion 0.02 0.000 0.01 0.001

(0.003)

(0.004)

Interest Groups 2.22 0.000 2.09 0.000

(0.157)

(0.348)

IC x Interest Groups -0.59 0.000 -0.56 0.000

(0.050)

(0.128)

R2

0.7971

0.9963

N

367

367

ρ 0.7570

Note: a Indicates that the natural log of the original variable was used in the

model.

138

variance in government spending.35

Positive coefficients represent greater spending on collective goods such as defense and

economic development relative to particularized benefits such as social protection and foreign

aid. Negative coefficients represent greater spending on particularized benefits relative to

collective goods.

Controls

Table 6.2 omits the coefficients for the year dummies. The year dummies do not carry

directional hypotheses and were included to control for time trends. A joint F-test of the year

dummies shows that the effect of year dummies was statistically significant at the 0.05 level with

a χ2 value of 5191.52 and a p-value of 0.000. The positive values on the year dummies indicate

that each year nations spent more on collective goods like defense and economic development

compared to nations in 1990.36

After 1990, the nation dummies carry positive coefficients

indicating that each year in the model, relative to 1990, spent more on collective goods. This

shift to greater spending on collective goods relative to 1990 suggests support for a retrenchment

of the welfare state argument, which argues nations moved to reduce spending on welfare items

like housing vouchers, unemployment benefits, and old-age pensions over the past two decades

(Pierson 1996; Starke 2006).

35

The empirical F-statistic for the interaction model compared to the constraint model is 28.89

which is larger than the critical F-statistic of 2.37 necessary to reject the null hypothesis that the

interaction model does not explain more variance in the spending priorities variable than the

constraint model. 36

An alternative specification using a single variable to express the year yields similar results to

using the year dummy in terms of signs, magnitudes, and statistical significance of the

independent variables. In this form, the year variable carries a positive coefficient representing

nations spending more on collective goods each successive year after 1990, which follows the

retrenchment argument in the literature.

139

The traditional socio-economic factors concerning wealth, the unemployment rate, and

the aged population all exhibit the predicted relationships with government spending priorities

outlined in Chapter 4 and supported in the spending priorities model in Chapter 5. Greater

wealth is associated with governments that have more resources to spend on particularized

benefits like family and children’s benefits without decreasing the goods and services provided

to society more broadly. In the interaction model (Model 3), higher levels in the natural log of

gross domestic product per capita are associated with governments that spend more on

particularized benefits, such as pensions and spending on families and children represented by

the negative coefficients. The relationship is statistically significant at the 0.05 level for a

directional test.

Additionally, as the unemployment rates increases, the expectation was that governments

spend more on services addressing the needs of the unemployed, such as unemployment benefits

or housing vouchers. The coefficient shows increases in the natural log of the unemployment

rate are associated with greater spending on particularized benefits confirms this hypothesis.

The proportion of the aged population in a nation should increase government spending

on particularized benefits that address the needs of the elderly population. The interaction model

shows that as the percentage of the aged population increases, governments spend more on

particularized benefits like unemployment. The effect of the aged population is based on the

negative coefficient and represents greater spending on particularized benefits and is statistically

significant for a one-tailed test at the 0.05 level.

Nations belonging to the European Union are predicted to behave differently in terms of

spending patterns based on their integrated economies as noted by the OECD, “common policy

goals regarding economic growth, agriculture, energy, infrastructure, and research and

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development (among others) may also affect the structure of expenditures” (At a Glance 2009).

The interaction model indicates that member nations of the European Union spend more on

particularized benefits, including aspects such as housing assistance and unemployment benefits,

compared to non-member nations of the European Union. This relationship supports the

hypothesis for European Union nations outlined in Chapter 4 and is statistically significant for a

directional test at the 0.05 level.

Institutional Constraints

As the number of national constraints increase, it should be harder for any group to obtain

its ideal spending priorities. This expectation extends previous work showing constraints alter

government spending priorities, specifically, increasing spending on collective goods relative to

particularized benefits. My argument is based on the effect of increasing the number of actors

involved in the decision making process. As the number of actors increases, single preference

holder should find it more difficult to increase spending on policy areas that may benefit their

group at the expense of others. Therefore, more national institutional constraints should be

associated with spending on policy areas that benefit the entire society at least somewhat versus

particularized areas that only benefit parts of it. The positive and statistically significant

coefficient for a directional test at the 0.05 level in the interaction model, which includes the

additive index based on the number of institutional constraints present in a nation and the

interaction between the number of constraints in a nation with different measures of elite and

mass preferences, lends support to the hypothesis that more institutional constraints increase

government spending on collective goods.

The results from the interaction terms involving each of the four measures of elite and

mass preferences provide additional insights on the question presented at the beginning of the

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chapter: Do the number of institutional constraints alter government policy outputs? The

interaction model demonstrates that governments are less responsive to both mass and elite

preferences as the number of institutional constraints present in a nation increases. Again,

responsiveness is measured as the degree to which policy outputs match expectations and

demands on government, which are captured here by the composition of political parties’ in

office, public opinion, general beliefs about the role of government and interest group strength.

My expectation is that as the number of institutional constraints increases and thus more groups

exist that must reach agreement, bartering will intensify and each group will obtain less spending

on any particular policy area than they would if they had sole discretion over spending. The

resulting increase in bartering and compromise produces less responsiveness, as policy outputs

will not match the demands of different groups. The results of the interaction terms show that

regardless of the measure of preferences, increasing the number of institutional constraints

decreases the degree to which governments respond to groups’ expectations; measured as the

cumulative effect of each preference based on the number of existing constraints.

The total effect of institutional constraints on government composition indicates that

increasing the number of institutional constraints decreases the effect of left parties in

government on spending priorities. Previous research suggets that more seats held by leftist

party members is associated with greater spending on particularized benefits, which conforms to

prior expectations. However, when I include the interaction term between government

composition and institutional constraints, in the presence of zero institutional constraints, the

results show that governments spend more on collective goods. The interaction term for the

coefficient is statistically significant at the 0.05 level for a directional test.

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What this result may indicate is that when leftist parties have fewer actors to appease in

the decision making process they are able to spend on a range of policy areas that support their

party platforms, including spending on education and health. However, as the number of

constraints increase requiring more bartering to pass legislation, leftist parties forgo policy areas

that are more collective in nature and fight for spending on policies that would receive less

spending without leftist parties, like social protection.

Table 6.3 Government Composition and Spending Priorities in the United Kingdom

Year

Percentage of Left Seats

Spending Priority Score

1990 39.28 54.49

1991 39.28 54.00

1992 39.28 53.67

1993 39.28 53.36

1994 39.28 53.23

1995 45.64 53.05

1996 45.64 53.10

1997 45.79 52.95

1998 46.42 52.95

1999 46.73 53.34

2000 72.61 53.58

2001 72.61 53.80

2002 72.61 54.08

2003 72.61 54.27

2004 73.00 54.28

2005 73.00 54.36

2006 73.00 54.56

2007 73.00 54.50

2008 66.83 54.88

The result under zero institutional constraints is highlighted by examining the United

Kingdom during the time period examined (Table 6.3). From 1990-97, increases in the

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percentage of seats held by leftist party members are associated with greater spending on

particularized benefits. During this period the left was not in control of the government and the

best the party could do was fight for a few of their causes. When Prime Minister Tony Blair took

office in 1997, the Labour party presented a new party manifesto to the public that focused on

education, crime, health, jobs and economic stability (Labour.org.uk). Four of the five focuses

of the traditionally leftist party correspond with collective goods policies, namely education,

public order and safety, health and economic development, and with control over the parliament,

the left was able to shift priorities to match their full policy agenda. With control of parliament

and no institutional constraints to promote compromise, the left could implement its full policy

agenda.

The spending pattern of leftist parties, in the presence of zero institutional constraints,

was similarly demonstrated in the American state context (Alt and Lowry 2000). In unicameral

states when Democrats held office, there is greater spending on the public sector than when

Republicans held office. However, in a bicameral systems the constraint, “… induces bargaining

when different parties control the legislature and executive” (Alt and Lowry 2000, 1039). The

product of bargaining forces the parties in office to choose among the policy objectives that are

feasible for it to achieve.

Regardless of the spending priority under zero institutional constraints, as the number of

constraints increases, government policy responsiveness to party preferences decreases in

relation to spending priorities. This result is shown by the negative coefficient for the interaction

term between government composition and the institutional constraint variable and is statistically

significant at the 0.05 level for a directional test.

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Figure 6.4 provides a visual display of the effect of government composition under zero

and four institutional constraints, respectively. Holding all else constant, when a nation has four

constraints, leftist parties are unable to obtain the same level of spending on policy areas as they

would under zero constraints. For example, moving from a nation with zero institutional

constraints (e.g., the United Kingdom) and a legislature dominated by leftist parties, switched to

a nation with four institutional constraints (e.g., Germany) holding all else constant, the model

predicts a 3% shift in spending from collective goods to particularized benefits; 3% is equivalent

to roughly 170 billion 2008 US dollars.

Figure 6.4 Predicted Spending Priorities for Government Composition

Note: Predicted values across the range of government composition from left parties

holding between 0 and 100 percent of the seats in the legislature. Based on

coefficients from the interaction model, with the remaining independent variables

held at their respective mean values, for the year of 1995.

Particularized Benefits

Collective Goods

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Particularized benefits are expected to increase when the population favors more

expansive roles for government involvement in a society (higher values on the role of

government variable) and increase spending on collective goods when views favor a more

limited role focusing on defense and the economy (lower values on the role of government

variable). In the interaction model, when there are zero institutional constraints, the negative

coefficient for the role of government indicates governments spend more on particularized policy

areas such as pensions and unemployment as the proportion of the population favoring more

expanded roles of government is higher and is statistically significant at the 0.05 level for a

directional test. Therefore, as the public expects government to be involved in areas such as

promoting equality and creating a humane society, governments spend more on particularized

policy areas like housing, and family and child benefits to address issues in these areas.

As the number of national institutional constraints increase, however, the effect of

government expectations diminishes, which is represented by the positive coefficient for the

interaction term between role of government and institutional constraints. As the number of

constraints increases, the cumulative effect of beliefs regarding government on spending

priorities decreases and is statistically significant at the 0.05 level for a directional test. The

decrease in government policy responsiveness to expectations can be examined graphically in

Figure 6.5, which depicts the predicted spending priorities across the range of expectations for

government under zero and four institutional constraints, respectively. When there are zero

institutional constraints, there is greater government responsiveness to beliefs compared to when

there are four constraints. The decrease in policy responsiveness to public perception for the role

of government under four constraints can be seen by comparing the absolute slopes under zero

and four constraints.

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Figure 6.5 Predicted Spending Priorities for Role of Government

Note: Predicted values across the range of expectation from societies dominated by

restricted views on governments role (-100) to expansive views on the role of

government (100). Based on coefficients from interaction model, with the

remaining independent variables held at their respective mean values, for the year

of 1995.

In nations with more liberal publics, the interaction model shows that in the presence of

zero institutional constraints, government spending priorities favor spending on particularized

benefits, indicated by the negative coefficient. The relationship between spending priorities and

public opinion supports the hypothesis from Chapter 4 and is statistically significant at the 0.05

level for a directional test. The result implies that when governments are not forced to barter to

pass legislation they can better represent and incorporate the preferences of public opinion into

Collective Goods

Particularized Benefits

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policy expenditures.

Figure 6.6 Predicted Spending Priorities for Public Opinion

Note: Predicted values across the range of public opinion from societies dominated by

right political priorities (-100) to societies dominated by left priorities (100).

Based on coefficients from interaction model, with the remaining independent

variables held at their respective mean values, for the year of 1995.

Similar to government composition and expectations regarding the role of government, as

the number of institutional constraints increases, government responsiveness to public opinion

decreases. The diminished response to public opinion is shown in the positive interactive

coefficient between the number of institutional constraints and public opinion. As the number of

institutional constraints increases, holding all else constant, the total effect of public opinion on

government spending priorities decreases. The interaction term is statistically significant for a

directional test at the 0.05 level. Figure 6.6 depicts the responsiveness of government to public

Particularized Benefits

Collective Goods

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opinion when there are zero and four institutional constraints, respectively. The absolute slope

under zero institutional constraints is larger than when there are four constraints, implying a

diminished role for public opinion in shaping spending priorities compared to when there are

zero constraints. The interaction term shows that as the number of institutional constraints

increase public opinion, like the preferences of the actors themselves will play a smaller role in

influencing the final policy outputs of government as a result of bartering and negotiations.

The final variable examined in the interaction model is the role of interest groups in

shaping spending priorities. Higher densities of interest groups in nations are expected to

increase government spending on collective goods; more interests mean more preferences to

appease, which overwhelm the system such that no group ultimately gets what it wants. When

there are zero institutional constraints in a nation, the interaction model shows that higher levels

of interest group density are associated with greater spending on collective goods, indicated by a

positively signed coefficient. The effect of interest groups, when there are zero constraints, is

statistically significant at the 0.05 level for a directional test. This finding supports the

hypothesized relationship between interest groups and government actions. In nations where

fewer pressure groups exist, it is easier for government to respond to these groups’ demands. As

the number of interest groups increases, however, it becomes difficult for governments to

respond to the multiple groups because resources are limited. Additionally, it is more

challenging to determine what organized interest is speaking for what sets of individuals in the

public.

As the number of constraints increases, the effect of interest groups on spending priorities

is diminished, represented by the negative coefficient for the interaction term between interest

group density and institutional constraints. Decreased government responsiveness to interest

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Figure 6.7 Predicted Spending Priorities for Interest Groups

Note: Predicted values across the range of interest group density from low levels of

density (-1) to high levels of interest group density (3). Based on coefficients

from interaction model, with the remaining independent variables held at their

respective mean values, for the year of 1995.

group density can be seen in Figure 6.7. The decrease in the absolute slope from zero constraints

to four constraints demonstrates the diminished policy responsiveness to organized interests as

the number of institutional constraints increases. The smaller slope could also be interpreted as

interest groups acquiring greater spending on the particularized benefits they desire. Institutional

constraints create access points to more decision makers that the organized interests can target

and push their spending agenda upon; however, as the graphs of spending priorities show, the

effect of interest groups at four constraints is not statistically different than zero, and it may

Particularized Benefits

Collective Goods

150

actually indicate that governments cease to be influenced by interest groups as more actors with

preferences over spending enter the decision making process.

IMPLICATIONS FOR NATIONAL SPENDING PRIORITIES

What does the interaction model imply for national spending priorities? In order to

understand how institutional constraints can alter national spending priorities, I begin by

examining the priorities within the United Kingdom. Over the time period from 1990 through

2008, the United Kingdom saw its spending priorities influenced by a range of factors favoring

spending on both collective goods, such as education and health, and factors fostering greater

spending on particularized benefits, such as housing vouchers.

Table 6.4 Observations for the United Kingdom from 1990-2009 used in the Interactions Model

GDP/Capita

Unemployment

Rate

Aged

Population

Government

Composition

Role of

Government

Public

Opinion

Interest

Groups

27,210.54 9.00 15.51 39.28 -5.21 -3.53 0.72

26,144.00 7.40 15.63 39.28 -3.31 -4.55 0.70

29,137.02 7.00 15.73 39.28 -1.41 -5.58 0.68

29,065.17 8.60 15.81 39.28 -1.26 -3.64 0.66

29,096.55 9.80 15.87 39.28 -1.10 -1.71 0.63

25,326.70 10.30 15.90 45.64 -0.95 0.22 0.61

26,627.25 9.70 15.93 45.64 -0.80 2.16 0.59

28,175.56 8.70 15.94 45.79 -0.64 4.09 0.57

28,770.50 8.20 15.93 46.42 -0.49 6.03 0.56

31,261.40 7.10 15.92 46.73 -0.33 7.96 0.56

32,881.25 6.20 15.89 72.61 -0.18 9.89 0.56

33,090.05 6.00 15.88 72.61 -0.18 9.82 0.56

31,369.53 5.60 15.88 72.61 1.83 9.75 0.57

30,252.46 4.70 15.89 72.61 3.84 9.67 0.59

32,519.36 5.00 15.93 73.00 5.85 9.60 0.61

36,552.37 4.80 15.98 73.00 7.87 9.53 0.63

41,922.89 4.60 16.04 73.00 9.88 9.45 0.65

41,736.57 4.80 16.10 73.00 11.89 9.38 0.67

42,987.19 5.40 16.16 66.83 13.90 9.31 0.68

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The United Kingdom saw increases in wealth, lower levels of inflation, a larger aged

population, greater expectations for the role of government and more liberal public opinions over

the time period. All of these variables are expected to result in governments spending more on

items covered by social protection with greater resources to spend and a greater demand for these

goods and services. At the same time, lower levels of unemployment and a left party in office

favoring a policy agenda that included education, public order and safety concerns, and

economic development also served to shape the spending patterns within the United Kingdom

during these years (Table 6.4).

The United Kingdom also serves as a case with zero institutional constraints. Although

the United Kingdom does have a bicameral legislature, the House of Lords does not have the

ability to veto legislation passed by the House of Commons, in essence creating a unicameral

legislature (Lijphart 1999). In Figure 6.8 the solid black line represents the predicted spending

priorities for the United Kingdom based on the interaction model. The dashed line represents the

spending priorities for the United Kingdom assuming it had three institutional constraints over

the same time period, retaining all other values for the variables in the model. The interaction

model predicts that if the United Kingdom had three institutional constraints during this period,

such as a nation like the United States or Italy, it would have spent more on particularized

benefits than it did.

Why is the United Kingdom predicted to spend more on particularized benefits with three

institutional constraints than zero? There are three primary explanations for this outcome. First,

while under zero constraints a more liberal public opinion will result in governments spending

more on particularized benefits, whereas with three institutional constraints, the effect of public

opinion is negated. Similar to public opinion, when the public expects the government to be

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Figure 6.8 Predicted Spending Priorities for the United Kingdom with Zero and Three Institutional Constraints

more involved on issues such as social protection, governments are predicted to spend more on

particularized benefits, under zero institutional constraints. However, expectations on the role of

government are cancelled out with three institutional constraints.

A second reason for expecting greater spending on particularized benefits in this situation

is the relationship between constraints and government composition. Left parties in government

Year

Pre

dic

ted

Sp

en

din

g P

rio

rity

53

54

55

1990 1995 2000 2005

0 Constraints

3 Constraints

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carry policy agendas that favor spending on a range of policy areas that include a number of

collective good like education and health. When left parties controlled government in the United

Kingdom under zero institutional constraints, they were able to implement a full range of policy

objectives and higher levels of spending on collective goods. However, the increase in the

number of institutional constraints predicts that the same left parties in office would not be in a

position to obtain their ideal spending outcomes and instead would barter away spending on

areas like education and health in favor of retaining spending on social protection.

A third reason for the constraints pushing spending toward particularized benefits under

higher levels of institutional constraints relates to the number of interest groups in the nation.

The initial argument around interest groups centered on more interests overwhelming the system,

making it difficult for government to cater to the needs of all the groups. However, an increase

in the number of institutional constraints provides more venues or access points for interest

groups to target with their agendas. The model predicts that for the United Kingdom, which was

already seeing a decrease in interest group density during this period (which would increase

spending on particularized benefits), an increase in the number of points for these interest groups

to target and sway regarding policy outputs would lead to further increases in spending on

particularized benefits. However, the influence of interest groups with three institutional

constraints on spending is very modest, at 0.2% shift in spending.

In this example, the predicted result of increasing the number of institutional constraints

is to shift spending towards particularized benefits. The United Kingdom is predicted to

experience an approximate 2.6% point shift from spending on collective goods like education

and defense to particularized benefits such as old-age pensions and unemployment insurance; a

difference equivalent to £17.8 billion in 2008. The influence of preferences on government

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spending priorities was altered under three institutional constraints. Additionally, the changes

under three constraints over time can be seen in the smaller changes to the predicted spending

priorities in Figure 6.8 compared to when there are zero institutional constraints.

CONCLUSION

The results confirm the hypotheses that institutional constraints influence the policy

process more than the prior literature suggests. Institutional constraints alter the final product of

government. As the example with the United Kingdom highlights, changing the number of

institutional constraints shifts the spending priorities of government. Not only do institutional

constraints shift spending priorities, but they also serve to moderate the ability of governments to

incorporate the preferences of both the elites and the mass publics into the decision making

process. The more actors that are introduced into the policy making process, the harder it will be

for any one actor to move spending in a direction that favors their ideal policy outputs or the

ideal outputs of any particular group.

The findings here are also applicable to different levels of government, particularly

within federal systems where different levels of government must make policy decisions within

specific policy domains. All levels of government face institutional constraints that should alter

their policy outputs. For example, Erikson, Wright and McIver (1989) find that within the

American states, once in office, parties move towards the center regarding nation policy outputs.

The findings in this chapter suggest that once in office, these same parties may still hold the

same preferences for policy outputs that are a part of their party platform and do not move in

terms of their policy preferences, but rather are forced into compromises that benefit all actors in

the decision making process based on the constraints present at the nation level. Within the

American state context, constraints may be found in bicameral state legislatures, within divided

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governments and in the form of bureaucrats whose careers are not tied to the election process and

represent a separate set of interests from elected incumbents.

A similar result is found in Alt and Lowry’s (2000) work on budgets within the American

states focusing on the ability of parties in office to change revenue dedications under unified and

divided governments between the legislature and executive branches and within bicameral

legislatures. Unified governments at the legislative level provide parties with a greater ability to

change revenues than when the houses are divided. In this context, divided government at the

legislative level serves as a constraint forcing compromise on ideal spending levels. Therefore,

while parties in office may change policy outputs, constraints limit their ability to completely

overhaul spending.

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CHAPTER 7 CONCLUSION

The work in this dissertation makes three strong contributions to the literature regarding

government spending priorities. First, the spending priorities variable examined in Chapter 3

expands upon the work done in the American states by Jacoby and Schneider (2001, 2009). The

results show that the particularized benefits/collective goods dimension, found at the state level

in the United States over time, also exists across 25 OECD member nations. Additionally, the

priorities model fills a gap in the literature by examining the factors that shape spending patterns.

This results in a model, which is more fully specified relative to previous work. Further, the

interaction model extends prior research on institutional constraints showing how constraints

slow down or impede the policy process. This research indicates that institutional constraints

also alter the final outputs of government as well as their ability to incorporate the will of its

citizens into the decision making process.

GENERAL FINDINGS

The spending priorities variable demonstrates that even though the process of

government spending across democratic nations appears to be a relatively complex, it can

actually be interpreted in a relatively simple, straightforward manner. Government spending is

based on whether expenditures target particular groups in society, through particularized benefits

(e.g., housing vouchers and unemployment benefits), or provide goods and services to the

society in more general terms through collective goods (e.g., defense and education). The

spending priorities variable shows an increase in spending on one set of policies decreases

spending on the other. For example, higher levels of spending on defense will decrease the

resources available to for particularized benefits, such as social protection.

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The spending priorities variable represents a single dimension of spending and explains

over 90% of the variance in government expenditures. Previous research in this area generally

examines one policy area (or at most, a few policy areas) pre-selected by the researcher based on

a priori assumptions about what policy areas represent similar types of government outputs. On

the other hand, the priorities variable combines expenditures across a fairly wide range of policy

domains. This includes such policies as government operations, social protection, health,

community development, education, economic development, defense, public order and safety,

recreation, and environmental protection. The unfolding analysis allows the expenditures to

determine how the policy areas relate to one another, based on the share of total government

expenditures policies receive relative to one another. Thus, government spending across a

variety of policy domains can be reliably measured and compared across nations in an

encompassing manner.

Furthermore, I establish how different factors traditionally characterized as influencing

government spending patterns affect a reliable measure of government outputs. Three different

sets of factors are examined, including socio-economic influences (predominant in the

functionalist literature), the role of mass and elite preferences for government actions, and the

influence of political institutions. Unlike prior work that tends to focus on one or two groups of

factors, I examine a more fully specified model of government spending that includes variables

from all three theories.

The results show that resources, public demands, and institutions all collectively shape

government spending priorities in democratic nations. Socio-economic variables represent the

resources available to the government and the influence of the societal groups dependent on

government to meet a minimum standard of living (like the elderly and unemployed). As

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expected within democratic nations, the preferences of both the elites in office and the masses

are mirrored in the policy outputs of government spending priorities. In addition, institutional

designs within a nation result in different expenditure patterns between nations.

The priorities model also shows that the fully specified model is able to account for more

variance in nations’ spending priorities than the individual arguments separately (See Appendix

B). The spending priorities model explains almost 80% of the variance in the priorities variable

which is 20 percentage points higher than the next best model running each set of factor

separately (Model 3, Appendix B). Further, the separate model specifications have omitted

variables producing biased coefficients. This has led to mixed findings in the literature about the

relationship between globalization and citizen mobilization relative to government outputs.

Expanding the understanding of government spending and institutions, I show how

institutional constraints influence expenditure patterns. Institutional constraints increase the time

it takes for governments to reach policy agreements because bartering and negotiations are

required to appease various expectations for policy outputs. This compromise affects the

responsiveness of governments to different groups’ preferences for government outputs. My

examination extends the work previously done regarding the nature of institutional constraints

and the ability to enact policies different than the status quo (Tsebelis 1995, 2000; Immergut

1990, 2010; Cox and McCubbins 2001; Shugart and Haggard 2001).

My findings suggest the bartering required to reach policy agreement in nations with

multiple institutional constraints also influences the spending priorities of government. In

nations that have more institutional constraints, actors in the decision making process are in less

of a position to obtain their ideal policy outputs. The bartering results in greater spending on

collective goods that provide all citizens makers with some general level of benefits for all

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groups. This is the case as collective goods benefit society more broadly, by funding programs

to protect the environment or promote a nation’s defense capabilities. On the other hand,

spending on particularized benefits would provide goods and services to only some sections of

the population, while leaving out direct benefits to others. My argument is supported with the

interaction model, where more institutional constraints are found to increase spending on

collective goods like education, environmental protection, economic development, and national

defense.

The interaction model also shows that as the number of constraints increase, the ability of

governments to respond to mass and elite preferences diminishes. Again, governments are

forced to compromise in the face of multiple decision makers with expectations for government

spending patterns; no group is in a position to obtain its ideal spending patterns. The effect of

constraints on preferences in society over policy outputs is evident in the diminished cumulative

effect for each set of expectation when interacted with the institutional design present in the

nation.

EXTENSIONS AND IMPLICATIONS

While the focus of this dissertation is on understanding how and why democratic nations

spend resources on different policy areas, the spending priorities variable has various

implications for future work. First, the unfolded policy dimension provides insight into research

on the modern welfare state. These studies have tended to group expenditures on education,

health, and social protection into one category representing “welfare” spending (Huber and

Stephens 2001). However, I show health and education are collective goods that provide

benefits to the community, more broadly. All individuals have access to primary schooling and

universal health care coverage in the majority of democratic nations. Grouping together policy

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areas that provide benefits to different segments of the population may provide confounding or

contradictory empirical results. For example, when social protection, education and health are

used as a single indicator of welfare a variable (such as globalization) might be found to decrease

spending resulting in support for the “race to the bottom” argument, but when multiple policy

areas are examined together with the spending priorities variable, globalization does not have a

statistically significant affect.

Additionally, the spending priorities variable allows for an examination of government

actions across an array of policy areas, simultaneously. The ten policy areas included in the

development of this variable capture most (if not all) of the spending commitments by the

general government, allowing use to control for any possible relationships between policy areas.

Previous research demonstrates that, the analysis of individual policy areas can produce results

that distort the influence of individual independent variables. Recall the Huber and Stephens’

(2001) model that uses a combination of health and pension spending as the dependent variable

and the replication of this model which includes the composite spending priorities measure as the

dependent variable. Adding together two expenditures like health and pension, which the

unfolding shows cover different types of government spending, resulted in explanatory factors

that are predicted to increase (or decrease) spending on these two different program areas in a

similar manner. The priorities variable preserves the actual relationship, in terms of spending

between these policies, and produces a different picture. In the replication, factors that increase

spending on pensions plans the aged population in a nation actually decrease spending on health

services.

The approach used to create the priorities variable allows researchers to predict examine

levels on individual policy areas after the analyses are run. Predictions are possible because the

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spending priorities variable preserves the proportion of spending on each policy area based on

the distances between nation spending priority points and the location of the policy points.

Using equation 3.4 discussed in Chapter 3, a researcher can use the predicted spending priorities

values from a model then to determine the proportion of spending on the individual policy areas.

In this manner, researchers who are interested in spending on a specific policy area are able to

use the priorities variable to analyze spending and then derive how their models affect spending

within the domain of interest.

The two sets of policies also support the depiction of government expenditures that is

becoming more prevalent in the literature (Banks and Duggan 2000, 2005; Lizzeri and Persico

2001; Volden and Wiseman 2007). The work done by Jacoby and Schneider (2001, 2009) and

Schneider and Jacoby (2006) shows similar results for the American states over time. Chapter 3

establishes that the same pattern exists for democratic nations. This increases the

generalizability of the particularized benefit/collective goods policy spending dimension.

Since the spending priorities variable captures government activities, it can also be used

to explain policy outcomes in democratic nations. For example, when examining how effective a

policy is at reducing the level of income inequality within nations, the priorities variable can be

used to operationalize government spending in relation whether expenditures promote policies

such as social protection or economic development. Figure 7.1 shows a scatter plot between the

Gini Index, which represents inequality in democratic nations, and policy spending priorities.

The graph shows that as governments spend more on particularized benefits (lower values of the

spending priorities variable) for example, social protection in terms of housing, unemployment,

and pensions—the level of income inequality in a nation decreases. However, as nations spend

162

more on collective goods (higher values of the spending priorities variable), the level of income

inequality in a nation rises.

FIGURE 7.1 Nation Gini Coefficients against Spending Priorities

Data sources: Gini coefficient data were obtained from the CIA World Factbook and

the World Bank.

Note: The slope estimate for the OLS regression line is 0.91 with a standard error of

0.434.

The analyses in Chapters 5 and 6 indicate that institutions matter for government

spending patterns; however, studying institutions, alone, in relation to policy outputs, does not

provide the full picture of government behavior, particularly in regards to political

163

responsiveness. Each individual institution provides insights into how actors behave in this

process. For example, presidential systems are more likely to promote spending on goods that

benefit society as a whole compared to parliamentary systems. However, the cumulative effect

of the increased number of actors introduced into the decision making process given the presence

of constraining institutions is overlooked in relation to policy outputs. Looking at the total effect

of constraints on government spending shows that the outputs of government vary when

bargaining must occur, under multiple constraints, and to when there is a single set of decision

makers with unified goals.

Studies that look at the role of institutional constraints acknowledge that these institutions

slow down the legislative process; however, the indirect effect of constraints on other influential

indicators has not been examined in the literature. By failing to look at how constraints alter the

role of preferences in shaping government activities, studies over state the role of citizens in the

democratic process. The interaction model run in Chapter 6 shows that expectations for

government outputs at both the mass and elite level still influence governmental activity;

however, the nature of the institutions present in the nation can diminish the responsiveness of

governments to translate citizens’ preferences into policy outputs.

The relationship between institutional constraints and societal demands or expectations

for government actions ties back to Lijphart’s (1999) examination of consensual versus

majoritarian systems. Consensual systems attempt to incorporate many preferences to make

democracy the “rule of as many people as possible;” on the other hand majoritarian systems

promote majority rule. Consensual institutions are the institutions that represent constraints in a

nation. These include presidentialism, proportional representation, increased district magnitude,

bicameralism, and federalism. The interaction model shows that by incorporating as many

164

preferences as possible no group in society is in a position to get what they want from

government.

The finding that institutional constraints influence the role of preferences is not limited to

the national government. Constraints also exist at the state and local levels of government. For

example, states may have bicameral legislatures. States may also have officials who are not

elected and have their own preferences over policy outputs. The constraints will again force

compromise on policy and alter government activities as shown in Chapter 6. Therefore, when

examining factors that influence government actions at any level of government, the constraints

present will affect how other factors influence policy outputs.

This point may offer an alternative explanation to the work done on the American states

by Erikson, Wright, and McIver (1989). Erikson, Wright, and McIver argue that once in office,

the political parties move to the center regarding policy decisions. However, the interaction

model would suggest that this is not necessarily true. Instead, the political parties may still hold

the same preferences for government spending as presented during elections, but as a result of

state constraints (such as divided government, bicameral legislatures, and lifelong bureaucrats),

parties may be forced to compromise on government outputs in order to reach agreements while

in office.

CONCLUSION

Democratic nations conceive of policies in a similar ways based on whether expenditures

target particular groups or the community in broader terms. However, governments have

different spending patterns on policy in accordance with the following: what they have to work

with, what citizens want, and how many people have to agree for government to act. No single

policy can capture the full range of what governments do. By examining the influence of

165

variables like national wealth and unemployment on expenditures for social protection, the

connection to other policy areas like economic development is omitted. Models only considering

an individual policy cannot confirm that the increases (or decreases) in spending explained by

the independent variables are unique to that policy area. For example, testing the effect of trade

openness on expenditures for economic development, alone, may show that greater openness

results in higher levels of spending on the economy. However, the same relationship may exist

between trade openness and spending on health, recreation, education, social protection, and

environmental protection. By examining policy areas in isolation from each other, the influence

of explanatory variables, revealed in the model may not be unique. In fact, these relationships

may be occurring across the range of government expenditures in various policy areas (i.e.

increases across total government spending instead of shifts in spending patterns across policies).

Without applying the link between preferences and constraints, the effect of citizens’

expectations and demands for government action can be overstated. The stronger association

between citizens’ preferences and government actions may lead researchers to underestimate the

trade-offs between institutional designs. For example, Lijphart (1999) suggests there are no

socio-economic trade-offs when adopting more consensual institutions. As previously noted,

consensual institutions increase the number of actors/groups present in the decision making

process (i.e. institutional constraints). Instead, Lijphart (1999) argues that the constraints create

a kinder form of democracy represented by greater spending on elements of social protection and

lower crime rates. However, the interaction model I present shows how Lijphart’s argument

misses political trade-offs in terms of how much the public can influence government activities.

This leads to a false sense of security that democracies promote the will of the people. Instead,

166

government outputs are based on compromise that does not mirror any groups’ ideal preferences

for government actions.

167

APPENDICES

168

APPENDIX A

DISTRIBUTIONS OF SPENDING BY POLICY AREA

Figure A.1 Distribution for the Proportion of Spending on Defense

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

169

Figure A.2 Distribution for the Proportion of Spending on Economic

Development

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

170

Figure A.3 Distribution for the Proportion of Spending on Education

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

171

Figure A.4 Distribution for the Proportion of Spending on Environmental

Protection

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

172

Figure A.5 Distribution for the Proportion of Spending on Government

Operations

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

173

Figure A.6 Distribution for the Proportion of Spending on Community

Development

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

174

Figure A.7 Distribution for the Proportion of Spending on Recreation

Note: The histogram is based on the percentage of spending out of total spending across

the ten policy areas for all 379 nation years in the dataset.

175

Appendix B

SEPARATE SPENDING MODELS BY SET OF INFLUENCES

Table B.1 Influence of Socio-Economic Factors on Spending Priorities

Note: a Indicates that the natural log of the original variable was used in the model.

b Indicates that p-value for the variable is for a non-directional test, all other variables

based on directional tests.

Model 1 Model 2 Model 3

Variable Coefficient

(s.e.) p-value Coefficient

(s.e.) p-value Coefficient

(s.e.) p-value

GDP/Capita a

-1.00 0.000 -0.83 0.000 -0.77 0.000

(0.160)

(0.111)

(0.087)

Unemployment a -0.65 0.000 -0.84 0.000 -0.79 0.000

(0.077)

(0.083)

(0.077)

Inflation 0.000 0.500 -0.02 0.065 -0.02 0.099

(0.017)

(0.014)

(0.013)

Female

Participation 0.02 0.001 0.005 0.335 0.007 0.252

(0.007)

(0.012)

(0.010)

Dependent

Population 0.02 0.347

(0.060)

Aged Population

-0.21 0.000 -0.26 0.000

(0.032)

(0.040)

Youth

Population

0.05 0.142

(0.045)

Openness b

0.004 0.006 -0.002 0.195 -0.002 0.189

(0.001)

(0.001)

(0.001)

European Union -2.20 0.000 -1.62 0.000 -1.63 0.000

(0.103)

(0.068)

(0.065)

R2 0.5152

0.5992

0.5967

N 367 367 367

176

Table B.2 Influence of Group Preferences on Spending Priorities

Model 4 Model 5

Variable Coefficient

(s.e.) p-value Coefficient

(s.e) p-value

Government Composition -0.003 0.245 0.001 0.386

(0.004)

(0.005)

Voter Turnout b

-0.10 0.000 -0.03 0.000

(0.006)

(0.004)

Role of Government -0.01 0.036 -0.02 0.000

(0.008)

(0.005)

Public Opinion 0.005 0.065 -0.02 0.000

(0.003)

(0.003)

Corporatism -0.12 0.000

(0.032)

Interest Groups

0.52 0.000

(0.414)

R2

0.5707

0.2548

N 214 369

Note: b Indicates that p-value for the variable is for a non-directional test, all

other variables based on directional tests.

177

Table B.3 Influence of Institutions on Spending Priorities

Model 6

Variable Coefficient

(s.e.) p-value

President 1.93 0.000

(0.262)

PR -1.79 0.000

(0.107)

Mixed Voting -1.47 0.000

(0.124)

District Magnitude a

0.34 0.000

(0.053)

Bicameralism 0.37 0.000

(0.068)

Federalism -0.75 0.000

(0.073)

R2

0.2484

N

369

Note: a Indicates that the natural log of the original

variable was used in the model.

178

Appendix C

DIAGNOSTIC TESTS AND MODEL SELECTION

Time Dummies

Year time dummies are included in the model in order to address any potential time

trends present in the spending priority variable. The year dummies do not carry directional

hypotheses. 1990 is used as the base category for the time dummies and the remaining time

dummies would be interpreted in relation to 1990. After the analysis a joint F-test is performed

to test for joint significance of the time dummies. If the year dummies are omitted the results

estimates from the resulting model are similar to the estimates in interaction model in terms of

signs, magnitude, and statistical significance. The only change is that the interaction term

between institutional constraints and role of government is not statistically significant at the 0.05

or 0.10 level for a directional test.

Lags

The independent variables included in the analysis are lagged two time periods. The

lagging of the independent variables is theoretically justified based on the general process of how

government expenditures occur. National governments’ expenditures tend to run on a two year

lag in order to established the budget and execute spending (Hofferbert and Budge 1992;

Klingmann et al. 1994). One year is required to discuss and approve spending and an additional

year is required for spending to occur. As there is a two year lag, it would not make theoretical

sense to determine the relationship between current events and observations with spending

patterns that are established and executed previously.

179

The process of a two year lag also helps to alleviate concerns over the direction of the

relationship between the dependent and independent variables. As I am using independent

variables that have been lagged two years, it would not be expected to find the actual

expenditures at time t affecting what happened at time t-2.

A lag of the spending priorities variable is not included for theoretical reasons. If a lag of

the spending priorities is included it would explain a large portion of the variance seen in

spending priorities the following year. However, from an information standpoint, this approach

fails to provide insight into what influences the actual pattern in the level of spending as it does

not contribute to the understanding of why governments spend. Further, there is degree of

multicollinearity between the lagged dependent variable and the other independent variables

increasing the difficultly of separating the relationships between the variables in the model. As

the focus here is on explaining spending priorities and not predicting spending priorities, I have

chosen to omit the lag of spending priorities that would improve predictions in favor of variables

that are expected to influence spending.

I also do not include a lag of the spending priorities in the model as it would theoretically

support an incrementalist argument in regards to the spending process. Incrementalism is based

on the assumption that a full review of the budget each year requires too much time, therefore

small changes are made each year but complete overhauls do not occur. It has been repeatedly

shown, however, that while this approach can explain the majority of changes that are minor

increases and decreases in spending that occur, it cannot account for the rapid changes in policy

explained by punctuated equilibriums (Baumgartner and Jones 1993; Baumgartner, Green-

Pedersen, and Jones. 2006; Baumgartner, Foucault, and Francois 2006; Baumgartner et al. 2009;

Jones et al. 2009). Therefore, if I include a lag of spending in the model I could predict the small

180

changes from year to year, but not large changes, nor could I explain why governments are

spending at the level they are on different policy areas.

Achen (2000) makes the point examining models that predict budgets, that, “budgets are

generally quiet stable over time and well predicted by the prior year, but nothing follows about

the bounded rationality or incrementalist thinking of the decisionmaker” (11). The expenditures

that are studied here behave similarly to budgets where the prior year’s values can explain

roughly 95% of the variance in the current year’s expenditures. However, this does not provide

and insight into what resulted in last year’s expenditures beyond what was spent the year before,

and so on. Achen (2000) also argues that, “lagged budgets will falsely appear to be the sole

cause of future budgets when the political environment is stable, but not otherwise,” applying

this to the expenditure data, when the influences of expenditures remain relatively similar to last

year, last year’s expenditures will appear to be a good predictor of the current year’s

expenditures(10). However, when the influences on expenditures change dramatically, the

ability of last year to predict would not accurately reflect why governments are spending. For

example, if extreme conservatives like Tea Party members or Libertarians gained office in the

United States, the ability of the prior years to predict expenditures patterns would likely fall

short. Instead models that focus on what factors influence spending and test the effect of

political parties in office and public opinion; that would have changed to elect these candidates,

the spending priorities would be more appropriately modeled.

Transformation

Having established the estimation procedure, I begin with the final model examined in

Chapter 5 that includes the socio-economic, elite and mass preferences and institutional variables

(priorities model) and replace the separate institutional variables with the institutional constraint

181

variable (constraint model). After examining the results of the constraint model, I rerun the

model omitting the variables that failed to reach statistical significance at the 0.05 level which

include trade openness, the inflation rate, female participation, and voter turnout (Model 2).

After running Model 2, I compare the variance explained by the two models and find the

variance explained by the model that includes the variables that failed to reach statistical

significance is not statistically different than the model that does not (Model 2, Table 6.2). The

empirical F-statistic calculated based on Model 1 and Model 2 is 0.827 which is less than the

critical value of 2.37 at the 95% level, meaning I fail to reject the null that Model 1 explains

more variance in spending priorities than Model 2.

Having established the base model, I run a full model that includes the interaction effects

between the institutional constraints and the measure of elite and mass preferences (interaction

model, Table 6.3). In order to confirm that openness, the inflation rate, female participation, and

voter turnout would not affect the finding in interaction model, I ran an additional model that

included the openness inflation rate, female participation, voter turnout, and voter turnout

interacted with the number of institutional constraints. The model finds produced similar results

in terms of signs, magnitude, and statistical significance as interaction model. Further, openness,

the inflation rate, voter turnout and the interaction between voter turnout and constraints were

not statistically significant either the 0.05 or the 0.10 level. Female participation carried a

positive coefficient and was statistically significant at the 0.05 level for a directional test.

However as the model does not increase the variance explained in a statistically significant

manner I chose to use the more parsimonious interaction model.

Because the models I use are based on the assumption of linearity I examine the

relationship between spending priorities and the independent variables before moving forward.

182

Figures 6.2 through 6.8 present the component plus residual plots for each of the interval level

variable in the model, each figure includes a line for the linear fit and a lowess curve

representing the relationship between the independent variable and government spending

priorities.

Examining each of the component plus residual plots shows that the relationships

between the independent variables and the spending priorities variable exhibit predominately

linear relationships, controlling for the other variables in the model. The natural logs of gross

domestic product per capita and unemployment were logged in the prior models from Chapter 5,

following the conventions of prior works that had examined the relationship between gross

domestic product per capita and unemployment, and government spending. Figures C.1 and C.2

show the natural logs of gross domestic product per capita and unemployment produce a linear

relationship with the spending priorities variable, controlling for the other variables in the model.

The aged population (Figure C.3), government composition (Figure C.4), role of

government (Figure C.5), public opinion (Figure C.6), and interest groups (Figure C.7), also

produce linear relationships with the spending priorities variable. The relationship between

expectations for government and spending priorities in Figure C.5 shows a small divergence

from linearity at lower values of expectations; however, at the lower end there are fewer

observations to use to establish the relationship. A linear relationship exists through the majority

of the data between role of government and spending priorities suggesting a transformation is

unwarranted; as the relationship is statistically significant in the model and a transformation

would only increase the difficulty of interpreting the effect of expectations for government on

spending priorities.

183

Multicollinearity

The nature of the data indicates prior to the analysis that multicollinearity is likely to be

present. In situations where multicollinearity is high, the standard errors will be inflated and

may produce estimates that appear to fail to reach traditional levels of statistical significance. In

order to address any issues of multicollinearity I examine the variance inflation factors after the

analysis in interaction model is performed. In situations where multicollinearity appears to be

high and the variable fails to reach statistical significance I note the potential for the standard

errors to have been inflated by the other variables in the model. In the future as more data

become available, additional analyses will help sort out the effects of different variables that may

suffer under current conditions of multicollinearity.

After running the interaction model, the variance inflation factors appear high; however,

as the four key interaction terms are a product of four preference variables in the model and the

institutional constraints variable this is not surprising. Table C.1 shows the variance inflation

factors for interaction model and the variance inflation factors for Model 2 where the interactions

are omitted. The remaining multicollinearity in Model 2 exists between the socio-economic

variables that are expected to be related to one another. Table C.2 shows the basic correlations

between the independent variables and also demonstrates that the majority of multicollinearity

occurs between the interaction terms, preferences, and the institutional constraint variables.

Residuals

After running the analysis I examine the residuals to test that there is constant error

variance. The examination of the residuals begins with looking at scatter plot of the residuals

versus the fitted values (Figure C.8). The scatter plot shows that the residuals appear to be

184

randomly distributed around zero. However, around both the minimum and maximum fitted

values, the residuals appear to deviate from a random distribution around zero.

In order to confirm what is seen in the scatter plots if Figure C.8, I run a White test for

heteroskedasticity. The null hypothesis for the White test is that homoskedasticity exists in the

residuals. After running the White test on residuals from the analysis I am unable to reject the

null hypothesis with a p-value of 0.1108. Using these two approaches to examine the residuals I

am comfortable stating that the residuals are homoskedastic. Had heteroskedasticity been an

issue, however, the panel-corrected standard errors would have accounted for it in the estimation

of the standard errors in the model.

Influential Observations

In order to ensure that influential observations are not affecting the results I create a plot

of the leverage values against the normalized residuals squared (Figure C.9). After examining

the leverage versus residuals plot in Figure C.9, there do not appear to by any particular

observations that have both high residuals and high leverage to bias the results of the interaction

model.

In addition to examining the plot of leverage against normalized residuals squared, I also

examine the actual values of the leverage statistics and the residuals. There were no observations

that had a both a leverage statistic greater 0.1744 and a residual greater than 1.312 or less than -

1.312.37

To further ensure that there are no influential observations I also examine the Cook’s

Distance (or Cook’s D) values. None of the Cook’s D values are close to 1, indicating that the

37

Here the threshold for the leverage value is greater than 2*32/367=0.174386921 and the

threshold for the residuals is absolute value is greater than 1.312052 (+/- two standard deviations

away from the mean).

185

influence of any individual observation is minimal.38

I use this as further evidence that there are

no individual observations influencing the results in the model.

38

The largest cook’s D value is 0.0635107.

186

Table C.1 Variance Inflation Factor Scores

VIF

Model 3 VIF

Model 2

GDP/Capita 55.03 44.45

Unemployment 22.90 18.44

Aged Population 78.76 73.88

European Union 7.01 6.80

Institutional Constraints (IC) 75.70 8.11

Government Composition 53.48 10.17

IC x Government Composition 67.27

Role of Government 14.66 1.42

IC x Role of Government 13.76

Public Opinion 29.69 1.63

IC x Public Opinion 25.59

Interest Groups 20.86 1.34

IC x Interest Groups 22.55

Mean VIF 16.89 7.74

187

Table C.2 Correlations Matrix for Independent Variables in Interaction Model

GDP/

Capita Unemployment

Aged

Population

European

Union

Institutional

Constraints

Government

Composition

IC x

Government

Composition

GDP/Capita 1.0000

Unemployment -0.5143 1.0000

Aged Population 0.1909 0.0787 1.0000

European Union -0.2927 0.3174 0.2575 1.0000

Institutional Constraints -0.0888 0.0974 0.0365 0.0307 1.0000

Government Composition -0.2328 0.0902 0.1309 0.0045 -0.0795 1.0000

IC x Government

Composition -0.2322 0.1680 0.0947 0.0279 0.7330 0.5775 1.0000

Role of Government 0.5622 -0.2371 0.1562 -0.1035 0.0510 0.1225 0.0892

IC x Role of Government 0.5342 -0.2497 0.1520 -0.0817 0.0652 0.0775 0.0770

Public Opinion -0.3271 0.2706 0.1835 0.0739 -0.1850 0.3880 0.1403

IC x Public Opinion -0.2335 0.1681 0.1937 0.0158 -0.1233 0.3776 0.1832

Interest Groups 0.2303 -0.0194 0.0186 -0.3221 0.1141 0.0702 0.1385

IC x Interest Groups 0.2356 -0.0505 -0.0093 -0.3424 0.2376 0.0673 0.2367

188

Table C.2 (cont’d)

Role of

Government

IC x Role of

Government

Public

Opinion

IC x Public

Opinion

Interest

Groups

IC x Interest

Groups

GDP/Capita

Unemployment

Aged Population

European Union

Institutional Constraints

Government Composition

IC x Government Composition

Role of Government 1.0000

IC x Role of Government 0.9520 1.0000

Public Opinion -0.2269 -0.1561 1.0000

IC x Public Opinion -0.1554 -0.0956 0.9672 1.0000

Interest Groups 0.2156 0.2404 0.1749 0.1850 1.0000

IC x Interest Groups 0.2072 0.2429 0.1630 0.1975 0.9636 1.0000

189

Figure C.1 Component plus Residual Plot for the Natural Log of GDP/Capita

190

Figure C.2 Component plus Residual Plot for the Natural Log of Unemployment

191

Figure C.3 Component plus Residual Plot for the Aged Population

192

Figure C.4 Component plus Residual Plot for Government Composition

193

Figure C.5 Component plus Residual Plot for Role of Government

194

Figure C.6 Component plus Residual Plot for Public Opinion

195

Figure C.7 Component plus Residual Plot for Interest Groups

196

Figure C.8 Scatter Plot of Residuals against Fitted Values

197

Figure C.9 Plot of Leverage and Residuals by Observation

198

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