The Political Economy of Tariff Revenue Dependence [Manuscript]-1

The Political Economy of Tariff Revenue Dependence:

Does Electoral District Magnitude Matter?

Objective. This paper investigates whether electoral district magnitude in democratic developing countries

influences government dependence on trade tax revenue through the aggregation of voters’ preferences over

taxation. These preferences over the use of different tax instruments tend to vary with voters’ income levels.

Method. The author uses fixed effects panel regression, with the dependent variable being tariff share of

government revenue. Results and conclusion. This study finds that higher electoral district magnitudes are

significantly associated with lower tariff revenue shares, controlling for confounders and alternative factors. It

also finds that income inequality moderates the negative association between district magnitude and tariff

revenue share.

1. Introduction

Tariffs are an important source of government revenue. Developing countries, in particular, rely more on tariff

revenues than their developed counterparts (Tanzi, 1987; Hitiris, 1990; Kubota, 2005). In fact, Tanzi (1987)

found that in developing countries, import duties account for 25% of total tax revenue, making them ‘by far the

single most important revenue source’. One proposed reason for this is that developing country bureaucracies

are too ineffective to collect other forms of taxes (Aizenman, 1987; Gardner and Kimbrough, 1992; Khattry and

Rao, 2002). On the other hand, political economy suggests that democratisation drives tariff revenue

dependence up in developing countries by strengthening the influence of the poor median voter, who imports

few high-quality goods and is less sensitive to hikes in tariffs than other taxes (Moutos, 2001; Adam, 2009).

However, existing research has yet to consider whether democracies with different electoral systems will

produce the same equilibrium level of tariff revenue dependence. Electoral systems with larger district

magnitudes, which allow representation of more different voters than just the poor median voter, might be

expected to produce lower tariff revenue shares in equilibrium. Thus, this paper investigates the effect of

varying electoral district magnitude on tariff revenue dependence in developing countries. It finds significant

evidence of a negative association between district magnitude and tariff revenue share in developing countries,

particularly when the effect of income inequality and its interaction with district magnitude is accounted for.

This study endeavours to provide a better understanding of the challenges faced by governments in developing

countries when they undertake trade liberalisation policies and are constrained by fiscal needs.

2 THE POLITICAL ECONOMY OF TARIFF REVENUE DEPENDENCE

2. Existing Literature

2.1 Explaining tariff revenue dependence using bureaucratic capabilities

A longstanding explanation for developing countries’ significant tariff revenue shares involves weak

administrative and bureaucratic capabilities, which prevent governments in developing countries from

effectively collecting income tax and other domestic direct taxes. This explanation – termed the Administrative

Capabilities Hypothesis (ACH) in current research – posits that the costs of collecting income taxes are high in

developing countries, leading bureaucracies to collect more revenue from tariffs. Developing countries tend to

have archaic tax administrations which are unable to competently assess tax liabilities and monitor domestic

compliance to tax laws effectively (Khattry and Rao, 2002). Structural characteristics of taxpayers in developing

countries further weaken the bureaucracy’s ability to raise revenue through direct taxes. Firstly, given that

literacy is required for citizens to file income taxes (Riezman and Slemrod, 1987), low levels of education

amongst citizens of developing countries hamper the government’s efficacy in collecting income taxes. Kenny

and Winer (2006) showed that educational attainment and school enrolment were positively related to individual

income tax revenue share and negatively associated with trade tax rates. Secondly, income tax compliance

monitoring and collection is hindered when the rural taxable population is geographically dispersed. Meanwhile,

tariffs are less costly to collect because taxable trade activity tends to be centralised in a few ports (Aizenman,

1987; Gardner and Kimbrough, 1992).

Yet, the ACH fails to consider the costliness of tariff collection due to complex tariff codes (Adam, 2009).

Empirical evidence for it has also been mixed. In Mahdavi (2008)’s study of tax revenue mix in developing

countries, the association between urbanisation and trade tax revenue share of GDP was insignificant.

Additionally, while Kenny and Winer (2006) found a negative association between educational levels and trade

tax rates, the association of the former with trade tax revenue share was insignificant.

2.2 Explaining tariff revenue dependence using political economy

Alternative explanations for tariff revenue dependence built on political economy theories have also emerged.

Kenny and Winer (2006) and Mahdavi (2008) proposed that in countries with authoritarian institutions

(regardless of the country’s level of economic development), the regime lacks legitimacy in the eyes of the

citizenry. This makes it harder for the government to enforce direct taxation, pushing it to collect more revenue

from indirect taxes instead. However, this “democratic legitimacy” explanation is unable to account for the

insignificant relationship between democracy and trade tax share found in both studies, particularly when the

THE POLITICAL ECONOMY OF TARIFF REVENUE DEPENDENCE 3

sample is not restricted to developing countries as was the case for Kenny and Winer (2006). Adam (2009)

suggested an alternative political explanation by focusing on voters’ interests and using a theoretical model

developed by Moutos (2001). He showed that in an environment where a vertically-differentiated good is traded,

with developing countries exporting low-quality versions to the rest of the world and importing high-quality

ones (Schott, 2004), tariff revenues encompass greater proportions of developing countries’ government

revenues as they become more democratic. Democratisation expands the selectorate to shift decisive trade and

tax policy positions away from the rich elite to the poor median voter who consumes low-quality, locally-

produced goods (Fajgelbaum et al., 2011; Bils and Klenow, 2000). The prices of low-quality, domestically-

produced goods are not affected by tariffs, so the median voter consuming low-quality goods will be

‘impervious to the imposition of tariffs’ but not higher income taxes (Adam, 2009, p. 611). Adam thus predicted

that majority voting will result in higher tariff revenue shares. Using a sample of 64 countries labelled by the

World Bank as Low Income, Lower-Middle Income or Upper-Middle Income, which fitted his assumptions of

trade patterns, Adam found that democracy had the expected positive sign and remained significant even after

controlling for bureaucratic quality, while not all ACH variables were significant.

2.3 What role can electoral institutions play?

However, Adam’s theory does not consider that democracies vary in form, particularly in terms of electoral

district magnitude. His theory presupposes a majoritarian system of democracy and is highly dependent on the

median voter to explain outcomes. Yet, according to the Database of Political Institutions (DPI) (Beck et al.,

2001), approximately 50% of countries classified as Low, Lower-Middle and Upper-Middle Income have some

form of proportional representation (PR) electoral system. When district magnitude exceeds 1, the median voter

may not be solely decisive, resulting in different conclusions regarding equilibrium tariff revenue share. No

study to date has investigated whether this is the case.

Much of existing research on the effect of electoral institutions on trade policy has uncovered a “protectionist

bias” in majoritarian systems: the majority party maximises the joint welfare of a majority of but not all districts,

and therefore adopts higher tariffs to redistribute income to industries linked to districts controlled by the

majority party (Grossman and Helpman, 2005). In contrast, in a PR system, the legislature is more insulated

from regional protectionist pressures (Rogowski, 1987), or more likely to represent the interests of the country

as a whole (Evans, 2009). Such theories focus on producers’ trade preferences, usually expressed through

narrow interest groups, instead of the preferences of ordinary voting consumers. An extension of Adam (2009)’s


research might complement the majoritarian bias literature by providing an alternative, consumer-centric

perspective on how electoral institutions influence trade policy.

The broader literature on the representational consequences of electoral systems might similarly suggest that PR

systems exhibit lower tariff revenue shares. In developing countries, wealthy voters who demand high-quality

imports are the minority, and PR systems might lend these pro-trade minority interests greater influence over

policymaking. It has been argued that electoral systems with higher district magnitudes produce more

proportional electoral results (Taagepera and Shugart, 1989; Cox, 1997) that enhance political representation of

minority groups by lowering the vote share threshold required by a party to guarantee a legislative seat. Cross-

national and country-level studies of electoral district magnitudes in socially heterogeneous countries provide

evidence of a positive association between district magnitude and minority representation (Ordeshook and

Shvetsova, 1994; Clark, Gilligan and Golder, 2006). Since rich elites who import high-quality goods form the

minority of the electorate in developing countries, we might expect that higher district magnitudes enhance the

electoral representation of interests favouring imports and lower tariffs in developing countries, resulting in

lower tariff revenue shares.

2.4 Inequality, tariffs and voter preferences

Political economy explanations for tariff revenue dependence imply that tariffs serve a redistributive function

alongside other taxes: due to income-based differences in patterns of consumption and tendencies to import

high-quality goods, poor voters desire that the government raise more revenue via tariffs than income taxes so

that the tax burden is shifted from the poor to rich importing voters (Moutos, 2001; Adam, 2009). It has been

suggested that a rise in income inequality in a democracy – defined as an increase in mean income relative to

median income – increases voter demand for redistribution by taxation (Meltzer and Richard, 1981). Hence we

might expect inequality to influence how an electoral system aggregates voter preferences over tariff revenues in

a developing country.

To date, we know that inequality affects tariff revenue share directly, but the interaction between inequality and

electoral systems in determining tariff revenue share has yet to be investigated. Katsimi and Moutos (2010)

suggested that in a developing country engaging in vertically-differentiated trade, inequality is nonlinearly

related to tariff revenue share. In their model, higher inequality increases demand for redistribution through both

income taxes and tariffs. However, the optimal weight placed on each tax instrument depends on the existing

level of inequality. This is because the level of inequality determines the size of the tariff tax base and the extent


to which it shrinks in response to higher tariffs (assuming changes in inequality are mean-preserving), which in

turn affects the revenue that can be collected from tariffs. Dutt and Mitra (2002) obtained similar findings in the

context of horizontally-differentiated trade: they found that the relationship between inequality and

protectionism depends on a country’s present capital endowment. These studies suggest that mean-preserving

increases in income inequality change the relative intensities of pro- and anti-tariff preferences among voters,

and may therefore influence the aggregation of policy preferences over tariffs.

In light of the findings and shortcomings of existing literature, I develop a theory that explains the effect of

varying electoral district magnitude on the aggregation of tariff preferences and the eventual tariff revenue share

allocated by the government.

3. Theory and Hypotheses

3.1 Setup

I propose a theory which extends Adam’s (2009) framework, explaining how tariff preferences are aggregated

under electoral systems of varying district magnitude. To do so, I first make the same assumptions regarding

world trade and consumer preferences as Adam did.

I assume that world trade operates such that specialisation of production takes place within a product type and

across quality levels of a vertically-differentiated good. Schott (2004) found that factor endowments can explain

within-product specialisation but not across-product specialisation in trade between the United States and

various developing and developed country trading partners. Capital-abundant, developed countries use their

endowment advantage to produce and export high-quality good varieties, while developing countries conversely

export low-quality goods and import high-quality ones from the developed world.

I also use the model developed by Moutos (2001) to define individuals’ preferences regarding tariff revenue

dependence in a developing country based on income and consumption patterns. Consider a vertically-

differentiated good � with quality �. Each consumer purchases 1 unit of � or none of it, but can choose quality

� from a range of continuous values to maximise utility. There is a “dividing” level of quality ��, below which

the developing country produces � at lower marginal cost than developed countries. Since the consumer would

like to minimise the price he/she pays for the good and price varies positively with marginal cost, the consumer

purchases quality levels below �� domestically at the price of the locally-produced good �� , and imports

quality above �� from developed countries at price �� under free trade. The consumer thus faces the

following budget constraint:


� = �� + ��if� < �� + ��if� ≥ �� (1)

where � is pre-tax income, � is the number of units of a homogeneous good consumed and � is the quality of a

fixed unit of the vertically-differentiated good consumed. The consumer maximises utility �(�, �) subject to

this budget constraint. Low-income consumers are assumed to maximise utility by consuming � < �� , while

high-income consumers maximise utility by consuming � > ��1.

The government has 2 tax instruments at its disposal – tariffs and income taxes – and raises revenue either by

implementing only tariffs (tariff revenue share 100%) or only income taxes (tariff revenue share 0%). With

income tax of rate 0 ≤ � ≤ 1 the budget constraint becomes:

�(1 − �) = �� + ��if� < �� + ��if� ≥ �� (2)

and with a flat tariff " > 0 the budget constraint becomes:

� = � �� + ��#$� < ��′�� + (�� + ")�if� ≥ ��′� (3)

where �� ′ is the new dividing quality, and ��& > �� since high-quality imports have become more expensive.

Figure 1 illustrates these new budget constraints and the corresponding new in utility-maximising (�, �) bundles for low-income consumers in a developing country. It shows that low-income consumers have higher

utility when government revenue is collected from tariffs rather than income tax. Low-income consumers spend

a larger share of their incomes on low-quality, locally-produced goods instead of high-quality imports, and are

therefore less sensitive to tariff hikes. High-income consumers, however, are more likely so spend a larger

share of their incomes on high-quality imported goods where � > ��, and thus obtain higher utility when

government revenue is collected from income tax rather than tariffs, making them more sensitive to tariff hikes.

[Figure 1 here]

3.2 Tariff preference aggregation under different district magnitudes

Based on these assumptions and the context of a developing country, I first consider the outcomes of political

competition in terms of tariff revenue share when district magnitude equals 1. My reasoning is guided by

Hotelling (1929)’s framework of competition as applied to party competition by Osborne (1995). There are ' citizens, and each citizen # has a complete and transitive preference relation over tariff rates such that each

citizen has a unimodal, symmetric distribution of utility over tariff rates, and gains highest utility from a most

preferred tariff rate ()* . Assume that citizens vote sincerely for candidates contesting a district based on tariff rate


preferences, which vary with income as described in Section 3.1. There is a one-dimensional policy space ,

identified by a real line, which contains a unimodal distribution of voters’ ideal tariff rates. By restricting our

analysis to the single dimension of tariffs, we assume that preferences over other taxes are implicitly determined

by preferences for a particular tariff rate; for instance, a voter that has a high ideal tariff rate will have a low

ideal income tax rate, since tariffs and income taxes are substitute sources of government revenue.

As for the election candidates, there are - of them, each with a different party affiliation for simplicity and a

preference relation over ,. When a candidate . takes the policy position (/, his/her vote share is 0/((/). Candidate .’s preferences over , are then structured such that he/she gains highest utility from a position that

yields him/her the highest vote share amongst all candidates (. wins by plurality), followed by a position that

yields him/her a vote share equal to another candidate with the next highest vote share (. ties), followed by a

position that does not give him plurality (. loses).

The game of district-level electoral competition is as follows. It is a simultaneous one-shot game in which there

are 2 players2, candidates . and 1, who choose tariff rate platforms (/ and (2 respectively from the distribution

of voters’ ideal tariff rates. Candidates earn payoffs in terms of vote shares 0/3(/4 and 02((2). It can be shown

that the outcome of this game is a unique Nash equilibrium where candidates . and 1 both adopt the median

voter ideal tariff rate 5 and tie in vote shares (see Online Appendix A). In a developing country, the proportion

of society that is poor outweighs the proportion of the rich. Hence, the median voter is poor, and as explained

earlier, has a greater propensity to consume low-quality goods. Low-quality goods are produced domestically in

a developing country and their prices are not affected by tariffs. Assuming citizens vote based on economic

preferences, the median voter is less sensitive to tariffs than his/her richer counterpart, and is more supportive of

the government raising tariffs than other taxes such as income tax. Therefore, when district magnitude is 1, the

elected legislator in each district represents the poor median voter’s tariff preferences, and will vote to increase

tariff revenue share in the legislature. Replicating this result across all single-member electoral districts in the

country, most if not all politicians with a seat in the legislature favour increased tariff collection, and the

legislature votes to collect a high proportion of government revenue from tariffs.

I now consider political competition over tariff policy positions when district magnitude exceeds 1. In this case,

several features of the aforementioned game are modified. Firstly, the number of electoral contenders in a

district is likely to increase beyond 2 candidates3. When there are more than 2 candidates, it can be shown that

there is no pure strategy Nash equilibrium where all candidates converge to the same position, at the median or


elsewhere, even if each aimed to win a plurality of votes to guarantee winning a seat (see Online Appendix B).

Therefore, candidates may not pander to the poor median voter’s preferred tariff policy in their campaigns.

Some candidates may choose to represent the rich minority’s preference for lower tariffs, while other candidates

may continue to represent poor voters. This increases the likelihood of candidates contesting elections on

platforms advocating lower tariffs.

Secondly, on top of being more likely to contest elections, candidates representing the rich minority are also

more likely to guarantee a legislative seat in a multimember district under PR arrangements when they contest.

When more than 1 legislator can be elected in a district, the minimum vote share needed by any candidate to

guarantee a seat, or the exclusion threshold, decreases. To illustrate, consider a list PR system that allocates

seats in a multimember district using quotas. A quota 6 is the minimum number of votes that guarantees a

candidate a seat in a district ":

6 = 7�8� + 9 (4)

where 7� is the total number of valid votes cast in district ", 8� is the number of seats contested in district "

(i.e. district magnitude), and 9 is a modifier that varies in value with the type of quota used (Clark et al., 2009).

The exclusion threshold : is then given by the ratio of the quota to the total number of votes cast4:

: = 67� =

18� + 9 (5)

Equation (5) implies that : decreases as 8� increases. Hence, as district magnitude increases, the threshold for

guaranteed representation falls towards the vote share obtained by candidates representing wealthy minority

interests, and candidates advocating tariff rates lower than the median voter’s ideal rate are more likely to be

elected to the legislature. Overall, under large district magnitudes, wealthy pro-trade interests gain political

representation and moderate the poor majority’s preferences for high reliance on tariffs revenues. Hence, I

expect to see lower tariff revenue shares when district magnitudes are greater than 1, and I test the following:

Hypothesis 1: In comparing developing countries with democratic elections, higher electoral district

magnitude is associated with lower tariff revenue share of government revenue.

Note that Hypothesis 1 reflects a “majoritarian bias” similar to that found by Grossman and Helpman (2005).

Therefore, my hypothesis may provide an alternative explanation for the “majoritarian bias”, one that considers

the role of consumer preferences in influencing protectionist policies.

3.3 The effect of inequality on tariff preference aggregation under different district magnitudes


A second hypothesis concerns the effect of income inequality on the aforementioned institutional explanation

for tariff revenue dependence. Extending Katsimi and Moutos’ (2010) reasoning, I propose that a mean-

preserving increase in inequality causes the number of voters who can afford high-quality imports to fall relative

to voters who cannot afford high-quality imports. As a result, the proportion of voters with high incomes that

would vote for lower tariffs and tariff revenue share falls. This reduces the probability that a candidate

representing wealthy pro-trade minority interests wins a seat. Using threshold : defined in Section 3.2, suppose

that the probability that candidate . wins a seat in an 8-seat district " with - contesting candidates is �;<=>: �;<=> = � 1if0/ ≥ :

ℎ30/ , -,8�4if0/ < :� (6)

where 0/ is .’s vote share, and ℎ&(0) > 0, ℎ&(8�) > 0 (i.e. �;<=> increases with own vote share and district

magnitude respectively). Section 3.2 showed that increasing 8� to 8�′ reduces the exclusion threshold to, say,

:’ < :. The expression for �;<=> becomes:

�;<=> = � 1if0/ ≥ :′ℎ30/ , -,8�′4if0/ < :′� (7)

where ℎ30/ , -,8�4 < ℎ30/ , -,8�′4; that is, holding all else constant, the probability that . wins a seat increases

as district magnitude increases, as shown in Section 3.2.

Now consider the effect of a mean-preserving increase in income inequality on �;<=> . If . were a candidate

representing wealthy pro-trade minority interests, increased inequality lowers his vote share to 0/′, and �;<=> falls since ℎ&(0) > 0. Figure 2 illustrates how �;<=> falls with increased inequality. If inequality is severe, �;<=> could even fall below the probability of winning under a lower district magnitude. Consequently, high inequality

reduces the efficacy of large district magnitudes in increasing the political influence of minority interests

through the electoral mechanism. The extent to which large district magnitudes lower the equilibrium tariff

revenue share is thus diminished with high inequality.

[Figure 2 here]

This leads me to test:

Hypothesis 2: In comparing developing countries with democratic elections, an increase in district

magnitude should lower tariff revenue share of government revenue by less when inequality is high.

4. Methodology and Data

4.1 Model


I empirically test the aforementioned hypotheses using the following fixed effects regression model:

A-_:CD'EE*> = FG8H8�*> + FIJ'-'*> + FKJ'-'I*> + FL8H8�*> ∗ J'-'*> + NOPQ--QRR*> + ST*> +UV*> + WX*> + YZ*> + [* + \*> where i indexes the country and t the year. LN_TARIFF measures natural log of tariff revenue share, MDMH

measures district magnitude, the main treatment for Hypothesis 1, and MDMH*GINI measures the interaction

between the Gini inequality index and district magnitude to test Hypothesis 2. Data used to construct these

variables are described in Section 4.2.1. OPENNESS measures trade share of GDP (see Section 4.2.3). C is a

vector of controls for MDMH. A is a vector of variables for ACH explanations. E is a vector of variables for

elite dominance explanations. N is a vector of variables for nontax revenue sources as alternative explanations.

[* denotes country fixed effects. Standard errors are clustered by country.

4.2 Data

The overall dataset describes 139 developing countries between the years 1990 and 2013. The dataset is

restricted to 139 countries as I only use countries classified by the World Bank as Low Income, Lower-Middle

Income or Upper-Middle Income to satisfy the assumption that developing countries export low-quality

products. Only the period between 1990 and 2013 is covered by this study due to data availability.

4.2.1 Treatment and outcome variables

The outcome variable is LN_TARIFF, which describes the log of customs and import duties as a share of tax

revenue from the World Development Indicators (WDI). The treatment testing Hypothesis 1 is district

magnitude, captured by the variable MDMH. MDMH describes mean district magnitude in the lower house from

the DPI. Only lower house data is used because not all countries in the sample have multi-chamber legislatures.

Country-years where district magnitude is coded as Not Applicable or Indirect are excluded5. The treatment

testing Hypothesis 2 is income inequality, as captured by the variables GINI, its square GINI2 and the interaction

MDMH*GINI. These variables are constructed based on Gini indices from the WDI. While Gini indices are

reported only for 740 out of 7367 country-year observations in the dataset, its strength lies in its common usage

by many researchers, which facilitates comparison across studies.

4.2.2 Control variables (C)

As controls, I include variables that potentially cause the non-random assignment of district magnitudes to

national economies and confound the relationship between political institutions and trade and tax policy

outcomes. The first set of controls comprises dummy variables that indicate whether a country has based its


legal system on English, French, German, Socialist or Scandinavian law: LEGOR_UK, LEGOR_FR,

LEGOR_GE, LEGOR_SO and LEGOR_SC respectively (La Porta et al., 1999). The second set of controls

comprises dummies for structural adjustment agreements that potentially affect variations in both political

institutions and trade and tax policies: IMF_SBA and IMF_EFF indicate whether a country signed an IMF

Stand-By Agreement or Extended Fund Facility agreement respectively in a year (Dreher, 2006). Finally, the

level of economic development as measured by real per capita income from the WDI, and captured by the

variable INCOME, is controlled for.

4.2.3 Alternative explanations: Tariff tax base

A country may be highly dependent on tariff revenues simply because its taxable tariff base is large. I use the

variable OPENNESS, which measures total trade (exports plus imports) share of GDP from the WDI, to proxy

the size of its tariff base in a given year.

4.2.4 Alternative explanations: Nontax revenue sources (N)

A government can rely less on tax revenues if it obtains funds from other sources. To account for these

alternative revenue sources, I firstly include variables OIL and MINERAL, which measure oil and mineral rents

respectively as a percentage of GDP. I also include the variable ODA, which measures the amount of net official

development assistance received as a share of GDP, as well as IBRD, which measures loans from the

International Bank for Reconstruction and Development and credits from the International Development

Assistance as a share of GDP. Data was obtained from the WDI.

4.2.5 Alternative explanations: Administrative Capabilities Hypothesis (A)

As suggested by the ACH literature in Section 2.1, numerous factors affect the government’s administrative

capabilities of collecting directing taxes and the extent it might have to rely on indirect taxes like tariffs as a

substitute. I therefore include BUREAUQUAL as a measure of bureaucratic quality according to the

International Country Risk Guide (ICRG), measures of population size POP and population density POPDEN,

rural population share as a measure of urbanisation and secondary school enrolment rates SECONDARY to

measure the population’s education level. I also include the ICRG corruption index CORRUPT since corruption

also negatively affects the government’s ability to collect various taxes (Bai and Wei, 2001; Gatti, 1999).

4.2.6 Alternative explanations: Elite dominance (E)

My proposed theory requires that legislatures are ‘mature and autonomous’, capable of ‘injecting the interests

and concerns of their constituencies into the policy process’ (Diamond, 1997). Yet, numerous developing


country democracies suffer from weak legislatures that face obstacles in representing voters’ interests. This

potentially renders voter preference aggregation mechanisms irrelevant in determining tariff revenue share.

To begin, financial crises may provide opportunities for wealthy elites with pro-trade views to exercise

influence over policymaking, as was the case in Latin America during the 1990s. To estimate the effect of this

phenomenon, I include a variable BANKCRIS, which codes the occurrence of a banking crisis in a particular

country-year, as a proxy for instances of economic distress. Data was obtained from Caprio et al. (2003) and

Laeven and Valencia (2008).

Dominant parties in developing countries may also mediate the influence of the poor majority through the

legislature by over-representing elites’ policy positions. Dominant parties are distinguished from majority

parties in democracies by their use of state resources to reward supporters for their loyalty and prevent citizens

from holding elected legislators accountable to remain in power. They offer elites opportunities to influence the

policymaking process in exchange for their political support (Bogaards, 2004; Smith, 2005; Magaloni, 2006).

Given that elites possess large amounts of wealth, dominant parties reflecting wealthy elite interests are likely to

favour import-intensive consumption preferences and choose lower tariff revenue shares. To measure the

influence of dominant elite parties in developing countries, I use the variable GOV1RLC from the DPI, which

codes the orientation of the largest government party as Right (1), Centre (2) or Left (3). If GOV1RLC equals 1,

there exists a dominant elite party in the legislature in that developing country in that year. To validate that this

variable does capture elite dominance, I look at country-years in the sample that had a value of GOV1RLC equal

to 1, and refer to the Inter-Parliamentary Union’s Parliaments Online (IPU PARLINE) database to verify

economic policy positions of the largest party in each case. Where GOV1RLC was 1, the largest party tended to

have views supporting of trade liberalisation (see Online Appendix C). However, GOV1RLC also risks being

endogenous to district magnitude. Hence I run alternative models using PARCOMP, an index of the

competitiveness of political participation from the Polity IV database (Marshall et al., 2013), to proxy the

potential for elite dominance in a developing country democracy. In developing countries, wealthy elites will

always be the minority even under the most proportional form of electoral representation, thus they should not

be expected to competitively gain dominance. Being coded based on constitutional features, PARCOMP is

exogenous to district magnitude.

A chief executive with a strong mandate could also render the influence that voters have through the legislature

less relevant to policymaking. To measure the degree of executive dominance, I use the variable XCONST from

the Polity IV dataset, which measures the degree of institutionalised horizontal constraints on the decision-


making powers of chief executives. The variable is coded from 1 to 7, with 7 indicating the highest degree of

limitations imposed by the legislature and other veto players. Executive influence may also weaken the

legislative mandate if the executive controls legislative decisions through a majority party in the legislature that

he/she is aligned with. I code such a scenario using the variable ALLHOUSE from the DPI, a dummy indicating

whether the executive’s party has an absolute majority in the houses that have law-making powers. Since both

XCONST and ALLHOUSE pertain to constitutional characteristics, they are exogenous and do not pose bad

control problems. However, they do not adequately capture the policy positions of strong executives. I therefore

run alternative specifications using the right-left orientation of the chief executive EXECRLC from the DPI as a

measure of elite dominance in place of XCONST and ALLHOUSE. EXECRLC takes on 3 possible values: Right

(1), Centre (2) or Left (3). Overrepresentation, and therefore dominance, of economic elites leads to a higher

probability of right-wing chief executives taking office (Albertus and Menaldo, 2013). Such executives, then,

are free to influence policymaking to reflect policy interests of the wealthy should they face few horizontal

constraints. EXECRLC is useful in distinguishing between cases where chief executives led the implementation

of pro-trade policies in line with wealthy interests in our model, and where they opposed such liberalisation (see

Online Appendix D). It is, however, a bad control like GOV1RLC.

5. Results and Analysis

5.1 Initial empirical results

Table 1 presents the results of the fixed effects panel regressions. As within-estimators are used, the regressions

drop legal origins, which are time-invariant controls; their effects are instead captured by country fixed effects.

Models 1 and 2 show uncontrolled regressions of tariff revenue share on district magnitude, the Gini coefficient,

and their interaction. In both models, the coefficients on MDMH are negative and significant at the 1% level. A

1 unit increase in district magnitude reduces tariff revenue share by approximately 0.0079 log points. This

supports the prediction of Hypothesis 1. The coefficients on MDMH*GINI are positive and significant in both

models as well. To interpret this coefficient, consider how it changes estimated coefficient on MDMH when

GINI increases by 1 unit; that is, the marginal effect of MDMH on LN_TARIFF. The marginal effect is given by:

Marginal effect of MDMH = (coefficient on MDMH) + (coefficient on MDMH*GINI)*GINI

= FG + FL ∗ J'-' Table 1 shows that the estimated FG is negative and that FL is positive. This means that as GINI increases by 1

unit, the marginal effect becomes less negative by FL units. Since FL is statistically significant, there is


significant evidence that increased inequality moderates the negative effect of higher district magnitudes on

tariff revenue share. This supports Hypothesis 2, which predicts that higher inequality enhances the influence of

poor interests for higher tariff revenues even under more proportional electoral systems. Model 2 also includes

the quadratic term GINI2; this reduces standard errors on the coefficients of MDMH and MDMH*GINI.

Model 3 sees the addition of controls, and Models 4 through 7 the addition of alternative explanations. The

coefficients on MDMH and MDMH*GINI remain at the 5% level in all models. In Model 7, which includes all

alternative explanations and controls, a 1 unit increase in district magnitude is associated with a 0.01 log-point

reduction in tariff revenue share. Every 1 Gini unit increase in inequality then reduces the magnitude of this

decrease in tariff revenue share by 0.00034 log-points.

[Table 1 here]

[Figure 3 here]

The effect of inequality on the relationship between district magnitude and tariff revenue share is further

illustrated in Figure 3. As GINI increases, the marginal effect of MDMH on LN_TARIFF becomes less negative.

Beyond GINI value of approximately 29.5, an increase in district magnitude increases tariff revenue share as the

coefficient of MDMH becomes positive. This positive coefficient becomes significant at the 5% level beyond

GINI index of approximately 33. This is the minimum level of income inequality at which the influence of a

poor majority overcomes the effect of having proportional electoral representation of economic elites.

5.2 Relationship with existing research: Administrative Capabilities Hypothesis

My results suggest that the political explanation considering district magnitude may be complementary to ACH

explanations offered by existing research. The addition of ACH variables in Model 6 in does little to change the

coefficients and standard errors on MDMH and MDMH*GINI. Additionally, the introduction of ACH variables

produces a relatively large increase in R2 from Model 5 to 6, implying that district magnitude explains a

statistically orthogonal dimension of variation in LN_TARIFF compared to bureaucratic quality and does not

overlap with ACH in explanatory power.

5.3 Relationship with existing research: Producer-centric “majoritarian bias” literature

[Table 2 here]

The traditional “majoritarian bias” literature places great emphasis on the way different electoral systems

aggregate producer preferences over trade protection. For this reason, scholars subscribing to this view need not

consider consumer characteristics, such as spending power and inequality thereof, when carrying out research.


In contrast, I focus on explaining how different electoral systems aggregate consumer preferences over trade tax

instruments; therefore I account for variations in consumer characteristics, specifically income inequality. To

compare the efficacy of consumer-centric explanations against producer-centric ones in explaining tariff

revenue dependence, I compare Model 7 to other regression models which have omitted direct inequality effects

(Model 10), interactive inequality effects (Model 9), or both (Model 8), ceteris paribus. The results in Table 2

show that the coefficient on MDMH is negative and significant only when the interactive effect of district

magnitude and inequality is included. This suggests that the electoral system cannot, by itself, explain variations

in the government’s dependence on trade tax as a revenue source; rather, electoral systems can only account for

trade tax revenues when income inequality is considered. If producer preferences were primarily driving trade

tax revenues, district magnitude should by itself significantly account for variations in trade tax shares, without

interaction with inequality (for instance see Evans, 2009). Yet, an interaction with inequality is required for

significant results. Since the concept of income inequality is industry-blind and linked to consumer rather than

producer preferences, Table 2 suggests that explaining trade tax revenue variations using electoral systems

requires that consumer preferences, rather than sector-specific producer preferences, are also taken into account.

5.4 Robustness checks

5.4.1 Alternative measures of elite dominance

As explained in Section 4.2.6, problems belie the use of GOV1RLC as a measure of elite party dominance, and

XCONST and ALLHOUSE as measures of elite-allied executive dominance. I therefore test whether the earlier

conclusions remain robust when different measures of elite dominance are used. The results are shown in Table

3: I find that the conclusions regarding the directions of postulated relationships remain unchanged.

[Table 3 here]

Model 11 uses competitiveness of political participation, PARCOMP, to measure the likelihood of single parties

with elite support dominating legislatures. Using PARCOMP in place of GOV1RLC hardly changes the

coefficients on MDMH and MDMH*GINI as well as their standard errors. In Model 12, GOV1RLC is retained

while XCONST and ALLHOUSE are replaced with the left-right orientation of the executive, EXECRLC.

Consequently, the coefficient on MDMH becomes less negative but remains significant. The coefficient on

MDMH*GINI, on the other hand, retains its hypothesised sign but becomes insignificant. Similar results apply

to Model 13, which modifies Model 12 by also replacing GOV1RLC with PARCOMP. It therefore appears that

the effect of district magnitude is robust to alternative specifications of elite dominance, but the same cannot

always be said for the interactive effects of district magnitude and inequality.


5.4.2 Alternative inequality metrics

A shortcoming identified with using the WDI Gini index as a measure of income inequality is the paucity of

observed data points. This limits the size of the sample used for the regression, potentially reducing population

representativeness as well as the precision of estimates. As a check on initial results, therefore, I repeat the

regression using an alternative inequality metric – multiple imputation estimates of inequality measures from the

Standardised World Income Inequality Database (SWIID) (Solt, 2009). Since estimates are provided for

country-years where the WDI Gini index is unavailable, this dataset is more complete and expands the sample

size for regression analysis. Table 4 shows the results of regressions run using SWIID data; these results show

that even with alternative inequality metrics, the coefficients on MDMH and the interaction between district

magnitude and inequality retain the hypothesised signs and statistical significance.

5.4.3 Year fixed effects

Events in particular years may systematically affect governments’ dependence on protectionism and tariffs

across the developing world. To rule out the possibility that district magnitude is simply capturing this effect, I

run regressions which include year fixed effects as shown in Table 5. In Model 16, dummies for all years

covered in the dataset are included, while in Model 17, I only include a dummy COLDWAR for the year 1990,

the approximate year by which the Cold War is understood to have ended. The end of the Cold War marked the

triumph of neoliberalism and could have weakened protectionist tendencies, reducing tariff revenue dependence

systematically across the developing world. I find that in both models, coefficients on MDMH and

MDMH*GINI all retain their hypothesised signs and significance.

6. Conclusion

In this paper, I extend existing research on political factors driving developing countries’ dependence on tariff

revenues by exploring the impact of varying electoral district magnitude across democracies on tariff revenue

share in developing countries. While existing literature has found that democracy increases tariff revenue shares

by aggregating the preferences of the poor majority of voters over tax structures and trade policy, I propose that

it is not just the level of democracy but also its form that matters. Against a backdrop of developing countries

participating in vertically-differentiated trade and residents with consumption-based trade preferences, I

hypothesise that larger district magnitudes result in lower tariff revenue shares as they allocate wealthy, anti-

tariff economic elites greater influence over policy, whilst softening the influence of the poor median voter who

is less affected by higher tariffs than rises in other voluntary direct taxes. I also propose that as income

inequality rises, tariff revenue shares increase even under large district magnitudes. Using panel data fixed


effects linear regression, I find empirical evidence supporting both propositions: lower house district magnitude

is negatively associated with tariff revenue share of government revenue, and this association becomes less

negative at higher levels of inequality. These results are robust even after accounting for alternative factors.

Certainly, further research can build on these results to enhance our understanding of political factors behind

trade policy and tax structures. Future studies may consider the impact of voter turnout on the results found

here. I have assumed full turnout, but in reality the poor may be less likely to vote, resulting in the electorate’s

aggregated preferences reflecting those of voters wealthier than the average citizen (Larcinese, 2007).

Additionally, democracies vary along numerous other dimensions, and it may be worth studying whether other

institutions affect a developing country’s dependence on tariff revenues. Nevertheless, the findings of this paper

emphasise the importance of considering variations in forms of democracy when using political economy to

explain policy outcomes. It also provides additional insight to the challenges potentially faced by governments

in developing countries in balancing trade liberalisation with other economic policy goals, such as fiscal

sustenance.


Tables and Figures

Table 1: Within-estimates of determinants of tariff revenue share

(1) (2) (3) (4) (5) (6) (7)

Regressors Linear GINI Nonlinear

GINI

Incl.

controls

Incl.

openness

Incl.

nontax

revenues

Incl. ACH Incl. elite

dominance

MDMH -0.0079*** -0.0082*** -0.0057** -0.0059** -0.0056** -0.0047* -0.010***

(0.0011) (0.00083) (0.0024) (0.0023) (0.0021) (0.0023) (0.0024)

GINI -0.00050 -0.17*** -0.20*** -0.19*** -0.20*** -0.17*** -0.19***

(0.0093) (0.052) (0.055) (0.056) (0.054) (0.058) (0.058)

GINI2 0.0019*** 0.0022*** 0.0022*** 0.0021*** 0.0019*** 0.0020***

(0.00054) (0.00058) (0.00058) (0.00056) (0.00059) (0.00061)

MDMH*GINI 0.00025*** 0.00025*** 0.00018** 0.00017** 0.00017** 0.00014* 0.00034***

(0.000045) (0.000032) (0.000072) (0.000067) (0.000072) (0.000080) (0.000082)

IMF_SBA -0.091 -0.11 -0.092 -0.015 0.058

(0.11) (0.11) (0.14) (0.10) (0.090)

IMF_EFF 0.011 -0.059 -0.12* -0.17* -0.26

(0.086) (0.095) (0.062) (0.082) (0.17)

INCOME -0.00010 -0.000075 -0.00013 -0.00012 -0.00014*

(0.00011) (0.00011) (0.00011) (0.000082) (0.000083)

OPENNESS -0.010* -0.0050 0.0050 0.0054

(0.0058) (0.0060) (0.0036) (0.0037)

OIL 0.0020 0.014 0.031

(0.019) (0.018) (0.021)

MINERAL -0.033 -0.048** -0.063***

(0.027) (0.021) (0.015)

IBRD -0.53 0.24 -1.39

(2.77) (1.98) (1.87)

ODA 0.025 -1.07 0.47

(1.61) (2.71) (2.18)

BUREAUQUAL -0.18 -0.21

(0.15) (0.16)

POP 7.4e-11 2.6e-09

(2.5e-09) (3.2e-09)

RURAL 0.023 0.031

(0.036) (0.030)

POPDEN 0.00034 -0.0034

(0.0072) (0.0091)

SECONDARY -0.0046 -0.012

(0.012) (0.0084)

CORRUPT 0.18*** 0.20***

(0.058) (0.055)

XCONST 0.045

(0.053)

ALLHOUSE -0.53**

(0.20)

GOV1RLC 0.049

(0.052)

Constant 2.15*** 5.75*** 6.82*** 7.37*** 7.71*** 5.31** 6.37**

(0.40) (1.23) (1.47) (1.49) (1.54) (2.38) (2.41)

Observations 322 322 319 319 196 140 124

R-squared 0.017 0.043 0.070 0.096 0.264 0.538 0.676

Number of id 77 77 75 75 39 31 30

Notes: Dependent variable is LN_TARIFF in all models. STATA dropped BANKCRIS because of multicollinearity.

Hausman test rejected random effects in favour of fixed effects. These regressions were also run with country fixed effects

entered as dummy variables, and F-tests rejected the joint null hypothesis that all country dummies have coefficient 0.

Clustered standard errors are shown in parentheses. *** p<0.01, ** p<0.05, * p<0.1.


Table 2: Comparing producer-centric and consumer-centric models

(7) (8) (9) (10)

Variables Unrestricted model MDMH only MDMH + direct

GINI effects only

MDMH +

interactive GINI

effects only

MDMH -0.010*** 0.00015 -0.00031 -0.0082***

(0.0024) (0.00038) (0.00061) (0.0026)

GINI -0.19*** -0.18***

(0.058) (0.058)

GINI2 0.0020*** 0.0021***

(0.00061) (0.00059)

MDMH*GINI 0.00034*** 0.00029***

(0.000082) (0.000088)

Constant 6.37** 3.87** 6.59** 2.25

(2.41) (1.76) (2.60) (1.95)

Observations 124 330 124 124

R-squared 0.676 0.423 0.620 0.623

Number of id 30 37 30 30

Notes: Dependent variable is LN_TARIFF in all models. Other variables for alternative explanations and controls are

included in all regressions here, but estimates of their coefficients are not shown for brevity. Clustered standard errors are

shown in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 3: Within-estimates using alternative measures of elite party and executive dominance

(7) (11) (12) (13)

Elite dominance

variables

XCONST,

ALLHOUSE,

GOV1RLC

XCONST,

ALLHOUSE,

PARCOMP

EXECRLC,

GOV1RLC

EXECRLC,

PARCOMP

MDMH -0.0100*** -0.010*** -0.0041** -0.0048*

(0.0027) (0.0026) (0.0019) (0.0025)

GINI -0.20*** -0.18*** -0.18*** -0.17***

(0.057) (0.062) (0.055) (0.058)

GINI2 0.0021*** 0.0018*** 0.0020*** 0.0019***

(0.00059) (0.00066) (0.00055) (0.00059)

MDMH*GINI 0.00034*** 0.00034*** 0.00010 0.00014

(0.000093) (0.000086) (0.000066) (0.000089)

XCONST 0.045 0.090

(0.053) (0.068)

ALLHOUSE -0.53** -0.51**

(0.20) (0.19)

PARCOMP -0.14 0.094

(0.15) (0.12)

EXECRLC -0.22*** -0.12**

(0.054) (0.053)

GOV1RLC 0.049 0.13**

(0.052) (0.060)

Constant 6.37** 6.24** 5.58** 4.83**

(2.41) (2.50) (2.27) (2.25)

Observations 124 124 140 139

R-squared 0.676 0.678 0.567 0.551

Number of id 30 30 31 31


included in all regressions here, but estimates of their coefficients are not shown. Clustered standard errors are shown in

parentheses. *** p<0.01, ** p<0.05, * p<0.1.


Table 4: Within-estimates using SWIID inequality data

(7) (14) (15)

Inequality metric WDI Gini index GINI_EST: Estimated

Gini index of inequality

in household disposable

(post-tax, post-transfer)

income

SHARE1: Estimated share

of (pre-tax, pre-transfer)

income reported by top

1% of taxpayers

MDMH -0.010*** -0.00427* -0.00275**

(0.0024) (0.00222) (0.00112)

GINI_EST -0.19*** 0.00763

(0.058) (0.0643)

GINI_EST2 0.0020*** -0.000211

(0.00061) (0.000662)

MDMH*GINI_EST 0.00034*** 0.000167**

(0.000082) (7.30e-05)

SHARE1 -0.0409

(0.0407)

SHARE12 0.000751

(0.000716)

MDMH*SHARE1 0.000475***

(0.000166)

Constant 6.37** 2.227 2.125

(2.41) (2.570) (1.967)

Observations 124 286 286

R-squared 0.676

Number of id 30 33 33


included in all regressions here, but estimates of their coefficients are not shown. For comparability, XCONST, ALLHOUSE

and GOV1RLC are used as elite dominance measures. Clustered standard errors are shown in parentheses. *** p<0.01, **

p<0.05, * p<0.1.

Table 5: Within-estimates with year fixed effects

(7) (16) (17)

Year effects None 1990-2013 1990

MDMH -0.010*** -0.012*** -0.0098***

(0.0024) (0.0039) (0.0022)

GINI -0.19*** -0.25*** -0.19***

(0.058) (0.056) (0.058)

GINI2 0.0020*** 0.0025*** 0.0020***

(0.00061) (0.00056) (0.00059)

MDMH*GINI 0.00034*** 0.00040*** 0.00033***

(0.000082) (0.00013) (0.000072)

COLDWAR -0.81*

(0.43)

Constant 6.37** 7.25** 5.55**

(2.41) (3.01) (2.66)

Observations 124 124 124

R-squared 0.676 0.809 0.690

Number of id 30 30 30


included in all regressions here, but estimates of their coefficients are not shown. For comparability, XCONST, ALLHOUSE

and GOV1RLC are used as elite dominance measures. Clustered standard errors are shown in parentheses. *** p<0.01, **

p<0.05, * p<0.1.


Figure 1: Consumer utility with income tax or tariff collection

Figure 2: Pro-trade candidate’s probability of winning a seat in large district with lower vote share

Figure 3: Marginal effect of district magnitude on tariff revenue share

uL

0 = uL

2

uL

1

Qd Qd’ Q

H

B0

B1

B2

Low income

Qd Qd’ Q

H

uH

0 uH

1 uH

2

B0

B1

B2

Legend

B0: No taxes

B1: Income tax

B2: Tariffs

High income

Gini = 29.5

0.0

1.0

2.0

3.0

4

Ke

rne

l den

sity

est

ima

te o

f G

ini

-.0

05

0.0

05

.01

.015

.02

Marg

inal eff

ect

of

dis

tric

t m

agn

itude

on

ln(t

ariff

)

20 30 40 50 60 70

Gini coefficient (World Bank)Thick dashed lines give 95% confidence interval.Thin dashed line is a kernel density estimate of gini.

psea

pseat

vj’ T T’ T’

1

ℎ30/ , -,8�4 ℎ30/ , -,8�4 ℎ(8�) ℎ38�′4


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Notes

1 Fajgelbaum et al. (2011) showed that the utility-maximising quality of a vertically-differentiated good

consumed increases with consumer income if quality levels are higher than average quality consumed by

individuals in the income group, and conversely decreases with income if quality is lower than average quality

consumed by individuals in income group; simply put, a low-income consumer to prefers a low-quality good if

the difference between high-quality and low-quality goods must be adequately substantial.

2According to Duverger's Law (1954), the mechanical and psychological effects produced by single-member

district plurality or majoritarian systems push the number of effective contesting parties to the minimal of 2 in a

democracy.

3Weaker Duvergerian mechanical and psychological effects due to more proportional seat allocation encourage

more small parties to contest elections.

4 Similar exclusion thresholds are also applicable to divisor systems of seat allocation, such as the d’Hondt

system (Lijphart and Gibberd, 1977; Taagepera, 1989).

5In doing so, I implicitly use a minimal procedural definition of democracy to restrict my sample, such that the

presence of elections (suggested by the existence of a legislature) indicates the presence of democracy. District

magnitude is only meaningful where there is at least procedural electoral democracy. I also tried restricting the

sample to developing countries that are at least “Partly Free” or “Free” according to Freedom House while

running the regressions in Section 5; doing so did not change my conclusions.

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