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Campaign Expenditures and Other Factors Affecting Electoral Outcomes:Major Race in the 72 Municipalities of the Mexican State of Sonora, 2003.

1. INTRODUCTION

The general objective of this term paper is to explore the

relationship between electoral outcomes and campaign

expenditures. We suspect that electoral results are not only related

or affected by expenditures relative to a particular campaign, but

also by expenditures related to different, but simultaneous, electoral

races. It also could be the case that other factors, such as

candidate’s gender or the ruling party prior to the election, play an

important role in the electoral results.

The importance of financial advantage over electoral

outcomes has been studied for some time with conflicting

conclusions, some claiming that increase spending makes an

appreciable difference in the number of votes gained and others

claiming the complete opposite. If campaign spending matters for

election outcomes, this would lead to conclude that campaign

finance could have a significant influence on who runs and wins

elections and what the winners do in office.

Campaign expenditures have mainly three financial sources:

candidate’s party, private sector support and the candidate himself.

Our case of study is focused on local elections of the Mexican State

of Sonora, where the electoral authority establishes maximum

expenditure levels by law. The Electoral Code of Sonora establishes

(article 95) the distribution of campaign expenditures’ budget limits

as follows (including private and public financing): 2% of the total

electoral budget will go to recently created parties; 30% of the

remaining budget will be divided among all old parties in equal

shares; and 70% will be shared by the parties according to past

elections’ global electoral results. Furthermore, public financing for

political parties is established by Mexican legislation and

Paula Núñez, Julieta Suazo & Vicente Rivera 1




distinguishes three concepts for its granting: support of the political

party’s ordinary activities1, expenses of electoral campaign2 and an

item destined to subsidize parties’ specific proven expenses in

education and political qualification, socioeconomic and politicalinvestigation, and publications.

Our case of study is particularly focused on the elections in

Sonora State for 2003, when during the month of July concurrent

elections for municipal presidents (majors) in 72 municipalities, local

congressmen in 21 local districts, and a governor were held.

Candidate’s participation in the electoral race was as follows:

• Major race: 266 candidates

• Congress: 134 candidates

• Governor: 5 candidates

It is important to note that while some parties had candidates

for majors, they not necessarily had candidates for congressmen orgovernor.

We will focus on the influence that campaign expenditures has

on the number of votes each candidate obtains. The reason to

explore the relationship between electoral outcomes and budgets of

different, but simultaneous electoral races, is that generally in

Mexico, and particularly in the Sonora State, the electoralcampaigns of diverse candidates running for electoral positions

have a propensity to be very connected. This implies that governors’

candidate campaign has a strong influence, through his image and

electoral budget, on voters to elect a Major candidate of the same

1 Which is determined annually and distribute 30% of the available budget inegalitarian form between all the parties with parliamentary representation and70% according to the voting obtained in the preceding election, there is also a

provision for new parties that have still not entered the electoral dispute.2 This amount is assigned in the year of the respective election following ananalogous formula.





party. The same happens with the congressman candidates, who

through his public image, presence and campaign in the

municipalities of his district, and using his own electoral budget, not

only draw voters for his own election, but also for the Majorcandidates of his own party.

To meet the objectives stated above, this article proceeds as

follows: Section 2 reviews the literature on the effects of campaign

spending describing the main results in the most prominent papers.

It also notes that these papers reach radically different conclusions

regarding the impact of candidate spending. Section 3 establishes

the most important hypothesis we want to prove in the present

paper. Section 4, reports the econometric models, estimation

methods and assumptions used. Section 5 carefully describes the

data used in the empirical analysis. Section 6 discusses the

interpretation and strength of the empirical results. Section 7

provides concluding observations. It is important to stress that more

research is required before the arguments considered can be

decisively established, not only in the case of Sonora but in other

political environments as well.

2. THEORETICAL FRAMEWORK

Research in spending and electoral outcomes has generated what is

called the “Spending Electoral Theory”. Payne (1991) establishes

some postulates of this theory:

1) The idea that voters are reflecting their own rational interest

when they vote for the candidate that spends more.

2) Voters are misinformed and irrational, leaving the elections to

be dominated by special interest voters.

3) Campaign spending is the key to success.





From Paynes’ perspective there are two hypothesis related to

the influence of campaign spending on electoral outcomes:

a) The electoral consequence hypothesis: incorporates the idea

that the candidates who spend more obtain more electoral

benefits because of it.

b) Legislator insecurity hypothesis: Electoral competition and the

fear of failure forces candidates to approve more spending

that even they believe necessary.

Based on the first hypothesis, more spending entails a greater

probability of success, since greater spending permits a better

identification of the characteristics of the electorate; a better

analysis of the opponent facilitates a good elaboration of a political

platform, favors a better preparation, coordination and stimulation

of organization in all the electoral process making even the

communication between the candidate and the electorate better.

Although Mexican electoral legislature and processes varies

greatly from the United States it is useful to cite the main

conclusions on the effects of campaign spending of the most

prominent papers. In the US, there have been several studies

performing regressions of candidate vote totals on candidate

spending levels. The basic model used is

Voteinc = α + B1(spendinginc) + B2(spendingchal) + B3x +μ

Where Voteinc is the incumbent share of the two-party vote,

spendinginc is total incumbent campaign spending. Spendingchal is the

total challenger campaign spending and x represents a set of

variables other than campaign spending that are thought to

influence candidate vote totals, such as challenger quality or





constituency partisanship. The particular function of campaign

spending is usually either the spending level itself or the natural

logarithm of spending. There has been significant disagreement on

how the basic model should be estimated. The main methodologicalapproaches and results are summarized in the 2 tables below.

Table 1 shows the predicted change in the candidates vote

share associated with a $100,000 increase in candidate spending.

The Jacobson (1985) model predicts that spending an extra

$100,000 will yield only a slightly increase in the incumbent’s share

of the vote (about a 0.1% increase), whereas a similar spending

increase results in a large boost for the challenger (about a 2.2%

increase). Green and Krasno (1988) predict large increases in vote





share for both the incumbent and challenger, an approximately 2-

percentage point increase for each. Erikson and Palfrey (2000) find

that an extra $100,000 is worth about 1% for the average

challenger and 0.6% for the average incumbent. Levitt (1994) incontrasts predicts that increasing spending has only a minimal

effect, regardless of whether the candidate is incumbent or

challenger. Table 1 illustrated the dramatic differences in estimated

spending effects. The coefficient values vary widely across studies.

Table 2 converts the values in table 1 into the cost per additional

vote implied by the Table 1 estimates. The dollar figures listed in

Table 2 are the cost of changing the vote margin by one vote. Table

2 also shows that depending on the model the cost of changing the

vote margin ranges from moderate to exorbitant.

Returning to Mexico, Garcia and Sandoval (2004) also

analyzed the results of campaign spending in the federal electoral

outcomes of 1997 concluding that there is a weak relationship

between campaign spending and electoral outcomes.

With regard to influence of the political party in power,

evidence suggests that voters hold political civil employees

responsible for the prevailing macroeconomic conditions and

therefore award good economic performance. Papers related to this

topic are Abrams y Butkiewicz (1995) that analyze US 1992

elections; Panzer y Paredes that focus on Chilean elections; Meloni

(1997) that empirically studies Menem reelection in 1995 and

Abuelafia y Meloni (2000) that work with a panel of Argentinean

presidential electoral outcomes. Authors that analyze governor

elections are Peltzman (1987) who constructs Panel data for the US

stated for the 1949-1984 period and find that voters respond to

local variables that the governor can control (mainly the budget)

and that national economic conditions affect electoral outcome.





Finally, before proceeding with the econometric model and

estimations it is important to remember that electoral outcomes are

a function not only of campaign spending but involve a complex

mixture of political, economic, social and cultural factors whoseweights can vary through time and electoral jurisdictions.





3. HYPOTHESES

The hypotheses we are considering are based on intuition and past

empirical observations. In the case of Mexico, and all its States,

electoral spending has always played an important role in the

results obtained by candidates in an election. Usually the candidate

with more financial resources to spend tends to win an election.

Generally, this is because expenditures are allocated for giving

people gifts, improving the candidate’s image, and increasing the

idea of security that people may see in a rich candidate.

Our central hypotheses are the following ones:

A. Major candidates with the strongest financial support,

including not only their own campaign budget, but also the

electoral budget of the congressman candidates and governor

candidates of the same party, will have better electoral

results.

B. Among the many factors that affect voting outcomes, we will

also consider the gender factor and the impact that the

governing party in the previous period generated on the

voters. If the ruling party in the period 2000-2003 had a

positive performance in terms of public investment, public

safety and government management the people would prefer

to vote for the candidate of the current ruling party.

Additionally due to the “machismo” culture prevailing in

Mexico, a male major candidate would be preferred on

average to a female one.





4. ECONOMETRIC MODELS AND ESTIMATION

METHODS

The key explanatory variable is the campaign expenditures of the

major candidates. The dependent or explained variable is the

electoral results of major candidates. We are also interested in

seeing if it is possible to estimate the separate effect that electoral

expenditures of congressman and governor candidates have on

municipal elections. Other factors of considerable importance in the

present study are the gender and ruling party dummies. In order to

explore this, the project will develop two functional forms, one is the

level-level and the other is the log-log.

The level-level model contains as a dependant variable the

electoral outcomes received by every major candidate (YPV)

measured as a percentage of total votes. The explanatory variables

are the percentage share of campaign expenditures of the major

candidates (X1PEM), the percentage campaign expenditures of thecongressman (X2PEC) and the percentage share of campaign

expenditures of the governor candidates (X2PEG) that could be use in

favor of a major candidate of their own parties or coalitions. We also

include two dummy variables: ruling party (X4RULPAR) and gender of

the major candidate (X5GENM). Hence, the population model we want

to test is the following one:





YPV = β 0 + β 1 X 1PEM + β 2 X 2PEC + β 3 X 3PEG + β 4 X 4RULPAR +β 5 X 5GENM + μ (1)

The log-log model consists of the logarithm of the number of

votes received by the major candidates [LOG(YV)], being this thedependent variable. The independent variables are the logarithms

of campaign expenditures of major [LOG(X1EM)], congressman

[(LOG(X2EC)] and governor [(LOG(X3EG)] candidates, measured in

Mexican pesos. We are also including the same dummy variables

X4RULPAR and X5GENM defined in the level-level model. Thus the

population model would be the following:

Log(YV) = β 0 + β 1 Log(X 1EM ) + β 2 Log(X 2EC ) + β 3 Log(X 3EG ) + β 4 X 4RULPAR + β 5 X 5GENM + μ (2)

We are not considering a level-level model that includes

directly the number of votes and campaign expenditures in Mexican

pesos due to wide variability in the data. While some major

candidates have 3,000 Mexican pesos, others have 4 millionMexican pesos to spend. The same happens with the number of

votes, some municipalities have 200 voters, while others have 300

thousand voters. (For more details see Table 4). In order to avoid

the heteroskedasticity problem, large standard errors in the

parameters or many other typical problems in econometric

calculations, we have decided not to run a model with these

variables.

We are going to estimate the ceteris paribus effect campaign

expenditures on major race electoral results. Of course, there are

many other factors that affect electoral outcomes, such as good

image of the candidate, previous professional or political activities,

educational level, empathy with people, and age, among others

factors. Unfortunately, we can not measure many of these

“variables” because the electoral information is limited. For this





reason, the unobservable or immeasurable factors are included in u.

In the next section we will discuss in detail all the aspects related to

the variables and data we are going to manage in order to test

these models.

Given the functional form of these models and that the

normality assumption applies in this case, we will rely on the

Ordinary Least Squares (OLS) method in order to make all the

estimations of the econometric models.

5. DATA ANALYSIS

The data used was obtained from the data base of the

Electoral Council of Sonora State (EC, Consejo Estatal Electoral). We

have obtained the number and percentage of votes received by

each one of the 266 major candidates of the Major race in the 72

municipalities of the Mexican State. Since the real campaign

expenditures of each candidate, either for major, congressman or

governor, are quite difficult to obtain, we will use the total budget

limit allowed by the electoral authority in Mexican pesos as a

measure of the effective campaign expenditures of each candidate.





It is important to notice that the electoral authority fixes the

values of the levels of expenditures by law and also establishes all

the expenditure limits as a function of the number of voters amunicipality or district has. This imply that the money spent

depends upon the size of the municipality in which a candidate is in.

This should not be an important problem when studying the

campaign expenditures of the major candidate, but could pose a

problem when we study the impact of the campaign expenditures of

the respective congressman or governor candidate, of a determined

party, in favor of the major candidates of his same party. An

important caveat is that these expenditures of the congressman and

governor candidates do not mean that they give this money to the

major candidates, but they are amounts they have to spend by law

in those municipalities. So we account these campaign expenditures

as a kind of support for the majors, which due to the law constraint

would be related to the size of the municipalities. There are very few

cases where a party carries candidates for major but not for

congressman. In those cases the expenses are considered zero. The

data collected appears in the appendixes.

Table 3 explains in detail the variables used for the

regressions. You can also see a summary of the most important

descriptive statistics in Table 4.





TABLE 3. DETAILED DESCRIPTIONOF VARIABLES USED IN THE MODELS

Variables Description Used in Model

DEPENDENTS

Y PV Percentage vote received by Major candidates(party or coalition). Level-Level (Jointand Separate

Expenditures)

LOG(YV ) Logarithm of the Number of votes received by

each Major candidate (party or coalition). (YV)

Log-Log

INDEPENDENTS

X1PEM Percentage Share of Campaign Expenditures

of Major Candidates. It is the authorized

amount of expenditures of a particular major

candidate in a municipality divided by the total

amount of expenditures of the candidates of the

different parties in that municipality.[=(Expenditures MCi)/(Total Expenditures MCi+

…+MCn in municipality j)]. MCik=Major

Candidate i.

Level-Level (Separate

Expenditures)

X2PEC Percentage Share of Campaign Expenditures

of Congressman Candidates. Of the total

amount authorized by the electoral authority to

the congressman candidate of a particular party

in a district, he/she spends a quantity that should

be proportional to the number of voters in the

municipality of its electoral district. To get these

numbers we divided such quantity by the total

spending amount of other congressmancandidates of other parties in the same

municipality. [=(Expenditures of CCi in

Municipality j)/(Total Expenditures of CCi+…

+CCn in municipality j)]. CCi=Congressman

Candidate, i=party.


Expenditures)

X3PEG Percentage Share of Campaign Expenditures

of Governor Candidates. This variable is the

result of dividing the amount spent by the

governor candidates of the different parties in a

specific municipality (in favor of the Major

candidates of their parties) by the total amountspent by all the governor candidates, of the

different parties, in the same municipality.

[=(Expenditures of GCi in Municipality j/Total

Expenditures of GCi+…+GCn in municipality

j)]. GCi = Governor Candidate of a particular

party, i=party.


Expenditures)

X123PEMCG Percentage Share of Joint Campaign

Expenditures of Governor, Congressman &

Major Candidates. It is the aggregate amount of

campaign expenditures in favor of a Major

candidate of a determined party or coalition in a

particular municipality divided by all theexpenditures made by the Major Candidates in

Level-Level (Joint

Expenditures)





that municipality.

LOG(X1EM ) It is the logarithm of Campaign Expenditures

of Major Candidates (Authorized Total Budget

Limit in Mexican pesos).

Log-Log (Separate

Expenditures)

LOG(X2EC ) It is the logarithm of Campaign Expenditures of

Congressman Candidates (Authorized TotalBudget Limit in Mexican pesos). In its absolute

value is the authorized financial limit amount

spent by a Congressman candidate in favor of the

Major candidate of his party or coalition in the

municipalities of its electoral district.

Log-Log (Separate

Expenditures)

LOG(X3EG ) It is the logarithm of Campaign Expenditures

of Governor Candidates (Authorized Total

Budget Limit in Mexican pesos). It is the

authorized financial limit amount spent by a

Governor Candidate in support of the Major

Candidate of his party or coalition in the 72municipalities of Sonora State.

Log-Log (Separate

Expenditures)

LOG(X123EMCG ) It is the logarithm of Aggregate Campaign

Expenditures of Major, Congressman &

Governor Candidates (=X1EM +X2EC +X3EG in

Mexican Pesos). It is the total amount spent on

campaign in favor of the major candidate of a

particular party or coalition.

Log-Log (Joint

Expenditures)

X4RULPAR Dummy variable that indicates whether the

party of the major candidate was governing in

the immediate past period (2000-2003 in this

case). If the candidate’s party was governing the

variable takes the number 1, 0 otherwise.

All

X5GENM Dummy variable for Major candidates’ gender,

=1 if Male, =0 if Female.

All





TABLE 4. SUMMARY STATISTICS

VARIABLES Mean S. D. Min Max Skewness Kurtosis

DEPENDENTS

YV 2,939.18 10,431.08 3 116,617.0 7.915696 74.82526

YPV 27.06 21.54 0.0011 0.6569 -0.043671 1.411654

LOG(YV) 6.34 1.859 1.09 11.666 0.0162664 3.224935

INDEPENDENTS

X1EM 229,859.2 477,697.9 2,902.94 4,270,600 4.392991 27.70981

X2EC 252,386.7 515,739.5 0 4,361,630 4.030857 23.45411

X3EG 437,573.3 1,079,570. 0 9,412,935 4.513047 27.88342

X123EMCG 919,819.2 1,915,445 11,856.19 13,738,588 3.731162 18.43039

X1PEM 27.06 12.44 10.86 66.13 0.815374 2.832928

X2PEC 27.06 14.99 0 66.85 0.78064 2.664907

X3PEG 27.06 16.78 0 66.62 0.65372 3.173511

X123PEMCG 27.06 13.87 5.91 62.68 0.722211 2.718711

X4RULPAR 0.2744 0.4470 0 1 1.010977 2.022074

X5GENM 0.906 0.2923 0 1 -2.782757 8.743734

LOG(X1EM) 11.05 1.46 7.97 14.68 0.4838 2.5598

LOG(X2EC) 11.17 1.46 8.39 14.79 0.5424 2.5253

LOG(X3EG) 11.48 1.78 8.39 16.06 0.4774 2.3182

LOG(X123EMCG) 12.39 1.57 9.38 16.44 0.5534 2.4688Number of Observations = 266

Note: S.D. Standard Deviation.





6. RESULTS OF ESTIMATED MODELS.

The results of the estimation of the population models are

presented in this section. As we written in the theoretical framework

many authors and researchers have encountered different and

controversial estimates. We are not the exception.

Fortunately, we had the luxury of using the whole set of major

candidates that participated in the 2003 election of Sonora State.

We also used the valuable information of the dummy variables of

major candidate’s gender and ruling party.

The Level-Level Model

Evaluating the model of the form level-level, with percentages

votes received by major candidates as the dependent variable and

the separate effect of candidates expenditures, we see the

following:3

Y PV = -5.72 + 0.299X1PEM + 0.5421X2PEC – 0.0133X3PEG +

14.43X4RULPAR + 7.083X5GENM

General Analysis

This model presents an R-squared of 0.5135, which means

that a little more than 51% of the variations in the percentage of votes are explained by percentage expenditures, ruling party and

gender of the candidate. The adjusted R-squared is 0.5041.

Testing the overall significance of the regression we see that

the F-statistic is 54.88, which allows us to reject the null hypothesis

3 The standard errors of the coefficients, t-values and other statistics are in table 5. The estimated parameters will use the letter B.





of Ho: βX1PEM=βX2PEC=βX3PEG=βX4RULPAR=βX5GENM=0. All the coefficients are

significant at the 1% level, because the Fc=3.02.

The coefficients of percentage expenditures of major (X1PEM)and congressman (X2PEC) candidates are positive, while the sign of

the percentage campaign expenditures of the governor (X3PEG) is

negative. Certainly, it is not credible that the expenditures of the

governor candidates do not affect positively the percentage vote

received by major candidates, so we suspect the data could have

some problems. However, the expected signs of the dummy

variables are according to our view (positives).

The sign on the intercept coefficient could be explained

because the percentage expenditures for most candidates are much

greater than zero and also it would be logical to think that minimum

campaign expenditure is needed to be known by the population and

obtain their votes; furthermore, the low percentage of female

candidates could also have an impact in this coefficient. All the

other coefficients were statistically significant except BX3PEG. We

suspect that this estimated model, due to the nature of the data,

could present multicollinearity and also heteroskedasticity

Multicollinearity Analysis

Making regressions with the independent variables against

each others in the same model, we encountered a relatively high

correlation among them. Especially, the regression of the

percentage of campaign expenditures on all other percentage

expenditures gave us an R-squared of 0.67. The less correlation we

encountered was that of the X1PEM with the X3PEG, which showed an R-

squared of 0.50. Thus, the percentage campaign expenditures are

less correlated with campaign expenditures of governor candidates.

In regressing the percentage campaign expenditures variables on





the dummy variables we saw low levels of correlation, 0.08 in the

case of X1PEM=X4RULPAR+X5GENM. We encountered relatively low

correlation between percentage expenditures of the congressman

and governor candidates on X4RULPAR and X5GENM (0.22 and 0.24 R-squared coefficients, respectively). However, when we looked to the

correlation coefficients, especially on campaign expenditures

variables, we saw 70 to 80% of correlation (See Tables 7 and 9). As

a result, all of these measures give us the idea that this model

suffers of certain level of multicollinearity.

Heteroskedasticity Analysis

Regressing the residuals of this model on all the independent

variables we got a R-squared of 0.79, and a F-statistic of 195.97

with a p-value of 0.000. These are strong numbers that permit us to

reject the null hypothesis of homoskedasticity, so this model suffers

of heteroskedasticity in a high degree. This means that the usual

standard errors are not reliable (See Table 12).

We also applied the Breusch-Pagan Test for heteroskedasticity

in order to detect if only the campaign expenditure variables

presented the problem, obtaining the following results: and F-

statistic of 279.38 with a p-value 0, which allows us to reject the Ho.

(See Table 13.)

Although in the case of making the test in the dummy

variables we encountered a smaller F-statistic=55.18, we had to

reject the hypothesis of homoskedasticity. (See Table 14)

Therefore, this is a real problem that can cause conflicts at the

time of calculating confidence intervals of the parameters. However,

when we calculated the significance of the parameters with the





robust standard errors we obtained the same or improved results

that those obtained by the ordinary standard errors.

The presence of heteroskedasticity in this model is a clearresult of the limits imposed by the electoral authority on electoral

budgets, which does not allow sufficient independence among the

different expenditures.

Coefficients Analysis

In testing the significance of the parameters we have obtained

the following results: we set the null hypothesis Ho: βj=0 against

the H1: βj > 0, with 260 degrees of freedom. This means that we are

looking for positive values of all our coefficients B.

The BX1PEM has a t-value of 2.29 which is greater than the

critical value of tc=1.645 at the 5% significance level; so we reject

the null hypothesis in favor of H1. The population parameter is

positive and statistically significant. When using the

heteroskedasticity-robust t-statistic (hr t-statistic) (together

with the robust standard errors), the BX1PEM is also significant at the

1% level. Hence, the expenditures of the major candidates have a

positive effect on electoral results.

The BX2PEC holds a t-value of 3.91, so this coefficient is

statistically significant at the 1% level (tc at 1%=2.326), the same

holds with robust statistics. In the case of BX3PEG we failed to reject

Ho, because this parameter seems to be no significant even at 10%

of significance. The B4RULPAR and X5GENM are also statistically greater

than zero at the 1% of significance level. The same happens when

we use the robust statistics.





Our analysis and interpretation of the magnitudes of the

coefficients seem to make sense, except by the problem of the

negative sign encountered in the BX3PEG. At the moment, we could

say that holding fix the campaign expenditures of congressman andgovernor candidates, and not taking to account the fact of the ruling

party and candidate’s gender, an increase of 1% in the financial

budget campaign for major, the candidate will receive 0.29

percentage points more vote. So, with 10% of increasing in

campaign expenditures, the major candidates could obtain 2.9%

more vote. This is particularly good if we think about close electoral

races, as it has recently happened in the electoral campaigns in

Mexico.

At the same time, and holding all other variables fixed, an

increase of 1% in the campaign expenditures of the congressman,

will help the major candidate to get 0.54 percentage points more

vote. This is interesting because the strength of the congressman

candidate is a crucial factor in supporting a major candidate. The

negative and low value of BX3PEG represents a problem in this model.

On the other hand, it is quite remarkable that when the party

of a candidate was governing in the previous period to the election,

could lead the major candidate of that party to have 14.4

percentage points more than other no-ruling party candidates in the

election. Consequently, the political, administrative and government

good performance of a major of a particular party helps to gain

votes for the major candidate of that party.

Another crucial aspect is that related with BX5GENM, which tells

us that if the candidate is male he will receive 7 percentage points

more of the voting than a candidate that is female, supporting our

preliminary intuition about this fact.





However, these interpretations could be weakened given the

existence of multicollinearity and heteroskedasticity in this model.

The Log-Log Model

We expect the log-log model reduces the problem of

heteroskedasticity we detected in the last functional form. This is

the estimated model with the separate effect of candidate’s

expenditures (you can see more detailed information in Table 6):

Log YV= -3.93 + 0.20LogX1EM + 1.00LogX2EC – 0.34LogX3EG +

1.19X4RULPAR + 0.49X5GENM

General Analysis

This model presents an R-squared of 0.4505, which means

that a little more than 45% of the variations in the percentage of

votes are explained by percentage expenditures, ruling party and

gender of the candidate. The adjusted R-squared is 0.4385.

The F-statistic = 37.55, which makes us to reject the null

hypothesis of insignificance of all the parameters of the model. This

statistic is almost a half of the one of the former model.

It is obvious that the log-log model could present some

troubles at the time of using it, because in this case we have some

zero values in the variables of X2EC and X3EG. Hence, when we make

the regression with the log-log model we lose many observations.

Probably this is the trouble with the model.






It is important to say that we encountered a strong correlation

among the independent variables, and we also see a serious

problem of multicollinearity. The regressions of X1EM (campaign

expenditures of the major candidates) are highly correlated with theother sort of expenditures: we observed R-squared between 0.90

and 0.93 (See Table 7 and Tables 10 and 11). We did not encounter

this problem in the dummy variables when were run with

expenditures variables.


We made the Breusch-Pagan Test for heteroskedasticity in

this model, and it resulted positive for heteroskedasticity. It is

important to note that the heteroskedasticity we encountered is not

as big as we obtained in the past model. We have an R-squared of

0.64 and an F-statistic of 82.44. (See Table 14).

When we made the same test to detect the presence of

heteroskedasticity in the explanatory variables of expenditures, we

still reject the Ho of homoskedasticity. Even though, the “level” of

heteroskedasticity is much smaller than in the level-level model.

Here the F-statistic is just 4.22, with a R-squared of 0.05. (See Table

15)

In this model the heteroskedasticity problem was translated to

the dummy variables, as we could see in Table 16. The F-statistic is

155.42, so we are able to reject the null hypothesis of

homoskedasticity.

The problem of heteroskedasticity does not disappear from

the model, and we also have a big problem of multicollinearity, even

higher than the level-level model.

Coefficients Analysis





Again we have obtained a negative sign in the case of the

parameter of LogX3EG. This model confirm us the probable negative

effect of governor candidate expenditures on electoral results formajors.

At respect of the significance of the parameters of the model,

we should say the following: we failed to reject the null hypothesis

of Ho: βLX1EM=0. The parameter is not statistically significant even at

the 20%. This is a result we did not obtain in the past model,

however the magnitude of the coefficient is pretty similar. This

suppose that by 1% increase in campaign expenditures of the major

candidate, the electoral outcome will improve in 0.20%, or 2% with

an increase of 10% in expenditures, but we cannot state this

because of the low level of significance of the parameter. The

parameter of LogX2EC is two times bigger than that of the level-level,

and it is significant at 1%, so we rejected the null hypothesis in

favor of H1: βLX2EC >0. We also obtained that while BX4RULPAR is

significant at 1% significance level, BX5GENM it is not significant at 5%,

but it is at 10% level.

Besides the fact of the significance of parameters, there is

another important aspect we encountered in the parameters of the

dummy variables: these have economically unconceivable values. It

is not credible that a major candidate of a particular party have 119

percentage points of more voting when his own party was governing

in the period previous to the election. And it is also quite difficult to

assimilate that by the only fact a candidate is man has 50

percentage points more vote than a woman.

The results offered by the two models explored make us to re-

think about reordering the model. We will do this for the level-level

model, which offers better results than this last. Therefore, we give





up in our intention of getting isolated effects of the campaign

expenditures. Due to the problems of multicollinearity and

heteroskedasticity encounter in the two models, we will run a model

with three independent variables: one is the percentage jointcampaign expenditures for major, congressman and governor

candidates (X123PEMC), and we will keep the last two dummy variables

of ruling party and gender. We will see what kind of results we

obtain in order to have a model that helps to effectively estimate

the parameters and to make some forecasts for electoral outcomes.

The Alternative Level-Level Model

We are interested in running a population level-level model,

but considering the joint effect of candidate’s expenditures, as

follows:

YPV = β 0 + β 1 X 123PEMCG + β 4 X 4RULPAR +β 5 X 5GENM + μ (3)

The resulting estimated model was this (For more details see Table 5):

Y PV = -4.311 + 0.777X123PEMCG + 14.03X4RULPAR + 7.15X5GENM

General Analysis

The goodness of fit of this model, which is measured by the R-

squared, is of 0.49 and its Adjusted R-squared is 0.48. This means

that 49% in the variation of the electoral outcomes in a major

election (percentage of vote received) is explained by joint

campaign expenditures, gender of the candidate and the crucial





aspect if the party of the major candidate is currently ruling

(previous to the election).

The F-statistic, which measures the overall significance of theregression, is 84.63. This allows us to reject Ho:

βX123EMCG=βX4RULPAR=βX5GENM=0. Therefore, the independent variables

do help to explain the dependent variable of our study.

The intercept parameter still continues being a negative

coefficient, and it is also not statically significant. The sign of the

parameter of the variable X123PEMCG (which measures the percentage

joint campaign expenditures of the major, congressman and

governor candidates) is positive, as we would expect. The

parameters of X4RULPAR and X5GENM are also positive, just our

hypothesis claimed.


First of all, let us test this estimated model for multicollinearity

and heteroskedasticity. When we regress X123PEMCG on X4RULPAR and

X5GENM we get a R-squared of 0.21, which is just the third part of the

R-squared we got on the Level-Level model (1). The problem of

multicollinearity has been diminished. We also observed that

correlation within independent variables is very small (See Tables 7

and 11)


Let us now apply the Breusch-Pagan Test for

heteroskedasticity in this new model. The results are satisfactory:

we got an R-squared of 0.016 and a F-statistic of 1.45 and a p-value

of 0.22. Therefore, we fail to reject the null hypothesis of

homoskedasticity in the model level-level with three independent

variables (See Table 17).





This model does not have problems of strong multicollinearity

among the independent variables and is free of heteroskedasticity.

Coefficient Analysis

At respect to the impact these coefficients generate in

electoral outcomes, we should say the following: if campaign

expenditures of the three level of candidacy (major, congressman

and governor) increases by one percentage point, a major candidate

receives 0.77 percentage points more of the total vote. This is, if the

electoral expenditures of congressman and governor together with

those of the major candidate (all of the same party) increase by 10

percentage points, that major candidate will receive 7.7 percentage

points of the voting, holding the factors of major candidate’s gender

and ruling party fixed.

On the other hand, fixing the percentage of joint campaign

expenditures and the gender’s factor, we expect that a major

candidate will get 14.03 more percentage points of the vote if its

party is ruling in the municipality (X4RULPAR). This is, a major candidate

will receive 14.03 percentage points more of the voting than other

major candidates whose party is not currently ruling.

The last parameter (BX5GENM) is referred to the influence the

major candidate’s gender has in its electoral outcome. Holding

campaign expenditures and the fact of ruling party, a major

candidate who is man will receive 7.15 percentage points more of

the total vote than a woman.

It is worth to say that the parameters of X 123PEMCG, X4RULPAR and

X5GENM are statistically significant at the 1% level of significance,

which makes the parameters of the model very strong (the t-values





are those corresponding to robust standard errors). However, if we

calculate the significance of the coefficient of X5GENM with the

ordinary standard errors, the parameter will not be significant at 1%

but at the 5%.

The interesting results we obtained, tell us that campaign

expenditures are an important determinant of the electoral

outcomes, and also the gender factor plays an important role

together with the positive influence a governing party plays in favor

of the “official” candidate (official because the major candidate’s

party is governing).

CONCLUSIONS

• We first estimated two models, one in the level-level functional

form and the other in log-log form. We did not encounter

satisfactory results due to the presence of high multicollinearity

and heteroskedasticity in both models. These models were

regressed in order to get the partial and separate effects of each

one of the campaign expenditures of major, congressman and

governor candidates on electoral outcomes; however, the level-

level model has low levels of multicollinearity among

independent variables, but high heteroskedasticity. The log-log

model, on the other hand, presented the exact opposite

problems.

• Almost all the coefficients obtained confirm the sign expected by

the hypothesis, except by the sign of the parameter of campaign

expenditures of governor candidates, which was negative in both

models and not statistically significant.





• In light of the issues with multicollinearity and heteroskedasticity

of these two models, we determined to change the perspective of

our model and decided to measure the joint effect the campaign

expenditures of major, congressman and governor candidatesproduces in the electoral outcomes of a major race. The main

reason for changing the number of variables in the original

model, gathering the three different campaign expenditures in

only one variable, is because we saw that these problems were

inherent to our data. This is because the electoral authority in

Sonora State has strong rules about electoral expenditures, and

very strict limits of campaign expenditures in order to avoidfinancial advantages among candidates. Another problem is that

all expenditures are subject to the size of number of voters a

municipality has. Thus, municipalities with large nominal lists

(number of voters) will give their candidates the opportunity to

spend more or to have more dispersion in their expenditures.

• The results of our last and definite model gave us more

statistically and economically significant information. We

obtained: statistically significant parameters, a high goodness of

fit (50%), the expected signs and magnitudes of the parameters

with high economic and political sense. We could not correctly

measure the separate effect of campaign expenditures, as we

wanted in our preliminary hypothesis, but we gained the

opportunity to show that total campaign expenditures due

exercise an important influence on a major race.

• In conclusion, if a major candidate, according to our model,

belongs to a party which is currently governing at the day of the

election, is male and increase his campaign expenditures

together with an increase of the other two categories of

candidates, he could increase his electoral results in 17.64

percentage points.





APPENDIX A: TABLES AND CHARTS

TABLE 5. OLS RESULTS FOR THE LEVEL-LEVEL MODEL (1)DEPENDENT VARIABLE: PERCENTAGE VOTING (YPV)

INDEPENDENTVARIABLES

PARAMETERS OF SUBMODELS1

A B C

INTERCEPT

-5.7262

(3.4874)

-1.6419

-4.3117.

(3.4048)

-1.2663

-6.6872

(3.6058)

-1.8545

X 1PEM

0.2993

(0.1304)

2.2955

[0.1195]

2.5044

-

0.7664

(0.0812)

9.4380

[0.0643]

11.9178

X 2PEC

0.5421

(0.1384)

3.9155[0.1185]

4.5748

- -

X 3PEG

-0.0133

(0.1042)

-0.1280[0.0818]

-0.1632

- -

X 123PEMCG -

0.7774

(0.0771)

10.0798[0.0641]

12.1207

-

X 4RULPAR2

14.4313

(2.4452)

5.9017[2.1753]

6.6342

14.0338

(2.3684)

5.9254[2.1240]

6.6071

18.7788

(2.2481)

8.3530[1.8794]

9.9919

X 5GENM2

7.083

(3.2305)

2.1925[3.0355]

2.3334

7.1555

(3.2845)

2.1785[3.0771]

2.3255

8.6711

(3.3255)

2.6074[3.0397]

2.8526

DIAGNOSTICS R-Squared 0.5135 0.4921 0.4740

Adjust. R-Squared 0.5041 0.4863 0.4680

F-Statistic 54.8895 84.6330 78.7091

S.E. of Regression 15.1732 15.4436 15.7167

SSR. 59,859.5 62,488.68 64,718.08

Observations 266 266 266

Note: 1 The data in parentheses are the standard errors of the estimateparameters, while the numbers below them are the t-statistic values. Thevalues in brackets are the heteroskedasticity-robust standard errors. The scriptterms are the t-robust values. 2Dummy variables.





TABLE 6. OLS RESULTS FOR THE LOG-LOG MODEL (2)DEPENDENT VARIABLE: LOG(YV), Log of Number of Votes

INDEPENDENT

VARIABLES

PARAMETERS OF SUBMODELS1

A B C

INTERCEPT

-3.9397

(0.9194)-4.2849

-3.5282

(0.8067)-4.3733

-2.7692

(0.8092)-3.4220

LOG(X 1EM )

0.2018

(0.2245)0.8991

[0.2160]0.9344

-

0.6914

(0.0626)11.0448

[0.0661]10.4659

LOG(X 2EC )

1.0044

(0.2646)3.7956

[0.2223]4.5188

- -

LOG(X 3EG )

-0.3470

(0.2388)

-1.4528[0.2361]

-1.4697

- -

LOG(X 123EMCG ) -

0.6920

(0.0574)12.0425

[0.0613]11.2856

-

X 4RULPAR2

1.1925

(0.2041)5.8406

[0.1510]7.8987

1.4094

(0.1992)7.0739

[0.1420]9.9255

1.5262

(0.2068)7.3771

[0.1535]9.9406

X 5GENM2

0.4952

(0.3405)

1.4542

[0.3894]1.2717

0.8192

(0.3045)

2.6903

[0.3394]2.4140

0.9285

(0.3151)

2.9468

[0.3432]2.7054

DIAGNOSTICS

R-Squared 0.4505 0.4075 0.3720 Adjust. R-Squared 0.4385 0.4007 0.3648

F-Statistic 37.5587 60.0804 51.7374

S.E. of Regression 1.3932 1.4379 1.4804

SSR. 444.5392 541.7632 574.2645

Observations 235 235 235

Note: 1 The data in parentheses are the standard errors of the estimateparameters, while the numbers below them are the t-statistic values. Thevalues in brackets are the heteroskedasticity-robust standard errors. The scriptterms are the t-robust values. 2Dummy variables.





TABLE 7. MULTICOLLINEARITY AMONG INDEPENDENT VARIABLES: R2 RESULTS

REGRESSANDS VS.

REGRESSORS

R 2

X1P=X2P+ X3P + X4 + X5 0.6701

X1P = X2P + X3P 0.6562X1P = X2P 0.6519

X1P = X3P 0.5034

X2P = X3P 0.6952

X1P = X4 + X5 0.0870

X2P = X4 + X5 0.2233

X3P = X4 + X5 0.2472

LX1=LX2+LX3+X4+X5 0.9231

LX1 = LX2 + LX3 0.9175

LX1 = LX2 0.8938

LX1 = LX3 0.9118LX2 = LX3 0.9382

LX1 = X4 + X5 0.0544

LX2 = X4 + X5 0.0184

LX3 = X4 + X5 0.0162

Note: By simplicity the variables X1PEM, X2PEC, X3PEG are X1P, X2P, X3P. The variablesX4RULPAR and X5GENM take the name of X4, X5. The Logs of X1EM, X2EC and X3EG are

LX1, LX2, LX3

TABLE 8. MULTICOLLINEARITY AMONG JOINT CAMPAIGN EXPENDITURESAND DUMMY VARIABLES

Dependent Variable: X123PEMCG

Method: Least Squares

Sample: 1 266

Included observations: 266

Variable Coefficient Std. Error t-Statistic Prob.

C 17.73878 2.492479 7.116923 0.0000

X4RULPAR 13.55610 1.698857 7.979545 0.0000X5GENM 6.190495 2.597875 2.382907 0.0179

R-squared 0.213990 Mean dependent var 27.0677

Adjusted R-squared 0.208012 S.D. dependent var 13.8729

S.E. of regression 12.34606 Akaike info criterion 7.87576

Sum squared resid 40087.81 Schwarz criterion 7.91618

Log likelihood -1044.477 F-statistic 35.8006

Durbin-Watson stat 1.479311 Prob(F-statistic) 0.00000





TABLE 9. CORRELATION MATRIX OF THE VARIABLES OF THE LEVEL-LEVELMODEL (1)

YPV X1PEM X2PEC X3PEG X4RULPAR X5GENM

YPV 1.0000 0.5641 0.6590 0.5851 0.5186 0.1898

X1PEM 0.5641 1.0000 0.8074 0.7095 0.2771 0.1157

X2PEC 0.6590 0.8074 1.0000 0.8338 0.4536 0.1566

X3PEG 0.5851 0.7095 0.8338 1.0000 0.4838 0.1405

X4RULPAR 0.5186 0.2771 0.4536 0.4838 1.0000 0.0537

X5GENM 0.1898 0.1157 0.1566 0.1405 0.0537 1.0000

TABLE 10. CORRELATION MATRIX OF THE VARIABLES OF THE LOG-LOGMODEL (2)

LYV LX1EM LX2EC LX3EG X4RULPAR X5GENM

LYV 1.0000 0.5347 0.5943 0.5503 0.2518 0.0644

LX1EM 0.5347 1.0000 0.9428 0.9553 -0.1491 -0.0923

LX2EC 0.5943 0.9428 1.0000 0.9686 -0.0700 -0.0303

LX3EG 0.5503 0.9553 0.9686 1.0000 -0.0977 -0.0661

X4RULPAR 0.2518 -0.1491 -0.0700 -0.0977 1.0000 0.0278

X5GENM 0.0644 -0.0923 -0.0303 -0.0661 0.0278 1.0000

TABLE 11. CORRELATION MATRIX OF

THE LEVEL-LEVEL MODEL (3)

X123PEMCG X4RULPAR X5GENM

X123PEMCG 1.0000 0.4439 0.1539

X4RULPAR 0.4439 1.0000 0.0537

X5GENM 0.1539 0.0537 1.0000





TABLE 12. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALS

OF LEVEL-LEVEL MODEL (1), ALL VARIABLES

Dependent Variable: U1AS


Date: 12/07/06 Time: 15:00Sample: 1 266



C -4850.241 1304.653 -3.717648 0.0002

X1PEM -82.32521 48.78944 -1.687357 0.0927

X2PEC 246.7653 51.79355 4.764402 0.0000

X3PEG 451.3069 38.99238 11.57423 0.0000

X4RULPAR 3782.394 914.7646 4.134828 0.0000

X5GENM -5006.379 1208.537 -4.142512 0.0000

R-squared 0.790303 Mean dependent var 8319.219Adjusted R-squared 0.786270 S.D. dependent var 12278.13


Sum squared resid 8.38E+09 Schwarz criterion 20.22911



TABLE 13. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALS

OF LEVEL-LEVEL MODEL (1), CAMPAIGN EXPENDITURES



Date: 12/07/06 Time: 15:37

Sample: 1 266



C -9059.751 886.781 -10.216 0.000

X1PEM -118.473 50.738 -2.335 0.020

X2PEC 273.993 53.744 5.098 0.000

X3PEG 486.518 40.188 12.106 0.000R-squared 0.762 Mean dependent var 8319.219



Sum squared resid 9513790015.410

Schwarz criterion 20.314



TABLE 14. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), ALL VARIABLES







Sample(adjusted): 1 265


Excluded observations: 30 after adjusting endpoints


C 1385.21111 138.66753 9.98944 0.00000

LX1EM 30.25869 33.86013 0.89364 0.37245

LX2EC 124.41237 39.91091 3.11725 0.00206

LX3EG -166.57622 36.02163 -4.62434 0.00001

X4RULPAR -11.10178 30.79418 -0.36052 0.71879

X5GENM -999.64857 51.35665 -19.46483 0.00000




Sum squared resid 10111250.97690 Schwarz criterion 13.64684







TABLE 15. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), CAMPAIGN EXPENDITURES



Date: 12/07/06 Time: 16:06Sample(adjusted): 1 265




C 422.0082 207.2018 2.036701 0.0428

LX1EM 120.5103 53.06014 2.271202 0.0241

LX2EC -4.397522 63.10246 -0.069689 0.9445

LX3EG -124.5653 58.32772 -2.13561 0.0338




Sum squared resid 26840578 Schwarz criterion 14.57665







TABLE 16. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), DUMMY VARIABLES



Date: 12/07/06 Time: 16:07Sample(adjusted): 1 265




C 1156.602 53.0794 21.79003 0

X4RULPAR 9.283113 32.32907 0.287144 0.7743

X5GENM -963.8307 54.67006 -17.62995 0






Durbin-Watson stat 1.41525 Prob(F-statistic) 0





TABLE 17. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF ALTERNATIVE LEVEL-LEVEL MODEL (3),

ALL VARIABLES

Dependent Variable: U1BS


Date: 12/07/06 Time: 15:34Sample: 1 266



C 392.0132 65.55805 5.979636 0

X123PEMCG -2.488568 1.485154 -1.675629 0.095

X4RULPAR 72.20292 45.60211 1.583324 0.1146

X5GENM -38.96815 63.24207 -0.616174 0.5383











CHART 1. OBSERVABLES OF YPV AND FITTED VALUES

OF LEVEL-LEVEL MODEL (1)

-250

-200

-150

-100

-50

0

50

100

1 16 31 46 61 76 91 106121136151 166181196 211226241256

Candidates

P e r c e n t a g e

YPV

YPV1est

CHART 2. OBSERVABLES OF LYV AND FITTED VALUESOF LOG-LOG MODEL (2)

-50

-40

-30

-20

-10

0

10

20

30

40

1 14 27 40 53 66 79 92 105118 131144157 170183196209 222235248 261

Candidates

P e r c e n t a g

LYV2est

LYV

CHART 3. OBSERVABLES OF YPV AND FITTED VALUES OF

LEV-LEV MODEL (3)

0

10

20

30

40

50

60

70

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239 253

Candidates

P e r c e n t a g

YPV

YPV13est





REFERENCES

• Erikson, R.S. & Palfrey, T.R. (2000). Equilibrium in campaignspending games: Theory and Evidence. American PoliticalScience Review, 94, 596-609

• Garcia y Sandoval (2004). Influencia de gastos de campaña enlos resultados de las elecciones federales de 1997. Espiral VolX. 29,81-108

• Gerber, A. (1998) Estimating the effect of campaign spendingon Senate election outcomes using instrumental variables.

American Political Science Review, 92, 401-411

• Green D.P. & Krasno, J.S. (1988). Salvation for the Spendthrift

incumbent . American Journal of Political Science Review, 32,884-907

• Jacobson, G.C. (1978) The effect of campaign spending incongressional elections. American Political Science Review,72, 469-491

• Jacobson, G.C. (1985) Money and votes reconsidered:congressional elections, 1972-1982, Public Choice, 47(1), 7-62

•

Levitt, S. (1994) Using Repeat challengers to estimate theeffect of campaign spending on election outcomes in theHouse. Journal of Political Economy, 102,77-98

• Meloni, Osvaldo (1997) Empleo, Desempleo y Elecciones. Elcaso de la elección

• presidencial argentina de 1995. Estudios de Economía(Universidad de Chile) Vol. 24, J

• Pags. 119-133.

• Payne, James (1991) Elections and government spending.Public Choice. Vol. 70, 71-82.

DATA SOURCES

• Consejo Estatal Electoral www.ceesonora.org.mx

• H. Congreso del Estado de Sonora, www.congresoson.gob.mx

IMPORTANT NOTE: You can also see the Electronic Appendixwhere we have put all the data and regressions we used in the


http://www.ceesonora.org.mx/

http://www.congresoson.gob.mx/

http://www.ceesonora.org.mx/

http://www.congresoson.gob.mx/




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