A definite reply to controveries related to moon sighting - Research paper
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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
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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.
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.
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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.
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.
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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.
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
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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.
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
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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.
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.
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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.
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.
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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.
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.
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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.
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:
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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.
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
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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.
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.
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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.
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.
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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.
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.
Level-Level (Separate
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.
Level-Level (Separate
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)
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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.
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
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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.
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.
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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.
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.
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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.
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
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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.
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
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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.
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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.
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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.
Multicollinearity Analysis
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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.
Heteroskedasticity Analysis
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
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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
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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
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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.
Multicollinearity Analysis
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)
Heteroskedasticity Analysis
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).
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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
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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.
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• 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.
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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.
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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.
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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
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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
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TABLE 12. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALS
OF LEVEL-LEVEL MODEL (1), ALL VARIABLES
Dependent Variable: U1AS
Method: Least Squares
Date: 12/07/06 Time: 15:00Sample: 1 266
Included observations: 266
Variable Coefficient Std. Error t-Statistic Prob.
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
S.E. of regression 5676.288 Akaike info criterion 20.14828
Sum squared resid 8.38E+09 Schwarz criterion 20.22911
Log likelihood -2673.721 F-statistic 195.976
Durbin-Watson stat 2.382215 Prob(F-statistic) 0.00000
TABLE 13. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALS
OF LEVEL-LEVEL MODEL (1), CAMPAIGN EXPENDITURES
Dependent Variable: U1AS
Method: Least Squares
Date: 12/07/06 Time: 15:37
Sample: 1 266
Included observations: 266
Variable Coefficient Std. Error t-Statistic Prob.
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
Adjusted R-squared 0.759 S.D. dependent var 12278.125
S.E. of regression 6025.958 Akaike info criterion 20.260
Sum squared resid 9513790015.410
Schwarz criterion 20.314
Log likelihood -2690.642 F-statistic 279.388
Durbin-Watson stat 2.372 Prob(F-statistic) 0.000
TABLE 14. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), ALL VARIABLES
Dependent Variable: U2AS
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Method: Least Squares
Sample(adjusted): 1 265
Included observations: 235
Excluded observations: 30 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.64288 Mean dependent var 273.54197
Adjusted R-squared 0.63508 S.D. dependent var 347.84515
S.E. of regression 210.12838 Akaike info criterion 13.55851
Sum squared resid 10111250.97690 Schwarz criterion 13.64684
Log likelihood -1587.12548 F-statistic 82.44734
Durbin-Watson stat 1.49388 Prob(F-statistic) 0.00000
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TABLE 15. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), CAMPAIGN EXPENDITURES
Dependent Variable: U2AS
Method: Least Squares
Date: 12/07/06 Time: 16:06Sample(adjusted): 1 265
Included observations: 235
Excluded observations: 30 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.052009 Mean dependent var 273.542
Adjusted R-squared 0.039698 S.D. dependent var 347.8452
S.E. of regression 340.8709 Akaike info criterion 14.51776
Sum squared resid 26840578 Schwarz criterion 14.57665
Log likelihood -1701.837 F-statistic 4.224421
Durbin-Watson stat 1.560914 Prob(F-statistic) 0.006234
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TABLE 16. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF LOG-LOG MODEL (2), DUMMY VARIABLES
Dependent Variable: U2AS
Method: Least Squares
Date: 12/07/06 Time: 16:07Sample(adjusted): 1 265
Included observations: 235
Excluded observations: 30 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.572631 Mean dependent var 273.542
Adjusted R-squared 0.568947 S.D. dependent var 347.8452
S.E. of regression 228.3765 Akaike info criterion 13.71255
Sum squared resid 12100147 Schwarz criterion 13.75672
Log likelihood -1608.225 F-statistic 155.4283
Durbin-Watson stat 1.41525 Prob(F-statistic) 0
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TABLE 17. BREUSCH-PAGAN TEST FOR HETEROSKEDASTICITY, RESIDUALSOF ALTERNATIVE LEVEL-LEVEL MODEL (3),
ALL VARIABLES
Dependent Variable: U1BS
Method: Least Squares
Date: 12/07/06 Time: 15:34Sample: 1 266
Included observations: 266
Variable Coefficient Std. Error t-Statistic Prob.
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
R-squared 0.016382 Mean dependent var 309.1627
Adjusted R-squared 0.005119 S.D. dependent var 298.1207
S.E. of regression 297.3567 Akaike info criterion 14.24267
Sum squared resid 23166299 Schwarz criterion 14.29655
Log likelihood -1890.274 F-statistic 1.454484
Durbin-Watson stat 2.085179 Prob(F-statistic) 0.227417
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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.
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
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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.
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
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present Project. There are two files, one in Eviews and other inExcel.