Vol. 01 no. 01 2014
Pages 1–10
Advanced Research Methods
Betraying the party to survive
Javier Brolo∗
MSc Political Science (candidate), Department of Government, University of Essex, UK
24 April 2014
Supervisor: Dr. Alejandro Quiroz
ABSTRACT
Transfuguismo occurrs when an incumbent defects the party
with which he or she was elected. It’s believed that incumbents
that switch parties are motivated to do so in order to gain reelection. This article tests whether transfuguismo increases the
probability of reelection using a discrete-time survival analysis
that follows 386 mayors from the period of their first reelection
until they leave office. It finds that having defected the fist party
reduces the probability of leaving office to mayors who began
the period with a party that didn’t figure among the two largest
parties in presidential elections. Confirming the existence of
this perverse incentive, and no effect of characteristics of the
party and electoral system, is consistent with observing a limited institutionalised party system in Guatemala. Further studies
that incorporate personal characteristics of mayors would be
able to illuminate on the mechanisms that determine successful
reelections.
INTRODUCTION
A yors in Guatemala are believed to switch parties in order to increase their chances of reelection. This article
evaluates whether such strategy successfully reduces the
probability of leaving office. To do so it uses a discrete-time survival
analysis. A total of 386 mayors elected after 1985 were followed
from the beginning of their second consecutive period until they left
office. The results show that loyalty, not defecting the party, can
indeed be detrimental for reelection for mayors not associated with
any of the two largest parties in presidential elections.
M
This findings are consistent with observing weakly institutionalised parties in Guatemala. Since mayors don’t have an incentive to
be loyal, parties never keep enough people to consolidate a political
protect over time. Parties can be seen as mere vehicles to come into
office. Also, Guatemala is clear example where incumbents have
a systematic disadvantage (Eggers and Spirling, 2014). Therefore,
mayors seeking reelection can feel justified in betraying the party in
order to survive in office.
The article is structured in four different sections. The first one,
data and theory, describes how data was obtained and the theoretical
connections between variables and the outcome. Special attention is
∗ [email protected]
c 2014
given to biases due to endogeneity, selection or measurement. The
second section, survival analysis in discrete time, explains the background of the mathematical model used to analyse the data. The
third section presents the results of the data analysis. Finally, the
conclusion summarises the main findings and discusses considerations for causal analysis and further studies.
1
DATA AND THEORY
The study follows 386 mayors elected in Guatemala after 1985 from
the beginning of their second consecutive period until they left office. This time interval is regarded as the spell. At the end of each
consecutive period the event of interest can be observed: whether
a mayor left office. There is a caveat for mayors who are currently
in office, those elected for the 2011-2015 period. Since we cannot
observe the future, we cannot know whether they will leave or stay
in office at the end of their period. This cases are called censored
and receive a different treatment in the analysis.
It’s important to clarity that although there may be different reasons for a mayor leaving office, this study assumes that it’s due defeat
by a challenger in elections. It would be possible, with some effort,
to record if mayors who left office had registered to run for reelection and lost, or if they didn’t register and left office unilaterally.
However, it would be impossible to distinguish if the reason for not
registering was due to anticipation of an electoral defeat.
Figure 1 shows the number of mayors according to the number of
consecutive periods they have stayed in office. The majority doesn’t
stay in office after the second period. This can be seen with more
detail in Table 1. We see that 386 mayors began a second period in
office. Of these, 221 left office by the end of their period, 108 began
a third period, and 57 are currently in office. The hazard rate indicates the proportion of mayors that left office, 221 out of 386, and the
survival rate the proportion of mayors didn’t leave office, 165 out
of 386.
The sample of mayors was chosen to avoid a selection bias. Since
the transition to democracy in 1985, Guatemala has held municipal
elections 10 times1 . In total, 2,177 different mayors have been elected for 2828 periods in office. Of these, only 392 mayors have had
1
Before the 1999 elections, not every municipality held elections on
the same year. Also, some municipalities were created after 1985 and
consequently have had less elections than the rest.
1
Javier Brolo
more than one consecutive period. We are interested in the effect that
switching parties has on the duration of a mayor in office. However,
in order to observe if a mayor switched party, they need to have
at least two consecutive periods in office. This study considers all
mayors with these characteristics except 6 cases that were dropped
because their measure of volatility couldn’t be computed.
The main independent variable is loyalty, loyal. It is a categorical
variable that identifies whether a candidate stayed with the same
party during their first reelection, ”Yes”, or not, ”No”. As mentioned before, loyal mayors are expected to leave office more often.
It’s important to point out that though mayors may have been loyal
to a party for their first reelection, they may change party afterwards.
Updating the loyalty of a mayor could be possible if introduced as
a time-varying variable. However, mayors may be forced to leave
parties that die over time. That means that a mayor who defects the
party after their fourth consecutive period doesn’t necessarily express the same opportunistic behaviour than a mayor who defects
the party immediately after their first time in office. This variable
identifies the initial loyalty behaviour of a mayor as a way to capture their support for institutionalised parties.
Another central independent variable is the association of a mayor
with the government party, GOV.l. This is a categorical variable that
indicates whether the mayor was elected with the same party that
won the presidential elections, ”First”, the second place, ”Second”,
or neither, ”Other”. Mayors that belong to the government party
could profit from its resources or damage from its reputation, thus
affecting their duration in office. More over, the association with the
government party could interact with loyalty, as government parties
recruit mayors from campaigning.
Figure 1. Frequency of mayors according to total number of consecutive
periods in office
Table 1. Life table: total number of periods in office for the 286 mayors who
had at least one reelection between 1985 and 2011 in Guatemala
Periods
in office
2
3
4
5
Began
Left
Censored
386
108
25
5
221
61
12
3
57
22
8
2
Hazard
rate
0.57
0.56
0.48
0.60
Survival
rate
0.43
0.19
0.10
0.05
The following paragraphs describe the variables available for each
mayor, and their expected effect on a mayor leaving office. Notice
that all explanatory variables refer to an observation strictly before
the event can occur.
There are three variables used to observe the duration of a mayor
in office: the entry period, enterP; the exit period, exitP; and if the
mayor left office, event. The first captures a mayors entry to the
number of consecutive periods in office, while the second indicates
moments before an additional period would begin. Entry and exit
periods have a range from 2 to 5. The event, leaving office, is a
dummy variable that indicates whether a mayor left office before
starting an additional period, 1, or 0 otherwise.
2
There are two geographical variables: depto, capital capital.
These categorical variables indicate the name of the district the municipality the mayor serves is in, and if that municipality is a capital
of the district. Geography is associated with differences in social
and economic characteristics. Therefore, the effect of geographic
location on a mayor leaving office would suggest that differences in
ethnic composition or economic development can affect the duration of mayors in office.
A series of variables that describe the electoral and party system
were considered. The size of the electorate, qPAD, was measured
as an ordinal variable with four categories, one for each quartile of
the distribution of all municipalities according to the logarithm of
their population. The reason of this grouping is that the effect of
electorate size may not be linear. Some would argue that in larger
municipalities the incumbent has more resources to stay in office,
but others that competition is stronger. Turnout, qTURN.l, was
measured as the percentage of valid votes with respect to the total
number of votes, and grouped acceding to quartiles. This is done
for the municipal elections before the period of a mayor started. It
would be expected that higher turnout would extend the duration in
office for the legitimacy it provides. Also, concur, is an indicator
that measures if the elections at the end of a mayors period were
simultaneous to the presidential elections. Concurrent elections are
expected to help the party in government.
Fragmentation, qNEP.l, was computed using the effective number of parties index (Laakso and Taagepera, 1979, p.4) grouped by
quartile. It’s expected that the more competition the less time before
a mayor would leave office. Advantage, qADV.l, is the percentage of
votes difference between the mayor and the strongest challenger at
the previous elections, grouped by quartile. It’s reasonable to think
that a mayor with an initial electoral advantage could stay in office
Betraying the party to survive
longer.
Volatility, VOL.l, is measured for the elections before the mayor’s
period using an variation of Pedersen’s Index (1979, p.1) inspired
by Powell and Tucker (2013) grouped by quartile. The continuous
value is given by the following equation:
2
Pn
A=
|pit − pi(t−1) |
+
2
Ps
Pm Ps
r=1 pjr(t−1) |
r=1 pjrt −
j=1 |
i=1
2
|
Pu
x=1
pxt −
Pv
y=1
Once data was computed at the municipal level, the database was reshaped to a long format. Spelling errors were cleaned, and the unit
of analysis was changed to mayors with one observation per period
along with time-fixed and time-varying variables. Finally, the duration variables were created, and the statistical analysis performed
using the eha package (Broström, 2014) in R (R Core Team, 2013).
+
(1)
py(t−1) |
2
Where i represent each of the n parties that participated individually at both time t and t − 1; r represent each of the s parties in the
alliance j; x each of the u parties that only participated in time t; and
y each of the v parties that only participated in election t − 1. This
measure becomes necessary given the instability of the Guatemalan
party system. What it captures is the change in electoral preferences between distinct political forces of three kinds: those who
are consolidated into stable parties, those that are desegregated into
different combinations of alliances, and those that are new or died.
Also, to measure volatility requires at least two elections before a
mayor enters office. Therefore, six observations had to be dropped
from the study, because reelection occurred before three elections
had taken place in the municipality. It is expected that the stability
in less volatile municipalities would lengthen the duration of mayors in office.
SURVIVAL ANALYSIS IN DISCRETE-TIME
In order to study the duration of mayors in office, we require a truly
discrete-time survival analysis. We are interested in the event that a
mayor fails to be reelected, and its determinants. This is not something that can happen any time, it occurs at a fixed moment: the day
of elections. Therefore, simultaneous or ”tied” events are not the
result of grouping observations into yearly measurements.
For discrete models (Singer and Willet, 1993, p.163), T is a random variable with positive real numbers as support (t1 , t2 , ..., tk )
that indicate the time period tj when the event occurs. The distribution of T is characterised by a probabilityP
mass function: pj =
P (T = tj ), j = 1, ..., k where pj > 0 and kj=1 = 1. Its cumuPj
lative distribution function is: F (tj ) = P (T ≤ tj ) =
i=1 pi ,
Pk
and its survival function S(ti ) = P (T ≥ tj ) =
p
.
i=j i These
characterisations can be interpreted as the probability that an event
occurs at a specific moment in time, before, or after, respectively.
However, for the occurrence of events, we are interested in the
conditional probability that an event has occurred given that it
hasn’t. In this case, the probability that a mayor will leave office
at period tj given that it has been in office for up to that period. This
is referred as the hazard function:
There are many more variables that would have been desirable
to include, but unfortunately aren’t easily available. For example,
variables describing the candidate such as age, education level, socioeconomic status, religion, profession, or ethnicity could explain
the personal characteristics preferred by voters. On the other hand,
indicators of performance delivering public goods such as crime
rates, number of public officers per capita, availability of schools,
hospitals, roads, social programs, public spaces can explain their
duration in office. Also, political indicators such as relatives in government positions, leadership positions, age of the party, ideology
of the party, popularity of the party, and political crisis can influence
the ability of mayors to gather support to remain in office. That is
there are many factors that can haver their own independent effects
on the duration of a mayor in office, and studying them could represent a research agenda. However, despite the fact that a mayor’s
duration in office can be explained by other factors, these aren’t necessarily correlated with the loyalty of a mayor at the same time,
and we can still investigate the independent effect of strategic, opportunistic calculations to remain in office.
The response, Y , in discrete-time models is a random variable indicating whether an event has occurred for a given point in time tj
and individual i. Following (Broström, 2012, p.121), such response
is modelled as a Bernoulli outcome Y ∼ B(1, p), where p is the
probability of the event occurring. One option to perform the analysis is through a logistic regression using a complementary log-log
link (cloglog). Different covariates to explain the response are introduced to the model as a vector X = [X1ij , X2ij , ...Xnij ], where
each X is measured for a given point in time tj and individual i.
Time is introduced as a vector D = [D1 , D2 , ...Dk ], where each D
is a dichotomous variable indicating the time at period j, 1 ≤ j ≤ k.
The general model is:
Before advancing to the methods section, a final note on how the
database was constructed. First, the electoral results for each municipal election between 1985 and 2011 was recorded by hand from
the printed reports published by the Supreme Electoral Court of
Guatemala. The original database is in wide format, with municipalities as the unit of analysis and electoral results per party and year.
The reason for choosing logistic regression using the complementary log-log link, instead of the logit or probit, is that it has a
distribution better suited for survival data and maintains the proportional hazards property of parametric models with continuous time.
The estimation is computed using maximum likelihood, taking into
account censured data.
pj
hj = P (T = tj |T ≥ tj ) = Pk
i=j
pi
, j = 1, ..., k
log(−log(1 − hij )) = αD + βX
(2)
(3)
3
Javier Brolo
3
Table 3. Effect of variables on mayor leaving office using a logistic
regression with complementary log-log link
DATA ANALYSIS
The first step was to run a full model, that is a logistic regression
including all the relevant variables. Then, a likelihood ratio test was
used to identify which variables have a significant effect.
The likelihood ratio test evaluates the null hypothesis, H0 , that
the effect of a variable is zero, βx = 0. To do so, it uses a test statistic that compares how well the full model fits the data with respect
to a nested model, where the effect of a variable is zero. The measures of fit are the likelihood function of the full, Lf , and nested Ln
models. The test statistic is given by T = 2(logLf −logLn ). Under
H0 , T has a χ2 (chi-square) distribution with d degrees of freedom,
T ∼ χ2 (d), where d is the difference in the number of parameters
of the full and nested models. In this story, for each variable, we
rejected H0 , no effect hypothesis, if T was significantly large at the
10% level.
The results of the likelihood ratio test are shown in Table 2.
The only variables that have a significant effect on the mayor leaving office are if elections were concurrent (concurr), the size of
the electorate (qPAD), and the interaction between being loyal to
the first party and the relationship with the party in government
(loyal:GOV.l).
Table 2. Variables that have a significant effect on mayors leaving office as
identified through a likelihood ratio test
Df
(Intercept)
exitP.f
capital
depto
concur
qPAD
qTURN.l
qNEP.l
qVOL.l
qADV.l
loyal:GOV.l
∗∗∗
3
1
21
1
3
3
3
3
3
2
Deviance
644.58
648.43
644.59
663.29
648.28
658.28
649.95
648.76
648.01
647.07
649.70
AIC
738.58
736.43
736.59
715.29
740.28
746.28
737.95
736.76
736.01
735.07
739.70
LRT
Pr(> χ2 )
3.85
0.02
18.71
3.70
13.70
5.38
4.18
3.43
2.50
5.12
0.2776
0.8921
0.6035
0.0544.
0.0033∗∗
0.1461
0.2426
0.3297
0.4759
0.0773.
p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05,. p < 0.1
It’s worth noting the implications that some variables don’t have
a significant effect on mayors leaving office.
First, how long has a mayor been in office, (exitP.f), does not seem
to affect if it will leave. This has substantive implications since it
suggests that the experience of being in office is not an advantage to
remain in office. Second, the geographical location doesn’t have an
effect. Neither mayors from district capitals (capital), nor mayors
from specific districts (depto) are more or less likely to leave office
than the rest. This implies that social and economic characteristics
associated with geographic locations such as ethnicity and resources
play a lesser role in the reelection patterns of mayors. Also, that
there is no initial evidence of a clustering effect due to municipalities belonging to the same district.
4
(Intercept)
exitP.f3
exitP.f4
exitP.f5
loyalYes
GOV.lFirst
GOV.lSecond
concurYes
qPAD(8.49,9]
qPAD(9,9.57]
qPAD(9.57,14]
loyalYes:GOV.lFirst
loyalYes:GOV.lSecond
AIC
BIC
Log Likelihood
Deviance
Num. obs.
∗∗∗
Reduced model
−0.02 (0.25)
0.05 (0.15)
−0.21 (0.30)
0.14 (0.60)
0.41 (0.19)∗
0.33 (0.23)
0.66 (0.25)∗∗
−0.55 (0.20)∗∗
0.30 (0.17).
0.07 (0.17)
−0.31 (0.19)
−0.32 (0.29)
−0.90 (0.33)∗∗
709.53
764.93
-341.76
683.53
524
p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05,. p < 0.1
Third, and most surprising, the characteristics of the party system
and elections when a mayor began a period aren’t associated with
him or her leaving office at the end of the period. It doesn’t seem relevant whether mayors competed with many or few parties (qNEP.l)
or won the election by a large or small margin (qADV.l), neither if
voters had stable or unstable preferences (qVOL.l) or turned out to
vote in large or small proportions (TURN.l). This reinforces the perception of a weekly institutionalised party system. It was expected
that mayors who began the period with less competition, more stability, and more legitimacy would be able consolidate an advantage
to provide them with continuity.
The second step was to run a reduced model only including variables with a significant effect. The results are shown in Table 3.
Interpreting the direction of the effect, we can see that mayors are
less likely to leave office when municipal and presidential elections
occur simultaneously (concur). Also, the coefficient of the interaction variable (loyalYes:GOV.lSecond) indicates that mayors are less
likely to leave office when they were loyal to the party for their first
reelection and were elected by the same party that won the presidential elections for the period under observation.
It is difficult to interpret the magnitud of the effect of each variable since the coefficients express an increase in the complementary
log-log shown in equation 3. A better way, advised by (King et al.,
2000, p.355) is to estimate the predicted probabilities for specific
cases. Here, the predicted probabilities were computed for the last
observation of each mayor and are shown according to each variable
in Figures 2, 3, and 4.
Mayors elected during concurrent elections have a median probability 4.6% lower than those elected between presidential elections.
The magnitude of this effect is certainly small, especially when it’s
Betraying the party to survive
observed that all mayors have more chances of leaving office than
staying, independently of the timing of elections. Also, there are
few cases of mayors who were reelected at least once and began or
ended their periods in non-concurrent elections. This calls for caution in the finding as mayors elected during in between periods may
be systematically different than the others.
Figure 3. Distribution of predicted probabilities of mayors leaving office
according to the size of the electorate
Figure 2. Distribution of predicted probabilities of mayors leaving office
according to simultaneity of presidential and municipal elections
Regarding the size of the electorate, Figure 3 shows that mayors
in the 25% of municipalities with the most voters have a lower probability of leaving office than the rest, a median of 60.8%, compared
to 64.0%, 66.4%, and 64.6 of the first, second, and third quartile respectively. Though this is only a small difference, it is worth
pointing out that municipal governments vary in size according to
their population. Therefore, local politics are qualitatively different
in municipalities of different size which can lead to differences in
the the probability that a mayor leaves office.
Finally, Figure 4 shows the differences in the distribution of predicted probabilities of mayors according to each combination of the
interaction variable. It can be observed that the mayors with the
least probability of leaving office are the ones who changed party
for their first reelection and were elected with a party that didn’t figure between the two largest parties in the presidential election. Their
median probability of leaving office is 60.6%, compared to 64.5%
of those who did stay with the same party for their first reelection.
Interestingly this phenomenon is reversed for mayors in the opposition party. Mayors who were loyal to their party for their first
reelection have less probability of leaving office if they began their
period with the opposition party than if they hadn’t been loyal. This
findings are consistent with the idea that institutionalised parties
aren’t preferred by voters. Therefore, mayors, aware that party
structures may hinder their ability to remain in office are better off
Figure 4. Distribution of predicted probabilities of mayors leaving office
according to their loyalty during first reelection and association to the
government party
betraying the party that served as the initial vehicle to enter office.
Figure 5 show the actual number of mayors who stayed and left
office according to each interaction. Notice that the first category is
the one where the largest proportion of mayors stayed in office than
left. It may also be of interest to observe the list of the predicted
probabilities for the mayors currently in office that have had three
or more consecutive periods in office provided in Table 6. This is
a static analysis, however, and it does not capture the proportional
rate at which mayors leave office according to subgroups, or if the
5
Javier Brolo
Table 4. Effect of variables on the hazard rate of mayors leaving office using
a cox regression
loyalYes
GOV.lFirst
GOV.lSecond
concurYes
qPAD(8.49,9]
qPAD(9,9.57]
qPAD(9.57,14]
loyalYes:GOV.lFirst
loyalYes:GOV.lSecond
Events
Total time at risk
Max. log. likelihood
LR test statistic
Degrees of freedom
Overall p-value
∗∗∗
Cox Model
0.41 (0.19)∗
0.33 (0.23)
0.66 (0.25)∗∗
-0.55 (0.20)∗∗
0.30 (0.17).
0.07 (0.17)
-0.31 (0.19)
-0.32 (0.29)
-0.90 (0.32)∗∗
297
524
-341.77
32.67
9
0.000152298
exp(coef)
1.51
1.39
1.94
0.58
1.35
1.07
0.73
0.73
0.41
p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05,. p < 0.1
Figure 5. Actual number of mayors leaving and staying in office according
to their loyalty during first reelection and association to the government party
Table 5. Test of the proportional effect of variables on the hazard rate of
mayors leaving office
effect is constant through time.
Using Cox regression, it is possible to evaluate if a variable has
a proportional effect in the rate at which mayors leave office (hazard rate). Table 4 shows the results from the cox regression model,
which are identical to the ones produced by the logistic regression
shown in Table 3.
The advantage is that cox regression allows to perform a test the
proportionality of the effect of the covariates on the hazard rate. This
is equivalent to testing the significance of the interaction between
the covariates and time in a logistic regression. The results are
shown in Table 5. It can be seen that time does not significantly
change the effect of most covariates; thus they have a proportional
effect. The only exception is mayors in the 25% of municipalities
with largest size of the electorate. It’s negative sign indicates that,
mayors leave office at a lower rate in larger municipalities. However, if it’s effect is not proportional, it may only hold for the first
periods of a mayor in office, and for later periods mayors leave at a
higher rate.
Cox regression can also be used to visually inspect the rate at
which mayors leave office. Figure 6 shows the cumulative hazard
function. The symmetric shape can be interpreted as a nearly constant rate of mayors leaving office, which resembles a geometric
distribution. What this implies is that permanence in office has ”no
memory”; that is, the rate at which mayors leave office is the same
regardless if its the second or fifth consecutive period. However, this
rate is high enough that we only see a few mayors surviving past the
three consecutive periods in office. As shown in Table 6, only 32 of
the 334 mayors currently in office have survived for more than three
consecutive periods.
As a complement to the cumulative hazards function, Figure 7
shows the survival function. This shows the decreasing probabilities
6
loyalYes
GOV.lFirst
GOV.lSecond
concurYes
qPAD(8.49,9]
qPAD(9,9.57]
qPAD(9.57,14]
loyalYes:GOV.lFirst
loyalYes:GOV.lSecond
GLOBAL
rho
0.04
0.10
0.05
-0.03
0.00
-0.04
-0.10
-0.02
-0.03
chisq
0.44
2.81
0.88
0.39
0.01
0.58
3.02
0.17
0.35
10.87
p
0.51
0.09
0.35
0.53
0.93
0.45
0.08
0.68
0.55
0.28
of surviving past certain number of consecutive periods in office.
That is, mayors with one reelection have less than 10% probability
of being more than three consecutive periods in office, and so forth.
Finally, the proportional effect of the interaction between loyalty
and affinity to the government party can be visually inspected in
Figure 8. In this case, three different groups were used to stratify
with respect to the baselines hazard (no covariates). These groups
were: (1) mayors who were loyal and were elected by a party
different than the two largest parties during presidential elections
(loyalNo:GOV.lOther); (2) mayors who were not loyal and were
elected by the second largest party in presidential elections (loyalNo:GOV.lSecond); and (3) all other mayors in the study. It is
evident that the proportionality assumption holds, since the lines
don’t cross: the hazard rate of the group 1 and 2 are always below
and above the reference group as expected. However, no mayors
from the first category are observed with five consecutive periods in
office, though several of them are currently in office.
Betraying the party to survive
Figure 6. Cumulative hazards function for mayors
Figure 8. Proportional Hazards
therefore, these conclusions can only apply to a proportion of mayors of a given type, not individual ones.
The study also exploits the benefits of a methodology relatively
new to the study of political phenomena: discrete-time survival analysis. Originally developed for medical science, it was shown that it
can be successfully adapted to study the causes of duration in office
of mayors. An approach that can easily be extended to the duration
of legislators in office or the lifetime of parties.
Figure 7. Survival function mayors
4
CONCLUSION
This study successfully shows that the loyalty of a mayor in combination with its type of association with the party in government have
a significant effect in the duration in office. Namely, the mayors
with the lowest probability of leaving office are: those who defected the party after their first time in office and entered the period
without association with the two mayor parties in presidential elections. Moreover, this is a proportional effect that persists over time.
This confirms the existence of a perverse incentive in the electoral
system of Guatemala that discourages the formation of institutionalised party systems. However, though the effect is significant, in
practice, the magnitude of the effect is small: about 5% difference.
All mayors were predicted more than 50% chance of leaving office;
Throughout the study, especial attention was given to thinking
causally about the determinants of the rate at which mayors leave
office. The significance and magnitude of the effects were considered, and, when found, they were consistent with the theoretical
expectations. Also, evident risks of simultaneity were avoided by
only considering determinants that occurred strictly before a mayor
could leave office. Finally the sample was selected to avoid bias or
undetermined results. Therefore, this study supports the claim that
opportunistic strategic behaviour such as defecting the party has an
effect on reelection.
However, big limitations remain to support the claim. First, this
is not a randomised experiment, and the existence of confounders
cannot be ruled out. However, a critique needs to show that there
are unmeasured factors that indeed cause both opportunistic behaviour and staying in office. Second, it might be necessary to further
evaluate correlation between units. Although no initial evidence was
found of effects of grouping in municipalities of the same district,
other hierarchical patterns need to be evaluated such as linguistic
regions, party ideology, and entry before or after the 2004 electoral
reforms.
Finally, the findings of the study can only be regarded as provisional explanations. Some explanations at a deeper level can be
uncovered with further studies. For example, considering competing risks, that is, acknowledging that mayors have different reasons
7
Javier Brolo
for leaving office, could significantly improve the predictions, as
few mayors could be believed to ambition more than five consecutive periods in office. Alternatively, the direction of party switching
could be incorporated into a model. It may be systematically different to switch from a small party to a larger one, after succeeding
during the first period than the other way around. Also, mayors may
update their strategies the longer they stay in office. This study is just
a first step, as more information to perform more detailed studies
becomes available.
ACKNOWLEDGEMENT
This work was possible thanks to the award provided by Chevening
Scholarships, the UK government’s global scholarship programme,
funded by the Foreign and Commonwealth Office (FCO) and parter
organisations.
REFERENCES
Asamblea Nacional Constituyente (1985). Ley Electoral y de
Partidos Polı́ticos (Decreto Número 1-85).
Box-Steffensmeier, J. M. and Jones, B. S. (2004). Event History
Modeling: A Guide for Social Scientists. Cambridge University
Press, Cambridge, UK.
Brolo, J. A. (2012). Evolución del sistema de partidos polı́ticos
guatemaltecos de 1985 a 2012. In Partidos polı́ticos guatemaltecos: Dinámica interna y desempeño, pages 41–53. Asociación de
Investigación y Estudios Sociales (ASIES), Ciudad de Guatemala.
Broström, G. (2012). Event History Analysis with R. CRC Press,
Boca Raton.
Broström, G. (2014). eha: Event History Analysis. R package
version 2.4-0.
Bueno de Mesquita, B., Smith, A., Silverson, R. M., and Morrow,
J. D. (2003). The Logic of Political Survival. The MIT Press,
Cambridge, Massachusetts.
Burgos, A., Lemus de Urrutia, D., and Silva Baeza, B. E. (2011).
Comportamiento electoral municipal en Guatemala. Elecciones
generales 2007. Technical report, Fundación Centroamericana de Desarrollo (FUNCEDE), Fundación Soros de Guatemala,
Guatemala.
Eggers, A. C. and Spirling, A. (2014). The Advantages and Disadvantages of Incumbency: Theory and Evidence from British
Elections, 1832-2001.
Geddes, B. (1990). How the Cases You Choose Affect the Answers You Get : Selection Bias in Comparative Politics. Political
Analysis, 2(1):131–150.
King, G., Tomz, M., and Wittenberg, J. (2000). Making the Most of
Statistical Analyses: Improving Interpretation and Presentation.
American Journal of Political Science, 44(2):347–355.
Laakso, M. and Taagepera, R. (1979). ”Effective” Number of
Parties. Comparative Political Studies, 12(1):3–27.
Pedersen, M. N. (1979). The Dynamics of European Party Systems:
Changing Patterns of Electoral Volatility. European Journal of
Political Research, 7(1):1–26.
Powell, E. N. and Tucker, J. a. (2013). Revisiting Electoral Volatility
in Post-Communist Countries: New Data, New Results and New
Approaches. British Journal of Political Science, 44(01):1–25.
8
R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna,
Austria.
Singer, J. D. and Willet, J. B. (1993). It’s about Time: Using
Discrete-Time Survival Analysis to Study Duration and the Timing of Events. Journal of Educational Statistics, 18(2):155–195.
Tribunal Supremo Electoral (1986). Memoria de las elecciones generales celebradas en los meses de noviembre y diciembre de 1985.
Technical report, Tribunal Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (1989). Memoria de elección de
corporaciones municipales 1988. Technical report, Tribunal
Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (1991). Memoria de las elecciones
1990/1991.
Technical report, Tribunal Supremo Electoral,
Guatemala.
Tribunal Supremo Electoral (1994). Elección de corporaciones municipales 1993: Memoria. Technical report, Tribunal Supremo
Electoral, Guatemala.
Tribunal Supremo Electoral (1996). Elecciones ’95: Memoria.
Technical report, Tribunal Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (1999).
Elecciones municipales
’98: Memoria. Technical report, Tribunal Supremo Electoral,
Guatemala.
Tribunal Supremo Electoral (2000). Elecciones 99. Technical
report, Tribunal Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (2004). Memoria elecciones generales
2003. Technical report, Tribunal Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (2008). Memoria elecciones generales
2007. Technical report, Tribunal Supremo Electoral, Guatemala.
Tribunal Supremo Electoral (2012). Memoria de elecciones generales y al Parlamento Centroamericano 2011. Technical report,
Tribunal Supremo Electoral, Guatemala.
Table 6. Predicted probabilities of leaving office for mayors in Guatemala with three or more consecutive periods who were elected for the period 2011-2015
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
∗
Mayor’s name
Municipality
District
Fredy Armando López Girón
René Vicente Osorio Bolaños
César Augusto Rodas álvarez
José Antonio Coro Garcı́a
Rubelio Recinos Corea
Rudi Eduardo Edelman Cop
Jorge Arturo Reyes Ceballos
Felipe Rojas Rodrı́guez
Carlos Francisco Pablo Félix
Francisco Pop Pop
Jorge Rolando Barrientos Pellecer
Joel Moscoso Garcı́a
Carlos Enrique Barrios Sacher
Humberto Santos Gómez Pérez
Edilma Elizabeth Navarijo de León
Juan José Mejı́a Rodrı́guez
Ramón Dı́az Gutiérrez
Álvaro Rolando Morales Sandoval
Edgar Arnoldo Medrano Menéndez∗
Marco Tulio Ramı́rez Estrada
Juan Francisco López Dı́az
Carlos Anibal Godoy Torres
Álvaro Enrique Arzú Irigoyen
Francisco Javier Vásquez Montepeque
Miguel Bernardo Chavaloc Tacam
Ernesto Calachij Riz
Carlos René Arrivillaga Jiménez
Vı́ctor Hugo Figueroa Pérez
Julio César Quiñónez Hernández
José Juventino Paredes Galindo
Miguel Antonio López Barahona
Carlos Enrique Calderón y Calderón∗
San Pedro Jocopilas
Santa Catarina Mita
Sanarate
Santa Catarina Pinula
Barberena
Zunilito
Cuyotenango
Casillas
San Rafael La Indepencia
Lanquı́n
Quetzaltenango
San Andrés Villa Seca
San Marcos
Tejutla
Ocós
Gualán
Jocotán
Quetzaltepeque
Chinautla
Los Amates
Rı́o Bravo
Yupiltepeque
Guatemala
La Gomera
Totonicapán
Zacualpa
Quesada
Uspantán
San Miguel Dueñas
Ciudad Vieja
Pastores
San José La Arada
Quiché
Jutiapa
El Progreso
Guatemala
Santa Rosa
Suchitepéquez
Suchitepéquez
Santa Rosa
Huehuetenango
Alta Verapaz
Quetzaltenango
Retalhuleu
San Marcos
San Marcos
San Marcos
Zacapa
Chiquimula
Chiquimula
Guatemala
Izabal
Suchitepéquez
Jutiapa
Guatemala
Escuintla
Totonicapán
Quiché
Jutiapa
Quiché
Sacatepéquez
Sacatepéquez
Sacatepéquez
Chiquimula
Note: these mayors have had at least two consecutive periods in office additional to the current streak recorded
Periods in
office
5
5
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Loyal
Yes
Yes
No
Yes
Yes
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Party
UNE-GANA
(independent)
UNE
PP
PP
UNE-GANA
PP
UNE-GANA
UNE
UNE-GANA
UNE-GANA
UNE-GANA
PP
UNE-GANA
UNE-GANA
UNE-GANA
UNE-GANA
UNE-GANA
PP
UNE-GANA
UNE-GANA
UNE-GANA
PU
PP
PP
UNE-GANA
UNE-GANA
UNE-GANA
UNE-GANA
PP
(independent)
UNE-GANA
Predicted
probabilities
0.63
0.63
0.57
0.60
0.60
0.61
0.62
0.63
0.63
0.63
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.61
0.61
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.63
0.64
0.65
0.67
Confidence interval
0.52
0.52
0.53
0.55
0.55
0.56
0.56
0.57
0.57
0.57
0.56
0.56
0.56
0.56
0.56
0.56
0.56
0.56
0.57
0.57
0.58
0.58
0.59
0.59
0.59
0.59
0.59
0.58
0.60
0.60
0.62
0.64
0.72
0.72
0.61
0.64
0.64
0.67
0.67
0.68
0.68
0.68
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.65
0.65
0.65
0.65
0.65
0.65
0.65
0.65
0.65
0.65
0.67
0.68
0.68
0.70