Should I stay or should I go? The unintended effects of enfranchisement

Wilber Baires,∗ Micaela Sviatschi,† Juan Vargas∗

This version:‡ March 10, 2019

Abstract

We empirically examine the effect of bringing voting centers closer to citizens’ residences on political and economic outcomes in the context of a violent country. In order to do this, we exploit a big-scale Public Policy implemented in El Salvador: “Voto Residencial” (Residencial Voting, RV). Building an unique dataset and using both non-parametric and parametric event study models, we find that RV in fact increased electoral participation, as previous literature stated. However, when using gangs leaders place of birth in El Salvador before their deportation from the USA as a potentially exogenous sources of variation of gangs presence across Salvadoran regions, we find that RV had a negative or null effect on regions with violent contexts. We argue that RV led to the disenfranchisement of some groups of voters, reducing their willingness to participate in elections because of fear of experiencing some act of violence (such as death). Furthermore, we find a positive effect of RV on public good provision and a reduction on inequality on regions without gangs presence, but a null or negative effect on those with gangs presence. Finally, we show that not only matter the presence of gangs in municipalities, but their territorial concentration or competition for the domain of territories.

Keywords: Residential Voting, Political Parties, Gangs, Regional Government, Growth.

JEL Codes: D72, H76, N96, O18, R23.

∗Facultad de Econom´ıa,Universidad del Rosario, Bogot´a,Colombia. †Department of Economics and Public Affairs, Princeton University, NJ, USA. ‡This is a very preliminary draft. Please do not cite or distribute without permission of the authors.

1 1 Introduction

Crime has been a detrimental factor for many economies throughout the world (Dixit, 2004; Gambetta, 1993; Pinotti, 2015; Acemoglu et al., 2013). At present, it is a problem of the first order in several countries in Latin America. Particularly, the phenomenon of “maras” (organized gangs) in Central America has been a manifestation of organized crime that has attracted the attention of many academics, politicians and policymakers. The power of the gangs has generated many anomalies in the economic activity of these countries, as well as in many variables of human development (Sviatschi, 2017, Baires and Dinarte, 2018). However, research on the effects of crime and violence has not only focused on economic variables, but also on electoral variables in other countries. For example, the political impacts of: paramilitary groups in Colombia (Acemoglu et al, 2013), terrorist groups in Israel (Berrebi and Klor, 2008), drug cartels in Mexico (Bateson, 2012; and Trelles, 2012), mafias in Italy (Alesina et al, 2018; Daniele and Dipoppa, 2017; De Feo and De Luca, 2017), among many others. Mainly, these works have focused on effects in the electoral market: exploring both effects on their size (measured by votes), as well as their composition (measured by the parties’ votes share). On the other hand, part of this literature has also focused on analyzing the effect of criminal groups on post-electoral variables, such as the behavior of elected politicians. In general, evidence has been found that the power of criminal groups has influenced the targeting of economic benefits and indulgences to them (Barone and Narciso, 2015; Alesina et al, 2018; Acemoglu et al, 2013). The case of “maras” is not the exception. Their power is such that, in order to obtain economic and legal benefits (mainly from the central government), they have come to demand meetings with high-ranking public officials, and to cause national strikes in some Central American countries where their demands are not heard, such as in El Salvador in 2015 (Hern´andez,2015; BBC, 2015). These examples suggest that it is mandatory to take into account the violent context of the countries when making public policies, since the presence of organized criminal groups can influence the outcome of these (Baires and Dinarte, 2018). In this paper, we analyze the large-scale implementation of a public policy in El Salvador, and the role played by the violent context of the regions throughout this country on local electoral and economic results. The policy analyzed is the “Voto Residenical” (Residential Voting, RV), which consisted of bringing voting centers closer to the homes of the voters, mainly benefiting those residents of rural areas, who had to move a lot of kilometers to go to vote before the RV policy (UNDP, 2015). In order to estimate these effects, we face the challenges of endogeneity in our specifications. To solve this concern, we exploit two sources of exogenous variation in our estimates. First, the random temporary allocation of public policy implementation, and, second, the use of instrumental variables for our measure of violence (the presence of gangs, “maras”). Thus, we take advantage of our data setting (as in Colonnelli and Prem, 2017), and we proceed to estimate both nonparametric and parametric event study models. In the first model, we obtain results that help us, on the one hand, to verify that the assumption of previous parallel trends in our variables of interest is fulfilled. On the other hand, this also allows us to see the temporal dynamics of the effect of the RV policy after its implementation. Second, the parametric model allows us to analyze the statistical significance and magnitude of the estimates. Thus, building a unique database, from different governmental and non-governmental sources (such as the National Police Department, the Supreme Electoral Tribunal, Newspapers, NOOA, etc.), we proceed to

2 calculate our results. First, with the use of our nonparametric model, we found that the implementation of the residential vote had a positive effect on the aggregate electoral participation of the country, but this was fundamentally driven by those regions without the presence of the criminal gangs, obtaining a null effect in those regions with the presence of these gangs. This is because, mainly, although the residential vote approached the polling stations, it was not taken into account that they could be located in areas with rival gangs to the one where the individual’s home was located (areas that could be divided by a road, for example). So, this situation would discourage individuals from voting because of fear of experiencing some act of violence (such as death). In addition, with the nonparametric model, we find that the effect of this policy on turnout has a U- inverted shape over time. We argue strongly that this is due to certain policies 1. that the government carried out lately (2012-2013) in order to stop the violence, but that ended up giving more power to gangs, mainly, in terms of territorial expansion (Dudley, 2013; BBC, 2017). Thus, municipalities that before the implementation of RV had no presence of gangs, then probably became invaded by them, causing the same effects as in municipalities that previously had the presence of these. The results of the parametric model go beyond the electoral participation, and also analyzes the com- position in electoral percentage of the political parties, as well as the effect on economic variables 2, such as economic growth (measured by the light density at night, which is also a very strong predictor of the provision of public goods in developing countries (Elvidge et al., 1997; Min, 2008)), and the change in the territorial distribution of this variable (as a proxy of the Gini index to measure inequality). Our results indicate heterogeneous effects of the residential voting policy on these variables, depending on the presence of gang violence in the regions (which we measure with a dichotomous variable that takes the value of 1 in case of a murder perpetrated by gangs before the implementation of RV, and 0 otherwise). In the first place, we found that in those areas with the presence of gangs, electoral support for the main left-wing party of the country was negatively affected, but not for the right-wing one. We argue that this could be driven by the fact that RV had a greater reach in the rural areas, regions that due to their sociodemographic characteristics concentrate the largest proportion of the voter registry of the left- wing party. Therefore, if municipalities had presence of gangs, a lower turnout would be mostly led by the militants of the left-wing party. On the other hand, regarding the local economy of the municipalities, we find a positive effect on the economic growth (or provision of public goods) for the regions without the presence of gangs, but a negative or null effect in those with a violent context. The same is true for the variable that measures the change in the territorial distribution of economic activity (or provision of public goods). This result contrasts with what has been exposed in works such as Di Cataldo and Mastrorocco (2018) or Daniele and Dipoppa (2017), in the way that local governments of unipalities with the presence of gangs, instead of giving economic favors or provide public goods to them, tended to move away in order to avoid interacting with criminal groups. Furthermore, it could be expected that, as a strategic response from politicians, if these violent groups

1Discovered later and declared illegal. This illegal policy was a negotiation between the central government and the main gangs? leaders , and was called “La tregua” 2Although in the nonparametric model the assumption of previous parallel tendencies is fulfilled for these variables, the dynamic effect after the implementation of the policy is not clear, which could be due to the fact that it is more difficult to capture the endogeneity of our violence measure with others variables

3 are politically disfranchising certain geographical areas, politicians will present less accountability on these places, and to focus on areas with higher electoral weight (this goes in line with the result of an increase in the inequality of the variable light density at night). Finally, to add robustness to our estimations, we also use alternative measures to our proxy violence in the regions of El Salvador. Thus, for example, to capture not only gangs presence, but intensity or competition between gangs, we compute a concentration index based on two variables: the geographic territory controlled by the gangs within a municipality (measured in km2), and the total homicides committed by each gang present within a municipality. Our results are consistent with those previously obtained, with the difference that the coefficients of the new estimates indicate that in the presence of a high degree of competition for gang territory (concentration index close to zero), the effects of RV on municipalities? electoral market and local economy with the presence of gangs was deepened. The reminder of the paper is organized as follows. Section 2 briefly describes the Residential Voting implementation characteristics and explains the gang?s structure and their features. Section 3 describes the data we use in this paper and describe our sample characteristics. Section 4 presents more in detail the two identification strategies we implement. In Section 5 we present our main results of our two econometric models, and section 6 presents additional results with other measures of violence. Finally, in section 7 we present a brief discussion of our main results and conclude.

2 Backgroud

2.1 Residential Voting

El Salvador has experienced many electoral reforms throughout its history, such as the implementation of the secret ballot in 1939, the right to vote for women in 19483, the creation of independent electoral authorities such as the Central Electoral Council in 1950 (abolished in 1992) and the Supreme Electoral Tribunal in 1992, among other major reforms (Puente, 2018). The last major change in the electoral system in El Salvador was the implementation of the residential vote, with the aim of reducing the transportation costs of exercising the vote of Salvadoran citizens. In this way, people in rural areas would no longer have to travel long distances to their polling stations in the urban centers of the municipalities, but instead would go to polling stations near their homes. The implementation of residential voting is one of the electoral reforms that had been postponed since the first elections in the country after the peace agreement in 1992. The initiative, which arose from a pact between the main political forces of El Salvador contenders in the 1994 presidential election, also included a reform to create a single citizen registry (DUI, for its acronym in Spanish) to facilitate the implementation of the residential voting (Fusades, 2012). This initiative was forgotten, and it was not resumed until 1998 (one year before the next presidential elections). Although the TSE developed a first pilot plan for the 1999 presidential elections, it was never carried out. This situation was repeated with the ”Plan de Acercamiento de Urnas (PAU)” in the year 2000, and with the Legislative Decree No. 293 in the year 2003. With the PAU was intended to increase from 384

3However, some time before in 1938 the Legislative Assembly issued a law recognizing that married women over the age of 25 had the right to vote, and for 30 years old single women with at least 6 years of schooling.

4 voting centers to 1,325, while with Legislative Decree No. 293 the establishment of the residential voting was explicitly ordered for the 2003 elections. Contrary to what was planned, in the 2000 midterm elections the number of voting centers decreased, and in 2003 the decree was repealed, arguing that the TSE did not have sufficient funds or technical capabilities to execute the project (Fusades, 2011). Finally, in 2006, the residential voting project was started, with a pilot plan that covered only 7 of the 262 municipalities4. The first municipalities with residential voting mainly corresponded to municipalities with small populations and territorial extensions, given the facilities of implementing a new logistics on a small scale (UNDP, 2018). With the success of the pilot plan in 2006, the plan was gradually implemented to larger municipalities. In the 2009 elections the plan was extended to 23 municipalities, and in 2012 to 185. The plan was completed in the presidential elections in 2014 and in the municipal ones of 2015, where the plan covered all the municipalities. In 2018 elections, El Salvador had a total of 1,595 polling stations (CV, by its acronym in Spanish) and 9,422 voting receiving boards (JRV, by its acronym in Spanish), with an electoral enrollment that amounted to 5,186,042 citizens (TSE, 2018). The electoral authority expects to increase the polling stations according to the background of each municipality. For example, one of the reasons why voting stations were increased or moved away was the context of violence of municipalities, where the presence of gangs has influenced Salvadorans in the decision to vote or not.

2.2 Salvadoran Gangs

El Salvador was the second most violent country in Central America during the 2009 to 2013 period. The average homicide rate was 69 deaths per 100,000 inhabitants (UNDP, 2013; IUDOP, 2015), almost three times the average homicide rate in Latin America. According to the WHO classification of violence as a health issue, El Salvador – and some other countries in the region – are categorized as being under endemic violence (WHO, 2012). Most of these homicides are officially attributed to local criminal organizations, which have been respon- sible for most crimes in most regions of the country during the last 20 years (Aguilar, 2007). Local gangs are rooted in the countries of Central America’s Northern Triangle.5 A typical gang member in El Salvador is a young male around 25 years old who decided to participate into the gang at approximately the age of 15. Most of them are born into low-income and broken families, live in vulnerable neighborhoods (Cruz et al., 2016), do not have either secondary education or formal employment, and earn less than $250 USD per month (International Crisis Group, 2017). Their motivation for enrolling gangs are a need to belong to a particular group due to a sense of exclusion or lack of opportunities. These criminal organizations emerged in El Salvador during the eighties. During that period, the country faced a civil war and economic crises that forced many El Salvadorans to emigrate to the U.S.6 Once living in the U.S., immigrant youths banded together to protect themselves, creating the 18th Street gang (Barrio

4Selected municipalities were Turin, Nuevo Cuscatlan, El Paisnal, San Juan Nonualco, Tecapan, Carolina and Meanguera Del Golfo 5The Northern Triangle is a geographic classification consisting of El Salvador, Guatemala and Honduras. 6The Central American immigrant population in the U.S. went from 354,000 in 1980 to 1.1 million in 1990. Most of them depended on low-wage job, and almost 21% lived below the poverty line. Many children and teenagers lived in disadvantaged urban neighborhoods, prominently in Los Angeles (International Crisis Group, 2017)

5 18) and the Mara Salvatrucha, which later was know as MS-13. In the mid-nineties, the U.S. increased deportations especially of gangs members. Unfortunately, during those years El Salvador was just emerging from its civil war, so the deportees faced limited school access and social services and no opportunities for reintegration into the formal sector, which made them look for illicit ways to obtain resources (Wolf, 2014). Gangs have evolved into violent and complex criminal organizations. Currently the two largest and most violent groups are MS-13 and the two factions of 18th Street gang: 18-Sure˜nos and 18-revolucionarios. They are better organized, have access to heavier weapons, and are more attractive to recruits than the many smaller pre-existing street gangs, or pandillas (International Crisis Group, 2017).7 Gangs’ emblematic crimes are micro-territorial (Aguilar, 2007). They extort businesses and individuals located in their territories as a source of revenue and impose deadly threats to reaffirm their control over those specific enclaves. There are two main characteristics of the gang structure that allow them to execute territorial control: size and recruiting process. First, estimations using official data show about 60,000 active gang members operating in El Salvador, and approximately 500,000 El Salvadorans – 8% of El Salvador’s 6.2 million population – linked with these members as social support base (Wolf, 2014; Crisis Group, 2017).8 They are also highly geographically dispersed. In 2008, each group had from 15 to 100 members, with an average of around 25 members. Second, gangs use violence to recruit – voluntary or not – teenagers and youths as part of their workforce. They usually recruit men between 12 to 25 years old (Cruz, 2009; Santacruz Giralt et al., 2001), who con- stitute the labor force of the gang, and oriented to perform different functions from extortions to homicides. Women are often recruited when they are between 16 to 25 years old (IUDOP, 2010), but their main role in the gang is to be the “wife” of one of the gang members. Occasionally they could also work as labor force within the gang (IUDOP, 2010). Unlike gangs in Guatemala, El Salvadoran gangs are more associated with drug use than with drug trafficking according to existing qualitative evidence. Specifically, narco-traffickers employ them sporadically as muscle in some operations (Farah, 2011; Cruz et al., 2016).

3 Data

For the main analysis of the residential voting effects on electoral and economic outcomes, we construct an unique panel dataset of aggregated variables for the 262 municipalities in El Salvador. We collect and merge data for the 2000 to 2018 period from different sources. The starting point for collecting data for this work lies at the base of electoral statistics of the Supreme Electoral Tribunal (TSE) of El Salvador, hosted on the website www.tse.gob.sv. The data is presented at both national and municipal level, and it has the timing of residential voting implementation, turnout, null votes, valid votes, and the number of votes obtained by each political party in each of the 262 municipalities since 1994. 7Although gangs may have been becoming more organized, there is no evidence that these groups are as specialized in their operations as are other transnational cartels. 8The social support base includes both active collaborators and ordinary citizens indirectly related to these groups, but who do not necessarily support them.

6 Second, our criminal data section come from the National Police of El Salvador (PNC, Polic´ıaNacional Civil). We have data for the period 2002 to 2010. Using this information we define whether a municipality has gang presence as the municipalities that experienced at least one homicide committed by gangs in 2002. Data on gangs? leaders comes from Sviatschi (2017), and it was collected from a special investigation done by one of the main newspapers in El Salvador, El Faro, which provided the names of the main gang leaders. Most of these gangs leaders grew up in the US but were born in El Salvador. Finally, we needed a measure of economic performance such as GDP. However, the first challenge was that there is no disaggregated GDP data at the municipal level. To overcome this, we follow Michalopoulos and Papaionnou (2014), Baires (2017) and other recent works to collect high resolution satellite data of night light density as a proxy for municipal economic activity. This data comes from the images reported by the Defense Meteorological Satellite Program’s Operational Line-scan System (DMSP-OLS)9. Then, using the ArcGIS software, we geo-reference the average pixels within the boundaries of the municipalities in El Salvador. We also use this data to measure inequality. Table 1 shows the descriptive statistics of some relevant variables to be considered in this work, such as income (measured by the density of light at night), inequality (using the Gini of the previous one), territorial extension (area in square kilometers) and homicide rate (homicides per 10 thousand inhabitants). Columns 3, 6 and 9 show the differences between the samples of the treated municipalities (T) versus the non-treated municipalities (C), for each of the waves of the Residential Voting interventions. It can be seen that the differences between the samples are not systematic in time for each of the variables, except for the territorial extension variable. This goes in line with the fact that, according to the UNDP (2018), the variable that had the greatest incidence in selecting a municipality was the fact that it was small in terms of extension, in order to facilitate the implementation of the program.

[INSERT TABLE 1 HERE]

4 Empirical Strategy

We estimate both non-parametric and parametric event study models. On the first place, we estimate a fully dynamic specification that allows us to capture the dynamics of electoral outcomes (like turnout) relative to the year of implementation of Residential Voting.

4.1 Non-parametric even study specification

The basic non-parametric even study specification is the following:

k=−2 k=3 X X Ym,t = φt + λm + θk + θkγXm,t + m,t (1) k=−3 k=0

where m and t stand for municipality and year, respectively, and θk capture the relative event time indicators. That is, θk is an indicator variable taking value 1 if it is year k relative to the Residential

9This data is captured at night at a height of 830 km. The measure is a six bits digital number (0-63) calculated for each 30 second output pixel that is averaged with respect to the overlapping input pixels and with all of the valid nights in during the year (Baires, 2017)

7 Voting implementation year. We choose a window of 15 years around the event (5 electoral periods of 3 years). As is typical in event study frameworks, we make the normalization θ−1 = 0, so that all coefficients represent differences in outcomes relative to the year before the implementation. The specification includes municipality fixed effects (m) and year fixed effects (t), which absorb fixed differences across municipalities and across years. m,t are standard errors clustered at the level of the municipality (Colonnelli & Prem, 2017; Bertrand et al., 2004).

4.2 Parametric even study specification

The parametric specification allows us to analyze the statistical significance and magnitude of the estimates. We use both OLS and IV empirical strategies.

4.2.1 Net Effect of Residential Voting (RV)

To estimate the impact of RV on turnout, we run the following OLS specification:

Ym,t = βRVm,t + φt + λm + γXm,t + m,t (2) where where m and t stand for municipality and year, respectively. Ym,t is the outcome of interest (turnout).

RVm,t is an indicator variable taking value 1 for all years after the implementation of RV in the municipality, and 0 otherwise. The λm are municipality fixed effect, and φt year fixed effects. Xm, t controls for time trends in baseline characteristics (that will be included in future estimations).

4.2.2 Heterogeneous Effects of RV

To estimate the impact of RV by Gang Presence on turnout, we run the following dynamic dif-in-dif speci- fication:

Ym,t = β(GangP resencem ∗ RVm,t) + φt + λm + γXm,t + m,t (3) where where m and t stand for municipality and year, respectively. Ym,t is the outcome of interest (turnout),

GangP resencem is a measure of gang activity for municipality m, which is defined by whether there was an homicide committed by gangs members in municipality m in 2002, the last year before the RV implementation

(Sviatschi, 2017). RVm,t is an indicator variable taking value 1 for all years after the implementation of RV in the municipality, and 0 otherwise. The λm are municipality fixed effect, and φt year fixed effects. Xm, t controls for time trends in baseline characteristics (that will be included in future estimations). We obtain our IV estimations using the previous OLS specification as our second stage, and we instrument

GangP resencem with a dummy indicating whether a main gang leader was born in that municipality. So, our first stage is defined as:

GangP resencem ∗ RVm,t = α(GangLeaderBornm ∗ RVm,t) + φt + λm + γXm,t + µm,t (4)

where definitions of all variables are the same, except for GangLeaderBornm, that is a dummy indicating whether a main gang leader was born in that municipality. We also estimate the impact of RV on other electoral and economic outcomes, like political parties participation (vote shares) of two main parties in El Salvador (ARENA - right wing- and FMLN -left wing-), economic growth and inequality. We use the same last specification.

8 5 Main Results

5.1 The Impact of RV on Electoral Participation

5.1.1 Non-parametric Estimations

When we run our specification 1, we can represent the results graphically. This will help us, on the first place, to verify that the assumption of parallel trends in our estimates is met. In fact, in Figure 1, when analyzing the effect of residential voting on electoral participation, it can be observed that in the periods before the implementation of RV, the coefficients are not statistically distinct from zero. This gives lights that there are no previous trends in this variable.

[INSERT FIGURE 1 HERE]

Secondly, it is possible to observe in figure 1 that, after the implementation of the RV, turnout increased in the municipalities. In addition, the effect seems to have an inverted u form, behavior that could be tentatively explained with the results obtained in figures 2 and 3.

[INSERT FIGURE 2 HERE]

In figure 2 we have the sample of municipalities with no gangs presence (measured as in Sviatschi (2017)), while in figure 3 we have the set of municipalities with gangs presence. The effect observed in figure 1 is practically pushed by what is observed in those municipalities without the presence of gangs, since according to figure 3, there is no effect of RV statistically different from zero in those municipalities with gangs presence.

[INSERT FIGURE 3 HERE]

Now, why is there no effect in those places with the presence of gangs? The explanation is given by the fact that in those municipalities where the residential vote was implemented, new voting centers were established (mainly in rural areas), although closer, if these were located in areas with a rival gang to the gang of the residence area of voters, people were virtually banned from voting (for fear of being killed). Therefore, this may explain the observed zero effect for municipalities with the presence of gangs. Then, in line with this argument, the inverted u shape of Figures 1 and 2 can be explained by the fact that in 2012 the gangs expanded throughout the territory, following a “Tregua” (truce) that the Salvadoran State mediated between the two main gangs in the country (Dudley, 2013 ). So, the probability of being killed because trespassing gang’s territories in electoral days rose. This “Tregua”, within many things, generated unintended effects. One of them was to increase the incentive to enter a gang of potential future members. Mainly, because the possibility of being killed would be lower as a result of the “Tregua”, so the cost of belonging to a gang decreased. Thus, municipalities that previously did not have the presence of gangs (in 2002 in our sample, to be more specific), it is likely to had some of them from 2012, leading results observed in Figures 1 and 2 to be more similar to those in Figure 3.

5.1.2 Parametric Estimations

We continue our analysis by obtaining our parametric estimates. First, using specification 2, we present our results for the aggregate effect of RV on turnout. As we can see in Table 2, the effect of RV on turnout is

9 between 1.1 and 2.4 percentage points (columns 1-3). This is, municipalities’ turnout increased between 1 and 2.4 percentage points after the implementation of RV.

[INSERT TABLE 2 HERE]

However, when we explore columns 4-6 of table 1, we can appreciate that this effect is mainly driven by that sample of municipalities without gangs presence. In fact, we have that the RV effect is negative or null in those places with gangs presence. These results go in line with the non-parametric estimations. Although we can not obtain a dynamic result based on this specification, we can conclude that the presence or absence of gangs is of the first order in the impact that the implementation of the VR may have on the electoral results of El Salvador. As before, in the presence of gangs, the effect vanishes. To give robustness to our results in table 2, we compute our results using a IV approach. In order to do this, we instrument the variable of gangs presence with a dummy indicating whether a main gang leader was born in a specific municipality. Our first stage results are presented in table 3.

[INSERT TABLE 3 HERE]

As we can see in Table 3, our first stage is strong, with high F statistics, and significant coefficients of my excluded instruments that remain similar throughout the different specifications (when including time and regional fixed effects). The magnitude of the coefficient indicates that our instrument explains to a very large extent the probability of gang presence in a municipality or not. Table 4 presents IV estimates using the dummy indicating whether a main gang leader was born in a specific municipality as my instruments for gangs presence in that municipality. As in the OLS estimations, I first present a parsimonious representation of the regression without including time neither regional fixed effects.

[INSERT TABLE 4 HERE]

The IV estimates are similar to the OLS estimates. As in the OLS estimates, IV estimates are higher when excluding regional fixed effects, and indicating a negative effect of RV on turnout for those places with gangs presence. However, this effects vanishes for regions with gangs presence when introducing regional effects, indicating that the negative effect observed was because regional differences rather than a effect of the RV implementation. The coefficients in the second row of column 4 imply that the implementation of RV in a municipality causes an increase in turnout of 2.4 percentage points. The coefficiente of the first row of the same column indicates that this effect is not statistically different from zero to those regions with gangs presence (when testing -0.023 vs 0.024 different from 0).

5.2 The Impact of RV on Economic and other Electoral Outcomes

In this section, we present the parametric estimations for other relevant economic and electoral outcomes, taking into account that when performing exercises similar to these in section 5.1.1, we obtain that the assumption of previous parallel trends is met (results under requirement).

10 [INSERT TABLE 5 HERE]

Thus, in table 5, making use of the empirical strategy of instrumental variables, we present the results of the effect of RV on economic variables such as economic growth (or provision of public goods, measured by the density of light at night), Gini of this variable (as a proxy for how the geographic distribution of economic growth or the provision of public goods has changed), and the change in the composition of votes obtained by the two main political parties (one on the left-wing and one on the right-wing). In row 2 of column 1, we can see that RV had a positive and statistically significant impact on economic growth in those municipalities without the presence of gangs. The coefficient indicates that, because of the implementation of RV, the municipalities increased their economic growth (or provision of public goods) by 7.4% (approximately 2.5% per year, given that the growth is computed based on the 3-year electoral periods) . However, the coefficient in row 1 of this same column indicates that this effect disappears (or becomes negative) in those places with a gang presence. A similar behavior is observed when exploring the effect in our Gini growth variable. In row 2 of column 2, we can appreciate that the distribution of the provision of public goods (or wealth measured by the density of light at night) improves in those regions without the presence of gangs, but that is exacerbated in those places with the presence of gangs. This result makes quite sense since those municipalities with the presence of gangs, have areas that becomes unenfranchised due to those people who stop voting due to fear of gangs. Thus, politicians, taking strategic actions, provide more public goods in those places where there are more potential voters. Finally, by looking at the last 2 columns, it can be inferred that RV had an effect on the composition of the votes of the left-wing party (FMLN), but had no effect on the right-wing party (ARENA). This makes sense because the median voter of the left party is characterized by having lower income and living in rural areas, where the residential vote was destined to have greater impact (PNUD, 2017). Thus, in those municipalities with the presence of gangs where RV was implemented, the votes obtained by the left-wing party are more likely to be negatively affected, since a large part of its members live in rural areas. This is not the case for the median voter of the right-wing party, which tends to live less in rural areas.

6 Additional Results

To give more robustness to our results, we proceed to estimate the effect of RV on our variables of interest making use of other gang presence proxies. Thus, in line with the argument that gang rivalry within the same municipality is what could lead to not voting in those places where RV was implemented, we proceed to calculate the intensity or dispersion of the presence of these gangs with a known concentration metric, the Herfindahl index (HHI). Then, we calculate the inverse of the HHI in two ways. First, taking into account the geographical territory controlled by the gangs (making use of a territorial map of gangs with restricted access), and, second, making use of the number of homicides perpetuated by each of the gangs within the same municipality as a proxy for its territorial domain. In both cases, in columns 1 and 3 of table 6, the coefficients by OLS are similar to those obtained previously using the previous gang presence variable (which simply took the value of 1 if there was some gang murder), however, for the case of IV estimates (columns 2 and 4), the coefficients

11 indicate that for those places with a high degree of dispersion or gang competition (or a low HHI), to such an extent that if the inverse of the HHI took the value of 1, the effect would be unequivocally negative (unlike the previous estimates by IV, where we had null effects if the dummy of gangs presence took the value of 1).

[INSERT TABLE 6 HERE]

Thus, these results give us some lights that does not only matter whether there are gangs or not in a place, but how scattered, numerous or competitive they are in the domain or territory in a specific region. In fact, if in a municipality there is only one gang (with an HHI = 1, or the inverse of the HHI = 0), it would not have to discourage people from voting, because wherever the closest voting center is located, they will always vote in the territory of the same gang. This exercise yields similar conclusions for the rest of the variables studied.

7 Concluding comments

The Economic Literature has a consensus that organized crime has negative effects on the countries’ economies. More recent literature has also found negative effects on the way democracy is exercised in these countries, and on the decisions made by politicians (Pinotti, 2015, Barone and Narciso, 2015, Alesina et al, 2018, Acemoglu et al. al, 2013). The case of El Salvador studied in this paper is in line with previous findings, in which the organized criminal groups called “maras” exert a great influence on the economic and political activities of the country. In this paper we find that the results of the large-scale implementation of a public policy in El Salvador were affected by its violent context. This policy, which consisted in bringing the voting centers closer to citizens’ residences, despite having increased turnout at the aggregate level, also decreased (or did not change) the electoral participation of the places with the presence of gangs. In addition, we found that the distribution of votes changed to only disadvantage the left-wing party, whose militants are presumed that RV had greater reach. On the other hand, when analyzing the effects on economic variables, we find that the RV positively affected those regions without the presence of gangs in terms of the provision of public goods (or economic growth, measured by light at night), but negatively those ones with the presence of criminal groups (in contrast to what might have been expected in the presence of other organized criminal groups, as in Di Cataldo and Mastrorocco (2018) and Daniele and Dipoppa (2017)). The same happened for the variable of Gini (that we used to measure territorial economic inequality), constructed from the same data of the density of light at night. We find that inequality decreased only for places without violence. These results are very important in terms of implementing public policies. As discussed in Baires and Dinarte (2018), it is necessary and of the first order to consider the violent context of the countries when implementing any public policy. In the case of countries with presence of criminal groups such as “maras” in El Salvador, where they strongly prevent the State from having a monopoly on violence, it is necessary that the central government take into account all the (undesired) collateral effects that could arise with any public policy, as was the case with the RV. Strengthening the security of polling stations only during elections will not solve the underlying problem.

12 References

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14 -.01 0 .01 .02 .03 .04 -3 -2 iue1 rs ffc fRV of Effect Gross 1: Figure -1 Years sinceRV 15 0 1 2 3 -.02 0 .02 .04 -3 iue2 VEeto ein ihu ag presence gangs without regions on Effect RV 2: Figure -2 -1 Years sinceRV 16 0 1 2 3 -.04 -.02 0 .02 .04 .06 -3 iue3 VEeto ein ih ag presence gangs witho regions on Effect RV 3: Figure -2 -1 Years sinceRV 17 0 1 2 3

Table 1: Descriptive Statistics First Implementation Second Implementation Third Implementation C T Diff C T Diff C T Diff (1) (2) (3) (4) (5) (6) (7) (8) (9) VARIABLES Light 1.59 2.1 -0.5 1.72 2.39 -0.68*** 2.06 1.58 0.48*** (0.05) (0.32) (0.34) (0.06) (0.12) (0.19) (0.1) (0.07) (0.12) Gini 0.34 0.23 0.11 0.35 0.18 0.18*** 0.43 0.42 0.01 (0.02) (0.04) (0.1) (0.02) (0.02) (0.05) (0.02) (0.01) (0.02) Area (km2) 80.01 51.49 28.52 82.42 46.08 36.34* 105.14 62.82 42.32*** (5.54) (18.87) (36.3) (5.78) (12.81) (19.48) (11.65) (4.44) (10.85) Homicides (/10k) 13.4 43.4 -30** 13.84 17.92 -4.08 26.21 6.55 19.66*** (1.95) (26.52) (12.47) (2.07) (8.61) (7.18) (4.49) (1.4) (3.99) Obs 255 7 262 239 23 262 102 160 262 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 2: The Impact of Residential Voting and Gangs on Turnout (OLS regressions) (1) (2) (3) (4) (5) (6) VARIABLES turnout turnout turnout turnout turnout turnout

VR 0.024*** 0.011*** 0.020*** 0.040*** 0.016*** 0.023*** (0.003) (0.003) (0.003) (0.005) (0.003) (0.004) VRxMara -0.107*** -0.026*** -0.019*** (0.017) (0.007) (0.006)

Observations 1,572 1,572 1,572 1,572 1,572 1,572 R-squared 0.012 0.012 0.582 0.071 0.021 0.587 Municipality FE NO YES YES NO YES YES Time FE NO NO YES NO NO YES Electoral Years 2003-2018 2003-2018 2003-2018 2003-2018 2003-2018 2003-2018 Number of mun. 262 262 262 262 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 3: The Impact of Residential Voting and Gangs on Turnout (First Stage) (1) (2) (3) (3) VARIABLES VRxMara VRxMara VRxMara VRxMara

VRxCabecilla 0.751*** 0.747*** 0.744*** 0.736*** (0.082) (0.08) (0.082) (0.081)

Observations 1,572 1,572 1,572 1,572 F statistic 83.741 86.95 81.838 83.74 Number of codmun 262 262 262 262 Municipality FE NO YES NO YES Time FE NO NO YES YES Electoral Years 2003-2018 2003-2018 2003-2018 2003-2018 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 4: The Impact of Residential Voting and Gangs on Turnout (IV Regressions) (1) (2) (3) (4) VARIABLES turnout turnout turnout turnout

VRxMara -0.192*** -0.035*** -0.181*** -0.023** (0.033) (0.011) (0.031) (0.010) VR 0.054*** 0.017*** 0.096*** 0.024*** (0.008) (0.003) (0.012) (0.004)

Observations 1,572 1,572 1,572 1,572 F-stat First Stage 83.741 86.95 81.838 83.74 Municipality FE NO YES NO YES Time FE NO NO YES YES Electoral Years 2003-2018 2003-2018 2003-2018 2003-2018 Number of codmun 262 262 262 262 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5: The Impact of Residential Voting on Other Outcomes (1) (2) (3) (4) VARIABLES Light growth GINI growth Votes Share FMLN Votes Share ARENA

VRxMara -0.085** 0.044** -0.077** 0.031 (0.039) (0.018) (0.035) (0.034) VR 0.074*** -0.031*** 0.019 -0.017 (0.020) (0.008) (0.012) (0.013)

Observations 1,305 1,305 1,568 1,572 F-stat 79.27 79.27 83.46 83.543 Estimation IV IV IV IV Number of codmun 261 261 262 262 Municipality FE YES YES YES YES Time FE YES YES YES YES Electoral Years 2003-2018 2003-2018 2003-2018 2003-2018 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 6: Alternative Measures of Gangs Presence: Competition for Territory (1) (2) (3) (4) VARIABLES turnout turnout turnout turnout

VR 0.024*** 0.028*** 0.021*** 0.023*** (0.004) (0.005) (0.004) (0.004) VRxInvHHImara_area -0.031** -0.078* (0.015) (0.043) VRxInvHHImara_presence -0.030** -0.058* (0.012) (0.030)

Observations 1,488 1,488 1,572 1,572 R-squared 0.588 0.584 F-stat 16.344 30.794 Estimation OLS IV OLS IV Number of codmun 248 248 262 262 Municipality FE YES YES YES YES Time FE YES YES YES YES Electoral Years 2003-2018 2003-2018 2003-2018 2003-2018 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1