Political geography of violence: Municipal politics and homicide in Brazil

World Development 123 (2019) 104592

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Political geography of violence: Municipal politics and homicide in Brazil Matthew C. Ingram a,⇑, Marcelo Marchesini da Costa b a b

University at Albany, SUNY, Milne Hall 314-A, 135 Western Avenue, Albany, NY 12222, United States Insper Instituto de Ensino e Pesquisa, R. Quatá, 300 – Vila Olimpia, São Paulo, SP 04546-042, Brazil

a r t i c l e

i n f o

Article history: Accepted 17 June 2019 Available online 30 August 2019 Keywords: Homicide Politics Spatial analysis Latin America Brazil Municipal

a b s t r a c t Violence has harmful effects on individuals and society. This is especially true in Latin America, a region that stands out globally for its high homicide rate. Building on research on subnational politics, democratization, and an inter-disciplinary literature that seeks to understand sources of violence, we examine the effect of municipal politics on homicide rates in Brazil while controlling for conventional sociostructural accounts. Specifically, we test the effect of four key political variables – party identification of mayors, partisan alignment of mayors and governors, electoral competition, and voter participation – and examine the locally varying effect of these variables with geographically weighted regressions (GWR). Our emphasis on political explanations of criminal violence is a rare departure from dominant accounts of violent crime, suggesting comparisons with the literature on political violence, and the spatial approach allows an analysis of the territorially uneven effect of political variables. The results show the statistical significance, direction, and magnitude of key political factors vary substantially across Brazil’s 5562 municipalities, showcasing the uneven effect of predictors of violence across space, and generating new hypotheses regarding the conditional effect of key predictors. In the time period examined (2007– 2012), the largest left party in Brazil, Workers’ Party (PT), had a beneficial effect, reducing violence in large parts of Brazil, the center party that held most local governments (PMDB) had a harmful effect in certain areas of Brazil, and the largest center-right party (PSDB) had mixed effects – helpful in some parts of Brazil and harmful in others. These results help us understand key features of the relationship between Brazilian politics and public security across different parts of the country, illuminating the political geography of violence in the region’s largest country. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Violence has harmful and costly effects on individuals and society, calling out for greater attention from scholars and policymakers. The problem of violence and its costs is especially acute in Latin America, which stands out globally for its high and increasing homicide rate over the last several decades (UNODC, 2013). On this topic, the role of electoral and partisan politics in city government in shaping violent crime receives little attention; rather, dominant socio- structural accounts emphasize demographic, economic, and environmental predictors of crime. Building on literatures in subnational politics, democratization, spatial analysis, and interdisciplinary studies in social and behavioral science, we examined the effect of municipal politics on homicide rates while controlling for conventional socio-structural accounts. Specifically, we tested the effect on homicide rates of four key political variables – (1) party identification of mayors, (2) partisan ⇑ Corresponding author. E-mail address: [email protected] (M.C. Ingram). https://doi.org/10.1016/j.worlddev.2019.06.016 0305-750X/Ó 2019 Elsevier Ltd. All rights reserved.

alignment of mayors with their governors, (3) electoral competition, and (4) voter participation. Further, we examined the locally varying effect of these variables using geographically weighted regression (GWR). Looking ahead, the findings show that the statistical significance, direction, and magnitude of key political factors vary substantially across Brazil’s 5562 municipalities. In the time period examined (2007–2012), the main center-left party (Workers’ Party, PT) had a beneficial effect, reducing crime in large parts of Brazil while having no effect in other areas. At the same time, the center party that held most local governments (PMDB) had a harmful effect in certain regions of Brazil and had no effect in others, and the main center-right party (PSDB) had the most heterogeneous effects: reducing violence in some areas, increasing violence in others, and having no effect in others. These results help us understand key features of the relationship between politics and public security in different parts of Brazil, illuminating the political geography of violence in the region’s largest country. The combination of political explanations and spatial methods justify framing this as a study of the political geography of violence. However, the spatial analysis also speaks to increasing

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attention to spatial interdependence in the social sciences, especially in political science (e.g., Cho & Gimpel, 2012; Franzese & Hays, 2008; Gleditsch & Ward, 2000), economics (Haddad & Hewings, 2005, 2009), and to the increasing attention to spatial connectivity among units in subnational comparative research (Harbers & Ingram, 2017, 2019; Soifer, 2019). This study also emphasizes the geographically constrained or uneven effect of explanatory relationships across geographic units, a point that we highlight in the discussion of findings. To be clear, the spatial portion of the analysis is not just a quick methodological fix; rather, the spatial approach offers insights into the uneven effect and causal heterogeneity of key explanatory factors across spatial units, contributing a clearer empirical understanding of existing theories and generating new hypotheses about potential unobserved factors which may condition the effect of known explanatory factors. The paper proceeds as follows. First, we offer a brief description of the outcome of interest – change in homicide rates at the municipal level during the time span covering the 2009–2012 mayoral administrations. Second, we outline our theoretical framework and working hypotheses. Third, we discuss our data and methods. Fourth, we present our results, and the following section expands the discussion of these results. We close with a summary of key findings and implications for policy and future research. 2. Homicide in Brazil Fig. 1 reports the geographic distribution of the outcome of interest, change in homicide rate. The map shows the difference between the two-year average of homicide rates in 2011–2012 and the two-year average from 2007 to 2008, i.e., average (2011– 2012) minus average (2007–2008), at the municipal level across Brazil’s 5562 municipalities. Positive values (shown in red) identify

municipalities that saw an increase in lethal violence; negative values (shown in blue) identify municipalities that saw a decrease. The explanatory analysis focuses on the data visualized in Fig. 1, capturing the change in violence over the four-year term of mayors who were elected in 2008, entered office in January 2009, and left office in December 2012. Presumably, if local politics and government practices affect violence, this local effect should be higher in the last two years of the mayor’s term, given that it takes time to implement actions and show results. Taking the average of two years of data at the beginning of the time span and two years at the end of the time span helps smooth the data and reduce the influence of any unusual events or measurement error. All municipal elections follow the same calendar in Brazil, so this variable is a valid measure of the change in homicide rates across all municipalities and mayoral administrations in our time span. As shown in Fig. 1, numerous municipalities became more violent in this time span, while many others became more peaceful. What explains this variation? 3. Theoretical framework and working hypotheses Our theoretical focus is on providing a political-geographic account of violence in Brazil while also accounting for conventional socio-structural explanations of lethal violence. Our key explanations of violence emphasize programmatic commitments, partisan alignments between mayor and governor, electoral competition, and participation. 3.1. Programmatic commitments All else being equal, we expect more programmatic parties, like the PT on the left and the PSDB on the right, to have a negative, beneficial effect on violence relative to centrist parties like the

Fig. 1. Change in Homicide Rate (between 2007 and 2008 and 2011–2012). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

M.C. Ingram, M. Marchesini da Costa / World Development 123 (2019) 104592

PMDB. We base this expectation on existing research that shows programmatic parties on both left and right of political spectrum seek to address crime and public safety issues, though for different reasons. For instance, rightist parties tend to emphasize policies about crime control and law and order, while leftist parties tend to emphasize policies that address sources of criminality, citizen insecurity, rights of the accused, and reintegration (Ingram, 2016; Mizrahi, 1999). All else being equal, we expect mayors from parties with more programmatic identities to have a helpful effect in reducing lethal violence. H1. Municipalities with programmatic parties (left or right) in the mayor’s office will have lower homicide rates relative to those with mayor’s that are centrist or non-programmatic.

3.2. Partisan alignment between mayor and governor Existing research on cooperation and contestation, government and opposition, and proregime vs. antiregime dynamics (Hoelscher, 2015; Ingram, 2013), especially in Brazil (Abrucio, 1998; Montero, 2004), suggests that partisan alignments between mayors and the governor of the same state mean that the mayor is more likely to be able to rely on a cooperative, co-partisan governor. Several benefits can flow from this alignment, including cooperation on policy coordination and supportive relationships regarding financial resources, personnel, training opportunities, and information sharing. This is particularly true for public security, given the state-level constitutional responsibility in this area. Thus, all else being equal, we anticipate mayor-governor alignment to have a negative, helpful relationship with violence. H2. Municipalities with mayors from the same party as the governor will experience reduced violence in comparison to those with mayors not affiliated with the party of the governor.

3.3. Electoral competition Existing research finds that electoral competition has a positive, helpful effect on government performance across a wide range of policy areas and institutions, and across different countries and subnational contexts. For instance, electoral competition improves legislative performance and institutionalization (Beer, 2003; Solt, 2004), electoral districting (Reynoso, 2005), fiscal policy and performance (Flamand, 2006), and educational spending (Hecock, 2006). Competition also translates into stronger human rights (Beer & Mitchell, 2004) and justice institutions (Beer, 2006; Finkel, 2008; Ginsburg, 2003; Ingram, 2012, 2013, 2016). Yet, regarding violence in Brazil, Hoelscher (2015, 35–37) finds a counterintuitive, positive, harmful relationship between competition and violence, though this finding is nuanced. Following most of the empirical findings, and all else being equal, we expect that municipalities that are more competitive electorally will experience reduced violence. We remain cognizant of findings to the contrary, but our main expectation is that competition will have a helpful, negative relationship with violence. H3. Municipalities with more political competition will have reduced violence in comparison to municipalities with less electoral competition.

3.4. Participation According to the social capital literature in political science (e.g., Putnam, Leonardi, & Nanetti, 1994) and also sociology and demog-

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raphy (Sampson, Raudenbush, & Earls, 1997; Yang, Shoff, & Noah, 2013), civic participation or engagement should exert a downward pressure on violence. Indeed, since civic engagement and political participation contrast with social disorganization (which has harmful effect; see below), any indicator of participation, engagement or social capital should be negatively associated with violence. All else being equal, we anticipate that participation, specifically electoral participation, should vary negatively with criminal violence. H4. Municipalities with higher electoral participation will experience less violence in comparison with municipalities with lower participation rates.

3.5. Alternative explanations: socio-structural accounts and Bolsa Familia It is challenging to distinguish between political and nonpolitical sources of violence, especially in Latin America (Moser & McIlwaine, 2006). Still, alternatives to our explicitly political explanations above include a range of specific propositions dominant in sociological and demographic studies of violence, and that can broadly be grouped under social disorganization theory. This account emphasizes the harmful effect of weak or absent community structure (Sampson & Groves, 1989; Sampson et al., 1997; Shaw & McKay, 1969). According to research in this tradition, explanatory factors associated with crime and violence include population density or movement (c.f., Bucheli, Fontenla, & Waddell, 2019, who find a helpful effect of return migration on reducing violence in Mexico), ethnic and linguistic heterogeneity, poverty, inequality, family instability, and can also extend to weak or absent institutions or organizations, including schools, neighborhood associations, etc. Also, education is widely regarded as having a protective effect against violence (Farrington, Gallagher, Morley, St Ledger, & West, 1986; Gottfredson, 1985; Ruhm, 2000, 624), especially against homicide (Ingram, 2014; Lochner & Moretti, 2004). Specifically in the Brazilian context, Melo, Andresen, and Matias (2017) examined social disorganization effects on crime in the city of Campinas. These authors found mixed evidence for and against expectations based on crime studies in the U.S. and other developed countries, which led them to call for a better understanding of urban spaces in Brazil and elsewhere (Melo et al., 2017). Our work aims to contribute to this better understanding. A major strand of social disorganization research argues that there is substantial correlation among the multiple structural conditions identified above, and that these can be reduced to three key bundles of causal factors: (1) population pressures, (2) resource deprivation, and (3) family disruption (Land, McCall, & Cohen, 1990; Parker, McCall, & Land, 1999). Still, even researchers in this line of research account for additional socio-structural factors, including inequality, age structure of population, and unemployment (e.g., Baller, Anselin, Messner, Deane, & Hawkins, 2001). We follow this line of research and expect that, all else being equal, violence will vary positively with population pressures, poverty, inequality, family disruption, and unemployment. Additionally, given that violence is associated with the age structure of a population and concentrated among young men, we anticipate that violence will vary positively with the size of the young male population. Lastly, some policies can exacerbate or ameliorate social disorganization. Bolsa Família (BF), a conditional cash transfer program, has the potential to soften or reverse disorganization. The cash transfers from BF hinge in part on children’s participation in school and family participation in health programs. Recent evidence from nonspatial research indicates that participation in BF decreases the incidence of homicide (Lance, 2014), and that BF increases a sense

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of belonging and efficacy (Hunter & Sugiyama, 2014), ‘‘countering social disorganization and resonating with the sociological literature on the violence-reduction effects of collective efficacy, social capital, and community resilience” (Ingram & Marchesini da Costa, 2017, 91; see also Sampson et al., 1997; Ingram, 2014). Georeferenced analyses on crimes in the city of São Paulo also found a beneficial effect of BF, especially in reducing the incidence of robberies (Chioda, De Mello, & Soares, 2016), while Ingram and Marchesini da Costa (2017) found a mixed effect of BF, though BF was more helpful than harmful. Thus, all else being equal, we expect that BF will vary negatively with violence. These expectations lead to the following additional hypotheses. H5. Municipalities with greater population pressures will experience increased violence in comparison to municipalities with lower population pressures.

H6. Municipalities with a larger proportion of young men will experience increased violence in comparison to municipalities with relatively fewer young men.

Table 1 Variable Descriptions. Name

Description

Source

HR Change

MS 2012

PT

Difference between the two-year average of homicide rates in 2011–2012 and the two-year average in 2007–2008 Margin of victory in mayoral election, calculated as difference between percentage of votes obtained by winner of election and percentage of votes obtained by second place Percent of voters that abstained from three consecutive elections as of 2010 Party alignment of mayor and governor (1 if party of mayor elected in 2008 was the same as party of governor; 0 otherwise) Mayors from PT

PSDB

Mayors from PSDB

PMDB

Mayors from PMDB

PopDensity

Total population divided by territorial area covered by municipality (logged) Percent of population consisting of males ages 15–29 (logged) GINI index (continuous variable; 0 for perfect income equality; 1 for totally concentrated income) Municipal Human Development Index (continuous variable; 0 low development; 1 high development) Percent of households headed by mothers with no education and child below 15 years-old Percent of residents age 18 or over who are employed (i.e., adult employment rate) Percent of poor families eligible for Bolsa Família who are actually covered by Bolsa Família (i.e., coverage rate)

Margin of Victory

Abstention Alignment

YoungMalePct GINI

H7. Municipalities with lower levels of general development will experience increased violence in comparison to municipalities with greater development.

HDI

SingleMotherHH

H8. Municipalities with more inequality will experience increased violence in comparison to municipalities with less inequality.

Employment BolsaFamília

H9. Municipalities with more unemployment will experience increased violence in comparison to municipalities with less unemployment. H10. Municipalities with more family disruption will experience increased violence in comparison to municipalities with less family disruption. H11. Municipalities with higher coverage of BF will experience reduced violence in comparison to municipalities with lower BF coverage.

4. Data and methods We clarified the outcome of interest – change in homicide rate – in the opening section, and Fig. 1 also visualized our measure. Data on homicide rates are from the Brazilian Ministry of Health. Turning to the explanatory variables, Table 1 provides a description of all variables and sources, and Table 2 provides summary statistics. Focusing on the political variables that constitute our key predictors of interest, we use dichotomous variables (0, 1) to capture the party of the mayor in office. In the time span examined, the PMDB held most mayorships in Brazil, while the center-right PSDB and center-left PT held the next largest proportion of offices. A dichotomous variable also captures partisan alignment between mayor and governor (1 = yes; 0 otherwise). Margin of victory is our main measure of political competition (for robustness sake, we also report partial results of models with effective number of candidates, ENC, in appendix; Laakso & Taagepera, 1979; there are no meaningful differences). To capture participation, especially the political form of participation that interests us, we use abstention rates, or the proportion of the eligible voting population that drops out of the political process. Notably, voting is mandatory

TSE 2008

TSE 2011 TSE 2008 TSE 2008 TSE 2008 TSE 2008 PNUD 2010 PNUD 2010 PNUD 2010 PNUD 2010 IBGE 2010 PNUD 2010 MDS 2010

in Brazil, so any abstention is an even more dramatic signal of political disengagement than in settings where voting is not mandatory. The map in Fig. 2 visualizes these key political predictors. All data for political variables come from the High Electoral Tribunal (Tribunal Superior Eleitoral, TSE). For population pressure, we use a measure of population density. This variable is the total population of a municipality divided by the geographic area covered by the city. The relative size of the population that is young and male is measured as the proportion of the total municipal population that is male and between the ages of 15 and 29. The municipal-level Human Development Index (HDI) captures aspects of income, education, and health, specifically, educational attainment, infant mortality, and income per capita, and provides a general measure of development. Adult employment rates capture local job market pressures. To measure family disruption we use the proportion of households that are headed by women who also have kids in the home, i.e., single-mother households. Lastly, the coverage of Bolsa Familia captures the per cent of eligible families who are actually covered by Bolsa Familia. Data come from official statistics (Instituto Brasileiro de Geografía e Estatística, IBGE), from the United Nations Development Program (PNUD in Portuguese), and from Brazil’s Ministry of Social Development. All data for explanatory variables are from 2010. Based on our interest in the uneven effect of predictors of violence and in the potential sources of this unevenness, we build a geographically weighted regression, or GWR (Brunsdon, Fotheringham, & Charlton, 1996; Fotheringham, Brunsdon, & Charlton, 2003). The GWR approach produces local coefficients for predictors, allowing ‘‘different relationships to exist at different points in space” (Brunsdon et al., 1996, 281), thereby facilitating the analysis of spatial heterogeneity (Shoff, Chen, & Yang, 2014, 558). To be clear, GWR enables the estimation of locally varying

M.C. Ingram, M. Marchesini da Costa / World Development 123 (2019) 104592 Table 2 Descriptive Statistics. Variable

N

Min

Max

Mean

S.D.

HR Change PopDensity YoungMalePct Gini HDI SingleMotherHH Employment BolsaFamilia Abstention Margin Alignment PMDB PSDB PT DEM PP PTB PR PDT PSB PPS PV PSC PC do B PHS PMN PRP PRTB PSDC PSL PT do B PTC PTN PRB Left Right Center

5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562 5562

84.47 2.03 2.54 0.28 0.41 4.01 21.18 1.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

96.98 9.47 0.99 0.80 0.86 29.11 95.60 553.45 9.29 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

1.79 3.21 2.02 0.49 0.65 14.40 63.34 99.15 0.77 0.20 0.21 0.21 0.14 0.10 0.08 0.09 0.07 0.06 0.06 0.05 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.44 0.21

14.44 1.42 0.10 0.06 0.07 3.24 9.08 22.60 0.54 0.24 0.41 0.41 0.34 0.30 0.28 0.29 0.26 0.25 0.24 0.22 0.14 0.11 0.10 0.08 0.04 0.05 0.04 0.04 0.03 0.05 0.03 0.04 0.05 0.09 0.42 0.49 0.41

coefficients for predictors of interest, and therefore the focus is on spatial heterogeneity, as opposed to the spatial homogeneity that is the core interest of the more common spatial error and spatial lag specifications (Anselin, 1988). Turning away from our substantive and theoretical interest in heterogeneous effects and towards more methodological concerns, spatial autocorrelation may be produced by diffusion or attributional processes – as examined by standard spatial models (e.g., spatial lag and spatial error models). However, Darmofal (2015, 119) advises that a separate source may be behavioral heterogeneity. Indeed, if autocorrelation is present, spatial analysts should always test for heterogeneous effects and, if present, model this heterogeneity because unmodeled heterogeneity is a ‘‘serious form of model misspecification” (Darmofal, 2015, 138). We found spatial autocorrelation in the dependent variable (Moran’s I = 4.478, p < 0.001), and then tested for heterogeneous effects. There are several ways to test for this heterogeneity (e.g., spatial Chow tests, bootstrap methods). We rely on Monte Carlo simulations following Lu et al. (2019), and the results provide firm support for our GWR approach (see Results and Discussion). For these reasons, we do not estimate spatial lag, spatial error, or other conventional spatial models, but rather focus on examining spatial heterogeneity with GWR models. In matrix notation, GWR can be expressed succinctly as:

yi ¼ bi X i þ ei Here, yi is the outcome of interest at location i (identified by coordinates [u,v], where u is the x-coordinate at location i, and v is the y-coordinate at location i), Xi is the set of predictors at location i, ei is a random error term at location i, and bi is a vector of coefficients associated with the predictors in X for location i. Location i is captured by the latitude and longitude of the centroid

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of each municipality, and estimating b is based on a kernel conditioned by other observations in the data set and therefore changes for each location, yielding local coefficients. In other words, for each predictor in X, the model estimates a unit-specific coefficient based on the data from that unit and neighboring units within a specified region or bandwidth. In this way, the GWR model promises results that can help identify regions of the country where the effect of key predictors vary in one or more of three ways: (1) statistical significance, (3) direction, and (3) magnitude. Although any variation in the nature of the effect across space is theoretically interesting, we are especially interested in results that show either (a) the significance of an effect changes meaningfully across different parts of the country, e.g., where this significance is above conventional thresholds in one place but below that threshold in another place, or (b) the direction of an effect switches in different parts of the country, e.g., where a predictor has a positive effect in one place, but a negative effect in another. As Table 2 shows, there are numerous dichotomous variables in the data set, related to the several parties that elected at least one mayor in Brazil in 2008, and at least a dozen of these parties each contributes less than one percent of observations. This raises challenges for the estimation of local coefficients in the GWR analysis. For instance, if all party dummies were included in a model, there would likely be large areas of the country where most of these variables would be zero, generating problems associated with multicollinearity. In order to overcome this challenge, we specified GWR models with a reduced number of variables, testing the effect of three major parties (PMDB, PSDB, and PT) against all other parties. We return to this issue below. Three cautions are in order regarding the use of GWR. First, Wheeler and Tiefelsdorf (2005) caution against using GWR when the estimated coefficients are highly correlated. Overall, diagnostics show this is not a concern for us. Second, Páez, Farber, and Wheeler (2011) caution against using GWR with small sample sizes. They recommend samples larger than 1000, and caution against samples smaller than 160. Our sample of 5562 municipalities meets and exceeds their recommended size. Finally, units of different shapes and sizes can distort the analysis across different parts of Brazil (e.g., as moving window of GWR estimation shifts from analyzing small, compact municipalities close to one another in the southern part of the country to analyzing large, dispersed municipalities covering large distances in the Amazon basin). Adaptive bandwidths are preferred to fixed, distance-based bandwidths where the units vary substantially in size across different regions, so all GWR models use adaptive bandwidths based on the same number of neighbors for each unit that include about 20% (1345) of the Brazilian municipalities in each estimation. Data and replication files are available on Dataverse (https://doi.org/10. 7910/DVN/NX5QIU). Overall, we proceed in the following steps: (1) estimate four OLS models with different specifications for robustness checks; (2) test stationarity of coefficients in our four main models; (3) estimate GWR models to examine the non-stationarity detected in step 2; and (4) visualize results of GWR analysis in a series of maps. We then interpret the combination of findings from OLS and GWR models (for coefficients that are stationary, we rely on OLS results; for coefficients that are non-stationary, we rely on GWR results) and robustness checks. Lastly, we add a small case study to help understand a dominant pattern of non-stationarity, namely the locally-varying but consistently helpful effect of the PT. 5. Results and discussion Table 3 reports results from four OLS regressions. Model 1 includes dummies for the three major parties (base category is all other, non-major parties), Model 2 includes only the PMDB,

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Fig. 2. Geographic Distribution of Key Explanatory Variables. (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

Model 3 includes only the PSDB (base category is all other parties), and Model 4 includes only the PT (base category is all other parties). The PMDB (21.63%), PSDB (14.22%), and PT (10.01%) account for almost half (46%) of all mayors, justifying the analytic focus on these three parties. In addition, robustness checks with a model containing all parties (and excluding PMBD as base category) show no meaningful improvement (see appendix) compared with focusing on the three major parties. Turning to the results reported in Table 3, key findings from the OLS models include the statistically significant and positive, harmful effect of the PMDB, and the non-significant effect of the PSDB and the PT. Among the other political variables, only Abstention has a consistently significant effect, and this effect is positive/ harmful. That is, there is a robust, positive association between voters dropping out of the political process and an increase in lethal violence. Notably, neither Margin of Victory nor Alignment are statistically significant (Alignment is significant at 0.10 level in only one of four models). Regarding control variables, population density, the percentage of young males, and the percentage of single-mother households, all exert the expected positive, harmful effects. HDI exerts the expected negative, helpful effect. Conversely, inequality has an unexpected negative, helpful effect on violence (p < 0.10). Employment and Bolsa Familia do not have consistent, statistically significant effects. With these baseline OLS results in mind, Table 4 reports our Monte Carlo (MC) tests for non-stationarity, i.e., coefficient hetero-

geneity. In these tests, the null hypothesis is homogeneous effects. If we reject this null, we have reason to expect heterogeneous effects that are best modeled by GWR. All tests were conducted in R (R Core Team, 2018) using package GWmodel (Lu et al., 2019; see replication code for details). Evidence suggests that the predictors that have a uniform, stationary relationship with outcome of interest (p > 0.10 in all models) include margin of victory, alignment, GINI, HDI, and adult employment. For these variables, a single coefficient captures the nature of their relationship with homicide rates across all municipalities, so we focus on findings from OLS models to guide our interpretations. Based on the OLS results, H2, H3, and H9 are not supported because the effects are not significant, and H7 is supported. Regarding inequality, this variable has an unexpected negative, beneficial effect, which does not support H8. This result is similar to results reported elsewhere regarding an unexpected beneficial effect of inequality and marginalization on violence in Brazil (Ingram & Marchesini da Costa, 2017). Thus, there may be something about inequality that indicates a latent or underlying quality of a community. For instance, one possibility is that local political bosses (coroneis) may be very strong. In such cases of persistent coronelismo, wealth may be distributed unequally and there may be a greater incidence of marginalization, but benefits are still doled out along old patrimonial networks. In highly unequal communities, local bosses may be strong enough to maintain a certain level of public safety, thus dampening violence in ways unexpected by socio-structural accounts. It might also be the case that a

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M.C. Ingram, M. Marchesini da Costa / World Development 123 (2019) 104592 Table 3 OLS Models DV: change in homicide rate (HR Change).

Margin of Victory Alignment Abstention PMDB PSDB PT PopDensity YoungMalePct GINI HDI SingleMotherHH Employment BolsaFamilia Constant Observations R2 Adjusted R2

Model 1

Model 2

Model 3

Model 4

0.730 (0.807) 0.765 (0.511) 1.078*** (0.380) 1.241** (0.507) 0.082 (0.625) 0.751 (0.668) 0.317** (0.159) 7.825*** (1.996) 6.234* (3.423) 17.682*** (4.045) 0.287*** (0.068) 0.047 (0.030) 0.004 (0.010) 23.145*** (5.018) 5,562 0.020 0.018

0.702 (0.807) 0.812* (0.470) 1.077*** (0.380) 1.360*** (0.473)

0.707 (0.807) 0.509 (0.495) 1.051*** (0.380)

0.744 (0.808) 0.613 (0.466) 1.052*** (0.380)

0.406 (0.588)

0.316** (0.159) 7.801*** (1.996) 6.416* (3.417) 17.748*** (4.033) 0.285*** (0.068) 0.046 (0.030) 0.004 (0.010) 23.191*** (5.018) 5,562 0.020 0.018

0.309* (0.159) 7.689*** (1.997) 6.497* (3.422) 17.820*** (4.047) 0.280*** (0.068) 0.050* (0.030) 0.004 (0.010) 23.264*** (5.021) 5,562 0.019 0.017

1.042 (0.643) 0.314** (0.159) 7.723*** (1.997) 6.137* (3.423) 17.970*** (4.034) 0.283*** (0.068) 0.052* (0.030) 0.003 (0.010) 23.194*** (5.020) 5,562 0.019 0.017

Model 1

Model 2

Model 3

Model 4

0.204 0.793 0.665 0.101 0.014 0.089 0.164 0.000 0.073 0.430 0.198 0.004 0.528 0.024

0.209 0.794 0.896 0.103 0.015

0.350 0.756 0.520 0.105

0.343 0.782 0.346 0.107

Note: Standard errors in parentheses *p < 0.1; **p < 0.05;

***

p < 0.01.

Table 4 Monte Carlo Tests for Stationarity.

Constant Margin Alignment Abstention PMDB PSDB PT PopDensity YoungMalePct GINI HDI SingleMothersHH Employment BolsaFamília

0.045 0.000 0.084 0.419 0.153 0.001 0.530 0.011

0.000 0.135 0.415 0.220 0.000 0.536 0.015

0.075 0.000 0.176 0.464 0.162 0.001 0.502 0.008

Note: values reported are p-values. Number of simulations: 1000.

single crime organization has such a strong and uncontested presence that there is less violence, despite the presence of sociostructural features that would predict violence, than what is experienced elsewhere, where there is more than one dominant group and therefore more contestation among criminal groups. In any case, this result deserves further attention in future work. All remaining variables have an uneven, spatially varying effect, including abstention (p just above 0.10), PMDB (p < 0.05 in all models), PSDB (p < 0.10 in at least one model), PT (p < 0.10 in at least one model), population density (p < 0.05 in all models), proportion of young males (p < 0.10 in at least one model), singlemother households (p < 0.05 in all models), and Bolsa Família coverage (p < 0.05 in all models). For these variables, we focus on findings from GWR models. Tabular reports of GWR results do not lend themselves to intuitive interpretation, requiring a comparison between the central

tendency of the local coefficients and the OLS estimate, as well as a closer examination of which local coefficients are statistically significant. Thus, we do not present any tables of GWR results. Fortunately, maps are an effective and efficient vehicle for communicating the variation across local coefficients estimated by GWR analyses (Matthews & Yang, 2012; Shoff et al., 2014), especially across multiple models and robustness checks (Ingram & Marchesini da Costa, 2017), so the discussion of GWR findings focuses on the results visualized in the following maps. Figs. 3–7 report the maps with GWR results. Figs. 3–5 report the results of Models 2–4, which include a dummy for each major party in turn, where the model specification facilitated an analysis of the effect of that single party (PMDB, PSDB, and PT, respectively) against all other parties. Fig. 6 then reports the local coefficients for Abstention (which MC tests suggested was non-stationary close to 0.10 level). For economy of presentation, we report only the local

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coefficients from Model 1 in Fig. 6. Lastly, Fig. 7 reports the results for the socio-structural control variables that MC tests indicated were non-stationary. Fig. 3 shows the uneven effect of the PMDB. Specifically, the party has a non-significant effect on homicide in most of Brazil (areas in white). However, where the PMDB has a statistically significant effect, this effect is uniformly positive and harmful (areas shaded red). This harmful effect is concentrated across a wide swath of northeastern municipalities, though there is also a harmful effect in a small region between São Paulo and Paraná. Referring back to Fig. 2, we can see that the PMDB held numerous mayors’ offices in the northeast, suggesting that there is a meaningful association between the PMDB in this part of the country and increased violence that is not present in other parts of the country. The implication is that either (a) the PMDB is behaviorally different in this (red) part of Brazil than in other (white) parts of Brazil, or (b) there is a local, contextual factor present in the shaded areas (and absent elsewhere) that conditions the effect of the PMDB. Fig. 4 visualizes the heterogeneous effect of the PSDB. Specifically, we see that like the PMDB, the PSDB has a non-significant relationship with homicide across much of the country (white areas). However, where the effect is statistically significant, the direction of this effect switches (i.e., it is positive in certain areas and negative in others). In places shaded red the PSDB has a positive, harmful effect (e.g., in parts of Bahia and in large areas in the north, including Pará and Amapá). In places shaded blue it has a negative, helpful effect (e.g., in parts of São Paulo, Rio de Janeiro, and southern states). As was the case with the PMDB’s uneven effect, this heterogeneity suggests the PSDB is behaviorally different across these three regions (white, blue, red), or there are unobserved local factors in each of these areas that condition the effect of the PSDB to produce these results. Fig. 5 shows the heterogeneous effect of the PT. Again, like the PMDB and PSDB, the PT’s effect is not statistically significant across most of Brazil (white areas). Yet, where it does have a significant effect, this effect is uniformly negative, i.e., helpful (areas shaded blue). This beneficial, negative relationship with homicide appears in northeastern states and in a large part of Mato Grosso. As was the case with the PMDB and PSDB, the implication is that the PT in these blue places is either behaviorally different than the PT in

Fig. 4. Local coefficients of PSDB (GWR Model 3). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

Fig. 5. Local coefficients for PT (based on GWR Model 4). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

Fig. 3. Local coefficients for PMDB (GWR Model 2). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

other white places, or there is an unobserved factor in these locations that is conditioning the effect of the PT in important ways. Where results are significant, the GWR models indicate that the effect of the PMDB is uniformly positive (harmful), the effect of PT is uniformly negative (beneficial), and the effect of the PSDB is mixed – harmful in some place and helpful in others. In part, these results support H1, that programmatic parties governing municipalities have beneficial effects on local violence when compared to centrist or less programmatic parties. However, the unexpected harmful effects of the PSDB in northeastern Brazil cuts against our expectation. There are several potential explanations to this: PSDB in some parts of Brazil may be less programmatic than in others, their mayors may be less efficient in implementing their program, or multiple regional factors may be contributing to these different results.

M.C. Ingram, M. Marchesini da Costa / World Development 123 (2019) 104592

Fig. 6. Map of local coefficients for Abstention (GWR Model 1). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

A natural, logical next step from the GWR analysis is to ask why a predictor has an uneven effect across locations. Two explanations are likely: (1) behavioral heterogeneity (i.e., causal heterogeneity), and (2) the presence of an unobserved, omitted third variable that exerts a conditioning effect on predictor of interest. Regarding

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behavioral heterogeneity, it is likely that the major parties examined here behave differently across different parts of Brazil. For example, the PMDB in the north is not the same as the PMDB in the south, and similar statements could be made about the PSDB and PT. Regarding omitted conditioning variables, there may be an underlying interaction that is not examined and that is causing the effect of the PMDB and PT to vary across different values of the moderating variable. We return to these issues in sections on limitations and conclusions. Still, results from the models testing the three major Brazilian parties suggest that party identification of the mayor does influence violence, particularly in specific areas of the country. Turning to the only other political variable that exhibited nonstationarity – Abstention – only the local coefficients from Model 1 are reported here for economy of presentation. Fig. 6 shows that Abstention has a statistically significant and positive, harmful effect, associated with higher homicide rates in large parts of the northeast, southern Brazil, and a small area between São Paulo and Minas Gerais, supporting H4. Fig. 7 reports the heterogeneous effects of socio-structural control variables and offers some insights, as well. Population density exerts a statistically significant effect across much of the country (supporting H5), and this effect changes in direction and magnitude. A major focal area or hot spot of the positive, harmful effect of population density covers a larger section of the northern and northeastern parts of the country, where population density is notoriously lower than in the coast and southern parts of the country. That is, in much of the Amazon Basin and northeastern parts of the country, population density had a harmful effect that it does not have in some of the urban centers in southern Brazil. In some parts of south-central Brazil – in areas shaded blue in

Fig. 7. Local coefficients for non-political control variables (GWR Model 1). (For interpretation of the references to colours in this figure legend, the reader is referred to the web version of this paper.)

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western Sao Paulo and Minas Gerais, population density has a negative, helpful effect. Notably, population density also has a harmful effect in parts of the southernmost state of Rio Grande do Sul, but the peak harmful effect is center in northern part of country near northern Para and Amapa. The proportion of young males does not have a statistically significant effect in most of the country (white areas), but it exerts a statistically significant and positive, detrimental effect (supporting H6) in a large area around the central and central-western part of the country. Single-mother households do not have a statistically significant effect in most of the country, but where they do this effect is positive and detrimental effect, supporting H10. This effect is present in parts of southern Brazil ranging from Santa Catarina to Mato Grosso do Sul, in Tocantins, and in several northeastern states. The identification of these geographic locations can help policymakers tailor violence reduction policies to address this particular phenomenon, perhaps by investing more in family leave policies, child care, or after school programs, or at a minimum by approaching single-mother households to try to understand what specific challenges they are facing. Finally, despite not having a statistically significant effect in OLS models, BF coverage has a statistically significant effect in GWR models in two parts of the country, and the direction of the effect changes across these areas, both supporting and challenging H11. In São Paulo, Paraná, and Mato Grosso do Sul, BF coverage has the expected, negative/helpful effect on violence. However, in Rio de Janeiro, Espírito Santo and parts of Minas Gerais, BF coverage has an unexpected positive, harmful effect. Given its recent implementation relative to the time span examined here, our result may simply be a function of the program not having enough time to development and have an impact. This question could reexamine as more data become available.

5.1. A brief qualitative excursus To better understand our results regarding the PT’s effect, in 2017 we conducted brief, informal interviews with a former PT mayor and a former legal advisor of PT mayors and found the following. Many small municipalities in Brazil are highly dependent on funding from the federal government. Thus, during the PT years in the presidency, including our time span, federal funding strategies and policies likely exerted an influence on diminishing violence at municipal level. Specifically, our sources reported that among other initiatives from the federal government at that time, support for small farmers increased, more indigenous areas were formalized, and the PT promoted a national set of policies geared towards generating jobs and education for youth. The PT federal government likely favored directing funding and other support for these programmatic goals to municipalities governed by PT mayors. At the same time, PT mayors were likely more receptive and supportive to these federal funds and policies. More generally, both of these dynamics were likely taking place, complementing each other. Indeed, this can help explain why the presence of PT mayors (in comparison to mayors from other parties) had a helpful/negative effect on homicide rates, and at the same time helps explain why non-PT mayors had a harmful/positive effect on homicides in at least some parts of the country. Thus, while municipality-state alignment did not seem to matter (see earlier results), which lead us to reject H2, municipality-federal alignment may have exerted a meaningful effect. In other words, the meaningful hierarchical relationship typically associated with federalism mattered, but it was not the one between governors and mayors or governors and the federal government, but rather between the federal government and mayors.

6. Limitations One limitation of our study is that we do not include any data on policing or security capacity at either municipal or state level. These are important factors to consider in future work. Separately, there are other reasonable modeling strategies for examining our research question and data, and one of those is a multi-level model. Adding state-level data and a multi-level approach could provide insights about vertical, hierarchical dynamics that we did not examine here. Further, a potential limitation of the GWR approach we employed is that ultimately results point to need to identify causal pathways underlying observed heterogeneity. As we noted in results and discussion, two main explanations are possible: (1) behavioral heterogeneity and (2) unobserved conditioning variables. Future research involving qualitative work could examine individual cases to flesh out these explanations in a more finegrained fashion. Fieldwork would be necessary to answer questions about behavioral heterogeneity or omitted variables (see, e.g., Harbers and Ingram, 2017). For now, we leave additional data, modeling strategies, in-depth, qualitative fieldwork, and other opportunities for future research.

7. Conclusion This paper advances a political-geographic account of lethal violence in Brazil by offering a statistical analysis of the uneven, geographically-varying effect of political predictors of violence across Brazil’s 5562 municipalities while controlling for dominant socio-structural accounts. The combination of OLS and GWR models yields the following core findings. The three major parties (PMDB, PSDB, and PT) affect violence in different ways. The PMDB has a largely harmful effect, exacerbating violence, though this effect is geographically constrained to a large region in the northeastern part of the country; the PSDB has mixed effects, reducing violence in the South and enhancing violence in the North of the country; and the PT has a largely helpful effect, reducing violence, though this effect – like that of the PMDB – is constrained to two larger regions in the northeastern and central parts of the country, respectively. Abstention from voting (lack of participation) has an expected harmful effect on violence in large parts of the Northeast, the South, and a small area in the Southeast. Overall, these findings shape our understanding of the political geography of violence in Brazil. While analyses of the structural, socio-economic covariates of violence are more common, the results reported here show that political parties, politicians, and the political process also contribute to the incidence of violence. Indeed, a focus on political variables is appealing because, unlike slow-moving demographic and socio-economic phenomena linked to socio-structural accounts, a political account of violence yields clearer implications about organizational, institutional, or procedural changes that can be implemented in the short and medium term in order to reduce violence. For instance, the findings regarding the PMDB and PT suggest there is something that needs to change related to how the PMDB governs in northeastern Brazil, and conversely, that there is something beneficial that the PT is doing in two large regions of the country. Indeed, these findings motivate qualitative work in these regions and with these parties to examine what it is about specific local governments (e.g., policies, local relationships, interactions with other levels of government) that is operating to produce these results. That is, future in-depth research in the regions identified here could help clarify the mechanisms underpinning these findings. However, the analysis also cautions against excessive optimism regarding our ability to export positive results from one part of the

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country to another part. In fact, one of the key general findings that emerges from this research is that the effects of key political predictors, and even of conventional socio-structural predictors, can be uneven and vary substantially – in significance, magnitude, and direction – across different communities within the same country. While it may be possible for future research to identify unobserved, omitted variables that are conditioning included variables, it is also possible that some predictors are inherently different across regions, or that some predictors have an inherently different relationship relative to nearby units. For instance, it may be that the PMDB in central Brazil is simply a different kind of party, with different bases of power and different programmatic orientations, than the PMDB in southern or southeastern Brazil. In this regard, the geographically uneven nature of key relationships draws our attention to the importance of local or regional context. The combination of spatial methods with the analysis of political variables influencing violence at local level across a larger region opens numerous possibilities for research and policy analysis in different countries. Additional research could pursue some of the qualitative research suggested in the narrative to: (1) identify causal mechanisms underlying key relationships; (2) examine the implications of this study for understanding party loyalty (or lack thereof) and the perceived lack of a shared program among Brazilian parties; (3) examine implications for party alliances and coalitions across regions; and (4) seek to identify omitted or

unobserved variables conditioning the uneven effect of predictors. In sum, we identify spatial and political dimensions of homicide in Brazil, and suggest several lines of inquiry that would help clarify the political geography of violence. Declaration of Competing Interest None. Acknowledgments Matthew C. Ingram is an Associate Professor in the Department of Political Science in the Rockefeller College of Public Affairs and Policy, and a Research Associate at the Center for Social and Demographic Analysis (CSDA), University at Albany, State University of New York (SUNY). Marcelo Marchesini da Costa is an Assistant Professor at Insper – Institute for Teaching and Research in Sao Paulo, Brazil. Earlier versions of this paper were presented at the 2015 meeting of the Latin American Studies Association and the 2016 meeting of the Brazilian Studies Association, and we thank participants for their comments. Portions of this research were funded by the Rockefeller College Research Incentive Fund and the CSDA at the University at Albany. Please direct all correspondence to: [email protected]. Authors contributed equally to this paper and all remaining errors are our own.

Appendix A. Model with all parties. The four main models reported in the main narrative are included here alongside an additional model (Model 5) that contains all parties that held mayoral office in Brazil in the time span we examined. Excluded base category in Model 5 is PMDB, which is modal category. Core results from Models 1–4 remain, including the main party effects, counterintuitive effect of inequality (reduced violence), the helpful effect of overall human development (combination of education, income, and health). Dependent variable: DifHRElec

Margin Alignment Abstention PMDB PSDB DEM PP PTB PR PSB PDT

(1)

(2)

(3)

(4)

(5)

0.730 (0.807) 0.765 (0.511) 1.080*** (0.380) 1.240** (0.507) 0.082 (0.625)

0.702 (0.807) 0.812* (0.470) 1.080*** (0.380) 1.360*** (0.473)

0.707 (0.807) 0.509 (0.495) 1.050*** (0.380)

0.744 (0.808) 0.613 (0.466) 1.050*** (0.380)

0.719 (0.807) 0.717 (0.525) 1.040*** (0.380)

0.406 (0.588)

1.360** (0.670) 1.450* (0.782) 0.635 (0.746) 1.920** (0.836) 2.090** (0.860) 1.410 (0.923) 2.140** (0.871) (continued on next page)

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A. Model with all parties. (continued) Dependent variable: DifHRElec (1)

(2)

(3)

(4)

(5) 2.050 (1.340) 1.330 (2.290) 0.540 (3.990) 2.210 (2.260) 10.200*** (3.500) 2.510 (4.160) 1.140 (1.950) 9.000* (5.080) 4.520 (3.720) 8.170 (5.080) 5.590 (3.990) 3.310 (3.610) 0.100 (1.720) 3.290 (2.000) 2.020*** (0.736) 0.307* (0.160) 8.180*** (2.000) 5.980* (3.430) 17.000*** (4.060) 0.285*** (0.069) 0.048 (0.030) 0.004 (0.010) 24.600*** (5.060) 5562 0.027 0.021

PPS Pc_do_B PHS PMN PRP PRTB PSC PSDC PSL PT_do_B PTC PTN PV PRB PT PopDensity YoungMalePct GINI HDI SingleMotherHH Employment BolsaFamilia Constant Observations R2 Adjusted R2

Note: *p < 0.1; **p < 0.05;

***

0.751 (0.668) 0.317** (0.159) 7.830*** (2.000) 6.230* (3.420) 17.700*** (4.040) 0.287*** (0.068) 0.047 (0.030) 0.004 (0.010) 23.100*** (5.020)

0.316** (0.159) 7.800*** (2.000) 6.420* (3.420) 17.700*** (4.030) 0.285*** (0.068) 0.046 (0.030) 0.004 (0.010) 23.200*** (5.020)

0.309* (0.159) 7.690*** (2.000) 6.500* (3.420) 17.800*** (4.050) 0.280*** (0.068) 0.050* (0.030) 0.004 (0.010) 23.300*** (5.020)

1.040 (0.643) 0.314** (0.159) 7.720*** (2.000) 6.140* (3.420) 18.000*** (4.030) 0.283*** (0.068) 0.052* (0.030) 0.003 (0.010) 23.200*** (5.020)

5562 0.020 0.018

5562 0.020 0.018

5562 0.019 0.017

5562 0.019 0.017

p < 0.01

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B. Maps of GWR Results for Parties with ENC instead of Margin of Victory

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