The Political Boundaries of Ethnic Divisions Part 2

Null Average Effects. Estimation results for equation (1) can be found in Table 2, which shows that redistricting does not change the local incidence of conflict. Column 1 shows a null effect of splitting on the likelihood of conflict incidents at the original district level. The point estimate and standard error are very small relative to the pre-split mean of around 57 percent of original district–months with any incidents. Nor are the null effects explained by differential pre-trends or countervailing ups and downs in conflict after redistricting. This can be seen in the event study specification in Appendix Figure D.1.1(a). The average likelihood of conflict is relatively flat pre- and post-split. While informative about overall changes in violence, these original district level results may obscure the different implications of redistricting for child and parent districts. Nevertheless, estimating at this more granular level in column 2 leads to similarly null effects. Moreover, this is not due to differential, offsetting effects in parent or child districts as seen in columns 3 and 4. 5.4 Main Results: New Ethnic Divisions Amplify Conflict after Redistricting Next, we identify a much richer set of post-split conflict dynamics by explicitly considering how redistricting transforms ethnic divisions. At the original district level, we therefore augment equation (1): conf lictdt = ν + βpost-splitdt + φ (post-splitdt × ∆ diversityd ) + θt + θd + θd × t + εdt . (2) The φ coefficients identify whether areas that split into more homogeneous and less polarized units experience a differential reduction in conflict. As outlined in Section 3, if original district diversity shapes conflict pre-split while diversity within parent and child districts shapes conflict post-split, then postsplit changes in conflict incidence should be a function of ∆diversityd (φ > 0). Importantly, the fixed effects, θd , absorb the time-invariant effects of initial diversity on conflict. Unlike the simpler DiD in Figure 6, equation (2) accounts for common shocks across districts, as well as differences in district-specific conflict trends. Recall that the timing of redistricting is uncorrelated with ∆diversity, which also does not exhibit differential pre-trends in conflict (see Appendix Figure D.1.2). Moreover, we show that ∆diversity is not simply proxying for other initial district characteristics that amplify conflict after splitting. These robustness checks detailed in Section 5.5 further clarify that the identifying variation in ∆diversity is orthogonal to the timing of redistricting and driven largely by the policy-induced constraints on partitioning original districts. Baseline Results: Original District. Table 3 reports estimates of equation (2) at different administrative levels, normalizing the ∆diversity measures. Column 1 demonstrates the conflict-enhancing effects of creating more polarized districts. A one standard deviation increase in ∆P implies a 3.6 percentage point (p.p.) increase in social conflict that is significant at the 5 percent level. This is smaller than the simple difference-in-difference estimate (Figure 6) and implies a 6.3 percent increase in the likelihood of conflict relative to the mean before redistricting. Furthermore, while ∆ religious polarization also enters positively, it is less precisely estimated, perhaps due to the more limited range of districts with significant perpetrated for and against redistricting, such episodes are limited and do not explain the timing of splitting. Indeed, the leading conflict watchdog group in Indonesia highlights a case of violent pressure for splitting in a district of West Sulawesi, which is not in our sample, but notes that “In most cases, this fragmentation [redistricting] has taken part without violence and indeed without much opposition” (International Crisis Group, 2005). 16 religious divisions. Meanwhile, ∆F has a weaker effect close to zero, albeit less precisely estimated and hence not statistically different from ∆P (p-value=0.17). We address upfront two important concerns about the small effective sample size, leaving further identification and robustness checks to Sections 5.5 and 5.6. First, we remove outliers in post-split × ∆diversity following the residual-influence approach of Belsley et al. (2005). Second, we demonstrate that inference is generally robust to accounting for the small number of clusters (Cameron et al., 2008; MacKinnon and Webb, 2018a), “effective degrees of freedom” (Young, 2016), and spatial correlation in unobservables (Conley, 1999), among other concerns about overstating precision.30 We detail these important robustness checks in Appendix D.4 but demonstrate the main takeaway in Table 4. In short, in Panel B, the simultaneous removal of outliers in post-split × ∆P and robust inference adjustments— including a quasi-randomization inference procedure that permutes ∆diversity across districts—leads to the strongest evidence that border-induced increases in ethnic polarization exert a significant positive effect on social conflict. It is clear that outliers work against our core result in column 1 in terms of both precision and effect sizes.31 We consider several benchmarks for this baseline result to shed deeper light on the causal compo- nent of the diversity–conflict relationship that can be attributed to political borders. First, the estimated effects of ∆P and ∆F are smaller than the large positive correlations between conflict incidence and diversity across all districts as discussed in Section 3. In other words, the causal component due to political boundaries is slightly smaller for polarization and significantly smaller if not null for fractionalization. Second, the effect of ∆P on conflict is around one-half of the cross-country correlation between ethnic polarization levels (P )—defined similarly based on Ethnologue definitions of ethnolinguistic groups—and low-intensity civil conflict in Esteban et al. (2012).32 Third, the effect of border-induced ∆P in Indonesia is roughly one-quarter as large as the effects of migration-induced ∆P within the black population of South Africa (Amodio and Chiovelli, 2017).33 This large difference may be due to the fact that changes in local diversity due to migration capture additional conflict-relevant group divisions besides ethnic ones, e.g., between native “sons of the soil” and immigrants (Fearon and Laitin, 2011; Weiner, 1978). The strong effects of ∆P in column 1 of Table 3 highlight a potentially important unintended consequence of using redistricting to create homogeneous political entities. In diverse settings with many groups, while it is possible to reduce the number of groups and hence F , it may not be feasible to simultaneously ensure that the new boundaries do not engender new polarizing divisions. The two districts of Maluku Utara (MU) and Bolaang Mengondow (BW) illustrate the importance of ∆P . While both sig30 MacKinnon and Webb (2018a,b,c) suggest that randomization inference may work better than the widely-used wild cluster bootstrap of Cameron et al. (2008) given the imbalance in cluster sizes. We do not take a strong stand on which is the correct approach among the five considered in Table 4 but believe that the weight of the evidence is in favor of a statistically meaningful result based on the 52 original districts that comprise our core sample. For brevity, we stick to the baseline approach of clustering by original district in subsequent tables that go beyond the core baseline results. 31 It could of course have gone the other way, but the residual plots in Appendix Figure D.4.1 provide clear visual evidence for why the outsized influence of a few outlying observations works against us. We restrict attention to post-split × ∆P but find similar results when also removing outliers in post-split × ∆F . 32 This is based on re-estimating column 1 of Table 4 in their paper and comparing normalized effect sizes. We omit the Greenberg-Gini index of diversity to maintain a stricter comparison to our results, but the effect sizes are similar when including it. Their estimates are based on pooled OLS panel regressions at the five-year frequency, and the dependent variable equals one if the country experiences more than 25 conflict-related deaths with a mean of 0.28. By comparison, this same threshold and five-year frequency in our data implies a mean of 0.29. Interestingly, they too find weaker effects of ethnic fractionalization on conflict when using the full breadth of groups in Ethnologue. 33 This is based on rescaling their estimate in column 5 of Table 3 by the standard deviation of ∆P , 0.09, in Table A.2. 17 nificantly homogenized based on F (∆F M U = −0.125 and ∆F BW = −0.097), the new borders generated new ethnic divisions and a larger increase in polarization in MU (∆P M U = 0.005 and ∆P BW = 0.002). We observe a 36 p.p. increase in conflict in MU compared to a 7 p.p. decline in BW after redistricting. Baseline Results: Parent and Child Districts. To better understand the tradeoffs associated with re- districting, we estimate equation (2) at the smaller, child and parent district boundaries. Changes in violence within these smaller units should also be a function of ∆diversity, specified here as the difference in diversity between the given child or parent district and the original district. As with specification (2), the goal is to identify how changes in the salience of local diversity affect conflict. There are several advantages to running our specification at this lower geographic level. First, it leverages greater variation in ∆diversity. Second, it allows us to analyze violence around post-split elections, which occur at different times in the parent and child. Finally, it provides a natural way to investigate whether the effects of changing ethnic divisions vary with the scale of changes in local government. As documented in Appendix A, children experience larger increases in fiscal transfers and a reduction in distance to the capital. In the framework of Section 3, this arguably implies that the effects of ∆diversity should be amplified in child districts. Columns 2–4 of Table 3 estimate these relationships within the given child and parent boundaries. And, as before, Table 4 demonstrates robustness to outliers and small-sample inference. Together, the results reveal similar effects as those at the aggregate original district level, but also point to potential changes in the geography of violence. Pooling parent and child districts in column 2 yields effects that are statistically indistinguishable from those in column 1. Turning to columns 3 and 4, ∆P has similar effects when splitting the sample into parent and child districts, respectively. Our baseline hints at potentially larger effects in the child, although this difference is imprecise and not robust to outliers (see Panel B of Table 4). When turning to mechanisms in Section 6, we will see clearer evidence of differences between parent and child districts around local political cycles. Discussion. While recent models clarify how conflict responds to diversity in long-run equilibrium, our results identify how political boundaries can shock that equilibrium by changing the salience of different group cleavages in society. The estimates in Table 3 identify the magnitude of this shift over a 5–10 year period. That redistricting can alter the effects of diversity so quickly is perhaps surprising. In Section 6, we link the resulting conflict dynamics to the nature of politics and majoritarian elections. While these effects may yet die out over time, the new ethnic divisions may also deepen, bringing us closer to the long-run correlation between diversity and conflict (within stable boundaries). Our findings offer two policy-relevant, methodological innovations. First, prior work shows how the colonial partitioning of ethnic groups across international borders contributes to modern conflict (e.g., Michalopoulos and Papaioannou, 2016). Our strategy moves beyond static differences to show how changes in political boundaries reshape the ethnic divisions underlying conflict. While infeasible to study these dynamic changes in national boundaries, within-country boundary changes are pervasive and informative about general mechanisms linking diversity to conflict. Second, by isolating the contribution of political boundaries, we rule out a host of other confounding changes in local ethnic diversity often associated with migration flows, which are of independent interest in the study of conflict. Our focus on boundary-induced changes in ethnic divisions further contributes to an ongoing debate 18 over which type of diversity matters for conflict. To the extent that redistricting changes the incidence of local public goods, one expects greater heterogeneity in preferences among the governed population to lead to greater conflict. Moreover, if private resources are changing more slowly than public ones, one would expect changes in fractionalization to be less important than changes in polarization (Esteban and Ray, 2011a). Further probing the differences between polarization and fractionalization shows that border-induced changes in deeper ethnic divisions—as captured by linguistic distances (η) between groups—are driving the changes in violence around redistricting.34 5.5 Identification Checks: Isolating Policy-Induced Changes in Ethnic Divisions This section addresses three key threats to our interpretation thus far: (i) that the identifying variation in ∆diversity reflects endogenous boundary choices, (ii) that ∆diversity is a proxy for other features of Indonesian districts correlated with future conflict, and (iii) that changes in contestable public resources confound the effects of ∆diversity. Feasible Redistricting. Although initial diversity in 2000 is predetermined, the particular way in which the borders are drawn, and hence ∆diversity, may be endogenous. One concern is that districts that chose particularly unfavorable borders with high ∆P were the ones where future conflict would have risen anyway, say because of bad governance. We show here that the effects of ∆diversity on conflict are not explained by the particular way that districts chose to draw new borders but rather by institutional constraints on redistricting and ethnic geography that did not allow for more homogeneous new districts. To do so, we consider all the possible ways an original district could split along subdistrict lines into k new districts, given that regulations require each new district has at least three contiguous subdistricts. This provides us with a distribution of feasible partitions, and associated ∆diversity, for each district. Appendix D.3 provides full details on this NP-hard combinatorial problem. We use this set of feasible partitions to clarify that our identifying variation in ∆diversity comes from cross-district variation in the set of feasible partitions, as opposed to similar districts choosing very different ways of redrawing their borders. First, we re-estimate our baseline specifications from Table 3, replacing the realized changes in diversity with the mean, feasible ∆P , ∆F , and ∆Relig for each district. Table 5 shows that this produces very similar results to our baseline, providing initial evidence that strategic redistricting is unlikely to explain our core effects. To further clarify this point, we look not at the average but rather at random combinations of extreme feasible ∆diversity. Specifically, we re-estimate our baseline specification randomly assigning ∆P (and associated ∆F and ∆Relig) for each district to be either the maximum or minimum from the feasible set.35 We repeat this randomized procedure 50,000 times at the original district level and plot the resulting estimates in Appendix Figure D.3.2. All the estimated effects of min or max feasible ∆P are greater than zero and smoothly distributed around the baseline estimated effect size. This is consistent with the 34 Additionally, the key results for ∆F and ∆P hold up to inclusion of ∆G/N where G/N is the Greenberg-Gini diversity index scaled by population as in the structural equation of Esteban and Ray (2011a) tested by Esteban et al. (2012). 35 That is, on any given draw, one district would receive its minimum ∆P while another district would receive its maximum ∆P , thereby shuffling randomly across all districts. 19 fact that within-district variation in feasible splits is small relative to the between-district variation.36 Together, these results suggest that unobserved heterogeneity in boundary choice does not explain the effects of ∆diversity on conflict. This further highlights the importance of the next two exercises that rule out confounding interactions with post-split. More substantively, these results demonstrate the importance of designing redistricting schemes that account for constraints on strategic border formation. Accounting for Confounders. To rule out omitted factors correlated with ∆diversity and conflict, we follow a standard approach in heterogeneous DiD specifications and augment equation (2) with interactions of post-split and an array of potential confounders. Our approach is twofold. First, we separately consider groups of initial predetermined variables chosen based on intuition and prior work (see, e.g., Fearon and Laitin, 2003; Esteban et al., 2012). These include, among others: security presence; public goods access; remoteness, transportation infrastructure and access to markets; population size and age distributions; natural resource intensity; educational and occupational distributions; and topography, soil quality, and water access. Many are indeed highly correlated with diversity.37 As shown in Appendix Tables D.2.1–D.2.4, some of these factors also mediate the effects of redistricting on conflict. However, the key coefficients of interest on ∆P and ∆F remain mostly unchanged across these different specifications. There are of course hundreds of potential confounding variables that one could combine in various ways in this type of exercise. With limited degrees of freedom, this leaves the door open to cherry-picking (Gelman and Loken, 2014). Therefore, we adopt a second strategy that leverages machine learning to take a more disciplined approach to ruling out omitted variable bias. We utilize the Belloni et al. (2014) double-selection post-Lasso method to select a parsimonious set of influential controls from the large number of variables potentially confounding the relationship between ∆diversity and conflict. In practice, we expand upon the broad set of covariates noted above and use this approach to select additional interactions with post-split while penalizing the tendency towards overfitting through a penalty parameter λ.38 Acknowledging the limits of our natural policy experiment, we choose λ to ensure that the number of variables selected remain sufficiently smaller than the effective degrees of freedom. We set λ = 3, 000 as a baseline and consider alternative values in Appendix D.2. Panel A of Table 6 presents results based on this principled approach to variable selection. The main qualitative and quantitative findings remain unchanged. The point estimates on ∆P with this rich set of Lasso-selected controls are statistically indistinguishable from the baseline without controls except in column 3 where the estimates here are larger. Note that the fixed λ selects a different number of highly relevant controls across columns, which is due to variation in both the sample size as well as the relevant confounders of ∆diversity. Overall, the conclusions remain unchanged for other reasonable values of λ. Changes in Local Public Resources. While the confounding effects of other initial district characteris- tics is limited, it is also important to account for other factors associated with conflict that change with 36 Appendix Figure D.3.1 further illustrates this point via examples showing relatively little overlap in the distributions of feasible ∆P across original districts. Indeed, original district fixed effects explain 89 percent of the variation in feasible ∆P . 37 In a cross-section of 310 Indonesian districts, the full set of 65 covariates used in the Lasso procedure below explain 80 (51) percent of the variance in fractionalization (polarization). 38 Appendix F details the full set of 65 potential confounding variables, and Appendix D.2 details our application of this method, which is particularly effective at dealing with the problem of overfitting in a setting where one aims to learn about a particular causal effect of interest rather than simply develop a good prediction of the outcome. 20 redistricting. We consider here two significant changes motivated by the conceptual framework, namely, changes in local government transfer revenue, a proxy for the per-capita prize, and changes in the proximity to government institutions stemming from the new capitals in child districts, a proxy for conflict costs. Panel B of Table 6 augments the baseline specification in Table 3 with these two measures (detailed in Appendix A). We draw a few important takeaways from this exercise. First, the effects of ∆diversity remain similar, with changes in polarization significantly amplifying conflict. Second, both ∆transf ers and ∆distance generally enter as expected. Greater transfers and greater proximity to the district capital are associated with more conflict after redistricting, particularly in child districts where these changes are much more pronounced. These findings are suggestive, but we do not push the interpretation too far given concerns about endogeneity and limited degrees of freedom.39 Importantly, though, these results further establish that border-induced changes in ethnic divisions matter per se. 5.6 Additional Robustness Checks This section describes additional robustness checks fully detailed in the Appendix. Overall, these tests lend further support to a causal interpretation of the main findings. First, in Appendix D.5, we address the concern that SNPK coverage might be changing systematically with redistricting. The main concern is that newspapers differentially report on events in locations with greater changes in ethnic divisions, leading to overestimates of the actual effects. The comprehensive coverage of SNPK from many different outlets provides some reassurance. We provide further supportive evidence using auxiliary Google Trends data capturing the relative monthly frequency of search for the original, parent and child district names. While imperfect, this proxy reflects the frequency of general interest in the given location, some of which may be orthogonal to media reporting incentives. To the extent that Google trends are less prone to differential underreporting than the SNPK data, controlling for such trends should dampen the overall effects we estimate if reporting bias is substantial. Nevertheless, doing so leaves our key results unchanged. In the Appendix, we address other concerns about the baseline specification. Appendices C.3 and C.4 demonstrate robustness across alternative groupings of violence categories and show that our effects are driven by events with injuries and property damage but not deaths per se. Appendix D.6 details a battery of additional checks. First, we consider the number of conflict events rather than a binary indicator, leaving the key takeaways mostly unchanged, though introducing more noise. Second, we omit districts that enter the SNPK in 2005, which is important given that these later entrants were selected on account of policy concerns about recent violence. Third, we separately exclude the regions of Aceh and Maluku, which experienced intense conflict in the late 1990s and early 2000s at the onset of decentralization. Fourth, we consider an alternative identification strategy that includes as additional control areas those nearby districts that have not undergone redistricting. Finally, we offer evidence on the external validity of our findings across Indonesia by reweighting the sample of districts to account for the likelihood of redistricting following the standard Horvitz and Thompson (1952) approach. 39 Further exogeneity may be possible using transfer rules (Cassidy, 2017) and geographic constraints on capital locations (Campante et al., 2017). However, our goal here is to rule out confounders of ∆diversity, and the OLS results should assuage first-order concerns. 21 6 Mechanisms: Ethnic Reconfiguration and Political Cycles of Violence Our core results show that changes in ethnic divisions—arising purely from a reshuffling of political boundaries—can affect conflict within a matter of years. This suggests that the boundaries of political contests fundamentally shape the way that diverse groups interact and compete with one another. This section bolsters this interpretation by linking changes in violence to district politics and local elections. District governments play a large role in the local polity with mayors in control of vast public resources. Given the salience of ethnicity during mayoral elections, we investigate how border-induced changes in ethnic divisions affect violence around these political contests. We show that violence surges around new, closely contested mayoral elections, and that these political conflict cycles are amplified in high ∆diversity areas. Further, we provide evidence of ethnic favoritism in the allocation of public resources as a potential mechanism for generating grievances and amplifying incentives for ethnic mobilization around these majoritarian contests. We conclude by discussing a case of redistricting that nicely captures our broad empirical findings and clarifies the way that political boundaries shape ethnic divisions. 6.1 Diversity and Majoritarian Elections We begin by establishing a few stylized facts, detailed in Appendix E, that help to characterize the context of mayoral elections. First, ethnic diversity, and particularly polarization, is associated with closer mayoral elections (i.e., lower victory margins). This is especially the case for newly created child districts and for both the first and the second quinquennial mayoral election (see Table E.1.1). Second, closer mayoral elections are associated with greater violence during the period around the election (see Table E.2.1). Third, according to the Indonesian Family Life Survey (IFLS) from 2014, individuals in districts that experienced greater border-induced changes in ethnic polarization are significantly more likely to report mayoral ethnicity and patronage as important factors influencing their vote (see Table E.1.2).40 Consistent with the background in Section 2.3, these patterns suggest that new ethnic divisions may be particularly salient during mayoral elections, a hypothesis that we verify next. 6.2 New Ethnic Divisions and Electoral Violence The empirical evidence thus far suggests redistricting may create strong incentives for group mobilization and violence. In settings with weak institutions, such violence may help shape the degree of control that one’s group exerts over new institutions responsible for public goods or may help influence resource allocation after another group assumes control. Here, we provide empirical evidence of these mechanisms in the context of mayoral elections. In Table 7, we augment the parent/child specifications in Table 3 with indicators for direct mayoral election periods. Since these elections are specific to the new parent or child district, we focus on specifications at that administrative level. These quinquennial elections, which began in 2005, vary in their timing (i) in parent districts, due to predetermined path dependence from Suharto-era election schedules (see Martinez-Bravo et al., 2017; Skoufias et al., 2014), and (ii) in child districts, due to the timing 40 This is consistent with Tanasaldy (2012, p. 263) who notes that in several areas of West Kalimantan (in our study), “Due to ethnic polarization introduced in previous elections, masses from each ethnic group tended to rally for candidates from their own ethnic group.” 22 of redistricting (with elections typically occurring 1.5–2.5 years after the split).41 Following Harish and Toha (2017) and others cited therein, we define the election period as a six month window centered on the month of the election, but results are similar for other bandwidths. The coefficient on post-split×first (second) election identifies whether the incidence of conflict during the first (second) election deviates from the average incidence after redistricting. To see whether electoral violence cycles are more likely in newly polarized areas, we further interact post-redistricting election periods with ∆diversity. Looking across columns of Table 7, the likelihood of social conflict after redistricting is generally higher around the initial mayoral elections, particularly where redistricting sharply changed ethnic divisions. Column 1 shows this result pooling parent and child districts. Columns 2 and 3 show results separately for parent and child districts, pointing to important differences between pre-existing and new seats of government. Child districts with ∆P one standard deviation above the mean exhibit nearly 70 percent more violence around the first election (comparing 0.027 to 0.041). This result lines up with the fact that polarization is associated with closer elections, and violence is significantly more pronounced during those close elections. Moreover, the amplifying effect of ∆P on political violence persists and may even be larger during the second election period 5–8 years after redistricting. Meanwhile, in parent districts, initial elections are generally less violent. Border-induced changes in ethnic polarization seem less important than changes in fractionalization, which exerts a large albeit noisy positive amplification effect. The estimates may be consistent with the stronger correlation of F and Relig than P with narrow victory margins in parent districts (see Appendix Table E.1.1).42 Second election periods appear differentially less violent in high ∆P areas, which is unexpected and helps explain the null ∆P in that bottom section of column 1. The differential effects of changing ethnic divisions on electoral violence in parent and child districts admit several possible interpretations. The relatively stronger effects of changes in ethnic polarization in child districts might be explained by the greater stakes of winning initial elections to control a completely new local government as opposed to winning control of an existing one. Another explanation for these diverging results could be differential institutional capacity. Parent districts may not only run more effective and safer initial elections than child districts but also learn more quickly how to manage the changing ethnopolitical divisions. Overall, the results in Table 7 suggest that ethnic mobilization around mayoral elections are an important feature of conflict dynamics after redistricting. As in the baseline, these results are robust to controlling for confounding media attention using Google trends and to specifying the outcome as the intensive margin number of conflict events. Furthermore, we show in Table 8 that the amplifying effects of ∆diversity on conflict around mayoral elections are a distinctive feature of high stakes majoritarian contests for political control. First, in columns 1–3, we find that the effects of ∆diversity on conflict do not systematically differ during the quinquennial parliamentary (and presidential) elections taking place in April 2004, 2009, and 2014. 41 All direct elections in child districts follow splitting. Some original districts have a first direct election that precedes splitting, and hence we include a term for first direct elections in addition to the term post-split×first election. We observe a second quinquennial election for the three-quarters of new districts in existence long enough to hold a second round by 2014. 42 And like child districts, more closely contested elections are associated with greater violence in parents (along the intensive margin, see Appendix Table E.2.1). 23 Although violence is generally more likely during such periods, ∆diversity exerts no significant amplifying effects as it does during the (staggered) mayoral elections. This difference is consistent with the fact that proportional representation legislative elections impart very different group-based incentives than the majoritarian mayoral elections (see Esteban and Ray, 2008b).43 As a further validation test in columns 4–6, we show that there are no systematic differential effects of ∆diversity during the (lunar) holy month of Ramadan when conflict is generally lower but not differential with respect to ∆diversity. Across columns, ∆diversity exhibits small, insignificant heterogeneous effects that are statistically different from the amplifying effects of ∆diversity around mayoral elections. Finally, the changes in violence due to changing ethnic divisions are not merely a transitory phenomenon around elections. Across all results in Tables 7 and 8, the coefficient on post-split × ∆P is statistically indistinguishable from that in Table 3, meaning that the post-split differences in the incidence of social conflict extend beyond election periods. This is what one would expect if grievances among losing group continued to manifest in violent acts protesting governance- and resource distribution-related issues in the future.44 Next, we provide evidence for one potential reason for such grievances, namely ethnic favoritism in the allocation of public resources. 6.3 Ethnic Favoritism Given hotly contested mayoral elections and their interplay with ethnic divisions, one would expect political favoritism towards co-ethnics. Recent studies document favoritism in resource allocation towards newly elected leaders’ ethnic homelands in sub-Saharan Africa (see, e.g., Burgess et al., 2015; Hodler and Raschky, 2014). While a full accounting of this phenomenon in Indonesia is beyond the scope of this study, we present here evidence consistent with ethnic favoritism as a potential factor contributing to the patterns of discontent underlying the link between changes in ethnic divisions and conflict. Following prior literature, we use nighttime light intensity as a proxy for local economic development and targeted public resources.45 Importantly, electricity cannot be provided solely by villages and often requires support from higher levels of government. Mayors have been responsible for setting electricity policy within their districts since 2005 (Jayawardena, 2005). Moreover, mayors have significant discretionary power in their choice of how to allocate resources across villages (Aspinall and Asad, 2015; Pal and Wahhaj, 2016). Altogether, this suggests scope for rivalry across villages in access to this public resource, and indeed, publicly-provided electricity is a factor underlying some of the violent incidents reported in the SNPK data.46 43 Fjelde and Höglund (2016) provide supportive evidence across African countries with different electoral systems. More fully disaggregating the timing around elections is instructive. Although somewhat imprecise, we can show that during the first election after redistricting, the amplifying effects of ∆P are stronger in the few months just after the actual election with weaker effects just before and during the election. This would be consistent with more limited use of group-based violence leading up to the election—perhaps due to lack of clarity about the newly salient and relevant group divisions— but an uptick associated with grievances thereafter. However, by the time of the second election, the amplifying effects of ∆P show up before, during, and immediately after the election, consistent with the various electoral violence typologies highlighted in Harish and Toha (2017) and discussed at length in Appendix C.2. 45 Olivia and Gibson (2015) validate a strong correlation of light intensity with district-level output and expenditure data. In the predominantly rural areas of our study, nighttime lights tend to disproportionately capture public street lights. Moreover, electricity provision in Indonesia is almost exclusively concentrated in a single public utility company, which is often subject to the same sort of political manipulation identified in South Asia (see Baskaran et al., 2015; Min, 2015). 46 For example, in Aceh Selatan district on 17 August 2007, “Hundreds of residents of Meukek subdistrict damaged PLN (Public Electricity Company) office and head of Subranting PLN’s house by throwing stones. Citizens were induced to action by 44 We investigate ethnic favoritism by exploiting the fact that redistricting often changes either the identity or strength of the dominant group in the district. We examine changes in resources flowing to village v after redistricting as a function of that village’s initial ethnic composition. Specifically, we ask whether the new boundaries imply that the village’s initial population (N ) share of ethnics from the new district’s largest group is (i) larger, (ii) smaller, or (iii) the same as the village’s initial share of ethnics from the original district’s largest ethnic group. Formally, we compare NeO v Nv to Nei v Nv where eO is the largest ethnic group in original district O, and ei is the largest group in child or parent district i.47 For example, appealing to Figure 4, those Gayo majority villages in the new parent district will fall under (ii) since the Gayo are no longer the largest group. Meanwhile, the Gayo majority villages in the new child district of Gayo Lues will fall under (iii) as the largest group has not changed with redistricting. Note that, like most villages in category (iii), the size of their majority in Gayo Lues has increased, consistent with the general reduction in fractionalization that comes with redistricting. In Table 9, we show how nighttime light intensity evolves across these three different types of villages after redistricting. We consider interactions of post-split with indicators for the change in alignment status, conditional on year and village fixed effects. Villages that lose their alignment with the largest ethnic group (ii) exhibit differentially lower light intensity after redistricting compared to those that either remain (iii) or become newly (i) aligned with the largest group. The results are consistent at both the parent and child level albeit slightly larger and more precisely estimated for the latter. Column 1 suggests that villages that become newly (remain) aligned with the largest group have 1.1 p.p. (2.8 p.p.) more village area with light coverage post-redistricting relative to those that lose their alignment. These are large differences relative to the mean of 16.3 percent of village area covered with any lights. These results are suggestive of ethnic favoritism, which may be one vehicle for generating the sort of grievances that lead to persistently higher violence in areas where new boundaries create fresh ethnic divisions, even outside of election periods. We turn now to a case study highlighting some of the particular mechanisms underlying our empirical results. 6.4 Case Study We discuss here an illustrative case study in West Kalimantan. This region has a significant history of ethnic strife including Dayak violence against the Chinese, and repeated clashes between Dayak and Madurese as well as Malay and Madurese. As in the rest of Indonesia, these large-scale open conflicts largely subsided in the early 2000s, replaced by more sporadic violence that remains a serious concern to policymakers (see Barron et al., 2016). We briefly present this interesting case below. We focus on the original district of Sambas, which split into three separate districts: Sambas, Bengkayang (in 1999), and Kota Singkawang (from Bengkayang in 2001). Before splitting, Sambas was comprised of 52 percent Malay ethnics, 15 percent Dayak, and 15 percent Chinese, with other smaller groups including Javanese and Madurese. Redistricting significantly altered these group shares. irritation due to irregular schedules of power outages. Other examples are provided in Appendix C.1. More recent examples, not (yet) in SNPK, provide further insight into the politicization of electricity provision with demonstrations against both PLN and the mayor (KabarNias, 2015) or being led by village heads against PLN (ProKal, 2016). 47 Note that if the largest group does not change with splitting, the village will fall under category (iii). Nearly 70 percent of these villages are in districts where the share of the largest group increased. There are 1,764 villages in 32 districts in category (i), 558 villages in 28 districts in category (ii), and 12,182 villages in 119 districts in category (iii). After the fall of Suharto, demands for decentralization and local empowerment spread across Indonesia, and West Kalimantan was no exception. As Tanasaldy (2012, p. 269) notes “In West Kalimantan such [native empowerment] movements were initially led by Dayaks who demanded more top jobs in the government and competed zealously against the Malays, for those political positions. Held in check during the authoritarian New Order [Suharto era], political polarization between the two ethnic groups was now unavoidable.” With these mounting tensions came a push for redistricting, motivated in part by a desire to reduce ethnic divisions. Tanasaldy notes that “government officials thought that separating conflict-prone areas and allowing the Dayaks to govern their own areas was a solution to chronic ethnic conflicts there.” After the two splits, the now-parent district of Sambas was about 82 percent Malay, with the next largest group being ethnic Chinese at 8 percent. Bengkayang became 54 percent Dayak, 19 percent Malay, and 6 percent Chinese, while Singkawang was 37 percent Chinese, 20 percent Malay, 17 percent Dayak, and 10 percent Jawa. This split is often depicted as having cleanly separate the three groups: “the Malays in Sambas, the Chinese in Singkawang, and the Dayaks in Bengkayang” (Kobayashi, 2011, p. 374). However, as evident from the group shares, Singkawang remains far more polarized than Benkayang. In Sambas and Bengkayang, the split was successful in reducing violence as the changing ethnic divisions translated into more amicable politics. Kobayashi nicely summarizes, noting that “district head elections became less tense because the Dayaks and Malays understood each others’ rights to lead districts where they were dominant,” and more generally that “interethnic strife to obtain political positions has declined since 2000.” Government officials often argue that redistricting “contributed to prevention of ethnic violence,” drawing connections, for example, between increased Dayak representation in the civil service and the end of violent street demonstrations. Meanwhile, Singkawang presents an interesting contrast. Despite being a significant 40 percent plurality, the Chinese had generally stayed out of politics, with the “sons of the soil” Malay typically dominating. However, in 2007, the first direct mayoral election after redistricting brought their numeric advantage to the fore amidst a growing “desire within the Chinese community to increase the number of Chinese in the government and to elect a Chinese mayor” (Kobayashi, 2011, p. 295). In the 2007 election, much to the surprise of the Malay candidates, Hasan Karman, an ethnic Chinese beat the three Malay candidates (whose votes were split). Once in power, though, Karman “fumbled the delicate issue of ethnicity”, as he “irked Malays by building a [Chinese] dragon statue. . . in the heart of the city” and “disparaged the Malay community. . . by [publicly] linking them to pirates” (Sukarsono, 2012).48 In the 2012 election, tensions mounted amid Chinese accusations of intimidation and vote-tampering after the Malay candidate won. Violent clashes erupted between Malay security personnel and Chinese protestors outside the election commission office.49 Overall, West Kalimantan highlights both the promise and pitfalls of redistricting as a vehicle for reshaping ethnic divisions in society. Greater homogeneity in Sambas and Bengkayang may have helped to resolve some of the longstanding interethnic grievances. However, the new district of Singkawang 48 These violent incidents are reported in the SNPK data with event details such as “series of arson cases by unknown perpetrators believed to be related to ethnical issues” in May 2010, and “there was a clash in the parade of Singkawang Parliament, the village of Pasiran, the city of Singkawang. Clash involving two groups of the pro and contra period of the construction of a dragon statue that will be built at the crossroads (2 injured)” in July 2010. 49 This event is reported in the SNPK data on October 1st, 2012 as “a clash between the masses and the police when the Mayoral Candidate campaign handed over evidence of more than 3,000 people being denied the vote. [2 injured]” 26 gave rise to fresh grievances and cycles of violence by invigorating hitherto less salient ethnic divisions. These cases highlight the tradeoffs of redistricting in diverse societies where creating purely homogeneous political units is not feasible everywhere. 7 Discussion This paper identified the causal effect of political boundaries on ethnic divisions. We showed how redrawing subnational boundaries can alter the salience of different ethnic cleavages in society, and, in turn, affect conflict. By bringing the government closer to the governed, redistricting holds promise for increasing social stability. However, this common policy reform is not without pitfalls. Our natural experiment showed in particular that fresh cycles of violence may erupt when new borders increase ethnic polarization. We argued further that electoral democracy may amplify the underlying incentives for group mobilization that often lead to violence. Overall, our findings provide novel evidence on the interlinkages between ethnic and political divisions. These results help inform ongoing debates on the causes of violence as well as policy efforts aimed at curbing it. Some of our findings suggest that border-induced changes in ethnic divisions may have persistent effects on conflict. This persistence can be interpreted through the lens of models like Rohner et al. (2013b), which feature vicious cycles of inter-group violence and erosion of trust. Political violence in newly created districts may be particularly prone to such dynamics as seen, for example, in the case of Singkawang. While over the long-run redistricting may foster new interethnic interactions, learning and cooperation, it is important to understand and prepare for the scope for violence during the transition. Local government proliferation is a pervasive feature of decentralization today. The widespread prevalence of ethnic mobilization (Fearon, 2006) and favoritism (De Luca et al., 2017) suggest that similar conflict dynamics could play out in other diverse countries. For example, Green (2010) discusses some of the same unintended consequences of redistricting in Uganda that we identify empirically in Indonesia. Nevertheless, we acknowledge that ours is only a partial analysis of the vast political and economic implications of redistricting. We see four important directions for future research on redistricting in Indonesia and elsewhere. First, a small but growing literature highlights the importance of within-ethnolinguistic or -religious group heterogeneity in culture (Desmet et al., 2017), genes (Arbatli et al., 2015), or income (Esteban and Ray, 2008a; Mitra and Ray, 2014) in shaping conflict. This is an interesting question in the context of decentralization and one that can be explored using heterogeneity in responses to household survey questions on preferences, variation in vote shares for different parties of the same religion, and withingroup educational or occupational inequality. A second question is whether redistricting can be a vehicle for a central government to constrain secessionist tendencies. Coming on the heels of East Timor’s independence and concerns about breakaway regions in Aceh and Papua, Indonesian policymakers in the late 1990s strategically chose districts rather than provinces as the primary administrative units allowed to proliferate. According to observers like Booth (2011), their goal, among others, was to fracture the strength of broader regional identities. It would be interesting to explore whether this policy of “breaking up to stay together” stifles secessionist sentiments and ultimately shifts violence from higher to lower levels. 27 Third and relatedly, redistricting has the potential to activate more granular cultural distinctions. Although many areas created ethnically homogeneous districts, sub-ethnic distinctions may have emerged over time as groups sought new vehicles for political mobilization. Redistricting may have contributed to the dramatic growth in the number of self-reported ethnic identities from 1,087 in the 2000 Census round to 1,331 in the 2010 round. As recounted in Fearon (2006), Horowitz (1985, p. 66) provides a telling example of this burgeoning of local identity in the Indian context of state proliferation in the 1950s: In Madras state, “. . . with large Tamil and Telugu populations, cleavages within the Telugu group were not very important. As soon as a separate Telugu-speaking state was carved out of Madras, however, Telugu subgroups—caste, regional, and religious—quickly formed the bases of political action.” In the Indonesian context, one could explore empirically how political boundaries lead to new forms of identity related not only to sub-ethnic distinctions but also to shared national identity (Bazzi et al., 2018). Finally, there are several open questions about the public goods and welfare consequences of redistricting. Recent studies identify environmental externalities (Burgess et al., 2012; Lipscomb and Mobarak, forthcoming). There are other interesting implications of reduced government scale and changes in the network of administrative responsibilities; not to mention increased proximity to service providers in the new district capitals. A full account of the welfare implications of redistricting clearly extends beyond the effects of changing ethnic divisions. 28