The Political Boundaries of Ethnic Divisions

1 Introduction Political boundaries can shape group identity and behavior in fundamental ways. For example, individuals from the same two ethnic backgrounds may face incentives for conflict in one political unit and incentives for cooperation in another (Michalopoulos and Papaioannou, 2016; Posner, 2004). A vast social science literature takes boundaries as given and links diversity therein to weak public goods provision, underdevelopment, and conflict (e.g., Alesina et al., 1999; Montalvo and Reynal-Querol, 2005). Would redrawing political boundaries overhaul the ethnic divisions that fuel such adverse outcomes? If political control is a key source of intergroup contestation, then changes in the composition of the electorate could affect violence. Pioneering work on ethnicity and conflict recognized this possibility (Horowitz, 1985). We provide the first attempt to investigate this important policy question. This paper focuses on what is perhaps the largest shift in the locus of politics since the creation of new nation states after World War II. The proliferation of subnational administrative units over the last 30 years of decentralization has profoundly changed political boundaries, as Grossman and Lewis (2014) document across numerous, often diverse, countries.1 This massive wave of redistricting offers a unique opportunity to examine how changes in political boundaries affect ethnic divisions by changing the incentives for group mobilization. We study the implications for interethnic contestation and violence in Indonesia, the world’s fourth largest country and among its most ethnically diverse. The key contribution of this paper is to show that political boundaries endogenously determine whether ethnic diversity causes instability. We exploit a policy experiment in Indonesia to estimate the effects of redistricting on social conflict. Where feasible, creating homogeneous new political units reduces violence. Yet, in practice, redistricting along group lines is rarely so simple and may instead foster new divisions.2 We show that the potential gains in stability are often undone and even reversed in areas where new group divisions become salient and polarizing. These boundary-induced changes in ethnic divisions necessarily change the way different groups interact and compete for political relevance. In newly polarized settings, we find that the ensuing majoritarian electoral contests to control valuable public resources further amplify incentives for violence and may lead to fresh cycles of conflict beyond the first few years after redistricting. Indonesia offers a rich setting to study changes in ethnic divisions. First, like many other new democracies, political violence remains a major policy concern (Butcher and Goldsmith, 2017). After the fall of a highly centralized, authoritarian regime, many feared the diverse country would break apart as local conflicts tore at ethnic, religious, and regional seams. These large-scale internecine conflicts have subsided, replaced by more sporadic outbursts of low-intensity social conflict and recurring electoral violence. Amplified by decentralization, ethnic mobilization around elections and political patronage are pervasive.3 Meanwhile, institutional constraints on violence remain relatively weak. While distinct 1 For example, from 1990 to 2010, Nigerian states increased from 22 to 37, Ugandan districts from 34 to 112, and Kenyan districts from 47 to 70. Czechoslovakia and Hungary increased their municipalities by 50 percent from 1989 to 1993. Brazilian municipalities increased from 3,974 in 1980 to 5,560 in 2000. Vietnam increased its provinces from 40 to 64 from 1996 to 2003. 2 Consider a stylized case where two minority groups break away to form their own district. Previously pitted together against a larger majority, these two groups now face fresh incentives for conflict in the newly polarized district. Related arguments have been made in the context of the newly created country of South Sudan where the North–South ethnic divide between Arab and non-Arab in pre-partition Sudan gave way to interethnic divisions among the non-Arab Nuer and Dinka ethnic groups in South Sudan (Posner, 2017). 3 Aspinall (2011, p. 298) notes that, “Since the introduction of direct local government head elections, there has been a shift in from civil wars, these outbreaks of local conflict may accumulate over time and also exert lasting adverse effects by eroding trust in the political process (Dercon and Gutiérrez-Romero, 2012), by undermining local institutions (Michalopoulos and Papaioannou, 2013), by weakening social capital and deepening ethnic cleavages (Dower et al., 2017; Rohner et al., 2013a), and by planting the seeds for more serious, future conflict (Rohner et al., 2013b). Second, the Indonesian context allows us to overcome a host of endogeneity and measurement challenges typically associated with identifying the effects of changing political boundaries. Decentralization reforms beginning in the late 1990s led to a dramatic increase in the number of district governments across the archipelago from 302 in 1999 to 514 in 2014 (see Figures 1 and 2). Motivated by a desire to reduce sociopolitical tensions, the central government created a very favorable environment for such redistricting. This allows us to rule out first order concerns about strategic violence aimed at achieving or preventing certain types of partitions. Moreover, we show that an abrupt, centrally-imposed moratorium between 2004 and 2006 generates plausibly exogenous variation in the timing of redistricting across locations.4 We exploit this staggered process in a generalized difference-in-difference (DiD) framework focused on districts that split around the moratorium. This approach prioritizes internal over external validity and constrains the effective sample size, two limitations we explore at length. But, the combination of exogenous timing and granular data on diversity and conflict make Indonesia arguably the most compelling context to study the effects of political boundary changes on ethnic divisions. Using universal Population Census data from 2000, we measure how homogenized the new, smaller districts are relative to the original district based on the same, initial populations. Indonesia is home to over 1,000 self-identified ethnic groups, allowing us to distinguish two key measures in the literature: fractionalization and polarization. Ethnic fractionalization (F ), which captures the likelihood of meeting someone outside your group, declines substantially on average at the original district level. However, some of the newly drawn borders encompass fewer, large groups, thereby increasing ethnic polarization (P ).5 While homogenization was an objective of redistricting in many areas, policy constraints on the number of required contiguous subdistricts for a new district limit the set of feasible partitions. We exploit these constraints to address endogeneity concerns relating to the specific choice of new borders. We estimate the effects of these changes in ethnopolitical boundaries on conflict using new data developed by the Indonesian National Violence Monitoring System. Based on systematic coding of print newspaper archives, the data capture over 230,000 violent incidents at a high spatial and temporal frequency from 2000 to 2014. While limited initially to high-conflict regions, these data offer more comprehensive coverage than other sources for Indonesia and rival some of the best geospatial event-based sources in sub-Saharan Africa and South Asia (e.g., ACLED) in terms of depth and detail. Reported violent events include, among others, attempts to influence the allocation of resources and express popular dissatisfaction with local governance (Barron et al., 2014). Vigilantism and public mobilization along favor of alignment between the ethnic identity of local government heads and the local populations they govern. Electoral winners tend to be drawn from the largest ethnic group in the district or province.” We provide further qualitative context in Section 6.1, which also shows that greater diversity within newly created district boundaries is associated with closer elections and stronger individual preferences for politicians’ ethnicities and patronage. 4 We build upon Burgess et al. (2012) who use similar variation in their study of deforestation externalities due to redistricting.  e 5 Formally, ethnic fractionalization in district d is F = N j=1 gj (1 − gj ), where Ne is the number of ethnic groups in the district, and g j is the population share of group j. Adopting the Esteban and Ray (1994) metric, ethnic polarization is given by P =  Ne  Ne 2 j=1 k=1 gj gk ηjk , where ηjk is the distance between groups j and k, for which we use linguistic distance. 2 ethnic lines are common (Wilson, 2015) as is strategic violence around elections (Harish and Toha, 2017). Despite policymakers’ goals, redistricting does not change the average incidence of social conflict. This somewhat surprising finding can be explained by heterogeneous changes in ethnic divisions. Reductions in conflict in newly homogeneous units are offset by increases in newly polarized units. Our estimates suggest that political boundaries can explain around half of the endogenous long-run correlation between ethnic polarization and conflict in the cross-section. These changes arise in both the parent district, which retains the original seat of government, and the child district, which acquires a new local government and capital (see Figure 3 for an example). Robustness checks, including a machine learning approach to selecting controls, rule out numerous other confounding effects of redistricting and changes in media attention. We also rule out small-sample biases to estimation and inference. Overall, the abrupt reconfiguration of the diverse electorate gives rise to fresh incentives for violence that are amplified in polarized settings. These border-induced changes in ethnic divisions may have persistent effects on conflict as a result of political cycles. Violence ratchets up around the first mayoral elections, particularly where they are closely contested. New ethnic divisions, and polarization in particular, exacerbate violence around these majoritarian elections, especially in child districts where the stakes are high given the fundamental role of the initial administration in establishing the new government and its first budget. We see no such amplification around parliamentary elections, which is consistent with incentives for violence differing under proportional representation. Moreover, this amplification effect persists into the next mayoral election five years later and also drives violence in intervening periods, pointing to new cycles of political conflict. The more pronounced cycles in child districts are consistent with differences in the scale of changes in contestable public resources but may also point to nascent differences in local state capacity. Ethnic favoritism helps rationalize electoral violence and perpetuate grievances, a mechanism we validate using nighttime light intensity to capture village-level changes in access to publicly-provided electricity after redistricting. Our results line up nicely with case studies in Indonesia. In Section 6.4, we discuss a case of redistricting in West Kalimantan that illustrates how border-induced changes in ethnic divisions affect conflict. Diprose (2009) offers similar insights on Central Sulawesi as do Nolan et al. (2014) on Papua, noting that “local elections in relatively new districts can exacerbate existing social fault lines.” With a bottom-up approach to redistricting, it is sometimes difficult for local parties to foresee all the complex new divisions that might arise with new borders. Wilson (2015, p. 33) succinctly summarizes the Indonesian context, arguing that redistricting “created confusion regarding lines of authority” and “involved renegotiating the boundaries of collective identities” as “local government bureaucratic precincts were altered, and some networks of resource distribution that relied upon state agents were disrupted or excluded by these shifting boundaries.” As a result, “ethnic identities have been politicised as clientilistic networks. . . mobilising support along communal lines.” Of course, conflict is by no means a foregone conclusion. However, by localizing political contestation, redistricting may make it easier to solve collective action problems that might otherwise forestall violence. Similar findings may be at play outside Indonesia as well. In Uganda, for example, Green (2008) argues descriptively that “the huge expansion in the number of new districts has led to local-level conflict by altering relations between local ethnic groups.” Although dramatic, the wave of redistricting we 3 study is comparable in scale and purpose to efforts across sub-Saharan Africa, Asia, and Eastern Europe (see Grossman and Lewis, 2014; Grossman et al., 2017). A small but growing political science literature examines this process, documenting similar institutional underpinnings (see Pierskalla, 2016a, for a survey). In diverse countries, as in Indonesia, the resulting shift in the locus of politics can fundamentally change relevant group boundaries. How these changes affect incentives for conflict is very much an open question to which the Indonesian setting is uniquely well suited to answer. Related Literature. This paper makes several contributions to the political economy and development literatures. Our central contribution is to show that subnational borders can reshape the ethnic divisions underlying conflict. We argue that the ethnic divisions underlying widely-used diversity measures are neither fixed nor exogenous and instead depend on the political boundaries within which groups are organized. Case studies as well as lab and survey experiments across Africa find that interethnic preferences are context-dependent (Berge et al., 2015; Eifert et al., 2010; Habyarimana et al., 2007; Lowes et al., 2015; Posner, 2004), and Blouin and Mukand (forthcoming) and Yanagizawa-Drott (2014) show that public media can manipulate these preferences. We show that subnational boundaries are a policy choice that can change ethnic cleavages by reorienting which groups are relevant to one’s own.6 While prior literature has recognized this political endogeneity of ethnic divisions (see Fearon, 2006; Posner, 2017), we provide the first empirical test using border changes. Our findings highlight important policy tradeoffs and demonstrate how majoritarian elections may exacerbate the new ethnic divisions. More generally, we offer policy lessons that go beyond the otherwise pessimistic conclusions from the literature on diversity and conflict. Alesina et al. (2011) and Michalopoulos and Papaioannou (2016) identify adverse long-run effects of colonial partitioning of ethnic groups across national borders. Although infeasible to undo most national borders, within-country redistricting may change the equilibrium relationship between diversity and conflict. Amodio and Chiovelli (2017) also study equilibrium changes but focused on geographic mobility, showing that abrupt, migration-induced changes in local polarization exacerbate conflict in post-Apartheid South Africa. Our effect sizes are smaller. This is intuitive because their results are based on changes in the underlying population, which introduces new groups and non-ethnic divisions (e.g., immigrant–native). By comparison, redistricting holds the local population fixed and instead reorients the relevant group divisions within new boundaries. This is important from a policy perspective given that redistricting is typically more feasible than resettlement. Furthermore, we provide a rich context to identify which configurations of diversity stoke political conflict. Esteban and Ray (2011a) show that in conflict over public goods (e.g., political power), polarization should be relatively more important than fractionalization as polarization captures differences in intergroup preferences and the strength of in-group ties.7 Esteban et al. (2012) provide supportive 6 In this respect, our identification strategy is somewhat akin to Hjort (2014) who uses the random assignment of workers to ethnically mixed teams to understand how diversity shapes productivity in a flower plant in Kenya around a period of national interethnic strife. Hence, our notion of time-varying diversity is distinct from variation due to migration or differential mortality and fertility, which may be confounded with a host of other factors whose effects on conflict are of separate interest. 7 Spolaore and Wacziarg (2017) make similar points in a related setup with the additional insight that contests over rival goods may be more likely between groups with similar preferences, a prediction borne out empirically when examining international conflict, which typically involves contestation of private resources such as oil (Caselli et al., 2015; Spolaore and Wacziarg, 2016). Other models of conflict emphasize the conditions giving rise to mobilization along ethnic as opposed to other group lines (e.g., Caselli and Coleman, 2013; Esteban and Ray, 2008a, 2011b). Consistent with some of these predictions, our results highlight the important role of ethnic markers in mobilizing around newly contestable local politics. cross-country evidence using proxies for the relative publicness of resources within countries. Local government proliferation offers a more precise public resource shock. We find that polarization matters relatively more than fractionalization, particularly in child districts, where new government institutions are being formed and elections are being held for the first time. We explicitly link polarization to violence around majoritarian elections but not proportional-representation elections, a distinction in line with predictions in Esteban and Ray (2008b). These results help clarify both the type of conflict and the type of diversity that might matter in new democracies undergoing similar decentralization processes. Finally, our results suggest that the growing number of studies exploiting subnational variation in diversity should use spatial boundaries at levels of aggregation that are outcome-relevant.8 This echoes recent work arguing that local diversity can have different effects than diversity at the aggregate, country level given the different nature of interaction within and between jurisdictions (Alesina and Zhuravskaya, 2011; Desmet et al., 2016; Montalvo and Reynal-Querol, 2017). Roadmap. Section 2 details the context of district proliferation and provides motivating evidence on diversity and conflict in Indonesia. Section 3 presents a conceptual framework showing how redistricting can reshape ethnic divisions. Section 4 proposes measures of border-induced changes in diversity and presents the new conflict data. Section 5 develops the empirical strategy and presents core results. Section 6 identifies political mechanisms linking the changing ethnic divisions to new conflict dynamics. Section 7 concludes with a discussion of policy implications and future research. 2 Background: Local Government Proliferation and Ethnic Divisions The number of districts in Indonesia increased by nearly 70 percent over a 15 year period. This background section details the central role districts play in government, key features of the redistricting process, and the fundamental changes in the scope of government that follow. For reference, Appendix Figure A.1 provides a timeline of key events discussed below. In a final section, we provide motivating evidence on the role of ethnicity in shaping politics and conflict. 2.1 Decentralization and the Transfer of Power to District Governments Indonesia has four main tiers of government. The largest tier is the province, of which there were 34 in 2014. Provinces are divided into districts known as kabupaten in rural areas and kota in urban areas. In 2014, there were 514 districts. Districts are in turn divided into 7,094 subdistricts (kecamatan), which are further subdivided into more than 80,000 villages, the smallest unit of government. The resignation of President Suharto in May 1998 ushered in a wave of decentralization reforms that shifted the balance of power away from the central government and provinces and towards the districts. Effective January 2001, districts assumed responsibility for nearly all public policy with the exception 8 As better data on subnational diversity become available (e.g., Gershman and Rivera, 2016), it is tempting to take the analysis to increasingly granular geography. This might be appropriate for questions about intergroup contact and preferences but perhaps less appropriate for studying conflict if the resources being contested are determined and allocated at higher levels of administration. This is in line with the warning by Michalopoulos and Papaioannou (2017) regarding the modifiable areal unit problem (MAUP) in the use of spatial data where results depend on the level of aggregation. 5 of the few areas naturally reserved for the central government (e.g., foreign affairs, fiscal and monetary policy). Accordingly, districts assumed extensive decision-making power over local expenditures. District heads or mayors, known as bupati and walikota, supervise the budget process, amalgamating spending requests from lower levels of government and submitting the final budget to local parliament for approval. The district executive plays a significantly larger role than local parliament in determining the composition and allocation of public goods (see Lewis, 2017b; Martinez-Bravo et al., 2017).9 While parliamentarians remain important, they tend to be less individually accountable than mayors and more beholden to national party politics than to the local population (Lewis, 2017a). Major electoral reforms also accompanied decentralization. Previously appointed by the central government, district heads were now elected via majority vote by members of the local parliament, who were in turn popularly elected according to a closed-list proportional representation system. Democracy deepened further in 2005 as district heads and their running mates were now directly elected via plurality/majority voting. Given the power of the district executive, these quinquennial mayoral elections, which occur at different times across districts, are a focal point of local political contestation.10 2.2 Creating New Districts Until the late 1990s, district boundaries were relatively stable (Booth, 2011). Many boundaries originated under colonial rule when the Dutch used local leaders for indirect rule. In practice, these administrative divisions spanned large swathes of territory with many different groups (Cribb, 2013). Postindependence, new districts arose with the goal of uniform population (Charras, 2005). However, due to imbalances in population density across islands, many districts continued to span large areas and multiple ethnic homelands. These expansive, arbitrary (post-)colonial boundaries remained largely in place until the fall of Suharto, when subsequent governments facilitated dramatic redrawing of the district map. The number of districts ballooned from 302 in 1999 to 514 in 2014 (see Figure 1) through a process known colloquially as pemekaran or blossoming. Redistricting Process. Subdistricts break off from their original district to create new districts. After a split, the original district is divided in (at least) two: The single parent district contains the original district name and capital with pre-existing institutions. The child district(s) receives a new name, capital, district head, parliament, and government apparatus. Figure 3 provides an example of this distinction with the original district of Aceh Tenggara splitting into the parent of Aceh Tenggara and the child of Gayo Lues. This also highlights the fact that original districts split up along contiguous, pre-existing subdistrict lines. Local elites and interest groups initiate the action to split in accordance with a redistricting law passed in 2000. The regulation stipulates that new districts must have: (i) at least three subdistricts, (ii) support among the original district mayor and parliamentarians, and (iii) demonstrated socioeco9 Mayors also appoint all subdistrict heads beginning in 2004 and have long appointed village heads in urban areas (MartinezBravo, 2014). These local officials play important roles in local public goods provision by navigating relations with the district government where the resources initially flow from the center (see, e.g., Section 6.3 on electricity). They also play an important role in mobilizing voters around elections, effectively greasing the patronage politics discussed in Section 3. 10 As Aspinall (2011, p. 305) notes, decentralization “shifted state resources and hence the focus of political contestation down toward the base of the political system.” Booth (2011, p. 46) argues, “Certainly the devolution of resources to the district level, where it has occurred has made the job of district head. . . very attractive to those who in the past had only managed to achieve lower-level positions in provincial or regional bureaucracies, or who had been largely excluded from official positions.” nomic capacity in terms of basic public goods and economic infrastructure.11 We discuss these policy constraints in Section 4.1 as they determined the scope of feasible changes in ethnic divisions. For identification purposes, we focus on the wave of redistricting from 2001–3 and 2007–8.12 The central government twice stopped the redistricting process via national moratoria, the first of which occurred from 2004–6 and the second from 2009–2012 as clearly seen in Figure 1.13 In both cases, the duration and enforcement of the ban was uncertain. Indeed, applications for new districts continued to arrive at the national parliament with more than 100 proposals at various stages of completion awaiting consideration in 2005–6 (BAPPENAS, 2007). Our main empirical strategy, which builds on Burgess et al. (2012), exploits this first moratorium by comparing districts that split around this policy shock. What is crucial for our identification is that the timing of redistricting is not driven by trends in conflict or correlated factors therein. Section 4 provides supportive evidence. Changes in Local Government. To understand how redistricting reshapes local government, a brief timeline is instructive. After a new child district is ratified, an interim executive along with local parliamentarians oversee the transition process with a focus on the first mayoral election. On average, those quinquennial elections first take place within 21 months after redistricting. The mayor is then tasked with operationalizing and staffing up to 30 new local government agencies in the new capital. By this time, roughly two years after redistricting, central government transfers have begun flowing into child district coffers. Local public expenditures begin increasing shortly thereafter, taking similar shape as in other districts with around 40–50 percent of spending on personnel. In the parent district, elections take place on the same local, five-yearly timeline as they would have absent redistricting, but in the meantime, local institutions undergo restructuring as the governed area and populace change. Taking the original district as a whole, there are considerable gains to redistricting in terms of political representation and public resources as shown in Appendix A. First, most child district residents experience a significant reduction in distance to government representatives and institutions in the district capital. Second, the number of legislators per capita always (weakly) increases with redistricting due to apportionment rules. Third, fiscal allocation rules imply a significant, roughly 20 percent long-run increase in annual transfers, relative to a base of around 200 USD per capita. While the scale of changes in local government may differ between parent and child districts, the significant shock to politicallyrelevant ethnic divisions is shared by residents of both. 2.3 Motivating Evidence on Diversity and Conflict Like other countries, ethnic diversity in Indonesia has long been associated with adverse social consequences ranging from weaker social capital to greater conflict. In the late 1990s, ethnic divisions were a factor in major conflicts across the archipelago, including, among others, separatist movements in Aceh 11 Given the favorable returns to parent and child districts (see below), splitting proposals were generally widely supported by original district parliamentarians who represent constituencies in both areas prior to redistricting (Pierskalla, 2016b). 12 The redistricting in 1999 occurred before the new government regulation on pemekaran and substantively differs from later rounds of redistricting. Several were long-standing requests from the Suharto era, and others were initiated by the central government (Fitrani et al., 2005). These areas only enter our main analysis if they later split again. 13 The primary stated reason for the moratoria was the drain on fiscal resources and lack of capacity to meet the staffing needs of new child district governments. Upon lifting the first moratorium, the government tightened the law on redistricting by increasing the minimum number of subdistricts to five and requiring original districts to have existed for at least seven years. and Papua, communal violence in Maluku and Central Sulawesi, and political violence in West and Central Kalimantan (Barron et al., 2014).14 Consistent with these pervasive divisions, Mavridis (2015) shows that residents of more ethnically diverse districts exhibit lower generalized trust and less community participation. Even as many of the most intense conflicts subsided by the mid-2000s, ethnicity remained a key vehicle for political mobilization across the country (see Allen, 2014; Aspinall, 2011; Fox and Menchik, 2011). The introduction of direct majority/plurality elections for mayors reinforced this tendency.15 Indeed, electoral competition is much stiffer in diverse districts, where fractionalization and polarization are associated with narrower victory margins, especially in newly created districts (see Appendix E.1). Relatedly, clientelism and patronage pervade local politics, with patronage networks often based on local ethnic identities and intermediated by lower-level officials such as village heads (Aspinall, 2013; Aspinall and Sukmajati, eds, 2016). These ethnic group dynamics occasionally manifest in conflict. Ideally, group-based contests over distributive goods occur peacefully through the political process. However, in settings with weak institutions (e.g., newly democratic countries), violence may be an effective means of influencing elections or the allocation of public resources. These low-intensity, localized bouts of violence are prevalent in modern Indonesia.16 While distinct from full-blown civil war, these episodes of violence are a major policy concern given their potential to undermine efforts to build local state capacity and to snowball into more systematic, large-scale conflict. Using new data detailed in Section 4.2, we document a strong positive correlation between ethnic diversity—as measured by either fractionalization or polarization—and the incidence of conflict in Indonesian districts since 2000. Simple regressions suggest that a one standard deviation increase in fractionalization (polarization) is associated with a 15 (8) percent increase in the likelihood of conflict. 3 Conceptual Framework: The Political Boundaries of Ethnic Divisions Implicit in much of the literature on diversity is the notion that ethnic divisions are shaped by political boundaries. The scope for ethnic mobilization and patronage networks depends on the size and cohesion of groups within the electorate. This suggests that by changing political boundaries, redistricting has the potential to reshape these intergroup divisions and, in turn, social tensions and conflict. But whether this actually happens, and how quickly it happens in practice, is unclear and yet crucial for informing policy. In the remainder of this section, we discuss a simple conceptual framework relating changes in conflict to border-induced changes in local diversity. Several models have linked ethnic divisions to between-group or social conflict (Ray and Esteban, 2016). For example, Esteban and Ray (2011a) show in a contest-based model that equilibrium conflict can be 14 Like Fearon (2006), we view ethnicity as determined by descent but subject to politics and history and not merely biological. Ethnic or regional political parties are effectively banned in Indonesia due to a host of legal requirements implemented with democratization in 1999, mandating that political parties must have widespread geographic coverage—in terms of institutional presence—in order to be eligible to contest elections (see Hillman, 2012, for details). Nevertheless, mobilization along identity lines in local elections is widespread (Aspinall, 2011). At the same time, class cleavages and family dynasties are less important in local politics than elsewhere in Southeast Asia (see Aspinall and Asad, 2016). 16 For example, Tadjoeddin (2012) finds that violence occurred in 23 percent of the first direct mayoral elections between 2005– 2007. Harish and Toha (2017) show that conflict around local elections is a persistent problem. They classify over 1,000 electoral violence episodes into voter-targeted, candidate-targeted, and government-targeted. We provide examples using our data in Section 4.2 and discuss their findings in Appendix C.2. 15 approximated by a function of diversity measures, including polarization and fractionalization, the size and relative publicness of contestable resources, and within-group cohesion. These models take political boundaries to be fixed. However, if the contest depends on the groups within a given boundary (as it may for conflict relating to local government), then changing boundaries has direct implications for conflict. This section discusses these implications for the purposes of guiding our eventual empirical specification. We keep the discussion general here, avoid taking a stance on the exact underlying model, and refer the interested reader to Appendix B for two specific examples of possible models. Effects of Redistricting. Suppose that per-capita violence in the original district (O) before redistricting is a function h(·) : RN → R1 of the original district’s underlying diversity DO ∈ RN , measured using one or more diversity indices: DO = (DO,1 , DO,2 , ..., DO,N ) for N ∈ Z+ . Under the assumption that splitting creates new, separate contests in parent (P) and child districts (C), per-capita violence in these districts post-split is determined by hP (DP ) and hC (DC ). If the functional relationship between violence and di- versity remains fixed, which corresponds to keeping per-capita resources and group cohesion constant in Esteban and Ray (2011a), then hP ≡ hC ≡ h. Splitting then implies that the change in total per- capita violence at the original district level will be given by the difference between population weighted functions of diversity in the newly created and original districts, ∆V GO = GP GO h(DP ) + GC GO h(DC ) − h(DO ), where GO , GP , and GC correspond to original, parent, and child district population, respectively. For typical specifications of h(·), this suggests that when groups separate into perfectly homogeneous child and parent districts, all violence in the original district ceases. In the special case of h(·) being linear and separable in diversity measures, the change in per-capita  violence at the original district level resulting from redistricting is: ∆V GO = h GP GO DP + GC G O DC − DO . This relationship informs our empirical specification relating conflict to border-induced changes in diversity at the original district level. When examining violence at lower levels of aggregation, such as within the (eventual) parent district, we need to take a stance on how violence is initially distributed across parent and child. Letting σ be the share of total violence falling in the change in violence within the parent  the parent district,  GO ∆V district boundary is given by: GP = h DP − σ GP DO . If violence is initially distributed according to   GP population σ = G , the change in violence within the parent boundary is given by the difference in O the diversity within that new unit and the overall diversity in the original district.17 The same holds for child districts. Discussion. Following the conflict literature, we focus empirically on (changes in) fractionalization and polarization. Fractionalization, a measure of the probability that two randomly selected individuals within a given boundary belong to different groups, always declines with splitting (in populationweighted terms). This is not the case for polarization. An original district with four equally-sized, different groups could split into two districts, each with two similarly sized groups contesting power. In this example, polarization, which is largest with two equally sized, competing groups, increases. Esteban and Ray (2011a) show that this specific type of diversity is relevant to conflict over public goods, which can be tailored to the winning group’s preferences but not fully excluded from losers. Fractionalization meanwhile is more relevant for private goods that can be fully captured and divided among the winners. We will let the data speak by considering both measures. 17 We validate this assumption in the data using the pre-split distribution of violence across parent and child districts. The Esteban and Ray setup also implies that changes in the value of the contestable resource (or its relative publicness) and changes in the costs of violence will affect conflict. Redistricting is accompanied by an influx of government resources, which plausibly increase the value of the public prize, and changes in the distance to the capital, which could alter the costs of violence. These changes amplify the effect of splitting on per-capita violence, particularly in newly polarized areas. With this in mind, we pursue a reduced form empirical approach that focuses on border-induced changes in diversity as a baseline while incorporating changes in the value of resources and costs of conflict for robustness. We also examine differences between parent and child districts, as child districts experience relatively larger changes in both resources and distance to the capital (see Appendix A). 4 4.1 Data: Measuring Changing Ethnic Divisions and Conflict Border-Induced Changes in Diversity Indonesia is the fourth most populous country in the world and among its most diverse. More than 1,000 self-identified ethnic groups speaking more than 400 languages span the archipelago. Indonesians are predominantly Muslim (87 percent) with minority Christian, Hindu, and Buddhist groups. From a policy perspective, diversity manifests at different levels of governance but became especially salient at the district level with decentralization. This section shows how we measure the changes in ethnic divisions that accompany redistricting. Initial Diversity. We measure diversity using microdata from the universal 2000 Population Census. This data allow us to link the initial subdistricts in 2000 to their final 2010 district boundaries, providing us with measures of diversity at the original, parent, and child district levels. These are all based on the initial population in 2000 and hence not subject to concerns about endogenous sorting in response to redistricting. The 2000 Census was the first since 1930 to record ethnicity, allowing respondents to report a single affiliation. This led to remarkable cultural distinction with over 1,000 self-identified ethnicities (Ananta et al., 2015).18 The sub-ethnic variation within broader ethnic groups may be relevant for conflict (Desmet et al., 2017). We also capture deeper interethnic cleavages using linguistic differences by mapping Indonesian ethnic groups to languages in the Ethnologue data. We focus on two widely-used measures of ethnic diversity. Ethnic fractionalization in district d is given  e by F = N j=1 gj (1 − gj ), where Ne is the number of ethnic groups in the district, and gj is the population share of group j. Given the large number of self-reported ethnic identities, fractionalization is quite high and indeed above 0.5 in many districts we study. Adopting the Esteban and Ray (1994) metric, ethnic  e  Ne 2 polarization is given by P = N j=1 k=1 gj gk ηjk , where ηjk is the distance between groups j and k. Polarization aims to capture the deeper cleavages in society and differs from fractionalization in two key respects. First, the squaring of the own-group term emphasizes that stronger within-group identification coincides with greater out-group alienation, which together exacerbate intergroup tensions. As such, polarization is maximized when there are two distinct, equally sized groups. Second, it formally incorporates distances between groups while the standard measure of fractionalization (F ) does not. 18 In our average original district, there are 549 distinct ethnic groups with 21 having more than 0.1 percent of the population. Consolidating subgroups based on language reduces these numbers to 271 groups, 18 with more than 0.1 percent. 10 We specify η using the same parametrization of linguistic distances as in Desmet et al. (2009, 2012) and Esteban et al. (2012) (see Appendix F for details). Although less pervasive, religious divisions may be important in some locations. In what follows, we focus our discussion around ethnic diversity but nevertheless incorporate religious diversity in our empirical analysis. We account for religious polarization, but lacking an obvious notion of distance, set ηjk = 1 ∀j = k. Given that religious diversity typically means one sizable non-Muslim group, polarization is nearly identical to fractionalization (rank correlation≈0.99).19 Changes in Diversity. Prior to redistricting, these measures of diversity at the original district level demarcated the boundaries of politically-relevant ethnic divisions. After redistricting, diversity within the new parent and child district boundaries becomes salient. Motivated by our conceptual framework, we propose simple but generalizable measures to capture these changes in ethnic divisions. As suggested by Section 3, we compute the difference between the population-weighted average diversity in the new units and diversity in the original 2000 district. For example, if original district  GP GC F C − FO , O splits into parent P and child C, the change in ethnic fractionalization ∆F = GO FP + G O where the first term captures the implied F within the new borders. By definition, ∆F (weakly) declines. However, the sign of ∆P is less clear, and sometimes, the new borders increase polarization. On average, ∆F = −0.059 (std. dev.= 0.083) while ∆P = −0.0002 (0.005). Figure 5 compares the original district diversity to the implied diversity after redistricting with the distance to the 45 degree line capturing the ∆. Importantly, there is variation in ∆diversity across districts with similar initial diversity. Later, we link this variation to constraints imposed by redistricting regulations. Note also that Indonesia’s remarkable diversity implies scope for differentiating between the two measures (the correlation between ∆F and ∆P is 0.38). Moreover, these ∆P reflect sizable shifts in ethnic divisions. To make these numbers concrete, consider two examples of redistricting in our setting. First, some areas, like the original district of Aceh Tenggara, were able to leverage the geographic distribution of groups across subdistricts to split along ethnic lines, and create homogeneous governing bodies. Aceh Tenggara split into one child with 93 percent ethnic Gayo while the parent comprised 47 percent Alas, 17 percent Batak, and 15 percent Gayo. This implied a significant reduction in diversity relative to the original district, which had 39 percent Gayo, 33 percent Alas, and 12 percent Batak. These changes led to ∆F = −0.180 and ∆P = −0.003 and can be seen in Figure 4(a), which plots the boundaries of villages (colored by ethnic majority), subdistricts, and districts, with the latter shown pre- and post-redistricting. Other districts split in ways that thrust hitherto less salient divisions into the limelight. One interesting example comes from Kotawaringin Timur, once the largest district in Central Kalimantan province and a legacy of Dutch administration in the 1930s, which was comprised of six relatively large groups spanning 26 subdistricts. It was not feasible to homogenize in the way that Aceh Tenggara did (see Figure 4(b)). Instead, the original district split into two child districts and one parent. This reduced fractionalization ∆F = −0.068 but increased polarization ∆P = 0.004 as the new districts comprised similar or fewer groups in more equal proportions than the original district. Section 6.4 discusses at length another interesting case from Bengkayang in West Kalimantan that further clarifies why these border-induced changes in diversity matter politically. 19 By comparison, ethnic polarization and fractionalization exhibit lower correlation (<0.2) and are statistically independent. 11 Together, these examples point to the institutional constraints on feasible redistricting schemes. With multiple groups spanning the same subdistricts, creating completely homogeneous new districts would have been unworkable given the policy regulations on economic viability, which required sufficient scale. Using these policy constraints, we show in Section 5.5 how the set of feasible partitions shaped the resulting changes in ethnic diversity and conflict. 4.2 New Conflict Data We estimate the effects of changes in ethnic divisions using new monthly data on conflict from the National Violence Monitoring System, referred to hereafter by its Indonesian acronym, SNPK (Sistem Nasional Pemantauan Kekerasan). Coverage begins in 1998 for nine conflict-prone provinces and increases to 15 provinces plus greater Jakarta beginning in 2005.20 Crucially, conflict locations are recorded at administrative levels that allow us to link event locations to parent and child districts prior to redistricting. Like other geospatial conflict databases such as the Armed Conflict Location & Event Data (ACLED), media reports of violence are the key input to the SNPK. Over a four-year period, project architects collected over 2 million images from the print archives of around 120 local newspapers, including multiple outlets for each province and excluding those with clear biases or no fact-checking (see Barron et al., 2009, 2014). Despite this rigor, as with all event-based conflict data, one may still worry about bias from selective reporting. We systematically address these concerns in two ways. First, we flexibly control for the number of papers available to coders for each province–month. Second, we use auxiliary Google Trends data to rule out confounding media attention due to redistricting. Coders used a standard template to assign incidents to 10 mutually exclusive categories based on the underlying trigger. They first code incidents as domestic violence, violent crime, violence during law enforcement, or conflict. Within conflict, coders further categorize based on what is being violently contested: elections and appointments, governance, resource, identity, popular justice, separatist, and other (could not be classified). When reported, coders also indicate the number of injuries or deaths and details on property damage. Further background can be found in Appendix C.1, which also highlights important advantages of the SNPK relative to other violence data. Outcome: Social Conflict Incidence. Our core outcome is the likelihood of any social conflict in a given district–month. We define this measure as any incident excluding domestic violence and crime. From 2000 to 2014, these events occurred in around 63 (36) percent of district–months based on the original district (parent/child) borders. Appendix C.3 demonstrates robustness to category misclassification, and Appendix C.4 shows that our results are driven by events with injuries and property damage rather than deaths per se. Such events are still costly and broadly reflect the sort of social instability that concerns policymakers in newly democratic countries. This is also consistent with our conceptual framework, motivated by Esteban and Ray (2011a) who note that “social conflict need not manifest itself in civil war alone, and there are various other measures . . . for instance, strikes, demonstrations, riots, . . . .” Before proceeding to results, we offer a few illustrative examples of these types of conflict that characterize our outcome measure. The following translated incident descriptions in Maluku Utara district 20 While the data is not representative of Indonesia, it spans all major island groups and covers a majority of the Indonesian population. We omit districts in Papua due to problems with the underlying administrative and census data. Data coverage is less reliable in the earliest years, and hence we exclude 1998 and 1999. help fix ideas: “(July 13, 2010): In Galela Selatan, supporters of Djasa (a mayoral candidate) destroyed the office of Galela subdistrict, 2 official cars, and billboards of other candidates.” “(August 18, 2011): Office of Morotai District Legislature was bombed; it is suspected as terrorism to prevent the inauguration of the elected mayor.” SNPK records point to various forms of political violence—protests over voter eligibility, clashes between supporters, direct targeting of candidates and government offices overseeing elections—often related to local, mayoral elections (see Harish and Toha, 2017, for a rich accounting). Such violence often involves building damage and injuries rather than deaths. Nevertheless, such incidents can and do escalate.21 Moreover, violence is not limited to election periods. Many events capture groups violently expressing grievances over policy and resource allocation issues (see Appendix C). 5 Results: The Effects of Political Boundaries on Diversity and Conflict This section presents our main empirical results. We first provide motivating evidence that boundaryinduced changes in diversity are associated with conflict. We then estimate a rigorous, generalized difference-in-difference (DiD) that recovers the causal effects of redistricting and links changes in ethnopolitical boundaries to violence. We show that despite policymakers’ goals, redistricting causes no change in the average incidence of conflict. We argue that this somewhat surprising finding can be explained by heterogeneous changes in ethnic divisions. In areas able to create homogeneous new districts, conflict falls. However, conflict increases in areas where redistricting led to greater polarization among the newly defined electorate. 5.1 Sample Construction Our main analysis comprises 52 original districts (d) in 2000 that split into 133 districts by 2014 and are covered by the SNPK. Nearly all redistricting occurs in the two years before and after the moratorium on splitting from 2004–6.22 By focusing on districts that split around the moratorium, our empirical strategy prioritizes internal over external validity. Prior studies identify incentives for creating new districts in Indonesia, including efficiency gains in the provision of public goods, ethnic homogenization, and rent seeking (see Fitrani et al., 2005; Nolan et al., 2014; Pierskalla, 2016b). We find similar evidence, but what’s important for causal identification is that these underlying incentives do not predict early versus later timing of redistricting.23 While our effective sample size is constrained, the Indonesian setting is plausibly the most compelling available to study the effects of changes in political boundaries. Granular data on diversity and 21 Consider these incidents from the districts of Kota Subulussalam and Maluku Tenggara Barat: “(November 2, 2013): Demonstrations involving hundreds of supporters of candidates for mayor and vice mayor. The masses demanded an explanation from the Independent Election Commissioner. [7 injured].” and “(May 30, 2002): The chaos of the mayoral election of West Southeast Maluku district is bad. Supporters of Heri Kadubun who were riding in boats were attacked by supporters of the Taher Hanubun group [3 killed, 8 injured].” 22 We observe 38 original districts with conflict data spanning pre- and post-split years with the remaining 14 only observed in the SNPK after splitting. This is due to the expansion of SNPK coverage to include additional provinces beginning in 2005. Only one district in our study splits again after 2008 (in January 2013), and for simplicity we drop observations in 2013 and 2014 for this district. Results are unchanged under other treatments. Four other areas split for the first time in late 2012–13. However, we exclude these from the analysis in order to focus on areas that were credibly affected by the moratoria. 23 In Section 5.6, we discuss a reweighting exercise that is consistent with our results not being driven by selection into splitting. conflict as well as plausibly exogenous timing of redistricting are simply not available in other countries undergoing similar decentralization reforms. Furthermore, we take several steps to address smallsample biases in Section 5.4. 5.2 Simple Difference-in-Difference: Changes in Ethnic Divisions and Conflict We begin with motivating evidence that changes in ethnic divisions are associated with changes in conflict incidence. We regress the change in the average monthly likelihood of social conflict after redistricting on the change in ethnic diversity implied by the new borders. Figure 6 presents results in graphical form, normalizing ∆diversity and including regression lines with robust 95 percent confidence intervals. In graph (b), a one standard deviation increase in ∆polarization (P ) is associated with a significant 6.8 percentage point (p.p.) increase in group conflict after redistricting. As a benchmark, consider two districts with roughly one standard deviation difference in ∆P . In Kupang, ∆P = −0.001, and conflict fell by 22.2 p.p., whereas in Kotawaringin Barat, ∆P = 0.002, and conflict increased by 12.4 p.p.24 Mean- while, graph (a) shows that conflict is less responsive to border-induced changes in fractionalization, despite the positive long-run correlation between initial fractionalization and conflict. The data in Figure 6 suggests a strong relationship between the political boundaries of ethnic polarization and conflict.25 Of course, there are many reasons why these simple DiD estimates might not reflect causal relationships. The rest of the paper aims to rule out these concerns, to understand the dynamics of conflict after redistricting, and to offer a deeper interpretation of the underlying changes in group divisions and social tensions. 5.3 Average Effects of Redistricting We use a standard generalized DiD specification to identify the effects of redistricting on conflict: conf lictdt = ν + βpost-splitdt + θt + θd + θd × t + εdt , (1) where post-splitdt is an indicator equal to one for all months after the original district’s first redistricting was passed into law.26 The month fixed effects, θt , sweep out shocks to conflict incidence that are common across all districts. The district fixed effects, θd , take out time-invariant level differences in conflict incidence across districts. Meanwhile, the district-specific time trends, θd × t, are important given differential long-run trends in violence and consistent with fixed effects specifications in the conflict literature 24 Recall that these small changes in polarization often imply large changes in ethnic divisions. In Kupang, for example, ∆P = −0.001 captures the split of an original district with three fairly large groups (38 percent Atoni Metto, 32 percent Rote, and 18 percent Sabu) into three homogeneous new districts for each (parent with 63 percent Atoni Metto, one child with 93 percent Rote, and another child with 98 percent Sabu). 25 These descriptive results exclude one extreme outlier with ∆P six standard deviations below the mean, are robust to controlling for all three ∆diversity regressors simultaneously, estimating robust Huber (1973) regressions rather than OLS, and conducting small-sample inference. Note, however, that our main generalized DiD estimates below retain the full sample of districts and are robust to small-sample inference and principled outlier removal procedures detailed in Appendix D.4. 26 Districts that split into three or more all at once pose no particular difficulty. Out of 52 original districts, 11 split at multiple points in time. Consider, for example, Manggarai district, which first created one child, Manggarai Barat in 2003, and then later the parent district was further subdivided to create Manggarai Timur in 2007. In our baseline, we code these using the first date of the split. Results are robust to dropping these multi-split areas or to assigning the date of the split to the month in which the most splits took place for the given original district. However, in all cases, ∆diversity is computed over the full period, taking the original district and final parent and child districts. 14 (e.g., Dube et al., 2016; Dube and Vargas, 2013; Miguel et al., 2004). As noted earlier, we define conf lictdt as a binary indicator for any reported incident of social conflict and show robustness to intensive margin specifications. We estimate all specifications using linear probability models (LPM). The coefficient β identifies the average post-redistricting deviation from district-specific conflict trends. We further estimate versions of equation (1) that disaggregate the original districts d and identify separate β for parent and child districts, allowing each to have its own fixed effect and trend. The monthly panel specification leverages the granularity of both the conflict data and the policy changes (as split approvals vary at the month level). This allows us to capture episodic as well as recurring violence associated with discontent. This is especially useful for exploring political cycles of violence in Section 6, which vary at a sub-annual frequency. In addition, it may offer power benefits relative to a coarser annual frequency given the considerable within-year variation and relatively weak autocorrelation of conflict (see McKenzie, 2012).27 We cluster standard errors at the original district level but demonstrate robustness to a battery of alternative approaches to inference. Identifying Assumptions. Two key assumptions underlie the generalized DiD strategy: (i) the timing of redistricting is orthogonal to conflict and its determinants, and (ii) districts would have exhibited parallel departures from their conflict trends in the post-split period absent splitting. We provide supportive evidence here and present further robustness checks in Section 5.6. First, we show that the timing of redistricting is plausibly exogenous. Cross-sectional regressions in Table 1 relate a normalized x variable to the timing of the initial split in original district d—measured either as the number of months since January 2000 or an indicator for whether splitting occurred after the moratorium. In Panel A, there are no statistically or economically significant effects of initial diversity within the original district borders in 2000, the eventual parent/child district borders in 2010, or the border-induced change, ∆diversity. Although diverse districts are more likely to split, they are no more likely to do so earlier. Moreover, as seen in Panel B, we find similarly insignificant timing effects for a large set of 65 time-invariant or predetermined confounding variables considered in robustness checks. To ensure that more violent districts did not split earlier, we estimate a Cox (1972) proportional hazards (survival) model for time to split. The results in Panel C of Table 1 provide no evidence of a systematic relationship between changes in any social conflict and the timing of redistricting, yielding an insignificant hazard ratio of 1.18 with a p-value of 0.8.28 Our results in Table 1 are consistent with Burgess et al. (2012) who present complementary evidence on exogenous timing. Second, we provide evidence of parallel pre-trends. While the estimates in Table 1 are reassuring, one might still worry about spikes or dips in conflict prior to redistricting, particularly if such trends were differential with respect to (changes in) diversity. Event study specifications discussed below rule out these concerns. Overall, this evidence is consistent with the favorable environment for redistricting and the arbitrariness of approval timing due to the moratorium and other factors.29 27 To be sure, all results are robust to controlling for up to twelve monthly lags of conflict. However, we do not use the lagged dependent variable specification as a baseline because it is subject to dynamic panel bias in the presence of fixed effects. 28 The survival model framework is appropriate given that, unlike the covariates in Panels A and B, conflict is time-varying, and the Cox (1972) estimator allows, for example, for conflict incidence closer to the time of splitting to matter differentially from incidence in earlier periods. 29 That redistricting is a largely peaceful process here is likely a consequence of the limited and often favorable fiscal and legislative consequences for the parent, combined with these obvious benefits for child districts. While there are cases of violence 15