The Political Boundaries of Ethnic Divisions Part 3

Figures Figure 1: Indonesia’s Remarkable Wave of Redistricting Notes: This figure captures the evolution of new districts across Indonesia from 1980–2014 based on the month each district was passed into law. Figure 2: Redistricting across the Country Notes: This map plots the original and new district borders based on district-level shapefiles for 2000 and 2010. 34 Figure 3: Example of Redistricting into Parent and Child Districts Notes: This figure provides an example of the redistricting process and our nomenclature for the different administrative divisions. Figure 4: Examples of Border-Induced ∆ Diversity (a) Homogenizing Case: Aceh Tenggara District (b) Newly Salient Divisions: Kotawaringin Timur District Notes: This figure provides two examples of the types of redistricting patterns that we see in our data in terms of changes in policy-relevant ethnic diversity. Figure (a) shows the original district of Aceh Tenggara as in Figure 3, and Figure (b) shows the original district of Kotawaringin Timur, which splits into two child districts, Seruyan on the left and Katingan on the right with the parent district in the middle. In both figures, we color code the villages based on the majority ethnic group in the village in the 2000 Population Census with red indicating the largest group in the original district as a whole, aqua the second largest, and so on, with a few villages in white with missing data or inability to match with shapefiles. Figure 5: Diversity Before and After Redistricting (b) Polarization 1 .025 .8 Kotawaringin Timur .6 Aceh Tenggara .4 .2 ethnic polarization, post-redistricting ethnic fractionalization, post-redistricting (a) Fractionalization .02 Kotawaringin Timur .015 Aceh Tenggara .01 .005 0 0 0 .2 .4 .6 .8 ethnic fractionalization, pre-redistricting (2000) 1 0 .005 .01 .015 .02 ethnic polarization, pre-redistricting (2000) .025 Notes: These figures plot diversity at the original district level baseline boundaries in 2000 (x-axis) against the 2010 boundaries after redistricting (y-axis). The latter measure is the population-weighted average of diversity in the new parent and child districts, but again based on the population residing in those areas at baseline in 2000. The dashed 45 degree line indicates the locus of points along which the new borders imply no change in diversity, and the vertical distance between each point and the line captures our ∆diversity measure. For presentational purposes, we omit the top 4 districts with baseline polarization >0.025, though they are of course included in all regression analysis. In both graphs, we identify the two example districts seen in Figure 4. Figure 6: Simple Difference-in-Difference: ∆ Conflict against ∆ Diversity (b) ∆Polarization .4 .4 .2 .2 ∆social conflict ∆social conflict (a) ∆Fractionalization 0 -.2 0 -.2 -.4 -.4 -4 -3 -2 -1 0 ∆ethnic fractionalization, normalized 1 -2 -1 0 1 ∆ethnic polarization, normalized 2 3 Notes: This figure presents a simple difference-in-difference regression relating ∆diversity to changes in social conflict captured by the difference in the mean monthly likelihood of any social violence before versus after redistricting. The results are restricted to those 38 original districts with conflict data pre- and post-redistricting. Graph (a) shows results for ∆fractionalization and (b) for ∆polarization. The thick dashed lines are robust 95 percent confidence intervals. The point estimate in (a) is 0.002 (0.050) and in (b) is 0.068 (0.029)** with robust standard and the HC3 degrees-of-freedom adjustment. We omit one extreme outlier (with ∆P six standard deviations below the mean) based on standard outlier detection methods. All districts, including this outlier, are retained in the main generalized DiD estimates in the paper, and Appendix D.4 presents a battery of small-sample robustness and inference procedures. 37 Tables Table 1: Plausibly Exogenous Timing of Redistricting Dependent Variable: no. months until split 1(post-moratorium split) mean: 53 mean: 0.31 Panel A: Diversity Standardized Coefficient original district ethnic fractionalization original district ∆ ethnic fractionalization child district ethnic fractionalization parent district ethnic fractionalization original district ethnic polarization original district ∆ ethnic polarization child district ethnic polarization parent district ethnic polarization original district religious polarization original district ∆ religious polarization child district religious polarization parent district religious polarization 2.183 (4.267) 2.328 (3.417) 2.641 (3.984) 4.416 (3.962) -1.829 (2.882) 2.168 (1.880) -1.545 (3.122) 1.412 (4.002) 1.530 (3.343) -1.461 (2.276) -1.107 (3.821) 2.653 (3.749) 0.011 (0.071) 0.033 (0.061) 0.009 (0.069) 0.051 (0.066) -0.006 (0.048) 0.039 (0.026) -0.002 (0.045) 0.045 (0.071) -0.024 (0.060) 0.022 (0.033) -0.063 (0.060) 0.013 (0.063) 0.096 4 3.3 -0.005 3 3.3 Panel B: 65 Potential Confounders (see Appendix D.2) mean standardized coefficient actual number of significant predictors at 5% level expected number of significant predictors at 5% level by chance Survival Analysis Months-to-Split Cox Proportional Hazard Ratio [p-value] Panel C: Changes in Prior Conflict any social conflict 1.180 [0.789] Notes: Each cell is a different bivariate OLS regression of the timing of the first split on initial district characteristics, each of which is measured in 2000 before the onset of redistricting. The dependent variable in column (1) counts the number of months between January 2000 and the month in which each original district split, and in column (2) is an indicator for whether the split happened after the moratorium from 2004–6. Coefficients are based on standardized variables. Panel A looks at ethnolinguistic and religious diversity, including the ∆ measure capturing differences between parent/child and original district diversity levels. Panel B looks at the 65 controls capturing a broad array of confounders associated with proximity to security forces, economic development, public goods, demographics, natural resource intensity, political factors, economic structure, geography/topography, and remoteness. See Appendix D.2 for a discussion of the variables and Appendix F for further details. The mean effect size is the average standardized coefficient. The sample size is the 52 original districts in our main analysis, and all regressions include an indicator if the district entered the SNPK data in 2005. Robust standard errors are in parentheses. Panel C estimates a Cox Proportional Hazards model relating the time-varying incidence of any social conflict to the likelihood of splitting in a survival regression framework. 38 Table 2: Average Effects of Redistricting on Social Conflict Administrative Unit: post-split Number of District-Months Number of Districts Dep. Var. Mean, Pre-Split Original District (1) Parent & Child (2) Parent Child (3) (4) -0.008 (0.026) 0.001 (0.022) 0.002 (0.028) 0.001 (0.025) 7,956 52 0.57 20,220 133 0.33 7,956 52 0.47 12,264 81 0.25 Notes: The dependent variable in all columns is an indicator equal to one if there is any social conflict in that district– month. post-split is an indicator equal to one for all months after which the original or parent district experiences its first redistricting and a child district is officially passed into law. Columns 1–4 are estimated at the respective administrative unit level listed at the top of the column. There are 52 original districts in column 1, 133 parent/child districts in column 2, 52 parent districts in column 3, and 81 child districts in column 4. All specifications include month FE, district FE, districtspecific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. Table 3: Redistricting, Changing Ethnic Divisions, and Conflict Administrative Unit Original District (1) Parent & Child (2) Parent Child (3) (4) -0.012 (0.025) 0.036** (0.018) -0.003 (0.019) 0.014 (0.013) -0.003 (0.021) 0.032 (0.019) 0.000 (0.012) -0.009 (0.011) 0.001 (0.026) 0.027** (0.013) 0.035 (0.026) -0.031 (0.021) -0.005 (0.025) 0.043* (0.025) -0.011 (0.019) -0.005 (0.014) 7,956 52 0.57 20,220 133 0.33 7,956 52 0.47 12,264 81 0.25 post-split post-split×∆ ethnic polarization post-split×∆ ethnic fractionalization post-split×∆ religious diversity Number of District-Months Number of Districts Dep. Var. Mean, Pre-Split Notes: The dependent variable in all columns is an indicator equal to one if there was any social conflict in that district– month. post-split is an indicator equal to one for all months after which the original or parent district experiences its first redistricting and the child district is officially passed into law. ∆diversity measures are normalized to mean zero, standard deviation one. Columns 1–4 are estimated at the respective administrative unit level listed at the top of the column. ∆ of the given diversity measure captures the difference in diversity between pre-redistricting (2000) and post-redistricting (2010) boundaries, based on the initial population in 2000. For the original district, this is a weighted average of the parent and child districts. For the parent and child districts, this is a simple difference of their diversity and the original district’s diversity. All specifications include month FE, district FE, district-specific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. See the notes to Table 2. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 39 Table 4: Robust Inference and Outlier Removal Administrative Unit Original District (1) Parent & Child (2) Parent Child (3) (4) Panel A: Baseline post-split × ∆ ethnic polarization baseline: clustering on original district (OD) spatial HAC, 500 km uniform bandwidth effective degrees of freedom adjustment wild bootstrap, clustering on OD [p-value] randomization inference [p-value] post-split × ∆ ethnic fractionalization baseline: clustering on original district spatial HAC, 500 km uniform bandwidth effective degrees of freedom adjustment wild bootstrap, clustering on OD [p-value] randomization inference [p-value] Number of District-Months Dep. Var. Mean Pre-Split 0.036 (0.018)** (0.018)** (0.029) [0.117] [0.090]* 0.032 (0.019) (0.010)*** (0.023) [0.180] [0.017]** 0.027 (0.013)** (0.014)* (0.019) [0.166] [0.032]** 0.043 (0.026)* (0.015)*** (0.030) [0.472] [0.003]*** -0.003 (0.019) (0.021) (0.022) [0.880] [0.536] 0.000 (0.012) (0.011) (0.013) [0.982] [0.480] 0.035 (0.046) (0.018)*** (0.030) [0.214] [0.012]** -0.011 (0.017) (0.016) (0.021) [0.587] [0.773] 7,956 0.57 20,220 0.33 7,956 0.47 12,264 0.25 Panel B: Residual Outlier Removal post-split × ∆ ethnic polarization baseline: clustering on original district (OD) spatial HAC, 500 km uniform bandwidth effective degrees of freedom adjustment wild bootstrap, clustering on OD [p-value] randomization inference [p-value] 0.081 (0.016)*** (0.015)*** (0.024)*** [0.013]** [0.051]* 0.025 (0.008)*** (0.008)*** (0.009)** [0.033]** [0.100]* 0.041 (0.014)*** (0.010)*** (0.021)* [0.069]* [0.027]** 0.028 (0.013)** (0.011)** (0.015) [0.134] [0.082]* post-split × ∆ ethnic fractionalization baseline: clustering on original district (OD) spatial HAC, 500 km uniform bandwidth effective degrees of freedom adjustment wild bootstrap, clustering on OD [p-value] randomization inference [p-value] -0.027 (0.018) (0.020) (0.020) [0.255] [0.881] 0.007 (0.013) (0.012) (0.013) [0.551] [0.296] 0.045 (0.025)* (0.018)** (0.028) [0.067]* [0.005]*** -0.007 (0.018) (0.015) (0.020) [0.733] [0.605] 7,696 0.59 19,753 0.30 7,788 0.47 11,927 0.21 Number of District-Months Dep. Var. Mean Pre-Split Notes: This table demonstrates robustness of the baseline results for ∆P and ∆F in Table 3 to alternative inference and outlier removal procedures detailed at length in Appendix D.4. The results for post-split and ∆religious diversity are suppressed for presentational purposes. Panel A reports several alternative approaches to inference besides our baseline of clustering by original district: (i) the Conley (1999) spatial HAC estimator that allows for contemporaneous correlation in unobservables between all districts within 500 km in addition to the usual within-district correlation over time; (ii) a new “effective degrees of freedom adjustment” due to Young (2016), who adjusts standard errors by the effective sample size implied by the influence of each observation; (iii) a cluster wild bootstrap procedure due to Cameron et al. (2008); and (iv) a quasi-randomization inference (RI) procedure that randomly permutes the ∆diversity vector across each of the districts in the given regression before estimation, repeating 10,000 times to recover the implied p-values. Panel B additionally removes outliers in post-split × ∆P following the residual-influence approach in Belsley et al. (2005). See the notes to Table 2 for further details on the baseline specification used in both panels. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 40 Table 5: Further Isolating the Effects of Changes in Ethnic Divisions (I) Mean Feasible ∆ Diversity Based on Potential Redistricting Schemes Administrative Unit post-split post-split× mean feasible ∆ ethnic polarization post-split× mean feasible ∆ ethnic fractionalization post-split× mean feasible ∆ religious diversity Number of District-Months Number of Districts Dep. Var. Mean, Pre-Split Original District (1) Parent & Child (2) Parent Child (3) (4) -0.015 (0.027) 0.053** (0.023) -0.021 (0.017) 0.009 (0.022) -0.010 (0.022) 0.032** (0.014) -0.009 (0.011) -0.009 (0.013) -0.005 (0.027) 0.046*** (0.017) 0.016 (0.021) -0.012 (0.026) -0.011 (0.026) 0.031** (0.014) -0.019 (0.016) -0.001 (0.014) 7,680 50 0.57 18,540 121 0.34 7,680 50 0.47 10,860 71 0.25 Notes: This table presents a robustness check on the main results in Table 3. Instead of actual ∆diversity, this table uses the mean of feasible ∆diversity based on the simulation of potential legal redistricting schemes (see Section 5.6 and Appendix D.3, which also considers other moments besides the mean). ∆diversity measures are normalized to mean zero, standard deviation one. These regressions omit two original districts for which a large number of feasible partitions (over which to compute reliable moments) was computationally intractable. The dependent variable in all columns is an indicator equal to one if there was any social conflict in that district–month. All specifications include month FE, district FE, district-specific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 41 Table 6: Further Isolating the Effects of Changes in Ethnic Divisions (II) Administrative Unit Original District (1) Parent & Child (2) Parent Child (3) (4) Panel A: Other Controls×Post-Split Selected via Double Lasso (λ = 3, 000) post-split post-split×∆ ethnic polarization post-split×∆ ethnic fractionalization post-split×∆ religious diversity number of post-split × Lasso-selected controls Number of District-Months Number of Districts Dep. Var. Mean, Pre-Split -0.013 (0.026) 0.028** (0.013) -0.024 (0.020) 0.019 (0.013) -0.001 (0.017) 0.032** (0.016) -0.003 (0.012) -0.005 (0.012) -0.017 (0.024) 0.079*** (0.016) 0.017 (0.040) -0.026 (0.022) -0.003 (0.022) 0.040 (0.026) 0.016 (0.019) -0.010 (0.016) 6 7,956 52 0.57 19 20,220 133 0.33 14 7,956 52 0.47 12 12,264 81 0.25 Panel B: Accounting for Changes in Local Public Resources After Redistricting post-split post-split×∆ ethnic polarization post-split×∆ ethnic fractionalization post-split×∆ religious diversity post-split×∆ transfer revenue post-split×∆ distance to district capital Number of District-Months Number of Districts Dep. Var. Mean, Pre-Split -0.006 (0.027) 0.038** (0.014) -0.006 (0.022) 0.013 (0.013) 0.037 (0.028) -0.002 (0.019) -0.001 (0.021) 0.032* (0.017) 0.006 (0.012) -0.013 (0.012) 0.027** (0.012) -0.012 (0.013) 0.002 (0.027) 0.023 (0.015) 0.035 (0.027) -0.031 (0.021) -0.004 (0.021) 0.010 (0.016) -0.002 (0.024) 0.034* (0.020) 0.005 (0.017) -0.014 (0.016) 0.038*** (0.012) -0.033 (0.020) 7,836 51 0.57 19,980 131 0.33 7,836 51 0.47 12,144 80 0.25 Notes: This table presents two robustness checks on the main results in Table 3. Panel A introduces additional interactions of post-split and initial district characteristics. We rely on the Belloni et al. (2014) double-selection post-Lasso approach to select a parsimonious set of influential confounders from the large set of potential covariates we marshal from various data (see Appendix D.2 for details). Subject to a penalty parameter, λ, that helps control overfitting, each column includes a given set of additional post-split interactions with the number varying with the specification. Alternative values of λ are explored in Appendix D.2. Panel B augments the baseline specification in Table 3 with two measures capturing changes in public resources and proximity to government institutions due to redistricting. ∆ transfer revenue is the difference in log average annual transfer revenue post-split and average annual transfer revenue pre-split under the assumptions of allocations proportional to population pre-split in the parent and child district specifications. ∆ distance to district capital is the population-weighted average village-level difference in log reported travel distance to the district capital in 2011 (post-split) and 2000 (pre-split) as reported by village officials. All variables are normalized to mean zero and standard deviation one. These regressions omit one original district in Jakarta on account of it being in the national capital and not receiving the same stream of general district transfer revenue. The dependent variable in all columns is an indicator equal to one if there was any social conflict in that district–month. All specifications include month FE, district FE, districtspecific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 42 Table 7: Changes in Ethnic Divisions, Mayoral Elections and Conflict Administrative Unit post-split post-split×∆ ethnic polarization post-split ×∆ ethnic fractionalization post-split×∆ religious diversity post-split× first election period post-split× first election period ×∆ ethnic polarization post-split× first election period ×∆ ethnic fractionalization post-split× first election period ×∆ religious diversity post-split× second election period post-split× second election period ×∆ ethnic polarization post-split× second election period ×∆ ethnic fractionalization post-split× second election period ×∆ religious diversity Number of District-Months Num Dist Pre-Split Mean Parent & Child (1) Parent Child (2) (3) -0.003 (0.022) 0.030 (0.019) -0.000 (0.012) -0.007 (0.012) -0.002 (0.027) 0.025* (0.014) 0.034 (0.027) -0.033 (0.022) -0.008 (0.025) 0.042 (0.025) -0.011 (0.019) -0.002 (0.015) 0.044 (0.044) 0.026** (0.011) -0.004 (0.018) -0.009 (0.016) -0.006 (0.051) 0.014 (0.022) 0.043 (0.034) 0.016 (0.035) 0.041* (0.021) 0.027*** (0.010) -0.010 (0.018) -0.017 (0.021) 0.049** (0.022) 0.009 (0.010) 0.001 (0.018) 0.025 (0.027) 0.042 (0.027) -0.039*** (0.014) 0.026 (0.030) 0.052** (0.024) 0.055* (0.032) 0.056** (0.022) -0.012 (0.019) 0.015 (0.031) 19,980 131 0.325 7,836 51 0.467 12,144 80 0.245 Notes: The dependent variable in all columns is an indicator equal to one if there was any social conflict in that parent or child district–month (see the notes to Table 2). post-split is an indicator equal to one for all months after which the child district is passed into law. ∆diversity measures are normalized to mean zero, standard deviation one. Since post-split mayoral elections are specific to the new parent or child district, we focus here on specifications at that administrative level rather than the original district level. The first election period is an indicator capturing the 6 month window around the district-specific date of the first direct election for the district head after splitting into child and parent districts. The parent district elections occur based on the predetermined schedule inherited from the Suharto era while the child district elections typically occur around 1.5–2 years after redistricting. Hence, parent and child district elections occur at different times. The second election period is defined similarly and occurs five years after the initial election. These second election coefficients are only identified for the three-quarters of districts observed for long enough to hold that second round during our study period. We also include controls for the pre-split election periods, which take place in 10 of the original districts. Hence, the reference period in all columns is the pre-redistricting, non-election period. See Appendix F for details. All specifications include month FE, district FE, district-specific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 43 Table 8: Political Mechanisms: Mayoral versus Parliamentary Elections versus Ramadan Administrative Unit Parent & Child Parent Child event: Parliamentary Election (1) (2) (3) post-split post-split×∆ ethnic polarization post-split ×∆ ethnic fractionalization post-split×∆ religious diversity post-split× mayoral election period post-split× mayoral election period ×∆ ethnic polarization post-split× mayoral election period ×∆ ethnic fractionalization post-split× mayoral election period ×∆ religious diversity post-split× event period post-split× event period ×∆ ethnic polarization post-split× event period ×∆ ethnic fractionalization post-split× event period ×∆ religious diversity Number of District-Months Number of Districts Pre-Split Mean ∆ polarization × election = ∆ polarization× event, p-value ∆ fractionalization × election = ∆ fractionalization× event, p-value Parent & Child (4) Parent Child event: Ramadan (5) (6) -0.008 (0.021) 0.031 (0.019) -0.000 (0.012) -0.011 (0.012) -0.009 (0.027) 0.027** (0.013) 0.034 (0.027) -0.038* (0.021) -0.010 (0.025) 0.041 (0.026) -0.011 (0.019) -0.005 (0.015) 0.007 (0.022) 0.031 (0.019) -0.000 (0.013) -0.011 (0.012) 0.003 (0.028) 0.029** (0.014) 0.035 (0.027) -0.038* (0.021) 0.005 (0.026) 0.040 (0.024) -0.012 (0.020) -0.005 (0.015) 0.065 (0.043) 0.016** (0.007) 0.001 (0.013) 0.007 (0.016) 0.053 (0.046) -0.010 (0.008) 0.037** (0.016) 0.041** (0.016) 0.046*** (0.017) 0.039*** (0.010) -0.008 (0.014) -0.003 (0.018) 0.059 (0.043) 0.017** (0.007) 0.000 (0.013) 0.007 (0.016) 0.046 (0.046) -0.009 (0.007) 0.036** (0.017) 0.040** (0.016) 0.046*** (0.017) 0.040*** (0.011) -0.010 (0.014) -0.004 (0.017) 0.154** (0.066) -0.013 (0.010) 0.008 (0.017) 0.019 (0.011) 0.266*** (0.077) 0.002 (0.029) 0.025 (0.040) 0.063** (0.029) 0.091 (0.085) -0.020 (0.016) 0.012 (0.018) 0.007 (0.013) -0.068** (0.029) -0.001 (0.006) 0.003 (0.010) 0.011 (0.009) -0.038 (0.038) -0.008 (0.009) -0.002 (0.012) 0.021 (0.016) -0.079* (0.041) 0.003 (0.008) 0.007 (0.014) 0.006 (0.011) 19,980 131 0.33 0.005 0.689 7,836 51 0.47 0.654 0.788 12,144 80 0.25 0.004 0.324 19,980 131 0.33 0.001 0.883 7,836 51 0.47 0.915 0.101 12,144 80 0.25 0.011 0.433 Notes: This table allows the effects of ∆diversity to vary with mayoral election periods and parliamentary election periods in columns 1–3 and holy month of Ramadan periods in columns 4–6. Mayoral election periods pool the first and second election windows considered separately in Table 7. Parliamentary election periods comprise the 6 month windows centered on the month of April in 2004, 2009, and 2014 when such elections take place simultaneously around the country. Ramadan periods include all Gregorian calendar months during which any of the lunar holy month falls. The own postsplit×∆diversity terms therefore capture the differential effects of diversity in all other months outside the given periods of interest. The dependent variable in all columns is an indicator equal to one if there was any social conflict in that parent or child district–month (see the notes to Table 2). post-split is an indicator equal to one for all months after which the child district is passed into law. ∆diversity measures are normalized to mean zero, standard deviation one. All specifications include month FE, district FE, district-specific time trends, and dummies for the number of papers used by coders for the given province–month. Standard errors are clustered by original district. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 44 Table 9: Light Intensity and Changes in Village-Level Alignment with the Largest Ethnic Group in the New Versus Original District post-split post-split × ∆ share of village in largest ethnic group in district > 0 post-split × ∆ share of village in largest ethnic group in district = 0 Number of Village–Years Dep. Var. Mean, Pre-Split Parent Child (1) Parent Child (2) (3) -0.020 (0.012) 0.011* (0.006) 0.028** (0.013) -0.006 (0.022) 0.012 (0.009) 0.017 (0.017) -0.033** (0.013) 0.021* (0.011) 0.040** (0.018) 164,594 0.163 85,401 0.229 79,193 0.114 Notes: The dependent variable is share of village area covered with any nighttime lights. The village-level panel spans 2000–2013. The regressions also control for village and year fixed effects. The regressor in row 2 (3) equals one if the share of the given village belonging to the largest ethnic group in the district increased (remained the same) as a result of redistricting. The ethnic shares are, as throughout the paper, defined based on the population in 2000. Standard errors are clustered at the original district level. Significance levels: ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1%. 45 A Further Background on District Proliferation Figure A.1 provides a timeline of events over our study period, including major decentralization, redistricting, and electoral reforms. Below, we provide further details on the implications of redistricting discussed in Section 2.2. Figure A.1: Timeline of Events Sample Period Parliamentary elections occurred in 2004, 2009, and 2014 Direct elections of district executive staggered throughout post-2005 period Major decentralization laws passed Government regulation on splitting Regulations tighten on splitting 1st moratorium 1999 2000 First wave of splits 2001 2003 2004 2nd moratorium 2006 2007 2008 2009 2012 2014 End of our sample First splits in our sample Start of our sample First splits post-moratorium Last splits in our sample pre-moratorium Last split in our sample Size of Government. In the typical district, between 1,200 and 2,000 new jobs are created (according to interviews and province-level yearbooks). We have found no evidence that the total number of offices and jobs decrease in the parent district. Thus, the overall number of civil servants per capita increases substantially, and these newly created jobs are important for setting and executing public policy. In addition, there are apportionment gains to redistricting due to the step function rule used to determine the seat-to-population ratio. Seats in local parliament always weakly increase with redistricting. For example, an original district with 400,000 people initially would have 40 seats. If it split into two equally sized districts, each would have 30 seats for a total of 60 compared with 40 originally. Fiscal Resources. Redistricting also leads to an increase in transfers from the central government. We estimate the effects of splitting on total per capita transfers in our sample using the within-district identification strategy detailed in Section 4.1 Once new funds for the child district start flowing in approximately two years after the split, real transfers at the original district level increase by 18–25 log points off a mean of roughly USD 200 (Table A.1, Panel A, Column 1).2 These revenue increases pass through to significant increases in local government expenditures in the following year. We cannot observe how transfers were divided between child and parent areas before redistricting. However, one natural benchmark is to  assume  that pre-split transfers (T) wereallocated according to N parent population with the parent receiving T and the child receiving Nchild T . We use this benchN N mark to perform two exercises that clarify the overall fiscal benefits of redistricting and the differential 1 Initial population is absorbed in the fixed effect, and while including time-varying population does little to change the point estimates, it introduces unnecessary noise as the data is incomplete and requires estimation and imputation. 2 Note that the decline in transfers in the year after splitting reflects a short adjustment period when child district transfers have only slowly started to flow into the new public coffers while parent district transfers have begun to adjust downward to account for their now smaller population. 47 gains to child districts. First, we take the original district transfers as given and compare realized transfers post-split to the expected transfers if they had continued to be allocated proportional to population. Second, we assume that parents and children receive their population shares of the original district transfers pre-split (and in the year of the split when nothing yet changes). Then, we continue this time-series post-split using the actual, observed transfers at these lower administrative units. This allows us to re-estimate regressions like that in column 1 of Panel A in Table A.1 at the smaller units. First, we simply compare realized transfers in post-split years at the parent/child level to expected transfers based on population shares of the realized original district level transfers in all post-split years. We plot the distribution of these differences between actual and expected transfers (based on population shares of the realized original district transfers) in Figure A.2. This shows the difference (in USD) for all post-split years and districts in our sample but looks comparable if plotted year-by-year. It is evident that children receive more than expected based on population shares and, consequently, parents less. In the average post-split year, parents receive USD 7.4 million less than expected (USD 16 per capita) and children receive USD 5.1 million more (USD 58 per capita). This strongly suggests that the gains from redistricting accrue disproportionately towards children. This finding is in line with the upfront costs of establishing new government institutions. For example, around 40–50 percent of expenditures go towards staff, which expanded greatly in the child but not the parent. Note that while children gain disproportionately from splitting, parents nevertheless tend to see an increase in transfers as well. To see this, suppose that parents received their population share of original district transfers pre-split. Parents receive a lower share of a larger total transfer ‘pie’. In practice, this still results in an increase in transfers per capita at the parent level as made clear in the second exercise. Second, columns 2–4 of Panel A in Table A.1 make these patterns even clearer by showing that both parents and children benefit from splitting in terms of real transfers, but children clearly benefit more. Parent districts experience roughly a 19 log point increase in long-run transfers relative to the pre-split period (column 3), whereas child districts experience a 59 log point increase (column 4). While these results are subject to strong assumptions about the pre-split allocation of transfers, different assumptions are unlikely to explain away the main takeaways that (i) overall transfers increase in both parent and child districts, and (ii) child districts benefit relatively more than parent districts. Proximity to Government. In addition to receiving increased transfers, child district residents experience a significant reduction in the average distance to government institutions. Panel B of Table A.1 shows how reported travel distance to the capital (in kilometers) changed after splitting. These estimates are based on reports by the village head in 2000 and 2011 from Podes, which we aggregate to 2010 district borders using population weights. While parent districts experienced little change in distance to the capital, child districts register an average reduction of around 55 km off of a pre-split mean of 100 km. Finally, note that these changes imply a significant reduction in the size of the population governed by any given district. According to Census data from 2000, districts based on 2000 boundaries have a median population of 400,000 whereas the 2010 district boundaries imply a median of 250,000. 48 Figure A.2: Comparing Fiscal Transfers Between Parent and Child Districts Notes: This figure plots the density of the difference in actual versus expected fiscal transfers for parent and child districts post-split under the assumption that the expected transfers are allocated proportional to population share of the original district. Table A.1: Splitting-Induced Changes in Transfer Revenue and Distance to Capital Panel A: Effects on ln(total fiscal transfers) Administrative Unit ≤ 2 Years Pre-Split 1 Year Pre-Split Year of split 1 Year after Split 2 Years after Split 3 Years after Split 4 Years after Split 5+ Years after Split No. of District–Years Dep. Var. Mean Original District (1) 0.073* (0.041) -0.029 (0.023) -0.113* (0.059) 0.093 (0.057) 0.180*** (0.058) 0.246*** (0.064) 0.207*** (0.053) 765 26.6 Parent Child (2) Parent Child (3) (4) -0.005 0.028 (0.032) (0.030) reference period -0.022 0.002 (0.026) (0.021) 0.073* 0.252*** (0.041) (0.051) 0.314*** 0.211*** (0.047) (0.051) 0.474*** 0.263*** (0.042) (0.051) 0.500*** 0.290*** (0.038) (0.056) 0.444*** 0.187*** (0.050) (0.058) 1,965 765 25.6 26.0 -0.017 (0.038) -0.035 (0.035) -0.047 (0.061) 0.368*** (0.060) 0.596*** (0.052) 0.620*** (0.047) 0.593*** (0.064) 1,200 25.3 Panel B: Effects on Distance to District Capital (kilometers) Parent Districts Child Districts Pre-Split Mean 48.9 [33.3] 99.8 [79.5] Mean Change -5.7 [18.2] -55.5 [8.04] Median Change -1.14 -38.5 Notes: Panel A reports a regression of log per capita transfer revenue in real 2010 USD (see Appendix F) on dummies pre- and post-split as well as district fixed effects, year fixed effects, and district-specific time trends. Details on the transfer time series are discussed in the text above. Standard errors are clustered at the original district level. Panel B reports the average change in distance to the capital in kilometers, constructed from the Podes 2000 and 2011 administrative censuses, for parent (and child districts separately. We are missing data for a small number of the districts in Aceh in 2003. Standard deviations in brackets. 49 B Conceptual Framework: Further Details In this appendix, we make explicit the general points made in Section 3 using two specific models. First, we present a simple two-group model of conflict over a public good, which shows that changes in violence can be derived from a simple, linear function of border-induced changes in diversity. Second, we present the implications of border changes for a special case of Esteban and Ray’s (2011a) approximation, which generates richer predictions. B.1 A Simple Two-Group Framework1 Baseline Setup. Suppose an original district (O) is composed of two groups with population GO,1 and GO,2 and total population GO = GO,1 +GO,2 . Denote by gO,i the share of group i in the population. These groups compete over a public prize. The prize, being non-excludable and non-rival, is not diminished by group size, but the winner of the contest gets to choose the mix of public goods that their group prefers. We assume the winner chooses a level that provides their own group with value R per person and the other group with 0. The leader of each group i chooses total violence VO,i given its per unit cost γ. The VO,i . probability of group i winning control over the public prize is given by the contest function VO,1 +V O,2 That is, each group i, taking as given the Each group leader chooses VO,i tomaximize per capita payoffs.  other’s choices VO,−i , maximizes VO,i VO,1 +VO,2 R γ − VO,i GO,i .   VO,1 +VO,2 VO VO R The Nash equilibrium level of conflict per capita, G = , is given by = g g O,1 O,2 γ . GO,1 +GO,2 GO O Thus, total violence per-capita in the district depends on the value of the public prize, R, the costs of conflict, γ, and a measure of diversity, gO,1 gO,2 , which is greatest when both groups are of equal size. For two groups, this measure equals 0.5× a standard fractionalization measure. Effects of Redistricting. We trace out the implications of changing borders on conflict under the assumption that splitting creates new, separate contests in parent and child districts. Conflict within each of the new districts will now be a function of the diversity within each new area. Assume for now that winning the prize continues to yield R per person within group and 0 otherwise and that the costs of conflict remain unchanged. Using P to denote the parent and C the child district, the change in total violence per capita at the original district level is:   ∆VO R GP GC , = gP,1 gP,2 + gC,1 gC,2 − gO,1 gO,2 GO GO GO γ where gi,j is the share of group j in new district i, and GO = GP + GC . That is, the change in violence per capita is explicitly a function of the difference in the population-weighted average of diversity within the new units relative to the diversity in the original district pre-split. In the event that the groups separate into perfectly homogeneous child and parent districts, all violence in the original district ceases. It is also interesting to consider changes within the new borders. This requires taking a stance on how violence is initially distributed across parent and child. Letting σ be the share of total violence falling in the parent district, the change in conflict within the parent district is given by:   ∆VP R GO . = gP,1 gP,2 − σ g1 g2 GP GP γ GP If violence is initially distributed according to population (σ = G ), the change in per-capita violence O within the eventual parent border is given by the difference in the diversity within that new unit and the overall diversity in the original district. 1 We thank Enrico Spolaore for suggesting this framework. 50 The model also implies that changes in the value of the public prize (R) or the costs of violence (γ) will change conflict. Redistricting is accompanied by an influx of government resources (R) as well as reductions in the distance to the new capital, which could affect γ. If R/γ increases in the newer units, then this will exacerbate ∆ conflict, particularly in newly diverse areas. Adapting the model to incorporate these changes, we find a similar link between changes in diversity and changes in conflict but one that puts greater than population-weight on diversity in the newer units. Finally, note that this simple model uses a single metric of diversity (a scalar multiple of fractionalization) and does not have the richer dynamics of a model like Esteban and Ray (2011a) which distinguishes between polarization and fractionalization, which we turn to next. B.2 An Esteban and Ray (2011a) Framework Esteban and Ray (2011a) model groups contesting a budget with per-capita value π + µ, some of which can be distributed privately and the remaining is public with the winning group choosing their preferred mix of public goods. For large N and for an isoelastic cost function c(r) = (1/θ)rθ , per-capita conflict is given by VG ≈ (α[πP + µF ])1/θ , where α is within-group cohesion, π (µ) is the population-normalized public (private) payoff of the conflict prize, P is ethnic polarization, and F is ethnic fractionalization. The paper explains the sense in which this is an approximation. Effects of Redistricting. We trace out the implications of changing borders on conflict in this model under the assumption that splitting creates new, separate contests in parent and child districts. Conflict within each of the new districts will now be a function of the diversity within each new area. Let O denote the original district boundaries, P denote the parent district, and C the child district. Further assume that per-capita payoffs remain unchanged within each new area. Then, the change in total violence per-capita at the original district level is:   ∆VO GP GC = α1/θ ([πPP + µFP ])1/θ + ([πPC + µFC ])1/θ − ([πP + µF ])1/θ , GO GO GO for original district population GO = GP + GC . That is, the change in violence per capita is explicitly a difference between population-weighted functions of diversity within the new units relative to a function of diversity in the original district pre-split. In the event that the groups separate into perfectly homogeneous child and parent districts, all violence in the original district ceases. It is also interesting to consider changes within the new borders. This requires taking a stance on how violence is initially distributed across parent and child. Letting σ be the share of total violence falling in the parent district, the change in conflict within the parent district is given by:   ∆V GO 1/θ 1/θ 1/θ ([πPP + µFP ] − σ =α ([πP + µF ]) GP GP GP ), the change in per-capita violence If violence is initially distributed according to population (σ = G O within the eventual parent border is given by the difference in the diversity within that new unit and the overall diversity in the original district. The model also implies that changes in the value of the prize (π, µ), social cohesion (α) or the costs of violence (which vary with θ) will change conflict. Redistricting is accompanied by an influx of government resources as well as reductions in the distance to the new capital, which could affect costs. If the value of the prize increases in the newer units, then this will exacerbate ∆ conflict, particularly in newly diverse areas. 51 C C.1 Measuring Conflict: Background and Robustness Indonesia’s National Violence Monitoring System (SNPK) Indonesia’s National Violence Monitoring System (NVMS) or SNPK by its Indonesian acronym (Sistem Nasional Pemantauan Kekerasan) is among the world’s largest single-country, geospatial conflict databases. After compiling several million images from over 120 carefully screened local newspapers, data entrants classify the nature of violence underlying each reported event into one of the 10 categories listed below in Table C.1.1.1 There are further subcategories within each category of conflict. For example, when available, each event also includes information on the number of deaths, injuries and buildings destroyed. Table C.1.1: Violence Categories in the SNPK Resource Conflict Violence triggered by resource disputes (land, mining, access to employment, salary, pollution, etc.). Governance Conflict Violence is triggered by government policies or programs (public services, corruption, subsidy, region splitting, etc). Popular Justice Conflict Violence perpetrated to respond to/punish actual or perceived wrong (group violence only). Elections Conflict Conflict Violence triggered by electoral competition or bureaucratic appointments. and Appointment Separatist Conflict Violence triggered by efforts to secede from the Unitary State of the Republic of Indonesia (NKRI). Identity-Based Conflict Violence triggered by group identity (religion, ethnicity, tribe, etc). Other Conflict Violence triggered by other issue. Violence During Law Enforcement Violent action taken by members of formal security forces to perform law-enforcement functions (includes use of violence mandated by law as well as violence that exceeds mandate for example torture or extrajudicial-shooting). Violent Crime Criminal violence not triggered by prior dispute or directed towards specific targets. Domestic Violence Physical violence perpetrated by family member(s) against other family member(s) living under one roof/same house including against domestic workers and violence between cohabiting couples. As discussed in Section 4.2, we rely on this rich, human-led classification system to isolate social conflict as opposed to (unorganized) interpersonal violence or crime. Of course, the lines between categories are often fuzzy.2 Nevertheless, in a robustness check in Appendix C.3, we effectively show that our core results are not driven by the particular measure of social conflict. Moreover, as proof of concept, it is reassuring that the differential social conflict around mayoral elections in Table 7 is indeed driven in large part by violence categorized as “elections and appointments conflict.” 1 The data report other information about each event such as the actors involved, the organizational form of violence (e.g., riot, kidnapping), weapons used, and outcome of external intervention. While potentially useful, this information is much less systematic and comprehensive than the categorization into types of violence, which is the most directly related to the conceptual framework and broader interest in the paper. 2 This description from the data manual provides further background that may be illustrative: “According to NVMS system, violent crime comprises acts of violence that occur without any prior dispute between parties. The motivation behind a criminal act can be monetary, for example, robbery or abduction; or personal pleasure, for example, rape or serial killings. In contrast, violence in the context of conflict occurs due to pre-existing disputes between those involved such as dispute over land, election, religion or other such matters. As such, in the NVMS system, an act of killing can be coded as ‘Conflict’ if there is a dispute behind it, e.g., in a killing of a certain group figure by other groups, or can be coded as ‘Crime’ if there is no pre-existing dispute between parties, for example, serial killings.” Event Descriptions. The following Appendix C.2 provides several examples of events in the “elections and appointment” conflict category. Below, we provide examples from a few of the other categories beginning with “governance”, which, like elections/appointments, is plausibly responsive to a similar sort of sociopolitical changes associated with redistricting. 1. Pontianak City, 24 July 2006: Hundreds of residents from 6 villages came to the office of Sungai Kunyit Subdistrict. They protested the perceived unfair distribution of the unconditional cash transfer (BLT) funds. They then threw a chair at the sight of a BPS (Central Statistical Agency) representative. Some community leaders and the subdistrict head calmed the masses. 2. Kotawaringin Timur District, 21 June 2012: People burnt a temporary bridge in Seruyan Hilir subdistrict because they argued that the government took too long to build the main permanent bridge. 3. Singkawang District, 5 December 2008: Protests led by Front Pembela Islam (FPI), Front Pembela Melayu (FPM), and Aliansi LSM Perintis Singkawang. They argued that dragon statue is a religious symbol, and hence a public road is not the proper place to build that symbol. In addition, the dragon statue is perceived as Chinese symbol. FPI claimed that symbols for particular ethnic groups cannot be placed in public places. Note that the last example above could clearly have also been classified as ‘Identity-Based Conflict’, pointing to the fuzziness across categories as noted earlier. As noted in Section 6.3, there are also numerous governance incidents involving violence directed at the public electricity monopoly (PLN) centered on frustration with electricity allocation. A few examples follow: 1. Tapanuli Selatan District, 23 November 2011: In the office of PLN . . . , about 200 people demonstrated and damaged the office. The action was triggered by anger over electricity being out for three months. 2. Sumbawa District, 14 December 2014: In the office of PLN . . . , there was an attack perpetrated by local residents on the head of Human Resources. The incident started when hundreds of residents went to the PLN office to protest the frequent power outages in the last 4 months and the recent total power outage for two days without notice. Angry demonstrators hit the HR’s head, leaving him injured. Fortunately, the action did not escalate further because the military, police and village heads intervened to quell emotions. A few other illustrative examples come from the “resource conflict” category: 1. Aceh Singkil District, 30 May 2011: Two hundred people demonstrated in front of the mayor’s office of Aceh Singkil in relation to land disputes with companies of Malaysian origin. They also demanded a fair and fixed land [compensation]. 2. Halmahera Tengah, 30 Jan 2012: Hundreds of East Halmahera residents burned tires and blocked roads at the PT Kemakmuran Pertiwi Tambang (PT Harita Grup) nickel mining site in Loleba village. Comparison to Other Conflict Data. The SNPK data offer several advantages over two alternative sources of information on violence in Indonesia. First, it offers more comprehensive temporal coverage than the triennial Potensi Desa (or Podes) data, which records information on the violent events at the village-level over the prior three-year period. This coarse coverage would not allow for the systematic generalized difference-in-difference identification strategy we deploy here. Moreover, Podes accounts are based on the self-reports of village leaders as opposed to the plausibly more objective, cross-validated newspapers reports in the SNPK. Second, the SNPK offers significantly more comprehensive coverage compared to a widely used, cross-country, subnational data source. The Uppsala Conflict Data Program (UCDP) Georeferenced Event Data (GED) (Sundberg and Melander, 2013) has been fruitfully deployed in a range of subnational conflict studies and with particular success in sub-Saharan Africa alongside the widely used Armed Conflict Location & Event Data Project (ACLED) data. The UCDP-GED is available for Indonesia whereas 53 the ACLED is not (yet). Mapping the UCDP-GED events to our original district monthly panel, we find very limited coverage of social conflict events in Indonesia. While SNPK covers 223 of the 230 original district–month incidents in the UCDP-GED data, there are 4,795 additional district–months with social conflict incidents in the SNPK. Together, these violent events involve nearly 5,000 deaths over a 15 year period. The more limited coverage by UCDP-GED is explained by both its more narrow focus on largescale conflict and by its reliance on international news sources and or English-based ones in Jakarta. The SNPK offers much deeper coverage precisely because it digitized millions of old newspapers from outlying regions of the country that allowed for coverage of violence that may have otherwise missed the attention of international reporters. Barron et al. (2016) offer a more systematic comparison (for all of Indonesia) by applying particular restrictions in the SNPK that more closely match those applied in the UCDP-GED. Their conclusion is similar to ours; the UCDP-GED cover around one-third of the events and and deaths reported in the SNPK. Costs of Conflict. The violent episodes in SNPK can be costly. Even if we examine the least violent years and restrict to social conflict, we observe around 500 annual deaths, 7,000 annual injuries, and 1,500 annual buildings damaged. Including crime and domestic violence more than doubles these numbers. Using a methodology due to Fearon and Hoeffler (2014), we estimate that the direct costs of social conflict in the post-2005 period range from 0.2–0.5% of GDP. C.2 Electoral Violence in the SNPK As discussed in Sections 2 and 3, Harish and Toha (2017) use the SNPK data to identify three salient types of electoral violence in Indonesia: (1) voter-targeting is “any kind of election-related violence that affects voters’ preferences participation in elections”, (2) candidate-targeting directs violence towards “candidates themselves and those around them by intimidating them into withdrawing and/or physically and forcefully removing them from the race”, and (3) government-aimed is “violence mounted against a government agency responsible for monitoring and enforcing rules of elections.” The authors use SNPK data combined with supplementary reporting to categorize over 1,000 episodes of local election violence in Indonesia since 2005. Attacks targeting candidates are the most common, occurring on 35 percent of the days in a six month window centered on the election. Voter-targeting occurred in 25 percent of those days, and agency-targeting on 17 percent of days. Not surprisingly, most candidate-targeting is concentrated in the lead-up to the election with attacks on election-related government agencies occurring thereafter. Drawing upon the same SNPK data, we provide some concrete examples of incident reports that clarify the types of electoral violence underlying these patterns. The following are district-specific examples that we translate from the SNPK: 1. Aceh Singkil District, 2 November 2013: Protest at Komisi Independen Pemilihan (KIP, Independent Commission for Elections) by supporters of Affan Alfian-Pianti Mala (Walikota-Wakil Walikota [mayor–vice mayor] candidate) regarding fraud in mayoral election. Seven people were reported seriously injured. The election took place on 29 October. 2. Aceh Barat Daya District, 28 June 2012: Supporters of FD (mayoral candidate for Aceh Barat Daya) were attacked by their competitors in Kuala Terubu Village and Alue Sungai Pinang village. The election took place on 9 April 2012. 3. Halmahera Utara District, 16 April 2005: Komisi Pemilihan Umum Daerah (KPUD, Local General Elections Commission) office and the house of the Partai Demokrasi Kebansaan (PDK) chairman were destroyed by people because one of the candidate was not selected in mayor–vice mayor ticket. Two buildings were damaged and one destroyed. The election took place on 27 June 2005. 4. Kepulauan Sula District, 12 May 2005: Molotov bombing of the local Electoral Commission office due to anger with the decision about four mayoral candidates. The election took place on 27 June 2005. 5. Pulau Morotai District, 21 May 2011: Mass supporters of RS and WP [mayoral candidate and running mate] who did not accept the decision of the Morotai Electoral Commission in the election took action in the Morotai air force base, South Morotai, northern Maluku, by trying to break. . . Four people were injured, and one building was damaged. Subsequent violent incidents were reported on May 26 and 27. The election took place on 16 May 2011. 6. Kotawaringin Timur, 6 June 2005: Incident between supporters of mayoral candidates Wahyu-Amrullah and Thamrina-Mullan Safri because one of them established billboard in the other candidates’ area (Seruyan) K Timur: On Jalan Mayjen Suprapto, Seruyan Hilir subdistrict, billboard of mayoral candidate was destroyed, occurred around mayoral election time. In Danau Sembuluh subdistrict, AS (legislative member candidate for Dapil [electoral region] II) was attacked by people (one of them was legislative member candidate for Dapil [electoral districdt] II). Two people were seriously injured. The election took place on 23 June 2005. 7. Bengakayang, 21 May 2010: In the Local Electoral Commission office, demonstrations took place with rioters throwing stones at the building and officials out of anger over the election outcome. One building was damaged. The election took place on 19 May 2010. C.3 Robustness of Main Results to Alternative Categorizations of Conflict Our main analysis considered a set of violence categories in the SNPK that aimed to capture group-based conflict. This appendix rules out two potential concerns with the measure of social conflict we use based on the SNPK groupings. First, some of the crime-related categories of violence may be shaped by similar (changes in) ethnic divisions as other categories deemed to fall under conflict.3 Hence, their omission may be deemed arbitrary at best and biasing at worst. Table C.3.1 shows that the main results in Table 3 are robust to not restricting the definition of violence. Indeed, the estimated effects of ∆diversity are very similar. The increase in precision may be due to the fact that the broader grouping reduces classical measurement error of the sort discussed in Appendix C.1. 3 Echoing this interpretation, one of the architects of the SNPK notes in a later reappraisal that “What may appear to be local violence (crime, interpersonal clashes over land) is often linked in complicated ways to the broader conflict” (Barron et al., 2016, p. 25). This would be consistent with the ethnic-related criminal gangs documented at length in the Wilson (2015) book that we cite in the paper. Indeed, many of these gangs are often mobilized for conflict by political actors during times of instability around elections. Another, broader interpretation of this concern would be that changes in ethnic divisions further undermine local state capacity that helps to forestall a breakdown in social order and prevent various types of crime. 55