Civil War and Social Cohesion: Lab-In-The-Field Evidence from Nepal

Civil War and Social Cohesion: Lab-in-the-Field Evidence from Nepal

Supporting Information

Michael J. Gilligan, Benjamin J. Pasquale, & Cyrus Samii Department of Politics, New York University

Contents

A Identiﬁcation Strategy and Sampling for the Lab-in-the-Field Component of the Study 2

B Sampling Design for the Full Study 8

C Non-Response Issues in the Broader Sample and the Lab-in-the-Field Sample 15

D Game Scripts 16

E Constructing the Distance to Road Measure 18

F Robustness Checks for Main Results 19

1 A Identiﬁcation Strategy and Sampling for the Lab-in-the-Field Component of the Study

Does conflict create social cohesion among community members or are pro-social communities more prone to conflict, perhaps because it it is easier for rebel groups and militias to recruit fighters there (Kalyvas, 2006)? We address this endogeneity problem by selecting a locale for study, Nepal, where communities were exogenously isolated from the conflict due to the unpredictable nature of the insurgency (as described above) combined with the country’s rugged terrain. In this way our study, by design, directly addresses the potential endogeneity question. We argue that optimal, purposeful strategic unpredictability interacted with the ruggedness of Nepal’s terrain in an interesting way. When forces decided to proceed down one corridor, villages in the other corridors were shielded from the conflict-related violence because of the terrain. Indeed, as we show in greater detail below, communities that were near each other in terms of distance, but separated into different corridors, strongly resemble each other in terms of social and economic conditions but were exposed to vastly different levels of wartime violence. We use these facts to identify community-level causal effects of exposure to wartime violence on post-war social cohesion. Our causal estimand is the effect of fatal war-related violence on social cohesion in places that stood some chance of being affected by such violence—a type of “average effect of the treatment on the treated” (ATT) (Angrist and Pischke, 2009, 70). Our identification strategy consists of finding matched sets of communities such that, 1. Within each set, the communities differ in their levels of exposure to violence. 2. The matched communities resemble each other on pre-violence covariates that ought to be prognostic of both likely exposure to violence and pro-social cohesion. 3. Our control communities are shielded from spill-over. 4. The only plausible reason for the difference in exposure to violence is due to the unpredictabile path of the insurgency and counter-insurgency.

For the first element of the identification strategy, we gathered data on accumulated levels of war-related fatalities per “Village Development Committee” (VDC) from the 1996-2006 editions of the Human Rights Yearbook produced by the Nepalese human rights NGO, Informal Service Sector (INSEC).1 INSEC’s reporting on the deaths contains enough information to determine the home VDC of 59.6% of those recorded as killed. Thus, at the VDC level, the data provide a somewhat incomplete picture of total deaths. To remove some of the resulting noise, we coarsened the measure into a three-level fatality rate score: high death rate refers to over 7.5 fatalities per 1,000 inhabitants in a VDC; medium death rate refers to rates above 0 but less than 7.5 per 1,000; and none means that there were no verified deaths.2 This coding was based on break points in the histogram of death rates. In the paper, we only compare VDCs with “high-death-rate” and “none.” Again, this should help to reduce some of the error due to incomplete data. The coarsened fatality data are displayed geographically in Figure 1. VDCs are shaded according to the score. District boundaries are also shown, and Kathmandu’s location is indicated as a reference point. Our research sites (VDCs) are circled. The fatality rate data exhibit extreme local variation. Within high- conflict-intensity districts there are low-conflict-intensity VDCs, and within low-conflict-intensity districts there are high-intensity VDCs.

1VDCs are Nepal’s second-tier, rural administrative units. There are 3824 VDCs across Nepal’s 75 districts; VDCs are exclusively rural, with urban areas demarcated separately as municipalities. VDCs are similar to counties in the United States, containing on average 5,000 individuals each, and consisting of 9-12 wards, which are like townships in the United States. 2Below, we discuss covariate stratification based on “Maoist control” and “armed confrontations” between Maoist and state forces. In the stratum defined by no Maoist control and no armed confrontations, the verified number of fatalities was never above 4 per 1,000. However, approximately half of fatalities could not be attributed to a VDC, and so we used various imputation strategies to allocate these fatalities to the VDCs in this stratum. This included simple proportional allocation as well as imputation based on a Poisson regression model. We assigned as “high” fatality VDCs in this stratum those VDCs that had higher than 7.5 deaths across all imputations.

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Figure 1: Death rates by VDC, with locations of games shown (the black circles). High refers to over 7.5 fatalities per 1,000 VDC inhabitants; medium refers to above 0 but less than 7.5 per 1,000; and none means no veriﬁed deaths. Sources: Informal Sector Service Centre (1996-2006) and Nepal National Geographic Information Infrastructure Program.

Because of the incompleteness of the INSEC data, we performed a validation check by comparing our data to a map that we obtained from the United Nations Office for the Coordination of Humanitarian Affairs (OCHA). This is displayed in Figure 2. The OCHA map was produced independently of INSEC, and shows the extent of wartime damage to VDC headquarters buildings reported by Chief District Officers in 2006. The clearly high correlation of the two measures gives reason to believe in their general accuracy. Because the mechanisms that we wish to study are related to fatal violence, we use the INSEC measure as our independent variable in this study. For the second and third elements of the identification strategy, we matched VDCs on the basis of available covariates subject to the restriction that pairs were not located in the same hill/mountain corridor or valley. The restriction is necessary specifically to realize the third element of the identification strategy (no spill-over). Communities that are very similar, enjoy similar accessibility, and are even within rather close proximity can nonetheless be very isolated from each other because virtually impassable hills and mountains define corridors in Nepal’s landscape and separate such communities. For the covariate matching, census data were available for 2001, and a Nepal Home Ministry assessment of district-level Maoist political control was available for late 2001. We thus use 2001 as the baseline year. 86.5% of war-related deaths occurred from late 2001-2006, and so controlling for background conditions as of 2001 allows for considerable leeway in studying the impact of violence. Categorical covariates include Maoist control, an indicator of when the VDC’s district first hosted armed confrontations between Maoists and state forces (war exposure) up to 2001, and an indicator of which ethnic group was the plurality in the VDC. Maoist control and timing of war exposure affect subsequent levels of violence and reflect aspects of pro-social cohesion that are linked to politics. Caste composition reflects local patterns of subordination/solidarity and may also affect the likelihood of being subject to higher levels of violence. Our continuous covariates include measures of elevation and demographics. Elevation, measured in terms of both the VDC-level mean and standard deviation, captures accessibility and agriculture mode, both of which may affect exposure to violence as well as pro-social cohesion. Demographic characteristics such as population, unemployment, illiteracy, and school absenteeism, determine strategic importance and possible Maoist support, and thus possible exposure

3 Figure 2: Source: Oﬃce for the Coordination of Humanitarian Aﬀairs.

4 High-fatality rate No fatalities District VDC Wards Fatalities By state Pr. by state VDC Wards fatalities Banke Khaskusma 4,8 21 14 0.67 Chisapani 1,9 0 Dolakha Pawati 6,9 4 3 0.75 Bocha 1,5 0 Doti Latamandau 8,9 55 54 0.98 Khatiwada 5,6 0 Rolpa Thawang 6,9 23 22 0.96 Jauli Pokhari 3,5 0 Udayapur Laphagaun 5,6 13 3 0.23 Hardeni 3,6 0 Palpa/Syangja Somadi 1,5 13 13 1.00 Pauwegaude 2,3 0

Table 1: Matched VDC pairs

to violence, as well as social context, which may affect social cohesion. Our resources permitted us to conduct games sessions in six pairs of VDCs, from which we randomly sampled two wards and then randomly sampled 12 respondents per ward.3 Our respondent population is household decision-makers in these rural areas.4 Because our estimand is an ATT, we began by randomly sampling high-fatality VDCs from within strata defined by the Maoist control and timing of war exposure covariates. We then exact-matched on ethnic plurality, and used the minimum Mahalanobis distance to identify pair-matches with the continuous covariates. Table 1 shows the matched VDC pairs, the wards sampled within, and information on the nature of the violence in each VDC. All VDCs were pair-matched within districts with the exception of the one pair in the middle of the country for which the high-fatality VDC of Somadi in Palpa district was matched to the no-fatality VDC of Pauwegaude in Syangja district. Syangja neighbors Palpa, and on the basis of other covariates Pauwegaude provided a better match than any VDC within Palpa. The selected VDCs are mapped in Figure 1 over levels of violence. Table 2 displays balance on VDC-level covariates. The weighted results apply weights that account for the stratified first stage of the sampling/matching procedure. For the categorical covariates, on the plurality ethnic group dimension there is an instance of inexactness in the matching: the Laphagaun-Hardeni pair matches plural Rai to Chhetri, respectively. Given other covariates, we felt that this provided the best match. For the continuous covariates, an exact omnibus test using Hansen and Bowers (2008)’s d2 statistic produces a p-value of about 0.70 under the hypothesis of random assignment to treatment within matched pairs, suggesting balance is quite good.5 Subsequent to the design of our survey we learned of work that established the importance of inequality as a source of conflict in Nepal (Murshed and Gates, 2005; Macours, 2011; Nepal, Bohara and Gawande, 2011) Since inequality is also a plausible predictor of pro-sociality we must be concerned about this confounding variable. In order to insure that inequality could not be driving our results we have since obtained the Gini and polarization data used by Nepal, Bohara and Gawande (2011) and completed a balance check. Our treated and control VDCs are very well balanced on their measures of inequality, as shown in Table 2. Such strong balance on a variable on which we did not stratify further bolsters the case that our identification strategy was successful. The final element of the identification strategy requires that the only plausible reason for differences

3Due to miscommunication between us and our field agents after we left Nepal games were never condcute in the second ward in Pawati so our study includes only 23 wards. Furthermore on occasion fewer than 12 survey respondents showed up to our game sessions so our actual sample size for the behavioral games is smaller than 288. Details of the matching and sampling algorithm are available in our supplemental documents, which also discuss larger samples that we have drawn as part of complementary studies on economic and psychological effects of the war. 4We choose household decision-makers as our units of analysis for two reasons. First, they are likely to be key actors in determining the quality of social life in the community. Second, concerns about nonresponse are less for a sample from this subpopulation than would have been the case if we had sought a sample from the general population of individuals. Among the latter, temporary relocation for work is very common, whereas the responsibilities of household decision-makers require that they remain near their homes. 5We used d2 in an exact test, rather than using Hansen and Bowers (2008)’s χ2 approximation, because of the small sample size. The rank condition for the test only allowed for us to test five of covariates jointly. We thus show the p-value for the test that omits illiteracy rate and inequality, as these are the one with the smallest standardized differences in means on average, providing a conservative test.

5 Table 2: Balance on VDC-level characteristics

Mean, Mean, Std. Wtd. mean, Wtd. mean, Wtd. std. no fatal. high fatal. diff.∗ no fatal. high fatal. diff.∗ (N=6) (N=6) Stratification variables (all binary) g. Maoist control (2001-3)a 0.50 0.50 0.00 0.75 0.75 0.00 h. Early war districtb 0.17 0.17 0.00 0.44 0.44 0.00 i. Late war districtb 0.50 0.50 0.00 0.28 0.28 0.00 j. Non-war districtb 0.33 0.33 0.00 0.28 0.28 0.00 k. Brahmin pluralityc 0.17 0.17 0.00 0.14 0.14 0.00 l. Chhetri pluralityc 0.67 0.50 -0.31 0.42 0.28 -0.26 m. Magar/Rai pluralityc 0.17 0.33 0.36 0.44 0.58 0.30 Distance matched variables and other continuous covariates a. Mean elevation (m)d 1291.67 1227.67 -0.09 1399.72 1676.44 0.40 b. S.d. elevation (m)d 297.67 320.00 0.14 266.36 298.78 0.21 c. Log-population (2001)c 8.11 8.16 0.23 8.11 8.17 0.27 d. Unempl. rate (2001)c 0.13 0.15 0.14 0.08 0.12 0.31 e. Illit. rate (2001)c 0.46 0.47 0.07 0.46 0.45 -0.07 f. School absent. rate (2001)c 0.42 0.44 0.15 0.45 0.43 -0.10 g. Inequality (Gini coefficient)e 0.23 0.24 0.14 0.23 0.23 -0.12 Omnibus balance test p-val∗∗ 0.70 0.67

∗The matching software standardizes the unweighted and weighted differences in covariate means relative to the treatment group standard deviation. ∗∗Exact test based on Hansen and Bowers (2008) d2 statistic. Covariate data sources: aSharma (2003); bDo and Iyer (2010); cNepal Central Bureau of Statistics (2001); dUSGS Digital Elevation Model panel E060N40; eNepal, Bohara and Gawande (2011). in exposure to violence within matched pairs is due to factors exogenous to social cohesion. This is not something that we can test directly, but is something we believe based on the nature of the conflict. The relevant details from qualitative and ethnographic accounts of the war are given in the main text. Another interpretation is that the unpredictability of the violence was an intentional result of both sides’ strategic choices. Insurgents and counter-insurgents played a cat-and-mouse game that required surprise and unpredictability. Insurgents and government forces faced repeated choices to move down one or another corridor in Nepal’s mountainous terrain throughout the course of the conflict. Their decisions to move down one or another corridor—effectively, to “go left” or “go right”—required unpredictability to maintain the element of surprise. In other words it is possible that the optimal prosecution of guerrilla war and counterinsurgency required playing mixed strategies to keep the opponent off guard. This strategic interaction resembles the familiar Colonel Blotto game (Borel, 1921; Borel and Ville, 1991; Golman and Page, 2009).6 Whatever the primary reasons for it, we make use of the apparently indiscriminate and conditionally unpredicatable character of the violence in our identification strategy to which we now turn. These accounts make clear how Maoist strategy relied heavily on surprise and unpredictability, mostly

6In this game one player (the insurgent) possesses a number of troops with which to attack a number villages. The other player, Colonel Blotto the counterinsurgent, must defend these same villages with the troops under his command. The players’ problem is to decide how many troops to allocate to each of the villages to win as many villages as possible. Decisions are made simultaneously so each player must allocate troops to the villages without knowing the allocation of the other. In the standard Colonel Blotto set up the player who allocates the larger number of troops to a village wins that village. Obviously there is no pure strategy equilibrium to this game, so a solution requries mixed strategies.7 No study of which we are aware has tested whether practitioners actually mix optimally as the Colonel Blotto game suggests they should.8 However Several excellent studies have indicated that practitioners at least in sports contests are able to implement optimal mixed strategies. One such study is Walker and Wooders (2001) who test whether servers in tennis matchers play mixed strategies. Another is Chiappori, Levitt and Groseclose (2002) who have studied penalty kicks in soccer.

6 because of their small numbers relative to state forces. The Maoist policy of unilaterally announcing Peo- ple’s Committees increased the level of strategic uncertainty for state forces, drawing them into areas with what Pettigrew and Adhikari (2009, 409) described as “aloofness and seemingly callous randomness.” The bulk of fatalities that occurred in our sample were state-inﬂicted and thus were largely a product of such circumstances. The rugged terrain added yet another layer of error, forcing both Maoist and state forces to make decisions on what directions to take in their forward movement with minimal ability to know what laid ahead deeper in the corridor and with little opportunity to change course.

7 B Sampling Design for the Full Study

Here we describe the sampling strategy that was used to construct the broader sample of 48 VDCs. This is the sample from which we draw, e.g., the results with respect to voting and community organization. The 12 games VDCs were a subset of this broader sample of 48 VDCs. Some of what is described here repeats points made in the previous section. In studying the effects of exposure to war-time violence, we want to minimize the risk of spuriousness in measured associations to social, economic, and political outcomes. To do so, we want to control for conditions that predict both where violence would occur during the war as well as how communities would operate socially, economically, and politically after the war. Available data permit us to control for such factors only for the years after 2001.9 Reliable census data are available only as of 2001, and a very useful Home Ministry assessment of “sensitivity” to Maoist influence is available only as about 2002. Close examination of violence- exposure data suggest that this may not be such a terrible restriction. Our measure of violence-exposure levels comes from data in INSEC’s human rights yearbooks. According to these data, approximately 85 per cent of the killings recorded by INSEC occurred in the years after 2001. Thus, controlling for socio-economic conditions as of 2001 still allows for considerable leeway in studying the impact of conflict. With the INSEC data, we operationalized “violence exposure” according to a three-level score: high death rate, for a death rate greater than 7.5 per 1,000 in a VDC; medium death rate, for death rates above 0 but less than 7.5 per 1,000 in a VDC; and no reported deaths.10 In the current paper, we only examine cases with ”high” or ”no” reported deaths. The data from the “medium” death rate communities are to be used in the separate studies on economic and political outcomes. The 2001 census included measures of socio-economic development and ethnic composition. These factors have been shown in numerous analyses to be important predictors of violence during the war (Do and Iyer, 2010; Hatlebakk, 2009). Thus, such information was incorporated into the sampling design. Violence during the war was associated with the struggle between the Maoist movement and government forces to assert control over the countryside. A Nepalese Home Ministry classification of districts “sensitive” to Maoist influence is useful for controlling for idiosyncratic factors that determined where the conflict would have the most potential to lead to violence. This classification is reported in Sharma (2003), who quoted ministry officials as considering it to be already “obsolete.” If the measure represents judgments by the Home Ministry that were accurate well before Sharma completed his research and obsolete as of the date of publication of Sharma’s research in 2003, then it works well for our purposes. Indeed, our interest is to control for conditions as of the end of 2001, and no later. This measure is the best available in that regard. This measure assigns 32 out of 75 districts as “Maoist sensitive.” All VDCs in a district are given the same district-level value. Since we limited our analysis to the effects of post-2001 violence, we wanted to distinguish between those communities that had already suffered considerable amounts of violence as of 2001, and those that did not. To do so, we use the breakdown over time reported in Do and Iyer (2010) to distinguish between districts that experienced early exposure (100 or more deaths in the district by 2001), later exposure (100 or more deaths in district by 2003), and little-or-no exposure (never reaching 100 or more deaths). All VDCs in a district are assigned the same district-level classification on this three-way score. A final consideration is the fact that quality pre-war data are available for the set of VDCs that were included in the Nepal Living Standards Survey 1995/6 (Central Bureau of Statistics, 1995/6)— hereafter NLSS I. We selected our sample in order to make use of these data in our separate analysis of economic outcomes. A cross-classification based on timing of war exposure, VDC death rate (high, medium, or low), Maoist sensitivity, and NLSS I inclusion yields 36 strata. Table 3 reports the distribution of VDCs over these strata.

9This coincides with the period following the State of Emergency that came into effect at the end of 2001. 10Below, we discuss covariate stratification based on “Maoist control” and “armed confrontations” between Maoist and state forces. In the stratum defined by no Maoist control and no armed confrontations, the verified number of fatalities was never above 4 per 1,000. However, approximately half of fatalities could not be attributed to a VDC, and so we used various imputation strategies to allocate these fatalities to the VDCs in this stratum. This included simple proportional allocation as well as imputation based on a Poisson regression model. We assigned as “high” fatality VDCs in this stratum those VDCs that had higher than 7.5 deaths across all imputations.

8 Table 3: Distribution of Actual VDCs Over Violence-exposure Strata.

District Control VDC death rate District War Exposure (NLSS inclusion) Early Late Little/none Maoist-controlled High 2 2 (NLSS I) Medium 6 31 20 Zero 5 15 19 Maoist-controlled High 14 4 5 (Non-NLSS I) Medium 99 351 291 Zero 74 253 504 Not Maoist-controlled High 1 (NLSS I) Medium 19 77 Zero 12 62 Not Maoist-controlled High 3 6 (Non-NLSS I) Medium 212 712 Zero 221 1132

Notes: Based on INSEC data, Sharma (2003), Central Bureau of Statistics (1995/6) and Do and Iyer (2010)).

Note that there are no VDCs that fall into the “non-Maoist-controlled” and “Early” VDC war exposure categories. In addition, there are no NLSS VDCs in the “High” VDC death-rate, “Little/no” district war exposure cell. Our objective was to draw a sample of 48 VDCs that allowed us to study differences between communities in VDCs that had high, low, and no exposure to war-related violence.We chose to sample NLSS VDCs because of the extra information that the NLSS provided. We assumed that sampling for the NLSS was done to a high enough standard such that NLSS and non-NLSS VDCs are exchangeable “in expectation.”11 Thus, exchangeability holds only when the distribution of VDCs over corresponding NLSS and non-NLSS cells are not too sparse in either the NLSS or non-NLSS cell. Using a cut-off of 20 observations to determine whether data in a cell is sparse, the values that are bold-faced in Table 3 are the ones for which we can be confident that exchangeability holds. For VDCs in these cells, we thus prefer to sample exclusively among VDCs that were included in the NLSS. For others, we take at least one VDC from the non-NLSS strata. All of these considerations lead us to set the distributions of sampled VDCs as it is displayed in Table 4. The letters are labels for the stratification cells. The accompanying numbers indicate the number of VDCs that we wish to sample within each cell. We sample 1,2, or 3 VDCs from each conflict stratum; the allocation is determined based on interests and needs for maximizing precision of estimates within and across categories. Our causal quantity of interest for the civilian VDC sample is the effect of war on places that stood some chance of being affected by war.12 We want to estimate this effect with minimal potential for bias due to confounding by socio-economic conditions, ethnic composition, and population size. For socio-economic conditions, we use a factorized “socio-economic underdevelopment” score extracted from 2001 census data on illiteracy, school-non-attendance, and economic inactiveness rates.13 For ethnic composition, we use data on the largest ethnic/caste group as indicated in the 2001 census as well as district (appreciating that within- district variation in ethnic composition is significantly less than between-district variation). For population size, we use the natural logarithm of total population size from the 2001 census. Evaluation of the distributions of the VDCs over the socio-economic underdevelopment scores and pop-

11In technical terms, we are assuming that across corresponding strata, the distributions of all relevant variables for NLSS VDCs converge to those for non-NLSS VDCs as the samples of each type of VDCs grow. 12For those familiar with the current causal inference literature, this is analogous to an “eﬀect of the treatment on the treated.” estimate. 13We factorize the logit transformation of these rates. The ﬁrst two are correlated highly (.81), though the last is only weakly correlated to the other two (around -.1 for both cases). The illiteracy rate has a factor loading of .997; school non-attendance is .81, and economic inactivity is -.11. Thus, illiteracy rates are the primary driver of the socio-economic underdevelopment score.

9 Table 4: Distribution of Sample VDCs Over Conﬂict Strata (Stratum Cell Label : # VDCs to Select)

District Maoist control VDC death rate District War Exposure Early Late Little/none Maoist-controlled High A : 1 B : 1 (NLSS I) Medium C : 1 D : 2 E : 2 Zero F : 0∗ G : 1 H : 1 Maoist-controlled High I : 4 J : 3 K : 3 (Non-NLSS I) Medium L : 1 Zero M : 4 N : 2 O : 2 Not Maoist-controlled High P : 1 (NLSS I) Medium Q : 1 R : 3 Zero S : 1 T : 3 Not Maoist-controlled High U : 3 V : 1 (Non- NLSS I) Medium W : 2 Y ∗∗ : 1 Zero X∗∗ : 3 Z : 1

Notes: The table gives the number of VDCs to be selected within each of the violence-exposure strata. ∗VDCs in cell F had incomplete matching data. ∗∗These strata labeled later in the sampling process, which is why stratum labeling switches direction for these two.

ulation size show that there is significant overlap on these values across the three death rate levels (see Figure 3).14 Nonetheless, conditioning on these factors in the sampling is important to ensure that we do not introduce confounding on these variables via the sampling. Given that we are working with a rather small number of sampled VDCs, this would be a concern were such precautions not taken. Such balance in the population is not the case with respect to dominant ethnic groups. High death rate VDCs are 27 percent Magar-dominated and 49 percent Chhetri dominated; Middle death rate VDCs are 8 percent Magar- dominated and 20 percent Chhetri dominated; and Zero death rate VDCs are 9 percent Magar dominated and 19 percent Chhetri dominated. Thus, there is a clear difference in the ethnic composition of high-conflict areas as is well known. Therefore the ethnic make-up of our communities must be accounted for. The sampling algorithm does that by conditioning on the ethnic profile of the High death rate VDCs. Finally it is evident from Table 3 that High death rate and medium and zero death rate VDCs differ markedly in the way that they are distributed over Maoist-control conditions and district-war-exposure conditions; these are accounted for in the sampling algorithm. Taking these facts into consideration, we define our sampling algorithm as follows15:

1. Randomly select “High” death rate VDCs from within the “High” death rate cells—i.e., cells A, B, I, J, K, P, U, and V . 2. Choose “Medium” VDC death rate observations that best match the “High” death rate VDCs in terms of District War Exposure, socio-economic conditions, ethnic dominance, and population size. To do so, we take “Medium” death rate VDCs that match the randomly selected “High” death rate VDCs on District War Exposure, district (or neighboring district if a within-district match is not available), and dominant ethnic group. From within these matched strata, we choose the VDCs that minimize the Mahalanobis distance, relative to the “High” death rate VDCs, over socio-economic underdevelopment score and population size. For cases where we are matching to only 1 or 2 “High” death rate VDCs, use the Euclidean distance from the means of the “High” death rate VDCs’ standardized underdevelopment scores and population measures.

14It is interesting to note that we find a dilution of the pattern relating poor socio-economic conditions and conflict intensity, as found at the district level in the papers by Do and Iyer (2010); Hatlebakk (2009), among others. This difference is somehow due to the fact that (1) we are operating at a different level of aggregation (VDC rather than district) and (2) we are using a coarsened measure (3 categories rather than numerical death rates). 15Contact *** for the R code used to implement this algorithm.

10 Table 5: Sample VDCs over Strata: Games VDCs are Highlighted in Bold

District Maoist VDC District War Exposure control death rate Early Late Little/none Maoist-controlled High A : Damachaur (Salyan) B : Satbariya (Dang) K : Mangalsen (Achham), I : Jailwang (Rolpa), J : Pawati (Dolakha), Laphagaun (Udayapur), Thawang (Rolpa), Ilampokhari (Lamjung), Dhaku (Achham) Khara (Rukum), Amale (Sindhuli) Tewang (Rolpa) ” Medium C : Nuwagaun (Rolpa) D : Sisahaniya (Dang), E : Bhatakatiya (Achham), L : Rangkot (Rolpa) Chakratirtha (Lamjung) Baraha (Udayapur) ” Zero M : Jauli Pokhari (Rolpa), G : Bocha (Dolakha) H : Hardeni (Udayapur) Marke/Bharke (Salyan), N : Bhedapu (Dolakha), O : Katunjebawala (Udayapur), Ghetma (Rukum), Santeswori/Rampur (Sinduli) Timilsain (Achham) Kavra/Kapra (Salyan) Not High P : Pandusain (Bajura) V : Somadi (Palpa) Maoist-controlled U : Khaskusma (Banke), Latamandau (Doti), Kolti (Bajura) ” Medium Q : Sitapur (Banke) R : Gorkhunga (Arghakhanci) W : Martadi (Bajura), Tansen N.P. (Palpa), Daud (Doti) Semalar (Rupandehi) Y : Dhanusadham (Dhanusa)

” Zero S : Gudukhati (Bajura) T : Bagauli (Rupandehi), X : Chisapani (Banke), Bahakot (Syangja), Khatiwada (Doti), Pauwegaude (Syangja) Jugada (Bajura) Z : Fulgama (Dhanusa)

Notes: The table gives the names of VDCs (and districts) selected with the sampling algorithm. ∗VDCs in cell F had incomplete matching data.

3. Choose “Zero” VDC death rate observations according to the same matching procedure.

The algorithm is a form of “minimum distance matching with ﬁne balance” as discussed by Rosenbaum, Ross and Silber (2009). We equalize marginal distributions over the key strata, while minimizing the distance between observations over continuous covariates. Table 5 shows the VDCs (and corresponding districts) that were selected with this algorithm. The sampling algorithm implies that we are conditioning throughout on the underlying socio-economic, ethnic, and population size proﬁle of the “High” death rate VDCs. As we noted above, this limits the generalizability of our inference with respect to ethnicity, but does not limit generalizability with respect to the other two covariates, so long as the sampling algorithm does not land us on some “extreme” end of the distribution of either. Figure 4 shows balance densities of covariates for the VDCs included in the sample. The distributions remain centered on the population medians (shown as the dashed vertical lines), and by visual inspection we see that compared to the densities in Figure 3, the shapes of the densities are quite close for the population and sample.

11 Figure 3: Density Plots Showing Balance Over Underdevelopment Scores and Log-Population for Three Death Rate Levels, pre-matching

Notes: The vertical lines show median values for the three plots.

12 Figure 4: Density Plots Showing Sampled VDCs’ Balance Over Underdevelopment Scores and Log-Population for Three Death Rate Levels, post-matching

Notes: The vertical lines show median values for the three plots; dashed is for the population values, solid for the sample.

Once we developed our list of VDCs we sampled two wards within each VDC based on probability proportional to the size of the ward’s population as recorded in the 2001 census. A ward is Nepal’s smallest administrative unit, and typically comprises a single village.16 Within each ward, we targeted a sample of 12 households. For the purposes of this study, the unit of observation is the household decision maker, who was identiﬁed by enumerators when they approached the household. We also obtained roster data on other household members. The sampling frame is the 2008 Constituent Assembly Voter List, which is publicly available and lists all adult voters registered in a ward. Individuals are grouped by household on the List. Anticipating possible non-response, we randomly selected two sets of 12 respondent households (twice the number of targeted

16The complete list of chosen VDCs and the selected wards is available upon request.

13 Table 6: Games VDCs and Wards District VDC Wards Banke Chisapani 1,9 Banke Khaskusma 4,8 Dolakha Bocha 1,5 Dolakha Pawati 6,9 Doti Khatiwada 5,6 Doti Latamandau 8,9 Palpa Somadi 1,5 Rolpa Jauli Pokhari 3,5 Rolpa Thawang 6,9 Syangja Pauwegaude (Pauwajoude) 2,3 Udayapur Hardeni 3,6 Udayapur Laphagaun (Lapagaun) 5,6

households) per ward from the List. We used two rounds of interval sampling to select the respondent households from the Voter List.17 This first sample from the two rounds of interval sampling was our target list. The second sample was a randomized refreshment list used to replace non-responding households. The randomized refreshment list helps to ensure efficiency while not introducing bias into the analysis. In sum, the total sample size for the Nepal Peacebuilding Survey is 48 VDCs times two Wards per VDC times 12 households per ward for a total of 1152 households. Data from 12 of these VDCs, with 2 wards each for a total of 24 wards, are used in the current paper. Implementation of the sampling plan took place through December 2009 and January 2010. Enumeration teams traveled to each ward and worked in a given ward for 2-4 days. Enumerators approached each of the selected households on the first day, and requested an interview with the household’s decision-maker. Appointments for interviews were scheduled to accommodate both the enumeration team schedule and the schedule of the respondent. For the games VDCs, we choose one randomly selected matched VDC pair (consisting of a high death rate and a zero deaths VDC) from each of the strata in Table 4, collapsing over the NLSS I/Non-NLSS I dimension. This yielded 5 pairs. For the sixth pair, we randomly selected a pair from the P/U/Q/W/S/X stratum so that all regions of the country would be covered. That yielded the six pairs (or twelve total) VDCs within which we ran games. The twelve VDCs and associated wards are listed in Table 6. Within each VDC, we ran games in both of the two wards. Respondents from all 12 households in each ward of the chosen games VDCs were invited to participate in a games session.

17 Nw First, we deﬁned the interval number as I = 12 , where Nw is the ward population and 12 is the ward sample size. We then randomly choose a number between 1 and I as the starting value, SV . We then choose respondent households corresponding to positions SV,SV + I,SV + 2I, ..., SV + 11I on the Voter List. Note that within wards, this creates an household-level self-weighting sample.

14 C Non-Response Issues in the Broader Sample and the Lab-in- the-Field Sample

We compute overall response rates based on the American Association for Public Opinion Research “RR1” response rate, including in the numerator the number of targeted households that responded and in the denominator the total number of targeted households (refreshment households are not part of the calcu- lation).18 All households listed in the Voter List were considered as “eligible” respondents for the survey and games. Households that were selected from the list but that did not provide for an interview were thus marked as “non-responding.” In practice, non-responding households included those that refused to participate or for which no one was present to respond to an enumerator attempts at contact during the working days in the ward. The response rate for the entire survey, including in the VDCs that did not include games was 92.9%. That is, there were 76 out of 1,152 targeted households that were survey non-responders, and so there are 76 refreshment sample households in the large dataset. Restricting attention to the 12 VDCs in which we ran games, the response rate was 89.9%. That is, 28 of the 288 targeted households were survey non-responders, and so there are 28 refreshment sample households in the games VDCs sample. With no attrition among households in the refreshment-completed sample, the number of participating subjects in the games would have been 288 but we did have some attrition and so our our total number of subjects in the end was 252. A potential concern to any field research in conflict or post-conflict settings is that researchers will be taken as affiliated with government bodies. We did apply, both prior to the the survey and behavioral games, extensive informed consent procedures to relieve respondents of such concerns. To get a sense of whether respondents did have anxiety during field research we examined patterns of non-response to sensitive survey questions and found low incidence of such non-response. For instance, between 95-97% of the games participants answered questions relating to whether the Nepali Army, Police, or Maoists are responsible for problems in their community at the time of the interview (questions from the household decision-maker survey 21.1, 21.2 and 21.5). Additionally all but 1 of the 235 games participants answered three questions related to whether they had ever attended a Maoist meeting (at least 75% reported they had attended such meetings). These patterns of response suggest to us that our respondents were relatively comfortable participating in the survey and games. In addition, as Table 7 shows, there is no evidence of differential response behavior across violence affected and unaffected communities.

Table 7: “Responsible for Community Problems Today?” (1) (2) (3) Nepali Police Nepali Army Maoists violence -0.00 0.02 0.08 (0.02) (0.04) (0.04) Observations 252 252 252 R2 0.044 0.034 0.061 Baseline (no violence) 0.03 0.03 0.03 Standard errors in parentheses WLS with matched-pair block FE. Robust standard errors clustered by ward. (p-values are for two-sided tests.) ∗ p < .1, ∗∗ p < .05, ∗∗∗ p < .01

18Refer to American Association for Public Opinion Research, Standard Deﬁnitions: Final Dispositions of Case Codes and Outcome Rates for Surveys, Lenexa, KS, 2011, available at http://www.aapor.org/.

15 D Game Scripts

Activity 1 (Lotteries) In this activity, we will give you 5 choices. Each choice will be a diﬀerent kind of gamble. In each gamble, there are two possible prizes. We will ask you to choose which gamble you like the best. Then, we will ﬂip a coin. If it is [heads], you will get the prize on the left of whichever gamble you picked. If it is [tails], you will get the prize on the right. (A table with the lottery payouts is provided in the main text) Example (demonstrate with coin and showing lottery example): Suppose you pick the third gamble. Then, if it is [heads], you get 20 rupees, if it is [tails], you get 60 rupees. [Flip coin and say what you would get.] Okay, does everyone understand? If yes: Okay, you will come up one by one, and we will play the game.

Activity 2 (Dictator Game—Do not tell people the name of the game!!!) In this game, you will have to decide how much money you wish to donate to a needy family in your community. We are required to guarantee the anonymity of this family so we cannot tell you their name. The family that will receive your donations has been picked by us in consultation with local community leaders. When it is your turn will each be given 40 rupees and you will be asked to decide how much of that 40 rupees to give to this needy family and how much to keep for yourself. You will indicate how much you wish to give to the organization by pushing the 5Rs notes that you wish to give over the line on a sheet of paper. Those bills that you keep on your side of the line are yours to keep. You will be awarded that amount of money along with your other winnings at the end of the game. [Demonstrate on the drawing] Any questions?

Activity 3 (Trust Game – Do not tell people the name of the game!!!) This is a new game. It is completely diﬀerent from the last two games. In this game, you will be either a “sender” or a “receiver.” Both the sender and the receiver get 12 rupees to start. Then, the sender person decides how much of his or her 12 rupees to send to the receiver and how much to keep. Whatever the sender sends to the receiver is sent is then tripled by us. So if 2 rupees is sent, we will make it 6 rupees. If 4 rupees is sent, we will make it 12 rupees. If 6 rupees is sent, we will make it 18 rupees. If 8 rupees is sent, we will make it 24 rupees. If 10 rupees is sent we will make it 30 rupees and if 12 rupees is sent we will triple it to 36 rupees. Then, we will give this tripled amount to the receiver. The receiver will decide how much of it to keep and how much to send back to the sending person. Example (demonstrate with coins): Suppose the sender sends 2 rupees and keeps 10. We will make the 2 rupees that were sent into 6 rupees and give it to the receiver. The receiver now has this 6 rupees plus the 12 rupees that he or she starts the game with, so in this particular example the receiver would have a total of 18 rupees. The receiver now decides how much of this 18 rupees to send back to the sender and how much to keep. He or she can send all of the 18 rupees back and keep nothing, send 16 rupees back and keep 2, send 14 rupees back and keep 4, send 12 rupees back and keep 6, send10 back and keep 8, send 8 back and keep 10, send 6 back and keep 12, and so on including sending nothing back and keeping all 18. Here is another example. Suppose the sending person sends 12 rupees and keeps none. We will triple this amount so that it is 36 rupees and give it to the returning person. These 36 rupees plus the receiving persons 12 rupees gives him or her a total of 48 rupees. The returning person can keep all of the 48 rupees and send nothing back, or keep 46 of the rupees and send 2 rupees back, or keep any other amount—44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 20, 18, 16, etc.—and send the rest back.

Here are the steps: The game proceeds in two rounds. In the ﬁrst round you will come up one by one and draw your number. We will tell you if your number means that you are a sending person or returning person. We will randomly match up each sending person with a returning person. But no one will know who is their partner in this game. • If you are a sending person: Your ﬁrst job will be to show us how much you want to send to your receiving person and how much to keep. Remember that we will triple whatever you send before the

16 returning person gets it. You will show us your choice and then return to your seat. Do not tell anyone what you sent. • If you are a receiver you do nothing this time. You just return to your seat. Then we will proceed to the second round. Once again each person will be called up one by one.

• If you are a sender you have no further decisions to make. You can return to your seat • If you are a receiving person we will tell you how much was sent to you. Your job will be to decide how much to send back and how much to keep. You will show us this decision by pushing the amount of money you want to return to the sender over the line on the sheet of paper between you and the game facilitator. Do not tell anyone what you sent back.

You will ﬁnd out how much you are paid after all the games are ﬁnished.

Activity 4: Public Goods/Obligation game [DO NOT TELL THEM THE NAME OF THE GAME] We will play our final game of the day all at once in a group here in the discussion area. In this game each person will be handed two folded cards like these [show them a pair of cards]. As you can see on the front of each these folded cards is an identifying letter for each playe—A through L. Each pair of cards will be given to the proper player according to the identifying letter. The inside of the two cards is different. Inside one of the cards is completely blank. Nothing is written in it. Inside the other card we have draw an X. So, each of you will have two cards with you identifying letter on the front of both. The inside of one of the cards will be blank and the inside of the other card will be marked with an X. Here is how the game is played. In the first round I will ask you to turn in one of your cards. You will choose which of the two cards you wish to turn in and give it to one of the game facilitators. It is very important that you do not show anyone else which card you are turning in. Do not show which card you are turning in to anyone else and when you turn your card keep the card folded so no one else can see which one you are turning in. Once everyone has turned in their first card as a group we will proceed to the second round. IN the second round we will have you come up one by one and turn in your remaining card. Here is how we will figure out the money you will win: For every card with an X in it that is turned in in the first round we will give every player 4 rupees. So, if five people turned in cards with an X in them in the first round every player here will receive 20 rupees. In addition to that amount we will give every player 20 rupees for every card with an X that they turn in one by one in the second round. So if 5 people turned in cards with an X in them in the first round but you kept your card with an X in it and turned it in in the second round you would receive a total of 40 rupees: 20 rupees for the five X cards that were turned in and 20 rupees for turning in your own X card in the second round. If one the other hand you were one of the five people who turned in your X card in the first round you will receive only 20 rupess: the 20 rupees for the X cards turned in in the first round. Obviously you have no X card to turn in in the second round because you turned it in in the first round. So in summary, we will first collect one card from each of you. For each X card we collect we will give everyone 4 rupees. Then you will come up one by one and turn in your remaining card. We will give you 20 rupees if you turn in an X card and nothing if you turn in a blank card. To put it another way, If you want to increase the amount of money that everyone receives by 4 rupees, turn in this card in the first round [SHOW THEM THE CARD WITH THE X IN IT]. If instead you want to increase the amount that you and only you receive at the end of the game by 20 rupees, keep this card [SHOW THEM THE CARD WITH THE X IN IT] in the first round and instead turn in this card [SHOW THEM THE BLANK CARD] in the first round. Does everyone understand the rules of this game? Are there any questions?

17 E Constructing the Distance to Road Measure

We used relevant tiles from the Nepal National Geographic Information Infrastructure Program GIS data on Nepal’s road network. Figure 5 displays the relevant road information that we used.

Distances to nearest road (km)

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 29.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Khatiwada● ● ● (3.9) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Latamandau (3.9) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Thawang (18.2) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 28.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Chisapani● ● ● ● (1.7)● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● JauliPokhari● ● ● ● ● ● ● ● ● ● ● ● (10.5) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pauwegaude● ● ● ● ● ● ● ● ● (0.4) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Khaskusma● ● ● ● ● ● (0.9)● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Somadi● ● ● ● ● ● ● ● ● (3.8) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Bocha (0.6) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Lat. (dec. deg.) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Pawati (1.7) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 27.5

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Hardeni (6.7) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Laphagaun (12.7) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 26.5

80 82 84 86 88

Long. (dec. deg.)

Figure 5: Map shows local fragments of the national road network, in proximity to each of the games locations. Distances (in km) are shown in parentheses. Source: Nepal National Geographic Information Infrastructure Program.

18 F Robustness Checks for Main Results

In the tables below, we check for robustness of our main results using diﬀerent standard error estimates, controlling for covariates, and excluding matching strata where balance was relatively poor.

Table 8: Main results with VDC clustered standard errors (1) (2) (3) (4) (5) Lottery risk Dictator Cooperate Trust sent Trust return Violence -0.11 2.04* 0.16*** 1.68*** 0.07** (0.09) (0.96) (0.03) (0.15) (0.03) Observations 252 252 252 124 128 R2 0.033 0.075 0.058 0.139 0.124 Baseline (no violence) 2.53 15.28 0.60 4.82 7.20 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 WLS with matched-pair block FE.

19 Table 9: Main results with covariates (1) (2) (3) (4) (5) Lottery risk Dictator Cooperate Trust sent Trust return Violence -0.18 1.52 0.20*** 1.55** 0.04 (0.21) (1.38) (0.07) (0.56) (0.03) elevmean s 0.11 3.93*** 0.04 1.41** 0.08*** (0.18) (1.24) (0.07) (0.65) (0.02) elevsd s 0.07 -21.55** -0.56 -5.85* -0.10 (1.60) (9.48) (0.36) (3.02) (0.19) lpop 0.47 2.58 -0.30 -0.15 0.17* (0.62) (5.98) (0.22) (3.16) (0.08) unemp 1.03 -5.94 -0.24 0.29 -0.09 (0.74) (5.95) (0.31) (2.90) (0.20) illit -0.69 25.99 1.23 9.77 -0.64* (2.59) (22.42) (0.93) (6.73) (0.32) noschool 1.75 -19.46 -0.28 0.39 0.36* (1.33) (12.16) (0.64) (6.58) (0.18)

Constant -1.97 -8.23 2.70 0.96 -1.12 (4.56) (47.03) (1.71) (23.16) (0.66) Observations 252 252 252 124 128 R2 0.023 0.084 0.048 0.137 0.150 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Covariance adjustment and ward-level clustering

20 Table 10: Main results with covariates and VDC clustering (1) (2) (3) (4) (5) Lottery risk Dictator Cooperate Trust sent Trust return Violence -0.18 1.52*** 0.20*** 1.55*** 0.04*** (0.10) (0.29) (0.04) (0.20) (0.01) elevmean s 0.11 3.93*** 0.04 1.41*** 0.08*** (0.11) (0.37) (0.04) (0.24) (0.01) elevsd s 0.07 -21.55*** -0.56* -5.85*** -0.10** (0.72) (2.11) (0.27) (1.06) (0.03) lpop 0.47 2.58** -0.30* -0.15 0.17*** (0.55) (1.09) (0.16) (1.10) (0.05) unemp 1.03 -5.94** -0.24 0.29 -0.09 (0.64) (2.42) (0.21) (0.84) (0.05) illit -0.69 25.99*** 1.23* 9.77* -0.64*** (1.68) (6.06) (0.58) (4.44) (0.10) noschool 1.75 -19.46*** -0.28 0.39 0.36*** (1.37) (4.28) (0.40) (2.53) (0.07)

Constant -1.97 -8.23 2.70* 0.96 -1.12*** (4.19) (8.95) (1.26) (8.11) (0.35) Observations 252 252 252 124 128 R2 0.023 0.084 0.048 0.137 0.150 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Covariance adjustment and VDC-level clustering

Table 11: Main results excluding Udayapur (1) (2) (3) (4) (5) Lottery risk Dictator Cooperate Trust sent Trust return Violence -0.12 2.10 0.18** 1.62** 0.07* (0.28) (1.27) (0.07) (0.66) (0.04) Observations 209 209 209 103 106 R2 0.022 0.054 0.057 0.126 0.091 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Block FE and ward-level clustering, excluding Udayapur

21 Table 12: Main results excluding Udayapur, with covariates (1) (2) (3) (4) (5) Lottery risk Dictator Cooperate Trust sent Trust return Violence 0.02 1.78 0.16** 1.31 0.03 (0.13) (1.25) (0.07) (0.76) (0.03) elevmean s -0.05 3.68** 0.09 1.44** 0.08*** (0.13) (1.31) (0.07) (0.59) (0.02) elevsd s 0.67 -20.50** -0.76** -5.39* -0.08 (1.36) (9.50) (0.36) (2.61) (0.17) lpop -1.29 -0.16 0.21 0.09 0.22 (1.33) (5.68) (0.32) (5.17) (0.20) unemp 0.02 -7.44 0.04 0.80 -0.04 (0.52) (6.06) (0.29) (2.43) (0.22) illit -0.14 26.50 1.15 7.97 -0.76** (2.51) (21.96) (0.88) (5.90) (0.35) noschool 2.85* -17.52 -0.65 1.35 0.40* (1.44) (11.49) (0.63) (6.99) (0.21)

Constant 11.87 13.34 -1.30 -0.69 -1.45 (10.12) (45.36) (2.49) (38.70) (1.59) Observations 209 209 209 103 106 R2 0.029 0.066 0.062 0.126 0.122 Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01 Covariance adjustment and ward-level clustering, excluding Udayapur

22 References

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