Miguel_effect Of War On Comm

War and Local Collective Action in Sierra Leone

John Bellows

Edward Miguel *

First version: January 2006 This version: November 2006

Abstract: We estimate the effects of the brutal 1991-2002 Sierra Leone civil war using unique nationally representative household data on conflict experiences, postwar economic outcomes, and local politics and collective action. Individuals whose households personally experienced more intense war violence are robustly more likely to attend community meetings, more likely to vote, more likely to contribute to local public goods, and are more aware of local politics. Several tests indicate selection into victimization is not driving the results. The relationship between conflict intensity and postwar outcomes is weaker at more aggregate levels, suggesting that the war’s primary impact was on individual preferences rather than on institutions or local social norms. More speculatively, the findings could help partially explain the rapid postwar economic and political recovery observed in Sierra Leone and after several other recent African civil wars.

*

John Bellows, Department of Economics, 549 Evans Hall #3880, University of California, Berkeley, CA 94720-3880, USA, [email protected] Edward Miguel, Department of Economics, 549 Evans Hall #3880, University of California, Berkeley, CA 94720-3880, USA, [email protected]. U.C. Berkeley and NBER

Acknowledgements: We are grateful to Rachel Glennerster, Gerard Roland, Katherine Whiteside, Yongmei Zhou, and David Zimmer for helpful discussions and for their valued collaboration on related research projects. Berndt Eckhardt of Sierra Leone Information System assisted in acquiring and analyzing GIS data. We thank Eva Arceo, Chris Blattman, Karen Feree, Christina Paxson, Daniel Posner, Uri Simonsohn, Leonard Wantchekon, Jeremy Weinstein, and seminar audiences at the 2006 AEA Meetings, the University of Wisconsin at Madison, February 2006 NBER Economics of National Security Meeting, 2006 Pacific Development Association Conference, 2006 NBER Africa Meeting, the Working Group in African Political Economy, and U.C. Berkeley Development Workshop for useful comments. The usual disclaimer applies.

I. Introduction Civil war has been a prominent feature of recent history in Sub-Saharan Africa: more than two-thirds of countries in the region experienced an episode of civil war during the past 25 years. Some scholars have claimed these wars have played a role in the region’s disappointing recent economic performance. For example, a recent World Bank report claims: “[t]he legacy effects of civil war are usually so adverse that they cannot reasonably be viewed as social progress…[Civil war] has been development in reverse” (World Bank 2003: 32). Yet the rapid postwar recovery experiences of some African countries after brutal civil wars – notably, Mozambique and Uganda – suggest that war need not have persistent negative economic consequences: in the decade following the end of their wars, Mozambique and Uganda experienced annual per capita income growth of 3.9% and 4.6%, respectively, well above the African average (United Nations 2003). This paper analyzes a novel nationally representative dataset from postwar Sierra Leone with the goal of better understanding the short-run economic and political impacts of civil war. Recent research has shown that the long run effects of war on population and the economic growth are typically minor. Studies that focus on United States bombing – including in Japan (Davis and Weinstein 2002), Germany (Brakman et al 2004) and Vietnam (Miguel and Roland 2005) – find few if any persistent impacts of the bombing on local population or economic performance. To the extent that war impacts are limited to the destruction of capital, these findings are consistent with the predictions of the neoclassical economic growth model, which predicts rapid catch-up growth postwar. However, the neoclassical growth model has little to say about the impact of war on institutions, politics, social norms, or individual preferences. Given the extreme trauma experienced by civil war victims, it is plausible that effects along these human dimensions could be more substantial and longer lasting than any impacts on capital investment levels.

1

War could also potentially generate large impacts on both national and local institutions. Tilly (1975) argues that wars historically promoted state formation and nation building in Europe, ultimately strengthening institutional capacity and promoting economic development. A broader definition of institutions might include the social equilibrium reached by individual rational actors. In experimental economics evidence from Honduras, Castillo and Carter (2004) find that people in locales that experienced extensive destruction from Hurricane Mitch – which, although traumatic, is arguably not as severe as the violence experienced by many civil war victims – shared significantly more of the “pie” with their partner in a Dictator Game. This suggests traumatic experiences could also have a positive impact on altruism or on local social norms regarding cooperation. At the individual level, the experience of being a victim of war violence could also profoundly change individual beliefs, values, and preferences. An emerging psychological literature has documented some of these individual responses to conflict-related trauma. Studies often focus on symptoms of post-traumatic stress syndrome (e.g. Dygrove et al 2002), but a subset of the literature now also explores positive responses to trauma, the so-called post-traumatic growth theory (Tedeschi and Calhoun 1996, Powell et al 2003), including changes in political action and beliefs. For example, Israelis who survived the Holocaust are more religious, more optimistic and at the same time more extreme in their political views (Carmil and Breznitz 1991), while Palestinians who personally survived aerial attacks are more likely to engage in political activism (Punamaki et al 1997). One key limitation of this literature is the use of small respondent samples of unknown representativeness. The distinction between how individuals react to their own personal experiences versus by observing others is critical for understanding the nature of civil war impacts in general as well as in this study of Sierra Leone. It is also an issue that is amenable to laboratory experiments: Simonsohn et al. (2006) find that individuals’ own personal experience (playing the Prisoner’s Dilemma and

2

other standard experiments) is far more influential in shaping subsequent game play than the firsthand observation of others’ experiences in the lab. Many studies of the determinants of U.S. political participation have focused on how costs and other economic factors influence the choice to vote, although it is unclear these rational choice voting models have been successful.1 In fact, there is growing evidence on how psychological and social factors affect political participation. Green and Garber (2004) find that subtle changes in the framing of political messages can have major impacts on voter turn-out. U.S. parents who lost in school choice lotteries for their children are significantly more likely to vote in subsequent school board elections, compared to parents who won (Hastings et al 2005). In a related finding, U.S. voters whose county suffered a seemingly random misfortune – including local floods, shark attacks, or flu epidemics – tend to punish political incumbents in later elections (Achen et al 2004). Theoretical explanations for this “expressive voting” are based on the assumption that victims derive some additional utility from voting relative to non-victims. Given these positive political participation impacts among school lottery losers and those whose town suffered a shark attack, a finding that political activism increases among civil war victims seems intuitively plausible. Unfortunately, the extreme scarcity of household survey data from contemporary conflict and post conflict societies has limited research progress on questions related to the economic and political aftermath of civil wars. One exceptional aspect of this project is the availability of high quality nationally representative household data from Sierra Leone containing detailed information on household experiences with war violence as well as on immediate postwar political and collective action behaviors, in addition to the more standard socioeconomic questions. The main empirical results focus on the individual level analysis made possible by this remarkable dataset. We also draw

1

Green and Shapiro (1994) argue these models have performed especially poorly with regards to explaining determinants of voting behavior.

3

on a collection of other new data sources from Sierra Leone to estimate the relationship between local conflict and postwar outcomes at the more aggregated chiefdom level.2 In our main result, we find that individuals whose households directly experienced war violence are much more politically active than non-victims. War victims are significantly more likely to vote (by 2.4 percentage points in our preferred specification), attend community meetings (by 5.7 percentage points), contribute to local public goods, and be knowledgeable about local politics.3 This relationship is robust across two independently collected survey samples and multiple econometric specifications, including a specification with village fixed effects, which compares neighbors within the same village, but with different violence experiences, to one another. Several tests indicate that systematic individual selection into victimization is unlikely to be driving the results; for instance, violence effects are also strong for those too young in age to have been community leaders at the start of the civil war, among whom conflict-related violence victimization is arguably more random than is violence against adults. Yet two to three years after the end of the war, there are – perhaps surprisingly – no lasting impacts on household socioeconomic status measures, including asset ownership, income earning activities, as well as consumption expenditures and child nutrition, so socioeconomic differences do not appear to be behind the observed differences in political mobilization. In contrast, we do not detect significant additional impacts of violence at the more aggregated chiefdom level. Chiefdoms that experienced greater violence do show greater overall political activism in certain dimensions, but these broader impacts are less robust than the individual level effects, suggesting that the war’s primary impact was on individual preferences rather than on local political institutions or social norms. 2

The chiefdoms in Sierra Leone are administrative units that were formalized by the British in the 1930s. These colonial boundaries remain salient today as most people identify their residential location by the chiefdom. The average chiefdom has roughly 20,000 people. 3 In a related result, Blattman (2006) finds that former child soldiers in Uganda are significantly more likely to vote than other youth.

4

Civil war experiences are transformative for many, and our analysis suggests that one shortrun legacy is increasing individual political participation and local public good provision. As we discuss in the conclusion, this finding echoes the observations of other scholars of Sierra Leone and speaks to the remarkable resilience of ordinary Sierra Leoneans. More speculatively, the findings of this paper also contribute to the recent debate on the underlying causes of Africa’s terrible recent economic performance, and speak against claims that civil war legacies are major long-run impediments to economic and political development in Africa. II. The Sierra Leone Civil War Sierra Leone was ravaged by a civil war that started in 1991 and lasted until January 2002. During the war an estimated 50,000 Sierra Leoneans were killed, over half of the population was displaced from their homes, and thousands were victims of amputations, rapes, and assaults (Human Rights Watch 1999). A. Origins of the war Just before the war began, Sierra Leone was the second poorest country in the world (United Nations 1993). For the preceding two decades the country had been ruled by dictators who enriched themselves through illicit deals involving diamonds, while doing next to nothing to provide needed services such as health care and education (Reno 1995). Partially as a result of the widespread discontent towards the corruption and ineffectiveness of the government, a small group of rebels, who had entered the country from Liberia in 1991, were successful in recruiting disenfranchised youth to rise up violently against the status quo. As their numbers swelled by early 1992, these rebels, known as the Revolutionary United Front (RUF), spread the armed conflict to all parts of the country. Some scholars have claimed that the initial motivations of the RUF were idealistic, and that the early rebels were guided by a strong sense of political grievances related to the failings of the corrupt regime (Richards 1996).

5

Another important factor in the RUF’s original motivations was access to Sierra Leone’s diamond wealth. Mining diamonds in Sierra Leone requires no heavy machinery or technology, since these alluvial stones sit close to the surface in dried riverbeds, so any armed group that controlled a diamond area could extract and then sell the diamonds for large profits. All armed groups participated to some extent in diamond smuggling during the conflict, and the control of these diamond areas was an extremely important objective for all groups. David Keen notes that “[a]ny battles were largely restricted to the areas with the richest diamond deposits” (Keen 2005: 212). Additionally, since large-scale diamond smuggling was possible so long as the country remained in chaos, profits from these “blood diamonds” represented an important incentive for armed groups to prolong the war (Keen 2005: 50). In contrast to most popular media coverage on African civil wars, neither ethnic nor religious divisions played a central role in the Sierra Leone conflict. The RUF rebels targeted people from every ethnic group and throughout the country, and statistical analysis of documented human rights violations shows that no ethnic group was disproportionately represented among RUF victims (Conibere et al 2004). There is also no evidence that levels of civilian abuse were higher when a particular armed faction and the community were predominantly from different ethnic groups (Humphries and Weinstein 2006: 438). B. The Revolutionary United Front (RUF) and the Sierra Leone Army (SLA) Although there were many different actors in the decade-long war, the majority of the violence was perpetrated by the RUF: the official government truth and reconciliation commission, which documented war atrocities reports that over 70% of all human rights abuses were committed by RUF fighters (Conibere et al 2004). Our own analysis of the No Peace Without Justice (NPWJ) conflict mapping project, which is a comprehensive record of all reported armed violence during the war, similarly concludes that 75% of all attacks and battles involved the RUF as the primary fighting force

6

(NPWJ 2005). The following incidents recorded in the NPWJ report are fairly typical of the brutal and seemingly arbitrary RUF raids on civilians: “In the early hours of 27 May 1997, the town of Karina (Biriwa Chiefdom, Bombali District) was attacked by RUF / AFRC forces carrying guns and other weapons. Soldiers surrounded the central mosque and killed 10 civilians celebrating the Muslim feast of “Jonbedeh”… An unknown number of people were injured trying to escape. RUF/AFRC forces raped an unknown number of women, and abducted 30 young civilian men and women. During the attack, numerous houses were burned down.” (p. 133) “RUF forces attack Koi town (Nongowa Chiefdom, Kenema District) early one morning in mid-1994, reportedly to terrorize the inhabitants. … They fired indiscriminately and many civilians were killed and others were wounded. The town was looted and people were forces to carry the stolen property to Peyama.” (p. 303) “On 11 March 1998, RUF/AFRC forces attacked the headquarter town of Jagbwema (Fiama Chiefdom, Kono District). RUF/AFRC forces entered the town firing indiscriminately. More than 70 houses were burnt and the town was massively looted. During the night, the RUF/AFRC forces abducted three people, including the Town Chief, who were all later killed. On 24 March 1998, RUF/AFRC forces coming from Jagbwema attacked Yeanoh, shooting and killing many people.” (p. 361) “On 26 December 1994, RUF forces attacked Mattru on the Rail (Tikonko Chiefdom, Bo District) in the afternoon, mutilating civilians’ arms and legs. The RUF then opened sporadic gunfire on the civilians, killing many people, looting their property and burning down their houses. They also abducted civilian youths who they conscripted into the RUF forces.” (p. 395) The extent of targeting of community leaders or other opponents during RUF attacks is important in the later analysis. It is useful here to distinguish between regions where the RUF did not establish permanent bases and thus mainly resorted to raids like those described above, versus regions with permanent bases that were occupied for extended periods. The ability to systematically attack particular types of civilians is inherently greater in areas the RUF occupied relative to areas they only briefly raided. The NPWJ report indicates that slightly more than half of all chiefdoms (86 of 152 chiefdoms) did not have permanent RUF bases during the war. In the analysis below we restrict attention to these areas, to gauge whether estimated war violence impacts are robust to a subsample where RUF violence against civilians was likely to be largely indiscriminate.

7

One feature of the fighting that has drawn attention from international observers is the cooperation between the rebels and the Sierra Leone Army (SLA). These two groups coordinated their movements in order to avoid direct battles, and at times worked out mutually beneficial profit sharing arrangements in diamond areas. This was especially true following the 1997 coup that formally brought the SLA and RUF together into a national coalition government called the Armed Forces Revolutionary Council, or AFRC (Keen 2005). Some soldiers apparently fought for the SLA by day and the RUF by night. As a result, the main victims of the violence were civilians, who were terrorized not only by the RUF but also by the army that was supposed to protect them. C. Civil Defense Forces (CDF) In order to protect themselves from the terror of RUF and SLA fighters, many communities organized their own local fighting groups, which became known collectively as the Civil Defense Forces (CDF). CDF fighters were overwhelmingly civilians and they relied primarily on local fundraising for supplies. While there were numerous manifestations of the CDF throughout the country, the command and organization of the CDF were often linked with traditional chiefly authorities. For example, the largest CDF, known as the kamajors, were an extension of traditional Mende hunter groups (Ferme 2001). There are many accounts of ordinary civilians going to heroic lengths to protect themselves from RUF attacks. One such account from Allister Sparks (2003:309), an international election observer in the 1996 Sierra Leone presidential election, describes how the citizens of Kenema Town bravely resisted the RUF in order to exercise their right to vote: “The polling stations were due to open at 7 am on 26 February, but at exactly 6.15 am the rattle of small-arms fire broke out around the centre. … For two-and-a-half hours the firefight raged. At times the rebels ran close past our building and we could hear them shouting: “No election! No election!” between their bursts of AK-47 fire. Then, indistinctly at first but gradually increasing in volume, we heard a counter-chant coming from the direction of the town: “We want vote! We want vote!” Thousands of people were pouring into the streets, and as the chanting crowd swelled they ran through the town waving palm leaves. … Whether it was this display of public

8

courage or a successful counterattack by the local military was unclear, but the rebels began to withdraw and the shooting subsided. As the observers made their way gingerly into the town, crowds lining the streets yelled impatiently at us: “Bring the boxes. We want vote!” The polling stations opened late, some not until the afternoon, but electoral officials worked frantically to open extra stations, and by the time the polls closed at 6 pm nearly every registered adult in Kenema had voted.” (p. 309) The CDF continues to be admired within Sierra Leone for their selfless defense of civilians. However, late in the conflict when their power and numbers had grown, some CDF units lost discipline and they too began to abuse civilians and enter the illicit trade in diamonds, although to less of an extent than the RUF or SLA (Keen 2005: 268). The rise of the CDF is illustrative of two points raised in the introduction. First, the CDF is an example of how war can create influential new institutions. Second, the account above presents a concrete example of how Sierra Leonean individuals responded to war violence with an increased desire to assert their political rights. We return to both of these points in the empirical analysis below. Following the brutal 1999 rebel attack on Freetown, a large deployment of United Kingdom and United Nations troops finally brought an end to the war. These foreign troops conducted a disarmament campaign and secured a peace treaty in early 2002. Donor and non-governmental organization (NGO) assistance has since played a major role in reconstructing physical infrastructure, resettling internally displaced people (almost all of whom had returned home by 2003), and funding other government expenditures. National elections for a president and members of parliament were held in 2002, and local government elections – the first in over thirty years – in 2004. III. Empirical Strategy and Data A. Estimation Strategy The literature reviewed in the introduction suggests that there are at least two plausible channels through which violence can impact postwar behavior and outcomes.

9

First, there may be individual level impacts, if the trauma associated with directly experiencing violence leads to changes in beliefs, identities, values and preferences. To investigate this relationship, we compare postwar outcomes and behaviors across individuals that suffered from different degrees of violence during the conflict. The preferred specification includes village (or enumeration area) fixed effects, so the analysis essentially neighbors within the same village.4 The identifying assumption is that, conditional on observable characteristics, violence victimization within villages is close to random at the household level. This might not hold if there was systematic targeting by fighters along some unobserved household dimension, in which case the observed relationship between victimization and postwar outcomes could in part be due to omitted variable bias. We carry out several tests to examine the extent of selection into war violence below, and these indicate that any selection bias is likely to be relatively minor. Concerns about selection are mitigated by the specific characteristics of Sierra Leone villages and the nature of the RUF attacks. Our surveys indicate that rural Sierra Leone villages consist almost entirely of subsistence farmers, and there is typically no conspicuous landowning elite for the RUF to target, as would be the case in some other societies. Additionally, Sierra Leone villages are very small, usually consisting of a handful of inter-related extended families5, so in the specifications including village fixed effects we effectively make comparisons across this small and relatively homogenous collection of households. As the above accounts demonstrate, RUF attacks on civilians were often brief, chaotic, and indiscriminate in nature, which provided little opportunity for precise targeting of community leaders.

4

In the GoBifo sample (described below) villages were selected, then individuals were randomly selected for surveys within the village. In the IRCBP sample, enumeration areas were selected first. In most IRCBP cases an enumeration area corresponds to a single village, but in some instances one enumeration area contains two smaller villages. In the urban settings, an IRCBP enumeration area is equivalent to a block or a neighborhood. 5 In the two districts (Bombali and Bonthe) where we have detailed data on village size, there are only 33 and 29 households per village on average, respectively.

10

According to many accounts of the war, the one group that the RUF was able to target was members of traditional authority families (chiefs), who were well known and visible in their localities (Keen 2005; Richards 1996). We have data at the household level on whether the households were members of the traditional authority and thus are able to control for this characteristic in the analysis. Other measures of socio-economic status, including education, are not robustly correlated with victimization, suggesting that targeting along other lines was relatively rare. While we admittedly cannot rule out that some targeting of politically active households occurred, we feel that the observed relationships we estimate primarily reflect the impacts of war victimization on postwar behavior, rather than selection bias, and we present several tests of this claim below. Second, there may also be war impacts at more aggregated levels due to changes in institutions or social norms brought on by the conflict. We investigate these effects by comparing chiefdoms that experienced different levels of conflict intensity. In the chiefdom level analysis, we rely on a rich set of local characteristics as explanatory variables to ensure we are isolating the effect of violence. These controls include the number of diamond mines, roads, population density, and in some specifications prewar socioeconomic measures. Additionally, district fixed effects are included to account for broader regional variation in unobservable characteristics. An important caveat of the entire empirical strategy is worth emphasizing: we focus on local comparisons across individuals and across chiefdoms, and cannot estimate the overall national impact of the Sierra Leone civil war. The data do no permit the estimation of national impacts because no suitable counterfactual exists.6 This caveat is important, as the net national effect of the war could be negative even in light of any positive local violence victimization impacts that we estimate. B. Individual Data 6

Liberia shares similar geography, history and culture with Sierra Leone, but Liberia was also experiencing a civil war during this time so it cannot be used a peacetime counterfactual.

11

Data on individual experiences with war violence is extremely rare for post conflict societies, and this has limited research progress in estimating civil war impacts. The broad collection of household level and chiefdom level data on conflict experiences and postwar outcomes makes ours among the most comprehensive datasets from a post-conflict society. In the present analysis we make use of data from two unusual household surveys that were collected in 2005, a mere three years after the war ended. The first survey is nationally representative7 and was conducted by the Institutional Reform and Capacity Building Project (IRCBP).8 The second survey was conducted as baseline data for a government assistance program called “GoBifo” and it covers only selected wards within two districts. The location of sample enumeration areas for the IRCBP and GoBifo surveys are presented in Figures 1 and 2, respectively. Details on these surveys, including sample sizes, can be found in the data appendix. These two surveys contain detailed questions on household war victimization experiences. The IRCBP survey contains the following three retrospective questions: “Were any members of your household killed during the conflict?” “Were any members injured or maimed during the conflict?” and “Were any members made refugees during the war?” We create a victimization index as the average of responses to these violence related questions (Table 1, panel A); as we discuss below, breaking the index down into its component questions does not substantively change the results. The data also includes information on household assets, some respondent characteristics (including education), and multiple measures of political engagement, voting, participation in collective action, and self-expressed levels of trust and cooperation (Table 1, panels B, C, D, and E). Because the two surveys were conducted independently and using different sampling frames, carrying out the analysis on both provides a robustness check.

7

The capital Freetown is excluded from the analysis. Freetown is Sierra Leone’s only large city and its local institutions and history are quite different from the rest of the country. 8 The IRCBP is affiliated with the government of Sierra Leone and its primary role is to support the ongoing decentralization of government services.

12

C. Chiefdom Data We also use the number of reported attacks and battles in each chiefdom as an additional dimension of violence. The number of attacks and battles is related to, but distinct from, the household reports of victimization, as it also includes the battles between troops that did not involve civilians. The 2004 No Peace Without Justice (NPWJ) conflict mapping project compiled all reports by human rights organization and the media on the location and intensity of violence during the conflict (Table 1, panel F). We construct a measure of attacks and battles from the descriptions included in this report. The correlation across the household victimization measure and the number of attacks and battles at the chiefdom level is moderate at 0.3. Note that the two measures of conflict-related violence are broadly analogous to the two types of commonly used crime data, crime victimization data versus official crime reports. Additional chiefdom level data is constructed from multiple sources. The 2004 Sierra Leone Integrated Household Survey provides data on nutrition, education and socioeconomic outcomes (Table 1, panel G). The 2005 School Survey provides data on the quality, monitoring and funding of local education facilities (Table 1, panel H). The 2003 Sierra Leone Data Encyclopedia provides information on the number of non-governmental organization (NGO) projects in each chiefdom (Table 1, panel H). The GIS data provides information on the location of diamond mines, roads, and population density (Table 1, panel I). The 1989 Sierra Leone Household Survey provides the only existing data we are aware of on prewar socioeconomic conditions (Table 1, panel I).9 Further details on data sources and variable construction are provided in the data appendix. The variation in chiefdom level civilian victimization is presented in Figure 3. As expected, violence is concentrated in the eastern part of the country near Liberia, but some violence was experienced in all regions. Figure 4 presents the residuals of the victimization index after the district

9

The sample for the 1989 household survey includes fewer than half of all chiefdoms in the country. The documentation for the data set is incomplete, making it impossible to know how exactly this sample was chosen.

13

means have been subtracted off. This measure of local violence is effectively used in specifications that include district fixed effects. As is apparent from the figure, subtracting off district averages emphasizes the considerable variation in violence across neighboring chiefdoms. We next investigate the relationship between war intensity and the different factors that are thought to have contributed to onset and to the length of the war. The most robust finding is that chiefdoms with diamond mines have significantly more attacks and battles during the war. In all specifications, including those with district fixed effects and controls for 1989 socioeconomic status, the relationship is large, positive, and statistically significant (Table 2, regressions 4-6). Our data thus confirms the widely held view that diamonds were related to the local intensity of fighting. Other geographic controls, including road density, distance to Freetown (the capital) and population density are only weakly related to both measures of violence. We find that there is no significant relationship between diamonds and household reports of victimization (Table 2, regressions 1-3). Humphries and Weinstein (2006: 444) similarly find no relationship between diamond mines and brutality towards civilians, in data reported by fighting units. It appears that the fighting around diamond mines primarily involved soldiers and did not disproportionately affect civilians in those areas. Turning to the other factors, prewar 1989 school enrollment is negatively related to civilian victimization (Table 2, regressions 3 and 6). This is consistent with the explanation that violence was higher in areas with poorer public services, possibly due to more severe political grievances in those areas (Richards 2003), or possibly fewer youth employment opportunities (Collier and Hoeffler 2004). Finally, we find that 1989 average log per capita consumption expenditures are positively related to the number of chiefdom attacks and battles, consistent with the explanation that lootable resources attracted armed groups (Collier and Hoeffler 2004). We do not place too much emphasis on these 1989 data because the sample size falls to just 64 chiefdoms. Yet this is suggestive evidence that prewar chiefdom socioeconomic conditions may be associated with later violence.

14

IV. Individual level analysis We document that households that experienced more direct civil war victimization are significantly more likely to be politically mobilized and engaged in local collective action than other households, but are no different in terms of assets, religiosity or self-expressed trust postwar. These findings hold across both the IRCBP and the GoBifo samples, using multiple measures of political mobilization, and across many specifications, including those with village fixed effects. Before turning to these results, we first establish that there is no strong evidence of individual selection into victimization, beyond the systematic targeting of village chiefs mentioned above. A. Correlates with victimization We would ideally only use prewar characteristics to predict conflict victimization, but these are unavailable at the household level. Instead we use postwar data on characteristics that are unlikely to change as a result of the war, for instance, adult educational attainment and demographic characteristics. Both the chiefdom level and village level victimization indexes are positively and significantly correlated with the household victimization index (Table 3, regressions 1, 2, 4 and 5), as expected. More importantly for our results, in the nationally representative IRCBP sample there are no statistically significant relationships between respondent age, gender, or education with the household violence victimization index across the six specifications, even in the village fixed effects specification (Table 3, regression 3). The lack of robust correlation between victimization and observable household characteristics is our first piece of evidence that violence was not systematically targeted along socioeconomic lines. In the smaller GoBifo sample, there is no significant correlation between respondent age or gender, but there is a positive relationship between education and victimization (Table 3, regressions 4-6). As discussed in subsection D, the chiefdoms in the GoBifo sample nearly all contained RUF

15

bases during the war; as the presence of a permanent base made it easier for the RUF to target community leaders or other opponents, it is perhaps not surprising that selection into violence is more of an issue in the GoBifo sample than in the nationally representative IRCBP sample. The one group that disproportionately suffered from war violence were traditional authority households (i.e. chiefs’ households and extended families). Traditional authority households are significantly more likely to be victimized across all six regressions. This lines up with many media accounts of the war, which describe how chiefs were systematically targeted by the RUF in an attempt to undermine the “corrupt” existing order. We account for this specific type of targeting in the analysis by controlling for traditional authority households in all later regressions. We also investigate below whether average chiefdom outcomes are affected by the killing of the local chief. B. Violence and Postwar Outcomes and Behaviors We begin with a detailed analysis of two specific individual behaviors: attendance at community meetings and registering to vote / voting in either of two recent elections.10 We estimate the relationship between household conflict victimization and these behaviors controlling for observable characteristics as well as different types of location fixed effects. Household war victimization is positively and statistically significantly related to respondent community meeting attendance (Table 4) and also whether the respondent registered to vote / voted in recent elections (Table 5), in both the IRCBP and GoBifo samples. The point estimates on victimization are remarkably stable across specifications with district fixed effects, chiefdom fixed effects, and village fixed effects, and are reasonably large: an increase from zero to one in the household conflict victimization index (which corresponds to going from no violence to experiencing all three types of violence) in our favored IRCBP village fixed effects specification is associated with a 5.7 percentage point increase in the probability of attending a community meeting (Table 4,

10

IRCBP survey respondents were asked if they had registered to vote, while GoBifo respondents were asked if they had voted. While not exactly the same, these two dependent variables are analyzed together in this section.

16

regression 3), on average village meeting attendance of 70%. The analogous change in household victimization is associated with a 2.4 percentage point increase in the probability of having registered to vote (Table 5, regression 3). Other determinants of meeting attendance and voting behavior that emerge in Tables 4 and 5 are sensible in the Sierra Leonean context. Women are relatively less involved in the political process – specifically, both less likely to attend community meetings and to vote – while people from traditional authority households attend more community meetings. Individuals with some education are more likely to participate in community meetings, but education is only weakly related to voting behavior. 11 Having established the robustness of the results for two outcome measures, we next expand the analysis to consider the relationship between conflict victimization and a wider range of postwar outcomes and behaviors. The specification shown includes village (enumeration area) fixed effects as well as respondent controls for gender, age, education, and traditional authority household, and each entry comes from a separate regression (Table 6). We first find no robust association between conflict victimization and either working for wages or owning a cooking stove. While the relationship between victimization and owning a radio is negative in the IRCBP sample, it is not statistically significant for the GoBifo sample (Table 6, panel A, rows 1, 2, and 3). Taken together, there is no clear evidence that individuals who experienced more conflict victimization are worse off along observable socioeconomic dimensions postwar, and we provide further evidence on this below. In contrast, those who were victims of war violence are very different in terms of political mobilization and participation in collective action. As before, conflict victimization is positively related to attendance at community meetings and registering to vote / voting (Table 6, rows 4 and 5). 11

These determinants of voting behavior are broadly similar to data from the U.S., where historically females have been less likely to vote (Timpone 1998). One difference is that education is positively related to voting in the U.S., a finding which does not find strong support in our estimates.

17

Conflict victimization is also robustly positively related to other measures of mobilization, including membership in a school management committee and membership in a political group (Table 6, panel B, rows 6 and 7). Not only is political mobilization greater among those who were conflict victims, but participation in local collective action is higher as well. The relationship between violence victimization and participation in road brushing, a crucial locally organized activity to keep bush paths between villages passable, is positive and statistically significant (Table 6, row 8). This is finding is important, as it appears to confirm that increased political mobilization is producing more local public goods, rather than just creating deadlock within communities.12 Recall that these specifications include village fixed effects, so these collective action impacts are unlikely to simply reflect greater local needs in war-torn areas. Nor does it seem likely that these collective action effects just reflect the lower opportunity cost of time among conflict victims, since there is no significant relationship between victimization and labor market activities (working for a wage or earning cash, Table 6, row 1). Political knowledge of the identity of both local government and traditional officials is also significantly higher among conflict victims, once again with large effects on the order of 9 to 11 percentage points (Table 6, rows 9 and 10), suggesting much greater engagement in local politics. We next investigate the impact of conflict victimization and leadership for those respondents who were too young to be prewar community leaders, those 30 years old or younger in 2005 (and 15 or younger in 1990, right before the war).13 Focusing on this demographic group reduces concerns about selection or omitted variable bias, since these individuals could not have been targeted for violence as a result of already being community leaders themselves prewar or in the early years of

12

Olson (1984) has noted that increased political mobilization could give rise to small, exclusive coalitions that lobby for narrowly targeted policies that do not benefit society at large. This type of mobilization could have a negative aggregate effect. 13 We thank Rachel Glennerster for suggesting this test.

18

the war. It is extremely rare for individuals under 20 years of age to be community leaders in our data. Thus it is plausible that treatment effects among this subgroup primarily reflect violence impacts rather than violence selection. This is not a perfect test, since these youths’ households may have been targeted during the war because their parents were community leaders, for instance, and leadership abilities may be somewhat correlated across generations. Nonetheless focusing on this youth sub-sample should considerably weaken the link between unobserved individual prewar political activism and targeted violence. Across both the IRCBP and GoBifo samples, conflict victimization effects are positive, large and statistically significant across political mobilization measures in the youth sample (Table 6, Panel B, columns 3 and 4). If anything, the point estimates tend to be somewhat larger in magnitude for youths, although differences across age groups are generally not statistically significant. (Any differential impacts may also be picking up heterogeneous violence impacts for youth.) The robustness of the conflict victimization impacts among youths, who were too young to have been explicitly targeted based on their role as prewar community leaders, is further suggestive evidence that omitted variables are unlikely to be driving the main results. The final portion of the individual analysis turns to issues of self-reported trust and religiosity. This is of interest since the increased provision of public goods reported in Table 6 could be a result of heightened levels of trust and cooperation across village members (what some would call “social capital”). The experience of being victimized could plausibly both make individuals less trustful of outsiders and more trusting of community members. However, the relationship between victimization and levels of trust for other community members is not statistically significant in the nationally representative IRCPB sample and the relationship is only marginally significant in the GoBifo sample (Table 6, row 11). Similarly, there are no discernable differences in the trust of outsiders among victims and non-victims (row 12). We also find no evidence of a statistically significant relationship between victimization and postwar religiosity in either sample (row 13).

19

Whatever effects victimization has on political mobilization and collective action do not appear easily explained by changing levels of trust or religiosity (Table 6, Panel C). The aggregate measures of conflict intensity, i.e. village level victimization and chiefdom victimization, are not robustly correlated with respondent behavior. For instance, village level victimization is never statistically significantly related to respondent meeting attendance and voting, conditional on their own household’s victimization experience (Tables 4 and 5, regressions 1-2 and 4-5). The relationship between chiefdom level victimization and these outcomes, again conditional on the household’s own experiences, is sometimes positive and statistically significant (for instance, in the case of community meeting attendance, Table 4 regressions 1 and 4), but not for most other outcome measures in Table 6 (e.g., not for voting, Table 5 regressions 1 and 4 – other regressions not shown). The stronger household level results suggest that violence victimization impacts are mainly driven by individual experiences and changes in preferences, rather than mainly by broader changes to institutions and social norms, since these would be reflected in the village or chiefdom level violence effects, and would affect both the households that directly experienced war violence as well as other households. It is noteworthy that differences across households within the same village are so pronounced given the residential proximity of these households, and the fact that all of them at a minimum witnessed extreme acts of violence during the Sierra Leone civil war, even if they did not experience violence directly. The gap we find between those who directly experienced violence and others provides real-world support for the experimental findings in Simonsohn et al (2006), who show in the lab that most people’s behavior is far more responsive to their own personal experience than to their observations of others’ experiences. C. Other specifications

20

We next investigate the possibility of heterogeneous effects of violence victimization for different population subgroups. When the explanatory variables for female, education, age, and traditional authority are interacted with the household victimization index, the coefficient estimates on these interaction terms are not generally statistically significant for the outcome measures in Table 6. The point estimate on the interaction of violence victimization with a youth indicator is often positive, and sometimes marginally statistically significant (as suggested by the findings in Table 6, Panel B, columns 1-2 versus 3-4), suggesting larger political mobilization impacts among youth, but this result is not robustly significant across samples, outcomes and specifications (regressions not shown). It is also theoretically possible that effects differ across different types of victimization, e.g., physical assault versus residential displacement, if these experiences are associated with different degrees of personal trauma. Yet, perhaps surprisingly, no single input into the conflict victimization index is a more important determinant of postwar behaviors than the others: when the three distinct components of the victimization index are included as separate independent variables, an F-test on the null hypothesis that the corresponding coefficients are all equal cannot be rejected at 95% confidence for any of the outcomes in Table 6, Panel B in the IRCBP sample (results not shown). D. Robustness to Chiefdoms without Permanent RUF Bases The main results are robust in a subsample of chiefdoms where the RUF did not establish permanent bases, and thus where the targeting of civilian violence is likely to be more arbitrary than elsewhere, as argued above. This analysis is conducted on the nationally-representative IRCBP survey sample.14 The main violence impact estimates remain large, positive and statistically significant for three of four political mobilization and collective action outcomes, and marginally significant for the fourth (Appendix Table A1, Panel B), and point estimates if anything strengthen slightly. E. Robustness to Retrospective Data on the Targeting of Violence 14

The smaller GoBifo sample contains only two chiefdoms without permanent RUF bases (according to the NPWJ report) and thus is not suitable for this analysis.

21

We next turn to retrospective household roster data to further investigate the targeting of violence. One specific concern for the identification strategy is that the RUF may have systematically targeted people who were community leaders (other than the traditional authorities) or otherwise very involved in local affairs. The analysis in Table 3, while informative regarding selection on socioeconomic status, does not allow us to rule out targeting on unobserved leadership qualities orthogonal to individual education. The retrospective household roster data was collected in the 2005 GoBifo survey, and contains information on all household members alive in 1990 (before the war). It includes two variables that allow us to test for targeting on leadership: whether the person was herself/himself a victim of violence during the war, and whether she/he ever held a community leadership position, for example, being the leader of a women’s group or a farmer’s group. We estimate the relationship between having held a community leadership position and victimization. Ideally, we could observe community leadership positions during the prewar period but unfortunately, the vague wording of the survey question left it unclear when exactly the person in question had held a community leadership position, since the wording did not explicitly specify that we were interested in the prewar period alone. In order to focus more precisely on prewar leaders, in the next analysis we restrict the sample to those aged 45 or older in 1990.15 These individuals were over 60 years old by the time of the 2005 survey, making it unlikely that they became community leaders only after the conflict ended. It appears more likely that any community leadership role was either before the conflict or in the early years of the conflict. This is not a perfect test, and some endogeneity (conflict victimization affecting the subsequent likelihood of being a community leader) remains possible. Still, to the extent the endogeneity bias is positive, it would lead us overstate the positive correlation between victimization and community leadership, making a finding of a near

15

The results are similar if the sample is restricted to people aged 40 years or older in 1990 (not shown).

22

zero correlation between these two variables even more persuasive evidence that community leaders were not in fact systematically targeted during the conflict. Among those aged 45 or older in 1990, the community leader indicator variable is unrelated to conflict victimization (Appendix Table A2). The point estimate is positive, but small and not statistically significant. As before, education is also unrelated to victimization during the conflict. Somewhat surprisingly, being a traditional authority is only weakly related to victimization in this older subsample, suggesting that younger members of chiefly families bore the brunt of the violence, although note that standard errors are relatively large here. V. Chiefdom level analysis Chiefdom level violence intensity is not robustly correlated with postwar outcomes in terms of socioeconomic status measures, public goods provision in education, and the number of NGO projects. Thus war violence appears to have a more decisive impact on preferences and values at the individual level than it does impacts on local institutions or social norms. A. Violence and Postwar Outcomes We find no substantial lingering negative effects of the war on 2004 consumption expenditure levels using either measure of conflict violence (the average conflict victimization, and the number of attacks and battles). The specifications include geographic controls, district fixed effects, and finally controls for prewar 1989 log per capita expenditures (Table 7, regressions 1-3). If anything, areas that suffered from more violence victimization have slightly higher postwar consumption, although effects are never statistically significant.16 In contrast, the number of diamond mines in the district is robustly positively associated with higher local living standards in all specifications.

16

One possible partial explanation for the rapid postwar economic recovery is improved soil fertility: land was often left fallow in areas that experienced more violence and population displacement, and this could have resulted in temporarily higher postwar yields. This remains speculative in the absence of detailed soil data, unfortunately.

23

We next estimate the relationship between conflict and a number of chiefdom level socioeconomic and public goods outcomes, focusing on a specification that includes all 152 chiefdoms and controls for district fixed effects and chiefdom geographic characteristics. Results are similar for regressions including the 1989 prewar per capita log expenditure control (regressions not shown), although the sample is considerably smaller in that case. Neither 2004 log per capita consumption expenditures (reproducing the Table 7 result), proportion of children enrolled in school, nor child body mass index (BMI) are significantly associated with conflict victimization in a chiefdom (Table 8, panel A). Conflict victimization is also not significantly related to local primary schooling outcomes, including teacher attendance, outside assistance, visits by chiefs, or local educational attainment levels (Table 8, panels B and C). The one exception is that chiefdoms with greater civilian victimization were significantly more likely to have successful community fundraising for their primary schools (row 5). This suggests that conflict-affected chiefdoms have perhaps slightly better local public goods fundraising three years after the war, consistent with the individual level findings on local collective action in Table 6. B. Chiefdom level Robustness Checks One concern with the chiefdom level results is that chiefdoms heavily affected by the war could have received increased amounts of NGO and donor funding in the postwar period. Not only do war affected chiefdoms not get more NGO projects, we find that they may even receive relatively fewer projects (Table 8, row 9). This could, in part, be due to the fact that some of the most conflictaffected areas were not declared safe for development workers until late 2002, up to a year or more after other regions. A second issue is whether chiefdom level impacts are larger in areas where chiefs were themselves attacked or killed in the violence. We use the NPWJ report to construct an indicator variable for this type of violence against traditional authorities, and when this measure is included as

24

an additional explanatory variable we find that the coefficient estimate is never statistically significant at traditional confidence levels for any of the variables in Table 8 or for those in Table 6 (regressions not shown). Thus attacks on traditional leaders do not appear to be the key drivers of the political and collective action impacts we estimate, again reinforcing the view that individual level changes are key. VI. Conclusion Using unique nationally representative household data for a postwar society, we find that individuals who directly experienced violence during the recent Sierra Leone civil war are no different in terms of postwar socioeconomic status, but they display dramatically higher levels of political mobilization and engagement, and contributions to local public goods than non-victims. Individuals whose households were conflict victims are more likely to attend community meetings, more likely to register to vote / vote, more likely to participate in road brushing (maintenance), and possess more awareness of local politics. This relationship is remarkably robust across two survey samples and multiple specifications with different levels of control. While we cannot rule out the possibility that omitted variable bias is playing some role – in that the types of people victimized tended to be those who would have become postwar local leaders anyway – there is no strong evidence that more educated people or community leaders were targeted. ,Additional tests – namely, demonstrating robustness in the youth subsample and in chiefdoms without permanent RUF bases, where conflictrelated violence victimization is likely to be more indiscriminate or random – argue against the hypothesis that the systematic targeting of community leaders is driving the results. Chiefdoms that experienced more intense fighting and abuse of civilians during the conflict are not relatively worse off three years later in terms of socioeconomic outcomes or child nutrition, or in terms of education public goods. If anything, there is a slight indication of perhaps somewhat better local collective action in the more affected chiefdoms, but these chiefdom level results are is

25

not as robust as impacts observed at the individual level. Taken together, it appears that the Sierra Leone civil war had its largest impacts on preferences and values related to political activity at the individual level. The increased local political mobilization we document could potentially help promote future economic development in Sierra Leone rather than hinder it. For example, we find that contributions to a pure local public good – road brushing – are higher among war victims. These individual contributions cannot simply be interpreted as a response to increased local problems, since the village fixed effects control for any village-wide needs, but rather appear to reflect changes in individual preferences and values. If this results in better provision of local public goods, it can be thought of as a positive legacy of the conflict. The finding that the civil war was transformative resonates with the observations of other Sierra Leone scholars. David Keen (2005: 170) has claimed that the “experience of displacement and to some extent the exposure to aid organizations seems to have produced a heightened awareness among many ordinary Sierra Leoneans”, and among youths in particular. Ferme also discusses the potential to forge something positive out of the horrors of war: “[Sierra Leonans] have sometimes turned [social instability] into a creative, though violent, opportunity to refashion themselves vis-àvis their own institutions” (2002: 228). More research needs to be done to understand the legacies of civil wars in Africa, especially since our empirical strategy only provides evidence on localized conflict impacts rather than overall national effects. Yet, more speculatively, the finding that war victimization can increase political mobilization and local collective action may help make sense of the rapid economic growth and political consolidation many African countries have experienced following protracted civil wars. The humanitarian costs of civil wars are horrific, of course, but it appears their postwar economic and political legacies need not be catastrophic.

26

References Achen, Christopher H. and Larry M. Bartles. 2004. “Blind Retrospection – Electoral Responses to Drought, Flu and Shark Attacks” Manuscript, Princeton Univ. Blattman, Christopher. 2006. “The Consequences of Child Soldiering” Manuscript, Univ. of California, Berkeley. Brakman, Steven, Harry Garretsen and Marc Schramm. 2004. “The Strategic Bombing of Cities in Germany in World War II and it Impact on City Growth.” Journal of Economic Geography 4 (1): 1-18. Castillo, Marco, and Michael Carter. 2005. “Identifying Social Effects with Economic Field Experiments.” Manuscript, Univ. of Wisconsin. Carmil, Devora and Shlomo Breznitz. 1991. “Personal Trauma and World View – Are extremely stressful experiences related to political attitudes, religious beliefs, and future orientation?” Journal of Traumatic Stress 4(3): 393 – 405. Collier, Paul and Anke Hoeffler. 2004. “Greed and grievance in civil war.” Oxford Economic Papers 56 (4): 563 – 595. Conibere, Richard, Jana Asher, Kristen Cibelli, Jana Dudukovich, Rafe Kaplan, and Patrick Ball. 2004. “Statistical Appendix to the Report of Truth and Reconciliation Commission, Report of Sierra Leone.” Human Rights Data Analysis Group, The Benetech Initiative. Davis, Donald R., and David E. Weinstein. 2002. “Bones, Bombs, and Break Points: The Geography of Economic Activity.” American Economic Review 92 (5): 1269-1289. Drèze, Jean. 2002. “Militarism, Development and Democracy.” Economic and Political Weekly April: 1171-1183. Dyregrov, Atle, Rolf Gjestad and Magne Raundalen. 2002. “Children exposed to warfare: A longitudinal study.” Journal of Traumatic Stress 15 (1): 59-68 Ferme, Mariane C. 2001. The Underneath of Things: Violence, History and the Everyday in Sierra Leone. Berkeley: Univ. of California Press. Green, Donald and Ian Shapiro. 1994. Pathologies of Rational Choice. New Haven: Yale Univ. Press Hastings, Justine, Thomas Kane, and Douglas Staiger. 2006. “Economic Outcomes and the Decision to Vote: the Effect of Randomized School Admissions on Voter Participation.” NBER Working Paper No. 11805. Human Rights Watch. 1999. “Sierra Leone: Getting Away with Murder, Mutilation, and Rape.” New York: Human Rights Watch. Humphries, Macartan and Jeremy M. Weinstein. 2006. “Handling and Manhandling Civilians in Civil War: Determinants of the Strategies of Warring Factions.” American Political Science Review 100 (3): 429-447. Keen, David. 2005. Conflict and Collusion in Sierra Leone. London: James Currey; New York: Palgrave. Miguel, Edward A. and Roland, Gerard. 2005. “The Long Run Impact of Bombing Vietnam” Manuscript, Univ. of California, Berkeley. Olson, Mancur. 1984. The Rise and Decline of Nations. New Haven: Yale Univ. Press. Powell, S. et al. 2003.“Posttraumatic Growth After War: A Study with Former Refugees and Displaced People in Sarajevo,” Journal of Clinical Psychology, 59(1): 71-83. Reno, William. 1995. Corruption and State Politics in Sierra Leone. Cambridge and New York: Cambridge Univ. Press. Richards, Paul. 1996. Fighting for the Rainforest: War, Youth and Resources in Sierra Leone. London: James Currey; Portsmouth, NH: Heinemann for the International African Institute.

27

Richards, Paul. 2003. “The Political Economy of Internal Conflict in Sierra Leone.” Working Paper no. 21, Netherlands Institute of International Relations. Punamaki, Raija-Leena, Samir Quota and Eyad El Sarraj. 1997. “Relationships Between Traumatic Events, Children’s Gender, Political Activity, and Perceptions of Parenting Styles.” International Journal of Behavioral Development 21(1): 91-109 Simonsohn, U., N. Karlsson, G. Loewenstein, and D. Ariely. 2006. “The Tree of Experience in the Forest of Information: Overweighing Experienced Relative to Observed Information” Manuscript Univ. of Pennsylvania. Smith, L. Alison, Catherine Gambette, and Thomas Langley. 2004. “Conflict Mapping in Sierra Leone: Violations of International Humanitarian Law from 1991 to 2002.” No Peace Without Justice March. Sparks, Allister. 2003. Beyond the Miracle: Inside the New South Africa. Chicago: Univ. of Chicago Press. Tedeschi, R.G., and L.G. Calhoun. 1996, “The Posttraumatic Growth Inventory,” Journal of Traumatic Stress, 9: 455-471. Tilly, Charles H. 1975. The Formation of National States in Western Europe. Princeton NJ: Princeton Univ. Press. Timpone, Richard J. 1998 “Structure, Behavior, and Voter Turnout in the United States” American Political Science Review, 92 (1): 145-158 United Nations. 1993. Human Development Report 1993. New York: United Nations Development Program, Oxford Univ. Press. United Nations. 2004. Human Development Report 2004. New York: United Nations Development Program, Oxford Univ. Press. World Bank. 2003. Breaking the Conflict Trap: Civil War and Development Policy. Washington, DC: World Bank.

28

Table 1: Descriptive Statistics Individual Level Data IRCBP Sample a GoBifo Sample b Mean Std Dev Mean Std Dev Panel A: Household Conflict Victimization Were any members of your HH killed during the conflict? Were any members of your HH injured/maimed during the conflict? Were any members of your HH made refugees during the conflict? Did you ever flee the place you were living because of the conflict? Were any children from your HH abducted during the conflict? Were any women from your HH abducted during the conflict? Was your house ever burned down during the conflict? Household conflict victimization index (average of above variables)

0.37 0.24 0.50 0.37

0.48 0.43 0.50 0.33

0.20 0.23 0.97 0.23 0.10 0.39 0.35

0.40 0.42 0.17 0.42 0.30 0.49 0.19

Panel B: Postwar Local Institutions Politics Did you attend any community meetings in past year? Did you register to vote / vote in either of the past two elections? Are you a member of a School Management Committee? Are you a member of a political group? Did you participate in road brushing in the past year? Can you correctly name the Local Councilor for this area? Can you correctly name the Paramount Chief of this chiefdom?

0.76 0.97 0.20 0.21 -

0.43 0.17 0.40 0.41 -

0.70 0.92 0.34 0.72 0.47 0.77

0.46 0.27 0.47 0.45 0.50 0.42

0.66

0.47

0.93

0.26

0.90

0.17

0.59

0.28

0.65

0.27

-

-

0.08

0.27

0.10

0.30

0.55 0.04

0.50 0.20

0.36 0.10

0.48 0.30

0.49 41.43 0.33 0.12 -

0.50 15.52 0.47 0.32 -

0.48 40.62 0.25 0.25 8.56

0.50 16.45 0.43 0.43 4.49

Panel C: Postwar Social Capital Are you a member of a church/mosque group / Have you attended church/mosque in the past month? Do you trust other members of your community / Would trust community members in hypothetical situations? (0 = low trust, 1 = high trust) Do you trust people from outside your community? (0 = low trust, 1 = high trust) Panel D: Postwar Socio-Economic Outcomes Did you do any work for wages / do any activities to earn cash in the past year? Does your household own a radio? Does your household own a stove? Panel E: Respondent Controls Female Age Have you ever been in school? Have you ever held a position of traditional authority? Is your household part of a traditional ruling family? Household size in 1990 Number of Household Observations Number of Districts ; Chiefdoms ; Enumeration Areas/Villages

5138 13 ; 152 ; 539

2694 2 ; 13 ; 235

29

Table 1 (continued): Descriptive Statistics Chiefdom Level Data Mean Std dev Panel F: Chiefdom Conflict Victimization Chiefdom conflict victimization index (Chiefdom average of IRCBP conflict index) a Number of attacks and battles in chiefdom, 1991-2002 c

0.46 9.41

0.17 9.70

Panel G: Postwar Socio-Economic Outcomes Average Log per capita expenditure (Leones), 2004 d Proportion of children enrolled in school (ages 5-18), 2004 d Average BMI for children (ages 0-5) d

13.00 0.64 22.15

0.44 0.17 8.44

Panel H: Postwar Education and Local Public Goods Proportion of teachers absent on day of school survey h Proportion of schools receiving financial/ in-kind resources from community h Proportion of schools receiving financial/ in-kind resources from donors/NGOs h Proportion of schools visited by a traditional authority in the last year h Proportion of adults in chiefdom who have ever been to school, 2004 d Total number of NGO projects in the chiefdom e

0.25 0.49 0.49 0.76 0.29 44.59

0.14 0.42 0.43 0.35 0.16 42.67

Panel I: Prewar Socio-Economic and Geographic Controls Average Log per capita expenditure (Leones), 1989 f Proportion of children enrolled in school (ages 5-18), 1989 f Number of diamond mines (per chiefdom) g Road density (km of road per sq km of land area) g Log distance to Freetown (km) g Log population density (people per sq km), 1985 g

8.00 0.26 2.59 0.09 11.94 3.75

0.72 0.19 5.49 0.06 0.57 0.75

Number of Districts ; Chiefdoms

13 ; 152

Notes: Sources: (a) Institutional Reform and Capacity Building Project, 2005 Household Survey (b) GoBifo Household Survey, 2005 (c) No Peace Without Justice Conflict Mapping Report, 2004 (d) Sierra Leone Integrated Household Survey, 2003-2004 (e) Encyclopedia of Sierra Leone, Sierra Leone Information Systems, 2003 (f) Sierra Leone Household Survey, 1989 (g) GIS Data, Government of Sierra Leone, 2002 (h) Sierra Leone School Monitoring Survey, 2005 There are some differences in questions across the IRCBP and GoBifo surveys. First, some questions in the IRCBP survey were not included in the GoBifo survey and vice versa. Second, the wording of some questions is different. These are indicated in the table and include: (1) The IRCBP survey asks “Did you register to vote for either of the last two elections?”; the GoBifo survey asks “Did you vote in either of the last two elections?”. (2) The IRCBP survey asks “Are you a member of a church/mosque group?”; the GoBifo survey asks “Have you attended church/mosque in the past month?”. (3) The IRCBP survey asks “How much do you trust members of your community?”; the GoBifo asks three hypothetical questions that measure trust in different situations, and the average of those three questions is the overall trust measure. (4) The IRCBP survey asks “Did you work for wages in the past year?”; the GoBifo survey asks about specific activities that earn cash. Freetown (the capital city) is excluded from every sample. The IRCBP sample is designed to be nationally representative. Each enumeration area in the IRCBP sample represents a distinct village because the only major city has been excluded from the analysis. Due to survey sampling design, there are 117 observations for the 2004 socio-economic variables (source (d)), 64 observations for the 1989 socio-economic variables (source (f)), and 104 observations for the school survey data (source (h)).

30

Table 2: Chiefdom Level Correlations with Conflict Intensity

Explanatory Variable

Dependent Variable: Conflict victimization index (3) (1) (2)

Dependent Variable: Number of attacks and battles (4) (5) (6)

-0.0016 (0.0024)

0.0010 (0.0014)

0.0011 (0.0012)

0.39*** (0.076)

0.33*** (0.077)

0.36*** (0.10)

Road density

0.11 (0.18)

-0.22 (0.17)

0.42 (0.46)

19.51* (10.69)

5.07 (16.32)

-25.12 (33.62)

Log distance to Freetown

0.13*** (0.043)

0.073* (0.039)

0.097 (0.084)

-1.94 (1.79)

0.59 (16.32)

4.64 (5.93)

Log population density, 1985

0.025 (0.026)

-0.0066 (0.015)

0.071* (0.036)

-0.36 (1.30)

0.23 (1.00)

1.90 (2.72)

Number of diamond mines

Proportion children in school, 1989

-0.21* (0.11)

5.00 (16.17)

Log per capita expenditure, 1989

0.011 (0.030)

3.76* (1.94)

R-squared Observations District fixed effects

0.22 152

0.60 152 X

0.69 64 X

0.070 152

0.21 152 X

0.34 64 X

Notes: Additional controls in all regressions include number of chiefdom non-diamond mines and the river density. In regressions (2), (3), (5), and (6) district fixed effects are included for Tonkolili, Pujehun, Port Loko, Moyamba, Kono, Koinadugu, Kono, Kenema, Kambia, Bonthe, Bombali, and Bo Districts; Western Area Rural District is the omitted district. Robust standard errors are reported. Standard errors are clustered at the district level in all regressions. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. The coefficient on log per capita expenditure in column (6) is robust to excluding Western Area Rural from the regression sample.

31

Table 3: Household Level Correlations with Conflict Intensity Dependent Variable: Household Conflict Victimization Index IRCBP Sample GoBifo Sample (2) (3) (4) (5)

Explanatory Variables

(1)

Respondent is female

0.0074 (0.0078)

0.0070 (0.0079)

0.0070 (0.0081)

-0.0001 (0.0067)

0.0004 (0.0068)

0.0000 (0.0070)

Respondent age

0.0003 (0.0003)

0.0002 (0.0003)

0.0003 (0.0003)

0.0000 (0.0002)

0.0000 (0.0002)

-0.0000 (0.0002)

Respondent has any education

0.0098 (0.0094)

0.011 (0.010)

0.015 (0.010)

0.018** (0.0085)

0.021** (0.0088)

0.019** (0.0096)

Traditional authority household

0.033*** (0.012)

0.034*** (0.013)

0.028** (0.013)

0.022** (0.0087)

0.024*** (0.0089)

0.025*** (0.0097)

0.0083*** (0.0008)

0.0084*** (0.0008)

0.0086*** (0.0009)

0.51*** (0.051)

0.48*** (0.054)

Household size in 1990 EA/Village conflict victimization index

0.47*** (0.040)

Chiefdom conflict victimization index

0.14*** (0.048)

R-squared Observations District fixed effects Chiefdom fixed effects EA/Village fixed effects

0.25 5138 X

0.28*** (0.054)

(6)

0.33*** (0.10) 0.27 5138

0.37 5138

X

0.13 2694 X

0.14 2694

0.24 2694

X X

X

Notes: Regression (3) includes enumeration area fixed effects. Regression (6) includes village fixed effects. The enumeration area conflict victimization index in regressions (1) and (2) is constructed as a simple average of the household conflict victimization indexes in the enumeration area excluding that household; it has mean 0.37 and standard deviation 0.20. The village conflict victimization index in (4) and (5) is constructed analogously; it has mean 0.35 and standard deviation 0.088. In the IRCBP sample, a traditional authority household is defined by whether the respondent has ever held a position of traditional authority. In the GoBifo sample, a traditional authority household is defined by whether that household is a member of ruling family, which does not necessarily mean somebody from that household has ever held a position of traditional authority. Respondent’s age has been demeaned. Robust standard errors are reported. Standard errors are clustered at the enumeration area in all the IRCBP regressions and at the village level in all the GoBifo regressions. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence.

32

Table 4: Community Meetings and Conflict Victimization

Explanatory Variables

Dependent Variable: Did you attend any community meetings in the past year? IRCBP Sample GoBifo Sample (2) (3) (4) (5)

(1)

Household conflict victimization index

0.072 (0.022)

**

0.057 (0.022)

**

0.057 (0.024)

**

0.098 (0.045)

0.10 (0.045)

0.12** (0.050)

Respondent is female

-0.13*** (0.011)

-0.13*** (0.011)

-0.13*** (0.012)

-0.16*** (0.017)

-0.16*** (0.017)

-0.16*** (0.018)

Respondent’s age

0.0006 (0.0004)

0.0006 (0.0004)

0.0004 (0.0004)

0.0010* (0.0006)

0.0011* (0.0006)

0.0014** (0.0007)

Respondent has any education

0.042*** (0.014)

0.060*** (0.014)

0.061*** (0.015)

0.054*** (0.021)

0.044** (0.021)

0.049** (0.023)

Traditional authority household

0.091*** (0.015)

0.077*** (0.016)

0.071*** (0.017)

0.067*** (0.021)

0.056*** (0.021)

0.049** (0.023)

0.0013 (0.0020)

0.0010 (0.0072)

0.0005 (0.0021)

-0.18 (0.13)

-0.17 (0.12)

Household size in 1990 EA/Village conflict victimization index

-0.011 (0.071)

Chiefdom conflict victimization index

0.49*** (0.11)

R-squared Observations District fixed effects Chiefdom fixed effects EA/Village fixed effects

0.13 5138 X

-0.004 (0.066)

**

(6)

***

0.67*** (0.32) 0.18 5138

0.29 5138

X

0.052 2694 X

0.074 2694

0.16 2694

X X

X

Notes: Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. Robust standard errors are clustered at the enumeration area and village level in all IRCBP and GoBifo regressions, respectively. The EA/Village conflict victimization index is as described in the notes for Table 3. In the IRCBP sample, a traditional authority household is defined by whether the respondent has ever been a traditional leader (chief). In the GoBifo sample, a traditional authority household is defined by whether that household is a member of ruling family, which does not necessarily mean somebody from that household has ever held a position of traditional authority. Respondent’s age has been demeaned.

33

Table 5: Voting and Conflict Victimization

Explanatory Variables

Dependent Variable: Did you register to vote / vote in the either of the past two elections? IRCBP Sample GoBifo Sample (1) (2) (3) (4) (5) ***

Household conflict victimization index

0.026 (0.0079)

***

0.026 (0.0082)

0.024 (0.0093)

0.10 (0.026)

0.10 (0.026)

0.094*** (0.029)

Respondent is female

-0.015*** (0.0049)

-0.015*** (0.0050)

-0.014*** (0.0052)

-0.033*** (0.010)

-0.0364*** (0.010)

-0.035*** (0.011)

Respondent’s age

0.0007*** (0.0002)

0.0007*** (0.0002)

0.0007*** (0.0002)

0.0012*** (0.0003)

0.0012*** (0.0003)

0.0013*** (0.0004)

Respondent has any education

-0.0050 (0.0052)

-0.0045 (0.0054)

-0.0021 (0.0057)

0.011* (0.012)

0.0049 (0.012)

0.0029 (0.013)

Traditional authority household

0.0048 (0.0061)

0.0066 (0.0064)

0.0077 (0.0072)

0.017* (0.012)

0.018* (0.012)

0.022* (0.014)

-0.0010 (0.0012)

-0.0008 (0.0012)

-0.0012 (0.0014)

0.076 (0.061)

0.076 (0.063)

Household size in 1990 EA/Village conflict victimization index

0.0093 (0.022)

Chiefdom conflict victimization index

0.012 (0.037)

R-squared Observations District fixed effects Chiefdom fixed effects EA/Village fixed effects

0.024 5138 X

0.0073 (0.021)

***

***

(6)

***

0.0044 (0.16) 0.044 5138

0.12 5138

X

0.033 2694 X

0.040 2694

0.13 2694

X X

X

Notes: Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. Robust standard errors are clustered at the enumeration area and village level in all IRCBP and GoBifo regressions, respectively. The dependent variable for the IRCBP sample is “Did you register to vote in either of the past two elections?”, in the GoBifo sample the dependent variable is “Did you vote in either of the past two elections?”. The EA/Village conflict victimization index is as described in the notes for Table 3. In the IRCBP sample, a traditional authority household is defined by whether the respondent has ever been a traditional leader (chief). In the GoBifo sample, a traditional authority household is defined by whether that household is a member of ruling family, which does not necessarily mean somebody from that household has ever held a position of traditional authority. Respondent’s age has been demeaned.

34

Table 6: Household Level Postwar Outcomes and Conflict Victimization Household Conflict Victimization Index: Coefficient (s.e.) Full Sample

Youth Sample (≤30 years old in 2005)

IRCBP (1)

GoBifo (2)

IRCBP (3)

GoBifo (4)

1. Have you worked for wages / Done any activities to earn cash in the past year?

-0.0087 (0.014)

-0.048 (0.036)

-0.032 (0.028)

-0.045 (0.080)

2. Does your household own a radio?

-0.058** (0.027)

0.012 (0.062)

-0.054 (0.062)

-0.047 (0.15)

3. Does your household own a stove?

-0.0004 (0.011)

0.046 (0.039)

-0.016 (0.029)

-0.0023 (0.084)

4. Did you attend any community meetings in past year?

0.057** (0.024)

0.12** (0.050)

0.13** (0.061)

0.19 (0.13)

5. Did you register to vote / vote in either of the past two elections?

0.024*** (0.0093)

0.094*** (0.028)

0.050* (0.029)

0.19** (0.092)

6. Are you a member of a School Management Committee?

0.062*** (0.021)

0.014** (0.055)

0.13*** (0.049)

0.12 (0.10)

7. Are you a member of a political group?

0.056** (0.023)

Dependent Variables Panel A: Socio-Economic Outcomes

Panel B: Mobilization and Political Action

0.050 (0.049)

8. Did you participate in road brushing in the past year?

0.22*** (0.052)

0.42*** (0.11)

9. Can you correctly name the Local Councilor?

0.11** (0.052)

0.29*** (0.11)

10. Can you correctly name the Paramount Chief?

0.089* (0.046)

0.17 (0.13)

Panel C: Trust and Group Memberships 11. Do you trust other members of your community / Would trust community members in hypothetical situations? (0 = low trust, 1 = high trust ) 12. Do you trust people from outside your community? (0 = low trust, 1 = high trust ) 13. Are you a member of a church/mosque group/ Have you attended church/mosque in the past month? Observations EA/Village fixed effects

-0.0020 (0.0095)

0.060* (0.036)

0.018 (0.015)

0.0046 (0.024)

0.047 (0.081)

0.0022 (0.040)

-0.031 (0.026)

0.039 (0.034)

-0.0053 (0.066)

0.11 (0.080)

5138 X

2694 X

1465 X

756 X

Notes: Each entry is from a separate OLS regression. Robust standard errors are reported. Standard errors are clustered at the enumeration area in all the IRCBP regressions and at the village level in all the GoBifo regressions. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. The specification is analogous to regressions (3) and (6) in Tables 5 and 6: additional controls include respondent’s gender, age, education, traditional authority household and household size in 1990. Enumeration area fixed effects are included in all IRCBP regressions and village fixed effects in all GoBifo regressions.

35

Table 7: 2004 Log Per Capita Expenditure and Conflict Victimization Dependent Variable: Log per capita expenditures, 2004 Explanatory Variable

(1)

(3)

0.53 (0.49)

(2) 0.42 (0.39)

0. (0.63)

Number of attacks and battles

-0.0054 (0.0041)

-0.0063 (0.0055)

-0.0037 (0.012)

Number of diamond mines

0.028*** (0.0032)

0.025*** (0.0042)

0.016** (0.0069)

Road density

0.29 (0.73)

0.80 (0.56)

0.69 (1.22)

Log distance to Freetown

-0.32** (0.11)

-0.12 (0.12)

0.33 (0.21)

Log population density, 1985

-0.096** (0.041)

-0.016 (0.042)

-0.042 (0.091)

Chiefdom conflict victimization index

Proportion children in school, 1989

0.034 (0.41)

Log per capita expenditure, 1989

0.065 (0.097)

R-squared Observations District fixed effects

0.22 117

0.47 117 X

0.65 55 X

Notes: Robust standard errors are reported. Standard errors are clustered at the district level in all regressions. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. Due to sampling in the 2004 household survey, the sample size is smaller then the full sample of 152 chiefdoms. Additional controls in all regressions include number of chiefdom non-diamond mines and the river density. In regressions (2) and (3) district fixed effects are included for Tonkolili, Pujehun, Port Loko, Moyamba, Kono, Koinadugu, Kono, Kenema, Kambia, Bonthe, Bombali, and Bo Districts; Western Area Rural District is the omitted district.

36

Table 8: Chiefdom-Level Outcomes and Conflict Victimization

Dependent Variables Panel A: Postwar Socio-Economic Outcomes

Chiefdom Conflict Victimization Index: Coefficient (std. error)

1. Log per capita expenditure, 2004

0.42 (0.39)

2. Proportion children enrolled in school, 2004

0.17 (0.17)

3. BMI for children, 2004

3.89 (8.94)

Panel B: School Quality Outcomes, 2005 4. Proportion of teachers absent on day of survey

-0.045 (0.16)

5. Proportion of schools receiving financial / in-kind resources from community

0.62* (0.33)

6. Proportion of schools receiving financial / in-kind resources from donors or NGOs

-0.11 (0.39)

7. Proportion of schools visited by a traditional authority in past year

-0.29 (0.44)

Panel C: Adult Education and NGO Projects in 2004 8. Proportion of adults with any education, 2004 9. Total number of NGO projects District fixed effects

0.038 (0.083) -27.89* (16.66) X

Notes: Each coefficient and standard error are from a separate OLS regression. Robust standard errors are reported. Standard errors are clustered at the district level in all regressions. Significantly different than zero at * 90% confidence,** 95% confidence,,*** 99% confidence. Due to sampling, there are 117 observations in rows 1-3; 104 chiefdoms in rows 4-7; and 152 chiefdoms in rows 89. The Number of NGO projects in row 9 includes all reported education, health, and agriculture NGO projects. The specification in these regressions is equivalent to regression (2) in Table 8: additional explanatory variables include number of attacks and battles, number of diamond mines, road density, log distance to Freetown, log population density in 1985, number of non-diamond mines, and river density, district fixed effects are also included (see notes in Table 8).

37

Figure 1: Location of Sample Enumeration Areas in IRCBP Sample

Notes: The IRCBP sample is nationally representative. There are a total of 539 enumeration areas in the sample. There are 23 enumeration areas that not included above due to missing GPS coordinates. The capital – Freetown – is not included in the sample; it is represented by a star on the map.

Figure 2: Location of Sample Villages in GoBifo Sample

Notes: There are 235 villages in the GoBifo sample. The sample covers two districts: Bombali in the North and Bonthe in the South. The sample is not designed to be representative of either district. There are 51 sample villages not included above due to missing GPS coordinates. The capital – Freetown – is not included in the sample; it is represented by a star on the map.

38

Figure 3: Chiefdom Conflict Victimization Index

Notes: The Conflict Victimization Index is the chiefdom average of three conflict related questions in the IRCBP survey. Chiefdoms are shaded in deciles according to the value of the conflict index. Data is missing for Gbonkolenken chiefdom, leaving a sample size of 151 chiefdoms.

Figure 4: Residuals of Chiefdom Conflict Victimization Index

Notes: The residuals in this figure are from a regression of the conflict index on a set of district fixed effects. Thus, this map shows the variation being used in all of the specifications that include district fixed effects. Chiefdoms are shaded in deciles according to the value of these residuals. Data is missing for Gbonkolenken chiefdom, leaving a sample size of 151 chiefdoms.

39

Appendix Table A1: Chiefdoms with no RUF bases Household Level Postwar Outcomes and Conflict Victimization

Dependent Variables Panel A: Socio-Economic Outcomes

Household Conflict Victimization Index: Coefficient (s.e.) IRCBP (1)

1. Have you worked for wages / Done any activities to earn cash in the past year?

-0.015 (0.021)

2. Does your household own a radio?

-0.068** (0.036)

3. Does your household own a stove?

-0.0048 (0.016)

Panel B: Mobilization and Political Action 4. Did you attend any community meetings in past year?

0.11*** (0.034)

5. Did you register to vote / vote in either of the past two elections?

0.024* (0.012)

6. Are you a member of a School Management Committee?

0.067** (0.029)

7. Are you a member of a political group?

0.046 (0.035)

Panel C: Trust and Group Memberships 8. Do you trust other members of your community / Would trust community members in hypothetical situations? (0 = low trust, 1 = high trust ) 9. Do you trust people from outside your community? (0 = low trust, 1 = high trust ) 10. Are you a member of a church/mosque group/ Have you attended church/mosque in the past month? Observations EA fixed effects

-0.0033 (0.013) 0.010 (0.021) 0.0055 (0.038) 2535 X

Notes: Each entry is from a separate OLS regression. Robust standard errors are reported. Standard errors are clustered at the enumeration area in all the IRCBP regressions. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence. The specification is analogous to regressions (3) and (6) in Tables 5 and 6: additional controls include respondent’s gender, age, education, traditional authority household and household size in 1990. Enumeration area fixed effects are included in all IRCBP regressions.

40

Appendix Table A2: Individual “Selection” into Victimization GoBifo 1990 Household Roster Data – 45 years and older in 1990 Explanatory Variable Ever held a community leadership position

Dependent Variable Victim of violence during the conflict 0.0011 (0.027)

Female

-0.034*** (0.013)

Age in 1990

-0.0007 (0.0006)

Ever been to school

0.017 (0.024)

Ever held a traditional leadership position

-0.0073 (0.021)

R-squared Observations Chiefdom Fixed Effects Mean of dependent Variable

0.054 1796 X 0.07

Notes: Robust standard errors are clustered at chiefdom level. Significantly different than zero at * 90% confidence,,** 95% confidence,,*** 99% confidence The data come from the 2005 GoBifo retrospective household roster. A complete roster was compiled for each respondent’s household in 1990. From that list, a random sample of five people were selected. The data consists of information on those five people, who all lived in the respondent’s household in 1990 but do not necessarily live with the respondent today. For reasons discussed in the text, this sample is restricted to persons over 45 years of age in 1990, which is approximately equivalent to keeping the top quintile of the sample. The means (std. dev) of the explanatory variables are as follows: Ever held a community leadership position 0.06 (0.24), Female 0.49 (0.50), Age in 1990 55.99 (9.75), Ever been in school 0.11 (0.31), Ever held a position of traditional authority 0.11 (0.31). Traditional authority includes Paramount, Section, and Village chiefs. Community leadership position includes women’s leader, youth leader, head teacher, school committee chair, imam/reverend and master farmer. Victim of violence corresponds to “injured, maimed or killed” during the conflict. Chiefdom fixed effects for the chiefdom of residence in 1990 are included in all regressions. Results do not change substantially without the fixed effects. Results do not change substantially if the sample is instead restricted to individuals over 40 years old in 1990 (not shown).

41

Data Appendix Institutional Reform and Capacity and Building Project (IRCBP) Survey, 2005 The IRCBP overall supports the ongoing decentralization in Sierra Leone, working closely with the newly elected Local Councils to strengthen local government. The 2005 IRCBP survey provides measures of conflict victimization and measures of local institutional outcomes. The IRCBP survey collected information on the provision of public services, attitudes and perceptions of local government, as well as some demographic and socioeconomic variables. The survey was designed to be nationally representative. Each of the 13 districts is included in the sample. Data is missing for Gbonkolenken chiefdom, which leaves 151 chiefdoms in all . A total of 539 enumeration areas were surveyed. The sample size for all individual level regressions is 5,138 individuals. GoBifo Household Survey, 2005 The GoBifo survey is baseline data for a randomized evaluation of a Community Driven Development project named GoBifo (in the local lingua franca, Krio, GoBifo means ‘Move Forward’). The GoBifo project is operating in two districts: Bombali and Bonthe. In these districts, specific villages were chosen randomly from wards designated for the program. Thus, the sample is not representative at either the district or the chiefdom level. 235 villages are included in the project. The sample size for all individual level regressions is 2,694 individuals. The GoBifo survey includes extensive household and individual data on community participation, social capital, as well as questions on experience during the conflict. The GoBifo survey also included a retrospective household roster. Each respondent was asked to name everybody who was living in the same household as them in 1990. This list was to include people who are no longer living with the respondent or are no longer alive. From that list, five people were chosen randomly and data was collected on them. The total sample size is 13,280 individuals. For reasons discussed in the text, the analysis included in this paper is restricted to individuals who were 45 years of age or older in 1990. No Peace Without Justice (NPWJ) Report, 2004 A measure of conflict intensity that focuses on troops and soldiers is provided by the number of attacks and battles in each chiefdom. This measure was coded from the No Peace Without Justice (NPWJ) conflict mapping report. NPWJ is a non-profit organization that works to promote an effective international criminal justice system and to support accountability mechanisms for war crimes. The conflict mapping report seeks to record all violations of humanitarian law that occurred over the entire conflict period. The ‘factual analysis’ section of the report is organized chronologically by district, and it reports the chiefdom where each incident occurred, allowing for the construction of chiefdom level war violence measures. The report is available online at: http://www.npwj.org. The measure used in our analysis is the number of attacks and battles that occurred within each chiefdom. An attack is defined to be an incident in which an armed group came into a village briefly, burned houses, raped or killed residents. It is common for attacks to be part of a larger military campaign and thus for human rights violations to be committed on a large scale (e.g. “during these attacks RUF forces burnt down fifty houses, killed nine people, abducted an unknown number of people and amputated a man’s hand with an axe” p. 189). A battle is defined to be a confrontation between two armed groups (e.g. “On 25 February, the RUF made a successful counter-attack at the rutile mining site, dislodging the SLA forces based there.” p. 430). Battles need not directly involve violence against civilians, although they sometimes do. There were 1,995 violent incidents recoded in the NPWJ report, and 1,363 of these incidents were classified as either an attack or a battle. To give the reader some sense of who the perpetrators are, of the 968 recorded attacks over 95% were committed by RUF rebels and less than two percent by CDF. The majority of the battles took place between RUF and CDF troops, with a smaller but still substantial number also involving the SLA and ECOMOG (West African forces led by Nigeria).

42

Sierra Leone Integrated Household Survey (SLIHS), 2003-2004 Data on chiefdom-level postwar household expenditures, enrollment of children in school, and child body mass index is available from the 2003-2004 SLIHS survey. The data collection was funded by DFID and the World Bank, with the intent of providing more complete poverty measures for use in postwar planning. The data was made available from the office of Statistics Sierra Leone. This national survey was designed to be representative at the district level. As with the IRCBP survey, the large number of households in each district allows construction of chiefdom level averages. All of the statistics used in the present analysis are based on the cleaned sample, which included households located in 117 (out of 152) chiefdoms. Due to sampling strategy, no data was collected for the remaining 35 chiefdoms. Sierra Leone Household Survey (SLHS), 1989 The 1989 SLHS household survey is, to the best of our knowledge, the only available household survey data source on prewar conditions outside of Freetown. The household and individual level data is used to construct measures of average log per capita expenditure and also child school enrollment. Regressions that include these variables should be interpreted with caution for two reasons. First, there is minimal existing documentation on the survey, so it is hard to assess data quality. Second, there is a small sample size: it is possible to construct measures for only 64 chiefdoms. Data collection under-sampled chiefdoms near Sierra Leone’s national borders, although the precise reasons why are unclear. Sierra Leone Data Encyclopedia, 2004 The number of non-government organization (NGO) projects located in each chiefdom is reported in the Sierra Leone Data Encyclopedia, 2004. The Encyclopedia compiles statistics from multiple government and donor agencies to facilitate information sharing and improve policy making. The Encyclopedia is produced by Sierra Leone Information Systems and the Development Assistance Coordination Office (SLIS/DACO) in Freetown. The Encyclopedia contains the WhoWhatWhere Humanitarian Database which compiles information on the activities of the international NGOs, large national NGOs, and other donors currently working in Sierra Leone. The measure used in the analysis is the total number of NGO projects across all sectors – including health, agriculture, and education – from 2001 to 2004. Geographic Information Systems (GIS) Data GIS data provides measures of resources and infrastructure in Sierra Leone. This data is managed and produced by Sierra Leone Information Systems and the Development Assistance Coordination Office (SLIS/DACO) in Freetown. GIS coordinates of all government registered industrial mining sites were combined with firm descriptions from site licenses to determine to location of all registered diamond mining sites. Non-diamond industrial mining plots, including rutile, bauxite, silver, gold, and ‘assorted minerals’, are also observed and included as controls in our regression analysis. Because of unregistered and illegal mining, these measures of mining activity may understate the true extent of diamond mining in Sierra Leone. However, since the civil war ended, the government of Sierra Leone has made a concerted effort to document and register all of the mining in the country, as these resources are a major source of government revenue. GIS data was also used to construct measures of road density, river density, distance of the chiefdom to Freetown, and the land area of each chiefdom. Sierra Leone School Monitoring Survey, 2005 This survey was conducted by IRCBP as part of their ongoing evaluation of local public service provision in Sierra Leone. The school monitoring survey featured two unannounced visits, in which the enumerators collected information on the number of teachers present, the number of children in school, whether the school was open, etc. In addition to this surprise component, enumerators also asked detailed questions regarding schools finances and operations. A total of 288 schools were surveyed, and we use chiefdom averages. There are 104 (out of a total 152) chiefdoms that have school data.

43