Who Benefited from Burundi’s Demobilization Program?

Abstract

Disarmament, demobilization and reintegration (DDR) programs are an essential component of most peace-building programs. The primary goal of such programs is to secure short-run stability in postconflict countries, clearing the way for structural reforms (Pugel 2009). Over the last two decades, such programs have been implemented in more than 30 countries. More than 2.5 million people have benefited from some form of demobilization worldwide since 1989, with an average annual cost of more than $US 600 million (Muggah 2009).

Despite their high costs and a seemingly widespread belief in their extreme importance for stability in postconflict countries, empirical evidence on the effectiveness of DDR programs remains scarce. Comparing soldiers who voluntarily participated in one of these programs to those who did not, Humphreys and Weinstein (2007) find little evidence that the DDR program in Sierra Leone facilitated demobilization and social reintegration. By exploiting a disruption in the DDR transfers in Burundi, Gilligan et al. (2013) found that in-kind allocations led to a decrease in poverty and improved livelihoods among beneficiary ex-combatants. While they concluded that the program was successful in terms of economic reintegration, they found no effect on ex-combatants’ overall satisfaction and attitudes towards norms.

Building on these insightful studies, we study the impact of the Burundian DDR program and extend the literature on DDR assessments in three ways: (i) by capturing the effect of potential spillovers to civilian households; (ii) by disentangling the short-run impacts from long-run dynamics; and (iii) by distinguishing between DDR phases. The DDR program in Burundi targeted 33,500 ex-combatants at the end of the 1993–2009 civil war. The program was conducted in two waves. The first wave started in December 2004 with the demobilization of the major Hutu rebel group, the CNDD-FDD¹ and the demobilization of a few other minor factions. The second wave started in 2009 with the demobilization of the second larger Hutu rebel group, the Palipehutu-FNL.² We denote by CNDD+ the beneficiaries of the first wave of demobilization and by FNL the beneficiaries of the second wave. The program featured two subsequent components. Through the reinsertion component, demobilized combatants received a cash allowance of at least US$ 515 distributed over 18 months. Demobilized combatants then benefited from a reintegration grant, worth US$ 545 and distributed in-kind.

According to economic theory, the impact of cash and in-kind transfers to demobilized combatants depends on whether they face financial, social or behavioral constraints which are relaxed following the transfers (Fafchamps et al. 2011; Blattman et al. 2014). If agents do not face such constraints, they should already produce at their efficient scale, and it follows that DDR allowances should be expected to be consumed or saved for future consumption. Greater demand should then generate a temporary economic boom at the local level. The resulting increases in price³ and turnover should benefit local vendors, but disadvantage buyers (Cunha et al. 2011; D’Aoust et al. 2013). If, however, there exist financial, social, or behavioral constraints that are relieved by demobilization transfers, demobilization allowances would be expected to be invested in productive assets. In this case, the impact on consumption and assets of beneficiary households should be positive and long-lasting, but the magnitude of the consumption increase should be rather small (Gertler et al. 2012). Spillovers on nonbeneficiaries at the community level are expected to be marginal. Examining which of the consumption or investment scenarios prevails is an empirical question, which we tackle in the empirical analysis.

We use a household panel dataset to capture the direct and indirect effect of the DDR program on beneficiary and nonbeneficiary households. The first wave of the panel was collected in 2006, while the reintegration phase of the demobilization of CNDD+ combatants was taking place. This wave is representative at the provincial level. The second wave was collected in 2010, in the midst of the reinsertion phase for FNL combatants, and about five years after the demobilization of CNDD+ combatants. The timeline below summarizes graphically the various components of our assessment (fig. 1).

Figure 1

Timeline

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Our identification strategy exploits several key facts: (i) not all households hosted ex-combatants; (ii) not all households with ex-combatants benefited from allowances; (iii) the waves of the demobilization program took place before and in between the two survey rounds of the panel; and (iv) some communities in the panel had ex-combatants among their populations whereas other communities did not. Our outcomes of interest include indicators of consumption, nonfood spending, livestock, and prices. We call all households hosting an ex-combatant returnees, and we call the subset of returnees which received demobilization allowances demobilized. In order to measure the short-run impact of reinsertion allowances on beneficiaries, we compare the outcomes of FNL demobilized households and FNL returnee households that did not benefit from reinsertion allowances. In the same vein, we measure the long-run evolution of the impact of program on CNDD+ by comparing outcomes of the CNDD+ demobilized households and CNDD+ returnee households that did not benefit from the DDR program. We measure the short-run (resp. long-run) spillovers of the program on non-beneficiaries by studying how households’ outcomes are affected by the density of demobilized FNL (resp. CNDD+) in their community.

We find that demobilization grants had a positive and significant impact on economic outcomes of FNL beneficiaries in the short run. Importantly, we also identify the presence of positive spillovers on nonbeneficiary households in the short run. This result is encouraging as it shows that reinsertion grants benefited the local economy, which may have increased local acceptance of ex-rebels. However, our analysis identifies a negative evolution of consumption and nonfood spending for CNDD+ ex-combatants. Similarly, we find that the density of demobilized combatants who benefited from the first wave of the DDR program is negatively correlated with outcomes of interest.

These results show that the consumption scenario prevailed in Burundi. When they returned home, demobilized FNLs spent a large part of their allowances on their immediate needs such as food and other basic spending. The demand for locally produced goods rose, which generated a short-run economic boom in host villages. Consistent with this narrative, we find that prices increased in the local market. The results on CNDD+ demobilized households are also consistent with the consumption scenario. Like demobilized FNL households in 2010, we argue that CNDD+ households are likely to have experienced a consumption boom when they benefited from reinsertion allowances as of 2004. Verwimp and Bundervoet (2009) found that the consumption of CNDD+ ex-combatants’ households increased by 34% between 1998 and 2007 compared to civilian households. Qualitative evidence also confirms that CNDD+ ex-combatants spent a large share of their grants in nonproductive ways (Uvin 2007). Under the assumption that CNDD+ households experienced a consumption boom similar to FNL households when they benefited from reinsertion allowances, our findings show that CNDD+ households were not able to maintain a high level of consumption after the exhaustion of their demobilization money.

Importantly, Gilligan et al. (2013) also assessed the impacts of the Burundian DDR and focused on the short-run impact of the reintegration phase for CNDD+ combatants. Their analysis exploited an exogenous bureaucratic failure in the implementation of the program, which resulted in delays of the distribution of in-kind reintegration allowances for demobilized combatants living in certain provinces. Our analysis complements the study of Gilligan et al. (2013) by using panel data methods to assess the impact of the reinsertion phase on FNL combatants, by studying long-run dynamics for CNDD+ combatants, and by capturing the impact of spillovers on civilian households.

The paper is organized as follows: Section I provides details on Burundian DDR. In section II, we present a theoretical framework describing the expected effects of demobilization allowances. In section III, we describe the data and introduce the identification strategy. Results are presented in section IV and interpreted in section V. Section VI concludes.

I. Background of the DDR Program in Burundi

The 1993–2009 conflict in Burundi was driven by years of ethnic discrimination (Vandeginste 2009). A few years after independence in July 1962, the Tutsi elite established a military dictatorship. Tensions between the Hutu majority and the Tutsi authoritarian government regularly triggered violent clashes. In 1988, Tutsi President Pierre Buyoya launched a process of political liberalization, establishing a government of national unity and organizing elections in 1993. In October 1993, four months after his election, the newly elected Hutu President Ndadaye was assassinated. This event set off a decade of civil war between Hutu rebel groups and the Tutsi-led Burundian army.

In 2000, the Arusha Peace Agreement laid the foundations for a peace process and a new constitution based on power-sharing and de-ethnicized political competition. Two Hutu rebel groups, the CNDD-FDD and the Palipehutu-FNL, refused to sign the peace agreement. The ethnic conflict turned into a civil war opposing the transitional government and the two Hutu rebel groups. In 2003, the CNDD-FDD agreed to lay down its weapons and its leaders were given positions in the government, in the national assembly and in the administration. Combatants from the national army (FAB) and from the CNDD-FDD were selected to join the national police and the new national army (The Forces de Défense de la Nation—FDN). Selection criteria were based on stated preferences, age, health status, and experience. Those who were not selected, about 23,000 combatants from CNDD-FDD and FAB, were assigned to the disarmament, demobilization, and reinsertion (DDR) program. The DDR program was officially launched in December 2004.

In 2009, the Palipehutu-FNL rebels in turn gave up their arms, and the FNL became a political party, with 33 minor posts given to the FNL leadership. Of the 20,000 Palipehutu-FNL members,⁴ 3,500 were reintegrated in the Burundian army and 6,500 benefited from the DDR program.

The program was coordinated by the National Commission for Demobilization, Reinsertion and Reintegration,⁵ under the supervision of the World Bank. The program was organized in three phases: the demobilization, the reinsertion and the reintegration. The demobilization phase started with disarmament, followed by the transfer of ex-combatants to a demobilization center. Ex-combatants spent eight days in the center, attending training on economic strategies and opportunities, HIV/AIDS, civic responsibility, as well as peace and reconciliation.

As part of the reinsertion phase, demobilized combatants received a cash allowance worth 18-month salary, paid in four installments over a period of eighteen months. Demobilized combatants received the first reinsertion payment when leaving the demobilization center. Called the Transitional Subsistence Allowance (TSA) by the World Bank, the reinsertion money was dedicated to “enable the ex-combatants to return to their community and to sustain themselves and their families for a limited period following demobilization” (The World Bank Group 2004). The total amount of the cash allowance increased with military rank, starting from a minimum of FBU 566,000 (US$ 515).⁶ The average benefit amounted to FBU 644,000 per demobilized combatant (US$ 586). Data from our survey in 2010 shows that civilian households consumed on average about FBU 190,000 (US$ 173) per adult equivalent per year, which is about one third of the minimum cash allocation to FNL rebels.

Finally, the reintegration phase consisted of a one-off in-kind transfer worth FBU 600,000 (US$ 545). The ex-combatants could choose from a range of options, including vocational training, support for agro-pastoral activity, and start-up material for a small business or a construction project. This phase was launched in September 2006 for the CNDD+.⁷ Of the 23,000 CNDD+ beneficiaries, 85% had received the reintegration support by December 2008. This phase was just starting for the FNL ex-combatants at the time of our 2010 survey.

Some rebels had ties with the factions, but did not benefit from the DDR program. A list of criteria was established in order to assess the military aptitudes of candidates, defining whether rebels were eligible or not for the DDR program. The DDR program also included the disarmament and the dismantling of militias. These were formed by people helping the factions, notably in terms of logistics. These people were called “Gardiens de la Paix” (GdP) if they belonged to the FAB, “Militants Combatants” (MC) if they were part of the CNDD-FDD and “Adultes Associés” (AA) if they supported the FNL. 20,000 GdP, 10,000 MC and 11,000 AA benefited from the program. They received FBU 100,000 (US$ 91) as compensation.

II. Theoretical Framework

The DDR program in Burundi was a social transfer program combining cash and in-kind benefits. We aim to assess its short- and long-run impacts, and to disentangle its direct impact on beneficiaries from its indirect impact on civilians. In this section, we rely on the theoretical and empirical literature on cash and in-kind transfers to predict the direction and the size of these effects.

Standard models of investment predict that agents produce at their efficient scale if financial markets function well (Blattman et al. 2014; Fafchamps et al. 2011). With well-functioning financial markets, cash and in-kind allowances are therefore not used to expand production, but rather to increase current consumption and future consumption through savings. Consumption smoothing depends on individuals’ discount rate: individuals with strong preference for the present, that is, a high discount rate, will save less. Cash and in-kind transfers have therefore no impact on capital and profit in the long run in this case.

These predictions change if agents face financial, social or behavioral constraints which are relaxed as a result of the cash and in-kind transfers. For example, businesses facing liquidity or credit constraints should grow rapidly when given additional capital (Besley 1995; Sadoulet et al. 2001). Whether this capital is provided in cash or in-kind should not affect the level of investment as long as people make decisions regarding asset accumulation in an integrated manner, that is, if consumption, profit, capital and saving are regarded as fungible (Fafchamps et al. 2011). However, cash and in-kind allowances can have systematically different effects if asset integration fails, for example because of self-control issues (Banerjee and Mullainathan 2010), intra-household allocation inefficiencies (Udry 1996) or redistributive pressures (Platteau 2000).

A large empirical literature documents the positive impacts of cash transfers on consumption (see, e.g., Duflo 2003; Hoddinott and Skoufias 2004; Fafchamps et al. 2011; Cunha 2014; Blattman et al. 2014; Haushofer and Shapiro 2014; Hidrobo et al. 2014). Studies on the dynamics of social transfers and consumption show that consumption tends to peak just after the payment (Stephens Jr. 2003; Shapiro 2005; Stephens 2006; Mastrobuoni and Weinberg 2009). The empirical literature also documents that cash transfers can have a positive impact on assets (Haushofer and Shapiro 2014; Blattman et al. 2014; de Mel et al. 2008). Investment in assets can in turn generate additional income and consumption, thereby generating a multiplier effect (Sadoulet et al. 2001; Gertler et al. 2012). The impact of transfers depends on the presence of conditionalities (Baird et al. 2011), on the timing and frequency of transfers (Stephens Jr. 2003; Haushofer and Shapiro 2014), on self-control issues, and on the extent to which preferences are present-biased (Shapiro 2005; Fafchamps et al. 2011; Ozdenoren et al. 2012; Evans and Popova 2014).

Recently, several studies in development economics have identified large externalities of development programs on nonbeneficiaries. These studies conclude that the impact of interventions may be substantially underestimated if such spillovers are not considered. Nonbeneficiaries may be affected by the interventions through three main channels (Glennerster and Takavarasha 2013). The first channel is physical. For example, untreated individuals can be positively affected by a treatment slowing the spread of a contagious epidemic (Miguel and Kremer 2004). The second channel is informational or behavioral: programs can have an impact on the untreated through imitation and communication with treated peers (Kim et al. 1999; Bobonis and Finan 2009). The third channel relates to market-wide effects. In our case, social transfer programs can generate externalities in village economies because the program’s money immediately passes from recipient to nonbeneficiary households as the cash is used to purchase goods and services or to transfer money to peers. Resulting general equilibrium effects have been highlighted in research on agricultural growth multipliers (Haggblade et al. 1991; Block 1999; Filipski et al. 2015) as well as in work on structural models of village economies (Kaboski and Townsend 2011; Attanasio et al. 2012).

As local demand increases, local traders’ revenue increases. Under general assumptions, prices of normal goods in the local market should then increase in the short run if the degree of openness and competition in the local economy is low (Cunha et al. 2011; D’Aoust et al. 2013). In the long run, prices should return to their equilibrium as local traders adapt to the increased demand and as excess money escapes the local economy for purchasing goods that are not produced locally (Cunha et al. 2011; D’Aoust et al. 2013). The short-run increase in price should further increase the profit of local traders, while being detrimental for buyers (Filipski et al. 2015). The price increase may be attenuated if cash allowances are partly invested in assets that increase productive capacity in villages. For goods that are imported locally and characterized by oligopolistic competition, prices could even decrease following the positive demand shock if local traders face fixed costs in a Bertrand competition model or if the demand shock causes more stores to sell the good, thereby increasing competition in a Cournot-Nash framework (Cunha et al. 2011; D’Aoust et al. 2013).

Few empirical studies have examined the price effects and the externalities of cash transfers on nonbeneficiaries. For example, using the data from Progresa in Mexico, Angelucci and De Giorgi (2009) and Barrientos and Sabatés-wheeler (2009) find that noneligible households in treatment areas show significantly higher levels of food consumption and asset holdings following the introduction of the conditional cash transfer program, compared to noneligible households in control areas. Studying the impact of the PAL program in Mexico, Cunha et al. (2011) find that cash transfers in remote villages led to higher food prices, which in turn increased agricultural profits and household wealth. They also show theoretically and empirically that prices are lower following in-kind transfers, which affect supply whereas cash transfers affect demand.

This review of the literature shows that the impacts and externalities of the Burundian DDR are expected to depend on how ex-combatants used their allowances. The use of allowances ultimately depends on socio-economic constraints faced by beneficiaries and socio-economic conditions in the village economies. Figure 2 shows how ex-combatants from our sample reported spending their grant. It summarizes the self-reported information provided by 22 demobilized ex-combatants and 9 GdP/MC/AA who were interviewed in 2010. Respondents could give a maximum of three answers.⁸

Figure 2

DDR Grants Spending by Ex-combatants

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In line with the literature, we observe that a large proportion of ex-combatants used a share of the money to buy consumption goods: 48% reported purchasing food and drinks, and 26% reported buying clothes. This consumption boom is expected to be short-lived, especially if the discount rate of ex-combatants is low and if saving opportunities are scarce. In the short run, the demand increase should induce a jump in prices. This change in prices should increase the sales of shopkeepers and farmers, who are therefore expected to benefit from the return of demobilized ex-rebels. Other households may suffer from inflation. However, the global effect on nonbeneficiaries is expected to be positive, as most households are involved in some kind of trading activity.

Ex-combatants also invested part of their allowances in productive assets. More than 50% of ex-combatants reported investing part of the grant in a plot of land, 23% of them invested in a small shop, 19% in working equipment, and one ex-combatant reported buying a cow. The economic situation of ex-combatants who invested in productive assets is expected to improve in the short run and in the long run following their enhanced production capacity. The resulting long-run increase in consumption is expected to be marginal compared to the size of the immediate consumption boom following the distribution of reinsertion allowances.⁹ As a corollary, the price effect and the externalities generated by investments of DDR money should be marginal except if investments were concentrated in specific domains. The in-kind allowances, which were distributed as part of the reintegration package, are likewise expected to have a positive but small impact on consumption in the long run.

In accordance with this line of reasoning, we propose to test the following hypotheses:

• - Hypothesis 1. Reinsertion allowances received by demobilized combatants had a positive impact on the levels of consumption and investment of their household.

• - Hypothesis 2. The short-run impacts on consumption of beneficiary households were short-lived. The impacts on investment lasted in the long run

• - Hypothesis 3. Nonbeneficiaries were also indirectly affected by the program. This indirect impact depends on the main occupation of households.

• - Hypothesis 4. Higher demand for normal goods induced a price increase in the short run.

By testing these hypotheses, we contribute not only to the scarce literature on the impact of demobilization programs, but also to the literature on the impact of cash and in-kind transfer programs on beneficiaries, nonbeneficiaries and prices.

III. Empirical Analysis

The Data

The empirical analysis draws on different types of data. The first dataset consists of a panel of households. The second dataset is a community questionnaire. The third dataset consists of administrative data from the National Commission for Demobilization, Reinsertion and Reintegration.

The Panel Data Set

The first data set constitutes a two-round household survey undertaken in Burundi. The first round is a QUIBB/CWIQ survey that is representative at the provincial level¹⁰ collected in February 2006. The survey was requested by the Ministry of Planning and designed by the World Bank. Data collection was coordinated by the University of Burundi. The CWIQ Survey Technology has been applied in different African countries for the purpose of generating standardized indicators of poverty and welfare. Developed by the World Bank in collaboration with other international agencies, it seeks to reduce the untimeliness of data and poor data quality with a less expensive alternative optimizing the sampling procedure and the structure of the questionnaire (Ajayi 2006; Zoyem et al. 2008). Besides the core modules on household characteristics and consumption behavior, each country can implement additional modules according to its specificities. In the QUIBB for Burundi, questions about displacement and return were included. The sample consists of 6,700 households. The QUIBB sample is characterized by two-stage cluster sampling. In the first stage, 548 sous-collines¹¹ were sampled and in the second stage, 15 households were sampled in each primary unit.¹² From the original data set, we applied a standard data cleaning process, where missing observations, data entry errors, potential measurement errors, distillation errors and outlier from unusual consumption were deleted.

The second round, undertaken by the authors in April 2010, only retained three provinces of the QUIBB sample: Bubanza, Bujumbura Rural, and Cibitoke, located in the North-West of the country. The choice of these provinces is justified by the concentration of ex-combatants in these three provinces in combination with high level of violence in the region over the last years, as well as by budgetary constraints. We traced 1284 households which were interviewed for the QUIBB survey in the three provinces of interest. A total of 1064 (or 83%) households were found and interviewed in 85 collines.¹³ We examine issues related to attrition more closely later in this section.

In order to maximize the reliability of the data, we trained interviewers for a week. Interviews were conducted in Kirundi, the local language. After the training, interviewers were selected on the basis of an exam and simulated interviews. The questionnaire was then tested during a pilot study in a colline that was not included in our sample. We assigned teams of five interviewers, each including a team leader and at least two women. Each interviewer did two interviews per day on average. The questionnaires were then checked for accuracy and entered in a CS-PRO program by data entry agents. We checked the accuracy of entry ex-post, too.

Community Data

During the 2010 survey, enumerators also undertook a community survey in each colline. This survey included data on past violence, public services, and community initiatives. The data on violent events is used as a control variable in our econometric model. We also use population data, based on the 2008 census, in order to scale the number of ex-combatants in each colline.

Official Demobilization Registers

The National Commission for Demobilization, Reinsertion, and Reintegration in Burundi provided us with registers of ex-combatants by colline and faction, along with their sex, age, military rank, colline of origin and of return, as well as the date of their demobilization. The registers contain precise information about each demobilized ex-combatant as well as the exact number of demobilized soldiers in each colline. The large variation in the number of demobilized ex-combatants per colline allow us to identify the spillovers of the DDR program in Burundi.

Identification Strategy

Our identification strategy is based on a lagged dependent variable model with province fixed effects. The lagged dependent variable model should be preferred to the difference-in-differences model when the assumption “that the most important omitted variables are time-invariant doesn’t seem plausible” (Angrist and Pischke 2008). The particular histories of civilian and ex-combatants households motivate an estimation strategy that controls for lagged dependent variables directly and dispenses with households fixed effects. Furthermore, when autocorrelation of outcomes is low or when data is imprecisely measured, as is the case for many economic variables such as household incomes and expenditures (McKenzie 2012), controlling for the lagged dependent variable is more powerful than either employing the difference-in-differences estimator or the single difference estimator using only the follow-up data. Intuitively, “when the baseline data have little predictive power for future outcomes, it is inefficient to fully correct for baseline imbalances between treatment and control groups” (McKenzie 2012). Because the coefficients of correlation correlation between our outcomes of interest in 2006 and in 2010 are low,¹⁴ we include the lagged dependent variable as a control variable in the regressions.

All the household welfare indicators have decreased on average between 2006 and 2010. This trend shows that living conditions have deteriorated in these three provinces of Burundi during that period. Meanwhile, household size increased during the same period. Table 1 suggests that attriting households purchased more, had fewer stocks, were smaller, and were more affected by the conflict. Attrition is discussed in more detail at the end of this section.

The dummies $RiFNL$ and $RiCNDD+$ are equal to one for returnee households. This category includes all households that declared having one member with ties to the factions, whether or not they benefited from DDR allowances.¹⁶

The dummies $DiFNL$ and $DiCNDD+$ refer to demobilized households, that is, returnee households with a member who benefited from the reinsertion grants. We constructed these dummies according to three definitions, provided in table 2 along with the corresponding number of ex-combatants falling into that category. In the first definition, demobilized ex-combatants are those who declared ties to a faction and were registered in the official demobilization registers (⁠$DiFNL1$ and $DiCNDD+1$⁠). In order to minimize misreporting, the second definition adds households from which individuals were matched¹⁷ with the official demobilization registers (⁠$DiFNL2$ and $DiCNDD+2$⁠). The third definition is based on self-reported data, and includes anyone who declared to be demobilized, that is, to have received the cash (⁠$DiFNL3$ and $DiCNDD+3$⁠). The last definition is more likely to contain measurement errors as it is based on self-reports; its associated coefficients will presumably be biased towards zero (Hausman 2001). The three different indicators will be compared in the empirical analysis.¹⁸

We argue that the second definition is the most appropriate for two reasons. First, the matching exercise with official demobilization registers should capture respondents who feared reporting that there is an ex-combatant in their family. Second, cross-checking self-reported demobilization status with official demobilization registers should exclude households who wrongly categorized themselves as having benefited from demobilization allowances. Indeed, many people helped the rebels during the war and they may have wrongly categorized themselves as demobilized ex-combatants. Similarly, some respondents may have claimed to be demobilized in expectation of rewards in exchange for their participation to the survey.

The short-run direct impact of the DDR program is measured by the dummies $RiFNL$ and $DiFNL$⁠. In particular, the coefficient β₁ measures the impact of having a FNL returnee in the household who did not benefit from reinsertion allowances. The coefficient β₂ measures the marginal impact of having benefited from the FNL reinsertion allowance.¹⁹ We therefore compare the returnee households that have received the grant (demobilized) to the returnee households that reported having a member who was part of the FNL but did not receive reinsertion grants (hypothesis 1).

The variables $RiCNDD+$ and $DiCNDD+$ capture the long-run evolution of outcomes for CNDD+ households (hypothesis 2). To interpret the coefficients associated with these variables, we need to carefully consider the timing of the demobilization of CNDD+ combatants, which started before the first survey at the end of 2004. Thus, by the time of the QUIBB survey, which is used as our baseline, these ex-combatants had already benefited from reinsertion allowances. Then, they benefited from the in-kind reintegration grants between both surveys. Hence, we are not able to measure the long-term impact of the program on their economic outcomes. What we can assess, however, is the evolution of their economic outcomes between the two surveys.

We explain this argument formally. In an ideal scenario in which we would have had data before the peace agreement, let σ denote the short-run impact of the demobilization program, that is, the one-year impact between the reception of the allowances and the QUIBB survey. Similarly, λ denotes the measure of the long-run impact of the demobilization program, that is, the five-year impact between the reception of the allowances and the 2010 panel survey. Unfortunately, we are not able to measure σ and λ separately. However, we can capture the difference $η=λ−σ$⁠, which can be thought of as the long-run evolution of the economic outcomes of CNDD+ ex-combatants. Therefore, in equation (1), the coefficient η₁ measures the evolution of outcomes for returnee CNDD+ households that did not benefit from the DDR, and the coefficient η₂ measures the differential evolution for CNDD+ demobilized households.²⁰ We recommend caution when comparing the impacts of the DDR on FNL and CNDD+ demobilized combatants given the differences in the timing, the type, and the size of the benefits received by these two groups.

We measure the indirect impact of the demobilization program on civilians by looking at the proportion per 1,000 people of ex-combatants living in each colline²¹ (hypothesis 3). The variables of interest are denoted $SiCNDD+$ for the proportion of ex-combatants demobilized between 2004 and 2006 and $SiFNL$ for the proportion of ex-combatants demobilized after 2009. The coefficient β₃ gives “a community average effect” of having more or less demobilized FNL combatants when the direct effect on those who received the money is controlled for. In this sense, it captures the indirect effect of the FNL demobilization program on civilians. Similarly, the coefficient η₃ captures the long-run evolution of spillovers generated by the demobilization of CNDD+ combatants. On average, there were 3.8 ex-combatants per 1000 inhabitants that came back following the first wave of the program from 2004 onwards. The FNL demobilization process of 2009 led to an average of 3 ex-combatant returns per 1000 inhabitants. There are substantial differences between collines, which are highlighted in fig. 3 for our provinces of interest. These maps present the distribution of ex-combatants in each colline, scaled by population.

Figure 3

Demobilized Ex-Combatants per 1000 Inhabitants in Bujumbura Rural, Bubanza and Cibitoke Provinces

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The vector X_i regroups several control variables that may explain changes in economic outcomes between the two rounds of the panel. It includes the sex, age, education, and marital status of the household head, a dummy that accounts for a change of household head between 2006 and 2010, and the main occupation of households. These variables are not first-differenced to avoid the multicollinearity problem. We also control for past violence, using two variables measuring the number of violent events that occurred in the colline before and after the baseline survey. Summary statistics are presented in table 1. Variables that we expect to have been affected by the demobilization program, such as production, land or other assets, are excluded from the regression (they can be considered as “bad controls” [Angrist and Pischke 2008]). We include province fixed effects Z_k. Standard errors are clustered at the colline level to account for intra-cluster correlation. Sampling weights are accounted for.²² Outliers are excluded from the regressions (see appendix B.1 for more details).

Before presenting the results, we discuss the problem of attrition by following the methods proposed by Becketti et al. (1988) and Fitzgerald et al. (1998) and applied by Alderman et al. (2001, 2006). The analysis of table 1 suggests that attriting households purchased more, have fewer stocks, are smaller and were more affected by the conflict. Attrition could bias the estimations if it is selective, that is, if the relationship of interest is different for households that were observed and for households that attrited. To test if selective attrition is likely to bias our results, we first estimated the determinants of our dependent variables in 2006 separately for traced and attriting households (Becketti et al. 1988). Fortunately, we do not reject the null hypothesis that the coefficients of these regressions are the same (table S.1 in appendix). Second, we estimated a Probit model to test whether attrition is correlated with dependent variables (Fitzgerald et al. 1998). As shown in appendix (table S.2), the dependent variables are not significantly correlated with the probability of being sampled both with and without controls. We conclude that differential attrition is unlikely to be a concern in our analysis.

IV. Results

Table 3 presents the estimation of the lagged dependent variable model in which the dependent variable is the change in the $log⁡$ of the total consumption per adult equivalent. It shows that there is no significant difference between the consumption per adult equivalent of FNL returnee households that did not benefit from reinsertion allowances and the consumption per adult equivalent of civilian households. However, having benefited from the demobilization program had a large impact on the consumption of FNL demobilized households (hypothesis 1). The coefficient of the variable $DiFNL$⁠, which measures the short-run direct effect of the cash transfer, is positive, significant, and strikingly high when self-reported information about demobilization status is cross-checked against official demobilization registers (columns [1] to [4]). It suggests that the consumption of demobilized FNL households is between 77% and 144% higher²³ than the consumption levels of FNL returnee households that did not benefit from reinsertion allowances but had the same consumption level in 2006 as the demobilized households. This effect is also positive when using the definition of demobilization status based on self-reported information (columns [5] and [6]), but it loses its significance. This reduced precision can be explained by the presence of noise due to misreporting (Hausman 2001).

The long-run evolution of total consumption per adult equivalent does not seem to differ between civilian households and CNDD+ returnee households that did not benefit from the DDR. In contrast, the long-run evolution of total consumption turns out to be negative for households that benefited from the first wave of the DDR program from 2004 (hypothesis 2). The coefficient of the variable $DiCNDD+$ is significant and large but negative. These estimates suggest that the consumption of CNDD+ households that benefited from the first wave of the program is between 40% and 50% lower in 2010 than the total consumption of nonbeneficiary CNDD+ households that had the same consumption level in 2006 as beneficiary households. This observation holds for all three definitions. The coefficients associated with the variables $DiFNL$ and $DiCNDD+$ are of similar size, but of opposite sign (the F-test p-values reported at the bottom of table 3 are above 0.48 for all specifications).

Table 3 also highlights the presence of spillovers (hypothesis 3). The coefficient associated with the proportion of ex-FNLs in collines $SiFNL$ is positive and significant. This result suggests that households living in collines with a large number of demobilized ex-combatants benefited from positive spillovers. By contrast, the coefficient of the variable $SiCNDD+$ is negative and significant, showing that in the long run, households living in areas with numerous CNDD+ ex-combatants consumed on average less in 2010 than households who had a similar standard of living in 2006 and who lived in areas with fewer CNDD+ ex-combatants. The coefficients associated with the variables $SiFNL$ and $SiCNDD+$ are of similar size, but of opposite sign across definitions (F-test p-values are all above 0.78).

Finally, the coefficient associated with the lagged dependent variable is negative and significant. This further illustrates the importance of including the lagged dependent variables in the regressions to account for low auto-correlation, noise, or catch-up effects in the outcomes of interest.²⁴

Table 4 displays the estimates of the lagged dependent variable model for the five other economic outcomes. This table relies on the second definition of demobilized ex-combatants, namely the one using self-reported information cross-checked with the official demobilization registers and supplemented with matches.²⁵