metadata: authors: 0: author: Marguerie,Alicia Charlene
1: author: Premand,Patrick
count: Cote d'Ivoire
docna: 0: docna: Savings Facilitation or Capital Injection ?
            Impacts and Spillovers of Livelihood Interventions in
            Post-Conflict Côte d’Ivoire
docty: Policy Research Working Paper
abstracts: cdata!: Policy makers grapple with the optimal
            design of multidimensional strategies to improve poor
            households&#x2019; livelihoods. To address financial
            constraints, are capital injections needed, or is savings
            mobilization sufficient? This paper tests the direct effects
            and local spillovers of three instruments to relax financial
            constraints, each combined with micro-entrepreneurship
            training. &#x201c;Cash grants&#x201d; and
            &#x201c;cash grants with repayment&#x201d; directly
            inject capital, while &#x201c;village savings and loan
            associations&#x201d; (VSLAs) promote more efficient
            group saving. The randomized controlled trial took place in
            western regions of C&#xf4;te d&#x2019;Ivoire that
            were affected by a post-electoral crisis in 2011 and an
            earlier conflict. The interventions had differential effects
            on the dynamics of savings and productive asset
            accumulation. The cash grant modalities generated
            investments in startup capital, although nearly 30 percent
            of the grant was saved. In contrast, village savings and
            loan associations did not increase total savings but
            gradually induced investments, so that productive assets
            caught up with cash grant recipients after 15 months.
            Positive local spillovers on savings and independent
            activities were also observed. Yet, investments in
            independent activities were not sufficient to increase
            profits, possibly because they were limited due to high
            precautionary saving motives in the post-conflict study setting.
display_title: Savings Facilitation or Capital
            Injection ? Impacts and Spillovers of Livelihood
            Interventions in Post-Conflict Côte d’Ivoire
listing_relative_url: /research/2023/09/34158084/savings-facilitation-or-capital-injection-impacts-spillovers-livelihood-interventions-post-conflict-côte-d’ivoire
topicv3: Financial Inclusion and Savings m1326073 369,Labor Markets m1326488 855,Social Protection and Labor m1327194 697,Livelihoods m1583202 4425,Self-Employment and Entrepreneurship
            m1583910 4423
publishtoextweb_dt: 2023-09-08T00:00:00Z
dois: 10.1596/1813-9450-10563
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 81
author_keys: 0: author_key: 1320298
1: author_key: 921098
model_extractions: Policy Research Working Paper 10563 Savings Facilitation or Capital Injection? Impacts and Spillovers of Livelihood Interventions in Post-Conflict Côte d’Ivoire _Alicia Marguerie Patrick Premand_ Development Economics Development Impact Evaluation Group September 2023
Policy Research Working Paper 10563

# **Abstract**

Policy makers grapple with the optimal design of multidimensional strategies to improve poor households’ livelihoods. To address financial constraints, are capital injections needed, or is savings mobilization sufficient? This paper tests the direct effects and local spillovers of three instruments to relax financial constraints, each combined with micro-entrepreneurship training. “Cash grants” and “cash grants with repayment” directly inject capital, while “village savings and loan associations” (VSLAs) promote more efficient group saving. The randomized controlled trial took place in western regions of Côte d’Ivoire that were affected by a post-electoral crisis in 2011 and an earlier conflict. The interventions had differential effects on the dynamics of savings and productive asset accumulation. The cash grant modalities generated investments in startup capital, although nearly 30 percent of the grant was saved. In contrast, village savings and loan associations did not increase total savings but gradually induced investments, so that productive assets caught up with cash grant recipients after 15 months. Positive local spillovers on savings and independent activities were also observed. Yet, investments in independent activities were not sufficient to increase profits, possibly because they were limited due to high precautionary saving motives in the post-conflict study setting.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at amarguerie@worldbank.org and ppremand@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Facilitation or Savings Capital Injection?

Impacts and Spillovers of Livelihood Interventions in Post-Conflict Cˆote d’Ivoire Alicia Marguerie (World Bank) Patrick Premand (DIME, World Bank) _JEL classification_ : D13, D14, O12, O17, I38.

_Keywords_ : Cash grant, Savings, Livelihoods, Poverty, Graduation, Economic Inclusion, Field Experiment.
_Acknowledgments_ : This paper is the result of a close collaboration with World Bank operational teams, the government of Cˆote d’Ivoire through BCPE, and IRC. We are very grateful to the World Bank team leaders (Steffen Janus and H. A. Wedoud Kamil), BCPE (Hermann Toualy, Adama Bamba, C ́esar G. Toassa, Boubacar Ndiaye, Ismahel A. Barry, Latif Doho, and Germain Kouadio) and IRC (K. Drissa Akou, Hubert K. Kassi, Akouli C. Seri, Mariame Kone, Louis Falcy, Cl ́ement Lorvao and Amaia Bessouet) for their many contributions throughout the program and RCT design and implementation. Horacio Vera Cossio, Eva Lestant and Daniel Corredor Vallejo provided excellent research assistance. We thank Lori Beaman, Christopher Blattman, Bruno Cr ́epon, Rema Hanna, William Parient ́e, Lore Vandewalle, a DIME reviewer, and seminar/conference participants at CREST, CSAE, IZA-WB, IPA, and Development Economics Network Switzerland for their helpful comments. The study was pre-registered in the AEA RCT registry (AEARCTR-0002737) and received IRB 2018-008 from J-PAL Europe. Financial support from the Knowledge for Change Program (KCP) at the World Bank is gratefully acknowledged. DIME analytics verified the computational reproducibility of the results. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the government they represent.

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# **1 Introduction**

Policy makers are looking for a combination of policies to improve the livelihoods, micro and small businesses that prevail in high-poverty settings (Filmer and Fox (2014), Jayachandran (2020)). Integrated interventions such as “graduation” or “economic inclusion” programs that provide capital, skill training, consumption support, coaching, and savings facilitation have had robust results.<sup>2</sup> Yet there are ongoing debates on how to optimize multi-faceted policies (e.g. Andrews et al. (2021)). In-kind assets, cash grants or microcredit are commonly used to inject capital, but evidence is lacking on which instrument is most effective to address capital constraints. Many multi-faceted programs also facilitate savings accumulation, through various modalities such as savings groups or formal savings accounts. Savings mobilization provides an alternative mechanism to facilitate productive investments, possibly reducing the need for capital injections in the first place. This multiplicity of instruments raises questions about the optimal mechanism to address financial constraints, which in turn depends on the nature of the capital or savings constraints that are binding for the poor.<sup>3</sup> In this paper, we report results from an RCT testing how three alternative instruments to alleviate financial constraints, combined with training, increase poor households’ investments, improve income-generating activities and influence social outcomes. Specifically, we document the relative effects of relaxing savings constraints through enhanced savings groups (Village Savings and Loans Associations, “VSLAs”) designed to facilitate savings accumulation and provide small credit opportunities without any capital injection, or relaxing capital constraints through a cash grant of 95,000 CFA (USD 384 PPP) (“cash grants”) or the same grant with a 50 percent repayment condition (akin to subsidized credit, “cash grants with repayment”).<sup>4</sup> These three financial support modalities are combined with a micro-entrepreneurship training on how to develop simple business plans, assess business opportunities and define capital needs. Of note, participants do not

> 2E.g. Banerjee et al. (2015b), Bandiera et al. (2017), Bedoya et al. (2019), Bossuroy et al. (2022). Some micro-entrepreneurship interventions have also successfully combined cash or in-kind grants with technical or business training (e.g. Cho and Honorati (2014) and Blattman and Ralston (2017)).

> 3Micro-credit has resulted in limited impacts on micro-enterprise growth (Banerjee et al. (2015a), Cr ́epon et al. (2015)), especially for households at the bottom of the distribution (Meager (2019)), which has also raised questions on how to optimize financial support for the poorest.

> 4Official IMF average exchange rate for 2017: USD 1=CFA 580.657 (IMF)). World Bank PPP conversion factor for Cˆote d’Ivoire in 2017, GDP (LCU per international $): 247.134.

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receive regular transfers for consumption support, which contrasts with most graduation programs studied in the literature.

The RCT took place in 147 localities in the western regions of post-conflict Cˆote d’Ivoire, covering both urban and rural areas. Localities were randomly assigned to one of the three interventions (“VSLAs”, “cash grants with repayment” or “cash grants”) or a control group. Within localities, program participants were selected based on a baseline survey designed to calculate a vulnerability proxy measure. We collected follow-up data approximately 22 months after the program started, or 15 months after the injection of cash grants or redistribution of savings. In both treatment and control localities, we surveyed individuals with a baseline proxy score below the program selection cut-off (potentially selected individuals), but also above the cut-off (non-selected individuals). We thus measure direct impacts (for beneficiaries) as well as local spillovers (for nonselected individuals) on economic and social outcomes. The analysis of local spillovers is particularly relevant in a post-conflict setting given concerns over lingering tensions.

Our primary contribution is to compare cash grants with savings groups to assess whether an external capital injection is necessary to generate investments in income-generating activities, or whether savings mobilization alone can trigger investments by increasing savings efficiency. Our second contribution is to compare two different types of capital injection modalities, cash grants with or without repayment, to test whether adding a repayment condition alters investment behaviors relative to unconditional capital.

Poor households can struggle to access capital for income-generating activities, for example facing imperfect capital markets, unattractive credit terms or collateral conditions. In credit scarce environments, cash grants can directly address capital constraints and high returns have been documented for micro-entrepreneurs (e.g., de Mel et al. (2008), De Mel et al. (2012) and Blattman et al. (2014)). However, compared with asset transfers, cash is more fungible, raising questions on how much will be used for productive investments. While there is evidence that poor households consume part of grants (e.g. Berge et al. (2015), Haushofer and Shapiro (2016)), unconditional cash grants have shown positive effects on economic diversification and earnings (Blattman et al. (2014), Macours et al. (2022)).<sup>5</sup> Cash grants with repayment conditions are akin to highly subsidized credit

> 5There is a large literature on the optimal design of small, regular cash transfers, but we focus here on one-off, lumpy cash grants for investments.

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and can also directly alleviate capital constraints.<sup>6</sup> The effect of repayment conditions is theoretically unclear. The reimbursement requirement may introduce an incentive (or possibly a behavioral nudge) to invest, similar to the idea that it is more tempting to consume cash in-hand compared to in-kind transfers (Fiala (2018)). Fafchamps et al. (2014) call this a “flypaper” effect, and show that in-kind transfers lead to larger business investments than cash grants. However, repayment conditions may hinder riskier but more profitable investments: Field et al. (2013) show that relaxing repayment requirement and extending grace periods in microcredit contracts increase profits by providing longer-term horizons for riskier investments. To shed light on the net effects between these potential mechanisms, we directly compare cash grants with and without repayment conditions.

Addressing savings constraints can also facilitate investments in income-generating activities, though it is unclear if it is sufficient for lumpy investments. Suboptimal savings can be due to market failures or behavioral constraints (Karlan et al. (2014)). The introduction of Village Savings and Loans Associations (VLSAs) offers a commitment device as participants meet with peers (every 1-2 weeks) to make small savings contributions. Evidence on the impact of relaxing savings constraints is mixed. Access to formal savings accounts has increased investments and revenues (Brune et al. (2016), Dupas and Robinson (2013)). Yet the literature on saving groups rarely reports improvements in business outcomes (Beaman et al. (2014), Ksoll et al. (2016)), with the notable exception of Karlan et al. (2017).<sup>7</sup> We are not aware of a direct comparison of instruments to address capital constraints (cash grants) and savings constraints (VSLAs).<sup>8</sup> Such a comparison can shed light on whether the underlying financial constraints faced by poor households are driven by a lack of capital or inefficiencies in savings instruments and behaviors. Afzal et al. (2018) show that loans (to be reimbursed) and savings (to be accumulated) are substitutes for microcredit clients in Pakistan. However, empirical evidence on the differential effects of saving “up” (using VSLAs) or “down” (using cash grants) is scarce, and there

> 6A 50 percent repayment condition on a cash grant is equivalent to an interest free credit on 50% of the grant amount.

> 7VSLAs were not used in the first wave of graduation programs studied in Banerjee et al. (2015b) or Bandiera et al. (2017), which included other forms of saving support (e.g. savings accounts, deposits with a collector, or ROSCAs). However, VSLAs have been increasingly considered in economic inclusion programming, adding to the relevance of their analysis.

> 8In the context of graduation programs, Sedlmayr et al. (2020) show that there is no additional impact of saving groups on top of cash grants, training and coaching. Bossuroy et al. (2022) find large impacts of a multi-faceted intervention that includes VSLAs, even when the package does not include a cash grant.

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is also limited evidence on the differential effects of cash grants and savings interventions. Our third main contribution is to add to the literature on integrated interventions addressing financial and skills constraints in fragile settings, including by analyzing both economic and social spillovers. There have been robust impacts of economic inclusion programs, but evidence is more mixed in fragile settings, with strong impacts in Afghanistan (Bedoya et al. (2019)) and South Sudan (Chowdhury et al. (2017)), but weaker results in the Republic of Yemen (Brune et al. (2022)). Our RCT took place in western regions of Cˆote d’Ivoire in 2015-2018, a few years after a decade-long conflict and a 2011 postelectoral crisis. In this context, there were concerns over lingering tensions between natives, internal migrants and foreign migrants. Our design generates comparable groups of non-selected individuals across treatment and control localities (similar to Angelucci and De Giorgi (2009)) and identifies local spillovers. Employment or entrepreneurship programs can in theory either fuel or crowd out economic activities among non-participants. There is limited empirical evidence on spillovers of economic inclusion programs, for instance with both Banerjee et al. (2015b) and Sedlmayr et al. (2020) finding no evidence of spillovers in treated villages.<sup>9</sup> Indirect effects may also arise in other domains such as social cohesion. Interventions improving economic outcomes could benefit the entire local economy, increase economic interactions and as such positively affect social cohesion. On the other hand, they could have negative side effects. Social impacts and spillovers may also depend on the nature of the interventions. For instance, VSLAs provide repeated contacts and interactions between participants from various groups over a long period, and as such could reduce tensions in line with the “contact hypothesis”.<sup>10</sup> In comparison, grants may induce tensions when allocated to some individuals but not others.

We find that the cash grants, cash grants with repayment and VSLAs have differential effects on the dynamics of savings and productive asset accumulation. Without increasing total savings, VSLAs shift savings towards a more efficient instrument and, over time, increase investments in independent activities. Cash grants and cash grants with repay-

> 9A larger literature studies spillovers from regular cash transfers. For instance, Angelucci and De Giorgi (2009) and Egger et al. (2022) find positive effects on the untreated.

> 10Lowe (2021) shows positive impact of repeated interactions between caste groups in cricket leagues in India. Feigenberg et al. (2013) shows that repeated social interactions in microcredit groups (through more frequent meetings) results in more economic cooperation (willingness to pool risk).

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ment induce investments in start-up capital, but recipients save a substantial share of the grants (approximately 30%). While start-up investments are twice as large for cash grants than VSLAs, endline impacts on productive assets are similar, suggesting that the VSLA treatment catches up with cash grants over time. This highlights that addressing savings inefficiencies can generate a similar level of productive asset accumulation (but at a lower cost) than injecting capital. Furthermore, and consistent with findings in Beaman et al. (Forthcoming) and Cr ́epon et al. (Forthcoming), adding a repayment condition to the grant does not alter saving and investment behaviors, and thus does not help overcome behavioral constraints. Despite these changes in savings and investment dynamics, we only find a limited increase in independent activities across treatments, and no significant impact on profits, earnings or household food security.

The intervention leads to some increase in the number of economic activities among beneficiaries, but does not reduce economic activities for non-selected individuals. On the contrary, it generates positive spillovers by increasing savings, which leads to marginally higher investments by non-selected individuals, too. An increase in transfers is found among beneficiaries, although without evidence of impacts on broader measures of social cohesion or spillovers on social outcomes for non-selected individuals in the community.

Overall, the results in our post-conflict setting are more muted than those found for other economic inclusion or graduation programs in the literature. The large share of the cash grant saved points to high precautionary savings motives, which may limit investments. This is noteworthy as the intervention did not include regular consumption support, which may be needed for individuals to make larger, higher return investments that may also be higher-risk.

The paper is structured as follows. Section 2 presents the intervention and data, and Section 3 the experimental design. Results on economic impacts and spillovers are discussed in Sections 4 and 5. Section 6 presents direct and indirect impacts on social outcomes. Section 7 concludes. Tables and figures are presented in the appendix.

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# **2 Intervention and Experimental Design**

## **2.1 Intervention**

The program we study aimed to promote economic empowerment and social cohesion in the Western regions of post-conflict Cˆote d’Ivoire. These regions were particularly affected by two episodes of conflict, and ethnic fragmentation created lingering intergroup tensions.<sup>11</sup> The program was implemented by the International Rescue Committee (IRC) under the supervision of a government agency in the Ministry of Youth Employment (BCP Emploi). It took place between 2014 and 2018 in 37 sub-prefectures across four regions. The RCT was embedded in the second implementation phase (between July 2015 to 2017).<sup>12</sup> The program was designed to improve livelihoods by addressing both human capital and financial constraints. Three alternative financial support modalities were tested: cash grants, cash grants with repayments and village savings and loan association (VSLA). A similar entrepreneurship training was delivered with each of the three intervention modalities. Table 1 provides a summary of the content of each modality.

In each locality, the program created small groups of participants. In cash grant and cash grant with repayment modalities, the groups mostly served as an entry point to deliver entrepreneurship training. In the VSLA intervention, the groups were established as savings groups in addition to being the entry point for entrepreneurship training. Specifically, beneficiaries created an association, elected a committee, and regularly met (weekly or biweekly) to save into a common pot. Facilitators from the locality were trained by the NGO to help participants set up the VSLA and keep books. After a VSLA reached a certain level of savings (usually after four months), participants could request loans (at a rate pre-determined at the start of the cycle). The possibility of taking loans is a key difference with ROSCAs, and one reason VSLAs are considered a more efficient

> 11A high fragmentation between natives, other Ivorians and migrants was considered a key driver of fragility and the 2002-07 conflict. Historically, tensions across groups were linked to land ownership. The post-electoral crisis in 2010-11 exacerbated these tensions. At the start of the program, the region had become peaceful again, but tensions remained.

> 12Appendix A2 provides additional details. The regions are Tonkpi, Cavally, Gu ́emon and Bafing. The program was rolled out in three phases. The 37 sub-prefectures identified during project preparation were assigned to a specific phase, so that there is no overlap between phases. The RCT was embedded in the second phase, which covered 16 sub-prefectures and had the most participants.

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Table 1: Intervention content by treatment arm

||T1|T2|T3|
|---|---|---|---|
||Village Savings and<br>Loan Association (VSLA)|Cash grant<br>with repayment|Cash Grant|
|**Basic training :**||||
|- Peace building and Social Cohesion|X|X|X|
|- Entrepreneurship 1 (“starting an activity”)||||
|**Development of business plan**|X|X|X|
|**Validation of business plan**||X|X|
|**Village Savings and Loan Association**|X|||
|**Complementary training :**<br>- Life skills|X|X|X|
|- Entrepreneurship 2 (“managing an activity”)||||
|**Cash Grant Disbursement**||X|X|
|**Follow-up counselling**||X||
|**Partial repayment**||X||

instrument.<sup>13</sup> At the end of the cycle (after 9-10 months), the pot is redistributed to participants proportionally to their savings share. The interests paid by borrowers are redistributed as part of the share-out. Hence, while there is no cash injection, participants mobilize savings over time and access a lump-sum transfer at the end of the cycle, which can in turn facilitate investment. They can also request a loan during the cycle to access capital earlier. NGO staff made regular follow-up visits to the VSLA groups during their first cycle, ending with the supervision of the pot redistribution.

The entrepreneurship training was common across financial support modalities. It aimed to build entrepreneurship and business skills and was delivered by NGO staff.<sup>14</sup> The training took place in two phases, lasting 55 hours over 8 days. A first phase (5 days) focused on how to start a business.<sup>15</sup> All participants then prepared a simple business plan for an income-generating activity. This involved searching for relevant information on prices, costs and competitors. A second phase (3 days) covered more advanced topics related to activity management, complemented with a life skills module.

In contrast with the VSLA intervention, the “cash grant with repayment” and “cash

> 13A ROSCA (known as _tontine_ in Cˆote d’Ivoire) is an informal group in which members contribute to a common pot which is awarded to a different member at each meeting. In ROSCAs, the timing to access the pot is constrained and usually predetermined. In contrast, in VSLAs small loans can be taken at any group meeting, and the amount is flexible.

> 14A “community expert” was chosen by the NGO in each locality to participate in the training and help explain some of the content to participants beyond the training duration. Those individuals were not in the study sample, and generally had higher levels of education.

> 15The basic entrepreneurship training curriculum covers topics such as how to choose a business, how to attract potential clients, how to deal with competition, how to manage costs, how to set a price, etc. A motivational module on peace building and community engagement was also included.

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grant” modalities provided a direct capital injection, which took place after training completion and approval of the business plans. In these two modalities, business plans were evaluated by a committee including NGO staff and representatives from a local bank or MFI. More than 95% of the business plans were approved. Cash grants were delivered after approval of the business plans, with no additional requirement and limited monitoring. The grants were paid through bank accounts setup with support from the NGO. The exact amount depended on the business plan, but averaged 95,000 CFA per beneficiary (USD 163 nominal, USD 384 PPP in 2017), similar across the two modalities.<sup>16</sup> In the “cash grant with repayment” modality, beneficiaries were told they would have to repay half the grant between three and six months later, with a grace period of one month. Following the disbursement, beneficiaries received light follow-up support from NGO staff.<sup>17</sup> Collectors went to the field to start recovering funds 4 months after disbursement of the cash grant with repayment modality, but the operation suffered from delays and understaffing. Less than one-fifth of beneficiaries reimbursed the required amount. On average across beneficiaries, only 20% of the grant amount (instead of the planned 50%) was repaid. Importantly, field visits by the NGO suggested that most of the participants remained convinced that they would have to reimburse half of the funds at some point. Given the clear upfront communication about the repayment condition and beneficiaries’ expectations, we consider the cash grant with repayment modality as different than the (unconditional) cash grants.

Table A1 documents the cost of the various treatments. Training, program administration and coordination have a similar cost across arms. In total, the program costs USD 223.4 per beneficiary for the VSLA arm, USD 269 for the cash grant with repayment arm, and USD 305.2 for the cash grant arm. VSLA facilitation costs USD 78.5, approximately half the amount of the cash grant and cash grant with repayment.<sup>18</sup>

> 16The amount depended on the capital needs from the business plan, but in practice 95% of grants were for the maximum amount (100,000 CFA). There is no difference in the amount across cash grant modalities: the average was 95,624 CFA per cash grant with repayment and 94,946 CFA per cash grant.

> 17This follow-up lasted between 3 and 8 hours per business, depending on needs. For agricultural activities, experts provided technical support and advice.

> 18Only part of the cash grant with repayment was recovered, as mentioned above. However, if 50% had been recovered as announced, then then cost of the cash grant net of repayment would have been similar to the cost of VSLA facilitation.

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# **2.2 Experimental design**

The study was designed as a cluster RCT to test the effectiveness of the overall intervention combining financial support and training, as well as to isolate the relative effect of the three alternative instruments to address financial constraints: (i) Village Savings and Loan Associations (VSLA), (ii) cash grant with repayment, and (iii) cash grants. The experiment was designed to identify both direct impacts and local spillovers on economic and social outcomes.

## **Two-stage locality randomization**

The RCT covered 16 sub-prefectures in four regions. A first lottery sampled 207 out of 354 eligible localities.<sup>19</sup> NGO staff then organized an assembly in each locality to provide basic information about the three possible interventions. Interested individuals were invited to enroll and baseline data was collected. A second lottery then assigned the 207 localities to three treatment or control arms. This two-stage process ensured that the same basic information was shared in all localities, which prevented differential enrollment of individuals into the three treatment modalities. The process also identified potential beneficiaries (individuals who would have been selected) and non-beneficiaries (individuals who would not have been selected) in localities ultimately assigned to the control group, which is key for the estimation of treatment effects and spillovers.

Through the second lottery, 60 localities were assigned to the control group (C), 53 localities to “VSLAs” (T1), 64 localities to “cash grants with repayment” (T2), and 30 localities to “cash grants” (T3).<sup>20</sup> Separate lotteries were held for 13 clusters of subprefectures, and for urban and rural areas in each cluster. Table A2 (Panel A) shows the number of localities assigned to each study arm. Approximately 20% of localities were urban. Figure 3 summarizes the experimental design.

> 19See Appendix A2 on the eligibility of localities.

> 20The lower number of localities (30) in the cash grant modality was due to budget envelopes determined at program design stage for each intervention modality, in particular because the cash grant modality was added for the RCT at a late stage in the program design process. To perform the randomization, localities in nearby sub-prefectures were grouped into clusters (with 13 clusters in total) so that there would be at least 10 localities per cluster. No locality was assigned to the cash grant intervention in a few smaller clusters.

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### **Beneficiary selection**

Across the 207 localities, individuals interested in the program were invited to enroll before the assignment to treatment or control groups. Enrollment data was collected by the implementing NGO. It was then used to check eligibility and screen applicants by level of vulnerability. Out of 14,880 interested individuals, 12,692 met the eligibility criteria as they were either 18-40 years old, a single mother above 15 years old, a widow or disabled individual (up to 60 years old) and were not benefiting from another program.

Among the applicants, 5,116 were selected for the program. The selection was based on an individual proxy score computed using the enrollment data.<sup>21</sup> Selected individuals are those above the vulnerability cut-off, i.e. those considered the most vulnerable. The cut-off is identical for all localities assigned to the same intervention, but varies across the three interventions based on the budget and target number of beneficiaries determined during the program preparation process. As further detailed below, the identification of treatment and spillover effects uses “common support” cut-offs across treatment modalities.

# **3 Estimation strategy and data**

## **3.1 Estimation strategy**

We estimate intent-to-treat treatment effects by taking differences in outcomes between treatment and control groups at endline. For an outcome _Y_ , we estimate the following ordinary least squares (OLS) regression by using the main sample to estimate direct effects, and the sample of non-selected individuals to estimate spillover effects: where i indexes the individual and j the locality. _β_ is the pooled Intention-To-Treat (ITT) estimate of the program’s overall impact. _Tj_ captures treatment assignment in locality _j_ and _Sj_ the randomization strata, i.e. a dummy for rural villages or urban districts within

> 21Table A3 details the composition of the score. The score weighs criteria including disability, marital status, education, employment, economic status, assets and economic responsibility in the household. Weights were chosen to maximize the dispersion of the score.

10
each sub-prefecture cluster.<sup>22</sup> Robust standard errors are clustered at the locality level. Monetary outcomes are winsorized at the 99th percentile.

To estimate the relative effect of each treatment modality, we estimate the following (OLS) regression, again using the main sample to estimate direct effects, and the sample of non-selected individuals to estimate spillover effects: _β_ 1 estimates of the effect of the VSLA treatment, _β_ 2 of cash grants with repayments and _β_ 3 of cash grants.

To estimate direct impacts using the main sample, we compare individuals above the vulnerability cut-off across treated and control localities, as illustrated in Figure 1. Because a different cut-off was used to select beneficiaries in each treatment arm (see dot, plain and dashed lines for each treatment arm in Figure 1.A) due to the budget envelopes for each intervention modality, we use a common support cut-off to pool observations across arms, providing a main sample of 2,936 individuals.<sup>23</sup> Another common support cut-off is used to estimate spillovers between comparable groups (below the cut-off) across treatment and control localities. The common support is lower in this case (as illustrated in Figure 1.B) and the spillover sample contains 1,201 individuals. Note that spillovers are identified among non-selected individuals ranked below the common support selection cut-off. Consequently, our estimates of spillovers are local and valid for “less vulnerable applicants”, and may not generalize to the whole population of non-applicant in sample localities. Also note that, by design, the spillover sample only contains rural localities. Urban districts were excluded since the data collection budget

> 22When estimating spillover effects, the sample does not include urban districts. Therefore, the strata become the sub-prefecture clusters.

> 23This approach lowers statistical power in comparison to estimating (1) separately for each treatment arm. As shown in Figure 1, the “common support cut-off” differs from the actual selection cut-off for two of the arms. This common support cut-off is used to estimate differences between the treatment arms and with control. In the control group, we can simulate “who would have been selected” if the locality was assigned to (each) treatment arm, using the actual cut-off. Pairwise comparisons between each treatment arm and the control group can thus be made using the whole sample of individuals above the selection cut-off for that specific arm. Since this does not require using a common cut-off, the approach improves statistical power. In practice these gains are marginal and results are robust. Also note that an alternative strategy using within-village differences between participants and non-participants would be biased due to local spillovers.

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<!-- Start of picture text --> Sample for pooled regressions<br>(measuring direct impacts)<br>Treatment 2: Treatment 1: Treatment 3: Pooled treatment | Control<br>Cash Grant with VSLA Cash grant<br>repayment<br>~ selecting 47% of<br>enrolled per locality | ~ selecting 53% of<br>“Vulnerability cater “shiglied perlocaliy Pct a a ae aia |<br>T2 ———————|_Vulnerabilityad cut-oMT enrolled~selecting per 69% locality of= | “Commonsupport” cut-off | simulatedEquivalent cut-off ||<br>Vulnerability cut-off |Pet locality |<br>T3<br><!-- End of picture text -->

<!-- Start of picture text --> Treatment 2: Treatment 1: Treatment 3:<br>Cash Grant with | VSLA Cash grant<br>repayment<br>~ selected 47% of<br>enrolled per ~ selected 53% of “<br>locality enrolled per Common Equivalent<br>Vuinerabilitycut. | locality ~ selected 69% of | support” cut-off simulated cut-<br>off T2 Vulnerability cut- enrolled per per locality off<br>off T1 locality 4<br>Vulnerability cut-<br>off T3<br>Pooled fg<br>treatment yyy<br>Sample for pooled regressions<br>(measuring local spillovers)<br><!-- End of picture text -->
was limited and spillovers were mostly expected in rural areas with a relatively higher coverage.

For all specifications, we use double-selection Lasso to include relevant baseline controls, starting with the full set of variables in balance Table A5 (for direct effects) or Table A6 (for local spillovers). To account for multiple hypothesis testing, we also calculate p-values controlling for the false discovery rate (FDR) following the step-up approach (Benjamini and Hochberg, 1995). The adjusted p values for outcomes in the main tables are presented in Table A9. Results are generally robust, but we note deviations in the text.

Lastly, we analyze treatment heterogeneity by estimating specifications in equations 1 and 2 with an added linear interaction between the treatment variable and the group of interest (and a control for the group of interest). We do so for rural areas (as this dimension is used in the stratified randomization), as well as for key subgroups of program participants such as women, youth and natives.

# **3.2 Timeline and Surveys**

**Enrollment and Baseline survey.** Figure 2 summarizes the study timeline. Enrollment took place in the 207 sample localities between December 2015 and January 2016. The NGO visited each locality at least twice. First, it organized a public meeting to describe the program and explain eligibility criteria. Second, the NGO collected enrollment (baseline) data for individuals interested in the program.<sup>24</sup> Baseline data was collected for 14,880 (interested) individuals, and 12,692 fulfilled the eligibility criteria.

As explained above, and key to the integrity of the experimental design, baseline data was collected prior to assigning localities to the three treatment and control groups. The baseline questionnaire included simple variables on employment, assets, education, and household characteristics.

The second public lottery took place in March 2016. The list of the 5,116 selected beneficiaries was publicly released in each locality between July and September 2016.<sup>25</sup>

> 24The baseline instrument was designed by the research team. NGO staff were trained as enumerators. An independent team of experienced enumerators was hired to perform field supervision and data quality checks. A double-blind data-entry process was set-up.

> 25The lag between the second lottery and the release of the beneficiary lists was due to delays in the

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<!-- Start of picture text --> 2015 2016 2017 2018<br>Aug ... Dec |Jan Feb Mar ... Sep Oct Nov Dec|Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec}... Jul Aug Sep<br>Program implementation<br>VSLA cycle<br>Treatment 1: VSLA Training_ 1st phase<br>Treatmentrea n2: Cash‘as Enrolment 8 j Partial: repayment<br>Grant with First ;<br>Treatmentrepayment samlotterylin: baselineand randomizedlottery: 4stTrainingphase (recollection)of grant Follow-upsurvey<br>Grant 3: Cash ing survey assignment<br>ome eene eee<br><!-- End of picture text -->
support cut-off). The endline sample size was determined based on power calculation to detect minimum effects between 0.15 standard deviations (for the pooled treatment) and 0.23 standard deviations (for comparisons across arms), for a power level of 80% and significance level of 5% and using baseline data for the intra-cluster correlation (ICC) parameter. Note that spillover effects are likely smaller than direct treatment effects, hence the design is not powered to detect very small spillovers, and we focus on analyzing spillovers for the pooled treatment.<sup>28</sup> The main sample includes 2,936 “selected” individuals (above the common vulnerability cut-off in treated and control localities) and is used to estimate direct impacts on economic and social outcomes.<sup>29</sup> The spillover sample includes 1,201 “non-selected” individuals (below the common vulnerability cut-off in treated and control localities) and is used to estimate local spillovers.<sup>30</sup> Control group individuals were sampled using the same vulnerability cut-off. The follow-up attrition rate was 10.6%, respectively 7.8% (using the common support cut-off), and was balanced between treatment and control groups.<sup>31</sup> Table A2 contains the detailed composition of baseline and endline samples.

The follow-up questionnaire collects data about household characteristics, employment, assets, food security, well-being, saving and debt, social relationships, community activities and interpersonal trust. A shorter version of the questionnaire was administered to the “non-selected” sample. Appendix A3 provides additional information on the main

> 28The endline survey sample was established based on power calculations to detect impacts between treatment arms and the control group. 15 “selected” individuals (respectively 17 and 22) were sampled out of localities in T1 (respectively T2 and T3). Finally, 10 “non-selected” individuals were sampled in each (rural) locality to study spillovers. When it comes to estimating spillovers, the sample size for the non-selected was determined to detect minimum effects between 0.18 standard deviations (for the pooled treatment) and 0.26 standard deviations (for comparisons across arms), for a power level of 80% and significance level of 5% and using baseline data for the intra-cluster correlation (ICC) parameter. Power was recalculated ex-post using the follow-up control group data to compute the mean, standard deviation and intra-cluster correlations for key outcomes. The minimum detectable effects remain between 0.15 and 0.22 standard deviations for the main outcomes. This means that the study is powered to detect a 20% impact (+5,300 CFA) on profits in independent activities, a 37% impact (+5,800 CFA) on starting capital or a 23% impact (+5,800 CFA) on the value of assets in the main activity.

> 29The sample includes 3,237 “selected” individuals, and the pooled specification with a common support cut-off contains 2,936 observations.

> 30The spillover sample includes 1,983 “non-selected” individuals, and the pooled specification with a common support cut-off contains 1,201 observations.

> 31In the sample of “selected” individuals, attrition is 10.56%, 11.30% and 10.44% for T1, T2 respectively T3 (using the common support cut-off). In the sample of “non-selected” individuals, attrition is 7.92%, 8.22% and 8.17% across T1, T2 respectively T3 (using the common support cut-off). In the total sample of selected individuals (not using the common support cut-off), attrition is on average 10.8% for “selected individuals” and 8.4% for “non-selected individuals”.

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outcome variables.

# **3.3 Descriptive statistics and balance**

Table A4 summarizes the characteristics of all applicants (column 4), selected individuals (column 2) and non-selected individuals (column 3). Of the applicants, 77% live in rural areas. They are on average 33.7 years old, with 3.3 children, and 62% are women. 80% do not have any diploma, meaning that they have not completed primary school. Nearly all applicants have an activity at baseline (96.3%). They are primarily self-employed in agriculture (54.7%) or non-agricultural (off-farm business) activities (25.4%), while only 9% hold a wage job.<sup>32</sup> Access to finance is very limited: only 2% of applicants have a bank account and 20% use mobile money. Half of the applicants report having saved some money over the last 3 months in ROSCAs.

Selected individuals differ from non-selected individuals due to the targeting based on the vulnerability proxy score (Table A4, columns 2, 3 and 5). Selected applicants are significantly less educated than non-selected applicants (30pp difference in the share without a school diploma). Selected applicants also tend to be poorer: they have lower baseline earnings (by roughly 30,000 CFA), they are less likely to use mobile money (by 12pp) and they are more likely to report facing constraints for education and health expenditures (by 4pp). Finally, the share of women is larger in the selected group: 71% of selected individuals are women (20pp higher than in the non-selected group).

Table A5 and A6 document balance between individuals in treatment and control localities (respectively for the main and spillover samples). A common support cut-off is used, consistent with the identification strategy used to estimate equations 1 and 2. The experiment achieved satisfactory balance between the pooled treatment and control group, as well as between treatment modalities. There are few statistically significant differences, and they remain of small magnitude.

Table A7 illustrates how the study sample compares with the general population of individuals 18-65 years old in program districts.<sup>33</sup> Sample individuals live in larger households, are more likely to be female and not to hold a diploma. The activity rates, types

> 32This is in line with the composition of employment in Cˆote d’Ivoire, especially in rural areas (Christiaensen and Premand (2017)).

> 33This is based on data from a national employment survey.

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of economic activities and overall asset holdings are otherwise comparable.

## **3.4 Take-up**

Table A8 presents take-up rates at various stages of each intervention modality. Take-up is high. 78.9% of selected individuals received the funds in the cash grant with repayment treatment, and 81.1% in the cash grant treatment. The VSLA intervention had a lower take-up rate (69.5%), which is mainly driven by urban areas, where participation is lower.<sup>34</sup> Participation in the entrepreneurship training is above 75% in all groups.<sup>35</sup> As discussed further below, we focus on intent-to-treat estimates of program impacts among all selected applicants.

# **4 Results: Direct economic impacts**

In this section, we discuss direct impacts on employment and independent activities (Table 2), earnings and other welfare proxies (Tables 3-4), savings and investments in independent activities (Tables 5-6). In each table, panel A provides estimates for the pooled treatment based on the specification in equation 1. Panel B presents estimates for each financial support modality based on equation 2 ( _β_ 1 =“VSLA”, _β_ 2 =“cash grant with repayment” and _β_ 3= “cash grant”). We provide p-values for a pairwise test of equality between treatment arms, and for a joint test _β_ 1 = _β_ 2 = _β_ 3. Recall that treatment effects are estimated approximately 15 months after the end of the interventions.

## **4.1 Employment and earnings**

### **_Employment and independent activities_**

The pooled treatment induces small adjustments in participants’ activities towards selfemployment. There is no change in the share of individuals employed at the extensive

> 34The vulnerability score is positively and significantly associated with program take-up, which means that those who did not fully participate were less vulnerable. There is no significant difference in program participation between the cash grants with repayment and cash grant interventions. Living in rural areas is a strong determinant of participation in VSLAs.

> 35Conditional on setting up a VSLA, participation in the entrepreneurship training is 85%. In both cash grant interventions, participation in the second training is conditional on business plan approval. More than 75% of individuals submitted a business plan, got a business plan approved, and participated in the second training.

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margin (Table 2, Panel A, column 1) as 95% of control individuals already have an activity. A small but significant impact on entry into self-employment is found (+2pp, Table 2, Panel A, column 2). It is offset by a small decrease in participation in wage employment (-3pp, Table 2, Panel A , column 3). Similar results are found for hours worked (Table 2, Panel A, columns 5-7), with no significant change in total hours, and a positive effect on hours in self-employment (not statistically significant, p=0.195) offset by a decrease in hours in wage employment.

The pooled treatment induces more substantial changes at the intensive margin by significantly increasing the number of independent activities by 0.24 per individual (Table 2, Panel A, column 4). This means that, on average, one out of four individuals added a new activity to his/her portfolio, representing a 8% increase relative to the control group. Yet it appears that a substantial share of beneficiaries did not launch the activity they proposed in their business plan. While agricultural activities account for 80% of independent activities in control, 70% of grant beneficiaries prepared a business plan for a non-agricultural activity (often retail trading). The impact on entry into non-agricultural activity is only 0.08.<sup>36</sup> To better understand the dynamics of activity creation, we also compared the business plans developed during the program with the activities listed by respondents in the follow-up survey. Only 40% of the activities in the business plans could be matched to activities reported as active in the 12 months preceding the followup survey. This is broadly consistent with the magnitude of the observed impacts on the net number of activities, and suggests that a large share of individuals either did not launch their proposed activity or stopped it quickly.

Importantly, we do not find differences in impacts on employment or time worked in wage or self-employment between treatment modalities (Table 2, panel B). We cannot reject that the treatment effects are equal between the three arms for any variable, aside from cash grant modalities leading to fewer hours in wage employment. There is no significant difference in the impact on the number of independent activities across treatment modalities either (p=0.6, panel B, column 4).

> 36The point estimate for the increase in independent activities at endline is 0.15 (not statistically significant) for agriculture, and 0.08 (significant at 10 percent) for non-agricultural activities (Table A10, Panel A, columns 1 and 2).

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# **_Earnings and Welfare_**

Table 3 documents impacts on earnings, including income from wage employment and profits from independent activities. Overall, earnings are unchanged by the pooled treatment. The increase in the number of independent activities does not induce a significant increase in total profits (Table 3, Panel A, column 2). The point estimate on profits has a small magnitude (+1,727 CFA), or less than 0.05 standard deviation. Since the study is well powered and the point estimate is close to zero, we conclude that the pooled treatment does not increase profits from independent activities.<sup>37</sup> Consistent with results based on self-reported profits in independent agricultural activities, we do not find impacts on revenues from crop or livestock sales over the last 12 months either (Table 3, Panel A, columns 3 and 4).<sup>38</sup> Impacts on wage earnings are also very close to zero and not statistically significant (column 1).

Similar results are found across the financial support modalities, with no significant impact on earnings or difference between treatment arms (Table 3, Panel B). The only exception is a slight reduction in earnings from livestock among cash grant recipients, suggesting lower sales of livestock in that group.

To explore potential differences in impacts on self-employment earnings along the distribution, Figure 4 displays the distribution of profits in the treatment and control group and Figure 5 shows estimates of quantile treatment effects. The profit distributions are not statistically different from each other at any point (Figure 4). Quantile treatment estimates suggest positive but very small and non-significant effects along almost the full distribution (Figure 5). We conclude that the lack of impact on average profits does not hide large changes in some parts of the distribution.

Results on other welfare outcomes are consistent with a lack of impact on earnings. We do not find impact or differences between arms in terms of food security (measured through the food consumption score, which captures dietary diversity, Table 4, column 1), the number of durable goods owned by the household (column 2), or household education

> 37No impact is found when analyzing profits separately from agricultural and non-agricultural activities either (Table A10, columns 3 and 4).

> 38Since a substantial share of independent activities relates to agriculture, Table A11 contains results for additional agricultural outcomes. We do not find impacts on whether beneficiaries hold livestock, cultivate crops, use fertilizers, or on the area or type of crops they cultivate. The pooled treatment effect is not statistically significant and we cannot reject equality between treatment arms in any case (except that cash grant beneficiaries spend a little less time raising livestock that other modalities).

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expenditures (column 3).<sup>39</sup> There is no change in beneficiaries’ psychological well-being either (column 4).

# **4.2 Investment and Savings**

## **_Differential Investment Dynamics_**

The cash grant and cash grant with repayment provide a direct capital injection. Although the grants are unconditional, they explicitly aimed to finance investments in the activity outlined in the business plan prepared following the entrepreneurship training.

Table 5 documents impacts on start-up capital and productive assets in independent activities. Results clearly show that interventions with capital injection induce investments in start-up capital (Panel B, column 1). They also increase the value of assets at endline (Panel B, column 2), which is net of potential investments or dis-investments between the launch of the activity and the follow-up survey. Specifically, the start-up capital of independent activities nearly doubles, increasing by 115% relative to control for cash grants and by 97% for cash grants with repayment (+17,813 CFA, respectively +14,828 CFA) (Table 5, Panel B, column 1). The total value of productive assets at follow-up is also significantly higher for cash grants (+12,245 CFA, or 31%) and cash grant with repayment (+7,788 CFA, or 20%) relative to control (Table 5, Panel B, column 2). However, the point estimates for the value of productive assets at endline tend to be lower (though not significantly so) than the point estimates for start-up capital, suggesting that the grants provided a one-off increase in capital, without investments continuing over time.

In contrast, the VSLA intervention shows different investment dynamics. The VSLA treatment does not provide a capital injection, but instead mobilizes savings which could, over time, be used for investments. VSLAs can contribute to investments by offering small loans from the accumulated group savings, or through the one-off redistribution of savings plus interests at the end of each cycle. Investments in start-up capital are substantially lower for the VSLA modality relative to the cash grants, yet there is no significant difference in the value of productive assets at follow-up. The VSLA treatment increases start-up capital by +7,259 CFA, or 48% relative to control (Table 5, Panel

> 39Panel B suggests a decrease in education expenditures among cash grant beneficiaries (p=0.04), but the FDR-adjusted p value is 0.16 in Table A9.

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B, column 1), which is a significantly lower impact on initial investment than the cash grant with repayment and cash grants (p=0.04, respectively p=0.07). In contrast, the VSLA intervention increases the value of productive assets at endline by 9,614 CFA (24% relative to control, Table 5, Panel B, column 2), which is not statistically different from the cash grant with repayment (p=0.64) or cash grant (p=0.68). This provides evidence that individuals in the VSLA treatment may be on a different trajectory than recipients of cash grants and cash grants with repayment. Also note that the point estimate of the VSLA treatment tends to be larger (though not significantly so) for the value of productive assets at endline than for initial start-up capital, which suggests that VSLA recipients (unlike cash grant recipients) continue investing over time.

# **_Differential Effects on Savings_**

We also find important differences in impacts on savings between financial support modalities, with the cash grants increasing total savings and the VSLAs mostly enhancing savings efficiency.

The cash grant interventions tend to increase savings, by 26,694 CFA for the cash grant and 11,832 CFA for the cash grant with repayment (Table 6, Panel B, column 4). The point estimates correspond to approximately 30% of the grant in both cases (taking into account the 50 percent to be repaid for the cash grant with repayment), or a 20%-45% increase in savings relative to control.<sup>40</sup> In absence of formal instruments, savings mostly take place through informal groups such as ROSCAs (“tontines”). For instance, 50% of the saving stock in the control group is held in ROSCAs. The overall increase in savings from cash grants is strongly driven by informal savings groups (+21,466 CFA) (Table 6, Panel B, column 6). While ROSCAs may not be the most efficient saving instrument, they offer a way for cash grant recipients to save in a setting with limited access to institutions.

The fact that a substantial share of the cash grant is saved also highlights that investments from cash grants, while statistically significant, remain relatively limited in magnitude. This likely contributes to explaining the lack of effects on earnings. Most economic in-

> 40Note that despite the magnitude of the coefficients, this is a noisy outcome. The coefficient for cash grant with repayment is not statistically significant, and the statistical significance of the cash grant coefficient is sensitive to multiple hypothesis corrections.

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clusion or graduation interventions in the literature provide small regular transfers to stabilize consumption and help households make higher-risk, higher-return investments. Regular consumption support was not included in the intervention we study, possibly leading households to use some of the financial resources for precautionary savings rather than investments. Precautionary savings motives may be particularly high in a rural, post-conflict setting with limited access to formal financial services. To further investigate savings behavior, we explore the distribution of savings stock in treatment and control. Precautionary savings motives would imply stronger impacts in the bottom of the distribution. Consistent with this, Figure 6 indicates that the increase in amounts saved arises mostly in the bottom of the distribution (up to approximately 40,000 CFA).

The VSLA intervention affects savings in markedly different ways than the cash grants: it increases savings efficiency but not total savings. There is a shift in the instruments used by individuals to save, as intended by the intervention. Among the control individuals, 30% save in ROSCAs, but only 17% in an enhanced savings group such as a VSLA (Table 6, columns 2-3). In contrast, 54% of individuals assigned to the VSLA treatment participate in a VSLA at follow-up (+37pp), with a substitution away from ROSCAs (-12pp) (Table 6, Panel B, columns 2 and 3). The 37pp impact on VSLA participation is noteworthy because it shows sustained effects 15 months after the end of the program. Given that VSLA cycles last 9 months, this means that groups launched new cycles after the program. These results contrast with some of the VSLA interventions studied in the literature that exhibit low take-up.<sup>41</sup> However, and importantly, the VSLA intervention does not increase the total amounts saved. The total savings stock remains similar as the increase in the amount saved through VSLAs (+9,505 CFA) is offset by a decrease in savings in ROSCAs (-16,470 CFA) (Table 6, Panel B, columns 5-6). The VSLA intervention thus mostly shifts savings toward enhanced savings instruments. As such, observed impacts on economic activities are not explained by a larger overall mobilization of savings: VSLA beneficiaries save similar amounts than individuals in the control group. However, offering access to enhanced savings mechanisms (combined with micro-entrepreneurship training) fuels investments in income-generating activities. This occurs without any injection of additional capital

> 41In constrast, participation is 31.6% across the three countries in Karlan et al. (2017), 37% in Beaman et al. (2014) and 45% in Ksoll et al. (2016).

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in the community. It is noteworthy that such an improvement in savings efficiency leads to investments that continue over time, as previously documented.

The higher participation in VSLAs is also associated with an increase in credits obtained from the groups, which likely contributes to the observed investments. The share of individuals who take any type of credit increases by 13pp (Table A12, Panel B, column 1), more than for cash grant recipients.<sup>42</sup> The total amount of credit taken increases by one-third in the VSLA treatment (+7,381 CFA, Table A12, Panel B, column 6), with no change for cash grant recipients. The results are fully driven by credit taken from VSLAs, which more than triple after the VSLA intervention (+9,780 CFA). Of beneficiaries in the VSLA modality, 41 percent have taken a credit with a savings group in the last two years (+31pp, Table A12, column 3). The sustained impact on VSLA participation after the program is also associated with sustained impact on access to credit: 35% of participants in the VSLA treatment have a credit in an ongoing VSLA cycle (+26pp, Table A12, column 5).

Finally, we also note a marginal increase in VSLA participation in other treatment modalities (+7pp for cash grant with repayment and cash grants, significant only for the former, Table 6, Panel B, column 5), although it did not provide direct support for the establishment of savings groups in other treatment arms. The literature shows that VSLAs can replicate (Beaman et al. (2014), Ksoll et al. (2016) and Karlan et al. (2017)). In our context, the increase in VSLA participation is substantially smaller for cash grants compared to VSLA localities (one-fifth of the coefficient, p=0.00).<sup>43</sup> Formally, we identify treatment effects inclusive of spillovers across localities. This means that, when comparing the VSLA and cash grant arms, differences might be underestimated. Still, spillovers appear limited and their magnitude not large enough to explain key results such as the lack of impact on profits.

> 42Only the coefficient for cash grant with repayment is marginally significant at 10%, and the 5pp difference is lower than the 13pp effect in the VSLA arm (p=0.01).

> 43Karlan et al. (2017) have a design to quantify replications and observe a 19.4% participation rate in VSLA in neighboring villages. Ksoll et al. (2016) find that 21% of control households joined a VSLA in treatment localities. We estimate participation rates around 24% in other localities.

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# **4.3 Additional interpretation of results on earnings**

The lack of impact on earnings can be further interpreted in light of the relative effects on savings and investments. On average, the cash grants provide 95,000 CFA. Among cash grant recipients, 18,000 CFA is invested in start-up capital and 27,000 CFA is saved. Among recipients of cash grant with repayment, 15,000 CFA is invested in start-up capital, 12,000 CFA is saved, and 15,000 CFA is repaid. This leaves 50,000 CFA from the cash grant and 53,000 CFA from the cash grant with repayment for other investments (e.g. for business operation) or expenditures. First, the observed investments do not appear sufficient to increase profits.<sup>44</sup> We cannot rule out that part of the grants was used for consumption or other household expenditures when they were received, although we do not detect impacts on food security, education expenditures, or household durables at endline. Second, the lack of impact on earnings also seems partly explained by a substantial share of the capital injection being saved. As mentioned above, this may reflect precautionary savings motives, in particular in a post-conflict setting and in the context of an intervention that did not provide regular consumption support.

What about the contribution of the cross-cutting training? The pooled treatment induces improvements in an index of business skills and an index of business practices that are statistically significant and in the order of 0.2 standard deviations (Table A13, columns 3-8).<sup>45</sup> Most of the literature on traditional entrepreneurship training finds changes in practices that are not sufficient to increase profits (McKenzie and Woodruff (2014)), and our results are consistent with this pattern.

Heterogeneity analysis reveals limited differences in impacts between subgroups such as women, youth, native or urban populations (Tables A14 - A17). There is some evidence that youth benefit slightly more in some dimensions compared to older individuals (Table A14), but the patterns are not fully robust. Notably, there is no subgroup exhibiting a statistically significant increase in profits.

> 44We also note that the timeline at which the study assesses impacts is similar to other graduation studies that find large effects on earnings (such as Bossuroy et al. (2022)). As such, it is not likely that it is too early for investments to generate profits. Most activities for which business plans were developed are not active at follow-up, which also makes it unlikely that larger returns would arise in the future.

> 45The increase in business knowledge is larger for the cash grant interventions, and the increase in business practices is larger for the cash grant with repayment modality that involved some additional post-training follow-up. The magnitude is similar to another experiment in Cˆote d’Ivoire where microentrepreneurship training was provided to public works participants (Bertrand et al. (2021)).

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# **5 Local spillovers**

In this section, we present estimates of local spillovers based on equations 1 and 2 and the sample of non-selected individuals (with a baseline vulnerability score below the common support vulnerability cut-off) across treated and control (rural) localities.

## **5.1 Earnings and Employment**

Economic spillovers on non-selected individuals could theoretically be either positive or negative. As shown above, the three intervention modalities lead to the creation of new independent activities that are more capital-intensive. If these new activities connect to local value chains or increase local labor demand, positive spillovers on non-selected individuals within the same village are possible. In the opposite direction, new activities may crowd-out opportunities for others. Similarly, on the one hand the creation of VSLAs may spur financial activities within the village, but, on the other hand, they may disrupt informal savings mechanisms, for instance by weakening traditional ROSCAs. We document the net between these forces.

Overall, we do not find evidence that non-selected individuals are economically worse-off than they would have been in absence of the intervention. There is no significant local spillover on the earnings of non-selected individuals (Table 8). The estimated coefficient for earnings from wage employment and self-employment have opposite signs, but are very close to zero and far from statistically significant.

If anything, Table 7 reveals some positive local spillovers on independent activities. There is no change in the share of individuals working, the participation in wage work or selfemployment, or hours worked. However, the number of independent activities significantly increases (+0.37 activities, a 11% increase relative to control, column 4). The spillovers are concentrated in independent agricultural activities (+0.33 activities, Table A18, column 2).<sup>46</sup>

> 46This increase in the number of independent activities could hide business closures due to competition induced by the program. However, we find no impact on the number of activities stopped by non-selected individuals in the last 12 months. In addition, a self-reported indicator on the level of local competition also remains unchanged. Overall, we do not find evidence that businesses were negatively affected by the intervention in the same locality.

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# **5.2 Capital, Savings and Other Spillover Channels**

We find evidence of spillovers associated with changes in financial dynamics in study communities, including savings and investments among non-selected individuals.

The point estimate for spillover on productive assets in independent activities at endline is substantial (+10,763 CFA, Table 9, column 2), but noisy and not statistically significant. There are positive spillovers on assets in agricultural activities that are also of substantial magnitude (+10,091 CFA, Table A18, column 6). The point estimate for impact on start-up capital is close to zero. Taken together, these results suggest that non-selected individuals increase investments in their agricultural activities over time.

As we have shown, the financial support modalities either increase the availability of capital or savings efficiency. The cash grant interventions increase beneficiaries’ savings, with a large share of the grants saved through ROSCAs. The VSLA intervention mobilizes beneficiaries’ savings and offers micro-loans to group members. Over time, a broader circulation of capital or access to enhanced savings technology might also benefit nonselected individuals. Table 10 shows that the propensity to save increases for non-selected individuals (+6pp, Panel A, column 1) and similarly across arms (panel B).

Positive spillovers on financial dynamics are observed among non-selected individuals in VSLA treatment villages, with a higher participation in savings groups (+10pp, panel B, column 2).<sup>47</sup> The amount saved in VSLA more than doubles (+4,553 CFA, Panel B, column 5), though this is not associated with an increase in total savings, a pattern similar to the one observed for beneficiaries. Point estimates suggest a reduction in the amounts saved in ROSCA (column 6), though standard errors are large and the coefficient is not significant. The same holds for the total savings. As such, we do not find conclusive evidence that the introduction of VSLAs disrupt informal savings arrangements among non-selected individuals. These results are consistent with VSLA expansion, which can take different forms (Beaman et al., 2014).<sup>48</sup> Results also show higher access to credit for non-selected individuals in VSLA villages (+8pp, Table A19, Panel B, column 1),

> 47We note that the spillover analysis was not powered for each treatment arm, and the results by treatment arm are sensitive to multiple hypothesis corrections.

> 48First, existing VSLA groups can expand if new members are added at the start of a new cycle. In our study, participation was restricted to beneficiaries during the first cycle but not after. Second, VSLAs can expand if groups replicate. Both mechanisms are likely to have occurred, since a given VSLA typically cannot expand beyond 30 to 35 members.

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although there is no significant impact on the amount of loans (Panel B, column 2). Some local spillovers on savings are also observed in villages assigned to cash grant modalities, with a higher share of non-selected individuals saving, an increase in VSLA participation, and an increase in amounts saved in VSLAs (Table 6, Panel B). These findings are consistent with those for cash grant recipients, and suggest that some VSLA replication also took place across localities.

We assess several other potential mechanisms for local spillovers. First, we can rule out that new activities were jointly started between beneficiaries and non-selected individuals.<sup>49</sup> Second, activities created by beneficiaries might be complementary to those from non-selected individuals in the same locality. We explore a set of proxies on upstream and downstream economic relations and find no such indication.<sup>50</sup> Also note that demand-side effects are unlikely as the program does not significantly increase income, as documented above. Lastly, non-selected individuals in treatment localities could theoretically benefit from the diffusion of skills taught during the entrepreneurship training, or imitate adopted practices. Table A20 provides no evidence for this mechanism: the index of business practices is unchanged among non-selected individuals in treated localities (although an improvement is found in the cash grant with repayment localities).

# **6 Impacts and spillovers on social outcomes**

This section documents how the interventions affect social outcomes among selected and non-selected individuals. This is directly relevant as the program also had a secondary objective to strengthen social cohesion in the post-conflict study setting. Consistent with the contact hypothesis, the intervention can potentially directly increase interactions between individuals, including those from different ethnic groups. In particular, the VSLAs involve much more frequent interactions relative to the cash grant modalities, with

> 49In cash grant localities, program participants could pool resources for activities requiring more expensive equipment. Approximately one-third of beneficiaries prepared a joint activity in their business plan. We do not find an impact on the number of jointly-owned activities at endline (pooled treatment estimate=0.13 ; standard error=0.11).

> 50In the control group, respondents declare that 54% of their customers and 31% of their suppliers come from the same locality. Upstream, there is no significant impact on the use of local suppliers among beneficiaries or non-selected individuals. Downstream, half of the market is local, but mostly involves business-to-consumer relations. Less than 15% of control individuals report selling to other businesses, and we do not find significant changes in the likelihood of serving the local market either, with a small exception in cash grant villages.

27
bi-monthly meetings over 9 months. More generally, improved livelihoods or economic outcomes may in turn increase social support and solidarity between community members.

We test whether there are lasting effects on social outcomes 15 months after the end of the program. We consider participation in economic or social groups, community activities, as well as financial help received and provided by sample individuals, which may increase either through improvements in economic outcomes or social ties. We also study broader measures of trust, victimization, and perception of safety.

Table 11 shows a moderate increase in the number of groups in which beneficiaries participate (+0.16, Panel A, column 1).<sup>51</sup> These effects are observed consistently across intervention modalities (Panel B, p=0.87), and thus are not solely attributable to addditional social contact in VSLAs. Results also show a significant increase in financial help (Table 11, column 2-3), including the number of times beneficiaries have received financial support (+21pp) or have supported someone else (+29pp). Again, we cannot reject equality of treatment effects between intervention arms (Panel B, p=0.23), suggesting that these effects are not only due to more frequent social interactions in the VSLA groups or higher availability of capital from cash grant modalities. No effect is found on broader measures of participation in community activities, trust, perception of safety or a victimization index capturing exposure to crime or other violent activities in the locality (Table 11, Panel A, columns 4-8).

Table A21 also shows that impacts on social outcomes do not extend to non-selected beneficiaries. Overall, the results thus point to relatively narrow changes in group participation and financial help between beneficiaries, which do not translate into more widespread changes in social outcomes in the broader community.<sup>52</sup>

> 51This is driven by economic groups. Beneficiaries participate in more group meetings and are also more likely to hold leadership positions. Nearly all additional groups in which individuals participate are mixed ethnic groups.

> 52We do not detect much heterogeneity in impacts on social outcomes between subgroups either. Tables A22 - A25 present results for heterogeneity analysis for subgroups including youth, women, rural, and native populations. If anything, impacts on some social outcomes (e.g. receiving financial help) are slightly stronger among non-native groups, which provides a weak indication of improved social inclusion in treatment localities.

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# **7 Conclusion**

In this paper, we report results from the randomized controlled trial of an economic inclusion program providing financial support - through capital injection or enhanced savings groups - combined with micro-entrepreneurship training to vulnerable individuals. We study the overall effectiveness of the intervention, which addresses both financial and skill constraints. We analyze the relative effects of alternative instruments to relax capital or savings constraints in increasing savings and investments in income-generating activities. Of particular relevance for fragile settings, we also document local spillovers on non-selected individuals’ economic and social outcomes.

The three financial support modalities increase the number of income-generating activities as well as productive assets, but not sufficiently to increase profits or welfare. Alternative financial support modalities affect savings and investment dynamics in different ways. Individuals appear to face binding constraints to save: the results show that capital injections strongly increase savings and that offering a new saving technology shifts savings and investment behavior. Specifically, cash grants increase investments in start-up capital, but also have large impacts on the total amount saved: recipients save 30% of the grant, which contributes to explaining why impacts on investments are not sufficient to increase earnings. Adding a repayment condition to cash grants does not encourage further investments, suggesting that repayment conditions do not help overcome behavioral biases. Remarkably, the VSLA intervention leads to similar impacts on productive assets as the cash grants at follow-up. While it has smaller impacts on start-up capital, beneficiaries invest more continuously over time. The VSLA intervention does not increase total savings, but rather redirects savings to a more efficient technology. In a low-income setting with thin formal financial markets, it is noteworthy that providing access to more efficient savings groups combined with training facilitates investments in income-generating activities, even in the absence of capital injection. However these impacts on investments are not sufficient to increase earnings.

We do not find evidence of negative spillovers or crowding-out effects for non-selected individuals in treated villages. This is in line with Banerjee et al. (2015b); Bandiera et al. (2017) and Sedlmayr et al. (2020), who do not detect spillovers from graduation programs. However, our results also suggest that the interventions increase the number of 29
independent activities among the non-selected in treated villages, partly due to increased savings flows. Some replication of VSLAs is also found, consistent with the literature (Ksoll et al. (2016), Karlan et al. (2017), Beaman et al. (2014)). Despite these increases in economic activities and savings flows, we do not observe broader changes in terms of earnings or social outcomes in the communities.

Results from the experiment contrast with the positive impacts documented for graduation programs that provide asset transfers (Banerjee et al. (2015b); Bandiera et al. (2017); Bedoya et al. (2019)) or cash grants (Bossuroy et al. (2022)). The absence of consumption support and high precautionary saving motives in the post-conflict study setting might partly explain the more muted impacts. Small regular transfers may be key for households to smooth consumption and enter into higher-risk, higher-return activities. Other studies have found impacts on savings but not earnings in South Sudan (Chowdhury et al. (2017)) and the Republic of Yemen (Brune et al. (2022)), where consumption support was disrupted. The results on VSLAs remain encouraging, however, as VSLAs appear to have initiated a dynamic of savings and investments. Yet a combination of capital injection and savings facilitation might ultimately be needed to overcome the constraints highlighted by the results in terms of both the level and the efficiency of savings. Such complementarities between regular cash transfers for consumption support, capital injections, and savings facilitation might be particularly relevant when seeking to optimize multi-dimensional graduation programs in fragile settings.

30
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34
<!-- Start of picture text --> fe<br>16 sub-prefectures<br>415 localities<br>First lottery : sampling<br>Eligible localities<br>354 localities<br>Second lottery : randomized<br>assignment to treatment /<br>control arms<br>Sample localities<br>207 localities Out of sample<br>Treated Treated Treated Control<br>T1:VSLA T2 : Cash Grant T3 : Cash grant<br>with Repayment<br>\ 53 localities \ 64 localities \ 30 localities \ 60 localities<br><!-- End of picture text -->
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Table 2: Employment and independent activities

||(1)<br>Employment<br>(Has an<br>activity of<br>any type)|(2)<br>Self<br>employed<br>(at least<br>1 activity)|(3)<br>Wage<br>employed<br>(at least<br>1 activity)|(4)<br># Independent<br>Activities|(5)<br>Total<br>hours<br>worked<br>(last 7<br>days)|(6)<br>Hours<br>worked in<br>self<br>employment|(7)<br>Hours<br>worked in<br>wage<br>employment|
|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates||||||||
|Treatment (ITT)|0.01|0.02**|-0.03**|0.24**|0.78|1.52|-1.10**|
||(0.01)|(0.01)|(0.01)|(0.10)|(1.50)|(1.17)|(0.51)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|95%|91.6%|10.4%|3.13|40.08|10.59|3.08|
|Observations|2,620|2,620|2,620|2,620|2,620|2,620|2,620|
|Panel B. Treatment Arm Estimates||||||||
|VSLA (T1) (ITT)|0.01|0.02|-0.01|0.21*|2.35|2.01|-0.44|
||(0.01)|(0.01)|(0.02)|(0.11)|(1.88)|(1.42)|(0.61)|
|Cash Grant with repayment (T2) (ITT)|0.02|0.02*|-0.04**|0.30***|0.09|1.12|-1.50***|
||(0.01)|(0.01)|(0.02)|(0.11)|(1.68)|(1.36)|(0.54)|
|Cash Grant (T3) (ITT)|0.01|0.03***|-0.04**|0.16|-0.89|1.40|-1.54**|
||(0.01)|(0.01)|(0.02)|(0.18)|(1.82)|(1.54)|(0.67)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|95%|91.6%|10.4%|3.13|40.08|10.59|3.08|
|p-value T1=T2|0.65|0.69|0.10|0.41|0.20|0.51|0.03|
|p-value T2=T3|0.84|0.40|0.93|0.41|0.56|0.85|0.95|
|p-value T1=T3|0.80|0.22|0.14|0.77|0.09|0.70|0.07|
|p-value T1=T2=T3|0.90|0.44|0.19|0.60|0.22|0.80|0.06|
|Observations|2,620|2,620|2,620|2,620|2,620|2,620|2,620|

Robust standard errors clustered at locality level. Hours worked are winsorized at the 99th percentile. Hours (columns 5-7) are measured for the last 7 days for the three main activities of the individual. The number of independent activities per individual is based on all activities operating in the last 12 months. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 3: Earnings

||(1)<br>Earnings from<br>wage employment<br>(monthly)|(2)<br>Earnings from<br>self employment<br>(Profts, monthly)|(3)<br>Revenues from<br>farming<br>activities<br>(last 12 mths)|(4)<br>Revenues from<br>livestock<br>activities<br>(last 12 mths)|
|---|---|---|---|---|
|Panel A. Pooled Estimates|||||
|Treatment (ITT)|-154.47|1726.78|19909.81|-2091.02|
||(700.90)|(1777.12)|(17808.79)|(1544.53)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|3057.36|24050.42|124989.51|8013.67|
|Observations|2,615|2,620|2,620|2,620|
|Panel B. Treatment Arm Estimates|||||
|VSLA (T1) (ITT)|230.73|859.10|26657.37|-685.07|
||(920.89)|(2372.50)|(24159.71)|(1990.21)|
|Cash Grant with repayment (T2) (ITT)|-170.29|2393.22|12659.68|-2162.26|
||(818.13)|(2217.70)|(19719.88)|(1609.86)|
|Cash Grant (T3) (ITT)|-905.88|2000.59|22505.26|-4839.52***|
||(807.37)|(3098.32)|(28995.56)|(1546.04)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|3057.36|24050.42|124989.51|8013.67|
|p-value T1=T2|0.67|0.57|0.55|0.36|
|p-value T2=T3|0.34|0.91|0.74|0.02|
|p-value T1=T3|0.19|0.74|0.89|0.01|
|p-value T1=T2=T3|0.38|0.85|0.83|0.01|
|Observations|2,615|2,620|2,620|2,620|

Robust standard errors clustered at locality level. Earnings and revenues are in CFA franc and winsorized at 99%. Earnings are computed over the three main activities of the respondent. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
# Table 4: Welfare indicators

||(1)<br>Food<br>consumption<br>score<br>(food security)|(2)<br># household<br>durable goods<br>owned|(3)<br>Education<br>expenditures|(4)<br>Psychological<br>well being<br>index<br>(z-score)|
|---|---|---|---|---|
|Panel A. Pooled Estimates|||||
|Treatment (ITT)|0.79|0.03|-5388.59|0.07|
||(0.99)|(0.17)|(3410.01)|(0.06)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|52.57|3.69|48798.88|-0.00|
|Observations|2,618|2,618|2,617|2,618|
|Panel B. Treatment Arm Estimates|||||
|VSLA (T1) (ITT)|0.22|0.04|-2669.95|0.03|
||(1.16)|(0.20)|(3816.59)|(0.07)|
|Cash Grant with repayment (T2) (ITT)|1.01|-0.05|-6082.24|0.07|
||(1.19)|(0.20)|(3974.22)|(0.06)|
|Cash Grant (T3) (ITT)|1.48|0.17|-9409.15**|0.14|
||(1.70)|(0.25)|(4559.82)|(0.10)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|
|Mean in Control|52.57|3.69|48798.88|-0.00|
|p-value T1=T2|0.50|0.67|0.33|0.50|
|p-value T2=T3|0.79|0.39|0.43|0.46|
|p-value T1=T3|0.47|0.62|0.11|0.26|
|p-value T1=T2=T3|0.69|0.69|0.26|0.50|
|Observations|2,618|2,618|2,617|2,618|

Robust standard errors clustered at locality level. Expenditures are in CFA franc. Expenditures and number of goods are winsorized at 99%.

The _Food consumption score_ (FCS) provides a measure of food security that captures the dietary diversity. It is commonly used by humanitarian organizations such as the World Food Programme.

The _Psychological well-being index_ includes the CESD Positive Affect scale, the Rosenberg self-esteem scale and a self-assessment of life satisfaction based on a Cantril ladder.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01

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Table 5: Investments and productive assets

||(1)<br>Total start-up<br>capital<br>(all activities)|(2)<br>Value of<br>productive assets<br>(all activities)|
|---|---|---|
|Panel A. Pooled Estimates|||
|Treatment (ITT)|12462.86***<br>(2355.42)|9320.74***<br>(3097.54)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|15260.21|39538.50|
|Observations|2,620|2,620|
|Panel B. Treatment Arm Estimates|||
|VSLA (T1) (ITT)|7259.34**<br>(2907.06)|9613.97***<br>(3674.09)|
|Cash Grant with repayment (T2) (ITT|)<br>14828.19***<br>(3066.31)|7788.24**<br>(3755.91)|
|Cash Grant (T3) (ITT)|17812.93***|12244.49*|
||(5341.51)|(6243.08)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|15260.21|39538.50|
|p-value T1=T2|0.04|0.64|
|p-value T2=T3|0.60|0.50|
|p-value T1=T3|0.07|0.68|
|p-value T1=T2=T3|0.06|0.77|
|Observations|2,620|2,620|

Robust standard errors clustered at locality level. Start-up capital and value of assets are in CFA franc and winsorized at 99%.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 6: Savings

||(1)<br>Has Saved<br>(last 2 yrs)|(2)<br>Participate<br>in a VSLA<br>(currently)|(3)<br>Participate<br>in a ROSCA<br>(currently)|(4)<br>Savings<br>stock<br>(Total)|(5)<br>Savings<br>stock :<br>VSLA|(6)<br>Savings<br>stock :<br>ROSCA|
|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||
|Treatment (ITT)|0.05**|0.19***|-0.04|5713.39|7615.26**|-603.14|
||(0.02)|(0.03)|(0.03)|(10699.54)|(3180.46)|(9444.95)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|16.8%|30.3%|59410.76|4983.39|29913.08|
|Observations|2,620|2,620|2,620|2,618|2,620|2,618|
|Panel B. Treatment Arm Estimates|||||||
|VSLA (T1) (ITT)|0.06**|0.37***|-0.12***|-11124.12|9504.65***|-16469.79*|
||(0.03)|(0.04)|(0.03)|(10329.50)|(2601.07)|(8907.05)|
|Cash Grant with repayment (T2) (ITT)|0.03|0.07**|0.01|11832.10|8143.30|4169.25|
||(0.03)|(0.03)|(0.03)|(13903.51)|(5257.53)|(11747.82)|
|Cash Grant (T3) (ITT)|0.07**|0.07|0.02|26693.63*|2496.31|21465.64*|
||(0.03)|(0.05)|(0.04)|(14127.84)|(2434.49)|(12985.60)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|16.8%|30.3%|59410.76|4983.39|29913.08|
|p-value T1=T2|0.19|0.00|0.00|0.04|0.76|0.02|
|p-value T2=T3|0.20|0.92|0.74|0.34|0.22|0.20|
|p-value T1=T3|0.85|0.00|0.00|0.00|0.00|0.00|
|p-value T1=T2=T3|0.30|0.00|0.00|0.00|0.01|0.00|
|Observations|2,620|2,620|2,620|2,618|2,620|2,618|

Robust standard errors clustered at locality level. Savings stock is in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 7: Local spillovers on employment and independent activities

||(1)<br>Employment<br>(Has an<br>activity of<br>any type)|(2)<br>Self<br>employed<br>(at least<br>1 activity)|(3)<br>Wage<br>employed<br>(at least<br>1 activity)|(4)<br># Independent<br>activities|(5)<br>Total<br>hours<br>worked<br>(last 7<br>days)|(6)<br>Hours<br>worked in<br>self<br>employment|(7)<br>Hours<br>worked in<br>wage<br>employment|
|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates||||||||
|Treatment (ITT)|0.01|0.01|-0.01|0.37**|-2.10|-0.60|0.19|
||(0.01)|(0.02)|(0.02)|(0.15)|(1.88)|(1.37)|(0.73)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|97.9%|93.2%|12.4%|3.23|44.11|9.32|2.83|
|Observations|1,102|1,102|1,102|1,102|1,102|1,102|1,102|
|Panel B. Treatment Arm Estimates||||||||
|VSLA (T1) (ITT)|-0.00|0.00|0.01|0.40**|-2.54|-2.76|0.44|
||(0.01)|(0.03)|(0.03)|(0.18)|(2.29)|(1.67)|(0.96)|
|Cash Grant with repayment (T2) (ITT)|0.01|0.02|-0.03|0.29|-2.60|0.93|-0.24|
||(0.01)|(0.02)|(0.03)|(0.19)|(2.33)|(1.83)|(0.91)|
|Cash Grant (T3) (ITT)|0.02*|0.01|-0.01|0.50**|0.09|0.01|0.75|
||(0.01)|(0.02)|(0.04)|(0.22)|(2.44)|(1.43)|(1.30)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|97.9%|93.2%|12.4%|3.23|44.11|9.32|2.83|
|p-value T1=T2|0.35|0.56|0.20|0.57|0.98|0.06|0.54|
|p-value T2=T3|0.48|0.77|0.61|0.37|0.29|0.61|0.47|
|p-value T1=T3|0.17|0.77|0.61|0.66|0.30|0.08|0.82|
|p-value T1=T2=T3|0.38|0.84|0.44|0.66|0.48|0.12|0.73|
|Observations|1,102|1,102|1,102|1,102|1,102|1,102|1,102|

Robust standard errors clustered at locality level. Hours worked are winsorized at the 99th percentile. Hours (columns 5-7) are measured for the last 7 days for the three main activities of the individual. The number of independent activities per individual is based on all activities operating in the last 12 months. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 8: Local spillovers on earnings

|(1)<br>Earnings in<br>wage Employment<br>(monthly)|(2)<br>Earnings in<br>self Employment<br>(Profts, monthly)|
|---|---|
|Panel A. Pooled Estimates||
|Treatment (ITT)<br>-1984.77<br>(1734.89)<br>PDS Lasso selected controls<br>Yes|1141.09<br>(3964.52)<br>Yes|
|Department X (Urban/Rural)<br>Yes|Yes|
|Mean in Control<br>6382.68|32513.35|
|Observations<br>1,099|1,102|
|Panel B. Treatment Arm Estimates||
|VSLA (T1) (ITT)<br>-1389.97<br>(2032.09)|1347.10<br>(5791.10)|
|Cash Grant with repayment (T2) (ITT)<br>-2773.07|969.78|
|(1744.84)|(4537.98)|
|Cash Grant (T3) (ITT)<br>-1232.74|1148.59|
|(2437.79)|(6034.99)|
|PDS Lasso selected controls<br>Yes|Yes|
|Department X (Urban/Rural)<br>Yes|Yes|
|Mean in Control<br>6382.68|32513.35|
|p-value T1=T2<br>0.37|0.95|
|p-value T2=T3<br>0.41|0.98|
|p-value T1=T3<br>0.94|0.98|
|p-value T1=T2=T3<br>0.57|1.00|
|Observations<br>1,098|1,102|

Robust standard errors clustered at locality level. Earnings and revenues are in CFA franc and winsorized at 99%.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 9: Local spillovers on investments and productive assets

||(1)<br>Total start-up<br>capital<br>(all activities)|(2)<br>Value of<br>productive assets<br>(all activities)|
|---|---|---|
|Panel A. Pooled Estimates|||
|Treatment (ITT)<br>PDS Lasso selected controls|1091.27<br>(5120.99)<br>Yes|10762.98<br>(7751.74)<br>Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|25358.29|59658.83|
|Observations|1,102|1,102|
|Panel B. Treatment Arm Estimates|||
|VSLA (T1) (ITT)|-3209.52<br>(5080.75)|6500.30<br>(9057.50)|
|Cash Grant with repayment (T2) (ITT)|3823.40<br>(6901.26)|12489.14<br>(9932.08)|
|Cash Grant (T3) (ITT)|3055.09<br>(7695.99)|15203.18<br>(9364.66)|
|PDS Lasso selected controls|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|
|Mean in Control|25358.29|59658.83|
|p-value T1=T2|0.27|0.54|
|p-value T2=T3|0.93|0.79|
|p-value T1=T3|0.38|0.35|
|p-value T1=T2=T3|0.44|0.63|
|Observations|1,102|1,102|

Robust standard errors clustered at locality level. Start-up capital and value of assets are in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Table 10: Local spillovers on savings

||(1)<br>Has Saved<br>(last 2 yrs)|(2)<br>Participate<br>in a VSLA<br>(currently)|(3)<br>Participate<br>in a ROSCA<br>(currently)|(4)<br>Savings<br>stock<br>(Total)|(5)<br>Savings<br>stock :<br>VSLA|(6)<br>Savings<br>stock :<br>ROSCA|
|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||
|Treatment (ITT)|0.06**|0.08**|-0.04|-10986.98|4770.73***|-7858.09|
||(0.02)|(0.03)|(0.03)|(14747.08)|(1502.82)|(10043.35)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|14.4%|28.1%|99043.92|3539.34|42099.05|
|Observations|1,102|1,102|1,102|1,101|1,101|1,102|
|Panel B. Treatment Arm Estimates|||||||
|VSLA (T1) (ITT)|0.06*|0.10**|-0.02|-14206.90|4553.32**|-11517.95|
||(0.03)|(0.05)|(0.04)|(16351.49)|(2134.31)|(11145.30)|
|Cash Grant with repayment (T2) (ITT)|0.05|0.04|-0.04|-22848.34|4737.46**|-18850.16**|
||(0.03)|(0.04)|(0.03)|(15239.54)|(1914.75)|(9355.21)|
|Cash Grant (T3) (ITT)|0.08***|0.12**|-0.09**|25366.19|5298.25*|27268.92|
||(0.03)|(0.05)|(0.04)|(28454.78)|(2823.74)|(25458.08)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|14.4%|28.1%|99043.92|3539.34|42099.05|
|p-value T1=T2|0.71|0.19|0.62|0.51|0.94|0.44|
|p-value T2=T3|0.28|0.14|0.24|0.08|0.85|0.06|
|p-value T1=T3|0.45|0.80|0.11|0.16|0.82|0.14|
|p-value T1=T2=T3|0.53|0.23|0.25|0.20|0.97|0.14|
|Observations|1,102|1,102|1,102|1,101|1,101|1,102|

Robust standard errors clustered at locality level. Savings stock amounts are in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
# Table 11: Social outcomes

||(1)<br>Participation in<br>groups or associations<br>(# groups)|(2)<br># times received<br>fnancial support<br>(last 12 mths)|(3)<br># times provided<br>fnancially support<br>(last 12 mths)|(4)<br>Participation in<br>community works<br>(# times in<br>last 12 mths)|(5)<br>Participation in<br>social activities<br>(# times in<br>last 12 mths)|(6)<br>Trust<br>Index<br>(z-score)|(7)<br>Victimization<br>(confict)<br>Index<br>(z-score)|(8)<br>Insecurity<br>(perception)<br>Index<br>(z-score)|
|---|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||||
|Treatment (ITT)|0.16***|0.21**|0.29**|0.03|0.15|0.03|-0.04|0.04|
||(0.05)|(0.10)|(0.12)|(0.03)|(0.36)|(0.06)|(0.04)|(0.06)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|1.19|0.88|1.31|61.10|6.59|0.00|0.00|0.00|
|Observations|2,620|2,620|2,620|2,620|2,620|2,374|2,614|2,617|
|Panel B. Treatment Arm Estimates|||||||||
|VSLA (T1) (ITT)|0.18***|0.15|0.27*|0.04|0.28|0.05|-0.01|0.03|
||(0.07)|(0.13)|(0.14)|(0.04)|(0.45)|(0.08)|(0.06)|(0.07)|
|Cash Grant with repayment (T2) (ITT)|0.14**|0.12|0.31**|0.03|-0.27|0.00|-0.05|0.03|
||(0.07)|(0.11)|(0.14)|(0.03)|(0.41)|(0.07)|(0.05)|(0.07)|
|Cash Grant (T3) (ITT)|0.16**|0.53**|0.28|0.01|0.84|0.04|-0.10*|0.07|
||(0.08)|(0.23)|(0.19)|(0.04)|(0.51)|(0.11)|(0.05)|(0.11)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|1.19|0.88|1.31|61.10|6.59|0.00|0.00|0.00|
|p-value T1=T2|0.59|0.84|0.78|0.69|0.20|0.53|0.58|0.99|
|p-value T2=T3|0.81|0.09|0.90|0.64|0.03|0.72|0.35|0.70|
|p-value T1=T3|0.82|0.13|0.94|0.45|0.28|0.93|0.17|0.71|
|p-value T1=T2=T3|0.87|0.23|0.96|0.75|0.07|0.81|0.37|0.92|
|Observations|2,620|2,620|2,620|2,620|2,620|2,374|2,614|2,617|

Robust standard errors clustered at locality level. Z-scores are centered in the control group. The _Trust index_ is based on 16 variables measuring different forms of trust (economic, financial, security) and trust in different types of groups or institutions (neighbors, same and other ethnic groups, leaders, foreigners). The _Victimization index_ is based on 2 variables, measuring if the respondent reports having been victim of an attack or involved in a conflict over the past 12 months. The _Insecurity (perception) index_ is based on 6 variables, measuring the insecurity perception of the respondent, including the perceived likelihood of a robbery, attack or shootings, a subjective assessment of the peacefulness of the locality, fear of conflicts, and knowledge of past conflicts in the locality. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01