metadata: authors: 0: author: Gazeaud,Jules
1: author: Khan,Nausheen
2: author: Mvukiyehe,Eric
3: author: Sterck,Olivier Christian Brigitte
count: Tunisia
docna: 0: docna: With or Without Him ? Experimental Evidence
            on Gender-Sensitive Cash Grants and Trainings in Tunisia
docty: Policy Research Working Paper
abstracts: cdata!: Is it possible to stimulate
            women&#x2019;s employment by relaxing their financial
            and human capital constraints? Does involving husbands help
            or hinder the effort? Using an experiment in Tunisia, this
            paper shows that providing cash grants and financial
            training to women stimulates their income generating
            activities, but only when their partners are not involved.
            The program did not alter traditional gender roles. Instead,
            it encouraged employment of other household members and
            investments in small-scale agriculture and livestock farming
            &#x2014; two activities traditionally undertaken by
            women at home. The impacts on household living standards are
            overwhelmingly positive, and suggest that the program is
            highly cost-effective.
display_title: With or Without Him ? Experimental
            Evidence on Gender-Sensitive Cash Grants and Trainings in Tunisia
listing_relative_url: /research/2022/07/33874483/or-without-experimental-evidence-gender-sensitive-cash-grants-trainings-tunisia
topicv3: Labor Markets m1326488 855,Social Safety Nets for Women m1326656 921,Cash Transfers m1327437 1608,Gender m1327621 615
publishtoextweb_dt: 2022-07-28T00:00:00Z
dois: 10.1596/1813-9450-10132
author_keys: 0: author_key: 1438710
1: author_key: 1484200
2: author_key: 972271
3: author_key: 1440608
model_extractions: Policy Research Working Paper 10132

# With or Without Him?

Experimental Evidence on Gender-Sensitive Cash Grants and Trainings in Tunisia _Jules Gazeaud Nausheen Khan Eric Mvukiyehe Olivier Sterck_ Development Economics Development Impact Evaluation Group July 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10132

# **Abstract**

Is it possible to stimulate women’s employment by relaxing their financial and human capital constraints? Does involving husbands help or hinder the effort? Using an experiment in Tunisia, this paper shows that providing cash grants and financial training to women stimulates their income generating activities, but only when their partners are not involved. The program did not alter traditional gender roles. Instead, it encouraged employment of other household members and investments in small-scale agriculture and livestock farming—two activities traditionally undertaken by women at home. The impacts on household living standards are overwhelmingly positive, and suggest that the program is highly cost-effective.

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 nkhan8@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_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
Jules Gazeaud, Nausheen Khan, Eric Mvukiyehe, and Olivier Sterck<sup>∗</sup> _JEL Classification:_ J16, L25, L26, O12 _Keywords:_ Cash Transfers, Financial Training, Gender Roles, Employment

> ∗Jules Gazeaud: J-PAL MENA, The American University in Cairo. E-mail: jgazeaud@povertyactionlab.org; Nausheen Khan: World Bank. E-mail: nkhan8@worldbank.org; Eric Mvukiyehe: Duke University. E-mail: eric.mvukiyehe@duke.edu; Olivier Sterck: ODID, University of Oxford. E-mail: olivier.sterck@qeh.ox.ac.uk. We are grateful to Arthur Alik-Lagrange, Diego Angel-Urdinola, Rabah Arezki, Mahdi Barouni, Simone Bertoli, Anush Bezhanyan, Theophile Bougna, Bruno Crepon, Carlo Del Ninno, Laura Derksen, Alison Fahey, Ugo Gentilini, Xavier Gin ́e, Alvaro Gonzalez, Afef Haddad, Mary HallwardDriemeier, Jesko Hentschel, Marcus Holmlund, Jason Kerwin, Elena Lanchovichina, Daniel Lederman, Arianna Legovini, Florian Leon, John Loeser, Fareeba Mahmood, David McKenzie, Lili Mottaghi, Khalid Ahmed Ali Moheyddeen, Yuko Okamura, Laura Ralston, Aminur Rahman, Imran Rasul, Emma Riley, Lea Rouanet, Tony Verheijen, Jan von der Goltz, Damien de Walque, Nahla Zeitoun, and numerous seminar participants for their inputs and support at various stages of the research. We thank Sarah Elven, Samih Ferrah, Joe St Clair, and Varada Shrotri for wonderful research assistance. During survey implementation, we were privileged to work with BJKA Consulting, including Samy Kallel (Director General) and an excellent team of enumerators. We are particularly indebted to Samir Ben Zineb for fieldwork coordination. Finally, we express our deepest gratitude to all households that participated to our surveys. This research would have not been possible without their collaboration. We gratefully acknowledge financial support from the World Bank through the Jobs MultiDonors Trust Fund, the Umbrella Facility for Gender Equality (UFGE), the MNA Gender Innovation Lab (MNAGIL), and the i2i Multi-Donors Trust Fund. This impact evaluation study received Institutional Review Board (IRB) clearance from _IRB Solutions_ , under protocol #2020/11/17 and was registered on the AEA RCT Registry under registration number AEARCTR-0007136. Computational reproducibility was verified by DIME Analytics. All errors and opinions expressed in the paper remain ours, the authors.
The potential of female workers and entrepreneurs is underused globally. In 2019, working-age women were 26 percentage points less likely to work (44.7% versus 70.4%) and, when they work, they earn significantly less than men with similar skills and employment status (ILO 2021). McKinsey (2015) estimates that the world GDP would be approximately USD 28 trillion higher (26%) if women were playing an identical role in labor markets as men.

The gender gaps in the labor market cannot be explained solely by financial and human capital constraints (Duflo 2012; Jayachandran 2021). Indeed, impact evaluations of cash grants and training programs aimed at stimulating entrepreneurship tend to find little effect on income and business outcomes of female recipients, but sizeable positive effects on male counterparts (De Mel et al. 2008; Klinger and Sch ̈undeln 2011; Fafchamps et al. 2014; Berge et al. 2015; Bernhardt et al. 2019; Gin ́e and Mansuri 2021). Recent research confirms that women face extra gender-specific constraints, including higher risk of expropriation by other household members and peers (De Mel et al. 2009; Jakiela and Ozier 2016; Bernhardt et al. 2019; Riley 2020), limited control over their bodies and over fertility choices (Bandiera et al. 2020; Carneiro et al. 2021), less-developed networks (Field et al. 2016), lower aspirations (Aloud et al. 2020), paucity of role models (Lafortune et al. 2018), and gender-related norms and beliefs stipulating the roles that women should and should not play (Field et al. 2010; Alesina et al. 2013; Bertrand et al. 2015; Bursztyn et al. 2020; Delecourt and Fitzpatrick 2021).

We report the results of a randomized controlled trial (RCT) of a multi-faceted program, which aimed to address not only women’s financial and human capital constraints but also some of the gender-specific constraints. The RCT was conducted in Tunisia, where gender gaps in the labor market are massive (ILO 2021). The RCT was targeted at women and had two treatment arms. First, 1,000 women received an unconditional cash grant worth USD 768 in PPP terms (TND 634).<sup>1</sup> This amount is large, about four times the median monthly income of the respondents at baseline. These women also received a one-day financial training, which covered themes such as money management,

> 1This paper uses the PPP conversion factor and the official exchange rate estimated by the World Bank.

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savings, credit, and investments. While the cash grant was unconditional, women were encouraged to invest the money in an income generating activity or in human capital (e.g., paying for vocational training of their choice).

The second treatment arm aimed at involving women’s partner in the program to encourage gender dialogue and address some of the gender-specific barriers in the labor market. A subset of cash grant recipients were encouraged to bring their male partners to the financial training. Previous research in Cˆote d’Ivoire had shown that women participating in gender dialogue groups were less likely to report economic abuse (Gupta et al. 2013). In line with these results, the objective of the joint training was to actively engage male partners in the training program, in order to minimize resentment or backlash in response to women’s empowerment and maximize impacts on women’s income generating activities (IGAs).<sup>2</sup> We study the impacts of the treatments after two years.

Results show that the cash grant and training program had positive impacts on women’s likelihood of having an IGA (+3.4 p.p.) and on their income (+61%), but only for women who had to attend the training alone. The involvement of partners in the training appears to have backfired: compared to women who had to attend the training alone, women who could invite their male partner are significantly less likely to have an IGA and they earn lower incomes. Their labor-market outcomes are very similar to labor-market outcomes in the control group. This evidence suggests that husbands’ involvement in gender-sensitive cash grants and trainings may be detrimental to the promotion of women’s IGAs, either because it may have reduced women’s privacy over the cash grants, or because men involved in the joint training may have felt part of the program, giving them more legitimacy to influence how cash grants were spent. Given strong gender norms and the difficulty to hide income in this context, we conjecture that the second explanation is more plausible. Overall, few women reported an income generating activity—even in the treatment groups.

> 2Bulte et al. (2017, 2018) implemented a similar program in Vietnam, with women in one treatment arm attending business training alone while women in a second treatment arm could invite their husband to the training. They did not offer cash grants to relax financial constraints. Bulte et al. (2017) find that training participation positively impacted knowledge, practices, and business outcomes, but inviting male partners had little marginal impact. Bulte et al. (2018) document that women participating into the training are more likely to hide income, and this effect is exacerbated by the presence of husbands.

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The cash grant and training program had significant positive effects on participation in livestock farming (+4.7 p.p.) and small-scale agriculture (+2.4 p.p.), two occupations traditionally undertaken by women at home. We also find positive effects on the IGAs of other household members. These results show that the program reinforced women’s traditional occupations, but was unable to dramatically shift gender roles. Our results are also consistent with Bernhardt et al. (2019)’s observation that households matter when assessing the impact of cash grants to female microentrepreneurs, because women’s grants are often invested in the IGAs of other household members.

Beyond the labor market, the impacts of the program were overwhelmingly positive. The cash grant and training program had positive effects on household living standards, as measured by food consumption (+10.7%) and asset ownership (+0.13 SD). Women in the treatment groups were also more satisfied with their lives (+0.18 SD), and had better mental health (+0.07 SD), and better access to finance (+0.41 SD). The program was highly cost-effective: we estimate that its material benefits exceed program costs after only 1.1 years.

Our paper contributes to three important streams of literature. First, it adds new evidence to the literature on cash transfers and training programs (see Bastagli et al. 2016 and McKenzie 2021 for reviews), and, in particular, to the burgeoning literature on the impact of large one-off grants in low- and middle-income countries (see e.g., Haushofer and Shapiro 2016, Cr ́epon et al. 2020, Kondylis et al. 2021). Our study also directly contributes to the nascent literature on multi-faceted programs that tackle multiple constraints at the same time (Banerjee et al. 2015; Bedoya et al. 2019; Sedlmayr et al. 2020; Banerjee et al. 2021; Bossuroy et al. 2021; Balboni et al. 2021). Second, our analysis sheds new light on the gender-specific constraints that women face in the labor market (see Duflo 2012 and Jayachandran 2021 for reviews). Consistent with e.g., Field et al. (2010), Bertrand et al. (2015), and Bursztyn et al. (2017), our analysis shows that tradition can shape women’s opportunities and interfere with programs aiming at stimulating women’s employment. Third, our paper relates to the literature on intra-household dynamics and information asymmetry (see e.g., Ashraf 2009, Castilla and Walker 2013, Ashraf et al.

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2014, Jakiela and Ozier 2016, Bulte et al. 2018, Lowe and McKelway 2021). In line with Field et al. (2021), our analysis shows that involving men in programs that target women may backfire.

The paper is structured as follows. Section 1 provides background information on the context of the experiment. Section 2 gives more details about the interventions. Section 3 introduces research hypotheses. Section 4 describes the data and the identification strategy. Section 5 presents the results, and Section 6 concludes.

# **1 Context**

The RCT took place in rural Tunisia. Until 2010, the World Economic Forum repeatedly ranked Tunisia as the most competitive economy in Africa (World Economic Forum 2010). Yet, the Tunisian economy had serious weaknesses, including inadequate job creation, pervasive restrictions to economic and political participation, cronyism and corruption, and deep regional disparities (World Bank 2014). The lack of political reforms to address these problems led to increasing popular discontent and eventually triggered a wave of protests, known as the Jasmine revolution. The popular upheaval culminated with the ousting of longtime president Zine El Abidine Ben Ali in January 2011. The protests eventually spread throughout the Arab world, in a chain reaction known as the Arab Spring.

Following the Jasmine revolution, Tunisia successfully transitioned to democracy and adopted one of the most progressive constitutions in the Arab world (Netterstrøm 2015). Yet, despite promising political reforms, authorities failed to undertake the necessary socio-economic reforms. GDP per capita has been shrinking since 2014, unemployment remains high, the business climate has deteriorated, and important regional disparities remain. Women and young university graduates are particularly disadvantaged in the labor market. The gender gap in employment is massive. In 2018, only 19% of workingage women versus 60% of working-age men were in paid- or self-employment (ILO 2021). The gender pay gap was 14.5% after controlling for age, education, working-time status, 5
and public-sector versus private-sector employment (ILO 2018).

Our experiment took place in Jendouba, one of the poorest governorates in Tunisia.<sup>3</sup> The Jendouba governorate is administratively divided into 95 Imadas (localities), 15 of which are classified as urban, and 80 of which are classified as rural. Our RCT targeted 2,000 women in the 80 rural Imadas. These women were recruited from the treatment and control groups of another randomized experiment, which evaluated the impact of a public-works program (The World Bank 2017).<sup>4</sup> Data from the 2014 census and from our baseline survey provide information about life in rural Jendouba (our data are described in Section 4.1). In 2014, only 37% of the adult population of rural Jendouba was economically active. Illiteracy rates were around 40%, and 48% of household heads had no education. In 2016, the value of daily consumption per capita at baseline was about TND 9 (less than USD 4). For most households, the main source of income is coming from casual work, agriculture, and animal farming. The labor market in rural Jendouba is highly segregated by gender. Female workers are typically working in agriculture, either as casual laborers (harvesting, weeding, sowing) or at home (Zuccotti et al. 2018). The United Nations General Assembly (2013) describes that _“In Jendouba, agriculture is the main source of income and depends primarily on female labour, particularly older women, who are ready to accept the sector’s low wages. These older rural women are predominantly casual agricultural labourers, who are collected every morning from the streets, transported, standing in the back of trucks, to the farms, and compelled to work long hours for meagre pay.”_ Women are also in charge of small-scale agriculture and livestock farming at home (Massin et al. 2016). About 18% of households reported owning land at baseline and 49% of households were owning livestock (usually

> 3See Cocco (2013) for a powerful photo documentary on living conditions in Jendouba.

> 4Between 2012 and 2016, 40 rural Imadas took part in a project of Community Works and Local Participation (CWLP) implemented by the Ministry of Vocational Training and Employment (MFPE) with the technical support of the World Bank. The CWLP program was implemented as a cluster RCT: 40 Imadas were randomly selected to benefit from public works and 40 Imadas were placed in a pure control group. Within each Imada, local associations identified 50 households willing to participate in the cash-for-work (CfW) program. In treatment Imadas, 35 households were randomly selected to participate in the CfW program and 15 households were chosen as control. Our baseline data are the endline data of the CWLP evaluation. We built our impact evaluation on the CWLP evaluation for three reasons: facilitate sampling, minimize costs, and allow for the evaluation of the long-term effects of the CWLP. Our interventions were cross-randomized without stratifying by the CWLP treatment status. We study heterogeneous treatment effects by treatment status in the CWLP evaluation in Appendix G.

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goats or chickens). By contrast, men tend to work in sectors requiring physical strength, such as construction work. Men are also in charge of specific tasks in agriculture, such as operating machines, transport, and portage.

# **2 Interventions**

Between September 2018 and January 2019, the 2,000 women of our study sample were randomly assigned to two interventions or to a control group. The design of the experiment is represented in Figure A.3.

## **_Treatment 1: Cash Grant and Training_**

One thousand women were offered an unconditional cash grant of TND 634 (USD 768 in PPP terms, USD 240 in nominal terms). This amount is relatively large: about four times the median monthly income of respondents with an IGA at baseline.

Before getting the cash grants, women had to take part in a one-day training, which covered the basic concepts of money management and investment. The training was coproduced and co-organized by Tunisia’s Center of Arab Women for Training and Research (CAWTAR) and the World Bank. It aimed at encouraging women to invest their money productively in physical capital (e.g., starting an income generating activity) or human capital (e.g., paying for vocational training of their choice). The training covered three main modules: i) financial planning and budgeting, ii) savings, and iii) debt management. Each module included a series of videos, exercises, and guided discussions. Details about training, including photos and links to the training material, are provided in Appendix A.

Women had to open a bank account to receive the cash grants. The grants were delivered, in one lumpsum, to these accounts. The take-up of the cash grant intervention was very high: 949 out of the 1,000 randomly selected women participated in the training sessions and received the cash grants.

## **_Treatment 2: Joint Training with Male Partners_**

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Half of the 1,000 women receiving cash grants (i.e., 500 women) were invited to bring their male partners to the financial training described above. Partners were asked to sit next to each other and do the exercises together. The six videos played during the training show two partners discussing, arguing about, and agreeing upon budget management and investments (see Figure A.2 in the Appendix). The opinions of the women in the video were presented by the trainers as the best practice. Partners were invited to reflect together on the content of the videos and share their joint experience with the group.

The rationale for adding this component was to encourage joint decision-making between the partners, limit expropriation of grants by male partners and other household members, and mitigate any negative perceptions or resentments that male partners may have as a result of the cash grant program. Previous research by Gupta et al. (2013) in Cˆote d’Ivoire found that women participating in gender dialogue groups were less likely to report economic abuse.

The take-up rate of the joint training program was also high: 444 partners attended the training from a total of 486 invited partners (and 502 women randomly selected into this treatment group). The take-up rate of this component is therefore 88.4%.

# **3 Hypotheses**

Our paper tests two series of hypotheses, which were pre-specified in our pre-analysis <u>plan</u> (Gazeaud et al. 2021b).

The first set of hypotheses is based on the naive assumption that the two treatment arms achieve their intended objectives. Previous research on cash grants to microentrepreneurs found positive impacts on a wide variety of business outcomes (De Mel et al. 2008; Fafchamps et al. 2014; McKenzie 2017). Training programs also generate positive impacts, but effect sizes are relatively small (McKenzie 2021). In light of this evidence, we hypothesize that the cash grant and training program relaxes women’s financial and human capital constraints, and that this is sufficient to stimulate women’s IGAs (H1.1) and enhance women’s agency (H1.2). In line with the findings of Gupta et al. (2013), 8
we also hypothesize that the joint training with partners is fostering gender dialogue and empowering women participants (H1.3) which, if this latter hypothesis is true, should in turn stimulate their IGAs (H1.4).

The second series of hypotheses recognizes that relaxing financial and human capital constraints may be insufficient because women face gender-specific constraints, including high risk of expropriation by other household members (De Mel et al. 2009; Jakiela and Ozier 2016; Bernhardt et al. 2019; Riley 2020). In line with the findings of Bernhardt et al. (2019), we acknowledge that focusing on women’s employment and income is insufficient because it ignores possible indirect effects on the household as a whole. For example, the cash grants to women could actually be invested in the IGAs of other household members. The interventions could therefore have positive effects beyond recipient women and directly impact household IGAs and household well-being (H2.1). If the joint training with partners increases women’s agency, the effect of the cash grant and training program on the IGAs of other household members is expected to be lower in households that participated in the joint training (H2.2). The program could also encourage migration if cash grants relax liquidity, credit, and risk constraints to migration (Gazeaud et al. 2021a) (H2.3). Jendouba—the governorate where this experiment is taking place—is one of the primary migrant-sending regions in Tunisia, with an out-migration rate of 13.2% in 2014 (Zuccotti et al. 2018).

# **4 Empirical strategy**

The empirical strategy is based on a pre-analysis plan, which can be accessed <u>here</u> (Gazeaud et al. 2021b). Supplementary analyses are described in Appendix G.

## **4.1 Data**

Baseline quantitative data on the 2,000 female participants were collected between April 2016 and January 2017, which is 12 to 18 months before the distribution of cash grants. The endline survey was conducted between December 2020 and March 2021, which is 9
between 2 and 2.5 years after the distribution of cash grants. Of the female participants, 91.2% were successfully surveyed at endline (N=1,824).<sup>5</sup> We study attrition in Section 4.4. The baseline and endline questionnaires included questions on household composition, economic activities, assets, consumption, women’s agency, subjective well-being, mental health, migration, economic shocks, and coping strategies.

Data collection was carried out in person and coordinated by a professional survey firm recruited by the World Bank. The survey firm was responsible for translation and adaptation of the survey instruments, recruiting and training enumerators, in-field quality control, and data collection logistics. The survey firm mobilized a large team of field personnel comprising field supervisors, enumerators, and independent back-checkers. Field workers were supported by back-office staff, including IT specialists to ensure efficient functioning of equipment and data transfer processes. This entire team participated in a week-long training in Jendouba facilitated by the World Bank DIME team. Figure A.4 in the Appendix depicts the overall timeline of the impact evaluation and data collection activities.

# **4.2 Estimation of intent-to-treat**

We estimate the intent-to-treat (ITT) effect of the cash grant and training program using a regression of the following form: where _yi_ is the outcome of interest for unit _i_ (where _i_ is an individual or a household depending on the outcome); _Ti_ is a dummy indicating whether unit _i_ was randomly offered the cash grant and training program; _Xi_ is a vector of control variables; _μe_ are enumerator fixed effects; and _εi_ is the disturbance term for the regression. The ITT effect of the cash grant and training program is given by the coefficient _β_ 1. The vector

> 5At endline, we also sought to interview the male partners of the 1,000 women who received cash grant and training program. We successfully interviewed 83% of these (N=830) and use the data to construct one measure of women’s agency. For male partners, we do not reject the hypothesis that attrition rates are equal in the two treatment groups (17.1% and 16.9% respectively).

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of control variables _Xi_ is selected using the double LASSO method of Chernozhukov et al. (2017), starting with the list of variables used in the balance table augmented with fourth-degree polynomials along with all first-order interactions the inverse hyperbolic sine transformation of continuous variables. We include enumerator fixed effects in all regressions (Di Maio and Fiala 2020) and the baseline levels of outcome variables when possible.<sup>6</sup> To test whether the cash grant and training program is more effective when partners are invited to the training, we estimate the following ITT specification: where _Ti_ 1 is a dummy indicating whether unit _i_ was randomly offered the cash grant and training program without partners’ involvement; _Ti_ 2 is a dummy indicating whether unit _i_ was randomly offered the cash grant and the joint training with male partners; and _yi_ , _Xi_ , _μe_ and _εi_ are defined as above. The ITT effects of the cash grant and training program without and with partners are given by the coefficients _β_ 1 and _β_ 2 respectively. We then test _H_ 0: _β_ 1 = _β_ 2 to study the significance of the marginal effect of joint participation into the training.

As mentioned in Section 2, the take-up rates of cash grant and training program and of the joint training with partners were very high (94.9% and 88.4% respectively) and no one from the control group received the treatments. As a result, ITT estimates are almost identical to estimates of Average Treatment Effect on the Treated (ATET). The ITT effects can therefore be approximately interpreted as Average Treatment Effects (ATE) with perfect compliance.

> 6Our pre-analysis plan did not specify how control variables and fixed effects would be selected and how the double LASSO procedure would be implemented. We follow the approach outlined in Derksen et al. (2021) to guide our decisions. Results are qualitatively similar without control variables and enumerator fixed effects (Appendix F).

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# **4.3 Outcomes**

In the main analysis, we consider 37 outcomes of interest, which are classified into five categories.

1. IGAs: To study impacts on female participants, we consider a dummy variable identifying whether they had an IGA at the time of the survey, a dummy variable identifying whether they had an IGA before the COVID-19 pandemic, several measures of income and profit, the number of business employees, and a dummy variable identifying women who report having unused skills. We also study various measures of involvement in agriculture and livestock ownership. To study impacts on other household members, we consider whether the household head has an IGA, a measure of his income, the number of other household members with an IGA and a measure of their income.

2. Women’s empowerment and well-being: We consider two indices of women’s agency (one using data from women and one using data from their male partners) and an index of women’s access to finance. We also consider three subjective measures of current, past, and future life satisfaction, one measure of perceived relative wealth, and a general measure of mental health (the Mental Health Inventory-5, or MHI-5).

3. Household living standards: We study three aggregate measures of consumption (total, food, and non-food consumption), an index of asset ownership, and an index of asset purchases.

4. Shocks and coping strategies: We consider four variables, identifying whether households faced economic shocks or other shocks, and whether they used extreme coping strategies in response to these shocks.

5. Migration: We study a dummy equal to one if the household has moved to a different Imada, a dummy equal to one if at least one household member has migrated since 2019, and a dummy equal to one if at least one household member reported being likely or very likely to migrate to another city or governorate in the next 12 months.

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The construction of these outcome variables is detailed in Table A.1 in the Appendix. Descriptive statistics are shown in Table A.2 in the Appendix.

# **4.4 Internal validity**

We discuss four threats to the identification of causal impacts: imbalances between the treatment and control groups, spillovers, attrition, and multiple testing.

**Balance** The treatment and control groups are well balanced across a series of baseline demographic and economic characteristics (Table A.2 in the Appendix). For all but one variable analyzed, the magnitude of the differences between the treatment and control groups is smaller than 0.1 standard deviations of the control group, which indicates good balance (Imbens and Rubin 2015, p. 310). Only two out of 75 differences are statistically significant at conventional thresholds. The p-values of omnibus F-tests of joint significance are above 0.90 for both treatment arms. Overall, these results suggest that the control and treatment groups are well-balanced.

**Spillovers** Important spillovers between the treatment and control groups are unlikely given the relatively small scale of the program—only 0.3% of the rural population of Jendouba received a cash grant.

**Attrition** Attrition could be an issue in this study, especially since the endline survey took place about 2-2.5 years after the intervention and 4-4.5 years after the baseline survey. Furthermore, previous research has shown that cash transfer interventions can affect migration decisions (Angelucci 2015; Adhikari and Gentilini 2018; Imbert and Papp 2019; Gazeaud et al. 2021a).

The overall attrition rate in our study is 8.8% (Table A.3), which is relatively low and on par with comparable studies in other developing country contexts. The attrition rate in the control group is 11.2%, which is significantly larger than the attrition rate of 6% and 6.8% observed in the two treatment groups. Differential attrition is mainly driven by higher migration rates in the control group. While the survey firm made its best efforts to 13
track all households—even those that migrated within Tunisia—not all of them could be traced because some households had moved abroad, or because neighbors and community leaders did not know where the households had moved. We study whether the baseline characteristics of households are balanced across the control and the treatment groups after dropping attrited observations (Table A.4 in the Appendix). Reassuringly, we find no evidence of serious imbalance, even if attrition rates are different in the treatment and control groups.<sup>7</sup> This provides suggestive evidence that the control group is a credible comparison group even if attrition is slightly higher in this group.

As a robustness check, we implement three methods that aim to address selection bias due to differential attrition. First, we use Inverse Probability Weighting (IPW), using baseline characteristics to estimate respondents’ probability to participate in the endline survey, and then using the inverse of these probabilities as weight in the regressions.<sup>8</sup> Second, we follow Kling and Liebman (2004) and estimate treatment effect bounds, assuming that attritors in the treatment and control groups have a 0.5 SD difference in their outcomes. Finally, we follow Lee (2009) and exclude 5% (48 observations) of the treatment group such as to fully close the attrition differential. We use the approach proposed by Behaghel et al. (2015) to identify the observations that should be excluded.<sup>9</sup>

> 7Only two out of 75 t-test p-values are statistically significant at conventional levels and normalized differences between the control and treatment groups are small. The p-values of omnibus F-tests of joint significance are 0.69 for the first treatment arm, and 0.92 for the second treatment arm.

> 8With IPW, baseline respondents that are more likely to participate in the endline survey are given a lower weight in regressions, and vice versa. This method can effectively address selection issues that depend on baseline observables, but is unlikely to fully address selection bias due to unobserved characteristics.

> 9The method of Behaghel et al. (2015) assumes that the willingness to respond to a survey depends on a latent variable _V_ and, while the treatment may affect _V_ , the treatment does not change the relative rank in _V_ within experimental arms. Under this assumption, the method proposes to exclude treatmentgroup observations that would not have been interviewed if they were in the control group. We exploit variation in the intensity of tracking and exclude the most-difficult-to-track respondents in the treatment group. We use two methods to identify these respondents. First, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village. Figure A.5 shows the distribution of interview dates relative to the median interview date in their Imadas. While 82% of interviews were done within 10 days of the median interview date, a series of interviews were done much later, typically because respondents were much more difficult to find and interview. For the treatment group, we trim 5% (48 observations) of the upper tail of this distribution in order to fully close the attrition differential. Second, we exploit the fact that the survey firm intended to stop endline data collection on March 4, 2021, but the research team asked them to put more effort to find and interview attritors. We exclude the 30 interviews in the treatment group that were done after this event. For the remaining 18 additional observations in the treatment group, we use standard Lee (2009) bounds. The two approaches yield similar results. On Figure A.5, the red line delimits observations that are excluded based on the relative date of interview, while black bars identify interviews done after

14
The results obtained with these three methods are qualitatively similar (Tables A.6 to A.8 in the Appendix),<sup>10</sup> suggesting that attrition is unlikely to drive our conclusions.

**Multiple testing** In the main analysis, we estimate 148 parameters of interest (37 outcomes and four t-tests per outcome). To address multiple testing concerns, we compute and report sharpened _q_ -values that control the false discovery rate (Anderson 2008).<sup>11</sup> Overall, _p_ -values and _q_ -values are extremely similar, suggesting that our inferences are robust to corrections for multiple testing.

# **5 Results**

We study impacts on IGAs in Table 1, on women’s empowerment and well-being in Table 2, and on household living standards, shocks, and migration in Table 3.<sup>12</sup>

**IGAs** Results show that the cash grant and training program had a positive effect on female IGAs (H1.1), but only for women who could not invite their male partners to the training. Compared to the control group, women who received the cash grant and the training without their partner were 3.4 percentage points more likely to have an IGA at endline. This effect is larger—4.0 percentage points—if we consider a dummy variable identifying women who had an IGA before the COVID-19 pandemic (in March 2020, i.e. 1.5 years after the intervention).

However, we find no effect on these outcomes if male partners are invited to the training. The gender dialogue intervention seems to have backfired: women who received the cash grant and the joint training with their partner were 4.2 percentage points less likely to have an IGA than women who could participate to the training alone (q-value = 0.044). The effect is larger—5.8 percentage points—for IGAs before the COVID-19

> March 4, 2021. The two sets of excluded observations are similar. They are also similar to attrited observations in the control group (Table A.5).

> 10As we impose increasingly conservative assumptions, the point estimate of the effect naturally diverges from the main estimate. However, in the large majority of cases it remains in line with the main estimate.

> 11The procedure considers all tests performed in Tables 1 to 3 together.

> 12Results with disaggregated indices are shown in Appendix E.

15
pandemic (q-value = 0.010). This result suggests that women had more leeway to invest the grant in their own IGAs if their partner was not involved in the training. Men’s involvement may have reduced women’s privacy over the cash grant. Another, slightly different, possibility is that men were aware of the cash grant in both treatment groups but, by involving them in the training sessions, men may have felt part of the program, giving them more legitimacy to influence how cash grants were spent. We conjecture that the second explanation is more plausible in this context, given strong gender norms and the difficulty for women to hide income. This interpretation is in line with the model of Lowe and McKelway (2021), which assumes that intra-household decision rights are not clearly delineated, and that interventions that make household decision-making more joint can give “veto power” to the spouse.

Overall, the impact of the cash grant and training program on women’s IGAs seems limited and far from being transformative. In fact, one of the most salient results of Table 1 is the very low percentage of women having an IGA in the control group (7.5%). This percentage is slightly higher than at baseline (5.2%), which could indicate slow improvement over time.

Interestingly, we find a large effect on a dummy identifying whether women report having unused skills. This result suggests that some women benefited from the training offered with the grant or used the grant to learn new skills. We note, however, that the latter interpretation rests on a rather myopic behavior: women invested in additional skills without anticipating that they would have been unable to use them productively.

We find some evidence that the cash grants were used to promote the IGAs of other household members (H2.1). The cash grant and training program significantly increased the number of other household members with an IGA (+28%), especially when the partner was not involved in the program (+49%). The effects on the IGAs of the household head and on the income earned by other household members are positive but not statistically significant at conventional levels. The effect on the IGAs of other household members suggests that women might be able to optimally invest in the activities of other household members, provided their partners are not involved in the training. Perhaps surprisingly, 16
partners’ IGAs do not seem to benefit significantly from the cash grants, even when they are involved in the training.

The effects of the cash grant and training program on agricultural and livestock farming—two activities traditionally done by women—are salient (H1.1).<sup>13</sup> Households that received the cash grant and training program are 2.4 percentage points (+39%) more likely to report agriculture as an IGA. While we find some evidence of change in agricultural practices, agricultural techniques remain traditional, with almost no use of fertilizers and pesticide. Agriculture is mostly small-scale: for households reporting an agricultural IGA, the average production in the 2019 agricultural season was 61kg, and the value of the production was TND 192 (USD 219 in PPP terms). Households who own land have an average area of 0.85 hectares. The impacts on the quantity and value of agricultural production are positive and highly significant. Households that received a cash grant are also 4.7 percentage points (+25%) more likely to report livestock farming as an IGA. Households in the treatment groups have more goats and chickens, and are significantly more likely to have purchased goats, chickens, and mules since January 2019 (Appendix Table A.10). The joint participation into the training had no significant marginal effect on agriculture and livestock farming.

**Women’s empowerment and well-being** We find no significant effect on an index of women’s agency. Coefficients are negative but insignificant at conventional levels. We assess impacts on each of the 16 questions used to construct the women’s agency index in Appendix Table A.11. A set of 11 questions capture whether women are involved in decision-making related to household finances (e.g., expenditure on food, furniture, livestock, or land, but also borrowing or lending money). A large majority of women report being involved in these decisions, regardless of their treatment status. Interestingly, the effect on women’s agency for the purchase and sale of livestock is negative and statistically significant at the 5% level. This result can be linked to the strong treatment effect

> 13We note that only 19% of households in the control group reported agriculture or livestock as an IGA. Many more households have a few animals or cultivate a small plot of land but do not consider their involvement in subsistence agriculture as an IGA. For example, 39% of households in the control group have goats and 53% of households have chickens.

17
observed on livestock ownership and suggests that women’s investments in livestock do not increase their agency, quite the contrary. The five other questions used to construct the women’s agency index examine whether women have agency to take personal decisions related to personal purchases, occupation, and social participation. A majority of women do not take these personal decisions alone, regardless of their treatment status. The margin for improvement is considerable, yet we find no evidence of impact. Overall, these results suggest that hypotheses H1.2 and H1.3 are not verified: the interventions did not affect women’s agency. Consequently, hypotheses H1.4 and H2.2 are irrelevant in our context. These results also suggest that the mechanism through which partners’ involvement in the training backfired is not related to a persistent negative effect of the joint training on women’s agency.

We find strong evidence that the cash grant and training program improved women’s access to finance. The treatment effect on a standardized index is 0.41 standard deviations. Women in the treatment group are more likely to have (1) a bank account, (2) higher levels of savings, (3) borrowed money, and (4) repaid their debt (Appendix Table A.12).

Women who received the cash grant and training program also report higher life satisfaction. The positive effect is identified not only for current life satisfaction (+0.18 SD), but also for life satisfaction one year before the endline survey (+0.16 SD), as well as for expected life satisfaction three years after the survey (+0.10 SD). It is worth noting that self-reported life satisfaction is very low: only 2.4 on average in the control group on a Cantrill’s ladder ranging from 1 to 10. The recipients of the cash grant and training program characterize their relative wealth as slightly higher than that of other households of their community (+0.15 SD). We also find a positive and significant effect of the cash grants on women’s mental health, as measured using the MHI-5 scale (+0.07 SD).

The impacts on women’s agency, access to finance, well-being, and mental health are similar in the two treatment arms, suggesting that partners’ involvement in the training had no marginal effect on these outcomes.

18
**Household living standards, shocks, and migration** The cash grant and training intervention had a positive impact on living standards (H2.1). The effect on total consumption per capita is positive and statistically significant. The average value of consumption per capita is 7.5% higher for recipient households compared to non-recipients. The positive effect on consumption is largely driven by the positive effect on food consumption per capita (+10.7%).

Households that received the cash grants also have more assets. The estimated effect on an asset index is +0.13 SD. Positive effects are observed on a wide range of assets, including ownership of motorcycles, televisions, generators, ventilators, tables, mattresses, and the number of rooms in the house (Appendix Table A.14). The effects on measures of living standards are very similar in the two treatment arms, suggesting that partners’ involvement in the training had no marginal effect on living standards.

The recipients of cash grants are 2.5 percentage points more likely to report having suffered from an economic shock in the two years preceding the survey (job loss, bad business, or loss of livelihood). This effect is quite large (+156%) given that this type of negative shock is extremely rare in the control group. This result suggests that some women used part of the grant to invest in an IGA, but these activities did not survive until the endline survey. We also find suggestive evidence that the grants help households cope with shocks without having to take extreme decisions such as skipping meals or taking children out of school.

Finally, we explore whether the cash grant and training program affected individual and household migration (H2.3). We find that households in the treatment groups are 3 percentage points less likely to have moved to a different Imada at endline. This result suggests that the opportunity cost of migrating may have increased thanks to the cash grants and the better livelihood opportunities and higher living standards that resulted from the program. We also examine a variable indicating whether some household members had left their household and migrated individually since January 2019, that is, about two years before the endline survey. Of households, 19% reported that one of their members had left the household to migrate. The cash grant and training program does 19
not seem to have affected the overall level of individual migration. However, when we look at the reasons why household members migrated, we find that the program reduced the likelihood of migrating domestically because of marriage or divorce by 1.9 percentage points, but increased the likelihood of migrating internationally by 1.7 percentage points (Appendix Table A.16). While this latter effect represents a 113% increase compared to the migration rate of the control group, it is worth noting that international migration is relatively rare. Only 1.5% of households in the control group reported one or more international migrant. The cash grant and training program positively impacted migration intentions for both the respondents and other household members.

**Cost-benefit analysis** We use two approaches to show that the cash grant and training program is highly cost-effective (Appendix Table A.9). First, we follow Banerjee et al. (2015) and assume that the ITT effect on consumption is permanent. Using a social discount rate of 5%, we aggregate the ITT effects on consumption and assets and find that the material benefits of the cash grant and training program are 17 times higher – than its cost. While the confidence interval is large [2.5 31.3], we note that even its lower bound is above 1.

Recognizing that the first approach is based on a rather optimistic assumption – that the ITT effect on consumption is permanent – we also estimate how long the ITT effect on consumption should last to fully cover the program costs. We find that only 1.2 years of benefits are needed to obtain a positive benefit-cost ratio, which shows that the program is highly cost-effective, even in the short-run.

# **6 Conclusion**

Our study sought to generate evidence on the potential of capital injections and financial training to address labor market constraints besetting vulnerable women. Our study also assessed the potential of a gender dialogue intervention to empower women and address gender-specific barriers in the labor market.

Our results are mixed. On the one hand, we find that households as a whole massively 20
benefited from the cash grant and training program. We identified positive effects on a broad range of outcomes, including food consumption, asset ownership, investments in agriculture and livestock farming, and employment of other household members. We also find that women in the treatment groups are more satisfied with their lives, have better mental health, and have greater access to finance. Our estimates suggest that the program is highly cost-effective. These results demonstrate the potential of cash transfer and training programs to stimulate economic activity and reduce household poverty.

On the other hand, the cash grant and training program had only limited impacts on women entrepreneurship and wage employment. Instead, the program had a strong positive impact on agriculture and livestock farming—two occupations traditionally undertaken by women. The cash grant and training program also increased the number of other household members with an IGA. Involving male partners in the training had no positive impact; instead, it seems to have harmed women’s employment.

Our results show the importance of redistribution and expropriation mechanisms within households, which should be accounted for, to understand the full impact of cash transfers to women. Our study also highlights the difficulty of stimulating women entrepreneurship and employment in contexts where women’s labor market is severely constrained by tradition. More generally, our research shows the difficulty of challenging gender roles in traditional societies and that attempting to do so can backfire. This highlights the need to very carefully develop and test unproven interventions that directly target norms.

21
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# **Tables**

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– Table 1 Treatment effects on income generating activities (IGAs)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women’s IGAs**||||||||
|Has an IGA|0.075|0.264|0.013<br>|0.034**<br>|-0.008<br>|0.016<br>|1,824|
||||(0.012)<br>|(0.016)<br>|(0.014)<br>|[0.044]||
||||[0.303]|[0.065]|[0.573]|||
|Had an IGA before COVID|0.091|0.288|0.011<br>|0.040**<br>|-0.018<br>|0.002<br>|1,824|
||||(0.013)<br>|(0.017)<br>|(0.014)<br>|[0.012]||
||||[0.448]|[0.053]|[0.251]|||
|Business proft (in Dinars)|9.505|66.500|0.420|3.132|-2.314|0.169|1,824|
||||(2.855)|(3.897)|(2.983)|[0.219]||
||||[0.693]|[0.478]|[0.487]|||
|Business employees|0.010|0.271|0.003<br>|0.001<br>|0.005<br>|0.779<br>|1,824|
||||(0.012)|(0.011)|(0.017)|[0.648]||
||||[0.648]|[0.722]|[0.648]|||
|Total income (in Dinars)|35.893|149.039|8.127<br>|22.029**<br>|-6.163<br>|0.005<br>|1,824|
||||(6.866)<br>|(9.716)<br>|(6.998)<br>|[0.020]||
||||[0.268]|[0.054]|[0.448]|||
|Has unused skills|0.276|0.447|0.080***<br>|0.077***<br>|0.083***<br>|0.816<br>|1,824|
||||(0.019)<br>|(0.023)<br>|(0.023)<br>|[0.648]||
||||[0.001]|[0.007]|[0.005]|||
|**IGAs of other household members**||||||||
|Household head has an IGA|0.341|0.474|0.025|0.021|0.029|0.796|1,824|
||||(0.020)|(0.025)|(0.025)|[0.648]||
||||[0.251]|[0.448]|[0.279]|||
|Number of other HH members with an IGA|0.140|0.400|0.039*|0.069**|0.008|0.045|1,824|
||||(0.020)|(0.028)|(0.022)|[0.078]||
||||[0.090]|[0.042]|[0.648]|||
|Household head income (in Dinars)|133.822|230.264|9.713<br>|4.270<br>|15.191<br>|0.436<br>|1,824|
||||(10.109)<br>[0.389]|(12.148)<br>[0.648]|(12.448)<br>[0.251]|[0.487]||
|Other HH members income (in Dinars)|64.950|198.728|6.563<br>|10.987<br>|2.082<br>|0.469<br>|1,824|
||||(8.666)|(10.817)|(10.417)|[0.511]||
||||[0.498]|[0.353]|[0.669]|||
|**Agriculture**||||||||
|Household has an agricultural IGA|0.062|0.241|0.024**|0.027*|0.022*|0.753|1,824|
||||(0.011)|(0.014)|(0.013)|[0.648]||
||||[0.053]|[0.089]|[0.121]|||
|Used chemicals|0.018|0.156|0.021**|0.018*|0.023*|0.735|1,824|
||||(0.009)|(0.011)|(0.013)|[0.648]||
||||[0.054]|[0.126]|[0.094]|||
|Quantity produced|1.666|20.371|6.093***|7.140***|5.038**|0.512|1,824|
||||(1.860)<br>[0.008]|(2.636)<br>[0.026]|(2.262)<br>[0.056]|[0.542]||
|Value of the production|8.736|76.197|12.282***<br>|15.290**<br>|9.250*<br>|0.406<br>|1,824|
||||(4.614)|(6.303)|(5.404)|[0.463]||
||||[0.028]|[0.043]|[0.124]|||
|**Livestock**||||||||
|HH has a livestock IGA|0.187|0.390|0.047***|0.047**|0.047**|0.999|1,824|
||||(0.016)|(0.020)|(0.019)|[0.727]||
||||[0015]|[0044]|[0041]|||
|Total stock (in Dinars)|187.204|492.600|.<br>44.994*|.<br>53.121*|.<br>36.776|0.633|1,824|
||||(23.211)|(28.781)|(28.874)|[0.606]||
||||[0.086]|[0.097]|[0.247]|||
|Bought since January 2019 (in Dinars)|4.087|49.656|27.850***<br>|27.835***<br>|27.866***<br>|0.998<br>|1,824|
||||(5.398)<br>[0.001]|(7.248)<br>[0.003]|(7.404)<br>[0.003]|[0.727]||

Notes: This table reports the intent-to-treat effects of the interventions on women’s IGAs, other household members’ IGAs, agriculture, and livestock. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

29
– Table 2 Treatment effects on women’s empowerment and well-being

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women empowerment**||||||||
|Women agency index|-0.000|1.000|-0.045|-0.033|-0.056|0.650|1,824|
||||(0.038)|(0.046)|(0.046)|[0.619]||
||||[0.274]|[0.511]|[0.251]|||
|Partners’ views index|-0.001|0.999|.|.|-0.030|.|830|
||||||(0.054)<br>[0.573]|||
|**Women fnancial index**|0.000|1.000|0.408***|0.408***|0.408***|0.998|1,824|
||||(0.088)|(0.134)|(0.088)|[0.727]||
||||[0.001]|[0.014]|[0.001]|||
|**Women wellbeing**||||||||
|Cantrill’s ladder||||||||
|Current life satisfaction|2.356|1.470|0.268***|0.275***|0.261***|0.879|1,824|
||||(0.062)|(0.075)|(0.079)|[0.693]||
||||[0.001]|[0.005]|[0.007]|||
|Life satisfaction one year ago|2.411|1.494|0.238***|0.267***|0.209***|0.517|1,824|
||||(0.060)|(0.075)|(0.073)|[0.542]||
||||[0.002]|[0.005]|[0.019]|||
|Predicted life satisfaction in three years|3.411|2.125|0.219***|0.252**|0.186*|0.558|1,824|
||||(0.081)|(0.098)|(0.099)|[0.563]||
||||[0.026]|[0.035]|[0.092]|||
|Relative wealth|2.821|1.656|0.243***|0.268***|0.217***|0.594|1,824|
||||(0.067)|(0.083)|(0.082)|[0.584]||
||||[0.005]|[0.008]|[0.028]|||
|Mental health (MHI-5 score)|43.154|19.500|1.426*|2.152**|0.693|0.170|1,824|
||||(0.783)|(0.941)|(0.952)|[0.219]||
||||[0.101]|[0.053]|[0.511]|||

Notes: This table reports the intent-to-treat effects of the interventions on women’s empowerment and well-being. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). _Women’s agency index_ : index constructed from the answers of the woman to 16 questions about her role in the decision-making of personal and household decisions (standardized average). _Partner views index_ : index constructed from the answers of the partner to 73 questions on women’s agency, spouses communication, and his perceptions of gender roles, women abilities for economic activities, genderbased violence, reproductive health, and women autonomy (standardized average). _Woman financial index_ : standardized average of nine questions on woman financial access and financial situation. _Cantrill’s ladder_ ranges from 1 to 10 (where 10 is the top of the ladder). _Mental health_ : the MHI-5 score is based on responses across five categories (happiness, peacefulness, nervousness, downheartedness, depression) with higher values indicating better mental health (0-100 point scale). Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

30
– Table 3 Treatment effects on living standards, shocks, and migration

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Household living standards**||||||||
|Total consumption per capita (in Dinars per day)|8.428|6.472|0.634**|0.734**|0.546*|0.624|1,824|
||||(0.267)|(0.336)|(0.321)|[0.606]||
||||[0.045]|[0.059]|[0.124]|||
|Food consumption per capita (in Dinars per day)|3.582|3.004|0.384***|0.418***|0.350**|0.679|1,824|
||||(0.116)|(0.144)|(0.140)|[0.641]||
||||[0.007]|[0.017]|[0.039]|||
|Non-food consumption per capita (in Dinars per day)|3.757|2.750|0.077|0.072|0.089|0.922|1,824|
||||(0.120)|(0.150)|(0.144)|[0.705]||
||||[0.543]|[0.606]|[0.559]|||
|Asset index (stock)|0.000|1.000|0.127***|0.148***|0.105**|0.424|1,824|
||||(0.038)|(0.049)|(0.043)|[0.478]||
||||[0.007]|[0.014]|[0.043]|||
|Asset index (bought since 2019)|-0.000|1.000|0.008|0.025|-0.009|0.551|1,824|
||||(0.042)|(0.052)|(0.049)|[0.563]||
||||[0.669]|[0.606]|[0.669]|||
|**Shocks and coping mechanisms**||||||||
|Economic shock|0.016|0.125|0.025***|0.020**|0.030***|0.399|1,824|
||||(0.007)|(0.009)|(0.010)|[0.458]||
||||[0.007]|[0.057]|[0.015]|||
|Other shock|0.250|0.433|-0.002|-0.020|0.015|0.184|1,824|
||||(0.019)|(0.022)|(0.023)|[0.229]||
||||[0.705]|[0.448]|[0.542]|||
|Extreme coping strategy|0.123|0.328|-0.028**|-0.018|-0.038**|0.248|1,824|
||||(0.013)|(0.015)|(0.015)|[0.275]||
||||[0.056]|[0.251]|[0.039]|||
|Other coping strategy|0.152|0.359|0.009|-0.023|0.041**|0.004|1,824|
||||(0.015)|(0.018)|(0.020)|[0.019]||
||||[0.573]|[0.236]|[0.078]|||
|**Migration**||||||||
|Household migration (since baseline)|0.094|0.295|-0.030**|-0.030**|-0.029**|0.953|2,000|
||||(0.012)|(0.014)|(0.014)|[0.722]||
||||[0.042]|[0.070]|[0.078]|||
|Individual migration (since 2019)|0.190|0.393|0.002|-0.001|0.005|0.784|1,824|
||||(0.018)|(0.022)|(0.022)|[0.648]||
||||[0.705]|[0.722]|[0.648]|||
|Expected likelihood of future migration|0.054|0.226|0.032***|0.015|0.049***|0.032|1,824|
||||(0.011)|(0.012)|(0.014)|[0.062]||
||||[0.014]|[0.251]|[0.005]|||

Notes: This table reports the intent-to-treat effects of the interventions on household living standards, shocks, and migration. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). _Asset index_ : standardized index using the method of Filmer and Pritchett (2001) on a set of 21 assets. _Economic shocks:_ dummy equal to one in case of job loss, failed business or loss of livelihood in the last 24 months. _Other shocks_ : dummy equal to one in case of death/disease of a household member or theft in the last 24 months. _Extreme coping strategy_ : dummy equal to one if the household reduced the number of meals, took children out of school or fostered children to friends to face a shock. _Other coping strategy_ : dummy equal to one if the household took debts, received help, sold assets, or used savings to face a shock. _Household migration_ : dummy equal to one if the household has moved to a different imada (value of one is imputed for attrited households who could not be surveyed because they migrated). _Individual migration_ : dummy equal to one if at least one household member has migrated since 2019. _Expected likelihood of future migration_ : dummy equal to one if at least one household member is likely or very likely to migrate to another city or governorate in the next 12 months. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

31
# **Online Appendix**

# **A Interventions and Design**

**CWLP program** Between 2012 and 2016, 40 rural Imadas took part in a project of Community Works and Local Participation (CWLP) implemented by the Ministry of Vocational Training and Employment (MFPE) with the technical support of the World Bank (The World Bank 2017). The CWLP program was implemented as a cluster RCT: 40 Imadas were randomly selected to benefit from public works and 40 Imadas were placed in a pure control group. In treatment Imadas, 35 households were randomly selected to participate in the cash-for-work program and 15 households were chosen as control. Our baseline data are the endline data of the CWLP evaluation. We built our impact evaluation on the CWLP evaluation for three reasons: facilitate sampling, minimize costs, and allow for the evaluation of the long-term effects of the CWLP. Our interventions were cross-randomized without stratifying by the CWLP treatment status. 710 households of our study sample had been randomly selected to benefit from the CWLP cash-for-work program while 1290 were part of the control groups. We study heterogeneous treatment effects by treatment status in the CWLP evaluation in Appendix G.

**Cash grants** The World Bank worked closely with the Ministry of Vocational Training and Employment (MFPE) and Tunisia’s Center of Arab Women for Training and Research (CAWTAR) to distribute unconditional cash grants to 1,000 women. While the grant was unconditional, women were encouraged to either invest the money in physical capital (e.g., starting a micro-enterprise) or human capital (paying for vocational training of their choice).

Those randomized into treatment received the cash grant in their bank account without any additional conditions. However, more than 80% of women contacted to receive a grant reported not having a bank or postal account. These women were invited to open a bank or postal account as soon as possible and provide account information at the training session. An official invitation was provided to them by the CAWTAR to facilitate 32
this operation. In practice, many women opted for prepaid ATM cards, which have low transaction costs but limited validity (typically one year). Most participants were able to open an account on time, but others did not. Just after the training sessions, local coordinators and supervisors continued the work on guiding project beneficiaries to open accounts and collect missing information. At the end of this process, the details of 939 bank or postal accounts were collected, which represents 99% of the women trained (and 94% of the target population). Ten participants could not open a bank account (typically because they did not have an ID card). For these ten women, it was agreed with the bank team to proceed with the payment in cash (the payment was video recorded).

**Training** Before receiving the cash grants, women had to attend a one-day training session. CAWTAR and the World Bank co-developed the trainer’s guide and the training material. The training covered three modules: i) Financial Planning and Budgeting Module, ii) Savings Module, iii) Debt Management Module. Each module included a series of definition, videos, exercises, and debates. Links to videos are provided in Figure A.2.

The first module of the training was devoted to financial planning and budgeting (2 hours). The trainers drew the attention of the participants to the difference between the different financial objectives (investment in an income generating activity, investment in human capital, consumption, coping with unforeseen situations). The participants were invited to choose a financial objective and prepare a plan and a budget for its achievement. A practical case was also presented.

The second module was focusing on savings (1.5 hours). The trainers presented the different savings services available locally, their characteristics, and modes of use. Scenarios were presented to the participants, who had to choose the savings products most suited to their needs.

The last module was devoted to the debt management (1.5 hours). The trainers presented the different sources of funding available locally to meet the needs of participants’ businesses. They also provided information about how to get a loan and how to calculate the cost of credit.

33
Under the supervision of the CAWTAR team, 8 trainers delivered 61 training sessions. An additional training session was facilitated by the field coordinator of the World Bank for women who could not attend their scheduled session. In total 949 women and 444 men attended the training (see photos in Figure A.1). There were 22 participants on average per session. Costs of transportation to the training venue were reimbursed at the end the training.

**Gender Dialogue** 500 randomly selected women were invited to bring their male partner to the training. Couples were asked to sit together and do the exercises together (this demand was not strictly enforced if partners preferred to sit apart). The content of the training was similar for women randomly selected in and out of the joint training with male partners. The videos shown during the training were specifically designed to stimulate gender dialogue (Figure A.2). Each video presents two partners discussing, arguing, and agreeing upon their opposite opinions with regard to household budget management, including the definition of financial objectives and the selection of expenses, investments, savings products. The opinions of the female were presented by the trainers as the best practice.

34
(a) Training session in Balta (b) Training session in Balta (c) Training session in Bouaouen (d) Training session in Bousalem (e) Training session in Ghardima (f) Training session in Jendouba (d) Training session in Bousalem – Figure A.1 Photos of training sessions 35
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<!-- Start of picture text --> Control<br>Sample Cash<br>Sample CWLP (N=1,000)<br>Grant Study<br>Study (N=4,000)<br>(N=2,000) T1: No Gender<br>T: Cash Grant<br>Dialogue (N=498)<br>+ Training<br>(N=1,000)<br>T2: Gender<br>Dialogue (N=502)<br><!-- End of picture text --> – Figure A.3 Experimental Design Notes: Women in the cash grant study were recruited from the treatment and control groups of the Community Works and Local Participation (CWLP) randomized experiment, which evaluated the impact of a public-works program. Out of the 2,000 women in the cash grant study, 710 had been assigned to the treatment group of the CWLP program. Random assignment in the cash grant study was not stratified based on prior exposure to the CWLP program.

39
October 2015 Impact evaluation design workshop for original CWLP program November— December 2016 Workers recruitment & randomization January —April 2016 Rollout of CWLP, cash for work activities = ——{ April 2016 - January 2017 Endline survey CWLP evaluation (baseline cash grants evaluation) ‘Sacrermber 2018 —ianuaw 2019 Rollout of the cash grants, Hannes, and gender dialogues interventions September 2018 Participants tracking, trainings, gender dialogues interventions October 2018 First cash grant delivered to participants

# January 2019

Last cash grant delivered to participants Wooo raranacecsccsaaaccoaaaacs---------2 years later (post intervention) December 2020 — April 2021 Follow-up survey data collection and qualitative research
# **B Variables**

## – Table A.1 Description of the variables analyzed

|**Women’s IGAs**<br>Has an IGA|Dummy equal to one if the woman currently has an IGA.|
|---|---|
|Had an IGA before COVID|Dummy equal to one if the woman currently has an IGA or if she re-<br>port having an IGA before the COVID pandemic started (in March<br>2020).|
|Business proft|Business proft in the last 30 days. Positive values have been win-<br>sorized at the 10% level.|
|Business employees|Number of employees in the woman’s business.|
|Total income|Sum of business proft and income from wage employment in the<br>last 30 days. Positive values have been winsorized at the 10% level.|
|Has unused skills|Dummy equal to one if the woman declares having skills that she<br>has not used yet.|
|**IGAs of other household members**||
|Household head has an IGA<br>Number of other HH members with IGA|Dummy variable equal to one if the household head has an IGA.<br>Count variable indicating the number of other household members<br>withanIGA.|
|Household head income|<br>Income of the household head. Positive values have been winsorized<br>atthe10%level.|
|Other HH members’ income|<br>Incomes of the other household members. Positive values have been<br>winsorized at the 10% level.|
|**Agriculture**||
|Household has an agricultural IGA<br>Used chemicals|Dummy equal to one if the household has an agricultural IGA.<br>Dummy equal to one if the household has used fertilizers or pesti-<br>cides since January 2019.|
|Quantity produced|Quantity of output produced during the 2019 agricultural season in<br>kilos.Positivevalueshavebeenwinsorizedatthe10%level.|
|Value of the production|<br>Value of the production for the 2019 agricultural season in Dinars.<br>Positive values have been winsorized at the 10% level.|
|**Livestock**||
|HH has a livestock IGA<br>Total stock|Dummy equal to one if the household has a livestock IGA.<br>Total value of goats, chickens, cows and mules owned by households<br>declaringlivestockasanIGA.ExpressedinDinars.|
|Bought since January 2019|<br>Total value of goats, chickens, cows and mules bought since January<br>2019 by households declaring livestock as an IGA. Expressed in<br>Dinars.|
|**Women’s empowerment**<br>women’s agency index|Index constructed from the answers of the woman to questions on<br>her involvement in the decision-making of 11 household decisions<br>(how to spend money from IGA; what food to buy and consume;<br>purchase of furniture of the house; purchase and sale of livestock;<br>purchase of plots of land; purchase of large pots/pans; gifts for rela-<br>tives who marry/have children; large household purchases; making<br>daily household purchases; borrowing money; lending money) and<br>questions on her involvement regarding fve personal decisions (per-<br>sonal purchases; occupation; place of work; working hours; partici-<br>ationinrous)|
|Partners’ views index|p gp.<br>Index constructed from the answers of the partner to 73 questions<br>on women’s agency, spouses communication, and his perceptions of<br>gender roles, women abilities for economic activities, gender-based<br>violence, reproductive health, and women autonomy (standardized<br>average).|
|**Women fnancial index**|Standardized average of nine questions on woman fnancial access<br>and fnancial situation. The questions are: dummy variables indi-<br>cating whether the woman has a bank account, saved money since<br>January 2019, lent money since January 2019, borrowed money<br>since January 2019, and repaid debt since January 2019, as well as<br>variables indicating the amount on the bank account, the amount<br>saved since January 2019, the amount borrowed since January 2019,<br>and the total amount of debt. The positive values of the following<br>outcome variables have all been winsorized at the 10% level: to-<br>tal amount of debt, amount borrowed since January 2019, amount<br>saved since January 2019, and savings in the bank account.|

**Women well-being**

41
|Current life satisfaction|Response to the question: ”Please imagine a ladder with steps num-<br>bered from 1 at the bottom to 10 at the top. The top of the ladder<br>represents the best possible life for you and the bottom of the lad-<br>der represents the worst possible life for you. On which step of the<br>ladder would you say you personally feel you stand at this time?”|
|---|---|
|Life satisfaction one year ago|Response to the question: ”Please imagine a ladder with steps num-<br>bered from 1 at the bottom to 10 at the top. The top of the ladder<br>represents the best possible life for you and the bottom of the lad-<br>derreresentstheworstossiblelifeforouOnwhichsteofthe|
||p p y. p <br>ladder would you say you personally feel you stood one year back?”|
|Predicted life satisfaction in three years|Response to the question: ”Please imagine a ladder with steps num-<br>bered from 1 at the bottom to 10 at the top. The top of the ladder<br>represents the best possible life for you and the bottom of the lad-<br>|
||der represents the worst possible life for you. On which step do you<br>thinkouwillstandaboutthreeearsfromnow?”|
|Relative wealth|y y <br>Response to the question: ”Please imagine a wealth ladder. I want<br>you to compare your wealth situation against other household of<br>this Imada. Imagine a ladder with 10 steps, poorer household of<br>this Imada being on frst step and wealthier on the 10th step. At<br>”|
|Mental health (MHI-5 score)|which step are you now?<br>The MHI-5 score is based on the responses (scores) to fve questions:<br>”During the past month, how much of the time have you felt: A.<br>Happy (1 = never; 6 = always); B. Peaceful (1 = never; 6 = always);<br>C. Nervous (1 = always; 6 = never); D. Downhearted (1 = always;<br>6 = never); E. Depressed (1 = always; 6 = never)”. The MHI-5<br>is computed by adding the scores of the fve questions and then<br>transforming the raw score to a 0-100 point scale: MHI-5 score =<br>100 _×_ ((score A + score B + score C + score D + score E) _−_5) /<br>25. Higher values indicate better mental health.|
|**Household living standards**<br>Total consumption per capita|Sum of household food consumption and non-food consumption per<br>capita.<br>Expressed in Dinars per day.<br>Positive values have been<br>winsorizedatthe10%level|
|Food consumption per capita|.<br>Sum of household expenses in 11 domains (bread, farine, four, orge,<br>sorgho; pasta, rice, semolina; fsh/sea product; meat; eggs and diary;<br>vegetables; fruits; oil; drinks; spices; tobacco, cofee, tea). Expressed<br>in Dinars per day. Positive values have been winsorized at the 10%<br>ll|
|Non-food consumption per capita|eve.<br>Sum of household expenses in eight domains (medical expenditures;<br>leisure; clothes; transportation; electricity, gas, water, frewood;<br>communication; soap, detergent, cosmetics; other services; school-<br>iEdiDidPitilhbi|
||ng). xpresse n nars per ay. osve vaues ave een wn-<br>|
|Asset index (stock)|sorized at the 10% level.<br>Standardized index using the method of Filmer and Pritchett (2001)<br>for questions on household stocks of 21 assets (rooms; mattress;<br>radio; regular cell-phone; smartphone; refrigerator; bicycle; mo-<br>torcycle; chair; generator; ventilator; AC; mat; head lamp; table;<br>equipped living room; library; dresser; electronic iron; sewing ma-<br>hiTV|
|Asset index (bought since 2019)|cne; ).<br>Standardized index using the method of Filmer and Pritchett (2001)<br>for questions on household purchases of 21 assets since January 2019<br>(rooms; mattress; radio; regular cell-phone; smartphone; refrigera-<br>tor; bicycle; motorcycle; chair; generator; ventilator; AC; mat; head<br>lamp; table; equipped living room; library; dresser; electronic iron;<br>h|
||sewing macine; TV).|
|**Shocks and coping mechanisms**<br>Economic shock|Dummy equal to one in case of job loss, failed business or loss of<br>livelihoodinthelast24months|
|Other shock|.<br>Dummy equal to one in case of death/disease of a household member<br>|
|Extreme coping strategy|or theft in the last 24 months.<br>Dummy equal to one if the household reduced the number of meals,<br>took children out of school or fostered children to friends to face a<br>|
|Other coping strategy|shock.<br>Dummy equal to one if the household took debts, received help, sold<br>assets, or used savings to face a shock.|
|**Migration**||
|Household migration (since baseline)|Dummy equal to one if the household has moved to a diferent<br>Imada.<br>We impute the value of one for attrited households who<br>ldbkdbhid|
|Individual migration (since 2019)|cou not e trace ecause tey mgrate.<br>Dummy equal to one if at least one household member has migrated<br>since2019|
|Expected likelihood of future migration|.<br>Dummy equal to one if at least one household member is likely or<br>very likely to migrate to another city or governorate in the next 12<br>months.|

42
# **C Internal validity: supplementary tables**

– Table A.2 Descriptive statistics and balance

||(1)|(2)|(3)|T-t|est p-val|ues|Norma|lized dif|erences|
|---|---|---|---|---|---|---|---|---|---|
||Control<br>group|Treatment<br>group 1|Treatment<br>group 2|(1)-(2)|(1)-(3)|(2)-(3)|(1)-(2)|(1)-(3)|(2)-(3)|
|**Respondent variables**||||||||||
|Age|43.110|42.556|42.761|0.348|0.549|0.769|0.051|0.033|-0.019|
||(10.535)|(11.179)|(10.854)|||||||
|Completed secondary school|0.181<br>(0.385)|0.181<br>(0.385)|0.155<br>(0.363)|0.990|0.215|0.284|0.001|0.068|0.068|
|Born in this Imada|0.756<br>|0.721<br>|0.731<br>|0.142|0.294|0.718|0.080|0.057|-0.023|
||(0.430)|(0.449)|(0.444)|||||||
|Married|0.757<br>|0.743<br>|0.775<br>|0.554|0.442|0.238|0.032|-0.042|-0.075|
||(0.429)|(0.437)|(0.418)|||||||
|Status in the household||||||||||
|Head|0.083|0.080|0.076|0.859|0.624|0.785|0.010|0.027|0.017|
||(0.276)|(0.272)|(0.265)|||||||
|Spouse of the head|0.716<br>|0.711<br>|0.729<br>|0.835|0.594|0.521|0.011|-0.029|-0.041|
||(0.451)|(0.454)|(0.445)|||||||
|Daughter of the head|0.163|0.169|0.157|0.781|0.780|0.629|-0.015|0.015|0.031|
||(0.370)|(0.375)|(0.365)|||||||
|Other|0.038|0.040|0.038|0.838|0.988|0.850|-0.011|0.001|0.012|
||(0.191)|(0.197)|(0.191)|||||||
|Had an IGA in the last month|0.052<br>|0.054<br>|0.038<br>|0.857|0.229|0.222|-0.010|0.067|0.077|
||(0.222)|(0.227)|(0.191)|||||||
|Attended a professional training|0.090<br>|0.090<br>|0.086<br>|0.982|0.780|0.793|-0.001|0.015|0.017|
||(0.286)|(0.287)|(0.280)|||||||
|Randomly assigned to prior CWLP program|0.361<br>(0.481)|0.343<br>(0.475)|0.355<br>(0.479)|0.502|0.807|0.710|0.037|0.013|-0.023|
|**Household demographics**||||||||||
|Household size|4.609|4.558|4.711|0.581|0.278|0.152|0.031|-0.059|-0.091|
||(1.707)|(1.619)|(1.749)|||||||
|Number of adults (18-65)|2.906<br>|2.876<br>|2.962<br>|0.726|0.510|0.378|0.019|-0.036|-0.056|
||(1.579)|(1.595)|(1.514)|||||||
|Number of children (_<_18)|1.279|1.211|1.317|0.371|0.624|0.213|0.050|-0.027|-0.079|
||(1.422)|(1.315)|(1.370)|||||||
|Number of elders (_>_65)|0.424|0.472|0.432|0.363|0.872|0.519|-0.049|-0.009|0.041|
||(0.941)|(0.997)|(0.943)|||||||
|**Household living conditions**<br>Daily consumption per capita (in Dinars)|8.122<br>(13.260)|7.338<br>(12.525)|6.551<br>(11.290)|0.273|0.023|0.296|0.061|0.128|0.066|
|Has dirt foor|0.100<br>|0.120<br>|0.104<br>|0.227|0.828|0.397|-0.065|-0.012|0.054|
||(0.300)|(0.326)|(0.305)|||||||
|Has thatched or steel roof|0.064|0.070|0.050|0.645|0.272|0.173|-0.025|0.061|0.086|
||(0.245)|(0.256)|(0.218)|||||||
|Owns land|0.146|0.144|0.155|0.907|0.660|0.631|0.006|-0.024|-0.030|
||(0.352)|(0.350)|(0.360)|||||||
|Has livestock|0.472|0.480|0.468|0.773|0.887|0.709|-0.016|0.008|0.024|
||(0.499)|(0.500)|(0.499)|||||||
|Walking distance (in minutes, one way)||||||||||
|Water source|27.591|28.492|28.758|0.632|0.538|0.905|-0.026|-0.033|-0.008|
||(34.179)|(34.613)|(35.706)|||||||
|Primary school|30.394|31.426|31.993|0.422|0.214|0.711|-0.044|-0.068|-0.023|
||(23.116)|(24.127)|(24.254)|||||||
|Food market|40.900|42.987|42.262|0.228|0.437|0.730|-0.066|-0.042|0.022|
||(31.069)|(32.515)|(33.892)|||||||
|Headquarter|61.409|63.818|62.681|0.168|0.463|0.584|-0.075|-0.040|0.035|
||(31.175)|(33.048)|(32.563)|||||||
|Public transportation station|21.254|23.308|23.106|0.080|0.108|0.886|-0.095|-0.087|0.009|
||(20.686)|(22.683)|(21.742)|||||||
|OmnibusF-testp-value|.|.|.|0.910|0.921|0.971|.|.|.|
|<br>Observations|1,000|498|502|1,498|1,502|1,000|1,498|1,502|1,000|

This table reports the balance checks for key variables measured at baseline. Columns 1-3 report the means (and standard deviations in parenthesis) of the different experimental groups. Columns 4-6 report the p-values for T-tests of equality of means between any two of the experimental groups. Columns 7–9 report the standardized differences between any two of the experimental groups (that is the difference in means in units of standard deviation).

43
– Table A.3 Attrition

||(1)|(2)|(3)|T-t|est p-valu|es|Norma|lized dife|rences|
|---|---|---|---|---|---|---|---|---|---|
||Control<br>group|Treatment<br>group 1|Treatment<br>group 2|(1)-(2)|(1)-(3)|(2)-(3)|(1)-(2)|(1)-(3)|(2)-(3)|
|Attrition|0.112<br>(0.316)|0.060<br>(0.238)|0.068<br>(0.252)|0.001|0.006|0.629|0.185|0.155|-0.031|
|Attrition reason||||||||||
|Migrated|0.063<br>(0.243)|0.038<br>(0.192)|0.028<br>(0.165)|0.046|0.004|0.364|0.113|0.169|0.057|
|Death|0.010|0.002|0.016|0.088|0.319|0.020|0.104|-0.052|-0.148|
||(0.100)|(0.045)|(0.125)|||||||
|Refusals|0.021<br>(0.143)|0.012<br>(0.109)|0.008<br>(0.089)|0.220|0.063|0.517|0.070|0.109|0.041|
|Other|0.018<br>(0.133)|0.008<br>(0.089)|0.016<br>(0.125)|0.131|0.773|0.252|0.088|0.016|-0.073|
|Observations|1,000|498|502|1,498|1,502|1,000|1,498|1,502|1,000|

Notes: Columns 1-3 report the attrition rate for each of the experimental group. Columns 4-6 report the p-values for T-tests of differential attrition rates across experimental groups. Columns 7–9 report the standardized differences in attrition rates between experimental groups (that is the difference in means in units of standard deviation).

44
– Table A.4 Balance table after dropping attrited households

||(1)|(2)|(3)|T-t|est p-val|ues|Norma|lized dif|erences|
|---|---|---|---|---|---|---|---|---|---|
||Control<br>group|Treatment<br>group 1|Treatment<br>group 2|(1)-(2)|(1)-(3)|(2)-(3)|(1)-(2)|(1)-(3)|(2)-(3)|
|**Respondent variables**||||||||||
|Age|43.243|42.630|42.880|0.312|0.543|0.726|0.057|0.035|-0.023|
||(10.315)|(11.121)|(10.702)|||||||
|Completed secondary school|0.167<br>(0.373)|0.177<br>(0.382)|0.145<br>(0.353)|0.619|0.307|0.183|-0.028|0.059|0.087|
|Born in this Imada|0.764|0.716|0.733|0.055|0.214|0.559|0.109|0.071|-0.038|
||(0.425)|(0.452)|(0.443)|||||||
|Married|0.769<br>|0.756<br>|0.782<br>|0.600|0.590|0.352|0.030|-0.031|-0.061|
||(0.422)|(0.430)|(0.413)|||||||
|Status in the household||||||||||
|Head|0.084|0.083|0.079|0.943|0.732|0.811|0.004|0.020|0.016|
||(0.278)|(0.277)|(0.270)|||||||
|Spouse of the head|0.730<br>|0.722<br>|0.739<br>|0.768|0.705|0.556|0.017|-0.022|-0.039|
||(0.444)|(0.448)|(0.439)|||||||
|Daughter of the head|0.151|0.152|0.143|0.969|0.703|0.713|-0.002|0.022|0.024|
||(0.358)|(0.359)|(0.351)|||||||
|Other|0.035<br>|0.043<br>|0.038<br>|0.472|0.739|0.741|-0.040|-0.019|0.022|
||(0.184)|(0.202)|(0.193)|||||||
|Had an IGA in the last month|0.053<br>|0.053<br>|0.037<br>|0.969|0.176|0.212|-0.002|0.079|0.082|
||(0.224)|(0.225)|(0.187)|||||||
|Attended a professional training|0.082|0.092|0.081|0.545|0.949|0.562|-0.034|0.004|0.038|
||(0.275)|(0.289)|(0.273)|||||||
|Randomly assigned to prior CWLP program|0.387<br>(0.487)|0.355<br>(0.479)|0.372<br>(0.484)|0.238|0.575|0.587|0.068|0.032|-0.036|
|**Household demographics**||||||||||
|Household size|4.650|4.579|4.754|0.463|0.291|0.113|0.042|-0.060|-0.104|
||(1.718)|(1.619)|(1.759)|||||||
|Number of adults (18-65)|2.909<br>|2.868<br>|2.972<br>|0.650|0.479|0.303|0.026|-0.041|-0.067|
||(1.590)|(1.588)|(1.521)|||||||
|Number of children (_<_18)|1.340|1.241|1.363|0.220|0.776|0.168|0.071|-0.016|-0.090|
||(1.451)|(1.322)|(1.376)|||||||
|Number of elders (_>_65)|0.401<br>(0.942)|0.470<br>(1.004)|0.419<br>(0.944)|0.209|0.740|0.421|-0.071|-0.019|0.053|
|**Household living conditions**||||||||||
|Daily consumption per capita (in Dinars)|8.299<br>(13.477)|7.326<br>(12.529)|6.672<br>(11.524)|0.196|0.027|0.406|0.075|0.130|0.054|
|Hasdirtfoor|0.097|0.115|0.105|0.287|0.646|0.602|-0.060|-0.026|0.034|
||(0.296)|(0.320)|(0.306)|||||||
|Has thatched or steel roof|0.061|0.064|0.049|0.811|0.378|0.323|-0.014|0.051|0.065|
||(0.239)|(0.245)|(0.216)|||||||
|Owns land|0.149|0.138|0.149|0.584|0.999|0.630|0.031|-0.000|-0.032|
||(0.355)|(0.344)|(0.355)|||||||
|Has livestock|0.479|0.481|0.466|0.940|0.654|0.647|-0.004|0.026|0.030|
||(0.500)|(0.500)|(0.499)|||||||
|Walkingdistance(inminutesoneway)||||||||||
|, <br>Water source|27.803|28.730|29.442|0.636|0.411|0.760|-0.027|-0.047|-0.020|
||(34.103)|(34.763)|(36.328)|||||||
|Primary school|30.369|31.044|31.774|0.613|0.296|0.644|-0.029|-0.059|-0.030|
||(23014)|(23916)|(24473)|||||||
|Food market|.<br>41.186|.<br>42.810|.<br>41.674|0.369|0.789|0.599|-0.051|-0.015|0.034|
||(31.136)|(32.487)|(33.564)|||||||
|Headquarter|61.678|63.222|62.423|0.392|0.679|0.709|-0.048|-0.023|0.024|
||(30.943)|(32.757)|(32.575)|||||||
|Public transportation station|21.134|23.245|23.035|0.083|0.111|0.885|-0.098|-0.090|0.009|
||(20.554)|(22.654)|(21.515)|||||||
|OmnibusF-test-value||||0693|0916|0984||||
|p<br>Observations|.<br>888|.<br>468|.<br>468|.<br>1,356|.<br>1,356|.<br>936|.<br>1,356|.<br>1,356|.<br>936|

Notes: This table reports the balance checks after dropping attrited households. Columns 1-3 report the mean (and standard deviations in parenthesis) of the different experimental groups. Columns 4-6 report the p-values for T-tests of equality of means between any two of the experimental groups. Columns 7–9 report the standardized differences between any two of the experimental groups (that is the difference in means in units of standard deviation).

45
<!-- Start of picture text --> fan)<br>m (NN All HH tracked (N)<br>HE HH intensively tracked (N)<br>So<br>So<br>a ol<br>QQ<br>q<br>v<br>><br>»o<br>=<br>:o ai ee ee, ee — —<— ——_—<—<br>-10 0 10, 20° 30 40 50 60 70. 80 90 100<br>Date of interview relative to median date in Imada<br><!-- End of picture text --> <!-- Start of picture text --> wy<br>- [-) AL HH tracked (N)<br>ME HH intensively tracked (N)<br>><br>QQ<br>q<br>v<br>><br>vo<br>wy<br>= an Hldia 1 iia dal<br>5 20° ; 35 : 50 ; 65 ; 80 95<br>Date of interview relative to median date in Imada<br><!-- End of picture text -->
– Table A.5 Comparison of attrited households in the control group and trimmed observations in the treatment group

||(1)|(2)|(3)|T-t|est p-val|ues|Norma|lized dif|erences|
|---|---|---|---|---|---|---|---|---|---|
||Attrited<br>households<br>in C|Trimmed<br>households<br>in T (using<br>relative<br>date)|Trimmed<br>households<br>in T (using<br>intensive<br>tracking)|(1)-(2)|(1)-(3)|(2)-(3)|(1)-(2)|(1)-(3)|(2)-(3)|
|**Respondent variables**||||||||||
|Age|42.054|39.646|37.633|0.256|0.087|0.506|0.195|0.342|0.154|
||(12.136)|(12.498)|(13.647)|||||||
|Completed secondary school|0.295<br>|0.271<br>|0.333<br>|0.762|0.685|0.562|0.052|-0.083|-0.135|
||(0.458)|(0.449)|(0.479)|||||||
|Born in this Imada|0.696|0.771|0.833|0.341|0.138|0.512|-0.168|-0.324|-0.155|
||(0.462)|(0.425)|(0.379)|||||||
|Married|0.661<br>|0.563<br>|0.433<br>|0.241|0.023|0.273|0.201|0.464|0.257|
|Status in the household|(0.476)|(0.501)|(0.504)|||||||
|Head|0.071<br>|0.063<br>|0.100<br>|0.839|0.606|0.551|0.035|-0.101|-0.136|
||(0.259)|(0.245)|(0.305)|||||||
|Spouse of the head|0.607<br>(0491)|0.521<br>(0505)|0.400<br>(0498)|0.314|0.043|0.305|0.173|0.419|0.241|
|Daughter of the head|.<br>0.259<br>|.<br>0.375<br>|.<br>0.433<br>|0.141|0.064|0.614|-0.249|-0.369|-0.117|
||(0.440)|(0.489)|(0.504)|||||||
|Other|0.063<br>(0243)|0.042<br>(0202)|0.067<br>(0254)|0.603|0.934|0.632|0.093|-0.017|-0.109|
|Had an IGA in the last month|.<br>0.045|.<br>0.063|.<br>0.100|0.637|0.246|0.551|-0.079|-0.212|-0.136|
||(0.207)|(0.245)|(0.305)|||||||
|Attended a professional training|0.152|0.167|0.233|0.814|0.293|0.474|-0.040|-0.205|-0.165|
||(0.360)|(0.377)|(0.430)|||||||
|Randomly assigned to prior CWLP program|0.152|0.354|0.367|0.004|0.008|0.912|-0.475|-0.499|-0.026|
||(0.360)|(0.483)|(0.490)|||||||
|**Household demographics**||||||||||
|Household size|4.286|4.667|4.467|0.174|0.586|0.612|-0.233|-0.110|0.118|
||(1.591)|(1.680)|(1.697)|||||||
|Number of adults (18-65)|2.884|3.021|3.067|0.598|0.547|0.894|-0.091|-0.127|-0.031|
||(1.493)|(1.523)|(1.388)|||||||
|Number of children (_<_18)|0.795<br>(1.058)|1.104<br>(1.309)|0.867<br>(1.306)|0.117|0.754|0.438|-0.260|-0.061|0.182|
|Number of elders (_>_65)|0.607|0.542|0.533|0.688|0.689|0.970|0.068|0.085|0.009|
||(0.914)|(1.010)|(0.819)|||||||
|**Household living conditions**<br>Daily consumption per capita (in Dinars)|6.716|7.421|5.397|0.727|0.564|0.457|-0.059|0.123|0.178|
||(11.343)|(12.484)|(10.069)|||||||
|Has dirt foor|0.125|0.042|0.033|0.109|0.149|0.855|0.303|0.342|0.043|
||(0.332)|(0.202)|(0.183)|||||||
|Has thatched or steel roof|0.089<br>(0286)|0.063<br>(0245)|0.100<br>(0305)|0.573|0.858|0.551|0.101|-0.036|-0.136|
|Owns land|.<br>0.125|.<br>0.083|.<br>0.067|0.448|0.373|0.791|0.136|0.197|0.062|
||(0.332)|(0.279)|(0.254)|||||||
|Has livestock|0.420|0.583|0.633|0.058|0.037|0.666|-0.329|-0.434|-0.101|
||(0.496)|(0.498)|(0.490)|||||||
|Walkingdistance(inminutesoneway)||||||||||
|, <br>Water source|25.907|32.539|27.896|0.296|0.789|0.624|-0.175|-0.053|0.115|
||(34.885)|(40.505)|(40.435)|||||||
|Primary school|30.587<br>(24019)|31.146<br>(21983)|27.833<br>(18227)|0.890|0.560|0.492|-0.024|0.129|0.164|
|Food market|.<br>38.634|.<br>43.583|.<br>42.567|0.351|0.523|0.882|-0.161|-0.137|0.035|
||(30573)|(30804)|(26872)|||||||
|Headquarter|.<br>59.282|.<br>61.515|.<br>60.550|0.689|0.850|0.892|-0.070|-0.040|0.032|
||(33.023)|(30.346)|(30.717)|||||||
|Public transportation station|22.205|25.771|23.733|0.354|0.737|0.707|-0.158|-0.068|0.088|
||(21.779)|(23.223)|(23.159)|||||||
|Omnibus F-test p-value|.|.|.|0.116|0.124|1.000|.|.|.|
|Observations|112|48|30|160|142|78|160|142|78|

Notes: This table compares attrited households in the control group to trimmed observations in the treatment group (we used the approach proposed by Behaghel et al. (2015) to identify the observations that should be trimmed in the treatment group to fully close the attrition differential). In Column 2, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village, and identify the 48 observations in the treatment group with the latest interview dates relative to the median interview date in their village. In Column 3, we identify the 30 interviews in the treatment group that were done during the intensive tracking. See notes to Table A.2 for more details.

47
– Table A.6 Treatment effects, accounting for attrition (IGAs)

||||KL bounds|+/- .25 SD|Beha|ghel et al. bo|unds|
|---|---|---|---|---|---|---|---|
||(1)<br>Main<br>specifcation|(2)<br>IPW|(3)<br>Lower<br>bound|(4)<br>Upper<br>bound|(5)<br>Using<br>relative date|(6)<br>Using inten|(7)<br>sive tracking|
||||||Exact|Lower|Upper|
||||||trimming|bound|bound|
|**Women’s IGAs**||||||||
|Has an IGA|0.013|0.013|0.001|0.024**|0.011|-0.009|0.011|
||(0.012)|(0.012)|(0.011)|(0.011)|(0.012)|(0.011)|(0.012)|
|Had an IGA before COVID|0.011|0.012|-0.002|0.022*|0.010|-0.011|0.010|
||(0.013)|(0.013)|(0.012)|(0.012)|(0.013)|(0.013)|(0.013)|
|Business proft (in Dinars)|0.420|0.635|-2.355|3.018|0.497|-7.623***|0.938|
||(2.855)|(2.895)|(2.626)|(2.625)|(2.932)|(2.041)|(2.943)|
|Business employees|0.003|0.003|-0.008|0.013|0.003|-0.009|0.004|
||(0.012)|(0.011)|(0.011)|(0.011)|(0.012)|(0.009)|(0.011)|
|Total income (in Dinars)|8.127|7.540|0.992|13.996**|7.051|-12.288**|7.754|
||(6.866)|(7.031)|(6.379)|(6.371)|(6.913)|(5.248)|(6.902)|
|Has unused skills|0.080***|0.079***|0.060***|0.099***|0.081***|0.073***|0.089***|
||(0.019)|(0.019)|(0.017)|(0.017)|(0.019)|(0.019)|(0.019)|
|**IGAs of other household members**||||||||
|Household head has an IGA|0.025|0.021|0.006|0.045**|0.019|0.013|0.027|
||(0.020)|(0.021)|(0.019)|(0.019)|(0.021)|(0.021)|(0.021)|
|Number of other HH members with an IGA|0.039*|0.040**|0.020|0.057***|0.043**|-0.008|0.044**|
||(0.020)|(0.020)|(0.019)|(0.019)|(0.021)|(0.017)|(0.021)|
|Household head income (in Dinars)|9.713|5.804|-0.031|19.419**|4.032|-5.013|9.483|
||(10.109)|(10.222)|(9.334)|(9.334)|(10.094)|(9.812)|(10.141)|
|Other HH members income (in Dinars)|6.563|7.062|-1.895|14.848*|6.878|-10.985|8.370|
||(8.666)|(8.756)|(7.974)|(7.973)|(8.792)|(7.862)|(8.772)|
|**Agriculture**||||||||
|Household has an agricultural IGA|0.024**|0.024**|0.014|0.036***|0.026**|0.010|0.028**|
||(0.011)|(0.011)|(0.010)|(0.010)|(0.011)|(0.010)|(0.011)|
|Used chemicals|0.021**|0.023**|0.012|0.029***|0.022**|0.007|0.022**|
||(0.009)|(0.009)|(0.008)|(0.008)|(0.009)|(0.009)|(0.009)|
|Quantity produced|6.093***|5.927***|4.580***|7.542***|6.299***|-0.789|6.523***|
||(1.860)|(1.810)|(1.709)|(1.709)|(1.911)|(0.695)|(1.970)|
|Value of the production|12.282***|12.177***|8.187*|16.174***|11.589**|-3.055|13.501***|
||(4.614)|(4.386)|(4.242)|(4.241)|(4.609)|(2.812)|(4.854)|
|**Livestock**||||||||
|HH has a livestock IGA|0.047***|0.052***|0.030**|0.066***|0.053***|0.041**|0.055***|
||(0.016)|(0.016)|(0.015)|(0.015)|(0.016)|(0.016)|(0.016)|
|Total stock (in Dinars)|44.994*|46.640**|22.962|67.245***|47.089**|0.409|55.141**|
||(23.211)|(23.150)|(21.418)|(21.422)|(23.140)|(19.922)|(24.259)|
|Bought since January 2019 (in Dinars)|27.850***|26.638***|23.595***|32.143***|27.194***|8.735***|29.339***|
||(5.398)|(5.198)|(4.988)|(4.981)|(5.277)|(2.680)|(5.655)|
|**Observations**|1824|1,824|2,000|2,000|1,776|1,776|1,776|

Notes: This table reports the results from three methods to test the sensitivity of our results to attrition. Column 1 reports the estimates from Table 1 for reference. Column 2 reports the results weighting the households by their inverse probability of responding to the survey (Inverse Probability Weighting or IPW). Columns 3 and 4 replace outcome values for the attrited households with semi-extreme values as in Kling and Liebman (2004). Missing treatment and control observations are set to have a 0.5 SD difference in their outcomes: that is we impute mean + 0.25 SD for missing treatment and mean – 0.25 SD for missing controls, and vice versa (KL bounds). Columns 5-7 use the approach proposed by Behaghel et al. (2015) to identify the observations that should be trimmed in the treatment group to fully close the attrition differential. In Column 5, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village, and we trim the 48 observations in the treatment group with the latest interview dates relative to the median interview date in their village. Columns 6 and 7 report the results excluding the 30 interviews in the treatment group that were done during the intensive tracking. For the remaining 18 additional observations in the treatment group, we use standard Lee bounds. Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

48
Table A.7 – Treatment effects, accounting for attrition (women’s empowerment and wellbeing)

||||KL bounds|+/- .25 SD|Behag|hel et al. bo|unds|
|---|---|---|---|---|---|---|---|
||(1)<br>Main<br>specifcation|(2)<br>IPW|(3)<br>Lower<br>bound|(4)<br>Upper<br>bound|(5)<br>Using<br>relative date|(6)<br>Using inten|(7)<br>sive tracking|
||||||Exact<br>trimming|Lower<br>bound|Upper<br>bound|
|**Women agency index**|-0.045|-0.036|-0.089**|-0.011|-0.047|-0.094**|-0.026|
|**Women fnancial index**|(0.038)<br>0.408***|(0.039)<br>0.414***|(0.035)<br>0.338***|(0.035)<br>0.478***|(0.038)<br>0.438***|(0.037)<br>0.141***|(0.038)<br>0.448***|
||(0.088)|(0.091)|(0.080)|(0.080)|(0.093)|(0.044)|(0.093)|
|**Women well-being**<br>Cantrill’s ladder:||||||||
|Current life satisfaction|0.268***|0.242***|0.196***|0.321***|0.253***|0.139**|0.285***|
||(0.062)|(0.062)|(0.057)|(0.057)|(0.062)|(0.056)|(0.062)|
|Life satisfaction one year ago|0.238***|0.231***|0.166***|0.294***|0.223***|0.139**|0.256***|
||(0.060)|(0.060)|(0.055)|(0.055)|(0.060)|(0.057)|(0.060)|
|Predicted life satisfaction in three years|0.219***|0.195**|0.120|0.303***|0.191**|0.089|0.231***|
||(0.081)|(0.081)|(0.075)|(0.075)|(0.082)|(0.079)|(0.082)|
|Relative wealth|0.243***|0.231***|0.166***|0.311***|0.216***|0.107*|0.251***|
||(0.067)|(0.068)|(0.062)|(0.062)|(0.068)|(0.063)|(0.068)|
|MHI-5 score|1.426*|1.480*|0.582|2.204***|1.000|0.035|1.800**|
||(0.783)|(0.790)|(0.721)|(0.721)|(0.791)|(0.756)|(0.772)|
|**Observations**|1824|1,824|2,000|2,000|1,776|1,776|1,776|

Notes: This table reports the results from three methods to test the sensitivity of our results to attrition. Column 1 reports the estimates from Table 2 for reference. Column 2 reports the results weighting the households by their inverse probability of responding to the survey (Inverse Probability Weighting or IPW). Columns 3 and 4 replace outcome values for the attrited households with semi-extreme values as in Kling and Liebman (2004). Missing treatment and control observations are set to have a 0.5 SD difference in their outcomes: that is we impute mean + 0.25 SD for missing treatment and mean – 0.25 SD for missing controls, and vice versa (KL bounds). Columns 5-7 use the approach proposed by Behaghel et al. (2015) to identify the observations that should be trimmed in the treatment group to fully close the attrition differential. In Column 5, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village, and we trim the 48 observations in the treatment group with the latest interview dates relative to the median interview date in their village. Columns 6 and 7 report the results excluding the 30 interviews in the treatment group that were done during the intensive tracking. For the remaining 18 additional observations in the treatment group, we use standard Lee bounds. Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

49
– Table A.8 Treatment effects, accounting for attrition (living standards, shocks, and migration)

||||KL bounds|+/- .25 SD|Behag|hel et al. bo|unds|
|---|---|---|---|---|---|---|---|
||(1)<br>Main<br>specifcation|(2)<br>IPW|(3)<br>Lower<br>bound|(4)<br>Upper<br>bound|(5)<br>Using<br>relative date|(6)<br>Using inten|(7)<br>sive tracking|
||||||Exact|Lower|Upper|
||||||trimming|bound|bound|
|**Household living standards**||||||||
|Total consumption per capita|0.634**|0.627**|0.355|0.906***|0.684**|0.477*|0.820***|
||(0.267)|(0.269)|(0.246)|(0.246)|(0.272)|(0.266)|(0.272)|
|Food consumption per capita|0.384***|0.395***|0.249**|0.509***|0.408***|0.319***|0.453***|
||(0.116)|(0.118)|(0.107)|(0.107)|(0.118)|(0.116)|(0.118)|
|Non-food consumption per capita|0.077|0.080|-0.042|0.188*|0.120|0.028|0.189|
||(0.120)|(0.122)|(0.111)|(0.111)|(0.122)|(0.120)|(0.122)|
|Asset index (stock)|0.127***|0.126***|0.079**|0.168***|0.132***|0.072**|0.150***|
||(0.038)|(0.038)|(0.035)|(0.035)|(0.038)|(0.036)|(0.038)|
|Asset index (bought since 2019)|0.008|0.009|-0.035|0.046|0.019|-0.066*|0.024|
||(0.042)|(0.042)|(0.038)|(0.038)|(0.041)|(0.035)|(0.041)|
|**Shocks and coping mechanisms**||||||||
|Economic shock|0.025***|0.024***|0.018***|0.031***|0.027***|0.007|0.027***|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.008)|(0.006)|(0.008)|
|Other shock|-0.002|-0.002|-0.021|0.015|0.002|-0.013|0.006|
||(0.019)|(0.019)|(0.017)|(0.017)|(0.019)|(0.019)|(0.019)|
|Extreme coping strategy|-0.028**|-0.029**|-0.042***|-0.017|-0.030**|-0.047***|-0.029**|
||(0.013)|(0.013)|(0.012)|(0.012)|(0.013)|(0.012)|(0.013)|
|Other coping strategy|0.009|0.008|-0.007|0.023|0.011|-0.005|0.014|
||(0.015)|(0.016)|(0.014)|(0.014)|(0.016)|(0.015)|(0.016)|
|**Migration**||||||||
|Individual migration (since 2019)|0.002|0.001|-0.014|0.018|-0.001|-0.018|0.001|
||(0.018)|(0.018)|(0.017)|(0.017)|(0.018)|(0.018)|(0.019)|
|Expected likelihood of future migration|0.032***|0.030***|0.022**|0.043***|0.033***|0.018*|0.035***|
||(0.011)|(0.011)|(0.010)|(0.010)|(0.011)|(0.010)|(0.011)|
|**Observations**|1824|1,824|2,000|2,000|1,776|1,776|1,776|

Notes: This table reports the results from three methods to test the sensitivity of our results to attrition. Column 1 reports the estimates from Table 3 for reference. Column 2 reports the results weighting the households by their inverse probability of responding to the survey (Inverse Probability Weighting or IPW). Columns 3 and 4 replace outcome values for the attrited households with semi-extreme values as in Kling and Liebman (2004). Missing treatment and control observations are set to have a 0.5 SD difference in their outcomes: that is we impute mean + 0.25 SD for missing treatment and mean – 0.25 SD for missing controls, and vice versa (KL bounds). Columns 5-7 use the approach proposed by Behaghel et al. (2015) to identify the observations that should be trimmed in the treatment group to fully close the attrition differential. In Column 5, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village, and we trim the 48 observations in the treatment group with the latest interview dates relative to the median interview date in their village. Columns 6 and 7 report the results excluding the 30 interviews in the treatment group that were done during the intensive tracking. For the remaining 18 additional observations in the treatment group, we use standard Lee bounds. Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

50
# **D Cost-benefit analysis**

– Table A.9 Cost-benefit analysis

|**Panel **|**A: Program costs per household (in 2021 Tunisian Dinars)**|||
|---|---|---|---|
|||**Estimate**||
|(1)|Direct cash grant costs (including bank charges)|734||
|(2)<br>|Cost of trainers (wages, transportation, per-diem)<br>|101<br>||
|(3)<br>|Transportation costs to training (participants)<br>|18||
|(4)|Other training costs (venue, cofee breaks)|18||
|(5)|Other costs (coordination, targeting, communication, printing)|158||
|(6)|Total costs|1,025||
|(7)|Total costs, infated to year 2 at 5% annual discount rate|1,130||
|**Panel **|**B: Benefts per household (in 2021 Tunisian Dinars)**|||
|||**Estimate**|**Confdence**|
||**Consumption**||**interval (95%)**|
|(8)|Year 2 ITT efect on yearly consumption|883|[127 - 1,639]|
|(9)<br>|Assumed ITT efect on yearly consumption in year 1<br>|883|[127 - 1,639]<br>|
|(10)|Assumed ITT efect on onward consumption in perpetuity (5% discount rate)|17,211|[2,471 - 31,951]|
|(11)|Total (ITT efect on total post-treatment consumption)|18,976|[2,724 - 35,229]|
||**Assets**|||
|(12)|Year 2 ITT efect on animals|45|[-0 - 90]|
|(13)|Year 2 ITT efect on household assets|75|[23 - 128]|
|(14)|Total (ITT efect on total assets)|113|[39 - 187]|
|(15)|Total benefts (ITT efect on total post-treatment consumption + assets)|19,094|[2,830 - 35,357]|
|**Panel **|**C: Cost-beneft ratios**|||
|||**Estimate**|**Confdence**|
||**Main estimate**||**interval (95%)**|
|(16)|Benefts-cost ratio (15)/(7)|16.9|[2.5 - 31.3]|
|(17)|Number of years, such that total beneft = total cost|1.2||
||**Sensitivity analysis**|||
|(18)|Beneft-cost ratio, at discount rate of 7%|12.4|[1.9 - 23.0]|
|(19)|Beneft-cost ratio, at discount rate of 10%|9.1|[1.4 - 16.8]|

Notes: The program costs reported in Lines (1) to (6) were provided by CAWTAR, the NGO in charge of implementation in 2018. IMF estimates of inflation are used to express values in 2021 Tunisian Dinars. In Line (7), we inflate the total costs to year 2 using a social discount rate of 5% per year. In Line (8), the Year 2 ITT effect on yearly consumption is obtained by estimating equation (1) with a measure of household yearly consumption. The ITT effect on yearly consumption in year 1 (Line 9) is assumed to be equal to the ITT effect on yearly consumption in year 2 (line 8). In Line (10), we assume a social discount rate of 5% to estimate the value of perpetuity of the ITT effect on yearly consumption (= _−_ 883 _/ln_ (0 _._ 95)). In Line (12), the ITT effect on the value of animals is taken from Table 1. In Line (13), the ITT effect on the value of household assets is estimated using equation (1) with a measure of the value of household assets. The unit-value of each asset is estimated using the median replacement value reported in the baseline survey, subject to data availability. In Line (14), the ITT effect on the total value of assets is estimated using equation (1) with a measure of the total value of assets, which is equal to the sum of the value of animals and household assets. In Line (15), the ITT effect on the total value of post-treatment consumption and assets is estimated using equation (1) with a measure of total benefits equal to the sum of the total value of post-treatment consumption and the value of assets.

51
# **E Impact on disaggregated outcomes**

– Table A.10 Treatment effects on livestock (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Stock**||||||||
|Total value in Dinars|187.204|492.600|44.994*|53.121*|36.776|0.633|1,824|
||||(23.211)|(28.781)|(28.874)|||
|Goats|112.613|327.841|26.291*<br>(15.159)|37.749*<br>(19.918)|14.740<br>(17.136)|0.285|1,824|
|Chicken|7.587|29.212|2.070*<br>(1.133)|1.726<br>(1.337)|2.418*<br>(1.331)|0.623|1,824|
|Cows|55.180|218.368|18.134<br>(12.310)|13.367<br>(14.066)|22.929<br>(17.186)|0.624|1,824|
|Mules|11.824|42.283|1.798<br>(2.010)|2.960<br>(2.686)|0.627<br>(2.265)|0.425|1,824|
|**Bought since January 2019**||||||||
|Total value in Dinars|4.087|49.656|27.850***<br>(5.398)|27.835***<br>(7.248)|27.866***<br>(7.404)|0.998|1,824|
|Goats|14.414|76.189|18.764***<br>(4.738)|14.757***<br>(5.549)|22.805***<br>(6.858)|0.321|1,824|
|Chicken|2.337|12.980|3.304**<br>(1.591)|2.253*<br>(1.281)|4.364<br>(2.660)|0.433|1,824|
|Cows|14.640|172.225|6.558<br>(7.038)|6.885<br>(8.283)|6.227<br>(7.855)|0.934|1,824|
|Mules|0.845|11.230|1.316*<br>(0.729)|1.103<br>(0.968)|1.532*<br>(0.920)|0.721|1,824|

Notes: This table reports the intent-to-treat effects of the interventions on livestock, disaggregating the results from Table 1. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

52
– Table A.11 Treatment effects on woman empowerment (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Woman sample**||||||||
|**Woman agency index**|-0.000|1.000|-0.045<br>(0.038)|-0.033<br>(0.046)|-0.056<br>(0.046)|0.650|1,824|
|Woman involved in the following **household decisions**:||||||||
|How to spend money from IGA|0.805|0.520|-0.029|-0.034|-0.025|0.745|1,824|
||||(0.019)|(0.023)|(0.024)|||
|What food to buy and consume|0.821|0.502|-0.009|-0.017|0.000|0.525|1,824|
||||(0.019)|(0.022)|(0.023)|||
|Purchase of furniture of the house|0.859|0.492|-0.022<br>|-0.026<br>|-0.018<br>|0.762|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Purchase and sale of livestock|0.784|0.549|-0.043**<br>|-0.041*<br>|-0.045*<br>|0.884|1,824|
||||(0.020)|(0.025)|(0.024)|||
|Purchase of plots of land|0.785|0.550|-0.025|-0.025|-0.026|0.979|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Purchase of large pots/pans|0.877|0.490|-0.020<br>|-0.020<br>|-0.021<br>|0.966|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Gifts for relatives who marry/have children|0.849|0.504|-0.021|-0.022|-0.020|0.961|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Large household purchases|0.780|0.517|-0.018<br>|-0.017<br>|-0.020<br>|0.906|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Making daily household purchases|0.807|0.505|0.009|0.014|0.003|0.689|1,824|
||||(0.019)|(0.023)|(0.023)|||
|Borrowing money|0.794|0.546|-0.023<br>|-0.015<br>|-0.032<br>|0.531|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Lending money|0.783|0.540|-0.008<br>(0.020)|0.006<br>(0.024)|-0.022<br>(0.024)|0.298|1,824|
|Woman decides for the following **personal decisions**:||||||||
|Personal purchases|0.402|0.618|-0.031|-0.013|-0.050*|0.267|1,824|
||||(0.024)|(0.030)|(0.029)|||
|Occupation|0.388|0.614|-0.027|-0.011|-0.042|0.358|1,824|
||||(0.024)|(0.030)|(0.029)|||
|Place of work (home vs outside)|0.364|0.608|-0.033<br>|-0.023<br>|-0.044<br>|0.533|1,824|
||||(0.025)|(0.031)|(0.029)|||
|Working hours|0.364|0.608|-0.031|-0.024|-0.038|0.687|1,824|
||||(0.025)|(0.031)|(0.029)|||
|Participation in groups|0.337|0.592|-0.030|-0.019|-0.041|0.506|1,824|
||||(0.024)|(0.030)|(0.028)|||
|**Partner sample**||||||||
|**Partner views index**|-0.001|0.999|||-0.030||830|
||||||(0.054)|||
|Perspectives on gender role index|-0.000|0.999|||0.004<br>(0053)||830|
|Perspectives on women abilities index|-0.000|0.999|||.<br>-0.049<br>0055||830|
|Perception of gender based violence index|-0.004|1.002|||(.)<br>-0.102<br>(0069)||830|
|Perspectives on reproductive health index|-0.002|0.999|||.<br>0.073<br>(0060)||830|
|Woman agency index|0.001|0.999|||.<br>0.031<br>(0057)||830|
|Qualityofcommunicationbetweenspousesindex|-0001|0999|||.<br>-0014||830|
||.|.|||.<br>(0.053)|||

Notes: This table reports the intent-to-treat effects of the interventions on woman empowerment, disaggregating the results from Table 2. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

53
– Table A.12 Treatment effects on woman financial index (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Woman fnancial index**|0.000|1.000|0.408***<br>(0.088)|0.408***<br>(0.134)|0.408***<br>(0.088)|0.998|1,824|
|Total amount of debt|816.824|1219.833|-67.647<br>(53.001)|-91.658<br>(63.487)|-43.467<br>(64.534)|0.502|1,824|
|Borrowed money since January 2019|0.305|0.461|0.057***<br>(0.020)|0.035<br>(0.024)|0.079***<br>(0.025)|0.129|1,824|
|Amount borrowed since January 2019|372.196|840.155|-2.806<br>(36.010)|-22.365<br>(43.114)|19.635<br>(44.555)|0.404|1,824|
|Repaid debt since January 2019|0.070|0.255|0.025**<br>(0.012)|0.019<br>(0.015)|0.030*<br>(0.016)|0.555|1,824|
|Saved money since January 2019|0.007|0.082|0.014***<br>(0.005)|0.013*<br>(0.007)|0.015**<br>(0.007)|0.774|1,824|
|Amount saved since Jan 2019|6.926|139.211|10.674<br>(8.111)|14.292<br>(11.996)|7.028<br>(7.199)|0.523|1,824|
|Has a bank account|0.044|0.205|0.085***<br>(0.011)|0.077***<br>(0.014)|0.094***<br>(0.014)|0.318|1,824|
|Savings on the bank account|0.338|5.806|4.558***<br>(1.482)|5.656**<br>(2.333)|3.452**<br>(1.485)|0.389|1,824|
|Lent money since January 2019|0.011|0.106|0.009*<br>(0.006)|0.005<br>(0.007)|0.013*<br>(0.007)|0.362|1,824|

Notes: This table reports the intent-to-treat effects of the interventions on a financial index, disaggregating the results from Table 2. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

54
– Table A.13 Treatment effects on woman mental health (disaggregated results)

||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|Eq (1)<br>(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|Eq (2)<br>(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|---|---|---|---|---|---|---|---|
|**MHI-5 score**|43.154|19.500|1.426*<br>(0.783)|2.152**<br>(0.941)|0.693<br>(0.952)|0.170|1,824|
|How much of the time during the past month have you felt:||||||||
|A. Nervous (1 = always; 6 = never)|3.264|1.435|0.065<br>(0.056)|0.100<br>(0.067)|0.028<br>(0.068)|0.338|1,824|
|B. Downhearted (1 = always; 6 = never)|3.249|1.464|0.079<br>(0.056)|0.096<br>(0.068)|0.062<br>(0.067)|0.652|1,824|
|C. Depressed (1 = always; 6 = never)|3.426|1.394|0.084<br>(0.054)|0.132**<br>(0.064)|0.037<br>(0.067)|0.197|1,824|
|D. Happy (1 = never ; 6 = always)|2.925|1.245|0.102**<br>(0.051)|0.141**<br>(0.063)|0.063<br>(0.062)|0.273|1,824|
|E. Peaceful (1 = never ; 6 = always)|2.855|1.097|0.058<br>(0.049)|0.099<br>(0.062)|0.017<br>(0.058)|0.229|1,824|

Notes: This table reports the intent-to-treat effects of the interventions on livestock, disaggregating the results from Table 2. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). _MHI-5 score_ : The MHI-5 is computed by adding the scores of the five question and then transforming the raw score to a 0-100 point scale: MHI-5 score = 100 _×_ ((score A + score B + score C + score D + score E) _−_ 5) / 25. Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

55
– Table A.14 Treatment effects on household asset index (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Asset index (stock)**|0.000|1.000|0.127***<br>|0.148***<br>|0.105**<br>|0.424|1,824|
||||(0.038)|(0.049)|(0.043)|||
|Rooms|2.274|1.352|0.124**|0.134**|0.115**|0.793|1,824|
||||(0.049)<br>|(0.062)<br>|(0.058)|||
|Mattress|4.012|1.976|0.140*<br>|0.231**<br>|0.048<br>|0.134|1,824|
||||(0.085)|(0.103)|(0.106)|||
|Radio|0.232|0.523|0.011<br>|0.004<br>|0.018<br>|0.597|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Regular cell-phone|1.729|1.302|-0.008|-0.032|0.017|0.476|1,824|
||||(0.053)|(0.065)|(0.062)|||
|Smart-phone|0.564|1.019|0.058|0.092*|0.023|0.251|1,824|
||||(0.043)|(0.055)|(0.050)|||
|Refrigerator|1.285|11.081|-0.367|-0.384|-0.349|0.542|1,824|
||||(0.362)|(0.376)|(0.350)|||
|Bicycle|0.052|0.232|0.006|-0.009|0.022|0.048|1,824|
||||(0.011)|(0.012)|(0.015)|||
|Motorcycle|0.050|0.217|0.019*|0.010|0.029**|0.229|1,824|
||||(0.011)|(0.013)|(0.014)|||
|Chair, bench|3.758|2.330|0.158|0.182|0.134|0.722|1,824|
||||(0.097)|(0.116)|(0.121)|||
|Generator|0.002|0.047|0.011***|0.009*|0.013**|0.622|1,824|
||||(0.004)|(0.005)|(0.006)|||
|Ventilator|0.101|0.407|0.029*|0.023|0.035**|0.520|1,824|
||||(0.015)|(0.018)|(0.017)|||
|AC|0.037|0.195|0.005<br>|0.011<br>|-0.001<br>|0.386|1,824|
||||(0.010)|(0.013)|(0.010)|||
|Mat|0.845|0.846|0.095***|0.062|0.128***|0.208|1,824|
||||(0.036)|(0.047)|(0.043)|||
|Head lamp|0.280|0.462|-0.012|0.004|-0.028|0.261|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Table|1.171|0.822|0.143***|0.161***|0.126***|0.497|1,824|
||||(0.034)|(0.045)|(0.041)|||
|Equipped living room (table, armchairs)|0.330|0.529|0.053**|0.061**|0.046*|0.636|1,824|
||||(0.022)|(0.029)|(0.025)|||
|Library|0.267|0.445|0.034*|0.058**|0.010|0.089|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Dresser|0.761|0.729|0.023|0.041|0.004|0.359|1,824|
||||(0.029)|(0.036)|(0.035)|||
|Electric iron|0.114|0.467|0.005|0.017|-0.007|0.222|1,824|
||||(0.017)|(0.021)|(0.018)|||
|Sewing machine|0.015|0.129|0.014*|0.016|0.012|0.739|1,824|
||||(0.008)|(0.012)|(0.008)|||
|TV|0.840|0.385|0.042**|0.029|0.054***|0.272|1,824|
||||(0.017)|(0.022)|(0.019)|||

Notes: This table reports the intent-to-treat effects of the interventions on a household asset index, disaggregating the results from Table 3. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

56
– Table A.15 Treatment effects on shocks and coping strategies (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Shocks in the last two years**||||||||
|**Economic shock**|0.016|0.125|0.025***|0.020**|0.030***|0.399|1,824|
||||(0.007)|(0.009)|(0.010)|||
|Job loss, failed or bad business|0.005|0.067|0.018***|0.016**|0.020***|0.668|1,824|
||||(0.005)|(0.007)|(0.007)|||
|Loss of livelihood due to unexpected large expenses|0.002|0.047|0.005<br>|0.005<br>|0.004<br>|0.854|1,824|
||||(0.003)|(0.004)|(0.004)|||
|Loss of livelihood due to natural disasters|0.009|0.095|0.002|-0.002|0.006|0.249|1,824|
||||(0.005)|(0.005)|(0.006)|||
|**Other shock**|0.250|0.433|-0.002|-0.020|0.015|0.184|1,824|
||||(0.019)|(0.022)|(0.023)|||
|Death of a household member|0.041|0.197|0.002|0.010|-0.005|0.243|1,824|
||||(0.009)|(0.012)|(0.011)|||
|Disease of a household member|0.220|0.461|-0.014|-0.042*|0.013|0.041|1,824|
||||(0.019)|(0.022)|(0.025)|||
|Confscation of felds|0.000|0.000|0.000|0.000|0.000|.|1,824|
||||(0.000)|(0.000)|(0.000)|||
|Theft|0.002|0.047|0.002|0.003|0.002|0.803|1,824|
||||(0.003)|(0.003)|(0.004)|||
|Other|0.014|0.116|0.009|0.004|0.015*|0.229|1,824|
||||(0.006)|(0.007)|(0.008)|||
|**Coping strategy to face the shock**||||||||
|**Extreme coping strategy**|0.123|0.328|-0.028**|-0.018|-0.038**|0.248|1,824|
||||(0.013)|(0.015)|(0.015)|||
|Reduced the number of meals|0.111|0.315|-0.020*|-0.012|-0.028*|0.312|1,824|
||||(0.012)|(0.014)|(0.015)|||
|Took children out of school|0.017|0.129|-0.012**|-0.011*|-0.012**|0.708|1,824|
||||(0.005)|(0.006)|(0.005)|||
|Sending children to friends|0.002|0.047|-0.001|0.000|-0.002|0.282|1,824|
||||(0.002)|(0.002)|(0.002)|||
|**Other coping strategy**|0.152|0.359|0.009|-0.023|0.041**|0.004|1,824|
||||(0.015)|(0.018)|(0.020)|||
|Took debts (friends, neighbors, cooperatives)|0.137|0.393|0.000|-0.016|0.016|0.203|1,824|
||||(0.017)|(0.020)|(0.023)|||
|Received help (community, NGO)|0.014|0.125|-0.002|-0.003|-0.001|0.806|1,824|
||||(0.005)|(0.006)|(0.006)|||
|Received from family members outside the village|0.026|0.159|0.000|-0.012*|0.012|0.010|1,824|
||||(0.007)|(0.007)|(0.010)|||
|Sale of household goods, felds, cattle|0.026|0.173|0.005|-0.003|0.012|0.199|1,824|
||||(0.008)|(0.009)|(0.010)|||
|Used the savings|0.015|0.120|-0.001|0.001|-0.003|0.512|1,824|
||||(0.005)|(0.007)|(0.006)|||

Notes: Notes: This table reports the intent-to-treat effects of the interventions on shocks and coping strategies, disaggregating the results from Table 3. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

57
– Table A.16 Treatment effects on migration reasons

||||Eq (1)<br>||Eq (2)<br>|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|Migration reasons||||||||
|Marriage, divorce|0.061|0.239|-0.019*<br>(0.010)|-0.023**<br>(0.011)|-0.016<br>(0.013)|0.588|1,824|
|Security reasons|0.001|0.034|0.000|0.001|-0.001|0.382|1,824|
||||(0.001)|(0.002)|(0.001)|||
|Study or health reasons|0.010|0.100|0.001|0.000|0.002|0.789|1,824|
||||(0.005)|(0.006)|(0.007)|||
|Domestic work migration|0.020|0.141|-0.004<br>(0.006)|-0.002<br>(0.007)|-0.006<br>(0.007)|0.619|1,824|
|International work migration|0.015|0.120|0.017**<br>(0.007)|0.013<br>(0.008)|0.021**<br>(0.009)|0.456|1,824|
|Other|0.029|0.169|-0.006|-0.001|-0.010|0.376|1,824|
||||(0.008)|(0.009)|(0.009)|||

Notes: Notes: This table reports the intent-to-treat effects of the interventions on migration reasons. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

58
# **F Results without control variables**

59
– Table A.17 Treatment effects on IGAs

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women’s IGAs**||||||||
|Has an IGA|0.075|0.264|0.007<br>|0.031*<br>|-0.018<br>|0.006<br>|1,824|
||||(0.013)|(0.017)|(0.014)|[0.035]||
||||[0.545]|[0.129]|[0.252]|||
|Had an IGA before COVID|0.091|0.288|0.006<br>|0.039**<br>|-0.027*<br>|0.001<br>|1,824|
||||(0.014)<br>[0.586]|(0.018)<br>[0.097]|(0.015)<br>[0.135]|[0.011]||
|Business proft (in Dinars)|9.505|66.500|-0.498|2.818|-3.813|0.103|1,824|
||||(3.022)|(4.089)|(3.131)|[0.171]||
||||[0.683]|[0.494]|[0.275]|||
|Business employees|0.010|0.271|0.003<br>|0.001<br>|0.005<br>|0.789<br>|1,824|
||||(0.012)|(0.011)|(0.017)|[0.652]||
||||[0.666]|[0.730]|[0.652]|||
|Total income (in Dinars)|35.893|149.039|5.266|22.141**|-11.609|0.001|1,824|
||||(7.305)<br>|(10.316)<br>|(7.476)<br>|[0.018]||
||||[0.471]|[0.096]|[0.192]|||
|Has unused skills|0.276|0.447|0.072***|0.060**|0.085***|0.411|1,824|
||||(0.022)|(0.027)|(0.027)|[0.440]||
||||[0.013]|[0.086]|[0.018]|||
|**IGAs of other household members**||||||||
|Household head has an IGA|0.341|0.474|0.025|0.018|0.033|0.635|1,824|
||||(0.022)|(0.027)|(0.027)|[0.580]||
||||[0.309]|[0.503]|[0.287]|||
|Number of other HH members with an IGA|0.140|0.400|0.037*|0.070**|0.005|0.044|1,824|
||||(0.021)|(0.030)|(0.023)|[0.108]||
||||[0.142]|[0.078]|[0.681]|||
|Household head income (in Dinars)|133.822|230.264|9.575|4.384|14.765|0.503|1,824|
||||(10.947)<br>|(13.210)<br>|(13.620)<br>|[0.503]||
||||[0.419]|[0.619]|[0.333]|||
|Other HH members income (in Dinars)|64.950|198.728|5.883<br>|11.655<br>|0.111<br>|0.383<br>|1,824|
||||(9.392)|(11.774)|(11.193)|[0.419]||
||||[0.506]|[0.355]|[0.738]|||
|**Agriculture**||||||||
|Household has an agricultural IGA|0.062|0.241|0.027**|0.032**|0.021|0.566|1,824|
||||(0.012)|(0.016)|(0.015)|[0.533]||
||||[0.095]|[0.105]|[0.225]|||
|Used chemicals|0.018|0.156|0.017*|0.016|0.018|0.890|1,824|
||||(0.009)|(0.011)|(0.013)|[0.691]||
||||[0.131]|[0.225]|[0.225]|||
|Quantity produced|1.666|20.371|5.796***|6.810**|4.782**|0.542|1,824|
||||(1799)|(2665)|(2214)|[0513]||
||||.<br>[0.018]|.<br>[0.058]|.<br>[0.095]|.||
|Value of the production|8.736|76.197|11.316**<br>|15.043**<br>|7.588<br>|0.324<br>|1,824|
||||(4.564)|(6.478)|(5.318)|[0.355]||
||||[0.065]|[0.079]|[0.225]|||
|**Livestock**||||||||
|HH has a livestock IGA|0.187|0.390|0.048**|0.052**|0.044*|0.758|1,824|
||||(0.019)|(0.024)|(0.023)|[0.635]||
||||[0061]|[0092]|[0129]|||
|Total stock (in Dinars)|187.204|492.600|.<br>48.275*|.<br>56.399*|.<br>40.150|0.662|1,824|
||||(24.869)|(30.994)|(31.106)|[0.586]||
||||[0.120]|[0.135]|[0.251]|||
|Bought since January 2019 (in Dinars)|4.087|49.656|26.872***<br>(5442)|27.742***<br>(7473)|26.001***<br>(7557)|0.867<br>[0683]|1,824|
||||.<br>[0.001]|.<br>[0.008]|.<br>[0.011]|.||

Notes: This table reproduces the results from Table 1 excluding control variables and enumerator fixed effects. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

60
– Table A.18 Treatment effects on women’s empowerment and well-being

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women empowerment**||||||||
|Women agency index|-0.000|1.000|-0.059|-0.049|-0.069|0.737|1,824|
||||(0.045)|(0.053)|(0.054)|[0.619]||
||||[0.246]|[0.398]|[0.252]|||
|Partners’ views index|-0.001|0.999|.|.|0.051<br>(0.068)|.|830|
||||||[0.464]|||
|**Women fnancial index**|0.000|1.000|0.413***|0.399***|0.427***|0.852|1,824|
||||(0.084)|(0.134)|(0.091)|[0.681]||
||||[0.001]|[0.026]|[0.001]|||
|**Women wellbeing**||||||||
|Cantrill’s ladder||||||||
|Current life satisfaction|2.356|1.470|0.227***|0.264***|0.191**|0.512|1,824|
||||(0.074)|(0.092)|(0.093)|[0.503]||
||||[0.023]|[0.029]|[0.103]|||
|Life satisfaction one year ago|2.411|1.494|0.211***|0.268***|0.153*|0.286|1,824|
||||(0.074)|(0.092)|(0.091)|[0.334]||
||||[0.029]|[0.029]|[0.153]|||
|Predicted life satisfaction in three years|3.411|2.125|0.179*|0.226*|0.132|0.526|1,824|
||||(0.103)|(0.128)|(0.126)|[0.506]||
||||[0.145]|[0.142]|[0.340]|||
|Relative wealth|2.821|1.656|0.190**|0.254**|0.126|0.284|1,824|
||||(0.082)|(0.103)|(0.100)|[0.334]||
||||[0.079]|[0.065]|[0.256]|||
|Mental health (MHI-5 score)|43.154|19.500|1.216|2.590**|-0.158|0.030|1,824|
||||(0.911)|(1.095)|(1.121)|[0.095]||
||||[0.242]|[0.078]|[0.691]|||

Notes: This table reproduces the results from Table 2 excluding control variables and enumerator fixed effects. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

61
– Table A.19 Treatment effects on living standards, shocks, and migration

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Household living standards**<br>||||||||
|Total consumption per capita (in Dinars per day)|8.428|6.472|0.546*<br>|0.771*<br>|0.320<br>|0.316<br>|1,824|
||||(0.313)|(0.398)|(0.372)|[0.353]||
||||[0.145]|[0.120]|[0.419]|||
|Food consumption per capita (in Dinars per day)|3.582|3.004|0.334**|0.399**|0.269|0.532|1,824|
||||(0.145)|(0.183)|(0.174)|[0.506]||
||||[0.081]|[0.095]|[0.192]|||
|Non-food consumption per capita (in Dinars per day)|3.757|2.750|-0.005|-0.010|-0.001|0.964|1,824|
||||(0.129)|(0.162)|(0.152)|[0.730]||
||||[0.730]|[0.730]|[0.738]|||
|Asset index (stock)|0.000|1.000|0.104**|0.128**|0.080|0.509|1,824|
||||(0.049)<br>|(0.065)<br>|(0.057)<br>|[0.503]||
||||[0.099]|[0.117]|[0.225]|||
|Asset index (bought since 2019)|-0.000|1.000|-0.011|0.024|-0.046|0.249|1,824|
||||(0.045)|(0.056)|(0.053)|[0.297]||
||||[0.659]|[0.586]|[0.419]|||
|**Shocks and coping mechanisms**||||||||
|Economic shock|0.016|0.125|0.023***|0.016*|0.029***|0.308|1,824|
||||(0.008)|(0.009)|(0.010)|[0.346]||
||||[0.025]|[0.142]|[0.034]|||
|Other shock|0.250|0.433|-0.015|-0.038|0.009|0.090|1,824|
||||(0.020)|(0.024)|(0.025)|[0.153]||
||||[0.464]|[0.171]|[0.619]|||
|Extreme coping strategy|0.123|0.328|-0.043***|-0.039**|-0.046***|0.718|1,824|
||||(0.014)|(0.017)|(0.017)|[0.613]||
||||[0.025]|[0.079]|[0.034]|||
|Other coping strategy|0.152|0.359|0.000|-0.043**|0.042*|0.000|1,824|
||||(0.017)|(0.019)|(0.022)|[0.008]||
||||[0.738]|[0.081]|[0.120]|||
|**Migration**||||||||
|Household migration (since baseline)|0.094|0.295|-0.028**|-0.026*|-0.030**|0.777|2,000|
||||(0.012)|(0.015)|(0.015)|[0.652]||
||||[0.081]|[0.145]|[0.102]|||
|Individual migration (since 2019)|0.190|0.393|-0.002|-0.009|0.004|0.616|1,824|
||||(0.018)|(0.022)|(0.023)|[0.573]||
||||[0.691]|[0.596]|[0.681]|||
|Expected likelihood of future migration|0.054|0.226|0.035***|0.012|0.057***|0.016|1,824|
||||(0.012)|(0.014)|(0.016)|[0.073]||
||||[0.029]|[0.419]|[0.011]|||

Notes: This table reproduces the results from Table 3 excluding control variables and enumerator fixed effects. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

62
# **G Supplementary analysis from the pre-analysis plan**

## **G.1 Supplementary outcome variables**

The analysis in the paper focuses on variables that were categorized as primary or secondary outcomes in our pre-analysis plan. Our pre-analysis plan also listed a series of tertiary outcomes, which were categorized as such because the direction of impacts and the theory of change were unclear. We explore impacts on these outcomes in this section.

We find no effect on wage employment when comparing all women who received the cash grants and training program to women in the control group (Table A.20). However, we find some evidence that the cash grants and training program increased wageemployment opportunities, but only for women who participated alone to the training. By contrast, we find some evidence that women who received the cash grant and the joint training with their partners were more likely to search for a job in the year before the endline survey. We find no clear evidence of impact on socio-political outcomes, antisocial behavior, victimization, and disputes.

## **G.2 Heterogeneous treatment effects**

We study heterogeneous treatment effects by adding interaction terms between treatment status and possible moderators to equation (1). We consider the following set of moderators: (1) a dummy identifying women who had an IGA at baseline, (2) women’s marital status at baseline, (3) a dummy identifying households with above-median levels of consumption at baseline, and (4) a dummy identifying participants to the CWLP, a cash-for-work program that was implemented in rural Jendouba between 2012 and 2016.<sup>14</sup> Results are reported in Tables A.21 to A.23. For two main reasons, these results should be interpreted with caution. First, except for the CWLP dummy, the analysis does not exploit exogenous or quasi-exogenous variation in the moderators. Omitted variable bias could therefore bias the results. In fact, 11 out of the 14 significant interaction

> 14In our pre-analysis plan, we also committed to study heterogeneous treatment effects by baseline level of women’s agency. This analysis is not feasible because baseline data on women’s agency was only collected for the few women with an IGA at baseline.

63
terms in Tables A.21-A.23 lose statistical significance at conventional levels when the specification further includes interaction terms between the treatment status and all the baseline variables listed in the balance table (Table A.2). The discussion below focuses on the four interaction terms that are robust to the inclusion of these supplementary interaction terms. Second, Tables A.21-A.23 study 140 interaction terms, 14 of which are statistically significant at conventional thresholds. Very few of the terms would remain statistically significant if we were to implement adjustments for multiple testing.

Results suggest that women with an IGA at baseline were less likely to invest in livestock following their participation to the cash grant and training program. Women with an IGA at baseline were also more likely to face other shocks (disease or death of a household member) if they were in the treatment group. We find evidence that the positive effect on the number of other household members with an IGA is driven by households that had below-median consumption levels at baseline as well as households that participated to the CWLP program. Other results are not robust to the inclusion of interaction terms between the treatment status and all the baseline variables listed in the balance table.

We also studied heterogeneous treatment effects by predicted outcomes, using the repeat split-sample (RSS) endogenous stratification procedure of Abadie et al. (2018). Results are reported in Tables A.24 to A.26. Very few results are statistically significant at conventional thresholds, suggesting that treatment effects do not strongly depend on predicted outcomes. We note two exceptions. First, the estimated treatment effect on women’s agency is negative and statistically significant for women with high predicted levels of agency, while the same treatment effect is low and statistically insignificant for women with low or intermediate predicted levels of agency. Second, the estimated treatment effect on the number of other household members with an IGA seems to be driven by women living in households with intermediate or high predicted number of other household members with an IGA.

64
– Table A.20 Treatment effects on tertiary outcomes

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women wage employment**||||||||
|Is wage employed|0.039|0.195|0.005<br>(0.009)|0.020*<br>(0.012)|-0.010<br>(0.010)|0.018|1824|
|Income from wage employment|27.417|110.614|6.586<br>(5.410)|18.485**<br>(7.787)|-5.410<br>(5.531)|0.003|1824|
|**Women job search**||||||||
|Searched paid work (last 30 days)|0.135|0.342|0.015<br>(0.016)|-0.007<br>(0.019)|0.037*<br>(0.020)|0.045|1824|
|Searched paid work (last 6 months)|0.146|0.354|0.016|-0.003|0.036*|0.088|1824|
||||(0.016)|(0.019)|(0.020)|||
|Searched paid work (last 12 months)|0.153|0.360|0.025<br>(0.017)|0.004<br>(0.020)|0.047**<br>(0.021)|0.067|1824|
|**Socio-political outcomes**||||||||
|Social and collective action index|-0.000|1.000|-0.006<br>(0.041)|0.021<br>(0.055)|-0.033<br>(0.044)|0.346|1824|
|Community and civic engagement index|0.000|1.000|0.046<br>(0.045)|0.054<br>(0.060)|0.038<br>(0.048)|0.792|1824|
|Contribution tax game (in Dinars)|2.048|1.662|0.087<br>(0.076)|-0.002<br>(0.093)|0.177*<br>(0.094)|0.097|1824|
|**Antisocial behavior, victimization and disputes**||||||||
|Anti-social behavior index|0.000|1.000|0.008<br>(0.037)|0.002<br>(0.043)|0.014<br>(0.042)|0.776|1824|
|Pro-social behavior index|-0.000|1.000|0.067<br>(0.045)|0.051<br>(0.050)|0.084<br>(0.060)|0.601|1824|
|Victimization index|0.000|1.000|0.051|0.017|0.084|0.293|1824|
||||(0.045)|(0.048)|(0.062)|||
|Conficts index|-0.000|1.000|0.050|0.117|-0.017|0.119|1824|
||||(0.058)|(0.082)|(0.060)|||

Notes: This table reports the intent-to-treat effects of the interventions on tertiary outcomes. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

65
– Table A.21 Heterogeneous treatment effects on IGAs (IGAs)

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP pa|rticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Women’s IGAs**|||||||||
|Has an IGA|0.009|0.082|0.015|-0.005|-0.005|0.022|0.014|-0.024|
||(0.012)|(0.093)|(0.030)|(0.032)|(0.024)|(0.028)|(0.032)|(0.037)|
|Had an IGA before COVID|0.008|0.075|0.021|-0.013|0.004|0.009|-0.003|-0.007|
||(0.013)|(0.094)|(0.032)|(0.035)|(0.026)|(0.030)|(0.036)|(0.042)|
|Business proft (in Dinars)|0.688|-3.566|2.716|-2.587|-6.667|9.205|3.057|-0.075|
||(2.866)|(18.121)|(5.355)|(6.360)|(5.299)|(6.215)|(7.320)|(8.467)|
|Business employees|0.004|-0.019|0.046|-0.055|-0.006|0.011|0.000|0.010|
||(0.012)|(0.024)|(0.035)|(0.039)|(0.045)|(0.046)|(0.001)|(0.007)|
|Total income (in Dinars)|4.262|88.088|5.157|3.959|-8.546|21.392|10.892|-6.236|
||(6.529)|(64.843)|(16.889)|(18.607)|(13.350)|(15.678)|(19.300)|(21.408)|
|Has unused skills|0.081***|-0.015|0.129***|-0.063|0.033|0.062|0.099**|-0.009|
||(0.019)|(0.080)|(0.037)|(0.043)|(0.038)|(0.044)|(0.048)|(0.057)|
|**IGAs of other household members**|||||||||
|Household head has an IGA|0.030|-0.075|0.024|0.002|0.040|-0.017|0.022|0.064|
||(0.021)|(0.081)|(0.038)|(0.045)|(0.041)|(0.047)|(0.055)|(0.063)|
|Number of other HH members with an IGA|0.035*|0.078|0.104**|-0.086*|0.068**|-0.036|0.019|0.029|
||(0.021)|(0.094)|(0.044)|(0.050)|(0.034)|(0.041)|(0.053)|(0.064)|
|Household head income (in Dinars)|10.664|-19.308|13.702|-5.915|33.690*|-32.707|-0.374|39.375|
||(10.334)|(44.094)|(20.146)|(23.199)|(19.594)|(22.876)|(27.383)|(31.899)|
|Other HH members income (in Dinars)|6.530|0.019|36.142**|-39.149*|20.851|-18.431|-23.677|40.129|
||(8.829)|(39.879)|(17.929)|(20.471)|(15.113)|(18.291)|(21.787)|(26.529)|
|**Agriculture**|||||||||
|Household has an agricultural IGA|0.026**|-0.048|0.012|0.016|0.052**|-0.039|0.000|0.017|
||(0.011)|(0.049)|(0.020)|(0.023)|(0.022)|(0.025)|(0.023)|(0.030)|
|Used chemicals|0.020**|0.028|0.025*|-0.006|0.049***|-0.039*|-0.004|0.012|
||(0.009)|(0.051)|(0.015)|(0.018)|(0.019)|(0.021)|(0.006)|(0.018)|
|Quantity produced|5.916***<br>|2.744<br>|4.711*<br>|1.885<br>|8.699**<br>|-3.495<br>|-1.732<br>|7.091**<br>|
||(1.892)|(5.984)|(2.763)|(3.357)|(3.582)|(4.099)|(1.311)|(3.042)|
|Value of the production|12.467***|-5.894|13.789*|-2.077|25.268***|-17.778*|-1.216|7.936|
||(4.713)|(15.793)|(7.146)|(8.666)|(8.154)|(9.636)|(3.569)|(9.237)|
|**Livestock**|||||||||
|HH has a livestock IGA|0.054***|-0.137*|0.036|0.019|0.080**|-0.042|0.027|-0.023|
||(0.016)|(0.071)|(0.032)|(0.037)|(0.033)|(0.038)|(0.042)|(0.049)|
|Total stock (in Dinars)|52.636**|-150.175|43.039|0.759|96.804**|-66.153|6.280|-32.712|
||(23.737)|(97.529)|(43.991)|(50.571)|(48.985)|(54.413)|(45.966)|(55.976)|
|Bought since January 2019 (in Dinars)|28.005***|-6.491|13.836**|17.438**|53.911***|-35.004**|22.122**|-3.060|
||(5.563)|(10.664)|(6.741)|(8.785)|(15.114)|(15.634)|(9.748)|(13.039)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

66
– Table A.22 Heterogeneous treatment effects on women’s empowerment and well-being

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP par|ticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Women empowerment**|-0.035|-0.271|-0.234**|0.241**|-0.011|-0.049|-0.108|0.015|
||(0.040)|(0.180)|(0.103)|(0.110)|(0.076)|(0.088)|(0.091)|(0.112)|
|**Women fnancial index**|0.363***|1.024|0.764***|-0.468**|0.232***|0.233*|0.678***|-0.414|
||(0.080)|(0.891)|(0.230)|(0.235)|(0.086)|(0.140)|(0.244)|(0.269)|
|**Women well-being**|||||||||
|Cantrill’s ladder:|||||||||
|Current life satisfaction|0.264***|0.075|0.447***|-0.236|0.145|0.153|0.323*|0.063|
||(0.063)|(0.270)|(0.133)|(0.149)|(0.119)|(0.139)|(0.167)|(0.193)|
|Life satisfaction one year ago|0.218***|0.316|0.341***|-0.142|0.274**|-0.067|0.195|0.181|
||(0.061)|(0.273)|(0.129)|(0.145)|(0.116)|(0.135)|(0.150)|(0.177)|
|Predicted life satisfaction in three years|0.200**|0.415|0.381**|-0.213|0.168|0.059|0.453**|-0.152|
||(0.083)|(0.313)|(0.170)|(0.193)|(0.155)|(0.182)|(0.210)|(0.247)|
|Relative wealth|0.236***|0.213|0.450***|-0.272*|0.233*|0.005|0.384**|-0.097|
||(0.068)|(0.308)|(0.140)|(0.160)|(0.129)|(0.150)|(0.175)|(0.204)|
|MHI-5 score|1.245|4.284|0.973|0.494|0.180|1.528|-1.133|2.749|
||(0.800)|(3.520)|(1.713)|(1.933)|(1.441)|(1.704)|(1.937)|(2.345)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

67
– Table A.23 Heterogeneous treatment effects on living standards, shocks, and migration

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP pa|rticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Household living standards**|||||||||
|Total consumption per capita|0.707***|-1.032|1.095**|-0.546|1.018**|-0.511|0.637|0.086|
||(0.272)|(1.392)|(0.548)|(0.630)|(0.494)|(0.588)|(0.732)|(0.839)|
|Food consumption per capita|0.391***|-0.073|0.824***|-0.572**|0.494**|-0.161|0.239|0.060|
||(0.118)|(0.589)|(0.232)|(0.268)|(0.198)|(0.241)|(0.308)|(0.358)|
|Non-food consumption per capita|0.098|-0.358|-0.039|0.150|-0.114|0.254|0.044|-0.007|
||(0.122)|(0.610)|(0.259)|(0.292)|(0.227)|(0.267)|(0.327)|(0.381)|
|Asset index (stock)|0.136***|0.197|0.129*|0.016|0.092|0.061|0.045|0.159|
||(0.039)|(0.186)|(0.078)|(0.090)|(0.080)|(0.091)|(0.095)|(0.115)|
|Asset index (bought since 2019)|0.006|0.021|0.049|-0.055|-0.008|0.022|0.004|0.001|
||(0.043)|(0.148)|(0.088)|(0.098)|(0.093)|(0.105)|(0.073)|(0.097)|
|**Shocks and coping mechanisms**|||||||||
|Economic shock|0.023***|0.037|0.027*|-0.001|0.020|0.006|0.042**|-0.022|
||(0.008)|(0.032)|(0.016)|(0.018)|(0.014)|(0.016)|(0.021)|(0.025)|
|Other shock|-0.009|0.156*|-0.016|0.018|0.043|-0.060|0.060|-0.088|
||(0.019)|(0.087)|(0.039)|(0.045)|(0.038)|(0.043)|(0.049)|(0.058)|
|Extreme coping strategy|-0.034***|0.097|-0.007|-0.030|-0.005|-0.034|-0.057*|0.023|
||(0.013)|(0.064)|(0.027)|(0.031)|(0.025)|(0.029)|(0.034)|(0.041)|
|Other coping strategy|0.007|0.037|0.008|0.001|0.031|-0.029|0.039|-0.037|
||(0.016)|(0.075)|(0.033)|(0.038)|(0.030)|(0.035)|(0.041)|(0.049)|
|**Migration**|||||||||
|Individual migration (since 2019)|-0.001|0.052|0.010|-0.012|-0.007|0.013|0.028|-0.031|
||(0.019)|(0.075)|(0.038)|(0.043)|(0.035)|(0.041)|(0.051)|(0.059)|
|Expected likelihood of future migration|0.032***|0.010|0.018|0.018|0.033*|-0.001|0.048*|-0.021|
||(0.011)|(0.035)|(0.020)|(0.024)|(0.017)|(0.021)|(0.027)|(0.032)|
|**Observations**|1,824|1,824|1,824|1,824|1,824|1,824|949|949|

Notes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

68
– Table A.24 Treatment effects on IGAs (endogenous stratification)

||Pr|edicted outco|me|T-|test p-valu|es|
|---|---|---|---|---|---|---|
||(1)<br>Low|(2)<br>Medium|(3)<br>High|(4)<br>(1)-(2)|(5)<br>(1)-(3)|(6)<br>(2)-(3)|
|**Women’s IGAs**|||||||
|Has an IGA|-0.002<br>|0.007<br>|0.022<br>|0.671|0.393|0.596|
||(0.015)<br>|(0.015)<br>|(0.024)<br>||||
|Had an IGA before COVID|-0.004|0.002|0.028|0.776|0.315|0.423|
||(0.014)|(0.015)|(0.028)||||
|Business proft (in Dinars)|-1.096<br>|-3.044<br>|3.714<br>|0.682|0.457|0.267|
||(3.705)|(2.982)|(5.301)||||
|Business employees|0.010|0.003|0.000|0.698|0.774|0.939|
||(0.011)|(0.014)|(0.032)||||
|Total income (in Dinars)|1.323<br>|0.609<br>|17.044<br>|0.951|0.309|0.320|
||(7.124)|(9.202)|(13.723)||||
|Has unused skills|0.054**|0.067**|0.117***|0.731|0.129|0.244|
||(0.026)|(0.028)|(0.032)||||
|**IGAs of other household members**|||||||
|Household head has an IGA|0.005|0.070**|0.021|0.124|0.729|0.274|
||(0.030)|(0.030)|(0.034)||||
|Number of other HH members with an IGA|0.007<br>|0.036<br>|0.073*<br>|0.447|0.149|0.443|
||(0.025)|(0.029)|(0.039)||||
|Household head income (in Dinars)|16.444|18.503|-1.115|0.921|0.457|0.430|
||(13.641)|(15.679)|(19.270)||||
|Other HH members income (in Dinars)|-2.864|3.580|18.764|0.694|0.292|0.477|
||(10.820)|(12.314)|(17.453)||||
|**Agriculture**|||||||
|Household has an agricultural IGA|0.021|0.028**|0.030|0.682|0.744|0.946|
||(0.013)|(0.012)|(0.024)||||
|Used chemicals|0.032*|0.021**|0.014|0.588|0.549|0.800|
||(0.019)|(0.010)|(0.024)||||
|Quantity produced|4.735*|6.618**|7.720**|0.629|0.510|0.814|
||(2.627)|(2.883)|(3.690)||||
|Value of the production|17.123**|10.902*|12.879|0.499|0.759|0.881|
||(7.087)|(5.858)|(11.890)||||
|**Livestock**|||||||
|HH has a livestock IGA|0.039**|0.056**|0.058**|0.597|0.594|0.958|
||(0.020)|(0.024)|(0.029)||||
|Total stock (in Dinars)|36.177|47.126*|62.596|0.785|0.643|0.785|
||(28.728)|(28.107)|(49.232)||||
|Bought since January 2019 (in Dinars)|29.460***|25.474***|30.283***|0.737|0.953|0.691|
||(9.701)|(6.848)|(9.983)||||

Notes: N = 1,824. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

69
– Table A.25 Treatment effects on women’s empowerment and well-being (endogenous stratification)

||Pr|edicted outco|me|T-|test p-valu|es|
|---|---|---|---|---|---|---|
||(1)<br>Low|(2)<br>Medium|(3)<br>High|(4)<br>(1)-(2)|(5)<br>(1)-(3)|(6)<br>(2)-(3)|
|**Women agency index**|0.046<br>(0.057)|-0.033<br>(0.050)|-0.214**<br>(0.085)|0.297|0.011|0.068|
|**Women fnancial index**|0.430***<br>(0.137)|0.323***<br>(0.090)|0.465***<br>(0.139)|0.514|0.857|0.391|
|**Women well-being**|||||||
|Cantrill’s ladder:|||||||
|Current life satisfaction|0.213**<br>(0.085)|0.259***<br>(0.080)|0.291***<br>(0.105)|0.693|0.564|0.808|
|Life satisfaction one year ago|0.203**|0.265***|0.190*|0.639|0.930|0.592|
||(0.096)|(0.091)|(0.105)||||
|Predicted life satisfaction in three years|0.301**<br>(0.132)|0.236**<br>(0.108)|0.063<br>(0.130)|0.703|0.199|0.306|
|Relative wealth|0.317***|0.220**|0.220**|0.294|0.535|0.673|
||(0.111)|(0.108)|(0.111)||||
|MHI-5 score|1.801|1.223|1.538|0.733|0.880|0.839|
||(1.325)|(1.052)|(1.138)||||

Notes: N = 1,824. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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– Table A.26 Treatment effects on living standards, shocks, and migration (endogenous stratification)

||Pr|edicted outco|me|T-|test p-valu|es|
|---|---|---|---|---|---|---|
||(1)<br>Low|(2)<br>Medium|(3)<br>High|(4)<br>(1)-(2)|(5)<br>(1)-(3)|(6)<br>(2)-(3)|
|**Household living standards**|||||||
|Total consumption per capita|0.891*|0.579|0.264|0.608|0.343|0.615|
||(0.455)|(0.403)|(0.478)||||
|Food consumption per capita|0.548***<br>(0.182)|0.388**<br>(0.168)|0.106<br>(0.207)|0.518|0.108|0.290|
|Non-food consumption per capita|-0.045|0.049|0.169|0.721|0.445|0.645|
||(0.201)|(0.171)|(0.197)||||
|Asset index (stock)|0.095<br>(0.060)|0.171***<br>(0.059)|0.201***<br>(0.069)|0.367|0.250|0.746|
|Asset index (bought since 2019)|-0.010<br>(0.051)|0.028<br>(0.051)|0.020<br>(0.085)|0.598|0.761|0.939|
|**Shocks and coping mechanisms**|||||||
|Economic shock|0.031***<br>(0.010)|0.023***<br>(0.009)|0.019<br>(0.012)|0.542|0.455|0.803|
|Other shock|0.025<br>(0.028)|-0.007<br>(0.024)|-0.029<br>(0.037)|0.390|0.249|0.619|
|Extreme coping strategy|-0.029<br>(0.018)|-0.033*<br>(0.017)|-0.022<br>(0.023)|0.898|0.799|0.710|
|Other coping strategy|0.021<br>(0.021)|0.013<br>(0.021)|-0.010<br>(0.030)|0.778|0.393|0.528|
|**Migration**|||||||
|Household migration (since baseline)|-0.007<br>(0.010)|-0.008<br>(0.010)|0.014<br>(0.017)|0.943|0.298|0.269|
|Individual migration (since 2019)|0.031<br>(0.025)|-0.019<br>(0.024)|0.007<br>(0.031)|0.141|0.547|0.497|
|Expected likelihood of future migration|0.032**|0.039***|0.033|0.717|0.973|0.824|
||(0.013)|(0.012)|(0.022)||||

Notes: N = 1,824. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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