metadata: authors: 0: author: Bedi,Tara Sylvia
1: author: Buehren,Niklas
2: author: Goldstein,Markus P.
3: author: Ketema,Tigist Assefa
count: Ethiopia
docna: 0: docna: Shifting Spousal Decision-Making Patterns :
            Whom You Target in an Agricultural Intervention Matters
docty: Policy Research Working Paper
theme: Financial Infrastructure and Access,Other Excluded Groups,Job Creation,Gender,Human Development and Gender,Data Development and Capacity Building,Rural Development,Social Protection,Social Development and Protection,MSME Finance,Environment and Natural Resource Management,Finance,Enterprise Development,Private Sector Development,Public Sector Management,Biodiversity,Land Administration and Management,MSME Development,Urban and Rural Development,Jobs,Landscape Management,Data production, accessibility and use,Renewable Natural Resources Asset Management,Social Inclusion,Rural Markets,Social Safety Nets
abstracts: cdata!: Does it matter whether poverty reduction
            programs target the female or male spouse? A randomized
            controllled trial in Ethiopia is used to study the
            differential impacts of easing information and financial
            constraints on agricultural productivity and household
            welfare, using data from 1,214 households in two regions of
            Ethiopia. The program targeted the husband, the wife, or
            both in a married household. The results indicate that the
            targeted spouse determines the type and channel of impacts.
            Targeting both spouses increased agricultural productivity
            in the short run and the monetary value of small ruminants
            and poultry in the long run, with a marginal positive impact
            on nonfood expenditure. Targeting only the female spouse
            resulted in increased business income from businesses with
            female involvement. This consequently increased household
            use of formal savings devices. This is in line with female
            preferences outside agriculture and for off-farm activities,
            and it results in little impact on agricultural
            productivity, despite an increase in women&#x2019;s
            access to extension services. Targeting only the male spouse
            has no impact on household savings or expenditure even
            though it increases men&#x2019;s wage income. The
            results suggest that the sharing of knowledge about the
            intervention changed household decisions. This would explain
            the different outcomes when both spouses were targeted,
            rather than only one.
display_title: Shifting Spousal Decision-Making
            Patterns : Whom You Target in an Agricultural Intervention Matters
listing_relative_url: /research/2023/12/34218381/shifting-spousal-decision-making-patterns-target-agricultural-intervention-matters
topicv3: Agriculture Extension m1325967 1009,Gender and Economic Empowerment m1327622 911,Gender and Rural Development m1504519 4203
publishtoextweb_dt: 2023-12-18T00:00:00Z
dois: 10.1596/1813-9450-10651
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 53
author_keys: 0: author_key: 761452
1: author_key: 1176301
2: author_key: 769522
3: author_key: 1174044
model_extractions: Policy Research Working Paper 10651 Shifting Spousal Decision-Making Patterns Whom You Target in an Agricultural Intervention Matters _Tara Bedi Niklas Buehren Markus Goldstein Tigist Ketema_ Africa Region Gender Innovation Lab December 2023
Policy Research Working Paper 10651

# **Abstract**

Does it matter whether poverty reduction programs target the female or male spouse? A randomized controllled trial in Ethiopia is used to study the differential impacts of easing information and financial constraints on agricultural productivity and household welfare, using data from 1,214 households in two regions of Ethiopia. The program targeted the husband, the wife, or both in a married household. The results indicate that the targeted spouse determines the type and channel of impacts. Targeting both spouses increased agricultural productivity in the short run and the monetary value of small ruminants and poultry in the long run, with a marginal positive impact on nonfood expenditure. Targeting only the female spouse resulted in increased business income from businesses with female involvement. This consequently increased household use of formal savings devices. This is in line with female preferences outside agriculture and for off-farm activities, and it results in little impact on agricultural productivity, despite an increase in women’s access to extension services. Targeting only the male spouse has no impact on household savings or expenditure even though it increases men’s wage income. The results suggest that the sharing of knowledge about the intervention changed household decisions. This would explain the different outcomes when both spouses were targeted, rather than only one.

This paper is a product of the Gender Innovation Lab, Africa Region. 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 tketema@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Shifting Spousal Decision-Making Patterns: Whom You Target in an Agricultural Intervention Matters**

**The Impact of the Farmer’s Innovation Fund in Ethiopia**

Tara Bedi, Niklas Buehren, Markus Goldstein, and Tigist Ketema<sup>1</sup> Keywords: Gender, Innovation fund, Agriculture, Ethiopia JEL Classification: J16, Q14, Q16

> 1 Buehren : World Bank;  nbuehren@worldbank.org; Bedi: Trinity College; bedit@tcd.ie; Goldstein: World Bank; <u>mgoldstein@worldbank.org; Ketema: World Bank;  tketema@worldbank.org.This paper is a product of the Gender</u> Innovation Lab, Office of the Chief Economist, Africa Region. The authors would like to thank Chimdo Anchala and Alaka Holla for helping design and formulate this evaluation. Amare Teklu Yirbecho and Nathalie Guilbert provided valuable early research assistance. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank Group and its member countries.
# **1. Introduction**

Agricultural growth is a critical driver for decreasing poverty in developing countries as it has a proportionately larger impact on poorer households than on the rest of society (Kilic et al. 2015). According to the World Bank (2008), GDP growth through agriculture is twice as effective in reducing poverty as growth through other sectors. Even more strikingly, investment in agriculture is 3.2 times better at decreasing extreme poverty (Christiaensen et al. 2011). Agricultural investment is especially important for countries where a large portion of farmers are smallholders. In Sub-Saharan Africa, more than 90 percent of the extreme poor are involved in agricultural activities (O'Sullivan et al. 2014). However, a lack of knowledge about new agricultural technologies and a lack of resources tend to constrain individuals from innovating in agriculture.

Gains in agricultural growth can be even more effective if the gender gap in agricultural productivity is also addressed. Women account for 43 percent of the agricultural labor force in developing countries (FAO 2011). However, they are less likely to have access to agricultural training and extension meetings (Quisumbing et al. 2014, Farnworth et al. 2015). Women also face a gap in credit (Huyer 2016). And this means lower yields: in a six-country study on agricultural productivity, O’Sullivan et al. (2014) find that women were 13 to 25 percent less productive per hectare than their male counterparts. The FAO (2011) estimates that addressing the gender gap in agriculture could increase total agricultural productivity by 2.5–4 percent, globally. At a country level, the returns from tackling the gender gap can be sizable. For example, in Malawi, it was estimated that addressing this gender gap could add USD 100 million to its GDP while lifting 238,000 people out of poverty (World Bank 2015).

This paper adds to the literature on the agricultural gender gap by looking at how addressing both financial and information constraints for agricultural households affects their agricultural productivity, livestock investments, and off-farm income. We study the Innovation Fund Project (IFP) in Ethiopia, an intervention designed to decrease the risk associated with trying new agricultural activities by increasing information access while simultaneously decreasing financial constraints. The intervention design allows us to disentangle the effect of easing these constraints for women versus men versus both. We map how men’s and women’s decisions and behaviors differ in reaction to whom the intervention has targeted within the household.

2
The majority of Ethiopia’s population is still rurally based and smallholders contribute over 95 percent of the agricultural output (Abebaw and Haile 2013). As with many countries in SubSaharan Africa, there is a large gender gap in agricultural productivity. Aguilar et al. (2015) found a 23.4 percent difference between men’s and women’s agricultural productivity in Ethiopia. Limited access to and low participation in agricultural extension services have been highlighted as major barriers to women’s economic empowerment in Ethiopia (World Bank 2010). According to O’Sullivan et al. (2014), a lack of financial and material resources limits not only women’s access to agricultural extension services, but also their ability to translate theoretical knowledge into practice. This suggests that the provision of agricultural extension services will be more effective in increasing productivity and reducing the gender gap in Ethiopia if it is coupled with inputs and financial resources.

The Innovation Fund Project (IFP), a two-stage agricultural extension and financial package intervention, ran from 2010 to 2011 in the Amhara and Tigray regions of Ethiopia. In the first stage, a Farmer Innovation Fund (FIF) was run in 51 kebeles across these two regions.<sup>2</sup> Each kebele had six eligible innovation proposals that were randomly allocated to control or treatment arms. To address the gender gap in FIF, the government of Ethiopia, together with the Gender Innovation Lab (GIL) of the World Bank, later added a pilot program, called the Women Innovation Fund (WIF) in 2011. The WIF formed the second stage of the intervention this paper is studying. From the 51 FIF kebeles, 20 were randomly selected to receive WIF funding. Each FIF and WIF treatment group received Br 3,000 (USD 220) and Br 10,000 (USD 730), respectively. Even though the intervention is designed to increase agricultural innovation, there are no formal conditions attached to the use of these funds.

Through analyzing whether the recipient’s gender matters for household productivity and welfare measures in agricultural interventions, as well as tracking the mechanisms behind these impacts, this paper makes an important contribution to the targeting literature. Recent studies on cash transfers have found little difference in education, production, consumption, and investment outcomes from targeting women versus men (Benhassine et al. 2015; Haushofer and Shapiro 2016). Other work shows that the gender of the recipient matters. For example, Akresh et al. (2016)

> 2 The kebele is the smallest administrative unit in Ethiopia.

3
find that while cash transfers to mothers did not lead to any significant difference, transfers to fathers improved young children’s health, increased livestock and cash crop investment, and led to improvements in housing.

We use a panel of 1,214 married households that we track over three rounds of data collection. Using an Analysis of Covariance (ANCOVA) model, we find that whom you target determines the type of impact achieved and the channels through which this impact is realized. From our results, we find that women’s access to extension services increases when only the male or female spouse is targeted, though the size of the increase is larger when the female spouse is targeted directly. We see no similar result for men, which could be because they already have higher access levels. Targeting both spouses also has no effect on access to extension services.

Similarly, we find that the gender of the targeted individual is important for the take-up of new technology. We find a significant increase in the take-up of new technology by women across all agricultural domains when only the male spouse is targeted. We also find a positive and significant increase in men’s overall technology take-up, and specifically for livestock, when the male spouse is targeted. However, when the female spouse is targeted directly, there is no effect on her technology take-up and instead, we find less adoption of technology by men in farming methods. Targeting both spouses does little for women but does have a complementary effect on men’s takeup of new technology in farming methods.

Targeting both spouses initially creates a positive interaction effect on agricultural productivity. This could be driven by the positive effect on technology use in farming methods for the men when both spouses are targeted. This impact is no longer significant by the endline, although it does remain positive. When only one spouse is targeted, we find no significant effect on agricultural productivity.

For livestock outcomes, targeting both spouses is better than just targeting one. Targeting both spouses has a positive effect on the stock value of small ruminants and poultry. Just targeting the female spouse has no similar effect, and actually has a negative effect on the stock value of poultry.

The impact of targeting the female spouse occurs outside of agriculture. Income from femalemanaged businesses increases when the female spouse is targeted by WIF. This suggests that 4
women are revealing their preferences by investing in nonagricultural activities where they potentially have more control over returns, as Swaminathan et al. (2009) found in Malawi. We do not see a similar impact when the male spouse is targeted. However, targeting both has a significant negative effect on income from female-managed businesses.

While we find no evidence that either intervention increases the likelihood of having wage employment, targeting either spouse individually causes an increase in the men’s wage income. Again, targeting both spouses is not as effective as targeting just one spouse. In contrast, it has a negative and significant effect on the men’s wage income.

Finally, when we look at measures related to savings and household welfare, we find that targeting the female spouse increases the household’s formal savings as reported by the husband. Regarding expenditure, none of the interventions have an impact on food expenditure, although targeting both spouses has a marginal positive effect on nonfood expenditure at the endline.

While we find some initial impact on agricultural productivity, the main impacts of these two interventions are found in off-farm and livestock activities. These results suggest that there is a need to take gender preferences into account when designing interventions. If increasing household welfare is the ultimate goal, the intervention design needs to provide space for these preferences to be acted on. In future studies, it would be important to understand if such impacts are a result of a change in preferences or if this is an outcome of bargaining.

The remainder of the paper is structured as follows. In Section 2, we discuss the country context and the related literature. Next, in Section 3 we outline the research design, followed by Section 4 where we describe our data and the baseline balance checks. Section 5 lays out our empirical strategy, including our research hypotheses, while we present our results and robustness checks in Sections 6 and 7. We conclude in Section 8.

# **2. Related Literature and the Ethiopian Context**

High agricultural growth in low-income countries can significantly reduce unemployment and poverty (for example, Mellor and Dorosh 2010). Agriculture is central to tackling poverty in Ethiopia, as it accounts for 75 percent of employment, constitutes 40 percent of GDP, and makes 5
up 80 percent of exports (USAID 2021).<sup>3</sup> With farmers facing limited access to improved agricultural technology, as well as limited dissemination and adoption of these technologies, there is a need to understand what can trigger take-up of technology to help increase productivity. This is important because currently most agricultural sector growth in Ethiopia comes from increases in the area cultivated rather than intensified production (Buehren et al. 2019).

In the agriculture sector in Ethiopia, while women’s share of agricultural labor is estimated to be between 29 percent (Palacios-Lopez et al. 2017) and 45.5 percent (FAO 2011), they have limited access to productive resources such as extension services and finance. Female farmers are less likely to access extension services than their male counterparts, and the quality of these services tends to be lower, which further affects the likelihood of technology adoption and rate of input use (Ragasa et al. 2012). These constraints affect women’s productivity (O'Sullivan et al. 2014), as well as their ownership of livestock (Kinati and Mulema 2020).

Therefore, men and women may face different levels of constraint on innovation in agriculture. If these constraints vary by gender, then easing knowledge and financial constraints for men and women separately may have differing effects on individual behavior and household welfare outcomes. This could also impact decisions related to livestock and poultry, where women’s bargaining power is related to whether her own resources are used in the purchase of them (Kinati and Mulema 2020). However, even if she contributed to the purchase of productive resources, this does not necessarily guarantee more bargaining power or the right to control the resources. Evidence from Ethiopia suggests that when productive resources, including large livestock, are jointly owned, control of the asset lies with the head of the household (Fafchamps and Quisumbing 2002).

Furthermore, women’s status and preferences within a household influence what agricultural activities they do and therefore what services they need. For instance, women may prefer agricultural advice from other women. A study from Tanzania found that both male and female farmers preferred female extension officers because they believed that female extension workers provided better agricultural information, and more female farmers were reached once more women were placed as extension officers (Due et al. 1997). On the other hand, from a field experiment in

> 3 https://www.usaid.gov/ethiopia/agriculture-and-food-security

6
Malawi, BenYishay et al. (2020) found that farmers were less willing to learn from female communicators (or lead farmers) because they perceived that female communicators were not as good at farming as their male counterparts. In that experiment, selected communicators were assigned to learn about new agricultural technology with the objective that they would in turn persuade other farmers to adopt it.

There has been a growing body of literature on what mechanism works for addressing the gender gap in agriculture. A study on Ethiopia found that expanding extension services and making them more responsive to farmers’ needs helped farmers move toward more commercial and marketable crops and that both female- and male-headed households were equally likely to benefit from these services (Buehren et al. 2019). Another study from Malawi found that participation in an input subsidy program improved productivity for both male and female farmers (Karamba and Winters 2015).

Yet, both studies found that neither intervention was able to overcome the gender gap in agricultural productivity. This could in part be due to lower initial endowments in labor and capital for women (Buehren et al. 2019). It could also be due to structural issues, such as differential returns for women from agricultural extension services (Aguilar et al. 2015). Finally, interventions that do not take gender preferences and responsibilities into account are less likely to succeed at addressing this gap (Doss 2018).

While the government of Ethiopia has put in place policies to address the gender gap in agriculture, gender equality is far from being achieved. Limited access to and low participation in the agricultural extension services have been raised as major barriers to women’s economic empowerment in Ethiopia (World Bank 2010). While women have access to certain extension services, such as horticulture and poultry, access to other extension services, such as credit, is more limited (Cohen and Lemma 2013; FAO 2019). Access is further limited by the design of these services, where most extension agents are male, and the scheduling of these services fails to take women’s time burdens into account (Cohen and Lemma 2013). Only 17 percent of development agents in Ethiopia are female (FAO 2019). Buchy and Basaznew (2005) found that women in Ethiopia comprised a small proportion of the farmers who received extension service training. Therefore, as mentioned in the introduction, linking agricultural extension services with inputs and 7
financial resources may be a more efficient way to increase productivity and reduce the gender gap.

# **3. Research Design**

As part of its efforts to promote agricultural growth, the Government of Ethiopia implemented the Rural Capacity Building Project (RCBP) in 2006. This sought to strengthen agricultural extension services, making it more responsive to the needs of smallholder farmers. It was launched in 10 regions of the country.

As a subcomponent of RCBP, the Innovation Fund Project (IFP) was implemented from 2010 to 2011. The first stage of the IFP is the Farmer Innovation Fund (FIF) program. In 2010 FIF provided funds directly to farmer groups for implementing innovative agricultural ideas. By easing financial constraints for the farmers, the FIF sought to substantially cushion any risks associated with trying out new and innovative ideas. In addition, by linking the farmers directly to development agents (DAs), who deliver extension services, the intervention was also increasing farmers’ access to extension service information.

With the assistance of the DAs, 10–15 farmers would form a group based on their interests, ideas, and willingness to work together. For married couples, by default, the head of the household became a member of the group. In the majority of households, this was the husband. As a result, 98 percent of FIF participants were male.<sup>4</sup> Groups were formed within a kebele. The FIF intervention covered a total of 51 kebeles in the Amhara and Tigray regions.

With the assistance of the DAs, each group developed innovation proposals that outlined their objectives and planned activities. These proposals were then screened by the DAs and woreda<sup>5</sup> officials based on how innovative the idea was, whether the proposal addressed a gap in the regular extension services, and the expected economic return from the innovation. In each kebele, six proposals were selected.

> 4 With this in mind, when we discuss FIF, we refer to it as targeted to the male spouse. In our robustness checks, we drop any female-headed households and find that the results remain consistent.

> 5 The woreda is one administrative level above the kebele (and two below the region).

8
Of these six eligible proposals, three were randomly allocated to the treatment arm of the study, while the remainder served as controls. Each of the resulting 153 treatment groups then received about Br 3,000 (USD 220).<sup>6</sup> In addition, the treatment groups received technical support from the DAs while implementing their project. The members of the treatment FIF group were expected to bear 5 percent of the nonlabor costs of implementing their ideas. Control groups did not receive any funding or technical support for their proposal.

As mentioned above, FIF had mostly male participants. The Government of Ethiopia and the World Bank Gender Innovation Lab therefore later designed a second component to this agricultural intervention that specifically targeted women. The Women Innovation Fund (WIF) was created not only to increase women’s participation but also to address some of the identified drivers behind women’s low productivity in agriculture. By increasing women’s access to extension services, and easing associated financial constraints, the WIF intended to generate empirical evidence on the impact this had on agricultural productivity, as well as off-farm incomegenerating activities and household welfare.

The WIF was implemented in the FIF kebeles. Of the 51 FIF kebeles, 20 were randomly allocated to receive the WIF. The WIF randomization used the household list from the FIF baseline survey as its sampling frame. Within each of the 20 treatment kebeles, 10 to 15 women were randomly selected from FIF eligible households to form a WIF group.<sup>7</sup> As previously for the FIF, the DA helped each group develop a proposal for its own income-generating activity. The 20 treatment groups each received roughly Br 10,000 (USD 730) in addition to the technical support from DAs while implementing their projects.<sup>8</sup> The money was used to finance the group’s income-generating activity. It should be noted that the WIF funding was more than three times the amount of funding that FIF groups received, and we have 3 FIF groups per kebele while it is only one WIF group. In addition, the FIF group is formed by self-selected farmers while the WIF members are randomly selected from FIF eligible households.

> 6 Thus, for an FIF group of 12 farmers, this means USD 18.33 on average per member. 7 These WIF households were from either the treatment or control arms of FIF. 8 For a WIF group of 12 women, this averaged to USD 60.83 per member.

9
Both FIF and WIF groups had similar types of proposed activities. These activities included crop production, natural resource management, livestock and poultry rearing, apiculture, and off-farm activities.

# **4. Data and Baseline Discussion**

In order to study the effect of these two interventions on agricultural productivity and income, three rounds of data were collected to form a panel data series from 1,595 households, of which 1,214 are used for the analysis in this paper. Round 1, the baseline, was collected from August to September 2010 and covered 2,675 households. Randomization only occurred after the baseline had been collected. Only 1,806 households that were members of a FIF group with an eligible proposal were kept in the study and followed up through to the endline. One and a half years after FIF was implemented, and a year after WIF, midline data were collected (March to April 2012). Finally, in June 2013 endline data were collected. In this section, we discuss existing baseline differences in our main outcome variables.

From the original sample of 1,806 FIF eligible households, 1,595 households were interviewed in all three rounds and 1,214 of them are used for this study. In our analysis, we only used households that satisfied our selection criterion: married couples.<sup>9</sup> Our sample has a total of 634 FIF and 158 WIF households. From these two groups, there are a total of 76 households that receive both FIF and WIF treatments. In addition, we have a total of 498 pure control households. Figure A1 in the appendix lays out the study treatment arms.

Our study sample is primarily male-headed, as shown in Table A1 in the appendix, where 98 percent of our sample at baseline was male-headed. At baseline, the average age of the household head was 44.5, with an average household size of 6.4 members and 0.03 members enrolled in school.

One critical issue for the robustness of our results is the comparability of households at baseline, prior to intervention. In appendix Table A2 , we present the baseline balance checks between treatment and control households for the FIF, WIF, and the sample that received both FIF and

> 9 The 381 single-headed households were dropped from the study as they did not satisfy the selection criterion for our analysis—married couples—which targets either female or male spouses or both.

10
WIF. Some of the differences between control and treatment groups are statistically significant.<sup>10</sup> However, when we look at the normalized differences between treatment and control groups, all but six<sup>11</sup> of the outcome indicators have a normalized difference below the rule of thumb value of 0.25 (Imbens, and Wooldridge 2009).<sup>12</sup> Hence, for a significant majority of our outcome indicators, we establish a balanced sample at the baseline of treatment and control households. The balance issue for the few indicators which are not balanced is also somewhat addressed by using an Analysis of Covariance (ANCOVA) specification, which controls for any baseline differences of outcome indicators.

Taking a brief look at the summary statistics, we disaggregate access to extension service information and use of new technology by gender in Table A1. In our sample, we see that men have much greater access to extension services, where 71 percent of male respondents received extension service advice, compared to 32 percent of female respondents at baseline. Similarly, at baseline, men are at least twice as likely to have used new technology for agriculture, livestock or farming than women.

# **5. Empirical Specification**

Our main empirical estimation investigates the direct effect of targeting the male spouse and the female spouse, as well as targeting both spouses, with an agricultural intervention comprising information and finance. We first estimate the impact of the two interventions on mechanisms that affect income and productivity outcomes. The relevant mechanisms through which the intervention can affect our outcomes are access to extension service information, use of new technologies, and the likelihood of having an off-farm income-generating activity. As we survey both spouses in a household, we can differentiate how the interventions impact men and women respectively in that targeted household. Next, we use the same specification to estimate the outcomes, which include agricultural productivity, the stock value of livestock and poultry, and

> 10 For example, of 51 baseline outcome indicators, we find significant differences in six between FIF and control households, five for WIF households, and six for the sample that received both FIF and WIF.

> 11 The normalized difference between treatment and control households is above 0.25 only for two outcome indicators for the WIF sample, whereas for the households that received both FIF and WIF it is above 0.25 for four variables.

> 12 We examine normalized differences in addition to t-statistics because normalized differences are not affected by the sample size. Also, regarding the rule of thumb, normalized difference values larger than 0.25 indicate that the estimates may be sensitive to the functional form of the linear regression.

11
household income from off-farm activities. Finally, we estimate whether these outcomes translate into any changes in household welfare by looking at the impact on food and nonfood expenditure.

We estimate the impact of FIF and WIF at two points in time, first in 2012, one and half years after FIF was implemented and a year after WIF rollout (Round 2), then in 2013 (Round 3). Estimating the impact at two points in time helps us see dynamics over time and to look at when the impacts occurred.

In this paper, we capture the intention-to-treat (ITT) effects using the Analysis of Covariance (ANCOVA) as follows: _Equation 1:_ _Yit = δRoundi3 + β1FIFRoundi2 + β2FIFRoundi3 +_ γ _1WIFRoundi2 +_ γ _2WIFRoundi3 +_ χ _1WIF*FIFRoundi2 +_ χ _2WIF*FIFRoundi3+ + αXi0_ + _εit_ ii0 σσYY Equation 1 captures our estimation specification. _Yit_ is our outcome of interest for household _i_ at time _t._ The variables _FIF_ and _WIF_ capture the treatment status, which equals 1 if a household member is participating, respectively, in a FIF and/or WIF group that received a fund and zero otherwise. _Roundt_ is an indicator variable for the data collection round, with _Round2_ equal to 1 for midline, and _Round3_ equal to 1 for endline. This enables us to estimate treatment outcomes separately for _Round2_ and _Round3_ . The coefficient _δ_ captures the time effect between the two rounds. is the outcome of interest at baseline _._ We also include a vector of control variables, _Xi0_ , that covers baseline sociodemographic characteristics including the sex of the household head, ii0 YY household size, age of the household head, and number of students in the household. This vector also includes controls for the region in which the household _i_ lives. Finally, _εit_ is our statistical error term and standard errors are clustered at the kebele level.

Coefficients _β1_ and _β2_ capture the extent to which targeting the male spouse via the FIF program impacts our specified outcomes in _Round2_ and _Round3_ , respectively. Similarly, coefficients γ _1_ and γ _2_ are the impact estimators of targeting the female spouse via WIF on our outcomes of interest in 12
_Round2_ and _Round3_ , respectively. On the other hand, coefficients χ _1 and_ χ _2_ tell us the additional impact of targeting both the male and female spouse in _Round2_ and _Round3_ , respectively.

Given this paper’s focus on estimating the differential impact of the intervention when it targeted the male spouse, the female spouse or both, we control for whether a household is female-headed. We do this because, first, such households may have been targeted by both WIF and FIF.<sup>13</sup> Second, these households may behave differently, as the woman will be the main decision-maker in the house and therefore decisions on how the resources from these interventions are used could be different.

Finally, we check the robustness of our results by rerunning our regressions with woreda<sup>14</sup> fixed effects to address any local differences that might affect the results. We also rerun the regressions by dropping observations with female household heads from our sample.

# **6. Results**

Based on our empirical specification, we test three groups of hypotheses. First, we explore the mechanisms that could explain how the interventions impact our productivity and revenue measures by looking at the male and female spouses’ uptake of extension services, technology adoption and off-farm activities. This enables us to better understand the channels through which these interventions have an impact. Second, we look at whether the interventions actually impact agricultural productivity and off-farm income. For this, we look at agricultural productivity and income at the household level, and off-farm activity both by gender and household. Finally, we look at whether there is any impact on household savings and welfare.

# **6.1 Mechanisms for Change**

The interventions mitigate information constraints on a household by facilitating access to extension providers and services, such as classroom training or the demonstration plot at the Farmer Training Center (FTC). As can be seen in Table 1, we find evidence that for women, as

> 13 Though of 25 female-headed households, only three received WIF (of which one received both WIF and FIF).

> 14 The woreda is one administrative level above the kebele (and two levels below the region).

13
long as at least either spouse was targeted, their access to extension improved, although the size of the impact did depend on who was targeted. When the male spouse is targeted through FIF, women are three percentage points more likely to have received advice in Round 2 compared to the control group, and this difference increases to five percentage points for Round 3. Households targeted via WIF saw a larger increase in access to extension services: by endline women were 14.2 percentage points more likely to have received extension advice compared to control households. One reason for this larger increase could be mechanical, women targeted by WIF worked directly with the DA to develop their income-generating proposals. This direct contact with extension service staff could explain women’s increased likelihood of accessing extension services.

However, we do not see any impact on men, regardless of who is targeted. This could be because, compared to women, men already have greater access to extension in Ethiopia (FAO 2019), as was also evident from our baseline data. Women are starting from a lower base—only 31 percent of women in control households accessed extension services at baseline, compared to 58 percent of men in control households (Table 1). When both spouses are targeted, our interaction term is not significant. This suggests no potential synergy from targeting both spouses for increasing access to extension services.

14
_Table 1: Impact on Access to Extension_

||Received ad<br>(ye<br>|vice from FTC<br>s=1)<br>|
|---|---|---|
||Men|Women|
|round 3|-0.028|0.028*|
||(0.033)|(0.015)|
|FIF*round 2|0.017|0.031***|
||(0.027)|(0.010)|
|FIF*round 3|0.018|0.051**|
||(0.037)|(0.020)|
|WIF*round2|-0.041|0.049|
||(0.038)|(0.033)|
|WIF*round3|0.085|0.142**|
||(0.060)|(0.057)|
|WIF*FIF*round2|0.035|0.037|
||(0.065)|(0.047)|
|WIF*FIF*round3|-0.064|0.053|
||(0.080)|(0.080)|
|Observations|2,428|2,428|
|R-squared|0.018|0.059|
|Estimates: FIF2 + WIF2 + FIF_WIF2|0.011|0.117|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.827|0.004***|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.039|0.246|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.503|0.000***|
|Control Mean(BL)|0.576|0.308|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household and region. FTC stands for Farmer Training Center. ***, **, and * indicate significance at the one, five and 10 percent critical level.

Next, we turn our attention to the impact of these two interventions on technology adoption ( Table 2). Through easing both information and financial constraints for households, we would expect that the interventions decrease the associated barriers to trying new technologies. We find that for technology adoption—unlike the impact on access to extension services—who was targeted does matter. Targeting the male spouse via FIF significantly increased men’s take-up of new technology overall, with an increase of 5.5 percentage points by round 3 (significant at 10 percent). This result is driven by innovation in livestock. Targeting the male spouse also increases women’s take-up of new technology overall in round 3 (significant at 10 percent), and it is driven by innovations in livestock, crops and farming methods. This strong impact contributed to the positive joint effect 15
on men and women’s take-up of livestock technology at the endline when both spouses were targeted, as can be seen by the result at the bottom of the table.

However, targeting the female spouse via WIF does not increase the take-up of new technology by women. At endline it actually has a negative effect on the male spouse’s take-up of new technology for farming methods.

Targeting both spouses only has a complementary effect on men’s take-up of new technology on farming methods at both midline and endline. The combination of targeting both spouses seems to be offsetting the negative impact on men’s take-up when the household is only targeted by WIF. We find a positive joint effect at the endline of men’s take-up of farming methods when both spouses are targeted, though we cannot reject the null hypothesis (that the overall effect is equal to zero).

These results imply that targeting the male spouse is the main way to influence technology adoption. Targeting the woman via WIF has little impact, and at worst negatively impacts men’s take-up of new farming techniques.

16
_Table 2: Impact on Technology Take-up_

||Tried new tec<br>by the D|h introduced<br>A (yes=1)|Live|stock|Tried new tec<br>Cr|h on: (yes=1)<br>op|Farming|Method|
|---|---|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|Men|Women|
|round 3|0.090***|0.088***|0.058*|0.098***|0.100***|0.106***|0.086***|0.082***|
||(0.029)|(0.032)|(0.030)|(0.034)|(0.028)|(0.031)|(0.025)|(0.027)|
|FIF*round 2|-0.027|-0.031|-0.042*|-0.019|-0.018|-0.002|-0.024|-0.003|
||(0.030)|(0.034)|(0.022)|(0.025)|(0.030)|(0.031)|(0.025)|(0.024)|
|FIF*round 3|0.055*|0.044*|0.069**|0.056*|0.037|0.056**|0.021|0.048**|
||(0.032)|(0.025)|(0.029)|(0.028)|(0.030)|(0.024)|(0.021)|(0.022)|
|WIF*round2|-0.032|0.032|-0.041|0.028|-0.061|0.010|-0.073|0.026|
||(0.060)|(0.062)|(0.044)|(0.042)|(0.054)|(0.051)|(0.044)|(0.058)|
|WIF*round3|-0.049|-0.044|-0.026|-0.022|-0.052|-0.060|-0.123***|-0.032|
||(0.058)|(0.037)|(0.042)|(0.040)|(0.052)|(0.040)|(0.044)|(0.038)|
|WIF*FIF*round2|0.070|0.083|0.073|0.025|0.076|0.010|0.094*|0.009|
||(0.077)|(0.071)|(0.063)|(0.066)|(0.069)|(0.057)|(0.055)|(0.061)|
|WIF*FIF*round3|-0.006|0.075|0.100|0.085|0.016|0.046|0.105*|0.026|
||(0.090)|(0.069)|(0.079)|(0.073)|(0.079)|(0.063)|(0.056)|(0.063)|
|Observations|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.040|0.028|0.053|0.040|0.036|0.030|0.042|0.030|
|Estimates: FIF2 + WIF2 + FIF_WIF2|0.011|0.084|-0.009|0.034|-0.003|0.017|-0.002|0.032|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.849|0.205|0.851|0.430|0.947|0.762|0.963|0.555|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.000|0.075|0.143|0.12|0.002|0.042|0.004|0.042|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.997|0.208|0.043**|0.058*|0.980|0.447|0.947|0.439|
|Control Mean(BL)|0.363|0.211|0.196|0.077|0.230|0.164|0.245|0.100|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household and region. DA stands for Development Agent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

17
The final channel through which the interventions could impact households is through the types of income-generating activities they engage in. There is no binding enforcement mechanism to ensure that intervention finance is spent on agricultural activities. Therefore, the interventions could affect the household’s likelihood of using these resources for nonagricultural incomegenerating activities, including business and wage employment activities. This outcome could occur if households want to diversify their income sources, if they believe the return from these activities is higher than from their agricultural activities, and/or if the recipient prefers an activity that is more likely to be in their locus of control.

In Table 3 we look at whether the two interventions affect the likelihood of the household having an off-farm activity. When the female spouse is targeted, the likelihood of the household having a business increases by the endline by 6.4 percentage points (significant at the 10 percent level). When the male spouse is targeted, we find no impact on the likelihood of a household having a business. One potential reason could be that the financial constraint for these households was not eased sufficiently as FIF provided less funding compared to WIF. WIF groups, formed by the female spouse, received three times more funding than did FIF groups formed by the male spouse. Targeting both spouses has no additional impact. This on the other hand could be an argument against a simple capital story, given that these households received both the FIF and WIF funding. It could also be related to how shared information on the interventions affects the types of decisions made. We find that none of the interventions have any impact on the likelihood of a household having wage employment.

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_Table 3: Impact on Likelihood of Having Household Off-farm Income-generating Activity_

||Have business<br>(yes=1)|Wage-employed<br>(yes=1)|
|---|---|---|
|round 3|-0.076***|-0.076**|
||(0.024)|(0.031)|
|FIF*round 2|0.040|-0.011|
||(0.029)|(0.026)|
|FIF*round 3|-0.012|0.017|
||(0.025)|(0.025)|
|WIF*round2|0.012|0.067|
||(0.046)|(0.078)|
|WIF*round3|0.064*|-0.015|
||(0.038)|(0.050)|
|WIF*FIF*round2|0.058|-0.075|
||(0.069)|(0.088)|
|WIF*FIF*round3|-0.036|0.016|
||(0.056)|(0.066)|
|Observations|2,428|2,428|
|R-squared|0.098|0.143|
|Estimates: FIF2 + WIF2 + FIF_WIF2|0.110|-0.018|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.064*|0.824|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.015|0.018|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.736|0.782|
|Control Mean(BL)|0.306|0.715|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household and region. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **6.2 Productivity and Income Outcomes**

Having looked at the mechanisms through which the interventions could impact the male and female spouses’ activities, we now turn to the interventions’ impact on household level agricultural productivity, the stock value of livestock and poultry, and off-farm revenue.

# **_Agricultural Productivity_**

We first assess the impact of the interventions on agricultural productivity at household level by looking at the total area of cultivated land, the total value of production per hectare (productivity), 19
and the income they generate through the sale of agricultural produce.<sup>15</sup> Given that we found an increase in access to extension services and the adoption of new technologies, we might expect a positive change in agricultural productivity.

We find that in order to increase agricultural productivity, it is important to target both spouses (Table 4). The interaction effect of WIF and FIF is positive on agricultural productivity in the short run. In addition, the combined effect of targeting both spouses on the household’s total agricultural productivity is positive and the joint significance test confirms that this result is significantly different from zero at midline. One explanation for this increase at midline is that targeting both spouses has a complementary effect on the household’s cultivated land and total harvest value. Another potential explanation for increased productivity at midline is via increased take-up of new farming methods and technology by men when both spouses are targeted—for which we found evidence in Table 2.

By endline, we find that while targeting both spouses still has a sizeable effect on agricultural productivity, the coefficient is insignificant. Importantly though, the joint effect on agricultural productivity from targeting the household with both WIF and FIF is positive, sizable and significant at the 10 percent level. This result is driven by both the interaction effect and WIF. Similar to midline results, one potential explanation for this result could be our result in Table 2 which indicated that when both spouses were targeted, the joint effect was an increase in men’s take-up of new farming technologies. Also, increased access to extension services for the female spouse, as found in Table 1, could be contributing to the impact of WIF on agricultural productivity.

Despite the increase in technology take-up as indicated in Table 2, targeting just one spouse has either a negative effect or none at all. We find no significant impact on household agricultural productivity when we target the male spouse (Table 4). Targeting the female spouse results in a decrease in cultivated land at round 2, although this does not impact household agricultural productivity. One possible reason would be the intensity of the adopted technologies, especially

> 15 The WIF and FIF were implemented at different times. Hence, if the weather was different during the implementation of the two interventions, the decisions made might be different. Unfortunately, we do not have data on the weather, and our regression model did not control for it. Therefore, our results might be affected as the weather is an important factor for agricultural productivity in Ethiopia.

20
given the positive effect of the interventions on agricultural productivity for the interaction term and the joined effects. On the other hand, for the female spouse, one possible explanation would be a lack of control over agricultural outputs. Controlling the final output than the process might be more important to invest her time and resources. Future studies might be benefited if they look into the intensity of the technology tried and the decision-making power of the female spouse on agricultural outputs.

_Table 4: Impact on Agricultural Productivity_

||Productivity<br>(birr/ha)|Cultivated<br>land (ha)|Value of<br>harvest (birr)|Total crop<br>sale (birr)|
|---|---|---|---|---|
|round 3|1,342.883***|0.184|1,293.943***|495.901**|
||(342.614)|(0.221)|(480.581)|(216.961)|
|FIF*round 2|-374.879|-0.045|-70.964|199.867|
||(313.285)|(0.082)|(305.701)|(170.585)|
|FIF*round 3|-331.530|0.182|-61.910|52.874|
||(436.389)|(0.183)|(495.877)|(272.181)|
|WIF*round2|-33.265|-0.397***|-515.366|-436.536|
||(624.458)|(0.142)|(591.326)|(267.706)|
|WIF*round3|1,106.025|0.081|764.276|723.916|
||(996.272)|(0.354)|(919.612)|(705.185)|
|WIF*FIF*round2|2,311.051***|0.296*|1,199.889*|7.982|
||(822.542)|(0.155)|(636.566)|(435.012)|
|WIF*FIF*round3|460.993|-0.629|-39.290|-476.813|
||(1,081.062)|(0.406)|(1,030.121)|(815.535)|
|Observations|2,428|2,428|2,428|2,428|
|R-squared|0.100|0.045|0.248|0.118|
|Estimates: FIF2 + WIF2 + FIF_WIF2|1903|-0.146|613.600|-228.700|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.003***|0.311|0.220|0.392|
|Estimates: FIF3 + WIF3 + FIF_WIF3|1235|-0.367|663.100|300|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.099*|0.055*|0.461|0.472|
|Control Mean(BL)|4936|2.302|5887|1165|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. Productivity is calculated by dividing the value of harvest by the size of cultivated land. Productivity, cultivated land, the value of harvest, and crop sales are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **_Livestock_**

We expect increased extension service information and the easing of financial constraints could lead to an increase in the reported value of livestock and poultry. Turning to Table 5, we find that such an increase only happens when both spouses are targeted.

21
We find that the combined effect of targeting both spouses via FIF and WIF is positive and large on the stock value of small ruminants and poultry (Table 5). Households that received both FIF and WIF saw an increase of Br 2,135 in the value of their small ruminant stock; this is almost double the baseline mean of Br 1,109 for the control households. We reject the null hypothesis in the case of small ruminants, though not for poultry. One channel that may have contributed to this effect is the positive joint effect on the take-up of livestock technology when both spouses were targeted. Importantly for this outcome, prior research from Ethiopia suggests that while these types of assets can be jointly owned, the control of such assets is in the hands of the household head (Fafchamps and Quisumbing 2002). Given that over 97 percent of the married household sample has male household heads, our result suggests that when both spouses are targeted, they invest in an asset that lies within his control.

However, targeting either the male or female spouse decreases the stock of poultry. Even though the intervention targets the female spouse, she is unable to increase the value of her poultry, an asset usually within her domain. This result, especially when the female spouse is targeted, suggests a takeover by the male spouse. The increase in the amount of savings reported by the male spouse could be one indication of such distortion of resources away from the female spouse. See Table 7.

22
_Table 5: Impact on Livestock_

|||Value of: (birr)<br>||HH has a<br>crossbre|t least one<br>ed: (yes=1)<br>|
|---|---|---|---|---|---|
||Large<br>livestock|Small<br>ruminant|Poultry|Cattle|Small<br>ruminant|
|round 3|5,684.438***|1,205.643***|68.052***|0.018|-0.010|
||(684.361)|(225.308)|(21.546)|(0.016)|(0.007)|
|FIF*round 2|64.113|-118.481|13.340|0.013|0.005|
||(659.644)|(215.374)|(21.148)|(0.018)|(0.009)|
|FIF*round 3|-605.558|49.192|-44.239**|-0.007|0.003|
||(853.432)|(297.188)|(17.822)|(0.022)|(0.008)|
|WIF*round2|868.754|237.752|-78.689***|0.074|0.040*|
||(1,275.121)|(412.703)|(26.310)|(0.047)|(0.023)|
|WIF*round3|-966.294|-892.280|-79.424***|0.043|-0.011**|
||(1,514.593)|(535.148)|(28.768)|(0.040)|(0.005)|
|WIF*FIF*round2|-1,470.739|-723.321|28.114|-0.002|-0.040|
||(1,728.361)|(626.306)|(55.762)|(0.055)|(0.027)|
|WIF*FIF*round3|2,870.331|2,978.463***|163.771***|0.030|0.010|
||(2,096.013)|(1,032.280)|(49.499)|(0.043)|(0.015)|
|Observations|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.369|0.208|0.122|0.113|0.007|
|Estimates: FIF2 + WIF2 + FIF_WIF2|-537.900|-604|-37.230|0.084|0.005|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.629|0.152|0.501|0.030**|0.854|
|Estimates: FIF3 + WIF3 + FIF_WIF3|1298|2135|40.110|0.066|0.002|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.371|0.021**|0.347|0.150|0.909|
|Control Mean(BL)|7608|1026|91.95|0.084|0.012|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. The values of large livestock, small ruminant and poultry are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **_Off-farm Income_**

For our final economic activity measure, we look at off-farm income (see Table 6). Although both interventions are designed to strengthen agricultural and livestock productivity in agricultural households, we found positive impacts on off-farm activities when either spouse is targeted. Targeting the female spouse in round 3 resulted in a significant positive effect on income from businesses that are managed or run by a woman. Income from these businesses increased by Br 974, which is 2.4 times the mean value of the same category of income for control households at baseline. Meanwhile, targeting either the male or the female spouse results in an increase in household wage income by the endline.

Targeting both spouses undoes both the business and wage impacts from targeting either spouse, and results in a significant negative interaction effect on income from businesses with female involvement. One reason for this could be that as the male spouse is also targeted, he may have 23
more information on the amount she received from WIF and on her project. This could then affect what type of activity she invests in, and the amount allocated to it.

When the male spouse is targeted, there is a significant positive impact on the household wage income, though there is no impact on the likelihood of the household having a wage income (see Table 3). This increase is driven by the significant positive increase in the male wage income. When the female spouse is targeted, it also has a significant positive impact on male wage income, though not wage income of the household as a whole. When both spouses are targeted, there is a significant negative interaction effect on men’s wage income, as well as on household wage income.

_Table 6: Impact on Off-farm Income_

||Bus|iness income (b<br>Ml|irr)<br>Fl|W|age income (bir|r)|
|---|---|---|---|---|---|---|
||Household|ae<br>Involved|emae<br>Involved|Household|Male|Female|
|round 3|76.321|-49.913|255.581|-4.625|98.255|29.392|
||(188.476)|(168.807)|(183.463)|(144.739)|(111.357)|(68.959)|
|FIF*round 2|365.768|228.147|222.286|-158.059|-15.834|-108.257|
||(239.300)|(231.391)|(184.740)|(110.766)|(86.415)|(81.515)|
|FIF*round 3|128.142|54.330|-71.649|279.876**|313.946***|46.971|
||(167.992)|(149.915)|(185.314)|(129.067)|(107.459)|(71.972)|
|WIF*round2|133.627|126.369|70.332|106.956|351.390|115.817|
||(488.915)|(494.487)|(256.201)|(293.143)|(231.216)|(153.292)|
|WIF*round3|794.135|745.616|974.264**|485.318|543.118*|110.809|
||(591.934)|(496.611)|(477.290)|(296.562)|(279.745)|(138.493)|
|WIF*FIF*round2|-135.500|-350.027|19.630|-112.445|-431.851|-75.048|
||(572.702)|(606.551)|(502.686)<br>-|(369.843)|(272.420)|(176.085)|
|WIF*FIF*round3|-225.808|-225.935|1,376.317**|-698.679*|-901.911**|-4.692|
||(778.345)|(661.222)|(573.448)|(387.249)|(354.416)|(180.453)|
|Observations|2,428|2,317|2,396|2,428|2,317|2,396|
|R-squared|0.057|0.072|0.025|0.079|0.073|0.060|
|Estimates: FIF2 + WIF2 + FIF_WIF2|363.900|4.489|312.200|-163.500|-96.290|-67.490|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.210|0.988|0.418|0.530|0.474|0.605|
|Estimates: FIF3 + WIF3 + FIF_WIF3|696.500|574|-473.700|66.510|-44.850|153.100|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.236|0.204|0.073*|0.840|0.809|0.301|
|Control Mean(BL)|574.800|451.700|284.900|850.400|632.500|342|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. All the income indicators are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

24
# **6.3 Welfare Impacts**

# **_Savings_**

As our first welfare measure, we look at household savings in Table 7. We start by looking at whether the interventions affected the types of savings devices used and find that there is no impact.<sup>16</sup> When we look at the value of savings held, we find that targeting the female spouse increased household savings in formal institutions. As we collect this information from both men and women, we can also look at whether there is any disparity in the results depending on who reported it. When the female spouse is targeted via WIF, the increase in male reported household savings in round 3 is twice the increase in female reported household savings. Furthermore, this result is only significant for savings reported by the male spouse.<sup>17</sup> One potential reason could be that men are more likely to access formal financial tools. In Ethiopia, there is a gender gap in access to formal bank accounts. More than 45 percent of men have a bank account, compared to 29 percent of women.<sup>18</sup> This gap also suggests an information gap between spouses on the amount of money in a bank account, even when an increase in savings is linked to the female spouse being targeted via WIF.

Alongside this increase in formal savings, we also see a decrease in the amount of money set aside using other<sup>19</sup> savings tools when the female spouse is targeted in round 3. The male spouse reports around Br 76 less in informal savings places. This suggests that the household is substituting formal savings mechanisms for informal ones. Given the magnitude of the increase in formal savings (Br 1,185.6), this story is not just about the substitution of savings tools. This increase in formal savings could also reflect increased precautionary savings for future income shocks or for future investments, or potentially a shift between savings and expenditure.

> 16 We asked both men and women what financial (savings) tools the household uses. We create a household dummy that takes a value of one if either the male or female spouse stated that someone in the household has a particular tool.

> 17 This is not comparing targeting the male versus female spouse, it is just comparing formal savings reported by the male versus female spouse when the female spouse is targeted by WIF.

> 18 <u>https://blogs.worldbank.org/africacan/financial-inclusion-in-ethiopia-10-takeaways-from-findex-2017</u>

> 19 Other savings include savings in other informal institutions such as _eqqub_ or _iddir_ (traditional ways of group saving systems in Ethiopia).

25
_Table 7: Impact on Household’s Likelihood of Using a Savings Device & Level of Savings by Type of Device_

||HH has<br>|savings in: (yes<br>|=1)<br>|Bank/<br>|Am<br>MFI<br>|ount of saving<br>R<br>|s in:<br>SCC<br>|O<br>|ther<br>|
|---|---|---|---|---|---|---|---|---|---|
||Bank/MFI|RSCC|Other|Male|Female|Male|Female|Male|Female|
|round 3|0.122***|0.004|-0.022|52.903|127.520|77.706**|82.536***|1.474|17.116|
||(0.033)|(0.029)|(0.020)|(165.290)|(139.451)|(36.847)|(28.054)|(30.067)|(13.380)|
|FIF*round 2|-0.006|0.006|0.008|-162.859|-97.796|13.798|50.851**|-44.805*|-2.640|
||(0.022)|(0.026)|(0.016)|(166.269)|(179.314)|(18.751)|(19.040)|(24.879)|(7.551)|
|FIF*round 3|-0.008|0.044|-0.016|322.457|240.554|37.104|-19.603|-26.981|-19.741|
||(0.030)|(0.027)|(0.015)|(237.506)|(194.287)|(44.193)|(34.429)|(33.143)|(14.811)|
|WIF*round2|-0.003|-0.014|-0.015|27.427|-169.133|-21.249|33.532|-82.752***|-16.541|
||(0.043)|(0.045)|(0.041)|(336.408)|(258.751)|(33.626)|(35.954)|(24.727)|(11.880)|
|WIF*round3|-0.016|0.031|-0.005|1,185.622**|624.322|-68.833|-48.723|-75.689***|-15.779|
||(0.053)|(0.070)|(0.031)|(563.074)|(374.630)|(59.899)|(47.621)|(23.812)|(29.962)|
|WIF*FIF*round2|-0.003|0.024|0.073|-72.613|-289.268|70.102|-35.382|137.207*|70.630**|
||(0.056)|(0.060)|(0.072)|(441.727)|(319.206)|(64.171)|(43.411)|(69.858)|(35.083)|
|WIF*FIF*round3|0.114|-0.062|0.057|-778.447|-416.698|53.109|104.212|82.623|19.826|
||(0.091)|(0.086)|(0.041)|(911.166)|(655.143)|(76.494)|(70.175)|(53.957)|(39.593)|
|Observations|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.091|0.074|0.011|0.133|0.054|0.042|0.044|0.016|0.008|
|Estimates: FIF2 + WIF2 + FIF_WIF2|-0.012|0.016|0.066|-208|-556.200|62.650|49|9.650|51.450|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.811|0.757|0.330|0.495|0.006***|0.266|0.195|0.897|0.187|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.089|0.012|0.035|729.600|448.200|21.380|35.890|-20.050|-15.690|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.142|0.791|0.376|0.238|0.370|0.804|0.673|0.691|0.564|
|Control Mean(BL)|0.123|0.161|0.048|373.500|163.600|58.840|40.030|16.980|12.510|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. MFI stands for Microfinance Institutions. RSCC stands for Rural Savings and Credit Cooperative. The amount of savings in Bank/MFI, RSCC, and other saving schemes are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

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# **_Household Welfare_**

For the final part of our analysis, we look at whether there is any impact on household welfare (see Table 8). We find no impact on food expenditure when either one or both spouses are targeted. This might be due to the fact that farmers in Tigray and Amhara regions are mainly engaged in crop production and do not spend much on food. When both spouses are targeted, we find that by endline households have increased their nonfood expenditure in the last twelve months—including school fees, agricultural service costs, clothing and household fabric— though this result is only significant at the 10 percent level. This result suggests an important added value for targeting both spouses, in addition to the effects of targeting either spouse individually. When we look at the joint effect, we find that it increased the nonfood spending by Br 1,358. To put that into context, the mean nonfood expenditure for control households at baseline was Br 4,618. Importantly, we can reject the null hypothesis that this joint effect is equal to zero. We find no similar effect when only one spouse is targeted.

When the female spouse is targeted, we find an initial significant negative impact on the values of productive assets. The size of this impact holds at the endline, though is no longer significant. This decrease in productive assets could potentially explain the increased savings reported when the female spouse is targeted via WIF.

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_Table 8: Impact on Household Welfare_

|||Household ex<br>|penditure on:<br>||
|---|---|---|---|---|
||Food|Nonfood<br>Items<sup>20</sup>|Production<br>Assets<sup>21</sup>|Consumer<br>Assets<sup>22</sup>|
|round 3|43.114**|719.410***|491.314|927.430***|
||(20.071)|(182.553)|(305.688)|(243.909)|
|FIF*round 2|9.992|-109.109|-151.678|-39.243|
||(15.386)|(250.716)|(299.787)|(214.353)|
|FIF*round 3|21.213|3.951|-337.491|-160.438|
||(23.135)|(294.774)|(400.873)|(275.181)|
|WIF*round2|31.089|-138.285|-589.934*|-425.624|
||(40.016)|(333.434)|(331.368)|(458.609)|
|WIF*round3|60.343|227.114|-522.029|-556.553|
||(47.467)|(416.846)|(524.702)|(366.886)|
|WIF*FIF*round2|-33.814|387.243|158.585|568.251|
||(44.657)|(524.301)|(462.332)|(630.851)|
|WIF*FIF*round3|-67.018|1,127.125*|615.481|907.723|
||(58.757)|(589.203)|(642.087)|(637.544)|
|Observations|2,428|2,428|2,428|2,428|
|R-squared|0.067|0.105|0.221|0.240|
|Estimates: FIF2 + WIF2 + FIF_WIF2|7.267|139.800|-583|103.400|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.788|0.768|0.194|0.797|
|Estimates: FIF3 + WIF3 + FIF_WIF3|14.540|1358|-244|190.700|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.727|0.019**|0.675|0.730|
|Control Mean(BL)|161|4618|1412|889.200|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. Food exp is defined as total household food expenditure in the last seven days. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. The values of all the outcome indicators above are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **7 Robustness Checks**

We run three robustness checks, first with woreda fixed effects, second, with only male-headed households, and third, by controlling for land ownership and decision-making power of the female spouse.

# **_Results with Woreda Fixed Effects_**

While our main specification controls for regional effects, there may be concerns that more local drivers are affecting the results. Therefore, we rerun our regressions with woreda fixed

> 20 Nonfood expenditure includes school fees, clothing, transport agricultural service expenses, building materials, as well as batteries, soap, wood, electricity payments, haircuts, and so forth.

> 21 Production Assets include plow, sickle, axe, hoe, spade, beehive, water pump, tiller, and so forth.

> 22 Consumer Assets include table, chair, bed, radio, television, phone, stove, jewelry, bicycle, cart, car, firearms and so forth.

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effects. A woreda is one administrative level above a kebele (and two levels below a region). Our overall results hold when we rerun the regressions with woreda fixed effects, though there are some changes in the size and significance of the coefficients. The five main differences are the following. First, we lose significance on the take-up of new technology for the male spouse when both spouses are targeted. Second, we lose our significant effect on the household having a business when the female spouse is targeted. Third, our effect on household wage income is now significant when either spouse is targeted. Fourth, our results on formal savings hold but are now only significant at the 10 percent level. Finally, our results on nonfood expenditure when both spouses are targeted become more significant at the 5 percent level.<sup>23</sup>

# **_Results without Female-headed Households_**

For our second robustness check, we rerun our regressions, dropping all female-headed households from our sample. Given that we have a proportion of married households (2 percent) who are female headed, these households may have different decision-making processes and have a different level of bargaining power. This may impact what mechanisms these households respond to and their final welfare outcomes. We find that the results on extension service (Table A 1), take-up of new technologies (Table A 2), livestock (Table A 5), wage income (Table A 6), and savings (Table A 7) for male-headed households are in line with the results from our full sample.

There are four main differences between our results. Targeting the female spouse no longer has a significant impact on the household having a business. At midline, we find that targeting the female spouse results in a significant negative impact on total crop sales, as well as increasing the likelihood of households owning crossbreed cattle. Finally, we no longer have a significant impact on household nonfood expenditure when both spouses are targeted, though the coefficient is still positive. We still find a positive joint effect and we can reject the null hypothesis (see Table A 8).

# **_Controlling for Land Ownership and Decision-making Power of the Female Spouse_**

In our main specification, targeting the female spouse via WIF does not increase women’s take-up of new technology despite its positive impact on access to extension services. To better

> 23 The results from these regressions are not in the paper but are available on request.

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understand whether these results are driven by the women’s ownership and decision-making power over the land, we rerun the same analysis but by controlling for the number of parcels the female spouse is controlling and the number of parcels where she makes decisions on agricultural activities.<sup>24</sup> The results remain the same for extension outcomes while there are a few differences in the size and significant level of coefficients from the technology tack-up variables. The two main differences are: (1) we find a positive joint effect on the likelihood of the female spouse trying new technology introduced by the DA, and (2) the likelihood of the male spouse trying new technology on farming method is no longer significant when both spouses are targeted.

Since extension services and technology take-up are defined as the mechanisms to increase agricultural productivity (see section 6.1), we rerun the agricultural productivity outcome variables to see if there is any difference after controlling for land ownership and decisionmaking variables.

When both spouses are targeted, the effect on the size of cultivated land is no longer significant at the midline while the decline by the endline becomes statistically significant at 10%. In addition, the value of harvest at the midline is now statistically significant when both spouses are targeted. Finally, the joint effect of the interventions on the value of harvest is now significant both at the midline and endline.<sup>25</sup>

# **8 Conclusion**

We contribute to the literature by showing that whom you target in a married household for agricultural interventions matters for the type of outcomes achieved. We also show that the reason for these different outcomes is that whom you target also determines the channel through which the impact is generated. Our results indicate that to impact agricultural productivity, it is important to target both spouses, which translates into an increase in take-

> 24 Note that the sample size has declined from 2,428 to 2,355 due to missing values for ownership and decisionmaking variables. Of the women in our sample, 68.5% and 72.7% either alone or jointly own the land and make decisions on the land, respectively.

> 25 The results from these regressions are not in the paper but are available on request.

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up of farming technology and a corresponding increase in agricultural productivity. In order to increase business income, targeting the female spouse is instrumental because it increases the likelihood of having a business and the income generated from any business run or managed by a woman. To strengthen the stock value of livestock, it is necessary to target both spouses; this increases the stock value of small ruminants and poultry. To increase household wage income, it is important to target the male spouse.

Whom you target also affects what type of welfare impact is achieved. Targeting both spouses increased nonfood expenditure on services, including school fees and agricultural services. Targeting the female spouse increased household level savings in formal savings devices.

In addition, our results suggest that targeting one spouse, rather than both, affects the type of decision made. This could well reflect a different levels of shared information and accountability between spouses when both are recipients, by contrast with targeting only one. For instance, targeting both spouses has a negative effect on men’s wage income, though this effect is positive when only one spouse is targeted. Similarly, targeting just the male or female spouse has a negative effect on the stock value of poultry, even though targeting both increases the stock value. These results have important policy implications because they underline the need to account for gender preferences and household gender roles during policy design. While increasing agricultural productivity may be the proximal goal of an intervention, the ultimate goal is to improve household welfare. If so, women may have different preferences for how they want to improve the welfare of their household. If interventions enable these preferences to be acted on, then the overall goal of improved household welfare may still be achieved, albeit not via the intended channel.

Importantly, when both members of a couple are targeted, their preferences may change, causing a change in the activities engaged in by the household. Targeting both spouses decreases the information gap between the spouses with regard to the interventions at hand. Our results suggest that this leads to different household decisions compared to when only one spouse is targeted.

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One additional policy consideration is related to the result of increased savings in formal bank accounts when the female spouse is targeted. This result is observed from savings reported by the male spouse, but no similar increase is found when reported by the female spouse. One potential reason for this result is the gender gap in access to financial tools. Future interventions could usefully offer access to financial tools for the targeted beneficiary. This would also give targeted beneficiaries more control of both the initial cash transfer and any income, including earnings from off-farm income-generating activities.

We encounter two important limitations with this study. First, our sample size for WIF, which targeted the female spouse, was small. Second, our two interventions vary in the size of their financial component and the time of their implementation. These may have had implications for choices made by both spouses and what preferences they revealed. A further extension of this study could involve a similar intervention targeting a larger sample of households, particularly for the intervention targeting the female spouse. An extension of this study would help to understand if there are any learning effects from the previous FIF and WIF activities. It would also be interesting to see the outcome if the intervention design for both spouses were the same. The results might perhaps prove to be more in line with those typically seen in the cash transfer literature, particularly if the same amount of money at the same time was transferred, further to an analogous group formation process.

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