metadata: authors: 0: author: Brudevold-Newman,Andrew Peter
1: author: Donald,Aletheia Amalia
2: author: Rouanet,Lea Marie
count: Cote d'Ivoire
docna: 0: docna: Steered Away from the Fields : Short-Term
            Impacts of Oxen on Agricultural Production and
            Intra-household Labor Supply
docty: Policy Research Working Paper
theme: Rural Markets,Agriculture Finance,Trade Facilitation,Economic Policy,Rural Development,Environment and Natural Resource Management,Finance for Development,Finance,Private Sector Development,Biodiversity,Rural Infrastructure and service delivery,Land Administration and Management,Urban and Rural Development,Jobs,Trade,Landscape Management,Renewable Natural Resources Asset Management
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
abstracts: cdata!: Mechanization has the potential to boost
            agricultural production and reduce poverty in rural
            economies, but its impacts remain poorly understood. This
            paper randomizes the subsidized provision of a pair of
            traction oxen among 2,546 farmers in C&#xf4;te
            d&#x2019;Ivoire through a matching grant. The analysis
            finds positive impacts on households&#x2019;
            agricultural production during the agricultural season
            overlapping with oxen delivery, and additional increases in
            total land holdings and use of complementary inputs in the
            subsequent season. The intervention affected household
            members in different ways, with wives and daughters
            substantially reducing their work on the
            farm&#x2014;especially in districts with more stringent
            gender norms around handling oxen. In these districts,
            introducing traction oxen resulted in women shifting to
            off-farm work. The intervention also improved
            girls&#x2019; health and reduced school dropout among
            boys. The results provide novel evidence on the human
            development effects of mechanization, while highlighting how
            social prescriptions mediate the impacts of technology
            within the household.
display_title: Steered Away from the Fields :
            Short-Term Impacts of Oxen on Agricultural Production and
            Intra-household Labor Supply
listing_relative_url: /research/2023/06/34109758/steered-away-fields-short-term-impacts-oxen-agricultural-production-intra-household-labor-supply
EnvironmentalCategory: B
topicv3: Impact Evaluation for Evidence-Based
            Policy m1326030 1157,Agriculture Technology m1326423 1950,Poverty m1326687 695,Gender m1327621 615
publishtoextweb_dt: 2023-06-30T00:00:00Z
dois: 10.1596/1813-9450-10516
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1415577
1: author_key: 1312021
2: author_key: 1393660
model_extractions: Policy Research Working Paper 10516

# Steered Away from the Fields

Short-Term Impacts of Oxen on Agricultural Production and Intra-household Labor Supply _Andrew Brudevold-Newman Aletheia Donald Léa Rouanet_ Africa Region Gender Innovation Lab June 2023
Policy Research Working Paper 10516

# **Abstract**

Mechanization has the potential to boost agricultural production and reduce poverty in rural economies, but its impacts remain poorly understood. This paper randomizes the subsidized provision of a pair of traction oxen among 2,546 farmers in Côte d’Ivoire through a matching grant. The analysis finds positive impacts on households’ agricultural production during the agricultural season overlapping with oxen delivery, and additional increases in total land holdings and use of complementary inputs in the subsequent season. The intervention affected household members in different ways, with wives and daughters substantially reducing their work on the farm—especially in districts with more stringent gender norms around handling oxen. In these districts, introducing traction oxen resulted in women shifting to off-farm work. The intervention also improved girls’ health and reduced school dropout among boys. The results provide novel evidence on the human development effects of mechanization, while highlighting how social prescriptions mediate the impacts of technology within the household.

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 abrudevoldnewman@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
# Steered Away from the Fields: Short-Term Impacts of Oxen on Agricultural Production and Intra-Household Labor Supply

Andrew Brudevold-Newman, Aletheia Donald and L ́ea Rouanet<sup>*</sup> _JEL Codes_ : O12, O13, J22, Q16, D13, J16 _Keywords_ : mechanization, productivity, gender, labor, social norms

> *Brudevold-Newman: World Bank, abrudevoldnewman@worldbank.org; Donald: World Bank, adonald@worldbank.org; Rouanet: World Bank, lrouanet@worldbank.org. This paper greatly benefited from comments by Kathleen Beegle and seminar participants at CSAE, PacDev, the NOVAFRICA Conference on Economic Development, and the International Conference on Development Economics. Ezechiel Djallo, Dede Houeto and Lacina Traore provided excellent research assistance. This paper is a product of the Gender Innovation Lab, within the Office of the Africa Region Chief Economist. We gratefully acknowledge financial support from the World Bank’s Umbrella Facility for Gender Equality (UFGE). The UFGE is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment through experimentation and knowledge creation to help governments and the private sector focus policy and programs on scalable solutions with sustainable outcomes. The UFGE is supported with generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, the Bill and Melinda Gates Foundation, and the Wellspring Philanthropic Fund. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank Group, its Board of Executive Directors, or the governments they represent. AEA RCT Registry ID: AEARCTR-0009714. All errors and omissions are our own.
# **1 Introduction**

Most rural households in lower-income countries rely on farming as their primary source of income, and on labor as the primary input to agricultural production. Mechanization, particularly in the form of animal traction, has the potential to raise agricultural incomes and facilitate the structural transformation of developing country economies. Previous research has identified which conditions favor the adoption of animal traction. Yet observational studies on its potential benefits yield nuanced results, and there is a dearth of rigorous evidence on impacts. In addition to knowledge gaps around how mechanization economically influences agricultural households, we also know little about whether (and why) technology adoption has differential impacts within the household. In this paper, we show that labor-saving technology can have large economic benefits, but that examining intra-household impacts—including gendered impacts—is crucial to understanding longer-term ramifications.

In a two-year randomized phase-in trial conducted with 2,546 cotton farmers in rural Cˆote d’Ivoire, we test the impacts of a matching grant that covered 50% of the cost and delivery of a pair of oxen. We find high take-up and use of the oxen, and positive impacts on agricultural production. In the first agricultural season, producers increased their cotton production and cotton revenues by 7%, despite only being notified about the program and receiving the oxen after planting decisions were made. Second season agricultural outcomes show that households increased their land area under cultivation by 6%, while also increasing spending on complementary non-labor inputs by about 15%. Moreover, household member time on plots decreased by about 1.5 hours per hectare, or 7%. These results represent a lower-bound of the impacts of oxen, as the phase-in trial resulted in a non-trivial share of control households receiving the oxen in advance of the endline survey.

These effects on household members’ time use are systematically related to gender. Assignment to treatment had no significant impacts on the producer’s time use or boys’ time use, while wives and girls decreased the time spent on household plots by almost 6% and 10%, respectively. In addition to this reduction in time spent working on household plots, girls were less likely to be sick, and the duration of illness over the last month reduced by about a quarter. Boys in treatment households were nearly 30% less likely to have dropped out of school, though we find no evidence of increased literacy or numeracy.

What is driving these gender-differential time use impacts? One answer comes from community gender norms around oxen use. Internationally, gendered social norms and assumptions have discouraged—or outright excluded—women from jobs that are perceived as relatively dangerous due to ‘benevolent sexism’ (Cuddy et al., 2015; Glick et al., 2000; Padavic and Reskin, 2002).<sup>1</sup> Tending to traction oxen is considered a

> 1This is true in Cˆote d’Ivoire; the country’s employment law explicitly states that women cannot engage in activities ‘beyond their strength’ and should be assigned ’suitable jobs’ (Code du Travail, Art. 23.13; D ́ecret No. 2018-272). While employment law is not binding in informal agriculture, it is indicative of this pervasive custom.

1
risky task due to their large size and potentially erratic behavior. According to Alesina, Giuliano and Nunn (2013), the plow requires significant upper body strength, and bursts of power, both to pull the plow and control the animal that pulls it. Women are thus commonly considered less suitable for the task–though there is substantial heterogeneity in adherence to this norm. We find that our observed negative treatment effects on the time women and girls spend on household plots are predominantly driven by households in districts with relatively lower female involvement in oxen tasks. Nationally-representative data shows that matrilineal ethnic groups are less prevalent in these districts, and norms are less gender-egalitarian. In these more conservative communities, women shift to working off-farm once oxen are introduced. These empirical patterns align with predictions from our simple model of time allocation under social prescriptions, which builds on Kevane and Wydick (2001).

There are three main contributions of this paper. First, we provide the first experimental evidence on the impacts of animal traction, and the first experimental evidence on the effects of agricultural mechanization as a whole in Sub-Saharan Africa–building on a rich literature that has examined the adoption and use of labor-saving agricultural technology in the region (Pingali, 2007; Owolabi et al., 2012; Guthiga, Karugia and Nyikal, 2007; Williams, 1997). The existing non-experimental research lays out several key stylized facts about animal-traction mechanization, including minimal yield gains, an increase in land under cultivation and devotion of the expanded land to market crops, as well as a reduction in labor required during the land-preparation period (Pingali et al., 1987). More recently, studies of rice production in Cˆote d’Ivoire highlight how mechanizing land preparation can contribute to agricultural intensification, thereby raising productivity (Mano, Takahashi and Otsuka, 2020), especially when agro-chemicals and mechanized land preparation are combined (Aihounton and Christiaensen, 2023). Within the limited literature demonstrating experimental evidence in developing country settings, our paper is most closely related to a recent study in Karnataka, India, which demonstrated that subsidized access to rental equipment markets lowered labor demand across all farming stages, and disproportionately so in stages not being mechanized, leading to an increase in non-agricultural income (Caunedo and Kala, 2021).

Our second contribution is to examine how the time-use impacts of mechanization vary within the household, speaking to the broader literature on the intra-household consequences of technology adoption (Wodon and Blackden, 2006; Bryceson, 2019; Doss, 2013; Theis et al., 2018). This analysis is particularly important in contexts where the social norm goes against women using the technology: in our case, plowing with oxen (Fisher et al., 2017; Starkey, 1995; Wanjiku et al., 2007). Modern agricultural technologies can affect women’s farm labor negatively, facilitating shifts to new tasks or activities, or positively, depending on the context. Importantly, these shifts have the potential to drive structural transformation if they free up women’s time for other market-based activities (Goldin and Sokoloff, 1984; Dinkelman and Ngai, 2022). This tension highlights that ‘one norm does not fit all’: sexist beliefs or practices limiting women’s engagement in one economic domain can spur women’s engagement in other economic domains.

2
In India, Afridi, Bishnu and Mahajan (2020) instrument suitability for mechanization by soil characteristics to estimate the gendered impact of mechanization. In line with our findings, they detect a significantly greater decline in women’s labor than men’s, explained by the complementarity of men’s labor and tilling machines, and by the fact that better quality tillage lowers the demand for weeding labor, which is mainly a female task in their setting. In other instances, new technologies might make women’s farm labor even more critical, owing to the sexual division of labor and limited technical innovations for women-led tasks (Wilson, 2003; Bassett, 1988). Although limited work has been done to rigorously measure the impacts of other mechanization equipment (Muhabaw and Muhabaw, 2014), to our knowledge, there are no randomized control trials testing the theory underlying improved productivity through animal traction, or its gendered impacts.

Lastly, our study also speaks to the literature on the interaction between crop choice and agricultural technology on the one hand, and education and child labor on the other. Part of this literature draws a causal link between child labor–intensive crops (such as cotton) and negative schooling outcomes (Baker, 2015), while others find that the income effect dominates and schooling outcomes improve (Kazianga and Makamu, 2017; Cogneau and Jedwab, 2012). However, there is little evidence on the relationship between the introduction of mechanization, which can replace tasks done by young children and potentially encourage schooling, beyond cross-sectional studies (Levy, 1985).

From a policy perspective, our findings reaffirm the broad potential for mechanization and productive animal traction to intensify agriculture: the significant short-run impacts suggest that recipient households shifted towards a larger, more capital intensive, and less labor intensive production approach. While receiving oxen shifted women off-farm in districts where the descriptive norm is that women engage minimally with oxen, it is unclear whether this facilitates structural transformation in the longer-term. With this in mind, policy makers seeking to implement agricultural mechanization interventions should consider complementary interventions that may help women retain some control over agricultural income in the face of their reduced participation, or support women in making their shift to off-farm labor market opportunities a profitable one.

The paper proceeds as follows: In Section 2, we present a simple model that highlights the links between agricultural norms, technology adoption and gender norms, and generates testable predictions for our empirical analysis. Section 3 provides information on the local context as well as the experimental design. We present the experimental results in Section 4, while Section 5 discusses the implication of our results and concludes.

3
# **2 Rural households operating under social prescriptions**

Our context is that of households in an agriculture-based rural economy, in which members maximize their utility over consumption and leisure. Households’ utility depends, inter alia, on how their members conform (or do not conform) to behaviors that are considered socially appropriate. The purpose of the framework is to i) illustrate the links between traction power, agricultural production and labor supply in this setting, and ii) predict what happens to household members’ labor supply in the face of gendered social prescriptions related to traction power.

## **2.1 Setup**

Our framework incorporates Akerlof and Kranton (2000)’s formalization of the insight that identity–including gender identity and the social prescriptions that accompany it–is a motivation for behavior, and draws on Kevane and Wydick (2001)’s model of time allocation under social norms.

In our simple setup, farm households maximize utility U subject to income, production technology and time constraints. Men own farm capital _K_ , including traction oxen, and female household members divide _T_ units of work time between farm work, _Tf_ , off-farm work _To_ , and domestic work _Td_ . The household has a production function _A_ ( _K, Tf_ ) that corresponds to output on the husband’s farm, and a function _H_ ( _K, Td_ ) that corresponds to domestic production, the value of which also depends on the household’s farm capital. Off-farm production, _To_ , is valued by the market at price _p_ . We assume that _ATT <_ 0, _AKK <_ 0, and _ATK >_ 0, and similarly for _H_ ().<sup>2</sup> The labor supply of the husband is assumed inelastic and its allocation is fixed across activities for the purposes of this simplified model, where we are interested in examining female household members’ labor supply.

As in Akerlof and Kranton (2000), an individual’s utility is a function of their social category and the extent to which their own and others’ actions correspond to behavior indicated by social prescriptions P. In our rural setting of northern Cˆote d’Ivoire, one example of such a ‘benevolently sexist’ social prescription is that women should not undertake activities perceived as risky, including handling large livestock (as discussed in Section 1). Since this social prescription does not regard women’s farm work overall, but specifically farm work when oxen are used ( _K_ =1), we modify Kevane and Wydick (2001)’s formalization of this norm’s impact on behavior by focusing on women’s deviation from average oxen use by other women in their community:

> 2 While an expansion of farm capital such as traction oxen increases the productivity of domestic production, e.g., through easing water/fuel transport, this impact may be offset by increased chore responsibilities such as animal care. We thus assume that _HTK_ , though positive, is close to zero.

4
where _j_ = _f , o, d_ denotes the set of activities available to the woman. The benefit from her behavior conforming to social prescriptions is given by _P_ . The parameter _a j_ represents the intensity of social penalties for violating norms regarding risky work in each of the different activities. _K j_ is the mean intensity of oxen use in activity _j_ for women in a particular community. Social penalties depend on the intensity parameter _a j_ , whether the woman performs relatively more tasks with oxen than women in her community (i.e., _K > K j_ ),<sup>3</sup> and how much time she spends on these tasks ( _Tj_ ). For simplicity and given our setting, we focus on social penalties related to farm work, setting _ao_ = _ad_ = 0.<sup>4</sup> The household allocates the woman’s labor to maximize: subject to the time constraint: This setup yields a set of tractable comparative statics for the relationship between a change in capital (oxen) and labor across the different categories.<sup>5</sup>

# **2.2 Predictions**

We start with the first equation. The marginal effect of capital (in our context, introducing traction oxen) on women’s farm work will depend on the sign of _ATKHTT − a f_ ( _K − K_ ) _HTT_ , since _|H|_ is negative as shown in Appendix A. Recall that _ATK_ is positive by assumption, while _HTT_ is negative by assumption. This implies that if _a f_ ( _K − K_ ) _> ATK_ —that is, the social penalty she incurs from spending time on-farm with oxen is greater than the return of her labor to farm production when capital increases marginally—then the intro-

> 3In our context, there are no penalties for women who are less involved in risky activities than the norm, so we do not consider the case of _K < K j_ .

> 4In line with the focus on farm norms and for simplicity, we drop the sector subscript for the mean intensity of on-farm oxen use, so that _K j_ = _K_ .

> 5Appendix A presents the derivation of these results.

5
duction of oxen will decrease the time she spends on the farm.

In the presence of social prescriptions that it is unsuitable for women to handle traction oxen, _a f_ ( _K − K_ ) is large and positive such that: Note that for lower values of the social penalty in less conservative communities, where the norm is for women to already be relatively more involved in handling oxen, introducing oxen will naturally have a smaller negative effect on women’s time spent on farm work.

Turning to domestic work, as explained above, we assume that _HTK_ is positive but only weakly so. Since _ATT_ is negative and _|H|_ is negative, this implies that the marginal effect of capital on domestic work is positive but close to zero:

Lastly, we turn to the case of women’s off-farm work. Since _HTK_ is near-zero, and _HTT_ is negative, the sign will again depend on the relative size of _a f_ ( _K − K_ ) and _ATK_ . If _a f_ ( _K − K_ ) _> ATK_ as above, then the first term of the numerator will be negative. Since we know that the second term of the numerator is positive and close to zero, this implies that in households where women would go against the norm by working with oxen on-farm and thus face a social penalty, _ceteris paribus_ : Another way to see this result is that<sup>_∂To∂Td∂Tf_Note also that</sup> _∂ K_<sup>is merely</sup> _∂ K_<sup>subtracted from the inverse of</sup> _∂ K_<sup>.</sup> as the social penalty shrinks, the increase in women’s off-farm work resulting from an increase in capital will also get smaller.

Next, we turn to describing our experiment in more detail before presenting household-level impacts and bringing our model to the data in Section 4.

6
# **3 Experimental design and implementation**

## **3.1 Experimental design**

We conducted a randomized evaluation of a traction oxen matching grant for cotton farmers working under formal cotton societies in four northern regions of Cˆote d’Ivoire under the World Bank’s Agricultural Support Program (PSAC).<sup>6</sup> Specifically, the project provided a matching grant of 50% of the cost of receiving two traction oxen and related equipment, with producers financing the remainder through their own funds or credit from cotton ginning companies and agricultural cooperatives.<sup>7</sup> In economic terms, this is a large treatment. Previous studies have found that an ox provides farm ‘labor’ equivalent to five to eight men, and that traction oxen are at least twice as powerful as smaller bovines: 400-500 watts of power versus less than 200 watts for small bovines (Smil, 2004; Starkey and Faye, 1990).

The participant cotton societies formed the farmer sampling frame using their member lists and a mix of objective criteria (such as being married, having been a member of a cotton society for at least 3 years, and belonging to a single cotton society) and more subjective criteria determined by cotton society staff (being credit-worthy, trust-worthy, and able to increase their cotton farmland). Few farmers in the sampling frame owned traction oxen before the intervention, although many owned other types of farm equipment (Appendix Table B1).

We used a randomized phase-in approach for our experiment: from an initial sample frame of 2,546 eligible farmers, we randomly assigned 1,273 producers to receive the oxen matching grant offer in the fall of 2016 (treatment group) and 1,273 to receive the offer starting in April 2018 (control group). We stratified the randomization by cotton zone, whether the cotton societies’ administrative data indicated that they already owned any oxen, whether they requested one or two oxen, and whether they owned a cart. We measure impacts using detailed household surveys conducted in July and August 2018.<sup>8</sup> As cotton cultivation is a key outcome of interest in this experiment, Figure 1 presents the timing of the distribution of the oxen under the experiment together with the key activities of the 2017 and 2018 cotton

> 6The cotton component of the project operated in Bere, Poro, Tchologo and Worodougou, and partnered with Intercoton—the cotton value-chain interprofessional organization in Cˆote d’Ivoire—and its five constituent cotton societies (CIDT, COIC, IVOIRE COTON, SECO and URECOSCI) to implement the matching grant intervention under the broader goal of increasing agricultural productivity in the country.

> 7The market value of two traction oxen at the time of delivery was FCFA 480,000, implying a matching grant value of FCFA 240,000 (US $408). In practice, there are small variations in treatment across farmers. While 96% of farmers requested two oxen, 4% instead requested one ox: households assigned to the treatment group were provided with the opportunity to purchase their requested number of oxen. Moreover, 7% of farmers in the treatment group requested and received a multicultivator, 6.4% a seed drill, 5.1% a plow, and less than 3.5% a received tillers or carts. Appendix Table B1 provides a full list, and shows that households own an average of 2 pieces of equipment, irrespective of their treatment status. The most commonly owned tools are multicultivators (52% households), followed by carts (46%) and plows (43%).

> 8We only collected information on agricultural inputs use from a random half of the sample, due to budgetary reasons. The data is balanced across this split.

7
**Figure 1:** Timeline of the experiment and of the 2017 and 2018 cotton seasons

<!-- Start of picture text --> Timing of oxen distribution<br>By treatment status<br>Harvest<br>Begin End<br>Planting<br>Land<br>Prep<br>Planting<br>Land<br>Prep<br>1/2016 7/2016 1/2017 7/2017 1/2018 7/2018<br>Date<br>Control Treatment<br>.6<br>.4<br>.2<br>Fraction received oxen<br>0<br><!-- End of picture text --> Note: No date reported for 13% of the sample that received oxen. These households are assumed to receive the oxen at the mean delivery date within their given strata.

seasons in Cˆote d’Ivoire. Delays compiling the lists of eligible farmers and procuring the oxen pushed the delivery of the oxen to April-November 2017, meaning that most treatment households received the oxen after the 2017 land-preparation and planting activities but before harvest. With the delivery timeline and the timing of the survey in mind, we focus on agricultural outcomes that span two seasons: agricultural production and harvest for the 2017 season, and agricultural inputs for the 2018 season.

# **3.2 Sample characteristics**

At endline, we tracked and administered a survey to 91% of farmers in the sampling frame, resulting in a sample size of 2,314 producers: 1,146 producers in the control group and 1,168 producers in the treatment group.<sup>9</sup> We focus on the 2,113 producers who reported comprehensive agricultural production, sales, and time use data.<sup>10</sup>

> 9The main survey respondent was the household head, with specific questions on the receipt and use of oxen addressed to the producer in instances where the two diverged. Moreover, we randomly selected and surveyed one girl and one boy in the household and administered age-appropriate numeracy and literacy assessments using the Uwezo instrument (accessible at `http: //www.uwezo.net/wp-content/uploads/2012/08/KE_2012_Tests.pdf` ).

> 10Of the 201 cotton producers surveyed at endline but dropped from the study, 136 reported partial agricultural output or sales data, 41 were secondary producers in multi-producer households, and 24 reported incomplete time use data.

8
Table 1 reports the socio-demographic characteristics of the sample at endline, together with the difference between treatment and control, and the level of significance of this difference. Sample households have large families with an average of over 8 members, including just under two children younger than 5 years old. Educational attainment is low, with 81% of household heads having received no formal schooling. The surveyed households are predominantly male-headed, with less than 1% being female-headed (only 17 households). Of the households in the sample, 42.3% are polygamous. There are a few imbalances between treatment and control: treatment households have fewer boys aged 6-16, are slightly more likely to have a household head that completed at least primary school, and are slightly less likely to have experienced a shock in the last year. We are limited on the range of variables we can test balance on given that we do not have baseline data in this experiment. However, this table also contains pre-program administrative data for a sub-sample of 86% of the cotton producers. Reassuringly, we do not find imbalances in the pre-program cotton area, which is around 3.65 ha in both experimental groups, nor in the cotton production, which is around 3770 kg.

The survey data provide a detailed picture of household agricultural tasks and cotton production in Cˆote d’Ivoire. While cotton producers are almost exclusively men, both men and women in these farm households are engaged in cotton production. Contrary to conventional wisdom detailing stark gender divisions in the performance of agricultural tasks, with men being more responsible for physically-intensive tasks like plowing and land clearing and women more responsible for weeding and sowing, Table 2 shows that there is little gender specialization in cotton-related tasks in our setting. The starkest gender difference is that household men are generally more involved than women in all tasks: over 80% of male producers perform each type of task and producers are over 10 percentage points more likely to conduct any given task than any other household member. Still, spouses are broadly engaged in agricultural tasks: 74% of spouses perform transport-related tasks while 68% plow (though consistent with the social prescriptions outlined in Section 2, rates are higher for plowing without oxen than with oxen). Similarly, while 55% of spouses sow and weed, 85% of male producers also perform these tasks. The only two tasks in which women (producer’s spouse and other women in the household) are relatively less involved are land clearing (40% for spouses) and marketing (9% for spouses).

Our data suggests systematic gender differences in perceived skill in handling oxen, which are endogenous to treatment. Figure 2 presents the distribution of different household members’ perceived skill levels, as reported by the cotton producer. Over 85% of girls and women are reported to have no oxen skills, about 30 and 60 percentage points above the equivalent rates for boys and for the male producer, respectively. The sizable difference in skill rates between boys and girls persists even for children under 10 years old, suggesting that social norms attribute higher perceived skills for boys and men, relative to girls and women.

9
**Figure 2:** Distribution of perceived oxen skill level for main household members

<!-- Start of picture text --> None 1 2 3 or higher<br>Reported oxen skill<br>Producer Boys<br>Spouse Girls<br>Note: Oxen skill reported by the household head for all household members on a 0-10 scale.<br>100<br>80<br>60<br>40<br>Percent of sample<br>20<br>0<br><!-- End of picture text --> The presence of social norms regarding handling oxen is reinforced by the existence of district-level patterns in the likelihood that women work with oxen. In our survey data, almost a third of households reside in districts where other households’ spouses performed fewer than one in three agricultural oxen tasks (sowing/weed, plow, or transport) over the prior year, while another third reside in districts where women performed at least 1.33 tasks. The intra-household labor impacts from the distribution of oxen are likely to critically depend on norms around the appropriateness of women working with oxen.

Women’s likelihood of working with oxen is highly correlated with broader district-level descriptive norms regarding women’s decision-making over their own health care, large household purchases, and visits to family and relatives (correlation coefficient of 0.75). Districts in which women are less likely to work with oxen are also those where women have less overall decision-making power. Regional patterns in these norms are shown in Figure 3.<sup>11</sup> Underlying these patterns in women’s decision-making is variation in kin-

> 11 We use the three standard DHS women’s decision-making questions (decision-making around women’s health care, large household purchases, and visits to family and relatives) to define a household-level decision-making index. We then take the median within each third-level administrative region, or districts (d ́epartements), and characterize districts as either high or low decision-making if they are above or below the median district. The main assumption underlying this approach relates to the mapping of DHS clusters to administrative geographic visions. Specifically, the DHS does not report the third-level geographic administrative division (d ́epartment) that we use to characterize the sample. We rely on the reported geo-coded coordinates to map clusters to departments. These coordinates are shifted up to 5km, with 1% of clusters shifted up to 10km. Our approach assumes that the clusters do not shift across department boundaries.

10
<!-- Start of picture text --> 7<br>.Ce 5 oT<br>ey<br>Pots<br>KB<br>L<br>oan owe<br><!-- End of picture text -->
# **4 Analysis**

In this section, we report intent-to-treat (ITT) estimates of the impacts of the mechanization treatment on households assigned to the treatment group. Treatment assignment was random within strata, so the impacts of the intervention on a given outcome _Yi_ can be measured using the following regression: where _Yi_ represents outcome _y_ for household _i_ , Treat _i_ is an indicator variable equal to 1 if household _i_ was assigned to receive oxen in the first wave, **Xi** is a vector of unbalanced demographic variables, and _δstratum_ is a series of strata fixed effects. As treatment was assigned at the individual/household level, we report heteroskedasticity-robust standard errors.<sup>13</sup> In addition, we examine impact heterogeneity by whether households live in districts where women are more or less involved in oxen tasks. Specifically, we run the following regression: where High _j_ is an indicator variable equal to one for households living in districts where the share of oxen tasks conducted by women within the district exceeded the median across all districts. We restrict to control households for this calculation, since our goal is get as close as we can to capturing baseline-level norms. As a robustness check, we-run this analysis using pre-program Demographic and Health Survey (DHS) data to divide districts into ones that are more conservative (below-median women’s decision-making power over their health care, making large household purchases and visiting family and friends) versus less conservative, as described in Section 3.2. We confirm that results are unchanged.

## **4.1 Household-level impacts**

Table 4 presents intent-to-treat impacts on agricultural yields and area cultivated, showing that traction oxen increase yields and lead farmers to bring a larger area under cultivation.<sup>14</sup> Panel A of Table 4 focuses on treatment effects for first season agricultural outcomes, showing that assignment to the oxen treatment arm led to significant increases in agricultural output, even though the oxen were distributed mid-season and recipient producers were not notified that they would receive oxen before they made their planting decisions. Producers increased cotton production by 7%, yielding an equivalent increase in cotton revenues.<sup>15</sup> Notably, the total market value of all crops did not increase, with the increase in cotton production partially

> 13For individual-level outcomes with more than one observation per household, such as children’s education and health, and wives’ initial labor outcomes, we cluster standard errors at the household level.

> 14As noted in Section 3.3, above, non-compliance within both the treatment and control groups suggests that our estimates represent a lower-bound for the treatment effects of the matching grant for the oxen.

> 15The cotton society-dominated market structure yields little variation in the price farmers receive for their cotton, with the median reported sales prices within communities in the sample varying by less than 2%.

12
offset by a negative but not statistically significant impact on other agricultural production.<sup>16</sup> We also find limited impact on total household income, which almost exclusively comprises agricultural sales. The fact that this increased cotton output stems from the early-season, unanticipated receipt of oxen after planting demonstrates that producers were able to increase output through increased productivity by using oxen for crop maintenance and harvesting.

Panel B of Table 4 presents the impacts on early second season agricultural outcomes. In contrast to the first season when households received oxen unexpectedly and after several key input decisions had been made, households were able to take the oxen into account for planning and land preparations in the second season. This added flexibility led to large shifts in land holding and cultivation decisions: households assigned to receive traction oxen increased their total land holdings by 8%, land area under cultivation by 6%, and land cultivated with cotton by 10%. This increased focus on cotton, an inedible cash crop, does not come at the expense of food security, as shown in Table 5.

Table 6 shows that, for the subsample of households asked about agricultural inputs, the oxen increased producers’ use of traction power, increased their use of other non-labor inputs, and decreased plot household labor. Panel A shows that treatment households increased the likelihood that households used traction oxen by 10 percentage points and the number of plots on which they used the traction oxen by almost 30%, while also decreasing the likelihood of renting in oxen, indicating that they are better able to meet their traction needs despite a larger area to manage.

Panel B focuses on non-labor inputs, showing that treatment households increased the share of plots on which they use organic fertilizer by almost 5 percentage points. There is little evidence that the oxen increased use of inorganic fertilizer or pesticides though extensive usage of these inputs was already high, with over 95% of households using at least one. Overall, households increase the value of non-labor inputs used by about 15%.<sup>17</sup> Finally, Panel C shows that households assigned to receive the oxen decreased their aggregate time spent on household plots over the last week without an accompanying increase in hired labor, indicating that labor productivity has increased due to the introduction of oxen. Overall, household member time spent on plots decreased by about 1.5 hours per hectare or 7%.

In the next section, we build on the observed decrease in aggregate household agricultural labor to examine the intra-household labor impacts of the oxen.

> 16Appendix Table B2 presents impacts on the production of other crops. Farmers assigned to receive the traction oxen increased rice production by 9%. Rice is primarily produced for household-consumption with only 8% of the rice-producing households selling any amount.

> 17Appendix Table B3 presents the estimated impacts of the treatment on the proportion of expenditures across six categories, finding that treatment households increased their spending on agricultural investments as a share of their overall spending.

13
# **4.2 Intra-household labor allocation**

This section unpacks the intra-household composition of the observed reduction in agricultural labor provision as a result of the intervention, as well as its determinants, testing the predictions in Section 2. We restrict attention to the 1,834 married producers with complete time-use data for spouses.<sup>18</sup> Table 7 shows that the oxen had different impacts for different household members, with time on household plots decreasing for women and girls. Columns 1 and 2 show that assignment to treatment had no significant impacts on the producer’s time use. Columns 3-8 show that treatment households decreased the time spent on household plots by almost 6% and 9% for wives and girls, respectively. The large decrease in household plot activities for girls represents around two-thirds of a larger decrease in their total reported active time.<sup>19</sup> In this table, we report results for both the first spouse and other spouses for polygamous households. The impact of the treatment on time use, including household plot activities, is not statistically different between first spouse (column 4) and other spouses (column 6). Results on other spouses being noisier but equivalent to results on first spouse, the rest of the paper studies the average impact of treatment on spouses, by taking the average of outcomes across the producer’s wives, when applicable. Finally, boys in treatment households increased time spent on household chores by almost 0.6 hours, representing an increase of over 30%.<sup>20</sup> These findings speak directly to our model: the significant decrease in female plot labor supports the presence of sizable social prescriptions dictating that women should not handle traction oxen ( _a f >_ 0). Relatedly, the lack of significant impacts on domestic work suggests a near-zero response of the marginal product of labor to a capital shock ( _HTK ≈_ 0). We examine the ambiguous observed impact on non-farm labor in more detail in Section 4.3, below, by exploiting district variation in social prescriptions regarding women working with oxen ( _a f_ ( _K − K_ )).

Building on the decreased time women and girls spend on household plots, Tables 8 and 9 examine treatment effects for education and health outcomes. Panel A of Table 8 shows that the program did not increase either overall school enrollment for children aged 6-16 years old or student performance on a simple literacy and numeracy assessment. However, this may have been a tall order, since it would have required many older children to enroll in school for the first time or to return to school after having dropped out previously. With this in mind, Panel B examines whether the oxen increased the likelihood that students who had previously

> 18Appendix C presents results equivalent to those presented above, showing that this sub-sample is balanced across the range of socio-demographic characteristics examined in section 3.1 and that the estimated impacts on household agricultural outcomes are consistent with those presented for the full sample, above, but less precisely estimated.

> 19The omitted category in the time use questionnaire is passive rest time that is not spent on hobbies or socializing. A reduction likely indicates an increase in rest, but may also stem from the fact that the treatment shifts these individuals away from household plots, decreasing the observability of their time use to the producer. Appendix Table B4 presents equivalent results for average weekly hours spent on household agricultural plots over the prior year, showing a similar pattern of decreased time on plots by women and girls.

> 20The low reported time spent on education for girls and boys is likely due to the timing of the survey, which took place during the summer school break.

14
attended school were still attending school, showing that boys in treatment households were almost 30% less likely to have dropped out of school. Among children that had attended school, we find no evidence of increased performance on the literacy or numeracy assessment.

Decreased time spent on agricultural plots also has the potential to affect child health by decreasing their exposure to agricultural chemicals and zoonotic diseases. Table 9 presents impacts of treatment on likelihood and duration of illness among the producers’ spouse and children. While all six of the coefficients are negative, the impacts are only statistically significant for girls, for whom treatment decreases the likelihood of illness and duration of illness over the last month by about a quarter. The coefficients for boys are about half as large as those for girls and are not statistically significant. This makes sense, since girls are reducing the time they spend working in agriculture at higher rates.

# **4.3 Role of community norms in driving intra-household impacts**

As discussed in Sections 2 and 3.2, community norms around women working with oxen are likely to play a key role in determining the intra-household impacts of a mechanization intervention. If the community norm is that women are not involved in oxen tasks, introducing oxen to agricultural work may shift women away from agricultural work. This subsection builds on these theoretical considerations by examining treatment effect heterogeneity for households residing in districts where women perform relatively more or relatively fewer oxen tasks.<sup>21</sup> Table 10 presents heterogeneous impact estimates for second-season time use outcomes. For this heterogeneity analysis, we average spouse outcomes across wives in households where the producer had more than one wife. Column 1 shows the estimated impact of delivering oxen to households residing in districts where women conduct more oxen tasks. In these households, oxen treatment had relatively limited time-use impacts with none of the 12 coefficients being statistically significant. This contrasts with the results presented in Columns 2-3, where we display the marginal impact of treatment within households who reside in districts where women perform fewer oxen-related tasks (Column 2) as well as the p-value of the test comparing these impacts to those in districts where men dominate oxen work (Column 3). In these households, we find large intra-household impacts of the oxen delivery. In particular, in districts where women are typically not engaged in oxen work, we see a significant 1.78 hour/week increase in how much time the spouse spends on off-farm work. This matches qualitatively with the fact that the spouses’ (and girls’) reduction in time spent working on the producer’s plots is concentrated in these same districts. This is consistent with our model: the impacts of introducing capital depend critically on the social penalty to capital intensive

> 21We define households as residing in districts with relatively high levels of women’s involvement in oxen tasks if the share of oxen tasks conducted by women in control households within the district exceeded the median across all districts. Appendix Table B5 presents summary statistics comparing households residing in districts with relatively high and relatively low rates of women’s involvement in oxen tasks, showing significant differences across several household and production characteristics. We include controls for each of the demographic characteristics in our heterogeneity specification. Controlling for production-related differences gives equivalent results.

15
work for different household members, _a f_ ( _K − K_ ). Taken together, these results show that gender norms around suitability of working with oxen lead to a concentration of household plot activities under men in the household, with women shifting to non-agricultural work.

One concern with the above heterogeneity analysis is the potential endogeneity of women’s involvement in oxen tasks. While we define this using endline data from control group households, the phase-in design means that many of these households also received the oxen and may have changed behaviors in response to treatment. With this in mind, we demonstrate the robustness of the heterogeneity results to using districtlevel differences in women’s decision-making power, shown in Figure 3, as an alternative construction of gender norms. Appendix Table B6 presents equivalent heterogeneity results using pre-program DHS data to characterize districts in the sample as either more or less conservative based on women’s decision-making power. Since, as discussed in Section 3.2, the two measures of district norms are highly correlated (0.75), this alternative way of analyzing heterogeneity correspondingly yields a similar pattern of results.

# **5 Conclusion and discussion**

In this paper, we provide experimental evidence on the short-term impacts of mechanization on household production and intra-household labor allocation among cotton farmers in Cˆote d’Ivoire. We find positive impacts on cotton production–a male-dominated cash crop–and intensification of agriculture, along with an increase in the value of complementary agricultural inputs used. Though our results are short-term and we do not detect a significant increase in household income in our survey time-frame, second season outcomes indicate that households expand their overall agricultural cultivation and not merely cotton. Moreover, our data indicates that animal traction can lead to yield–not just production–improvements, as farmers were able to increase their cotton production after land cultivation decisions were made.

Our results shed additional light on four stylized facts related to agricultural mechanization identified in observational studies. First, earlier observational studies analyzed by Pingali et al. (1987) suggested minimal yield gains for animal traction farms relative to hand-hoe farms. The increased cotton and rice production we detect in the first season, together with the fact that the oxen were distributed after land cultivation decisions were made, suggest that the oxen increased yields through either task quality or efficiency improvements during crop maintenance, harvest, and/or crop transport. Second, Pingali et al. (1987) documented a relationship between animal traction use and a larger area under cultivation. Our estimates indicate that this relationship is causal, with households who gain access to traction oxen bringing new land under cultivation. Third, earlier findings have suggested that additional area brought under cultivation as a result of mechanization was concentrated among market crops including cotton, rice, and groundnuts (Pingali et al., 1987). We find impacts concentrated among both cotton and rice but caution against the market crop characterization: few farmers in this setting report selling rice, suggesting that the impacts may stem from specific traction 16
oxen efficiencies of cultivating rice rather than a market crop designation. Finally, Pingali et al. (1987) note that there is “general agreement that a transition [to animal traction] reduces the amount of labor required during the land-preparation period”. We find evidence of broader labor savings from oxen: specifically, we document a reduction in household agricultural labor during crop maintenance, when households may have been expected to need more labor due to their increased land under cultivation. This is in line with results from Afridi, Bishnu and Mahajan (2020), who point to the fact that mechanization leads to higher-quality plowing, which in turn lowers the demand for weeding labor.

Our results show the broad labor-saving potential of animal traction beyond the land preparation period, but also how this labor-saving does not affect everyone in the household equally. Only wives and girls significantly reduce the time they spend working on household plots as a result of mechanization. The human development impacts are similarly gendered: girls appear to benefit in terms of their health, while boys benefit in terms of their schooling (possibly because of an anticipatory income effect by households).

Our findings indicate that the adoption of labor-saving technology in male-dominant activities can have large intra-household effects and welfare impacts. They also underscore the importance of complementary development efforts. For example, while boys are less likely to drop out of school as a result of mechanization, they do not observe an increase in education outcomes–pointing to the potential need for increased investment in school capacity. Moreover, our analysis shows how gender norms are tightly linked to mechanization impacts for women, with women in districts with stronger norms against women working with oxen shifting off-farm. Looking forward, it will be important to better understand how these relatively conservative norms interact with greater off-farm engagement.

In terms of consequences for structural transformation, it is worth noting that norms related to women working with technology and in capital-intensive tasks affect many more domains than simply working with oxen. Prescriptions regarding the acceptability of different types of work for women are not just relegated to informal norms, but are also enshrined in formal legislation. In 49 of 190 countries worldwide, women are not allowed to work in jobs deemed ’dangerous’ in the same way as men (World Bank, 2023). Beyond examining the effects of legal and normative changes regarding what sectors it is acceptable for women to work in, future research should examine whether offering complementary gender-targeted programming alongside mechanization can strengthen women’s economic empowerment, and more broadly continue unpacking the relationship between technology, labor and (gendered) power.

17
**Table 1:** Balance between treatment and control groups

|**Outcome**|**Obs**|**Control Group**<br>**Mean**|**Treatment**<br>**Difference**|
|---|---|---|---|
|Household head: age|2104|42.094|-0.249|
||||(0.484)|
|Household head: no education|2113|0.811|-0.012<br>|
||||(0.017)|
|Household head: some primary education|2113|0.102|-0.013<br>(0.013)|
|Household head: completed at least primary school|2113|0.087|0.025<sup>_∗_</sup><br>(0.013)|
|Household head: female|2113|0.007|0.000<br>(0.004)|
|Household head: producer|2113|0.936|0.003|
||||(0.010)|
|Polygamous household|2113|0.417|-0.006<br>|
||||(0.021)|
|Number of spouses|2113|1.464|-0.034<br>(0.032)|
|Age of frst spouse|1790|35.169|-0.644<br>(0.492)|
|First wife: no education|1983|0.944|-0.018|
||||(0.011)|
|Men in household|2113|1.763|-0.053<br>(0.047)|
|Women in household|2113|1.721|-0.011<br>(0.053)|
|Boys aged 6-16|2113|1.553|-0.171<sup>_∗∗∗_</sup><br>(0.063)|
|Girls aged 6-16|2113|1.209|-0.020<br>(0.058)|
|Number of elderly individuals over age 65|2113|0.095|0.000<br>(0.014)|
|Children under 5 years old|2113|1.829|-0.050<br>(0.066)|
|Number of shocks in last year|2113|1.856|-0.115<sup>_∗_</sup><br>(0.061)|
|Household assets (PCA)|2113|8.415|0.095<br>|
||||(0.132)|
|Pre-program cotton area (ha)|1804|3.648|0.027<br>(0.092)|
|Pre-program cotton production (kg)|1804|3769.341|10.267<br>(133.957)|

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals. Pre-program cotton area and production come from cotton producer database.

18
**Table 2:** Share of Household Members Performing Agricultural Tasks

|**Household**|**Land Clearing**|**Plowing**|**Sow/Weed**|**Transport**|**Harvest**|**Marketing**|
|---|---|---|---|---|---|---|
|Producer|0.85|0.88|0.85|0.90|0.81|0.86|
|Spouse|0.40|0.68|0.55|0.74|0.45|0.09|
|Other men|0.52|0.71|0.65|0.69|0.59|0.25|
|Other women|0.26|0.50|0.37|0.49|0.27|0.05|
|Boys|0.34|0.50|0.40|0.51|0.38|0.05|
|Girls|0.18|0.39|0.26|0.40|0.21|0.03|

Note: Variable equal to 1 if the individual performed the agricultural task over the prior year. Sample restricted to control group.

**Table 3:** Treatment compliance

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|Household has cattle|2113|0.88|0.02<br>(0.01)|
|Number of household cattle<sup>_⋄_</sup>|2113|4.87|0.22<br>(0.28)|
|Household received traction oxen|2113|0.46|0.09<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 09/17|2113|0.24|0.19<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 04/18|2113|0.38|0.16<sup>_∗∗∗_</sup><br>(0.02)|
|Household received traction oxen before 08/18|2113|0.47|0.10<sup>_∗∗∗_</sup><br>(0.02)|
|Number of traction oxen received<sup>_⋄_</sup>|1811|0.93|0.23<sup>_∗∗∗_</sup><br>(0.04)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions include controls for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

19
**Table 4:** Impacts on agricultural output and land cultivated

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|**Prior Season**||||
|Total cotton production (kg)<sup>_⋄_</sup>|2113|5292.93|387.23<sup>_∗∗_</sup><br>(179.58)|
|Value of cotton sold (USD)<sup>_⋄_</sup>|2113|2706.25|191.54<sup>_∗_</sup><br>(105.70)|
|Value of non-cotton agric production (USD)<sup>_⋄_</sup>|2113|1115.50|-72.52<br>(69.55)|
|Value of agricultural products sold (USD)<sup>_⋄_</sup>|2113|4263.35|45.79<br>(175.86)|
|Total non-farm income (USD)<sup>_⋄_</sup>|2113|41.60|2.87<br>(3.83)|
|Total household income (USD)<sup>_⋄_</sup>|2113|4342.99|56.53<br>(177.99)|
|**Current Season**||||
|Household plots cultivated|2113|1.67|0.04<br>(0.04)|
|Total land holdings (ha)<sup>_⋄_</sup>|2113|13.81|1.05<sup>_∗∗∗_</sup><br>(0.36)|
|Total land cultivated (ha)<sup>_⋄_</sup>|2113|12.64|0.74<sup>_∗∗_</sup><br>(0.31)|
|Total land cultivated w/ cotton (ha)<sup>_⋄_</sup>|2113|4.81|0.49<sup>_∗∗∗_</sup><br>(0.14)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

20
**Table 5:** Food Security Impacts

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|**Food Security**||||
|Food groups eaten in last 7 days|2113|7.302|-0.054<br>(0.081)|
|Food groups eaten in last 24 hours|2113|5.534|0.061<br>(0.082)|
|Meals skipped in last 7 days|2109|0.379|-0.012<br>(0.057)|

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively.All regressions include controls for number of boys 6-16 years old and stratum fixed effects.

21
**Table 6:** Impacts on agricultural inputs

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|---|---|---|---|
|**Traction ox use**||||
|Household used traction oxen|947|0.56|0.10<sup>_∗∗∗_</sup><br>(0.03)|
|Number of plots where traction oxen were used|947|0.91|0.27<sup>_∗∗∗_</sup><br>(0.07)|
|Share of plots where traction oxen were used|947|0.53|0.11<sup>_∗∗∗_</sup><br>(0.03)|
|Household rented oxen|947|0.16|-0.04<br>(0.02)|
|**Non-labor inputs**||||
|Share of household plots using organic fertilizer|947|0.82|0.05<sup>_∗∗_</sup><br>(0.02)|
|Share of household plots using inorganic fert|947|0.95|-0.00<br>(0.01)|
|Share of household plots using phytosanitary products|947|0.95|0.01<br>(0.01)|
|Value of non-labor inputs for household plots (USD)<sup>_⋄_</sup>|947|1267.11|164.07<sup>_∗∗_</sup><br>(77.72)|
|**Labor inputs**||||
|Household hired labor|947|0.63|0.02<br>(0.03)|
|Quantity of hired labor<sup>_⋄_</sup>|947|28.85|1.37<br>(2.49)|
|Total household plot hours in last week<sup>_⋄_</sup>|2113|219.58|-8.46<sup>_∗_</sup><br>(4.91)|
|Total household plot hours in last week/ha<sup>_⋄_</sup>|2113|21.21|-1.51<sup>_∗∗_</sup><br>(0.58)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. Agricultural input usage only collected for half the sample. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

22
# **Table 7:** Impacts on household member time use

||**_Pro_**|**_ducer_**|**_First_**|**_Spouse_**|**_Other_**|**_Spouses_**|**_G_**|**_irls_**||**_Boys_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Outcome**|**Control**<br>**Mean**<br>**[1]**|**Estimated**<br>**Impact**<br>**[2]**|**Control**<br>**Mean**<br>**[3]**|**Estimated**<br>**Impact**<br>**[4]**|**Control**<br>**Mean**<br>**[5]**|**Estimated**<br>**Impact**<br>**[6]**|**Control**<br>**Mean**<br>**[7]**|**Estimated**<br>**Impact**<br>**[8]**|**Control**<br>**Mean**<br>**[9]**|**Estimated**<br>**Impact**<br>**[10]**|
|HH plot activities|43.11|-0.48|35.31|-2.14<sup>_∗∗_</sup>|35.34|-1.50|26.68|-2.65<sup>_∗∗_</sup>|34.54|-0.96|
|||(0.86)||(0.91)||(1.38)||(1.12)||(1.21)|
|Other agric work|1.32|-0.30|1.19|-0.06|1.12|-0.05|0.28|0.00|0.20|0.05|
|||(0.23)||(0.21)||(0.30)||(0.11)||(0.08)|
|Non-farm labor|11.38|-0.61|5.47|0.61|5.09|0.02|4.10|0.06|12.90|-1.20|
|||(0.85)||(0.59)||(0.82)||(0.56)||(0.95)|
|Household chores|1.62|0.19|28.52|-0.53|28.87|0.06|16.37|-0.02|1.79|0.55<sup>_∗_</sup>|
|||(0.31)||(0.82)||(1.38)||(0.81)||(0.31)|
|Hobbies|9.26|-0.00|4.50|-0.02|5.63|-0.04|9.69|-0.63|9.47|-0.37|
|||(0.55)||(0.38)||(0.66)||(0.79)||(0.71)|
|Education|0.62|-0.02|0.19|-0.02|0.07|0.06|0.79|-0.19|0.69|-0.14|
|||(0.14)||(0.11)||(0.11)||(0.15)||(0.12)|
|Total inactive time|100.49|1.17|92.82|2.20|92.18|1.01|110.06|3.67<sup>_∗∗_</sup>|108.38|2.23|
|||(1.47)||(1.46)||(2.26)||(1.74)||(1.69)|
|Observations|1|836|1|781||757|1|199||1352|

Note: *, **, and *** indicate significance at the 90, 95, and 99 % confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. Regression reports impacts of treatment on weekly average household time use for given tasks: for households with multiple wives, daughters, or sons, the time use is averaged and regressions are reported at the household level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.
# **Table 8:** Household Member Education

||**_All C_**|**_hildren_**|**_G_**|**_irls_**||**_Boys_**|
|---|---|---|---|---|---|---|
|**Outcome**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|**Control**<br>**Mean**|**Estimated**<br>**Impact**|
|**Panel A: Households with children**|**6-16 years**|**old**|||||
|Children currently attending school|0.48|0.02<br>(0.02)|0.52|0.02<br>(0.02)|0.47|0.02<br>(0.02)|
|Education assessment score|-0.01|0.05<br>(0.04)|-0.00|0.01<br>(0.06)|0.01|0.06<br>(0.05)|
|**Panel B: Households with children**|**who have**|**attended sch**|**ool**||||
|Students dropped out of school|0.10|-0.02<br>(0.01)|0.09|-0.00<br>(0.02)|0.11|-0.03<sup>_∗_</sup><br>(0.02)|
|Education assessment score|0.44|0.06|0.41|-0.02|0.47|0.07|
|||(0.06)||(0.08)||(0.08)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

24
**Table 9:** Household Member Health

|**Outcome**|**_Sp_**<br>**Control**<br>**Mean**|**_ouse_**<br>**Estimated**<br>**Impact**|**_G_**<br>**Control**<br>**Mean**|**_irls_**<br>**Estimated**<br>**Impact**|**_B_**<br>**Control**<br>**Mean**|**_oys_**<br>**Estimated**<br>**Impact**|
|---|---|---|---|---|---|---|
|Sick in last 30 days|0.27|-0.01|0.15|-0.04<sup>_∗∗_</sup>|0.13|-0.02|
|||(0.02)||(0.02)||(0.01)|
|Days sick in last 30 days<sup>_⋄_</sup>|2.24|-0.03|0.61|-0.14<sup>_∗_</sup>|0.60|-0.09|
|||(0.20)||(0.07)||(0.07)|
|Observations|1|988|1|284|1|451|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

25
**Table 10:** Heterogeneity for HHs in districts where women perform relatively few oxen tasks

|**Outcome**|**Obs**|**Control**<br>**Mean**|**Impacts in Di**<br>**Women Perfor**<br>**More Ox Tasks**|**stricts where**<br>**m Relatively**<br>**Few Ox Tasks**|**p-value**|
|---|---|---|---|---|---|
||||**[1]**|**[2]**|**[1]=[2]**|
|Total land cultivated (ha)<sup>_⋄_</sup>|1718|12.64|0.76<sup>_∗_</sup><br>(0.45)|0.40<br>(0.51)|0.60|
|Producer total active time|1718|69.95|-0.85<br>(2.28)|-2.96<br>(1.95)|0.48|
|Producer household plot labor supply|1718|43.36|0.61<br>(1.27)|-1.66<br>(1.22)|0.20|
|Spouse total active time|1718|76.61|-1.90<br>(1.99)|-1.77<br>(1.99)|0.96|
|Spouse household plot labor supply|1717|37.73|-1.64<br>(1.29)|-2.77<sup>_∗∗_</sup><br>(1.24)|0.53|
|Spouse other agric work|1718|1.02|0.16<br>(0.28)|-0.06<br>(0.26)|0.55|
|Spouse non-farm labor|1716|5.55|-0.53<br>(0.76)|1.78<sup>_∗∗_</sup><br>(0.81)|0.04|
|Spouse household chores|1714|28.12|-0.36<br>(1.11)|-0.28<br>(1.24)|0.96|
|Spouse hobbies|1713|4.29|0.19<br>(0.50)|-0.35<br>(0.58)|0.47|
|Boys’ total active time|1264|61.99|0.01<br>(2.45)|-3.46<br>(2.56)|0.33|
|Boys’ household plot labor supply|1263|36.61|1.02<br>(1.67)|-1.72<br>(1.98)|0.29|
|Girls’ total active time|1124|59.76|-2.46<br>(2.44)|-3.62<br>(2.69)|0.75|
|Girls’ household plot labor supply|1124|29.61|-1.80|-3.04<sup>_∗_</sup>|0.60|
||||(1.63)|(1.71)||

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. † indicates hours in last week. Spouse outcomes are averaged across wives in polygamous households. All regressions include controls for household composition, household-head education, 26