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His, Hers, or Ours: Impacts of a Training and AssetTransfer Programme on Intra-Household Decision-Making in Zambia

Kashi Kafle, Hope Michelson & Alex Winter-Nelson

To cite this article: Kashi Kafle, Hope Michelson & Alex Winter-Nelson (2019) His,Hers, or Ours: Impacts of a Training and Asset Transfer Programme on Intra-HouseholdDecision-Making in Zambia, The Journal of Development Studies, 55:9, 2046-2064, DOI:10.1080/00220388.2018.1516868

To link to this article: https://doi.org/10.1080/00220388.2018.1516868

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His, Hers, or Ours: Impacts of a Training and AssetTransfer Programme on Intra-HouseholdDecision-Making in Zambia

KASHI KAFLE *, HOPE MICHELSON** & ALEX WINTER-NELSON***Research and Impact Assessment Division, International Fund for Agricultural Development (IFAD), Rome, Italy,**Department of Agricultural and Consumer Economics, University of Illinois, Champaign, IL, USA

(Original version submitted July 2017; final version accepted August 2018)

ABSTRACT This paper studies the effects of a multifaceted asset transfer programme on the decision-makingdynamics of smallholder households. Constructing separate indexes of participation in household decision-making for adult females and males, and using difference-in-differences to assess the impact of livestock transferand training, we find evidence that these interventions increased the share of decisions in which individualsparticipated, regardless of gender. Increases in decision-making participation by both men and women are drivenby an increase in joint decision-making within the household on the extensive margin. Decisions made jointly bymen and women increased by 16 per cent across all household activities, with statistically significant declines inindependent decision-making by men and women. Findings are encouraging given the evidence of welfare gainsassociated both with increases in participation in decision-making by women as well as increased cooperationwithin households.

1. Introduction

Numerous policies and interventions work to empower women through education, training, improvedaccess to credit, leadership development, and transfer of economic resources. These projects are informedby evidence that improving women’s economic resources can increase women’s decision-making partici-pation and lead to improvements in household welfare (Duflo, 2012; Malhotra and Schuler, 2005; Mehra,1997).We study the effects of a multifaceted asset transfer programme on the decision-making dynamics ofsmallholder households, paying attention to changes in the prevalence of decisions made by men, bywomen, or jointly. We construct separate indexes of participation in household decision-making for adultfemales and males and use difference-in-differences to assess the impact of livestock transfer and trainingon how household decisions are made. While our focus is on the impact on intra-household decision-making, we use the terms women’s empowerment and women’s decision-making participation inter-changeably given evidence that increased decision-making participation is associated with increasedempowerment in the household (De Brauw, Gilligan, Hoddinott, & Roy, 2014; Duflo, 2012; Karl, 1995;Pitt, Khandker, & Cartwright, 2006).

A large literature assesses the impact of a range of initiatives, including asset transfers, micro-credit, cashtransfers (conditional and unconditional), and training, on women’s empowerment, often emphasising

Correspondence Address: Kashi Kafle, Research and Impact Assessment Division, International Fund for AgriculturalDevelopment (IFAD), Via Paolo Di Dono 44, 00142, Rome, Italy. Email: krkafle@gmail.comSupplementary Materials are available for this article which can be accessed via the online version of this journal available athttps://doi.org/10.1080/00220388.2018.1516868

The Journal of Development Studies, 2019Vol. 55, No. 9, 2046–2064, https://doi.org/10.1080/00220388.2018.1516868

© 2018 Informa UK Limited, trading as Taylor & Francis Group

women’s participation in decision-making (Bandiera et al., 2014; De Brauw et al., 2014; Pitt et al., 2006;Yoong, Rabinovich, &Diepeveen, 2012).1 However, little research has examined how changes in women’sdecision-making participation affects cooperative decision-making or men’s participation in decision-making in the household. Recently, McCarthy and Kilic (2017) provided evidence that increased coopera-tion amongmen and womenwithin the household can yield welfare improvements. Relatively unexaminedin the literature is how decision dynamics within the household shifts between decisions made individuallyand decisions taken jointly; in particular, how programmes may cause such shifts and what the empower-ment implications of altering decision-making dynamics might be.

Existing studies often assume (implicitly or explicitly) that intra-household bargaining is a zero-sum-game such that improving women’s position within the household necessarily weakens the position of men(Hanstad, 2014; Miller, 2011). Whether women can experience increased participation in decision-makingwithout diminishingmen’s roles could be a critical question for asset transfer programmes, especially wheneffective management of the asset is facilitated by participation from both men and women, as can be thecase with assets like livestock that require significant amounts of labour or complementary investments togenerate returns. When household enterprises require the support or active involvement of men, negativeimpacts on the continued engagement of men in household decision-making can prove detrimental forwomen-led enterprises (Chant & Gutmann, 2000).

A shift from individual to joint decision-making in the household may expand the scope of decision-making for the household member who was previously excluded, but could represent diminishedempowerment for the individual losing independence as a decision-maker. Implications for women’sempowerment of a shift to joint decision-making differ substantively depending on whether a shiftimplies a disproportionate reduction in decision-making solely by women or solely by men. Examinationof men’s and women’s individual and joint decision-making can reveal whether changes in women’sdecision-making participation are consistent with increasing or diminishing cooperation within thehousehold. Many standard measures of women’s decision-making power, such as the Women’sEmpowerment in Agriculture Index (WEAI), focus on the prevalence of decisions involving women,combining those made jointly with those made independently (Alkire et al., 2013; Sraboni, Quisumbing,& Ahmed, 2013). However, a focus on women’s participation in decision-making that combines jointdecisions with female-controlled decisions can miss important changes in decision-making dynamics. Amore nuanced result emerges by analysing joint and independent decision-making separately beforecombining them into a single index.

Specifically, we assess the impact of a livestock transfer and training intervention on men’s andwomen’s participation in intra-household decision-making using household survey data from a fieldexperiment in Zambia. A programme rollout with two treatment arms allows us to identify the casualimpact of the intervention on decision-making. The intervention could impact decision-makingdynamics by altering the prevalence of decisions made solely by either women or men, or by changingthe share of one gender’s participation in joint decision-making in the household. Moreover, changesin decision-making dynamics could emerge at the extensive margin, affecting only those new manage-ment decisions that arise as a result of the livestock transfer (for example where to sell the milkproduced by the transferred dairy cow), or might permeate pre-existing decision spheres not directlyrelated to the intervention.

This analysis is among the first to evaluate the impact of a multifaceted intervention on gendereddecision-making in rural households. We find an increase in women’s decision-making participation,limited to areas directly related to the transferred asset and emerging primarily through growth in jointdecision-making. This result suggests that decision-making is more malleable on the extensive marginthan in areas where decision dynamics are well-established. Results also demonstrate that an increasein women’s participation need not imply reduction in men’s engagement because of increases in theprevalence of joint decision-making in the household. Finally, our analysis shows that combining jointand independent decision-making into a single metric can obscure the true impact due to possiblecountervailing effects on decisions taken alone versus decisions taken jointly.

Our finding that most changes in decision-making are on the extensive margin may reflect twofeatures of our sample. First, household economic activity is concentrated in a few areas and therefore

His, hers, or ours 2047

the number and variety of decisions being made is commensurately small. Of the 42 decisions that wecan measure using our survey instrument, households on average report activity in five decisions inbaseline and 10 decisions in endline.2 Second, the majority of decisions that we observe in our sampleare made jointly at baseline. Together, these factors may drive the majority of project impacts to theextensive margin of newly introduced decisions in the household.

The rest of this paper proceeds as follows. In Section 2, we discuss the livestock transfer interven-tion. Section 3 presents the research methods. Section 4 presents results and contextualises theseresults in existing theories of intra-household bargaining models. We discuss implications of thefindings in the conclusion.

2. The intervention

This study analyses an asset transfer programme initiated by Heifer International (Zambia) in Zambia’sCopperbelt Province in 2011. The intervention consists of provision of donated livestock, training, andsocial capital development to households who belong to organised groups who applied to HeiferZambia for the opportunity to participate.3 The groups that applied to Heifer Zambia were usuallyorganised and led by women but included male participants. Selected groups received support andtraining over the course of a year. After a year, Heifer transferred livestock to a subset of groupmembers. Based on local ecological and market conditions, group members received either onepregnant dairy cow, two pregnant draft cattle, or one male and seven female meat goats. Cattlerecipients also received one bull to share as a group to service the cows. The total value of theasset transfer was similar regardless of the species transferred. The programme requires that indivi-duals receiving a donated animal pass on the first female offspring of that animal to group memberswho did not receive a gift in the initial distribution. Eventually, all group members are expected toreceive animal asset transfers. Topics of trainings include: gender balance, accountability, sharedresponsibilities, sustainability and self-reliance, business management, social justice, environmentalsustainability, improved animal management, and nutrition. Both men and women receive the training.

A theory of change for the project is presented in Appendix Table A1 and suggests multiple impactpathways. The Heifer programme could expand women’s and men’s joint decision-making if trainingson topics like the benefits of collective decision-making, cooperation, mutual respect, and theimportance of women’s contributions in the household change men’s and women’s behaviour. Theintervention may enhance women’s decision-making participation if women are the primary managersof the transferred livestock and control income from animal sales or animal product sales. In addition,capacity building in groups that have significant female leadership might translate into greater voicefor women within households. Finally, by introducing a new asset that may require joint managementby men and women, the programme may introduce a new set of decisions that are made jointly.

2.1. Sample selection

Figure 1 details the sample selection procedure. First, Heifer Zambia solicited applications for the trainingand livestock programme from farmer groups in the Copperbelt Province. Applicants were primarilywomen-led self-help groups (SHGs). A minimum of 10 households were required to form a group but thenumber of households per group varied. Heifer screened the applications and selected eligible groups from12 different communities. The number of groups that were considered eligible exceeded the number thatcould be supported by the resources available in 2011 and the programme employed a rollout design toimplement the project. Livestock transfers and training were scheduled by order of application of theeligible groups assuring that eligible groups that were ahead in the queue received services first. Ouranalysis leverages the rollout design of the intervention to define a control group.

The evaluation design uses a two-stage sampling process. In the first stage, researchers randomlyselected three SHGs from the set of groups that were scheduled to receive support at the time of thebaseline survey and purposively drew two additional groups that were in queue for services at some

2048 K. Kafle et al.

future, undetermined date. In the second stage, researchers used the pre-determined sample size torandomly select households from each group proportional to the population of the respective com-munity. Households from the groups scheduled to receive services form our treatment sample. Controlhouseholds were drawn from the two SHGs that were next in queue for services, but had not beenscheduled. Households in the control sample were eligible for the programme but received nothingfrom Heifer during the three years after baseline and had no definitive information regarding whetherand when they would receive services.

The full sample size relevant to this study is 520 (260 in each round) and power calculations suggestthat we have adequate power to detect changes in joint decision scores (97%), and reasonably adequatepower for female only (78%), and ‘male and joint’ (78%) decision scores. We are underpowered(51%) for ‘female and joint’ and male only decision scores, meaning we are more likely to not rejectthe null when the alternative hypothesis is true for those variables and we may be more likely tooverestimate effect sizes in the case of a significant result. These power calculations are conservativeas they are based on unconditioned baseline means and standard deviations. Analyses in Jodlowski,Winter-Nelson, Baylis, and Goldsmith (2016) and Kafle, Winter-Nelson, and Goldsmith (2016) useddata from the same field experiment.

Treatment and control households self-selected to participate. Households in both treated andcontrol groups are similar in the group and household observables that Heifer considers when selectinggroups for participation (capacity and needs of members, cohesiveness of the group, and so forth).Since all households self-selected into the programme and are balanced in observables, we assume thatthe households are similar in their relevant non-observable factors.

All treatment households received training and benefits of enhanced social capital that come throughgroup membership, but the treatment consists of two arms: one subset of treated households receivedanimals from Heifer Zambia at the start of the project and a second subset received second-generationanimals as pass-on gifts from original recipients later.4 Those who received animals and training in theinitial distribution form the full treatment group for the analysis, while households who received the

Figure 1. Sample selection procedure.

His, hers, or ours 2049

offspring from the original livestock as pass-on gifts form a partial treatment group. Households in thepartial treatment group received immature animals, while households in the full treatment groupreceived more valuable mature livestock. Key informant interviews with group members indicatedthat selection into the two treatment arms was random, but the purity of that randomness is not known.The majority of households in the partial treatment group had already received animals by the endlinesurvey for this study (January 2015). The spatial proximity between the two types of treatmenthouseholds implies that they may be subject to spillover effects as well as the benefits of training;neither spillovers nor training are relevant for the control households because they are geographicallyseparated from the treated households and had not received the training by the endline survey.5

3. Data and methods

3.1. Data

Data collection included an initial survey in January 2012, at the time when livestock transfers werebeing made to the full treatment households (initial livestock distribution extended from November2011 through March 2012). Additional survey rounds were conducted in July 2012, January 2013,July 2013, January 2015, and July 2015.6 This study uses a two-period panel because questions relatedto decision-making were administered in two survey rounds only.

Figure 2 presents the survey timeline and corresponding sample sizes. The baseline for this study isthe first follow up survey (July 2012), conducted four to six months after the initial animal distribu-tion, and the endline for this study is the fourth follow up survey conducted in January 2015. If theprogramme affected decision-making during the first six months, using the first follow up survey asbaseline will underestimate the treatment effect.

The baseline includes 276 households of which 260 were successfully re-interviewed in the endline(Table 1). The full sample attrition rate is about 6 per cent but partial treatment households have a

Project Baseline

Follow up Rounds

TimelineJan

2012July2012

Jan 2013

July 2013

Jan2015

July 2015

Sample size 324 313 308 304 298 295

Baseline for this study

Endline for this study

Figure 2. Timeline of survey and corresponding sample size.

Table 1. Survey characteristics and attrition

Treatment Status Baseline Endline Attrition (%) Attrition (n)

Training and assets recipients (Full treatment) 104 100 3.8 −4Training and delayed assets recipients (Partial treatment) 105 96 8.6 −9Control 67 64 4.5 −3Total 276 260 5.8 −16

Notes: Baseline is the first follow up survey from a larger field experiment described in Kafle et al. (2016) andJodlowski et al. (2016).

2050 K. Kafle et al.

slightly higher attrition rate of 8 per cent while attrition among the control households was 3 per cent.We find no pattern in attrition related to observable characteristics of the households.

Given the design and implementation of the intervention and availability of longitudinal data onmen’s and women’s control over intra-household decisions, we test three hypotheses:

Hypothesis 1. The asset intervention increases women’s decision-making participation.

Hypothesis 2. The scope of both men’s and women’s decision-making within the household expandsas they shift from independent to collective decision-making.

Hypothesis 3. The intervention increases women’s decision-making participation in household activitiesrelated to the transferred assets only, with no spillover into other household decision spheres.

3.2. Measuring decision-making participation

Measuring women’s empowerment is complicated by the reality that empowerment encompassesmultiple dimensions of an individual’s lived experience. Measures that try to capture this richnessface problems of dimensionality and interpretation. Measurement strategies in the existing body ofrelated literature generally rely on the construction of indexes that aggregate, among other things,women’s independent and joint decision-making participation over multiple spheres. The Women’sEmpowerment in Agriculture Index (WEAI), for example, aggregates joint and independent decision-making across five dimensions into one index (Alkire et al., 2013; Malapit, Kadiyala, Quisumbing,Cunningham, & Tyagi, 2013). Our strategy is to focus on household members’ decision-making but toexamine separately decisions made individually and jointly within the household. Measures based onindividual and joint decision-making can provide a lens for gauging the voice of individual householdmembers and the prevalence of cooperation in the household.

Our focus on decision-making participation to gauge women’s empowerment is consistent with theWEAI and other existing measures of women’s empowerment (De Brauw et al., 2014; Doss, 2013;Wiig, 2013). We extract and adapt components of the WEAI to define decision-making indexes thatcan be applied to assess men’s role in decision-making, women’s decision-making, and joint decision-making in the household. As in the WEAI and in Wiig (2013), we focus on a count of decisions thatmen and women in the household make either independently or together. Our measure is distinct fromthe WEAI in that we only include decisions related to production and control over economic resourcesin the household; we analyse joint and independent decisions separately rather than aggregating theminto a single index; and we exclude indicators beyond decision-making such as time allocation. We usethis approach for both practical and theoretical reasons. First, we lack the detailed data across multipledecision spheres to construct the full WEAI. Second, we expect that production activities and controlover economic resources are the two spheres most likely to be affected by the programme. Third, weseek measures that can be extended to joint participation as a measure of cooperation.

We construct our measures using information about authority over eight decision spheres ofhousehold activities including decisions about sending children to school, slaughtering animals,sales of animal products and live animals, sales of crops, control over farm income, access to loanand credit opportunities, and crop storage. Within these spheres we measure 42 household decisions.We consider decisions directly related to the intervention (for example cattle and goat sale andslaughter; milk, meat, and draft power sale; and access to credit)7 to be ‘treatment-related’ decisions.We use remaining decisions to test for the existence of project spillovers into spheres of householdactivities unrelated to the project.

We first calculate individual decision scores for each of the eight decision spheres by averaging thenumber of women’s and men’s joint and/or independent decisions over total decisions made, givingequal weight to joint and independent decisions. Our approach of aggregating women’s joint andindependent decisions into a single measure is consistent with the approach of measuring women’sempowerment in the WEAI, which equally weights decisions in which women have independentcontrol and those in which they have a share of control. We then calculate aggregate decision scores by

His, hers, or ours 2051

averaging the scores for each decision sphere, giving equal weight to each of the spheres in which ahousehold is active. Since only a few households report activities in all eight decision spheres,decision scores for most households are averaged over fewer than eight decision spheres. This processimplies that each sphere is equally important, but that individual decisions are weighted less whenthere are more decisions in a specific sphere.

We apply this measure of decision-making participation to five different types of decision-making:(1) combined women’s independent and joint decision-making; (2) combined men’s independent andjoint decision-making; (3) joint decision-making by men and women; (4) women’s independentdecision-making; and (5) men’s independent decision-making.

Suppose T denotes the total number of decisions made in the household, Ti denotes the number ofdecisions under each decision sphere i, Wi denotes the number of decisions under sphere imade by femalemembers, Mi denotes the number of decisions under sphere i made by male members, and Ji denotes thenumber of joint decisions under sphere i. Let Nk be the number of decision spheres in which household khas reported some activity. Then decision scores for household k are computed as follows:

(1) Female and joint score WJkð Þ ¼ 1Nk

P

i

WiþJiTi

(2) Male and joint score MJkð Þ ¼ 1Nk

P

i

MiþJiTi

(3) Joint score Jkð Þ ¼ 1Nk

P

i

JiTi

(4) Female only score Wkð Þ ¼ 1Nk

P

i

WiTi

(5) Male only score MKð Þ ¼ 1Nk

P

i

MiTi

By construction, all five scores lie in the continuum from 0 to 1. The ‘Female (Male) and Joint’score for a household can be interpreted as the mean of the share of female (male) members’ joint andindependent decisions over the decision spheres in which the household has reported activity. Forexample, a ‘Female and Joint’ score value of 1 for a household means that female member(s) of thehousehold have participated in the decision-making of all activities considered. The ‘Female (Male)Only’ score for a household is the mean of the share of female (male) members’ independent decisionsover the decision spheres in which the household has reported activity. A ‘Female Only’ score value of1 for a household means that female member(s) of the household have full control in the decision-making in all areas in which the household made decisions.

When interpreting change in a decision score that aggregates women’s independent and jointly-made decisions, it is not possible to discern whether an increase is caused by growth in joint decisions,growth in women’s independent decisions, or both. Changes in the ‘Female and Joint’ score and the‘Male and Joint’ score will indicate changes in the scope of participation in decision-making, but theywill not reveal shifts from individual to joint decision-making, which may have implications forempowerment effects. In contrast, tracking changes in decision scores that separately measure theprevalence of uniquely male, uniquely female, and joint decisions allows one to track changes in thedecision dynamics from independent to joint decisions. We use the Joint score and the ‘Male Only’and ‘Female Only’ scores to test the effect of the asset transfer and training on the shares ofindependent and joint decisions, and thereby better gauge changes in empowerment.

Finally, as a robustness check, we aggregate the scores by decision sphere into an index using weightsfrom a principal components analysis, rather than using equal weights. We calculate five weighted indexesanalogous to the five decision scores. First, we calculate the proportions of joint or independent decisionsto total decisions by decision spheres and run principal component analysis (PCA) over the eightproportions to derive weights. In all cases, we retain only the first principal component because it capturesthe maximum variation present in the data (Filmer & Scott, 2008; Vyas & Kumaranayake, 2006).

2052 K. Kafle et al.

3.3. Econometric method

We apply a difference-in-differences (DID) estimation that captures two treatment arms (full treatmentand partial treatment) across two periods to estimate the impact of the livestock intervention. Thisapproach has the advantage of correcting for endogeneity arising from unobserved individual effects(Bertrand, Duflo, & Mullainathan, 2004). The estimating equation is:8

yit ¼ α0 þ α1Timeþ β1Full Treatment � Timeþ β2Partial Treatment � Timeþ �X þ ciþ εit (1)

where yit is the outcome of interest for household i at time t. Time is a time dummy variable equallingone for endline. Full Treatment is a treatment indicator that equals one for the fully treated households(Training and asset recipients) and zero for others. Partial Treatment equals one for the partiallytreated households (Training and delayed asset recipients). X is a vector of control variables thatincludes household size, number of children ages five years or below, number of children ages six to16, age of the household head, level of education of the head, gender of head, and marital status of thehousehold head. ci is the household level fixed effect, and εit is an idiosyncratic error term. Thecoefficient β1 represents the programme effect on the households experiencing full treatment and β2represents the effect on partially treated households.

Equation (1) provides unbiased estimates contingent on the identifying assumptions. Our main assumptionis that in the absence of the intervention changes in the intra-household decision-making would be similar intreatment and control households (parallel trends). We cannot test for pre-programme parallel trends as we donot have two rounds of pre-intervention data. Balance tests in Table 2 indicate that the decision-makingparticipation in baseline is statistically indistinguishable across treatment and control households. Even so,the parallel trends assumption could be violated if men and women from control households changed theirdecision-making participation after the treated groups received the asset transfers and training. We argue thatthis is unlikely to happen because: (1) the control households are spatially remote from the treatmenthouseholds leaving little chance of contamination, and (2) the control households have no certainty onwhether and when they will receive the livestock leaving little room for ‘expectation effects’. Anotheridentifying assumption is that the programme roll-out was random. This assumption also holds because,despite a purposeful selection of eligible groups, programme placement was done by queuing and thesequence of applications can be considered random, as applications arrived in a relatively short time period.

4. Results

4.1. Descriptive results

Summary statistics and the balancing test shown in Table 2 indicate that our sample is reasonablybalanced and the baseline characteristics are smoothly distributed across recipients and non-recipients.Based on the normalised differences (Imbens & Wooldridge, 2008) household size and age of thehousehold head for the treated groups are significantly different from that of the control group. Allother observed characteristics of the two groups are statistically equivalent. Household size and age ofthe household head play little role in the fixed effects regression because the changes in these variablesover time are identical across treatment and control groups. Balancing tests on the outcome variablesindicate that the various decision scores are similar across treatment and control households atbaseline. The baseline results indicate a high prevalence of joint decision-making in all groups andsimilar scores for male only and female only decision-making. Given that both control and treatedhouseholds applied to the programme, their unobserved characteristics are also likely to be similar. Wecorrect for any time invariant unobserved characteristics through the use of household fixed effects.

ND1 and ND2 indicate the value of normalised difference between full treatment (Training andassets recipients) and control households, and partial treatment (Training and delayed assets recipients)and control households, respectively.

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Table 3 presents changes in the share of household decisions made by male and female membersjointly or individually at baseline and endline. These descriptive statistics suggest a shift fromindividual decision-making by men and women to joint decision-making in the treated households.Among the households that received assets and training initially, the decline in individual decision-making and increase in joint decision-making results in an 8 per cent increase in the share ofindependent and joint decisions in which women participate and an 11 per cent increase in the share

Table 2. Summary statistics and balancing tests on baseline sample

Treatment status Normalised difference

Variables Full treatment Partial treatment Control ND1 ND2

Household Size 7.24 7.10 5.84 0.36 0.32(2.66) (2.90) (2.34)

No. of children 5 or under 1.19 1.22 1.00 0.13 0.15(1.002) (0.98) (1.07)

No. of children 6 to 16 2.28 2.52 1.84 0.18 0.27(1.68) (1.79) (1.59)

Female head (1 = Yes, 0 = No) 0.21 0.15 0.19 0.04 −0.08(0.41) (0.36) (0.39)

Married head (1 = Yes, 0 = No) 0.82 0.89 0.78 0.07 0.19(0.39) (0.32) (0.42)

Education of head 2.90 3.10 2.94 −0.02 0.08(1.41) (1.55) (1.30)

Age of head 51.1 44.6 43.7 0.35 0.04(12.99) (12.69) (13.70)

Female and joint decision score 0.74 0.80 0.79 −0.12 0.02(0.302) (0.26) (0.29)

Male and joint decision score 0.70 0.77 0.78 −0.16 −0.03(0.34) (0.31) (0.32)

Female only decision score 0.30 0.23 0.22 −0.25 −0.01(0.34) (0.31) (0.32)

Male only decision score 0.26 0.20 0.21 0.16 0.02(0.30) (0.26) (0.29)

Joint decision score 0.44 0.56 0.57 0.12 −0.01(0.34) (0.35) (0.36)

Observations 100 96 64

Notes: Point estimates are mean and standard deviations are in parentheses.

Table 3. Change in decision scores over time by treatment groups

Treatment status

Decision scores

Female and Jointa Male and Joint Joint Female only Male only N

Full treatment 0.08** 0.11** 0.18*** −0.11*** −0.07** 99(0.341) (0.268) (0.355) (0.270) (0.339)

Partial treatment 0.03 0.06* 0.09* −0.06 −0.03 96(0.314) (0.267) (0.390) (0.267) (0.315)

Control −0.02 0.04 0.02 −0.04 0.02 64(0.297) (0.305) (0.398) (0.304) (0.298)

Full sample 0.04* 0.08*** 0.11*** −0.07*** −0.03 259(0.322) (0.277) (0.383) (0.278) (0.321)

Notes: Point estimates are mean. Standard deviations are in parentheses, asterisks indicate the level of significancefor test of equality of means over time, *p < .10, **p < .05, ***p < .01. a‘Female (Male) and Joint’ score is thecombined score of adult women’s (men’s) joint or independent participation in intra-household decision-making.Joint and independent decisions are equally weighted.

2054 K. Kafle et al.

of decisions in which men participate. A similar pattern appears for the partial treatment group(training and delayed assets), but the differences are not statistically significant.

Table 4 presents changes in both women’s and men’s joint and independent decision scores over the eightdecision spheres. Post-intervention, both women’s and men’s participation in decision-making has increasedinmost decision spheres. For example, the ‘Female and Joint’ decision scores increased by 23 per cent and 13per cent for live animal sale and for animal slaughter, respectively, suggesting expansion inwomen’s decision-making participation in spheres related to the transferred assets. The ‘Male and Joint’ decision scores foranimal slaughter increased by 28 per cent, but the change is not statistically significant. This observationsuggests that even though both men and women’s decision-making participation related to transferred assetsincreased,women’s participation increasedmore in important decisions related to live animal sales. Regardingdecisions not directly related to the transferred asset, women had more say on sending children to school andmen gained involvement in crop produce sales and loan/credit services. Results for households that receivedtraining and delayed assets (partial treatment) show a similar pattern but there is no significant change inmen’sand women’s decision-making participation among the control households.

All decision spheres, except sending kids to school, involve multiple decisions and the women’s(men’s) empowerment score is the weighted score of women’s (men’s) independent and joint decisionsmade in the household.

Figure 3 depicts the change in Joint decisions for the household that received training and assets (fulltreatment) and control households. The pattern of change in decision-making shows that the expansionin ‘Female and Joint’ and ‘Male and Joint’ decisions largely comes from a growth in joint decision-making. These patterns of change in decision-making are explored further in the econometric analysis.

4.2. Empirical results and discussion

Table 5 presents estimates of the impact of the intervention on decision scores. The first columnpresents the impact of the livestock intervention on the ‘Female and Joint’ decision score, which is the

Table 4. Change in women’s and men’s independent and joint decision scores by decision spheres

Full treatment Partial treatment Control

Decision spheresFemale and

JointMale andJoint

Female andJoint

Male andJoint

Female andJoint

Male andJoint

Sending children toschool

0.14*** −0.08 0.03 −0.02 0.01 0.016

(0.429) (0.340) (0.446) (0.250) (0.402) (0.381)Animal slaughter 0.13*** 0.28*** 0.10* 0.16** −0.01 0.15

(0.438) (0.490) (0.481) (0.518) (0.382) (0.475)Live animal sale 0.23*** 0.10 0.16 0.31** 0.208 0.12

(0.501) (0.437) (0.437) (0.495) (0.396) (0.433)Animal product sale 0.091 0.23 - - - -

(0.684) (0.429)Crop produce sale −0.03 0.26*** −0.02 0.17*** −0.06 0.14

(0.622) (0.499) (0.529) (0.533) (0.532) (0.532)Off-farm income 0.10 −0.07 0.064 −0.14 0.17 −0.07

(0.632) (0.623) (0.604) (0.531) (0.698) (0.704)Access to credit 0.001 0.19*** 0.06 0.08* −0.02 0.06

(0.598) (0.511) (0.519) (0.450) (0.529) (0.452)Crop storage/sale 0.33 −0.33 0.33 −0.67 - -

(0.577) (0.577) (1.155) (0.577)Observations 99 99 96 96 64 64

Notes: Point estimates are mean decision scores for women’s and men’s independent and joint decisions. Standarddeviations are in parentheses, asterisks indicate the level of significance for test of equality of means over time,*p < .10, **p < .05, ***p < .01.

His, hers, or ours 2055

share of decisions in which the woman participated in any capacity. The second column reports impacton the ‘Male and Joint’ decision score capturing male participation and columns (3), (4), and (5)present the impact on Joint decisions, women’s independent decisions (‘Female Only’ decisions) andmen’s independent decisions (‘Male Only’ decisions). Recall that we were underpowered to detectchanges for ‘Male Only’ decisions and ‘Female and Joint’ decisions; we find statistically significantand economically reasonable (relative to the other estimated coefficients) changes for both of thesevariables, likely because some of the variation is explained by the household controls included in theanalysis. Results indicate that the intervention increases the ‘Female and Joint’ decision score by 8.5percentage points and the ‘Male and Joint’ decision score by 8.1 percentage points suggesting anincreased participation in household decision-making by both men and women.

Considering joint decisions and independent decisions separately reveals that the intervention led to anexpansion of joint decision-making by 16 percentage points accompanied by about an eight percentage

Figure 3. Change in the joint decision scores over time.

Table 5. Project impact on women’s and men’s decision-making based on decision scores

Decision Scores

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline −0.036 0.039 0.0024 −0.038 0.037(0.039) (0.036) (0.049) (0.035) (0.039)

Full treatment × Endline 0.085* 0.081* 0.16*** −0.079* −0.082*(0.050) (0.046) (0.059) (0.046) (0.050)

Partial treatment × Endline 0.057 0.030 0.085 −0.028 −0.055(0.049) (0.045) (0.062) (0.045) (0.049)

Female respondent (1 = Yes, 0 = No) 0.074* −0.048* 0.025 0.050* −0.073*(0.038) (0.029) (0.047) (0.029) (0.038)

Household Controls Yes Yes Yes Yes YesObservations 517 517 517 517 517

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head. The full results from allreported regressions are presented in Table 5 in the Supplementary Materials.

2056 K. Kafle et al.

point reduction in both women’s and men’s independent decision-making, although the latter is notstatistically significant. Because the number of decision spheres does not change over time, expansion injoint decisions comes from reduction in independent decisions or addition of new joint decisions withinexisting decision spheres. In this case, the results point to increased cooperation in the household with bothmen and women participating in more decisions, coupled with a reduction in independent decision-makingby men and women. The reduction in women’s independent decision-making (7.9%) is not statisticallydifferent from the decrease in men’s independent decision-making (8.2%).

In contrast to the fully treated households, households that received partial treatment exhibit nostatistically significant impacts from the treatment. The lack of evidence of change in men’s andwomen’s decision-making among these households suggests a possible difference in the impact oftraining with and without transfers of assets. Absent the asset transfer, awareness training about genderbalance does not impact decision-making in the short-run.

To assess whether impacts on decision-making are restricted to decision spheres related to thelivestock transfer, we combine the eight decision spheres into a ‘treatment related’ sphere and a spherefor all other decisions. The ‘treatment related’ sphere includes decisions about cattle and goat sale,milk, meat, and draft power sale, cattle and goat slaughter, and women’s and men’s access to credit.The number of decisions made in each of the five processes (Joint, Male Only, Female Only, Male andJoint, Female and Joint) is taken as the outcome of interest.

The programme led to a large and statistically significant increase in joint decision-making fortreatment-related activities (Table 6). Due to the intervention, both men’s and women’s decision-making participation in treatment-related activities increased by an additional unit. We find noevidence of a statistically significant change in the number of decisions that are made independentlyby men or women, implying that new decisions emerging from the asset transfers tend to be madejointly. The result holds for the partially treated households. Results suggest that the intervention hadno impact on joint or women’s independent decision-making for activities not directly related to theintervention (Table 7). The intervention appears to have decreased men’s independent decision-makingon non-treatment related decisions in both treatment groups.

Measured gendering of decision-making participation could be influenced by the gender of therespondent. Estimated coefficients on the binary indicator of respondent’s gender are presented inTables 5, 6, and 7. Results show that having a female respondent amplifies the measured positiveimpact of the intervention on women’s decision-making participation but tempers the observed impact

Table 6. Project impact on women’s and men’s decision-making based on ‘treatment related’ activities

Treatment related decisions

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline 1.05*** 0.96*** 0.75*** 0.30** 0.20**(0.22) (0.22) (0.21) (0.13) (0.092)

Full Treatment × Endline 0.95*** 0.70*** 0.78*** 0.17 −0.081(0.25) (0.25) (0.24) (0.16) (0.14)

Partial Treatment × Endline 0.49* 0.92*** 0.71*** −0.22 0.22(0.28) (0.27) (0.27) (0.15) (0.14)

Female respondent (1 = Yes, 0 = No) −0.18 −0.24 −0.22 0.043 −0.014(0.18) (0.18) (0.18) (0.083) (0.12)

Household Controls Yes Yes Yes Yes YesObservations 518 518 518 518 518

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head. The full results are presentedin Table 6 in the Supplementary Materials.

His, hers, or ours 2057

on men’s decision-making participation. The respondent’s gender has no influence on the reportedmen’s and women’s participation in joint decision-making.

In summary, findings suggest that transferring physical assets combined with training can expand thescope of cooperative decision-making and increases women’s participation in decisions while reducingmen’s and women’s independent decisions. The finding that providing training along with transferringphysical resources to women leads to expansion in the scope of women’s voice in decision-making isconsistent with the separate spheres bargaining model in which household members have full control overthe payoff of household resources under their control (Lundberg & Pollak, 1993). In their assessment of acash transfer programme, Lundberg and Pollak (1993) found that the programme’s impact differs accord-ing to the recipient’s gender. In our case, women’s control over decision-making regarding transferredassets has probably increased because women were identified as the primary owner of the transferred assetsand therefore likely have more control over the payoff of the transferred assets. However, our finding thatexpansion in women’s decision-making participation comes through increased joint decision-making onthe extensive margin of new decisions suggests that a decrease in men’s decision-making participation isnot inevitable. The findings may be better explained by the collective bargaining models. Even thoughwomen may have more individual control over transferred assets and related decisions, they may stillcooperate with men in the household because non-cooperation leads to Pareto inefficiency in householdresource allocation (Chiappori, 1988; Himmelweit, Santos, Sevilla, & Sofer, 2013).

Data limitations prohibit a test for Pareto-efficiency in resource allocation, but existing studies findevidence of Pareto-efficiency in intra-household resource allocation in agrarian settings (Bobonis, 2009;Browning & Chiappori, 1998). Evidence suggesting the absence of Pareto-efficiency in household resourceallocation is not uncommon (Basu, 2006; Lundberg & Pollak, 1993), but Chiappori (1992) argues that intra-household decision-making leads to a Pareto-efficient outcome irrespective of bargaining mechanisms.Meanwhile, McCarthy and Kilic (2017) provides a theoretical and empirical base to argue that increasedintra-household cooperation may increase productivity. Because our findings are consistent with the collec-tive bargaining model, this analysis suggests that the livestock transfer and training intervention may havecontributed to efficient intra-household resource allocation through increased cooperation.

Table 7. Project impact on women’s and men’s decision-making based on ‘other’ activities

‘Other’ decisions

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline 2.46*** 2.14*** 1.77*** 0.69*** 0.37*(0.40) (0.37) (0.42) (0.23) (0.20)

Full Treatment × Endline 0.67 0.37 0.83 −0.16 −0.46*(0.53) (0.51) (0.56) (0.33) (0.25)

Partial Treatment × Endline 0.42 0.032 0.52 −0.100 −0.49*(0.50) (0.46) (0.53) (0.30) (0.26)

Female respondent (1 = Yes, 0 = No) −0.005 −0.73** −0.19 0.19 −0.53**(0.39) (0.35) (0.42) (0.22) (0.21)

Household Controls Yes Yes Yes Yes YesObservations 518 518 518 518 518

Notes: Standard errors in parentheses, significance level, * p < .10, ** p < .05, *** p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head. The full results are presentedin Table 7 in the Supplementary Materials.

2058 K. Kafle et al.

4.3. Robustness check and sensitivity analysis

To assess the robustness of our results we run the model using decision scores with weightsgenerated through PCA; we merge the two treatment arms into a single treatment group; and weapply alternative exogenous weights to joint and independent decisions in calculating Female(Male) and Joint scores.

Results of the analysis using a PCA weighted index of decision-making participation (Table 8)confirm the finding that full treatment resulted in an increase in joint decisions and a decrease indecisions made independently by men. Women’s participation in decision-making increased throughtheir participation in joint decision-making. These results confirm our main results in Table 5 in thatthe intervention expanded joint decision-making at the expense of independent decisions, but suggestsa greater relative reduction in independent decision-making by men as opposed to women.

Since selection into the two treatment arms (full and partial treatment) may not have been purelyrandom, we re-estimate all model specifications with a single treatment group, combining the twotreatment arms. As one would expect, blending fully and partially treated households results moderatesthe measured impacts (Appendix Tables A2, A3, A4, and A5). For example, joint decision-making inthis analysis expands by only 12 per cent as opposed to 16 per cent in the full treatment group whenpartially treated households are excluded.

Whether an increase in participation in joint decisions combined with reduced independentdecision-making constitutes an increase in decision-making power is debatable. One might arguethat a preferable measure of empowerment would apply a lower weight to joint decisions than toindependent decisions and thus the ‘Female and Joint’ decisions score would be an improvedmeasure of women’s empowerment if lower weights were placed on joint decisions. Alternatively,one could argue that joint decisions may lead to systematically better welfare outcomes thanindependent decisions and should therefore be given greater weight in the decision scores. Weassess the sensitivity of our findings to the use of a range of weights for individual participation injoint and independent decision-making.

Table 9 presents results using four different weighting schemes for joint and independent decision-making. Results in the first row in Table 9 are equivalent to the Full treatment × Endline results in Table 5.The second two measures in the middle panel in Table 9 assign independent decision-making a higherweight than joint decisions. The next two measures assign smaller weights to independent decisions.

Table 8. Project impact on women’s and men’s decision-making based on PCA-weighted index

PCA-weighted index

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline −0.40 −0.051 −0.42* 0.17 0.50**(0.24) (0.19) (0.23) (0.18) (0.20)

Full Treatment × Endline 0.52* −0.008 0.57* −0.30 −0.94***(0.29) (0.23) (0.29) (0.23) (0.26)

Partial Treatment × Endline 0.32 0.001 0.40 −0.17 −0.39(0.28) (0.22) (0.29) (0.22) (0.26)

Female respondent (1 = Yes, 0 = No) 0.18 −0.38** −0.092 0.30** −0.38*(0.19) (0.16) (0.24) (0.14) (0.21)

Household Controls Yes Yes Yes Yes YesObservations 518 518 518 518 518

Notes: Standard errors in parentheses, significance level, * p < .10, ** p < .05, *** p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head. The full results are presentedin Table 8 in the Supplementary Materials.

His, hers, or ours 2059

Across weighting schemes there is a negative effect on both men’s and women’s independent decisions.The impact on women’s joint and independent decision-making participation shifts from positive tostatistically insignificant as the weight to joint decisions declines. Establishing the theoretically orempirically appropriate weights is beyond the scope of this paper, but convention in the WEAI suggestsacceptance of equal weights in assessment of empowerment based on decision-making participation.

5. Conclusion

This study assesses the impact of livestock transfers and training on the dynamics of intra-householddecision-making to investigate whether household decision-making patterns are affected by the pro-gramme and whether expansion in women’s decision-making within households can be reconciled withcontinued male engagement. Maintaining male participation may be critical to the success of activitiesthat benefit from men’s labour and management. Results show that transferring physical assets whileproviding training to both male and female members of recipient households can expand cooperation inintra-household decision-making. Results imply that women’s decision-making participation can beimproved without compromising men’s engagement when joint decision-making can expand. In thisstudy, the intervention increased the proportion of joint decisions by 16 per cent across activitiesexamined and led to an overall decrease in both women’s and men’s independent decision-makingparticipation by 8 per cent. We find, however, that effects on decision-making are largely limited todecisions that relate directly to the transferred assets.

Our results emphasise the importance of treating independent and joint decisions separately whenanalysing decision-making dynamics; when independent and joint decisions are combined, the estimatedimpacts differ because the expansion in joint decisions is offset somewhat by a reduction in individualdecisions. Recognition of the expansion in joint decisions influences interpretation of the reduction inindependent decisions by men. Likewise, interpretation of whether an increase in women’s participation indecision-making reflects growth in female empowerment requires an understanding of changes in inde-pendent and joint decisions involving women. Failure to attend to both joint and independent decision-making can lead to misrepresentation of impacts of interventions on decision-making and empowerment.

The relatively large estimated impact on the households that received training and assets initiallycompared to minimal impacts on the households that received training but assets after a delay, impliesthat programmes providing training on gender balance, social justice, and other themes may be more

Table 9. Impact of the intervention on intra-household decision-making with varying weights to joint andindependent decisions

Weighting scheme Female and joint Male and Joint Joint Female only Male only

Equal weightsJoint = 1 0.085* 0.081* 0.164*** −0.079* −0.082*Independent = 1 (0.050) (0.046) (0.059) (0.046) (0.050)Independent decisions given relatively higher weightJoint = 0.75 0.044 0.040 0.123*** −0.079* −0.082*Independent = 1 (0.042) (0.039) (0.044) (0.046) (0.050)Joint = 0.25 −0.038 −0.042 0.041*** −0.079* −0.082*Independent = 1 (0.039) (0.042) (0.015) (0.046) (0.050)Joint decisions given relatively higher weightJoint = 1 0.105** 0.102** 0.164*** −0.059* −0.062*Independent = 0.75 (0.049) (0.044) (0.058) (0.034) (0.037)Joint = 1 0.144*** 0.143*** 0.164*** −0.019* −0.021*Independent = 0.25 (0.053) (0.051) (0.058) (0.011) (0.012)Observations 517 517 517 517 517

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head.

2060 K. Kafle et al.

effective when combined with physical asset transfers. The observed increase in joint decisionsprimarily reflects shared responsibilities in activities on the extensive margin associated with theasset transfer itself. The introduction of new management opportunities with new decisions can createspace for more joint decision-making, expanding women’s overall engagement without diminishingmen’s involvement. A key policy implication is that interventions that aim to improve women’sempowerment may be more effective by integrating men in programme design and demonstratingpractical benefits of cooperation.

Notes

1. See (Garikipati, 2013) for a review of existing literature on the impact of developmental interventions on women’sempowerment.

2. Examining the growth in the mean number of decisions over time shows that, on average, both ‘treated’ and ‘control’households reported more decision-making activities at endline and the increase was greater among ‘treated’ households. Theexpansion in decision-making came from ‘treatment’ related decision spheres such as animal slaughter and animal sale as wellas activities related to access to credit and crop sales which are not directly related to animal transfer but may have beeninfluenced by the training.

3. Jodlowski et al. (2016) and Kafle et al. (2016) find positive effects of the same intervention on poverty and food securityoutcomes.

4. Heifer International projects require original recipients to pass-on the first female offspring of the animal they receive to otherparticipants who are eligible for the project.

5. A sample of 30 households who were not members of any organised group and therefore ineligible for participation was alsosurveyed in the villages with treatment households. These unaffiliated households are not included in this analysis due toconcerns over possible sample selection bias.

6. The process of data collection is described in more detail in Jodlowski et al. (2016) and Kafle et al. (2016).7. Access to credit is part of the treatment because the intervention establishes an insurance fund that beneficiaries can access

under specific conditions. In addition, the training emphasises financial literacy, financial planning, and business manage-ment. The NGO also introduces members to existing financial institutions.

8. The estimating model (Equation 1) excludes indicators for the treatment groups because these indicators are time invariant.

Acknowledgements

This research was made possible by a grant from Elanco Animal Health (USA), and by thecooperation of Heifer International. We are especially grateful to Kevin Watkins (Elanco), JamesKasongo (Heifer International), Joyce Phiri (Heifer International), Djondoh Sikalongwe (HeiferInternational), and Peter Goldsmith (University of Illinois). We thank Mary Arends-Kuenning,Kathy Baylis, Craig Gundersen, Dean Jolliffe, Paul Winters, Rui Benfica, Aslihan Arslan,Alessandra Garbero, and three anonymous reviewers for helpful comments. This paper alsobenefitted from comments from participants in the Women’s Empowerment and InternationalDevelopment (WEID) and the International Policy and Development (IPAD) seminars atUniversity of Illinois. Views, opinions, and remaining errors are ours.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Elanco Animal Health (USA) [grant number 2012-02615].

ORCID

Kashi Kafle http://orcid.org/0000-0001-8135-8423

His, hers, or ours 2061

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APPENDIX

Table A1. Project theory of change

Inputs Outputs Outcomes Impacts

(1) Training on genderbalance, self-reliance,animal care andmaintenance, businessmanagement, sharing,and so forth.

(2) Livestock transfer:dairy cattle, or meatgoats, or draft cattle

(1) Women and men frombeneficiary house-holds receivedtraining

(2) Early recipientsreceived 1 dairy cat-tle, 2 draft cattle, or 7meat goats along with1 bull for a group toshare or 1 male goatper goat recipient.

(3) Pass-on-gift recipientsreceived, after a delay,1 female dairy calf, 2female draft calves, or7 female meat goatkids.

(1) Men and women arebetter informed aboutimportance andpotential benefits ofjoint decision-making.

(2) Women are the pri-mary owner of thetransferred livestockand control incomefrom animal sales oranimal product sales

(3) Transferred assetshelp create new deci-sions that requirehouseholdcooperation

(4) Increase in women’sdecision-makingparticipation.

(5) Shift in intra-house-hold decision-makingfrom independentdecision-making tojoint decision-making.

Table A2. Project impact on women’s and men’s decision-making based on decision scores

Decision scores

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline −0.036 0.038 0.0011 −0.037 0.037(0.039) (0.036) (0.049) (0.035) (0.039)

Treatment × Endline 0.071 0.056 0.12** −0.053 −0.069(0.044) (0.041) (0.054) (0.041) (0.044)

Respondent is female (1 = Yes, 0 = No) 0.076** −0.046 0.029 0.047 −0.075*(0.038) (0.030) (0.047) (0.029) (0.038)

Household Controls Yes Yes Yes Yes YesObservations 534 534 534 534 534

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head. The full results from allreported regressions are available on request.

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Table A3. Project impact on women’s and men’s decision-making on ‘treatment related’ activities

Treatment related decisions

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline 1.04*** 0.96*** 0.75*** 0.29** 0.21**(0.22) (0.22) (0.21) (0.12) (0.09)

Treatment × Endline 0.72*** 0.81*** 0.74*** −0.020 0.067(0.24) (0.24) (0.23) (0.14) (0.12)

Respondent is female (1 = Yes, 0 = No) −0.15 −0.25 −0.22 0.068 −0.033(0.18) (0.18) (0.18) (0.086) (0.12)

Observations 535 535 535 535 535

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head.

Table A4. Project impact on women’s and men’s decision-making on ‘other’ activities

‘Other’ decisions

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline 2.46*** 2.13*** 1.76*** 0.70*** 0.37*(0.40) (0.37) (0.42) (0.23) (0.19)

Treatment × Endline 0.55 0.20 0.67 −0.13 −0.47**(0.46) (0.43) (0.49) (0.27) (0.22)

Respondent is female (1 = Yes, 0 = No) 0.011 −0.71** −0.17 0.19 −0.53**(0.39) (0.35) (0.42) (0.22) (0.21)

Household Controls Yes Yes Yes Yes YesObservations 535 535 535 535 535

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head.

Table A5. Project impact on women’s and men’s decision-making based on PCA-weighted index

PCA-weighted index

(1) (2) (3) (4) (5)

Female and Joint Male and Joint Joint Female only Male only

Endline −0.40 −0.051 −0.42* 0.18 0.51**(0.24) (0.19) (0.23) (0.18) (0.20)

Treatment × Endline 0.42 −0.004 0.48* −0.24 −0.67***(0.26) (0.20) (0.26) (0.20) (0.23)

Respondent is female (1 = Yes, 0 = No) 0.19 −0.38** −0.08 0.29** −0.41*(0.20) (0.16) (0.24) (0.15) (0.21)

Household Controls Yes Yes Yes Yes YesObservations 535 535 535 535 535

Notes: Standard errors in parentheses, significance level, *p < .10, **p < .05, ***p < .01. Household controlsinclude household size, number of children ages five or under, number of children ages six to 16, indicators forfemale head and married head, and level and squared of age and education of head.

2064 K. Kafle et al.