metadata: authors: 0: author: Benyishay,Ariel
1: author: Jones,Maria Ruth
2: author: Kondylis,Florence
3: author: Mobarak,Ahmed Mushfiq
count: Malawi
docna: 0: docna: Are gender differences in performance innate
            or socially mediated ?
docty: Policy Research Working Paper
subtopic: Human Rights,Educational Institutions & Facilities,Gender and Development,Food Security,Effective Schools and Teachers
teratopic: Agriculture
abstracts: cdata!: To explain persistent gender gaps in
            market outcomes, a lab experimental literature explores
            whether women and men have innate differences in ability (or
            attitudes or preferences), and a separate field-based
            literature studies discrimination against women in market
            settings. This paper posits that even if women have
            comparable innate ability, their relative performance may
            suffer in the market if the task requires them to interact
            with others in society, and they are subject to
            discrimination in those interactions. The paper tests these
            ideas using a large-scale field experiment in 142 Malawian
            villages where men or women were randomly assigned the task
            of learning about a new agricultural technology, and then
            communicating it to others to convince them to adopt it.
            Although female communicators learn and retain the new
            information just as well, and those taught by women
            experience higher farm yields, the women are not as
            successful at teaching or convincing others to adopt the new
            technology. Micro-data on individual interactions from 4,000
            farmers in these villages suggest that other farmers
            perceive female communicators to be less able, and are less
            receptive to the women's messages.  Relatively small
            incentives for rewards undo the disparity in performance by
            encouraging added interactions, improving farmers'
            accuracy about female communicators' relative skill.
geo_reg_key: 517195,517191
repnme: repnme: Are gender differences in performance
            innate or socially mediated ?
display_title: Are gender differences in
            performance innate or socially mediated
listing_relative_url: /research/2016/05/26420271/gender-differences-performance-innate-or-socially-mediated
topicv3: Teachers Management m1326119 1523,Education, Skills Development and Labor
            Market m1326135 1484,Agriculture and Food Security m1326205 435,Gender m1327621 615,Discrimination m1505707 3379
publishtoextweb_dt: 2016-05-31T00:00:00Z
historic_topic: Agriculture
no_of_pages: 50
author_keys: 0: author_key: 959947
1: author_key: 1053648
2: author_key: 797414
3: author_key: 418988
model_extractions: WPS7689 Policy Research Working Paper 7689 Are Gender Differences in Performance Innate or Socially Mediated?

_Ariel BenYishay_ _Maria Jones Florence Kondylis Ahmed Mushfiq_ Development Research Group Impact Evaluation Team May 2016
Policy Research Working Paper 7689

# **Abstract**

To explain persistent gender gaps in market outcomes, a lab experimental literature explores whether women and men have innate differences in ability (or attitudes or preferences), and a separate field-based literature studies discrimination against women in market settings. This paper posits that even if women have comparable innate ability, their relative performance may suffer in the market if the task requires them to interact with others in society, and they are subject to discrimination in those interactions. The paper tests these ideas using a large-scale field experiment in 142 Malawian villages where men or women were randomly assigned the task of learning about a new agricultural technology, and then communicating it to others to convince them to adopt it. Although female communicators learn and retain the new information just as well, and those taught by women experience higher farm yields, the women are not as successful at teaching or convincing others to adopt the new technology. Micro-data on individual interactions from 4,000 farmers in these villages suggest that other farmers perceive female communicators to be less able, and are less receptive to the women’s messages. Relatively small incentives for rewards undo the disparity in performance by encouraging added interactions, improving farmers’ accuracy about female communicators’ relative skill.

This paper is a product of the Impact Evaluation Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at fkondylis@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
**Are Gender Differences in Performance Innate or Socially Mediated?**<sup>**1**</sup>

Ariel BenYishay, College of William & Mary Maria Jones, World Bank Research Group Florence Kondylis, World Bank Research Group Ahmed Mushfiq Mobarak, Yale University _Keywords_ : gender, technology adoption, agriculture, discrimination _JEL Codes_ : J16, O12

> 1 Corresponding authors: Florence Kondylis <u>(fkondylis@worldbank.org)</u> and Mushfiq Mobarak (ahmed.mobarak@yale.edu). This draft benefited from comments from Manuel Bagues, Esther Duflo, David Evans, Arianna Legovini, Isaac Mbiti, David Rorhbach, Gil Shapira, Daniel Stein, and seminar participants at the 2013 World Bank Annual Meetings, IFPRI, DECRG, JPAL-Europe Labor Conference, UC-Merced and Cornell University. Siyao Zhu provided superb research assistance. Generous funding from the World Bank i2i, Gender Action Plan and Research Support budget, the Millennium Challenge Corporation, the Macmillan Center at Yale, and the Center for Business and Environment at Yale made this research possible, as well as support from the World Bank Development Impact Evaluation unit and Innovations for Poverty Action in Malawi. The views expressed in this manuscript do not reflect the views of the World Bank. All errors are our own.
# **1. Introduction**

Gender gaps in earnings and in other economic outcomes have been documented extensively by economists (Blau and Kahn 2000). While gaps favoring men exist in virtually every country, disparities in income, health, education, and decision-making power are more pronounced in developing countries (Jayachandran 2015). Understanding the causes of these gaps has received a lot of attention in the literature, with most of the empirical work focused in developed countries.<sup>2</sup> The early literature documented cases of discrimination against women in the market as a source of disparity in outcomes (Altonji and Blank 1999), while a more recent literature has increasingly focused on gender differences in innate preferences, attitudes and ability as underlying causes, inspired by insights from psychology and experimental research (Bertrand 2011).

Research documenting innate gender differences in risk preferences or attitudes towards competition relies heavily on lab experimental approaches (e.g. Gneezy et al 2003, Niederle and Vesterlund 2007), with very few field experiments (e.g. Flory et al 2015). The literature on discrimination has developed in parallel, and somewhat independently, using observational and experimental data in field settings, employing methods such as audit studies (Neumark 1996), blinded natural experiments (Goldin and Rouse 1996, Moss-Racusin et al 2012), and field experiments with fictitious resumes or emails (Bertrand and Mullainathan 2004; Riach and Rich 2006; Petit 2007; Milkman et al 2012).

The central thesis of this paper is that the two literatures and classes of explanations may be intricately tied to each other: even if women have innate characteristics (raw ability and attitudes) that are comparable to those of men, they may perform differently in the market if the task requires them to interact with others in society, and they are subject to discrimination in those societal interactions. We set up a field experiment and data collection strategy to explore this thesis, and then add a cross-cutting experiment to explore whether the provision of small incentives to promote interactions between women and men helps eliminate such differences between male and female actors.

> 2 See Bertrand (2011) and Niederle (2015) for reviews of the literature.

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We set up a field experiment motivated by this thesis, in which we randomly assign male or female farmers in Malawi the task of learning about a new agricultural technique, communicating about it to other villagers, and convincing them to adopt. To measure these communicators’ performance in the field, we collect two years of follow-up data on a random sample of other nearby farmers to track how well these other farmers learned and retained information about the technology from their assigned communicator, and whether they adopted it. In addition, we administer knowledge tests to the communicators to measure how well they themselves learned about the technology and retained that information. The latter allows us to compare male and female communicators in terms of their raw innate ability to learn, while the former allows us to track how that ability translates into performance in the field when those communicators have to interact with society at large to complete the assigned task. Comparing across the two stages allows us to infer whether women under-perform on the field task due to a raw ability gap, or because of issues related to social interactions, norms and attitudes.

To shed light on the mechanisms underlying gender discrimination, we provide performance-based incentives to a randomly assigned subset of communicators. These inducements generate additional interactions with both male and female farmers, improving the accuracy of farmers’ perception of the communicators’ skills. We can thus compare whether performance on the socially mediated task continues to lag among women when the individuals they interact with are better informed about their relative knowledge.

Most real-world labor market tasks are characterized by situations where a woman has to rely on or interact with male (or female) colleagues to be successful in her job. This is true for any job that requires managing or supervising teams of workers, teaching or training others, or interacting with customers. Labor markets in the modern economy rarely offer opportunities for individuals to work and succeed in isolation. In such situations, equally able women may underperform relative to men because of overt discrimination and lack of cooperation from colleagues (Bagues and Esteve-Volart 2010; Delfgaauw et al 2013), because of social norms and attitudes (Gneezy et al 2009, Bertrand, Kamenica and Pan 2013), or because gender identities require women to behave differently when interacting with male colleagues (Akerlof and Kranton 2000).<sup>3</sup>

> 3 Hoff and Pandey (2014) provide field experimental evidence on how low-caste children in India perform worse on cognitive tasks in the presence of high-caste children, especially when caste identity is made salient.

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Perceptions of women’s performance may also not accurately reflect their actual performance (Beaman et al 2009).

A lab experimental literature presents some related evidence that women either perform worse, or display some different innate attitudes when they are in mixed-gender environments compared to single-gender environments (Booth and Nolen 2012a, 2012b). In contrast, testing our hypotheses required us to set up a large-scale field experiment, because our core idea is that women’s relative performance may change when they move from single-gender or isolated environments (where true innate ability is expressed in private tasks), to a field environment where they have to interact with others to complete the task successfully.

We find that women’s performance drops in relative terms when they are reliant on others in the field. We use micro data on individual interactions collected from around 4,000 farmers to explore alternative hypotheses and underlying mechanisms. It is not simply the case that learning and teaching are different skills and women are better learners than they are teachers. Direct measures of teaching performance indicate that women put more effort into teaching, and farmers they teach experience better yields compared to those in male communicator villages. This does not seem to be about cross-gender frictions in communication either: Nearly half of all maize plots in our sample villages are farmed by women, and the gender gaps remain regardless of whether we examine the responses of other male or female maize farmers.

We do observe that both male and female maize farmers perceive male communicators to be more knowledgeable about agriculture, although by our objective measures those men are no more knowledgeable about the new technology introduced through the experiment. Accordingly, other farmers do not pay as much attention to female communicators. Biases in perception and attention appear to be the most likely underlying causes of the patterns we document.

We also find that adding performance-based incentives encourage communicators to intensify their interactions with both male and female farmers, and eliminates the gap in the socially mediated task. This is not because the incentives lead women to exert differentially more effort in the private task of learning about the technology. In fact, the incentives improve the relative performance of male communicators in the learning task, leaving their knowledge indistinguishable from that of female communicators, and erasing the yield gap. The resulting 4
similarity in performance on the socially mediated task across genders is thus parallel to that of the private learning task when added incentives are provided. These results indicate that providing a small incentive to communicators is extremely cost-effective at both improving farm profitability and eliminating discrimination against women in productive social tasks.

We conduct this experiment in an important setting for policy. Agricultural yields have remained low and flat in Sub-Saharan Africa over the last 40 years, and low adoption of productive technologies is thought to be a major cause (World Bank 2008, Conley and Udry 2010, Duflo et al 2011). Lack of persuasive sources of information may make farmers reticent about new technologies, which makes the communication task we assign to men and women in our field experiment extremely important. Yield and other outcome data collected from over 4,000 farmers living in our study villages suggest that success in the assigned tasks has large welfare implications. Use of the new technology increases yields by over 30% in arid areas (BenYishay and Mobarak 2014; Beaman et al 2015).

More broadly, gender disparities in the United States and other developed countries have been studied extensively, and we add to that literature by investigating the sources of disparities in developing countries where gender inequality is more pronounced (Jayachandran 2015). Gender discrimination also has more devastating consequences in developing countries, where 6 million women are reportedly “missing” at birth, and many do not survive into adulthood (World Bank 2011; Duflo 2012).

The rest of the paper is organized as follows: In section 2, we describe the context and our experimental design. Section 3 discusses the data, while results on communicators’ unmediated and mediated tasks are presented in sections 4 and 5. Mechanisms are discussed in section 6. We offer a brief conclusion along with implications for policy in section 7.

# **2.  Context and Experiment**

# **2.1 Extension Network**

Malawi is predominantly agricultural, and 85% of the population lives in rural areas (Malawi Integrated Household Survey 3, 2011). 56.6% of the rural population was classified as poor in 2011 (MIHS3). Maize is the primary staple food in Malawi. With one rainy season, there is 5
typically one maize harvest per year. The Ministry of Agriculture communicates with maize farmers through a national public, decentralized agricultural extension system. All agrarian areas are in principle staffed by an Agricultural Extension Development Officer (AEDO), but in practice, these frontline positions are chronically understaffed. Each AEDO is responsible for providing extension services to 1,465 households on average, and moreover, many AEDO vacancies remain unfilled. At the outset of our study in 2009, only 56% of all AEDO positions were filled across our study districts, according to Ministry records. As a result, only 18% of farmers participated in extension activity according to the 2006/2007 National Agricultural and Livestock Census. Informational deficiencies remain a key challenge that hinders adoption of yield-enhancing technologies.

In addition, the traditional extension network is male-dominated: the average ratio of male to female AEDOs from 2005-2010 was 8:1 (Masango and Mthinda 2012). The Ministry had adopted a ‘lead farmer’ approach to extend the reach of extension, in which a few farmers are chosen to act as extension partners to communicate with their neighbors. These positions are typically filled by men. These gender imbalances suggest that involving more women to communicate with farmers about new technologies may be a promising way to address informational deficiencies, especially since 48% of maize farms in our study area are cultivated by women. This creates a rich setting to study the role of innate ability vs. social norms and attitudes in the relative performance of men versus women in a field-based task, by involving women in the delivery of extension advice through experimental manipulation.

# **2.2 Experimental Design**

We partner with the Ministry of Agriculture and Food Security (MoAFS) in Malawi to conduct a large-scale field experiment with 142 maize farming communities in 8 districts across the country. In 2009, the MoAFS was preparing to roll out a new extension activity<sup>4</sup> in these eight districts, and we convinced them to incorporate ideas from social network theory to improve delivery. Under this approach, a few village residents are chosen as extension partners or _communicators_ , and are asked to serve as the interlocutor between the AEDO assigned to the

> 4 A decentralized T&V model of extension is set up such that information on new technologies flows from researchers to extension workers and on to producers via farmer communicators, points of contact between extension agents and other farmers (Kondylis et al. 2014).

6
area and the rest of the village farming community. A communicator has two main categories of tasks: _Task A:_ receive training on a new technology, and acquire, retain and use this knowledge on her own farm.

_Task B:_ communicate about the new technology to other (non-communicator) farmers in her village, and convince them to acquire, retain and use this knowledge on their farms. The reform carried a gender inclusion mandate, to give women more central roles in extension delivery. We exploit this policy interest to experiment with the gender dimensions of communication, and randomly assign the gender of the communicator at the village level. Measuring the relative performance of men and women in Tasks A allows us to isolate gender differences in innate ability, while the comparison to Task B allows us to gauge relative changes in performance across these men and women, when success is contingent on communicating with others and persuading them to accept their message.

In a second, cross-cutting treatment arm, we examine gender-specific constraints to participating in extension tasks by dispensing performance-based incentives to communicators. This makes up four treatment arms (male-assigned without incentives, male-assigned with incentives, female-assigned without incentives, and female-assigned with incentives), in addition to a fifth pure control arm.

Each extension officer (AEDO) is responsible for a group of villages, which is called a “section”. Our sampling frame is restricted to the 457 sections (out of 822) actually staffed by an AEDO in our study districts. Within this set, we randomly select 121 sections where we conduct the project. We first randomly assign 26 sections to “pure control” and the 95 other sections to one of the four treatment arms. 76 of those 95 are randomly selected to serve as a “pure treatment” section, in which only one randomly chosen village per section receives the treatment. In the other 19 sections we label “mixed treatment and control”, one village is randomly selected to be treatment, and we select one additional village as control.<sup>5</sup>

> 5 This assignment is stratified within each treatment assignment to ensure balance across arms. In three mixedtreatment sections we selected 2 control villages.

7
We chose to collect data in these 19 additional control villages in treated sections in order to track whether enhanced AEDO attention to the treatment leads to a displacement of effort away from other control villages he is responsible for. Such effort displacement is unlikely, because AEDOs are instructed to assist at most one treatment village in addition to his normal workload for this project. His normal workload requires him to serve 25 villages in the average section, so this project represents a small percentage increase in effort requirement.<sup>6</sup> The project therefore should not significantly deplete the AEDO’s attention. Robustness checks, excluding these mixed control villages, and introducing an indicator for mixed section as a regressor do not affect our main results in any significant way. This allows us to plausibly rule out concerns about SUTVA violations. Appendix A provides a detailed timeline.

# **2.3 AEDO Training**

At the outset (August 2009), prior to launching any intervention, AEDOs serving our 95 treatment sections received a three-day training on (a) the new technologies we were introducing for this project, and (b) how to transfer knowledge about those technologies to a village-based communicator. The training materials were prepared and delivered by the two agencies most relevant to the two technologies we introduced (Departments of Agricultural Research Services and Land Resources Conservation). The Department of Agricultural Extension Services (DAES) coordinated the trainings. The sessions were attended by all AEDOs and their direct supervisors (Agricultural Extension Development Coordinators, AEDCs). Each AEDO and AEDC was trained only on the one technology relevant to their assigned districts.

The first day of the training was devoted to familiarizing AEDOs and their supervisors with the aims of the project. The concept of _communicator_ was discussed, as well as the motivations for a gender reservation mechanism. This part of the training was key in ensuring good compliance to the study protocols, since the AEDO played a central role in the identification of communicators. The second and third days of training were allocated to classroom and hands-on training on the new technology that the AEDO would then disseminate to communicators. Classroom discussions took place at the training center, while hands-on modules were held in adjacent demonstration plots. The training closed with an explanation of

> 6 All other, non-selected villages continued to receive extension services as per the _status quo_ modality, as did our sampled control villages.

8
each AEDO’s specific village assignment, including any gender requirements for the extension partner or “communicator” they had to help identify.

# **2.4 Communicator Identification and Gender Assignment**

The first village visit was designed to identify the communicators in all treatment villages. The AEDO assigned to that village convoked and led a half-day meeting that was open to anyone in the community (including local leaders). Turnout was usually substantial. The concept of “communicator” was first discussed, and the eligibility criterion laid out was that communicators should be willing to learn and disseminate a new technology to others. The specific (random) gender assignment was shared in this first meeting.<sup>7</sup> To reduce gender stereotyping of the communicator role, AEDOs told the meeting attendees that men and women could be equally successful at this task.

After this first discussion, leaders worked with the assembled community members to produce a short list. In a second step, the AEDO selected the communicator from the short list, and verified the assignment and/or the good faith attempt of complying with the assignment. Third, AEDOs and leaders went back to the larger assembly to share their final choice of communicator. This last step ensured that the community endorsed the pick. These meetings were completed during August-September 2009.

While we essentially find perfect gender compliance in our male-communicator arm at baseline, this is not the case of our female assignment: in that arm, only 58% of communities actually had a woman communicator by midline. We therefore conservatively report _intent-totreat_ (ITT) estimates throughout the paper, which tracks the effect of the village getting _assigned_ a female communicator.<sup>8</sup> Different _types_ of women or men may have chosen to comply with the treatment assignment, especially across the incentive and no-incentive arms, and this makes the local average treatment effect of gender across incentive arms difficult to interpret.

> 7 In all communities, we asked community leaders to designate 5 “peer farmers” and 1 “lead farmer”. However, the specific communicator type is not the focus of this paper and, in the interest of statistical power, we pool these groups and focus on the gender assignment. In both _lead farmer_ and _peer farmer_ villages, our gender treatment implied that one woman should be designated as communicator. Again, we pool these two treatment arms into one general _communicator_ treatment and split the effects along the (random) gender assignment.

> 8 We do not present 2SLS results of actual compliance on assignment, since round-to-round compliance is likely endogenous to our gender and incentive assignments through actual communicators performance.

9
# **2.5 Shadow Communicators and Training**

We follow the same communicator identification process in control villages, to designate “shadow communicators” who _would have_ played the communicator role, had that village been treated.<sup>9</sup> These shadow communicators are useful for research purposes, because they represent the correct counterfactual against which to compare the actions of the actual communicators in treatment villages. Identifying and surveying the shadow communicators allows us to report experimental results on what actions and activities the communicators participate in as a result of the project, holding constant the social identity and other unobserved characteristics of the communicator. These regressions will prove to be useful to understand the mechanisms underlying the effects that we will observe.

This design implies that the only difference between treatment and control communities is that communicators in treatment villages receive subsequent visits and trainings from the AEDOs on a new technology, while shadow communicators don’t. AEDOs trained the communicators soon after identification, and ahead of the main planting season. These trainings lasted for half a day. It involved explaining the merits and demonstrating the application of the new technology. The AEDOs then made follow-up visits to treatment communicators over the course of the next two years.

# **2.6 Incentives**

In a cross-cutting experiment, we randomly assign performance-based incentive payments to half of the male and female communicators. We informed only the selected communicators about the incentives during individual meetings in October-November 2009, after the selection and training of communicators was completed, so that the assignment of incentives does not influence the identities of communicators.

The scheme consists of two payouts, one after the first agricultural season, and the associated midline data collection was completed, and the other after the second agricultural season and completion of endline data collection. The first payout was based on gains in knowledge score in their community, because we mostly expected information about the new

> 9 In control communities, the process ended after the community endorsed the choice of communicator. Unlike communicators in treatment communities, shadow communicators were not trained nor contacted beyond the second step of the identification process.

10
agricultural technologies to spread during the first year. The threshold for the incentive payment is set at 20 percentage point increase in knowledge scores among interviewed farmers, as measured by an exam administered during the midline that tested farmer knowledge of the details of how to implement the technology (see Appendix B). Communicators are given no prior information about the nature of the exam or the sampling frame for the survey, and therefore have no opportunity to teach others to the test. Furthermore, we rotated the set of households who were sampled in each round, so that there is not a perfect overlap of households across survey rounds. Not surveying the same households across rounds is costly because it creates imperfect panels, but we did it to make it more difficult for the communicators to target a minority of households in order to win the incentive payment.

Endline payouts are indexed on gains in adoption within the community. In order to avoid relying solely on respondent self-reports, we monitor adoption through an on-farm monitoring (OFM) survey administered between planting and harvest. Enumerators trained in the maize farming process visited the farms of 1,400 households to directly observe any evidence of technology use. These farm visits were timed to the agricultural cycle when that evidence would be visible. Gains in adoption of 20 percentage points and higher trigger a payment. The OFM surveys were also conducted on a rolling sample of farmers to ensure that communicators could not easily target effort on a narrow set.

Payouts were given in kind, in the form of demonstration kits and production inputs. The incentives are gender-neutral in nature since maize farms are equally cultivated by men and women in our sample. MoAFS financed the intervention and legislated their value, and project staff ensured that payouts across all villages were roughly equal in value (KW 12,000, or USD 80 per community per year).

# **2.7 Technology**

We promoted one yield-enhancing conservation farming technology in each village. AEDOs in four arid districts were trained on “pit planting”.<sup>10</sup> “Changu composting” was promoted in villages in the other four districts.<sup>11</sup> These conservation agriculture technologies

> 10 These are Rumphi, Neno, Chikwawa, and Balaka districts.

> 11 There are Zomba, Mchinji, Dedza, and Mzimba districts.

11
have proven yield benefits and overall positive returns, as documented experimentally for Malawi (BenYishay and Mobarak, 2014; Beaman et al., 2015), and observationally for the subregion (Thierfelder et al., 2015). These techniques do not confer any added benefit to one gender over another, and there is no gender gap in knowledge of these techniques in our control villages at baseline. We will briefly describe the two technologies here, and Appendix C provides the detailed training manuals.

Pit planting increases a soil’s capacity for storing water while minimizing soil disturbance and, thus, nutrient runoff. In practice, rectangular pits are excavated in a field. Seeds are planted inside the pits, rather than on ridges, which is the status quo practice in Malawi. The pits conserve moisture and allow farmers to use small quantities of inputs more efficiently. Once prepared, pits can be used for at least two consecutive seasons, after which farmers have to reshape the pits. Pit planting was virtually non-existent at baseline in our study area. Only 1 percent reported ever practicing it, and only 12 percent of farmers had ever heard of the technology.

The main benefits of “Changu composting” over other types of compost are (i) reduced heap maturity and (ii) use of easily accessible materials. At baseline, we record that only 7 percent of farmers in control villages had heard of this particular type of compost (overall, 53% had heard of compost in general), and only about 25 percent of those who had heard of Changu composting were able to correctly answer questions on preparation, properties and application methods.

The data analysis will pool all eight districts together and use district fixed-effects to absorb any unobserved technique-specific and geographic heterogeneity.

# **3. Data and Descriptive Statistics**

# **3.1 Data Sources**

We first conduct a full listing of all households in each of the 142 villages. We randomly select households to survey from this sampling frame. We chose all communicators (treated or shadow), plus 25 other randomly selected households for the surveys. We rotate the sample of 12
households participating in each survey round and create imperfect panels to both minimize survey fatigue for each household and prevent communicators from focusing on just a few households, after observing who we survey. This yields samples of 3,685, 3,496 and 3,314 households of ‘regular’ (non-communicator) farmers and 467, 474, and 329 communicators at baseline, midline and endline, respectively. The baseline was conducted in August-September 2009, a midline in July – September 2010 at the end of the first agricultural season, and an endline in July – September 2011 after the second agricultural season. We augmented our midline and endline instruments with a spouse/additional farmer module, increasing the total number of respondents to 6,006 and 4,693 at midline and endline, respectively.

These surveys capture a rich set of measures of knowledge and adoption of the promoted techniques, along with agricultural production, household demographics, individual characteristics, perception, and social network relationships. To test respondents’ knowledge about the technology introduced in their village, we ask seven questions regarding the details of how to apply the technology. The questions are based on the material used by AEDOs to train communicators in treatment villages.<sup>12</sup> We use these questions to construct a [0,1] knowledge index where 0 indicates a respondent could not correctly answer any question, and 1 that a respondent answered all questions correctly. Adoption of the technology is self-reported in the survey, and validated through direct observations during the on-farm monitoring.

The social network module records farmers’ interactions with and perceptions of (shadow or actual) communicators and other farmers in their village. Social network relationships with other non-communicator farmers were recorded as follows. For each respondent, we drew a list of six randomly-picked farmers in the community from our baseline listing. We then asked questions about the nature of the respondent’s interactions with each of these farmers, as well as their perceptions about those farmers. Appendix B details the construction of all main outcome variables.

> 12 See Appendices B and C for a detailed record of the knowledge exam, and training manuals, respectively.

13
# **3.2 Descriptive Statistics and Balance**

Table 1 displays sample characteristics of the communicators chosen for the experiment, and compares them to both shadow communicators and other non-communicators who were randomly selected to respond to our survey. Communicators are similar to regular farmers on most dimensions, with no significant differences by gender (cols 8, 9, 10). Reassuringly, shadow and actual communicators share similar characteristics (col 11). Randomly varying the gender of chosen communicators does not lead to statistical differences in the observable characteristics of the communicators (col 7). This is important to note for the proper interpretation of the statistical differences in post-treatment outcomes across arms that we will report later. Those outcome differences will be attributable to randomly assigning a task to either men or women who are similar along observable dimensions.

Table 2 tests for balance in randomization by comparing non-communicator respondents across treatment assignments. We achieve balance on individual observable characteristics across all treatment arms (cols 3, 6, 9), and reject joint significance of these characteristics in determining treatment status. Farmers in our study area are about 40 years old, work mainly in agriculture and cultivate just under 1 hectare of land, own one head of cattle and about 4.75 other assets. The farmers are quite poor on average: they live in dwellings made out of local material, with only 23 percent owning a tin roof. Their diet consists mainly of maize. Only 5% had taken any loan in the year prior to baseline.

# **4. Regression Results**

# **4.1 Communicators’ Performance in the Private Task** **_(Task A – Learning about the New Technology)_**

To explore whether there are any innate differences between genders in their ability to perform assigned tasks, we first compare communicators’ relative performance in a task that did not require them to interact with other farmers in society: acquiring, retaining and adopting the new technology themselves. We use the performance of shadow communicators in control villages as the omitted category. This implies that we are holding constant all socioeconomic characteristics, network positions and other unobservables associated with identity, and identifying 14
Vivat = B,NonIncentMale,qg + B,NonIncentFemale,q + B3lncentMale,q + B,lncentFemale,q + T Xivat + Dt + Da + Evat (1) Vivdt By Ba B, Bo Bs Ba B, Bs Bo Ba
Table 3 reports results on four measures of communicator effort in “private” tasks that do not require any interaction with other farmers in his/her society: extensive and intensive margins of participation in trainings on the promoted technology (cols 1, 2), post-training measures of knowledge about the technology to track how well the information was learnt and retained (col 3), and the propensity to adopt the technology on the communicator’s own farm (col 4).

Communicators in treated communities are 36-43 percentage points more likely to have been trained by AEDOs than shadow communicators, and we do not detect any significant gender difference in training participation.

Column 3 reports on how well communicators retained the information that they were trained on. Shadow communicators (the omitted category) obtain average knowledge scores of 0.15 across the two survey rounds, which can be interpreted as correctly answering 15 percent of the questions about the technology that they are tested on, or one of the seven questions on average. Trained communicators in treated villages acquire and retain significantly more knowledge about the technology than their shadow counterparts. Their test scores are about double or triple of shadows’ scores. Female communicators score slightly higher than men in the knowledge test, but the difference is not statistically significant.

Column 4 shows that only 2% of shadow communicators adopt the new technology, and that there is a sharp increase in adoption among treated communicators, especially with incentives. Communicators use own adoption as a strategy to teach and persuade others to adopt. There is a statistically significant female advantage: across the entire sample (pooling incentive and nonincentive arms), female-assigned communicators are 8 percentage points more likely than male communicators to adopt the technology themselves. The gender contrast is especially stark when incentives are not offered: only half as many male communicators adopt the technology as females (p-value = 0.06).

The promise of incentive payments has much greater impact on male communicators. Incentives significantly boost knowledge retention among male communicators, while female performance is almost equally strong with or without incentives. This pattern of more pronounced gender differences in the non-incentive arm will be repeated in subsequent tables, as performancebased incentives appear to mitigate the gender gaps we report on in this paper.

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# **4.2 Communicators’ Performance in the Socially Mediated Task (Task B – Teaching and Convincing Others about the Technology)**

Table 3 focused on tasks that do not require much social interaction with, or dependence on, the rest of the villagers. Table 4, in contrast, focuses on tracking performance in tasks that are _social_ : convincing others in the community to acquire and retain information and, ultimately, use the technology. We now use the sample of non-communicator farmers in all villages (randomly chosen, excluding the communicators) to evaluate how well information and adoption traveled from the communicators to others.<sup>15</sup> We present the same statistical tests as in Table 3, comparing the male and female-assigned villages and the effects of incentives. Table 4 report results for three different samples: separately for female and male respondents, and a pooled sample.

The change in the relative performance of women when we compare across Tables 3 and 4 is striking. While in the absence of incentives, female-assigned communicators were outperforming their male counterparts in adoption of the technology (Table 3, col. 4), they are systematically less successful at convincing others to accept their message without incentives (Table 4, col. 1, 2, 3). Without incentives, recipient farmers’ knowledge scores in femalecommunicator villages is 4 percentage points lower than that of recipient farmers in malecommunicator villages, and this gap is statistically significant. The size of the gap is equivalent to 0.20 standard deviations, or 50% of the average knowledge scores in the control group. Nonincentivized female communicators under-perform in transferring knowledge to both male and female recipients in the village, challenging the idea of cross-gender frictions in teaching.

Coefficient estimates in columns 4-6 indicate that female communicators also underperform in convincing others to adopt. Interestingly, there is a larger and more significant drop (of 4 percentage points) when they try to convince other women, casting further doubt on frictions in communication across genders as the underlying mechanism.

> 15 In these regressions, controls (coefficients not reported) include a constant, total landholdings, number of plots cultivated, baseline HH characteristics (HH head male dummy, dummy if respondent is HH head,  HH head completed primary school education dummy, improved water source dummy, dummy for dwelling with improved roof, dummy for dwelling with improved walls, dummy for whether any HH member took a loan) and village characteristics (matrilineal dummy, dummies for primary religion, dummies for primary language, dummies for primary economic activity, percentage of maize growers in the village, dummies for type of staple food), and district and survey-year indicators.

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In the row that reports the gender difference in performance pooling across both incentive arms, estimated coefficients are negative in Table 4 (suggesting a male advantage in transferring knowledge and convincing others to adopt), whereas all coefficients in the equivalent row in Table 3 were estimated to be positive (suggesting a female advantage in acquiring, retaining and using the new technology). This reversal in the female advantage is a key result in this paper. Without incentives, communicators in female-assigned villages are as knowledgeable and adopt more, but this relative zeal does not translate into other farmers accepting their message.

These gender differences are again more pronounced in the non-incentive arm, and in Table 4, female communicators catch up to their male counterparts in the incentive arm. For example, recipient farmers’ knowledge improves by 5 percentage points with the introduction of incentives in female-assigned villages (p-value = 0.04). Across villages with male and female communicators facing incentives, there is virtually identical knowledge (p-value = 0.88) and adoption (p-value = 0.78). This similarity parallels the similarity in the communicator’s own performance on the private learning task (p-value = 0.84). Thus, performance-based incentives appear to lead to socially mediated outcomes that more closely reflect the underlying skill of men and women.

# **4.3 Are Women Simply Worse at Teaching than at Learning?**

The results we have reported thus far are consistent with women under-performing in relative terms when they move from a private task that requires little social interaction to more public tasks that do. This may be evidence of discrimination against women in societal interactions, but it also may simply be the case that women are better learners than they are teachers. Differences in teaching performance could also arise due to gender disparities in human capital investment early in life. To explore these possibilities, in Table 5 we study crop yields and crop failures amongst a random sample of recipient farmers in each village.

Columns 1-3 report the effect of our treatments on a measure of maize yield on the farm captured at endline, after the second agricultural season post intervention. The technologies are productive on average, so maize yields are significantly greater across all treatment villages compared to control. In the absence of incentives, the yield increase is significantly higher in female communicator villages (p-value = 0.03). In this arm, yields are 17% greater when women teach. The female communicator advantage persists for both male and female recipient farmers, 18
so it does not appear to be the case that female communicators are only better at teaching other women.

In line with our results on the “private task” (technology adoption by the communicators), male communicator villages gain more with incentives, erasing female communicators’ advantage in producing higher yields. While female communicator villages retain an advantage overall in the full sample, the difference is no longer significant.

Columns 4-6 show that the likelihood of crop failure is smaller (but statistically indistinguishable) in the female communicator villages. Crop failure is a relatively rare event that year (5% in control villages), and there is no evidence that female communicators cause more disasters or are less able to transfer skills to other farmers.

We are reporting results on yields and failure on a random sample of all recipient farmers, not conditioning on adoption, to avoid any selection issue regarding who the male or female communicators choose to target. The fact that yields are the same or greater in female communicator villages suggests that when women manage to teach and convince others to adopts, the recipients do just as well with the technology. It does not appear to be the case that women are worse than men at teaching. Even if women are less confident as teachers, or in terms of the beliefs they express about the new technology, it does not undermine their performance as teachers. The differential gender performance in the private versus public tasks therefore does appear to be related to something else: social frictions, or perceptions of women, or willingness to accept messages when equally able or more able women are asked to complete a task that requires them to interact with and persuade others in society.

# **5. Mechanisms**

In this section we explore what types of communication inefficiency can explain why female communicators, despite being as knowledgeable and achieving better command of a new technology, are not as convincing as men in getting others to learn and adopt a new technology. First, women may be worse as teachers. The yield data strongly suggest otherwise. Second, there may be some “gender frictions” if farmers learn more from others of the same gender. Then our results could stem from a gender composition effect: there are more male farmers in our sample, 19
who are more receptive to male communicators, and women would be mechanically less effective. As previously reported, this hypothesis is not borne out by the data when we separately estimate effects for male and female controlled plots. This is in line with other findings in the literature, showing that discrimination against women is not solely perpetrated by men (Bagues and Esteve-Volart 2010; Jayachandran 2015).

Third, women may find it more costly to communicate with others. For instance, getting others in the community to pay attention to their message and trainings may be harder for female than male communicators, especially in the presence of gender bias on the demand side. We might observe lower attendance at the trainings women organize, should this be the issue. Fourth, other farmers may perceive women to not be as good at farming, and may not want to receive advice from them, or give as much credence to the advice they impart. Finally, other farmers may be less inclined to engage in discussions with female communicators due to social norms and attitudes about women’s place in society (e.g., “we do not want to talk to women, they should not teach”).

We combine our random assignment to performance-based incentives with rich data on all farmers’ and communicators’ social networks, perceptions and communication patterns to shed light on the relative merits of each of these mechanisms.

# **5.1 Interactions with Communicators**

Our treatment encouraged communicators to hold _formal_ training activities on the plots in which they adopted the new technology. We start by examining communicators’ self-reported provision of trainings for other farmers (col 1, Table 6). Male and female communicators are equally likely to organize trainings for others. Again, in the non-incentive arm female communicators put in more effort, but there is no statistical gap in the overall sample. However, other farmers report that they are significantly more likely (by 11 percentage points, p-value < 0.05) to participate in trainings organized by male communicators (cols 2-4). In summary, although the supply of trainings is comparable across men and women, the demand for that training is not.<sup>16</sup>

> 16 We note that female communicators are 7 percentage points less likely to provide training than males in the incentive arm (35% for men, 28% for women, difference not significant). A simple back-of-the-envelope calculation shows that this gap in training provision is unlikely to explain a 50% (7pp) drop in attendance (from 13% for men to 6% for women, significant at the 10% level). For men in the incentive treatment, a 1% increase in training provision

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We also collected data on more informal interactions between communicators and other farmers. We asked all farmers in the sample whether they have ever _discussed the new technology with a communicator_ , and columns 5-7 show that these informal discussions are significantly less likely to occur in female communicator assigned villages. The treatment evidently induces a lot of informal discussion across all treatment villages relative to control, but both male and female plot owners are 11 percentage points less likely to have those discussions with female communicators. Providing a small performance-based incentives significantly augments farmers’ interactions with communicators in both female and male communicator villages.

Columns 8-10 examine whether maize farmers report interacting with women less _in general,_ not necessarily about this new technology. 77% of all households in all villages report talking to the communicators, and this is equally true for shadow communicators in the control villages. Within treatment villages, the female communicators appear no less “visible” than male communicators in their respective networks.

In summary, farmers interact with females in general, and women do not suffer from differentially less visibility or greater introversion in this context. Farmers just appear to pay relatively less attention to messages about a new agricultural technology coming from a female communicator, both in informal and formal settings. As Table 5 shows, female communicators transmit the message effectively, despite getting less face time. All of this suggests that either women are not perceived to be good at farming, or that there are differentials in male and female “identity” in agricultural occupations (Akerlof and Kranton 2000), and the role of women in agriculture and training. Providing a small inducement intensifies interactions between farmers and communicators (Table 6), and reduces the gender gap on socially-mediated outcomes (Table 5). Hence, one possibility is that these gaps in perception may be driving female-assigned communicators’ relative lack of success in convincing others, rather than a more generalized gender communication gap: by increasing exposure to women communicators, incentives improve the accuracy of farmers’ perceptions. We explore these ideas using two further sources of data and

> leads to a 0.37% increase in attendance. For women in the incentive treatment, a 1% increase in training causes attendance to go up by 0.22%. Assuming constant attendance rates across trainings (linear training-attendance relationship), incentivized women would need to provide: (0.13-0.06)/0.22 = 0.32, 32pp, more trainings to close the gap.

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# **6. Perceptions of Male and Female Communicators**

Farmers engaging less with female communicators may be related to biased perceptions of women’s farming and training abilities (Beaman et al 2009). To investigate, we collect data on other farmers’ perceptions of the diligence, skills, and knowledge of communicators in their village. Each question elicits a subjective rating on a {1,2,3,4} scale on different dimensions of perception (cf. Appendix B). At midline, perception questions capture how hardworking and skillful a respondent considers the communicator to be, with no reference point. At endline, we ask whether the assigned communicator is (a) knowledgeable and (b) a good farmer, relative to the respondent herself. We construct two separate indices of these measures, and normalize them to be [0, 1].

Table 8 shows that farmers perceive male communicators to be more hardworking, skillful and knowledgeable than female communicators. This difference is imprecisely measured at midline, but significant at the 1% level at endline. This perception gap exists regardless of the gender of the respondent. Both men and women think that male communicators are better at agriculture. We consider these beliefs and perceptions to be biased against women, because the results in Table 3 and 5 indicate that women are just as knowledgeable about the new technologies introduced through the experiment, and farmers that women teach experience better yields. Again, this is particularly striking in the no-incentive arm, where female communicators are more zealous and induce higher yield increases. In that arm, female communicators are perceived to be as knowledgeable as the shadow communicators designated in our experiment, who did not acquire and teach a new technology, and less knowledgeable than their male counterparts (p-value = 0.07). In line with our thesis, incentives successfully combat this perception gap (p-value = 0.15).

# **7. Conclusions**

This study contributes to the large literature investigating the sources of gender disparities in market outcomes by setting up a large-scale field experiment to test whether women perform differentially worse at a ‘social task’ that requires them to interact with others to succeed, relative to a ‘private task’ that does not. Women are comparable to men (or even slightly out-perform men) when innate ability is measured through the private task, but this does not translate into greater success at teaching or persuading others.

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We explore underlying mechanisms using a cross-cutting experiment with performancebased incentives for the women and men, detailed micro data on social interactions, perceptions, effort expended, and finally, farm yields to obtain an objective measure of success in the field. We find that women teach better and expend more effort, but others do not perceive them to be as good at farming and teaching, and pay less attention to their message and trainings.

These gender gaps are more pronounced in the no-incentive arm, and assigning a performance-based incentive helps to mitigate these issues. Both male and female-assigned communicators exert more effort with incentives, making gains on different margins. Male communicators do relatively better in the private task with incentives, closing the gap with women. Conversely, incentives allow women to close the gap with men in terms of teaching and convincing others. Both males and females intensify their interactions with other farmers in their village.

Our data do not support a generalized gender communication gap in our study setting (e.g. “nobody talks to women”), nor is it consistent with gender frictions in communication (e.g. “women prefer talking to women, men to men”). Instead, our findings support the notion that farmers in general are less likely to pay attention to and accept a message from a female communicator. Incentives appear to close the gender gap by improving the accuracy of farmers’ perception of the communicator’s skills. This is further validated by a significant gender perception gap against female communicators that disappears when incentives are provided.

Our results illustrate the cost that gender discrimination imposes to society. Women transmit information about a productive new technology more successfully, and others taught by women experience greater farming yields in a poor, rural society where increases in productivity hold large welfare consequences. Yet, gender-based discrimination leads to inefficiencies in their transfer of this information to others. Talent is wasted, as communication and perception barriers prevent relatively more zealous and accomplished female communicators from efficiently spreading their knowledge.

Can a policy of assigning female farmers to teach others help overcome such inefficiencies? We show that a small in-kind incentive can induce female communicators to expend more effort and overcome demand-side biases. Differences in performance in the field stem from gaps in perceptions, so a class of policy actions where women are empowered to take 24
a leading role in acquiring and passing a message to others in their community, has the potential to change perceptions, narrow the gender performance gap, and make better use of the available talent. The welfare implications of this transition are large, as we show using adoption of a new technology which greatly enhances agricultural yields in a poor agrarian society.

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