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RTG 1666 GlobalFood ⋅ Heinrich Düker Weg 12 ⋅ 37073 Göttingen ⋅ Germany www.uni-goettingen.de/globalfood
ISSN (2192-3248)
www.uni-goettingen.de/globalfood
RTG 1666 GlobalFood Transformation of Global Agri-Food Systems:
Trends, Driving Forces, and Implications for Developing Countries
Georg-August-University of Göttingen
GlobalFood Discussion Papers
No. 99
Can private food standards promote gender equality in the
small farm sector?
Eva-Marie Meemken Matin Qaim
May 2017
Suggested Citation:
Meemken, E.M., M. Qaim (2017). Can private food standards promote gender equality in the small farm sector? GlobalFood Discussion Paper 99, University of Göttingen. http://www.uni-goettingen.de/de/213486.html.
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Can private food standards promote gender equality in the small farm sector?
Eva-Marie Meemken*1 and Matin Qaim1
1University of Goettingen, Goettingen, Germany
*Corresponding author: Eva-Marie Meemken, Heinrich Dueker Weg 12, 37073 Goettingen, Germany. Email: [email protected]. Phone: +49 551 39 20209
Abstract Agricultural commercialization can help to lift subsistence farmers out of poverty, but can also have adverse effects on gender equality. We explore whether private food standards – with their particular elements to regulate production and trade – could serve as a vehicle to promote gender equality in the small farm sector. We use gender-disaggregated data from coffee producers in Uganda and focus on two sustainability standards that explicitly address gender issues, namely Fairtrade and UTZ. Entropy balancing techniques, combined with estimates of farmers’ willingness to accept standards, are used to control for possible selection bias when comparing certified and non-certified households. We find that standards and their certification programs increase wealth in male-headed and female-headed households. In male-headed households, standards also change the intra-household distribution of asset ownership: while in non-certified households, assets are predominantly owned by the male household head alone, in certified households most assets are jointly owned by the male head and his female spouse. Standards also improve access to agricultural extension for both male and female farmers. Effects on women’s access to financial services are statistically insignificant. Private standards cannot completely eliminate gender disparities, but the findings suggest that they can contribute towards this goal.
Keywords: cash crops, certification, gender, sustainability standards, women’s empowerment
Acknowledgements This research was financially supported by the German Research Foundation (DFG) through the GlobalFood Program (grant number RTG 1666). We thank Marco Kruse for useful comments.
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1 Introduction
The United Nation’s Sustainable Development Goals emphasize the importance of gender
equality and women’s empowerment for poverty reduction and food security (UN, 2016). Yet,
achieving gender equality remains a challenge, especially in rural areas of developing countries
(FAO, 2011). Agricultural commercialization and linking farmers to high-value markets are seen
as promising strategies to lift subsistence farmers out of poverty (Maertens and Swinnen, 2009;
Rao and Qaim, 2011). However, as is well known, commercialization can also have adverse
effects on women’s empowerment and gender equality (von Braun and Kennedy, 1994). Given
gender disparities in terms of access to land, farm inputs, and rural services, women farmers often
find it more difficult to participate in modern value chains (Maertens and Swinnen, 2012; Oduol
et al., 2017; Quisumbing et al., 2015). Further, social norms and gender roles may limit women’s
engagement in cash crop production and marketing (Handschuch and Wollni, 2015; Njuki et al.,
2011; Orr et al., 2016; Waltz, 2016). Several studies also show that women may lose control over
agricultural income, when farming becomes more profitable and market-oriented (Chege et al.,
2015; Fischer and Qaim, 2012; von Braun and Kennedy, 1994). This is problematic not only for
women’s empowerment, but also from a broader welfare perspective, because female-controlled
income is often more important for family nutrition and child wellbeing than male-controlled
income (Doss, 2013; Hoddinott and Haddad, 1995; Malapit and Quisumbing, 2015). Here, we
explore whether private food standards could possibly serve to mitigate negative effects of
agricultural commercialization on gender equality. Private food standards – such as Fairtrade –
are gaining in importance in global food chains that involve smallholder farmers in developing
countries (Clark and Martínez, 2016; Lee et al., 2012; Maertens and Swinnen, 2009). These
standards cover a wide range of issues, such as food safety, human welfare, labor conditions, and
environmental stewardship. We focus on two particular standards that are aimed at promoting
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sustainability, namely Fairtrade and UTZ. Fairtrade and UTZ also include specific components to
promote gender equality and women’s empowerment (Fairtrade International, 2009; UTZ,
2015c). For instance, farmer organizations that are certified under these two standards need to
comply with non-discrimination policies. Certified organizations are also encouraged to organize
gender equality awareness workshops, implement special programs tailored to women farmers,
and promote female participation in agricultural training sessions. Understanding whether such
components are really effective in improving gender equality can be useful for further developing
food standards and related rural development initiatives.
A growing body of literature has analyzed welfare effects of sustainability standards on farmers
in different developing countries (Bolwig et al., 2009; Chiputwa et al., 2015; Chiputwa and
Qaim, 2016; Ibanez and Blackman, 2016; Jones and Gibbon, 2011; Kleemann et al., 2014;
Mitiku et al., 2017; van Rijsbergen et al., 2016). However, these existing studies typically focus
on the farm or the farm household as the unit of observation. Issues of intra-household
distribution of costs and benefits have hardly been analyzed. Hence, it remains unclear how
sustainability standards affect gender equality (Terstappen et al., 2013). A few quantitative
studies have looked at gender aspects from a broad perspective (Chiputwa and Qaim, 2016;
Ruben and Fort, 2012), yet without analyzing details of intra-household distribution. A few
qualitative studies have investigated experiences of female farmers or of employed female
workers in certified value chains (Bacon, 2010; Bonnan-White et al., 2013; Hutchens, 2010;
Loconto, 2015; Lyon et al., 2010; Lyon, 2008). Our study is the first to analyze gendered
implications of sustainability standards in the small farm sector in more detail with quantitative
approaches. The research objectives are to evaluate (1) whether standards benefit women and
men in male-headed households, (2) whether costs and benefits are equally distributed within
male-headed households, and (3) whether female-headed households can benefit as well. The
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analysis is based on gender-disaggregated data from a survey of coffee producers in Uganda.
Some of the sample households are certified under Fairtrade or UTZ standards, while others are
not certified. For the impact analysis, we use outcome variables that capture different dimensions
of women’s empowerment, such as gendered asset ownership, time allocation, participation in
farmer group meetings, and access to financial services. Entropy balancing techniques
(Hainmueller, 2012) are employed to reduce possible selection bias due to observed differences
between certified and non-certified farmers. To reduce possible bias from unobserved
heterogeneity, we additionally use estimates of farmers’ willingness to accept (WTA) standards
as a conditioning variable in reweighting the data.
2 Possible effects of food standards on gender equality
Depending on their focus in terms of certification requirements, not all standards can be expected
to improve gender equality. However, we hypothesize that those standards that specifically
address gender issues can contribute to promoting gender equality. In this section, we discuss
existing gender components of Fairtrade and UTZ standards and possible effects on women’s
empowerment. This discussion builds on existing qualitative studies and provides the framework
for the quantitative analysis below.
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2.1 Gender components of Fairtrade and UTZ
In this study, we analyze the gendered effects of Fairtrade1 and UTZ standards. We take these
two standards as examples of sustainability standards that include gender components. In the
analysis, we consider both standards together. While Fairtrade and UTZ differ in terms of several
aspects (Chiputwa et al., 2015), the gender components, which are the focus here, are very
similar for these two standards. Fairtrade and UTZ both highlight their commitment to promote
gender equality on their homepages and in several reports (Fairtrade International, 2011, 2009;
UTZ, 2015b, 2015c). Fairtrade has developed a gender strategy (Fairtrade International, 2009),
aimed at gender mainstreaming along the value chains. UTZ recommends the use of its ‘gender
checklist’ to promote gender equality along value chains. Further, UTZ is piloting gender-
sensitive approaches to auditing, including the training of auditors on gender issues (UTZ,
2015c).
Fairtrade and UTZ also specify a range of mandatory and suggested measures to promote gender
equality. The specific goal of these measures is to raise awareness and strengthen women’s
position within households, farmer organizations, and communities (Fairtrade International,
2011, 2009; UTZ, 2015b, 2015c). Measures can be broadly grouped into three categories. The
first category refers to non-discrimination policies. Farmer organizations that are certified under
Fairtrade or UTZ have to respect non-discrimination principles in recruiting, paying, and treating
staff. Further, women employees have the right to maternity leave. Sexual harassment must not
be accepted (Fairtrade International, 2011; UTZ, 2015a, 2015c). UTZ additionally encourages
farmer organizations to strive for equal representation of disadvantaged groups (incl. women)
among their staff (e.g. extension officers or farmer organization leadership) (UTZ, 2015a, 2015c).
1 We refer to Fairtrade standards set by Fairtrade International (Fairtrade International, 2011).
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The second category of measures relates to special gender programs. The implementation of
these programs is voluntary. Farmer organizations certified under Fairtrade or UTZ are
encouraged to implement workshops on gender equality, targeting both women and men. Further,
the establishment of training programs tailored to the specific needs of women farmers is also
encouraged (Fairtrade International, 2011, 2009; UTZ, 2015c). Fairtrade farmer organizations
sometimes use parts of the Fairtrade premium2 for implementing such programs (Fairtrade
International, 2011).
The third category of gender measures aims at increasing women’s participation in regular
(agricultural) trainings, group meetings, and other activities implemented by certified farmer
organizations. To promote this goal, farmer organizations certified under UTZ have to ensure that
women are informed about upcoming training sessions. Furthermore, trainings have to be held at
times feasible for women, and participation of women and men has to be documented (UTZ,
2015a).
2.2 Possible effects on economic empowerment
Individual control over economic resources (e.g. cash income, asset ownership) is a key driver of
women’s empowerment (Doss, 2013; Johnson et al., 2016; Kabeer, 1999). Women who are
employed (e.g. in the agro-processing sector) or who sell products in the market generate their
own income, which contributes to economic empowerment (Maertens and Swinnen, 2012).
However, women’s role in cash crop production and marketing may be limited; these crops and
the income generated from sales are often controlled by men (Njuki et al., 2011). Many of the
2 The Fairtrade premium is paid to Fairtrade farmer organizations and shall be invested in development projects (Fairtrade International, 2011).
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sustainability standards with relevance to smallholder farmers focus on traditional cash crops,
such as coffee, tea, or tropical fruits. Drawing from previous studies, we hypothesize that
sustainability standards may contribute to women’s economic empowerment (1) by improving
women’s access to markets and/or (2) by increasing women’s control over income from cash
crop production and sales.
Several studies suggest that standards and certification requirements can promote women’s
access to markets and their role in cash crop production. For instance, Lyon et al. (2010) used
examples from Mesoamerica to show that Fairtrade-Organic standards are associated with more
female involvement in coffee production and marketing. In these examples, many women
registered as farm operators and farmer group members. High rates of female participation may
have been related to certification requirements, coupled with the fact that men were often absent
due to migration. Certification required that registered farm operators be present during
announced and unannounced certification audits. For Organic standards, Kloos and Renaud
(2014) found that access to certified markets increased women’s involvement in cotton
production in Benin. Conventionally, cotton is cultivated with high rates of chemical pesticides
and fertilizers, the application of which is locally perceived as a male task. However, the use of
chemical inputs is prohibited in certified Organic production, making women’s involvement
more socially acceptable. Using case studies from Burkina Faso and Mali, Bassett (2010) also
showed that women’s role in cotton production increased with the introduction of standards.
Using data from Uganda, Chiputwa and Qaim (2016) showed that sustainability standards can
increase women’s control over coffee revenues. This result was attributed to non-discrimination
policies and the establishment of workshops on gender equality in certified farmer organizations.
While these examples are encouraging, some of the observed effects are likely related to the
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specific circumstances in the case-study settings. There are also a few other studies, suggesting
that women’s role in marketing would not change or could even worsen with the introduction of
standards (Bolwig, 2012; Loconto, 2015; Lyon, 2008; Sen, 2014). These studies conclude that
possible economic gains from standards and certification schemes are primarily captured by men.
2.3 Possible effects on division of labor and women’s workload
Agricultural tasks in the small farm sector are often gender-specific. Hence, agricultural
interventions may affect women’s and men’s workload differently (Doss, 2001; Quisumbing et
al., 2015). Women are often strongly involved in labor-intensive activities (e.g. weeding,
harvesting, post-harvest handling). Sustainability standards have specific requirements that can
increase the labor intensity, and thus possibly also the workload for women (Lyon et al., 2010).
For instance, in Organic standards the prohibition of chemical pesticides may increase the need
for weeding and other measures of pest control (Bolwig 2012). Many standards also require more
work for harvest and post-harvest operations (Chiputwa et al., 2015).
Whether additional demand for female labor in cash crop production is good or bad for gender
equality depends on the particular situation. On the one hand, higher involvement of women in
the operations for these crops may potentially also increase their role in decision-making (Orr et
al., 2016). This could be fostered through specific certification rules. On the other hand, women
often work longer hours than men anyway, so that additional tasks in farming may reduce their
ability to engage in other economic, social, or leisure-time activities. In any case, it should be
stressed that standards will not always increase the overall labor intensity in farming, as this
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depends on the type of standard and also on the adoption of labor-saving equipment (Bolwig,
2012; Lyon et al., 2010; Ruben and Fort, 2012).
2.4 Possible effects on social capital
Social capital and collective action are key for female empowerment and for linking women
farmers to markets (Fischer and Qaim, 2012; Handschuch and Wollni, 2015). Farmer
organizations can be an important platform to enhance social capital in the small farm sector
(Mojo et al., 2017). Yet, women farmers are often excluded from farmer organizations (Doss,
2001; Fischer and Qaim, 2012). Sustainability standards may increase women’s participation in
farmer organizations, especially if specific gender measures are implemented. Yet, the evidence
on whether standards actually deliver on this goal is mixed. A few qualitative studies suggest that
standards fail to increase women’s representation in farmer organizations (Lyon, 2008; Sen,
2014). This is supported by data from Fairtrade International, reporting that only around 20
percent of the members registered in Fairtrade-certified farmer organizations are women
(Fairtrade International, 2009).
Low female membership in farmer organizations may be problematic, because registered
membership is usually a precondition to influence decisions on important organizational matters,
such as the type of services provided (Lyon, 2008). Even in organizations with a higher
proportion of female members, men often occupy leadership positions (Bacon, 2010) and may
disapprove women’s active participation and ideas (Lyon, 2008; Sen, 2014). Additionally, or
possibly as a result, women’s participation in meetings may be passive or low (Lyon, 2008; Sen,
2014). However, Elder et al. (2012) argued that Fairtrade certification has helped to improve
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female participation and decision-making in farmer organizations. A few studies have also
described how women-led initiatives emerged within certified farmer organizations (Bacon,
2010; Elder et al., 2012). And there are also examples of certified women-only cooperatives, such
as ‘café feminino’ (Lyon, 2008).
2.5 Possible effects on access to rural services
Women farmers are often disadvantaged in terms of access to agricultural and financial services.
Such gender disparities can explain low adoption of agricultural technologies, lower yields, and
poor market access among women (Doss, 2001; FAO, 2011). Sustainability standards may
improve women’s access to rural services. Certified farmer organizations usually offer
agricultural trainings and other services to their members. Especially if specific gender measures
are implemented, women’s participation in such trainings may increase. Women’s access to
information may also improve if more female extension officers are employed by certified farmer
organizations.
3 Research context and household survey
3.1 Survey
The empirical analysis builds on a survey of coffee-producing households in Uganda conducted
in 2015. We employed a two-stage sampling strategy. First, we purposively selected two coffee
farmer organizations in central Uganda. One of these organizations was certified under Fairtrade,
the other under the UTZ standard. However, not all members of these farmer organizations were
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actually certified; participation is a voluntary decision. Second, we randomly selected certified
and non-certified households from complete membership lists provided by both farmer
organizations. The total sample includes 346 households, 174 of which were certified and 172
were non-certified (Table 1).
In all sample households, we collected data at household and individual levels through face-to-
face interviews. Whenever available, we interviewed the male or female household head.
Additionally, in male-headed households we interviewed the female spouse.3 The interviews with
male and female household members were conducted separately. Table 1 provides an overview
of the number of households and individuals interviewed by certification status. In the 346
sample households, we interviewed a total of 548 individuals, including 233 male household
heads, 244 female spouses, and 71 female household heads.4
Table 1: Number of sample households and individuals by certification status
Certified Non-certified Total Total no. of households 174 172 346 Total no. of individuals 311 (278) 303 (270) 614 (548) Male-headed households 137 131 268
Male household heads 137 (119) 131 (114) 268 (233) Female spouses 137 (126) 131 (118) 268 (244)
Female-headed households 37 41 78
Female household heads 37 (33) 41 (38) 78 (71)
Numbers in parentheses refer to the individuals from whom we received individual responses.
The interviews were conducted by local enumerators, who were trained and supervised by the
researchers. The questionnaire covered farm, household, and contextual characteristics. We also
3 A few households in our sample (N=5) are polygamous. In such cases, we asked the household head, who was interviewed first, to identify the female spouse with the greatest decision-making power. 4 Female household heads include widowed, single, divorced, or separated women, as well as married women whose husbands migrated.
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collected detailed information on household assets and individual asset ownership. The
questionnaire further included detailed questions on individual time allocation and participation
in training sessions and other services offered by the farmer organizations.
3.2 Gender measures implemented by farmer organizations
As discussed above, Fairtrade and UTZ standards have a range of mandatory and voluntary
measures to promote gender equality. In this subsection, we briefly summarize the concrete
measures implemented by the two farmer organizations that were selected for this study. Both
organizations follow policies on non-discrimination. For instance, men and women are recruited
as extension and certification officers, and also for administrative positions. Both organizations
organized workshops to raise awareness on gender equality. The specific purpose of these
workshops is to help household heads, spouses, and other household members to work as a team
and appreciate the work of others.
Additionally, both organizations provide agricultural services to their members, including credits
and agricultural trainings. In principle, any person from member households can join the training
sessions, regardless of whether or not the household is actually certified. However, certified
households are particularly encouraged to participate. Training sessions and regular interactions
with certification officers serve to ensure that certified farmers understand and comply with the
certification requirements. Compliance is important, because otherwise not only the individual
household but also the farmer organization as a whole may lose its certification status.
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4 Empirical strategy
Our goal is to analyze how certification under Fairtrade and UTZ standards affects female
household heads (in female-headed households), male household heads, and female spouses (in
male-headed households). We are particularly interested in the effects on women’s
empowerment, using the framework discussed above. That is, we analyze effects of standards on
female economic empowerment, division of labor and workload, social capital, and access to
different types of rural services. We first describe the concrete outcome variables, before
explaining the identification strategy.
4.1 Definition and measurement of outcome variables
We use asset ownership as an indicator of women’s economic empowerment. Asset ownership is
a suitable proxy, as it determines individual economic options and livelihood opportunities (Doss
et al., 2014; Johnson et al., 2016; Quisumbing et al., 2015). For instance, assets can serve as
collateral when seeking credit. In traditional societies, assets are predominantly owned by the
male household head or by other male household members, hence more assets being held by
females can be interpreted as a trend towards women’s empowerment (Deere and Doss, 2006;
Doss et al., 2014).5
We compare the gendered distribution of asset ownership in certified and non-certified
households to evaluate the possible impact of standards. Assets are measured in terms of their
5 One alternative indicator of women’s economic empowerment would be to look at gendered control over household income (e.g. Chiputwa and Qaim, 2016). However, control usually varies between different sources of in-kind and in-cash income and is therefore more difficult to aggregate for the household as a whole. Aggregation is easier when using asset ownership. Another advantage of assets is that these are less volatile than income and hence a more reliable indicator of wealth and economic development.
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current market value expressed in thousand Ugandan Shillings (UGX), as reported by survey
respondents. The assets considered include productive assets, such as agricultural equipment,
livestock, and means of transportation, as well as consumptive assets, such as furniture and
electronic devices. Very long-term assets, such as land and houses, are not considered in the
analysis. The reason is that most of these long-term assets were acquired long before certification
started in the study region.6 In most cases, the question who within the households owns a
particular asset is implicitly or explicitly determined when the asset is acquired. Hence, for very
long-term assets, measurable effects of standards on changes in the gendered structure of
ownership cannot yet be expected.
One challenge with using asset ownership as an empowerment indicator is that certain assets can
also be held jointly by male and female household members (Quisumbing et al., 2015).7 In those
cases, it is often unclear for the researcher who exactly has what types of rights, for instance,
when it comes to selling these assets (Johnson et al., 2016). We address this challenge by looking
at individual asset ownership and joint asset ownership separately. In male-headed households,
we look at the (1) total value of household assets, (2) the value of assets owned by the male
household head alone, (3) the value of assets owned by the female spouse alone, and (4) the value
of assets jointly owned by the head and his spouse. In addition to absolute values in monetary
terms, we also consider relative shares, such as the percentage of assets owned by the male head
relative to total household assets. In female-headed households, we look at the value of assets
owned by the female household head, both in absolute terms and as a share of total household
6 The two farmer organizations in Uganda started to be involved in certification around the year 2007. After that, the number of participating households increased. 7 Another relevant question is who in the household is actually being asked about asset ownership. Recent research shows that perceptions of male and female household members may differ Anderson et al. (2017). Our data on gendered asset ownership build on responses by the household head.
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assets. This share can be lower than 100 percent when other household members, such as the
brother or son of the female household head, also own some of the assets.
To analyze how standards may affect the workload of male and female household members, the
questionnaire for the individual interviews included a 24-hour time recall, capturing all the
activities of the interviewed individual during the one day prior to the survey (Alkire et al.,
2013). To construct an objective indicator of gendered workload, we added up the time spent on
farm, off-farm, and domestic work to obtain the total daily number of hours worked by male and
female respondents. In addition to this objective indicator, we also use a subjective measure.
Following Alkire et al. (2013), we asked respondents how satisfied they are with their own time
available for leisure activities. This variable is expressed in terms of a five-point scale, where one
indicates “very unsatisfied” and five “very satisfied”.
To measure social capital, we asked respondents whether they had participated in any meetings of
the farmer organization during the past twelve months. The answers are expressed as a simple
binary variable. In addition, we asked respondents whether they held a leadership position in any
group (e.g. the famer organization, women’s groups, religious groups) at the time of the survey in
2015.
To analyze the effects of standards on access to agricultural services, we asked respondents
whether they had interacted with an extension officer during the past 12 months. We also asked
whether they had participated in field days or agricultural training sessions during the past twelve
months. For the training sessions, we differentiated between sessions on soil fertility, pest
management, and coffee quality improvement. Regarding access to financial services, we asked
respondents whether they had a personal savings account, used mobile money services, or were
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member of a savings group at the time of the survey. All these indicators of access to services are
expressed as binary variables.
4.2 Identification strategy
To assess the effects of standards on the outcome variables described, we compare certified and
non-certified households. Specifically, we compare (1) male household heads in certified and
non-certified households, (2) female spouses in certified and non-certified households, and (3)
female household heads in certified and non-certified households.
As households decided themselves whether or not to participate in certification, we have to
account for the fact that certified and non-certified observations may differ systematically, which
would lead to selection bias in the impact analysis. To reduce such bias, we use a technique
called “entropy balancing” (Hainmueller, 2012). Entropy balancing belongs to the family of
weighting and matching approaches, such as inverse probability weighting (IPW) and propensity
score matching (PSM) (Hirano et al., 2003; Pirracchio et al., 2012). Weighting and matching
approaches are used to address systematic differences (imbalances) in the distribution of
covariates between the treatment group (in our case certified households) and the control group
(in our case non-certified households). When such systematic differences are controlled for, the
two groups become sufficiently similar so that remaining differences in the outcome variables
can be interpreted as “treatment effects” of certification.
Entropy balancing is a novel approach that has recently been used for policy impact evaluation in
various sectors (Freier et al., 2015; Huang and Yeh, 2014; Marcus, 2013; Neuenkirch and
Neumeier, 2016), but – to our knowledge – not yet in the context of agriculture and rural
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development. Entropy balancing calculates weights for each untreated individual such that
differences in the distribution of covariates between treatment and control group are reduced.
Technically speaking, this is a minimization problem, subject to the balancing and non-negativity
constraints. Entropy balancing has advantages over more established methods of weighting and
matching, such as PSM. With PSM, simultaneously balancing a larger number of covariates can
be challenging. Observations for which a proper match cannot be found have to be dropped from
the PSM analysis, sometimes resulting in small comparison groups that are not representative
anymore. With entropy balancing, low levels of covariate balancing can be avoided, and
information from all observations is used, because no observation is given a zero weight
(Hainmueller, 2012).
To obtain entropy weights, we first have to select conditioning variables, i.e., variables that are
accounted for in reweighting control group observations to make the treatment and control groups
more similar. All factors that may simultaneously affect households’ certification decision and
the outcome variables should be included. We condition on a rich set of covariates, such as
household members’ age, education, religion, and other socioeconomic variables. Reweighted
data are then used to calculate the average treatment effect on the treated (ATT) for each outcome
variable. For continuous outcome variables, we use ordinary least squares (OLS) models in the
second stage. For binary outcome variables, we use probit models. In all second-stage
regressions, we include certification status as the only explanatory variable. As the observations
are balanced, other control variables are not required in the second stage regressions.
One shortcoming of all weighting and matching approaches (including entropy balancing) is that
they can only control for selection bias resulting from observed differences between the treatment
and control groups. In our case, it is also possible that certified households differ from non-
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certified households in terms of unobserved factors, such as personal motivation or
entrepreneurial skills of household members. When there is such unobserved heterogeneity, the
estimated ATT could still be biased. To reduce possible bias from unobserved factors, we include
estimates on farmers’ willingness to accept (WTA) sustainability standards as one of the
conditioning variables for entropy balancing estimation. These estimates on farmers’ WTA were
obtained from a choice experiment carried out with male and female respondents in all certified
and non-certified households (Meemken et al., 2017). The choice experiment was conducted to
better understand farmers’ preferences for sustainability standards and certification requirements.
The farmer-specific WTA estimates are very useful for our impact analysis, because these
estimates are likely correlated with a range of unobserved factors that influence farmers’
certification decision. Hence, using the WTA estimates as a conditioning variable is a neat way to
control for unobserved heterogeneity. A similar approach was employed by Verhofstadt and
Maertens (2014) in their impact analysis with PSM. Combined with entropy balancing this
approach to control for unobserved heterogeneity has not been used previously.
5 Results
5.1 Factors influencing the certification decision
We start the presentation of results by looking at the factors that influence the farmers’ decision
whether or not to participate in certification under a sustainability standard. The probit model
estimates are shown in Table 2, separately for male-headed households (column 1) and female-
headed households (column 2). In addition to these household-level models, we also estimated
individual-level models (i.e. for male household heads, female spouses, and female household
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heads). These individual-level models are shown in Table A1 in the Appendix. The results
suggest that various socioeconomic characteristics influence the certification decision. For
instance, households with older and better educated household heads and those located in higher
altitudes are more likely to participate, whereas Muslim households are less likely to participate
in certification (Table 2). We refrain from further interpreting these results, because the focus in
this study is on the effects of standards on gender equality.
Table 2: Factors influencing the probability of certification (probit estimates at household level)
(1) (2) Male-headed households Female-headed households Household size 0.02 -0.02 (0.01) (0.02) Household head Muslim (1/0) -0.18* -0.65** (0.10) (0.30) Age household head (yrs.) 0.02*** 0.01 (0.00) (0.01) Age gap (age head - age spouse) -0.01** (0.00) Schooling household head (yrs.) 0.05*** 0.03* (0.01) (0.02) Education gap (schooling head- schooling spouse) -0.03*** (0.01) Yrs. growing coffee -0.00 0.00 (0.00) (0.01) Altitude (m) 0.00*** 0.00*** (0.00) (0.00) Distance to input market (km) -0.01** 0.01 (0.01) (0.01) Distance to output market (km) -0.01 -0.03 (0.01) (0.02) Wealthy ancestors (1/0) -0.15* 0.10 (0.08) (0.17) WTA sustainability standards 0.00 0.06*** (0.01) (0.02) Observations 268 78 Pseudo R2 0.21 0.25 LR chi2 76.74*** 27.32*** Marginal effects are shown with standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
5.2 Economic empowerment
Figure 1 provides an overview of the distribution of wealth within male-headed households.
Overall, female spouses own few assets alone. Most assets are either owned by the male
20
household head or jointly by the head and his spouse. Women’s most valuable assets are
livestock. Men’s most valuable assets are means of transportation, followed by livestock. The
differentiation by certification status in Figure 1 suggests that certified households own more
assets than non-certified households. The distribution of wealth differs as well. While in non-
certified households the largest share of all assets is owned by male household heads alone, in
certified households the largest share is owned jointly by the head and his spouse.
Figure 1: Asset ownership in male-headed households by type of asset and certification status
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Non-certified Certified Non-certified Certified Non-certified Certified
Male household head Jointly Female spouse
1000
UG
X Other assets
Electronic devicesAgricultural equipmentFurnitureMeans of transportationLivestock
21
The results in Table 3 confirm significant differences in asset ownership between certified and
non-certified households. Columns (1) to (3) shows simple comparisons without controlling for
possible confounding factors. Column (4) shows the differences after controlling for confounding
factors through entropy balancing; these differences are the estimated average treatment effects
(ATTs) of participating in sustainability certification. In male-headed households, sustainability
standards increase total household assets by 1163 thousand UGX (column 4), which is equivalent
to a gain in wealth of about 50 percent. The largest increase (913 thousand UGX) is found for
assets that are jointly owned by the household head and his female spouse. Standards also have a
statistically significant and economically relevant positive effect on assets owned by female
spouses alone (147 thousand UGX, equivalent to an increase by 71 percent). The ATT for assets
owned by male household heads alone is positive but statistically insignificant (column 4). The
results suggest that standards reduce the share of assets owned by male household heads alone,
whereas standards increase the share of assets owned jointly by about 13 percentage points.
The lower part of Table 3 shows results for female-headed households. In female-headed
households, standards increase the total value of assets by 2138 thousand UGX, which is
equivalent to almost a tripling of wealth. Most of these gains in assets accrue to the female
household heads themselves.
How can the increases in wealth and female economic empowerment through standards be
explained? For most sample households, coffee is the main income source, and selling in certified
markets is associated with income gains. Coffee revenues are predominantly controlled by the
household heads, which is true both in male- and female-headed households. Specifically, in 92
percent of the female-headed, and in 67 percent of the male-headed households, coffee revenues
are controlled by the household head alone. The redistribution of wealth in male-headed
22
households may be attributable to female spouses gaining more control over coffee revenues
through certification. Indeed, Figure 2 shows that female spouses have more control over coffee
revenues in certified than in non-certified households. Such changes in the control over coffee
revenues through sustainability standards were also reported by Chiputwa and Qaim (2016).
However, Figure 2 suggests that the gendered effects of certification may also apply to other
crops produced and sold by sample households. Even for local food crops, which are not sold in
certified markets, female spouses in certified households have stronger control over revenues
than their counterparts in non-certified households.
Table 3: Effects of standards on asset ownership (in thousand UGX)
Simple comparison Entropy balancing (1) (2) (3) (4) Certified
Mean a Non-cert.
Mean a Mean Diff. b
ATT c
Male-headed households (N=137) (N=131) (N=268) (N=268) Value of household assets 3684.52 2299.45 1385.07*** 1162.94*** (2896.86) (2358.02) (0.00) (376.62) Asset ownership male head Value 1434.89 1160.90 273.99 229.14 (1842.77) (1697.26) (0.21) (250.40) Percent of total household assets 37.80 46.71 -8.91** -9.04* (32.67) (31.08) (0.02) (5.46) Asset ownership female spouse Value 322.90 205.95 116.95 146.78** (782.05) (417.41) (0.13) (74.08) Percent of total household assets 9.80 11.69 -1.88 -1.16 (18.93) (19.39) (0.42) (2.81) Joint asset ownership Value 1842.12 846.99 995.12*** 912.63*** (2089.72) (1348.84) (0.00) (261.63) Percent of total household assets 50.32 38.48 11.83*** 13.18** (32.27) (29.71) (0.00) (5.62) Female-headed households (N=37) (N=41) (N=78) (N=78) Value of household assets 2899.39 1106.97 1792.42*** 2137.64*** (3371.77) (1873.99) (0.00) (617.88) Asset ownership female head Value 2430.50 922.45 1508.05*** 1741.91*** (3272.33) (1529.37) (0.01) (601.53) Percent of total household assets 77.67 78.67 -1.00 -0.94 (29.61) (30.61) (0.88) (12.83) a Standard deviations in parentheses. b p-values in parentheses. c Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
23
Figure 2: Who controls crop revenues in male-headed households? (Percent of households)
Only households that produced and sold a particular crop were included in this graph (coffee: 346 households, maize: 134, cassava: 21, matoke: 72, beans: 79, groundnuts: 27)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%N
on-c
ertif
ied
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Coffee Maize Cassava Matoke Beans Groundnuts
Female spouseJointlyMale household head
24
5.3 Division of labor and workload
Figure 3 displays the gendered tasks in coffee production in male-headed households. In most of
these households, the male household head alone controls production and marketing decisions.
Only for harvesting, female spouses play an almost equal role. These patterns apply to both
certified and non-certified households, with no significant differences. The only observed
difference is for control over coffee revenues, where female spouses seem to gain through
certification, as was already discussed above.
Figure 3: Tasks in coffee production in male-headed households by certification status (percent of households)
In Table 4, we compare women’s and men’s workload. Especially in male-headed households,
female spouses work longer hours than their husbands, irrespective of the certification status
(columns 1-2). Overall, the workload seems to be somewhat higher in certified households,
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Non
-cer
tifie
d
Cer
tifie
d
Responsiblefor production
decisions
Responsiblefor harvest
Responsiblefor marketing
Control overrevenues
Female spouse
Joint
Household head
25
although the differences (column 3) and the estimated ATTs (column 4) are not statistically
significant. Also in terms of satisfaction with the time available for leisure activities, certification
has no significant effects in male-headed households. In female-headed households, certification
seems to have a negative effect on the level of satisfaction of the female household heads
(column 4).
Table 4: Effects of standards on workload and social capital
Simple comparison Entropy balancing (1) (2) (3) (4)
Workload and satisfaction
Certified
Mean a Non-cert.
Mean a Mean Diff. b
ATT c
Total workload (hrs./day) Male heads 8.57 8.48 0.09 0.11
(3.28) (3.69) (0.85) (0.64) Female spouses 10.32 9.95 0.38 0.41
(2.66) (2.68) (0.27) (0.59) Female heads 8.93 8.20 0.73 1.04
(3.09) (3.82) (0.39) (1.27) Satisfaction with leisure time (1-5 )
Male heads 2.12 2.30 -0.18 0.04 (1.10) (1.17) (0.23) (0.20)
Female spouses 2.49 2.42 0.07 0.06 (1.14) (1.14) (0.64) (0.26)
Female heads 2.30 2.37 -0.07 -0.93* (1.31) (1.32) (0.84) (0.51)
Social capital Participation in farmer meetings (1/0)
Male heads 0.85 0.84 0.01 -0.05 (0.36) (0.37) (0.89) (0.05)
Female spouses 0.64 0.58 0.07 0.12 (0.48) (0.50) (0.29) (0.10)
Female heads 0.64 0.58 0.06 0.00 (0.49) (0.50) (0.63) (0.19)
Administrative / leadership position (1/0) Male heads 0.67 0.47 0.20*** 0.15
(0.47) (0.50) (0.00) (0.10) Female spouses 0.48 0.24 0.25*** 0.10
(0.50) (0.43) (0.00) (0.10) Female heads 0.48 0.34 0.14 -0.06
(0.51) (0.48) (0.23) (0.19) a Standard deviations in parentheses. b p-values in parentheses. c Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
26
In the lower part of Table 4, we look at social capital. Male household heads are more likely to
attend meetings of the farmer organization than female spouses and female household heads
(columns 1-2). Standards and certification do not seem to change this pattern. Concerning group
leadership positions, the simple comparisons in column (3) suggest that male household heads
and their female spouses are more often involved when the household is certified than when it is
not certified. However, these differences do not seem to be caused by certification, as the
estimated ATTs in column (4) are statistically insignificant.
5.4 Access to rural services
In Table 5, we look at the effects of standards on access to rural services. Farmers in certified
households are more likely to have interactions with agricultural extension officers than farmers
in non-certified households. The effects are significant for male household heads and also for
female spouses in male-headed households; in both cases sustainability standards increase the
probability of interactions with extension officers by 31 percentage points (column 4). Strikingly,
the effect of standards is even stronger for female household heads, whose likelihood of
interactions with extension officers increases by 58 percentage points.
Similar patterns are also observed for the other outcome variables related to agricultural
information and training in Table 5. Male and female heads of certified households are
significantly more likely to participate in agricultural field days or training sessions on soil
fertility, pest management, or coffee quality than their counterparts in non-certified households.
For female spouses in male-headed households the effects are more varied. Standards
significantly increase the probability of female spouses to participate in field days and trainings
27
on coffee quality, but not in trainings on soil fertility and pest management. These patterns reflect
the gendered division of agricultural tasks in male-headed households.
Table 5: Effects of standards on access to rural services
Simple comparison Entropy balancing (1) (2) (3) (4) Certified
Mean a Non-cert.
Mean a Mean Diff. b
ATT c
Interaction with extension officer (1/0) Male heads 0.72 0.35 0.37*** 0.31***
(0.45) (0.48) (0.00) (0.10) Female spouses 0.53 0.21 0.32*** 0.31***
(0.50) (0.41) (0.00) (0.10) Female heads 0.64 0.24 0.40*** 0.58***
(0.49) (0.43) (0.00) (0.13) Participation in field days (1/0)
Male heads 0.48 0.11 0.36*** 0.39*** (0.50) (0.32) (0.00) (0.07)
Female spouses 0.23 0.10 0.13*** 0.17*** (0.42) (0.30) (0.01) (0.04)
Female heads 0.27 0.11 0.17* 0.19* (0.45) (0.31) (0.07) (0.10)
Training on soil fertility (1/0) Male heads 0.76 0.40 0.35*** 0.29***
(0.43) (0.49) (0.00) (0.10) Female spouses 0.48 0.25 0.23*** 0.09
(0.50) (0.43) (0.00) (0.10) Female heads 0.73 0.26 0.46*** 0.54***
(0.45) (0.45) (0.00) (0.17) Training on pest management (1/0)
Male heads 0.76 0.31 0.46*** 0.42*** (0.43) (0.46) (0.00) (0.10)
Female spouses 0.49 0.19 0.31*** 0.16 (0.50) (0.39) (0.00) (0.10)
Female heads 0.70 0.21 0.49*** 0.59*** (0.47) (0.41) (0.00) (0.16) Training on coffee quality (1/0)
Male heads 0.76 0.43 0.33*** 0.32*** (0.43) (0.50) (0.00) (0.10)
Female spouses 0.48 0.23 0.25*** 0.21** (0.50) (0.42) (0.00) (0.10)
Female heads 0.73 0.26 0.46*** 0.59*** (0.45) (0.45) (0.00) (0.17)
Access to financial services (1/0) Male heads 0.85 0.64 0.21*** 0.22**
(0.36) (0.48) (0.00) (0.09) Female spouses 0.64 0.48 0.16** -0.02
(0.48) (0.50) (0.01) (0.09) Female heads 0.76 0.58 0.18 0.05
(0.44) (0.50) (0.12) (0.15) a Standard deviations in parentheses. b p-values in parentheses. c Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
28
The effects of standards on access to financial services are shown in the bottom part of Table 5.
Male heads of certified households are 22 percentage points more likely to have access to
financial services than male heads of non-certified households (column 4). However, the effects
of standards on women’s access to financial services are statistically insignificant.
5.5 Robustness checks
Weighting and matching procedures can produce unbiased treatment effects when relevant
confounding factors are controlled for and the number of observations is sufficiently large
(Hirano et al., 2003; Pirracchio et al., 2012; Wooldridge, 2007). What exactly “sufficiently large”
means depends on the context and the actual distribution of treated and control observations. For
our estimates related to male-headed households, we have more than 200 observations and an
almost equal split between certified and non-certified households (Table 1). However, for female-
headed households, we only have 78 observations. Pirracchio et al. (2012) used Monte Carlo
simulations to compare the performance of propensity score methods and inverse probability
weighting (IPW) with small sample sizes. Both methods were efficient in reducing bias even with
samples of only 40 observations. In the range between 60 and 100 observations, IPW performed
better than propensity score methods (Pirracchio et al., 2012). We are not aware of similar tests
for entropy balancing methods.
Against this background, we decided to carry out robustness checks by re-estimating all ATTs
with the IPW method. The IPW estimator first calculates the treatment probability (in our case
the probability of being certified). For this step, we used the same conditioning variables as for
entropy balancing, including the WTA estimates. Then, the inverse of the treatment probability
29
was used as a weight in estimating the mean outcomes for certified and non-certified
observations and the ATT. Results are displayed in Tables A2-A4 in the Appendix. Overall, the
ATTs obtained with the IPW method are similar to those obtained with entropy balancing,
confirming the robustness of our findings. In some cases, the IPW estimates are larger and have
higher levels of significance, suggesting that the estimates obtained with entropy balancing are
more conservative.
6 Discussion and conclusion
Achieving gender equality remains a challenge, especially in rural areas of developing countries.
The literature suggests that agricultural commercialization may further increase gender
disparities, even though gender-sensitive approaches may possibly prevent such undesirable
trends. We have analyzed whether private sustainability standards can contribute to more gender
equality in the semi-commercialized small farm sector. We have particularly looked at two
standards that try to address gender issues in cash crop production, namely Fairtrade and UTZ.
Using gender-disaggregated data from male-headed and female-headed coffee-producing
households in Uganda, we have analyzed the effects of these standards on a set of outcome
variables that characterize various dimensions of women’s empowerment. The results suggest
that private standards may not completely eliminate gender disparities, but they can at least
contribute towards this goal.
Our findings related to economic empowerment are promising and challenge earlier conclusions
that economic benefits from sustainability standards are primarily captured by men (Bolwig,
2012; Loconto, 2015; Sen, 2014). We hypothesized that sustainability standards may contribute
30
to women’s economic empowerment (1) by improving women’s access to markets and/or (2) by
increasing women’s control over income from cash crop production. While the first mechanism
was confirmed for female household heads, the second one is particularly relevant for female
spouses in male-headed households. In female-headed households, sustainability standards
increase total household assets (our proxy for wealth) and thus also the assets that are
individually owned by women. The positive welfare effects are the result of higher coffee
revenues in households that are certified under a sustainability standard.
In male-headed households, standards also increase total household assets, but additionally they
also affect the gendered distribution of wealth. In our case, standards have particularly strong
positive effects on assets owned by female spouses and on assets that are jointly owned by male
household heads and their spouses. In other words, standards decrease the share of assets that are
owned by male household heads alone. These effects can be explained by women in certified
households having greater control over cash revenues from coffee and other crops than their
counterparts in non-certified households. Obviously, women can benefit from standards, even
when they are not directly involved in marketing. In fact, our data suggest that standards do not
reduce the dominant role of male household heads in coffee production and marketing. Hence,
other aspects of standards seem to be more important drivers of women’s empowerment in male-
headed households. One likely explanation are non-discrimination policies and workshops on
gender equality that are implemented by certified farmer organizations. Such policies and
measures may gradually contribute to behavioral change among males and females in certified
households (Chiputwa and Qaim, 2016).
Unlike a few previous studies (Bolwig, 2012; Lyon et al., 2010), we find no evidence that
sustainability standards significantly increase the workload of women. Our results may differ
31
from previous studies, because we did not look at Organic standards. Hence, in our sample of
certified households we do not observe a substitution of manual labor for chemical inputs.
However, the finding of no significant effects of standards on labor should not be over-
interpreted. Labor use in farming varies seasonally. Such variation is not captured in our cross-
section data, so the effects during other times of the year may be different. For instance, Fairtrade
and UTZ have specific requirements on coffee quality that involve higher labor requirements for
harvest and post-harvest operations. While harvesting is usually an activity undertaken by all
household members, the workload during the harvest seasons may be unequally distributed
within households.
Our results further indicate that sustainability standards improve farmers’ access to agricultural
extension services and related information, irrespective of gender. However, male and female
household heads benefit more in this regard than female spouses. Female spouses are less likely
than their husbands to be a registered member of the farmer organizations and to participate in
group meetings. As Lyon (2008) points out, being a registered and active member is a
precondition to influence decisions on the types of services offered by certified farmer
organizations. In terms of access to financial services, we find that standards have positive and
significant effects for males, but not for females. More could be done to improve access to rural
services for female spouses in particular and to encourage their registration as formal members of
the farmer organizations.
One could argue that sustainability standards fail to challenge traditional gender roles and
inequalities, if men’s dominance in farmer organizations and in cash crop marketing persists
(Loconto, 2015; Sen, 2014). However, traditional social norms can change only gradually, so a
quick fix through sustainability standards cannot be expected. Standards alone will not eliminate
32
gender disparities, but our results clearly suggest that they can instigate and support trends in the
right direction. In terms of the design of sustainability standards, it should be emphasized that
many of the gender measures are suggestions to certified farmer organizations rather than
mandatory certification requirements. As a result, it depends on the particular farmer organization
whether or not such gender measures are really taken up. In the farmer organizations that we
studied in Uganda, gender policies were effectively implemented. Elsewhere this may not always
be the case. Hence, we agree with previous qualitative studies (Hutchens, 2010; Lyon, 2008) that
gender issues could be addressed more explicitly in standards – for instance in the form of
mandatory certification requirements.
Two limitations of our study should be mentioned. First, we have used cross-section
observational data, which are less suitable for inferences on causal effects than panel data or
experimental approaches. While we have tried to control for observed and unobserved
confounding factors, the possibility of remaining bias in the estimated effects cannot be ruled out
completely. Second, with our data we were not able to analyze possible spillover effects.
Certified households are strongly encouraged to participate in trainings on gender equality and
farm management, but non-certified households from the same farmer organizations are not
excluded when they also wish to participate in these trainings. Hence, positive spillovers to these
non-certified households in the control group could occur, which would lead to underestimation
of the effect of standards on gender equality. Against this background, the exact magnitude of the
estimated effects should be interpreted with caution. Our study is the first to use a quantitative
approach in evaluating the effects of private standards on gender equality. Follow-up research
will be useful to test the findings and further add to the knowledge base.
33
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Appendix
Table A1: Factors influencing the probability of certification (probit estimates at individual level)
(1) (2) (3) Male heads Female spouses Female heads Household size 0.02 0.01 -0.03 (0.01) (0.01) (0.03) Muslim (1/0) -0.20* -0.18* -0.64** (0.10) (0.10) (0.30) Age (yrs.) 0.02*** 0.02*** 0.00 (0.00) (0.00) (0.01) Age gap (age head - age spouse) -0.01** 0.01* (0.00) (0.00) Schooling (yrs.) 0.05*** 0.06*** 0.03 (0.01) (0.01) (0.02) Education gap (schooling head- schooling spouse) -0.03** 0.02 (0.01) (0.01) Yrs. growing coffee -0.00 0.00 0.00 (0.00) (0.00) (0.01) Altitude (m) 0.00** 0.00*** 0.00*** (0.00) (0.00) (0.00) Distance to input market (km) -0.01 -0.02** 0.01 (0.01) (0.01) (0.01) Distance to output market (km) -0.00 -0.01 -0.03 (0.01) (0.01) (0.02) Wealthy ancestors (1/0) -0.09 -0.16* 0.18 (0.09) (0.09) (0.18) WTA sustainability standards 0.00 0.00 0.05** (0.01) (0.01) (0.02) Observations 233 244 71 Pseudo R2 0.19 0.22 0.25 LR chi2 61.63*** 74.84*** 24.90*** Marginal effects are shown with standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
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Table A2: Effects of standards on asset ownership, estimated with inverse probability weighting (in 1000 UGX)
ATT SE Male-headed households Value of household assets 1148.58*** (348.19) Asset ownership male head Value 109.06 (254.95) Percent of total household assets -10.52** (4.80) Asset ownership female spouse Value 147.65** (69.78) Percent of total household assets -0.55 (2.41) Joint asset ownership Value 926.86*** (220.69) Percent of total household assets 12.43*** (4.65) Female-headed households Value of household assets 1775.60*** (554.28) Asset ownership female head Value 1409.25*** (530.30) Percent of total household assets -7.13 (6.31) * p < 0.1, ** p < 0.05, *** p < 0.01
Table A3: Effects of standards on workload and social capital, estimated with inverse probability weighting
Workload and satisfaction
ATT SE
Total workload (hrs./day) Male heads -0.07 (0.50)
Female spouses 0.17 (0.59) Female heads 0.19 (0.68)
Satisfaction with leisure time (1-5)e
Male heads 0.00 (0.17) Female spouses -0.01 (0.22)
Female heads -0.60 (0.40) Social capital Participation in farmer meetings (1/0)
Male heads -0.04 (0.05) Female spouses 0.16* (0.08)
Female heads 0.07 (0.16)
Administrative / leadership position (1/0)
Male heads 0.17** (0.08) Female spouses 0.20*** (0.08)
Female heads -0.03 (0.16) * p < 0.1, ** p < 0.05, *** p < 0.01
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Table A4: Effects of standards on access to rural services, estimated with inverse probability weighting
ATT SE Interaction with extension officer (1/0)
Male heads 0.35*** (0.08) Female spouses 0.34*** (0.07)
Female heads 0.45*** (0.13)
Participation in field days (1/0) Male heads 0.39*** (0.05)
Female spouses 0.15*** (0.05) Female heads 0.09 (0.12)
Training on soil fertility (1/0)
Male heads 0.31*** (0.08) Female spouses 0.19*** (0.07)
Female heads 0.43*** (0.13)
Training on pest management (1/0) Male heads 0.44*** (0.07)
Female spouses 0.26*** (0.07) Female heads 0.50*** (0.13)
Training on coffee quality (1/0)
Male heads 0.32*** (0.08) Female spouses 0.27*** (0.07)
Female heads 0.46*** (0.13)
Access to financial services (1/0) Male heads 0.22*** (0.07)
Female spouses 0.02 (0.07) Female heads 0.07 (0.12)
* p < 0.1, ** p < 0.05, *** p < 0.01