Sustainability Standards, Gender, and Nutrition

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The Journal of Development Studies

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Sustainability Standards, Gender, and Nutrition among Smallholder Farmers in Uganda Brian Chiputwa & Matin Qaim To cite this article: Brian Chiputwa & Matin Qaim (2016): Sustainability Standards, Gender, and Nutrition among Smallholder Farmers in Uganda, The Journal of Development Studies, DOI: 10.1080/00220388.2016.1156090 To link to this article: http://dx.doi.org/10.1080/00220388.2016.1156090

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The Journal of Development Studies, 2016 http://dx.doi.org/10.1080/00220388.2016.1156090

Sustainability Standards, Gender, and Nutrition among Smallholder Farmers in Uganda

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BRIAN CHIPUTWA* & MATIN QAIM** *World Agroforestry Centre (ICRAF), Nairobi, Kenya, **Department of Agricultural Economics and Rural Development, Georg-August-University of Goettingen, Goettingen, Germany

(Final version received December 2015; final version accepted December 2015)

ABSTRACT Sustainability standards are gaining in importance in global markets for high-value foods. While previous research has shown that participating farmers in developing countries may benefit through income gains, nutrition impacts have hardly been analysed. We use survey data from smallholder coffee farmers in Uganda – certified under Fairtrade, Organic, and UTZ – to analyse impacts on food security and dietary quality. Estimates of instrumental variable models and simultaneous equation systems show that certification increases calorie and micronutrient consumption, mainly through higher incomes and improved gender equity. In certified households, women have greater control of coffee production and monetary revenues from sales.

1. Introduction Global food systems are undergoing a rapid transformation, with high-value market segments, private standards, and certification schemes gaining in importance (Reardon & Timmer, 2012). This transformation is partly driven by changing consumer preferences and growing concerns for food safety and environmental and social consequences of agricultural production (Mergenthaler, Weinberger, & Qaim, 2009; Narrod et al., 2009). To address these concerns, various sustainability standards were introduced. In rich and emerging countries, market shares of products with sustainability labels are rising. Especially for high-value foods imported from developing countries – such as coffee, tea, cocoa, or tropical fruits – voluntary sustainability standards like Fairtrade, Organic, UTZ, or Rainforest Alliance are increasingly used for product differentiation (Henson & Humphrey, 2010; Holzapfel & Wollni, 2014). Many of these standards involve smallholder farmers. Hence, this trend towards ‘sustainable consumption’ in rich countries may possibly contribute to poverty reduction and rural development in poor countries. There is a growing body of literature looking at impacts of sustainability standards on smallholder farmers in developing countries. Many of these studies analysed effects of participation in Fairtrade and Organic certification schemes for producers of coffee (Arnould, Plastina, & Ball, 2009; Chiputwa, Spielman, & Qaim, 2015; Jena, Chichaibelu, Stellmacher, & Grote, 2012; Ruben & Fort, 2012), cocoa (Jones & Gibbon, 2011), and tropical fruits (Kleemann, Abdulai, & Buss, 2014; Ruben, 2008). The results are mixed and not all studies properly accounted for possible selection bias (Dragusanu, Giovannucci, & Nunn, 2014). In many situations, Fairtrade and Organic farmers receive higher and sometimes more stable prices than their non-certified colleagues. Yet studies from various countries in Correspondence Address: Brian Chiputwa, World Agroforestry Centre (ICRAF), PO Box 30677, 00100, Nairobi, Kenya. Email: [email protected] An Online Appendix is available for this article which can be accessed via the online version of this journal available at http://dx. doi.org/10.1080/00220388.2016.1156090 © 2016 Informa UK Limited, trading as Taylor & Francis Group

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2 B. Chiputwa & M. Qaim Latin America have shown that higher prices do not always translate to higher household incomes (Barham & Weber, 2012; Ruben & Fort, 2012; Ruben, Fort, & Zuniga, 2009; Ruben & Zuniga, 2011). Especially when there are restrictions on the use of certain inputs, certification is sometimes associated with lower yields. And not all standards offer incentives for farmers to produce high quality. Several recent studies from countries in Africa show more significant farmer income gains (Chiputwa et al., 2015; Jena et al., 2012; Kleemann et al., 2014). One possible explanation for dissimilar impacts across regions is that average yield and quality levels are still lower in Africa and that markets for certified cash crops are not yet as developed as in Latin America. Concrete impacts seem to depend on the particular context. Beyond income, one question that has received much less attention in the literature is whether sustainability standards can help improve food security and nutrition among smallholder farmers. Undernutrition is still a widespread problem in many developing countries. A large proportion of the undernourished are smallholder farmers. Hence, it is critical to better understand the linkages between agriculture and nutrition and include nutrition dimensions into impact evaluation of agricultural programmes (IFPRI, 2015). We are aware of only one study that has looked at the effects of sustainability standards on food consumption with a quantitative approach: using data from a small sample of farmers in Kenya, Becchetti and Costantino (2008) showed that Fairtrade certification is positively associated with food expenditures and dietary quality. Becchetti and Costantino (2008) used a relatively simple dietary quality index. They did not analyse pathways to explain observed differences in diets between certified and non-certified households. It can generally be expected that income gains that may result from participation in high-value markets would contribute to improved nutrition. However, as is well known, agricultural commercialisation can change gender roles within the farm household, often resulting in a lower share of the income being controlled by women (Chege, Andersson, & Qaim, 2015; Fischer & Qaim, 2012; Njuki, Kaaria, Chamunorwa, & Chiuri, 2011; von Braun & Kennedy, 1994). Since women tend to spend more on food and healthcare than men (Hoddinott & Haddad, 1995; Quisumbing & Maluccio, 2003), this shift in income control might possibly entail negative effects for dietary quality and nutrition. The gendered effects of sustainability standards may differ from those of other types of commercialisation, because these standards tend to consider the promotion of gender equity as an important element in the certification process (Lyon, Bezaury, & Mutersbaugh, 2010; Terstappen, Hanson, & McLaughlin, 2013). Several studies looked at the gender implications of Fairtrade and other standards (Lyon, 2008; Maertens & Swinnen, 2012; Ruben et al., 2009; Utting-Chamorro, 2005), although none of these studies explicitly linked gender effects with nutrition. While the employment opportunities and conditions for female labourers often improve through certified production, the effects for female farmers are less clear. In their recent review, Terstappen et al. (2013) concluded that more research on gender and wider social effects in different contexts is needed. We contribute to the existing bodies of literature by analysing the impact of sustainability standards on farm household nutrition in Uganda, where undernutrition is a sizeable problem. Using survey data, we evaluate effects on household calorie and micronutrient consumption. We also develop simultaneous equation models to examine impact pathways with a particular focus on income and gender roles.1 Given data limitations, our intention is not to provide conclusive evidence, but to offer preliminary insights and stimulate follow-up work to better understand nutrition impacts of sustainability standards and other developments in high-value markets.

2. Coffee Production and Certification in Uganda Uganda is one of the top coffee producers in Africa, accounting for 2.5 per cent of global coffee production. Coffee is also the country’s most important export crop. 80 per cent of the coffee grown in the country is Robusta, which is indigenous to Uganda, while the rest is Arabica (GAIN, 2015). It is estimated that in Uganda the coffee sector employs more than 3.5 million people directly and indirectly. The crop is mostly grown by smallholders; an estimated 90 per cent of the coffee in

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Sustainability standards, gender, and nutrition 3 Uganda is produced by farm households with less than seven acres of total land. In smallholder production systems, coffee is often intercropped with staples like banana, maize, and cassava; coffee is the main source of cash income, while the other crops are predominantly grown for subsistence consumption. Smallholder coffee producers in Uganda are relatively poor; around 25 per cent live below the international poverty line of 1.25 dollars a day (Chiputwa et al., 2015). Prior to 1991, the Ugandan coffee sector was centrally controlled by a marketing board. Coffee producers were organised in cooperatives, and through these cooperatives the marketing board paid farmers a fixed price upon delivery, and a premium based on quality at a later stage. The fixed prices were often below world market levels, and the quality premium was paid with significant delays. The system was liberalised in 1991. The Uganda Coffee Development Authority (UCDA) was established to monitor and regulate the market and promote value addition and competitiveness among local farmers. UCDA is not directly involved in purchasing or marketing coffee; this is left to independent private traders and companies. While liberalisation helped to improve efficiency in Uganda’s coffee sector, production and exports declined in the late-1990s and early-2000s due to various reasons, including low productivity, increasing international competition, and declining world-market prices. Given the sector’s economic and social importance in Uganda, several public and private sector initiatives were launched trying to improve coffee productivity and quality (Baffes, 2006). One strategy is to expand the production of speciality coffees through certification schemes, including sustainability standards. While the share of certified coffee in Uganda’s total coffee production is still relatively low, it has increased considerably during the last 10 years (Chiputwa et al., 2015). Sustainability standards with growing importance in Uganda include Fairtrade, Organic, and UTZ, which are also those that we focus on in this study.2 Each sustainability standard has its own principles, but all of them involve certain environmental and social objectives. One important social objective is to improve the livelihoods of smallholder producers through fair participation in international value chains. While nutritional targets are not explicitly mentioned by any of these standards, it is known that changes in smallholder incomes and market access can significantly affect dietary quality (Sibhatu, Krishna, & Qaim, 2015). Understanding these effects is important from a food security and nutrition perspective.

3. Methodology 3.1 Measuring Nutrition To analyse nutrition impacts of sustainability standards, we first need to identify appropriate indicators of nutrition. The most precise indicators of nutritional status are clinical measures (for example, blood samples) and anthropometric data (IFPRI, 2015; Masset, Haddad, Cornelius, & Isaza-Castro, 2012). However, clinical and anthropometric measures are less suitable to assess patterns of food security and dietary quality, which is what we concentrate on here. To analyse dietary patterns, data from household food consumption recalls are frequently used, which can be converted to calorie and nutrient values using food composition tables (Ecker & Qaim, 2011; Fiedler, Lividini, Bermudez, & Smitz, 2012). We follow this approach and use calorie consumption levels to assess food security. Furthermore, we use the consumption of important micronutrients to assess dietary quality. We focus on iron, zinc, and vitamin A. Deficiencies in these micronutrients cause large public health problems in developing countries (IFPRI, 2015; Stein et al., 2008). Details of the household survey are provided further below. Here, we describe how the food consumption data were collected and used to derive the nutrition indicators. We conducted a food recall, asking survey respondents to report quantities of all foods consumed by the household during the last seven days from own production, purchases, or any other source. This food recall was carried out with the person in the household responsible for food preparation. The survey questionnaire included a breakdown of over 100 different food items. Reported food quantities were converted to edible portions. These edible portions were then converted to quantities of calories and micronutrients,

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4 B. Chiputwa & M. Qaim using recent food composition tables for Uganda (Hotz, Abdelrahman, Sison, Moursi, & Loechl, 2012). To enable comparison across households of different size and composition, consumption at the household level was divided by the number of adult equivalents (AE) living in each household. We define a food-secure household as one whose calorie consumption per AE is greater than or equal to the minimum daily requirement of 2400 kcal for adult men. The recommended dietary threshold levels used for the three micronutrients are 18 mg/day/AE for iron, 15 mg/day/AE for zinc, and 625 μg RE/ day/AE for vitamin A (FAO, WHO, & UNU, 2001). While using household food consumption data has advantages to assess food security and dietary quality, this approach also has a few limitations (de Haen, Klasen, & Qaim, 2011; Fiedler et al., 2012). First, by using a single seven-day recall we cannot account for seasonal variation in food consumption. The timing of the survey was shortly after the main harvest season, so that consumption levels may be somewhat higher than during other times of the year. Second, we are not able to account for intrahousehold food distribution. Third, the seven-day recall data measure consumption levels, which are only a proxy for actual food and nutrient intakes. Food wasted in the household or portions given to guests or fed to pets cannot be fully accounted for, which may result in overestimated consumption levels. However, while these issues reduce the accuracy of the dietary assessment, they are unlikely to bias the impact estimates systematically because they apply equally to certified and non-certified households. 3.2 Modelling Nutrition Impacts We want to evaluate the impact of farmer participation in sustainability certification schemes on household nutrition. We start with a reduced-form model as follows: Ni ¼ α0 þ α1 Ci þ α2 Xi þ ε1 ;

(1)

where Ni is the nutrition indicator. In different regressions, we use household consumption of calories and micronutrients per AE as indicators of food security and dietary quality, as explained above. Ci is the certification treatment variable, which we define in two different ways: (i) We use a treatment dummy that takes a value of one for certified farm households and zero otherwise. (ii) We use a continuous treatment variable measuring the number of years that a farm household has been certified already (duration); for non-certified households this variable takes a value of zero. Xi is a vector of farm, household, and contextual variables that may influence nutrition, such as asset ownership, characteristics of the household and the household head, and infrastructure conditions. ε1 is a random error term. To evaluate whether certification has an impact on nutrition, we are particularly interested in the coefficient α1 . A positive and significant coefficient would imply that certification contributes to improved nutrition. However, one problem in estimating equation (1) is that Ci is likely endogenous. In the sample, farm households decided themselves whether or not to participate in certification. It is possible that this decision is correlated with unobserved factors that also influence nutrition, in which case the estimated treatment effect would suffer from selection bias. We deal with this problem by using an instrumental variable (IV) approach. The challenge is to identify a valid instrument that is correlated with the treatment variable but not directly correlated with the nutrition outcomes. 3.3 Choice of Instrument We tried various instruments, but most of the variables that influence the certification decision – such as human capital, asset ownership, and infrastructure conditions – also affect household living standards and nutrition directly. The variable that we eventually identified as the most suitable instrument for Ci is the altitude above sea level at which the farm is located. Farm altitude was previously used as an instrument by Wollni and Zeller (2007) in their study on the welfare effects of

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Sustainability standards, gender, and nutrition 5 farmer participation in specialty markets for coffee in Costa Rica. Altitude has an effect on coffee quality (Avelino et al., 2005). High-quality Arabica coffee is only grown in Uganda at altitudes above 1500 metres. The farmers in this study are located at altitudes between 1100 and 1300 metres; all of them grow Robusta coffee. Certified farmers tend to be located in lower altitudes, hence certification is negatively correlated with altitude (p < 0.01). This correlation may also be due to clustering effects, as certification takes place in groups to reduce costs for the participating smallholders. Interestingly, the relatively small altitude differences do not matter in non-certified markets: in our sample, coffee sales prices of non-certified farms are not correlated with altitude. We also tested whether farm altitude has any direct effect on household living standard in the full sample and for the sub-sample of non-certified farmers. In both cases, the estimated coefficient for altitude was insignificant. To test the validity of the instrument further, we used an approach suggested by Di Falco, Veronesi, and Yesuf (2011) and regressed the nutrition outcome variables on altitude and other relevant controls for the sub-sample of non-certified farms. Altitude was not significant in any of these models (results of these tests are provided in Table A1 in the Online Appendix). We conclude that farm altitude does not have a direct influence on household living standard and nutrition and is therefore a valid instrument for certification in this study. 3.4 Modelling Impact Pathways The reduced-form model in Equation (1) is useful to analyse whether sustainability certification has an impact on nutrition, but it cannot explain impact pathways. We hypothesise that participation in certification affects nutrition primarily through two pathways, namely through effects on income and gender roles within the household. Concerning the income pathway, several recent studies showed that sustainability standards like Fairtrade and Organic can contribute to income gains in the African small farm sector through price premiums and reduced risk (Chiputwa et al., 2015; Jena et al., 2012; Kleemann et al., 2014). Holding other things constant, income gains are likely to improve food security and nutrition. Concerning the gender pathway, certification may also affect the roles of men and women within the household and thus food availability and nutrition. Previous research showed that agricultural commercialisation is often associated with women losing control of production and income (Chege et al., 2015; Fischer & Qaim, 2012; Njuki et al., 2011; von Braun & Kennedy, 1994). However, sustainability standards explicitly try to strengthen women’s role, hence loss of income control may possibly be prevented. For example, the promotion of gender equity and ensuring that women’s work is properly valued and equally rewarded is one of the 10 key principles of the Fairtrade standard (Fairtrade, 2009). Similarly, the UTZ code of conduct promotes policies of non-discrimination and gender equity by providing gender training and awareness programmes to its members and extension workers (UTZ, 2009). While the evidence is limited, a few studies showed that sustainability standards can indeed improve women’s incomes, autonomy, and access to information and cooperative networks (Bassett, 2010; Lyon et al., 2010). To formally analyse the two impact pathways, we develop a system of simultaneous equations as follows: Ni ¼ β0 þ β1 Ii þ β2 Gi þ β3 Xi þ ε2

(2)

Ii ¼ θ0 þ θ1 Ci þ θ2 Yi þ ε3

(3)

Gi ¼ ω0 þ ω1 Ci þ ω2 Zi þ ε4

(4)

Ci ¼ γ0 þ γ1 Ai þ γ2 Li þ ε5

(5)

where Ni is the nutrition indicator of household i, as defined above, Ii is per capita income, and Gi is gender, which we measure in terms of a dummy that takes a value of one when revenue from coffee

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6 B. Chiputwa & M. Qaim sales is controlled by a male household member. We hypothesise that income and gender are both endogenous and influenced by certification (Ci ), as shown in Equations (3) and (4). Ci is also endogenous, so that in Equation (5) we use farm altitude (Ai ) as a valid instrument (see above for validity tests). Xi , Yi , Zi , and Li are vectors of socioeconomic controls that are expected to influence nutrition, income, gender, and certification. ε2 , ε3 , ε4 , and ε5 are random error terms that may be correlated. We employ a mixed-process maximum likelihood procedure to estimate this system of simultaneous equations (Roodman, 2011). The vectors of socioeconomic controls may overlap but are not identical across the four equations. The inclusion of variables is based on economic theory and plausibility criteria. In all four equations, we include household size as well as age and education of the household head as socio-demographic variables that are likely to affect decisions about farming, other economic activities, and intra-household resource distribution. Furthermore, in the income and certification equations we control for gender of the household head. Gender of the household head is not included in the other two equations because of its close correlation with Gi . In the income and certification equations, we additionally control for asset ownership. The most important source of income for sample households is farming, so we include farm size in terms of land owned in Equation (3). Certification may also be influenced by other asset and wealth categories, which we proxy by the size of the homestead (number of rooms), and by market access and infrastructure conditions, which we proxy by distance to roads. Asset variables in the certification equation are lagged by five years, because farm households in our sample made their certification decisions in the past. By using lagged values in Equation (5) we avoid possible problems of reverse causality.3

4. Data and Descriptive Statistics 4.1 Farm Household Survey This research builds on a structured survey of coffee-producing households carried out in 2012 in Uganda. We used a multi-stage sampling procedure. At first, we contacted the main coffee associations in Uganda to obtain lists of existing farmer cooperatives, including information on their location, the number of cooperative members, and certification details. Based on these lists and visits to many of the locations, we purposively selected three cooperatives for inclusion in the study. These cooperatives have similar agro-ecological and infrastructure conditions. All three are located in the Central Region of Uganda; two of them in Luwero District, and the third in Masaka District. In all three cooperatives, farmers produce only Robusta coffee. Luwero and Masaka are among the top four districts that account for over 50 per cent of Uganda’s Robusta coffee production. All three cooperatives had acquired UTZ certification around 2007; two of them had added a second certification scheme shortly thereafter. At the time of the survey, one cooperative had only UTZ, the second had UTZ plus Fairtrade, and the third had UTZ plus Organic certification. Farmers have to be members of a cooperative to participate in the certification schemes, but not all members of the three cooperatives actually participated in certification. Hence, all three cooperatives comprise certified and non-certified farm households, based on individual household decisions. Cooperative management provided us with lists of all members, including details on the location of each farm household and their participation in certification schemes. In each cooperative, we randomly selected two parishes, and in each parish, we randomly selected three villages. In these villages, we randomly selected households for the interviews. In total, we interviewed 271 certified households. Of these, 108 households were certified under UTZ and Fairtrade, 101 under UTZ and Organic, and 62 only under UTZ. In addition, we randomly selected a control group of 148 non-certified farm households in the same villages. Some of these control households were cooperative members while others were not. The total sample size is 419.

Sustainability standards, gender, and nutrition 7 Table 1. Summary statistics by certification status

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Non-certified (N = 148)

Farm and household characteristics Male household head (dummy) Age of household head (years) Education of household head (years) Cell phone ownership (dummy) Household size (AE) Number of rooms in house Years growing coffee Total land owned (acres) Number of rooms in house (5 years ago) Per capita expenditure per day (UGX) Total land owned 5 years ago (acres) Farm altitude (m) Distance to all-weather road (km) Control of coffee activities Male controls production (dummy) Male controls revenue (dummy)

Certified (N = 271)

Mean

S.D.

Mean

S.D.

Difference

0.791 47.378 6.534 0.750 4.848 4.128 16.662 4.533 3.757 3176.39 4.344 1210.03 18.793

0.408 15.444 3.329 0.434 2.930 1.481 12.745 3.296 1.519 1582.18 3.496 47.698 15.401

0.738 55.432 6.590 0.775 5.360 4.613 26.786 6.220 4.557 3579.32 5.995 1168.85 14.998

0.441 12.816 3.785 0.418 2.683 1.508 15.590 4.702 2.237 1821.21 5.287 71.652 8.307

** *** *** *** * *** *** **

0.574 0.601

0.496 0.491

0.369 0.439

0.483 0.497

*** **

***

Notes: UGX, Ugandan shillings; AE, adult equivalent; S.D., standard deviation. Differences in mean values are tested for statistically significant differences; *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

4.2 Descriptive Statistics Table 1 shows descriptive statistics, disaggregated by certification status. The heads of certified households are older and have longer experience with coffee cultivation than the heads of non-certified households. Certified farmers also have more land and larger homesteads. They are located closer to all-weather roads than non-certified farmers and have slightly higher incomes. We proxy income by per capita expenditure, which is a better indicator of living standard. As explained, we use altitude as an instrument for certification. Certified farms are located in somewhat lower altitudes than non-certified farms. These comparisons suggest that there are systematic differences between certified and non-certified households, which need to be accounted for in the impact analysis. For instance, higher asset endowment and better infrastructure conditions can influence nutrition directly and may also facilitate household participation in certification schemes. Meeting the certification criteria is easier for farmers with certain minimum human and financial capital endowments. Hence, we control for these factors in our regression approach. In addition, there could be unobserved factors that jointly determine certification and nutrition, such as farmers’ motivation or entrepreneurial skills. The IV approach controls for unobserved heterogeneity between certified and non-certified households and therefore produces consistent impact estimates.

4.3 Gender Roles in Coffee Production In the survey, we also tried to capture gender roles within the household. To the extent possible, in households with married couples the interviews were conducted with both partners present. While the couples answered the general interview sections and the questions about gender responsibilities jointly, some of the more specific questions were asked individually without the other partner present. For the household coffee enterprise, survey respondents were asked to identify who in the household is the primary decision-maker for coffee production activities – such as weeding, input use, and harvesting – and who controls the revenues from coffee sales. Based on the answers, the decisions were categorised as

8 B. Chiputwa & M. Qaim 100%

Share of households

80%

60% Joint Female

40%

Male

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20%

0%

Certified

Non-certified

Coffee production

Certified

Non-certified

Coffee revenue

Figure 1. Male and female control of coffee production and revenues in certified and non-certified households.

being made by (i) the male household head, (ii) the female spouse or female household head, or (iii) jointly by male and female household members. The lower part of Table 1 shows descriptive statistics for these gender roles. In certified households, women have significantly more control of coffee production and revenues than in non-certified households. In 56 per cent of the certified households, women control coffee revenues either alone or together with a male household member (Figure 1). This is a first indication that certification may have a positive influence on women’s empowerment. To further examine potential effects of sustainability certification on gender roles, we analyse the relationship between the duration of being certified and gender control of coffee production and revenues in Figure 2. This is possible with the cross-section survey data, because households in the sample were certified at different points in time. The longer households have been certified, the less likely it is that males alone control coffee production and revenues. This supports the hypothesis that certification contributes to profound behavioural changes towards gender equity in participating households.

4.4 Household Nutrition by Certification Next, we compare nutritional indicators between certified and non-certified households. Table 2 shows levels of consumption, deficiency, and depth of deficiency for calories, iron, zinc, and vitamin A. The numbers confirm that food insecurity and micronutrient malnutrition are widespread problems among coffee farmers in Uganda, affecting more than 40 per cent of the households. Notable differences are observed between certified and non-certified households. Certified households have higher mean calorie and micronutrient consumption levels. They also have lower levels of nutritional deficiencies. Whether or not these differences can be interpreted as causal effects of certification will be analysed in the next section.

5. Econometric Results 5.1 Impact of Certification on Nutrition We start this analysis by specifying and estimating the reduced-form model in Equation (1). In separate regressions, we use the consumption of calories, iron, zinc, and vitamin A per AE as dependent variables. Certification is the treatment variable, which is included on the right-hand side together with a vector of controls. As control variables, we include gender, age, and education of the household

Sustainability standards, gender, and nutrition 9

.8 .6 .4 .2 0

0

.2

.4

.6

.8

1

Men control revenues

1

Men control production

0

2

4 6 Number of years certified

0

Fitted values

2

4 6 Number of years certified

95% CI

8

Fitted values

.8 .6 .4 .2 0

.2

.4

.6

.8

1

Both spouses control revenues

1

Both spouses control production

0

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95% CI

8

0

2

4 6 Number of years certified

95% CI

8

0

Fitted values

2

4 6 Number of years certified

95% CI

8

Fitted values

Figure 2. Relationship between duration of certification and gender control of coffee production and revenues. Notes: Fitted values are predictions based on simple linear regressions with proportion of male control or both spouses as dependent variable and number of years certified as independent variable (CI, confidence interval). Zero years represent non-certified farmers.

Table 2. Household calorie and micronutrient consumption Non-certified (N = 148) Mean Calories Daily consumption (kcal/AE) Prevalence of deficiency (%) Depth of deficiency (%) Iron Daily consumption (mg/AE) Prevalence of deficiency (%) Depth of deficiency (%) Zinc Daily consumption (mg/AE) Prevalence of deficiency (%) Depth of deficiency (%) Vitamin A Daily consumption (μg RE/AE) Prevalence of deficiency (%) Depth of deficiency (%)

S.D.

Certified (N = 271) Mean

S.D.

2867.710 0.439 0.289

1408.336 0.498 0.204

3151.453 0.354 0.217

1353.307 0.479 0.148

20.722 0.486 0.344

10.770 0.502 0.225

23.266 0.395 0.248

11.324 0.490 0.152

10.661 0.784 0.460

5.974 0.413 0.220

12.263 0.745 0.379

6.392 0.436 0.192

1203.388 0.358 0.455

1218.732 0.481 0.276

1266.426 0.303 0.437

Difference * * * *** * ***

1148.831 0.460 0.269

Notes: AE, adult equivalent; S.D., standard deviation; RE, retinol equivalent. Differences in mean values are tested for statistically significant differences; *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

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10 B. Chiputwa & M. Qaim head, household size, and infrastructure conditions, which may all affect nutrition. Furthermore, we include farm size and number of rooms in the homestead as asset variables, which were found to differ significantly between certified and non-certified households.4 Inclusion of these variables controls for bias due to observed heterogeneity. The IV approach additionally controls for unobserved heterogeneity. As explained, we specify the treatment variable in two different ways, as a certification dummy and as a continuous variable measuring the number of years a household has been certified. Table 3 shows the estimation results for the models with the certification dummy. These estimates are based on the IV estimator. For comparison, OLS results are shown in Table A2, first-stage results of the IV models are shown in Table A3 in the Online Appendix. The Durbin-Wu-Hausman test statistics shown in Table 3 are significant for all models, confirming that there are issues of unobserved heterogeneity that are controlled through the IV approach. The results in Table 3 show that certification has a positive and significant effect on the consumption of calories, iron, and zinc. Controlling for other factors, certified households consume 541 kcal more per AE and day, which implies a 19 per cent increase over mean consumption levels of noncertified households. Certified households also consume 7.3 mg/AE more iron and 5.1 mg/AE more zinc, representing increases relative to non-certified households of 35 per cent and 48 per cent, respectively. Also for vitamin A, we observe a positive effect of certification, although this coefficient

Table 3. Impact of certification status on calorie and micronutrient consumption (IV models) Calorie consumption Iron consumption Zinc consumption Vitamin A consumption (kcal/AE) (mg/AE) (mg/AE) (μg RE/AE) Certified (dummy) Male household head (dummy) Age of household head (years) Education of household head (years) Household size (AE) Number of rooms (5 years ago) Total land owned 5 years ago (acres) Distance to all-weather road (km) Constant Observations Log likelihood Wald (Chi-squared) Durbin-Wu-Hausman (Chi-squared)

540.909* (327.795) −140.605

7.274*** (2.418) −1.656

5.137*** (1.217) −0.295

441.029 (307.128) −140.829

(149.889) 6.347

(1.265) 0.030

(0.727) 0.015

(142.071) −0.379

(5.248) −30.377*

(0.043) −0.325**

(0.024) −0.189**

(4.960) −37.720**

(18.165) −201.577*** (22.849) 55.714*

(0.153) −1.388*** (0.192) 0.119

(0.088) −0.758*** (0.110) −0.141

(17.217) −33.124 (21.651) 25.484

(32.243) 8.465

(0.270) 0.003

(0.155) 0.086

(30.542) −3.292

(13.929) 12.603**

(0.117) 0.123**

(0.067) 0.078***

(13.193) −0.426

(5.914) 3227.684*** (327.200) 419 −3789 105.60 4.01*

(0.049) 24.106*** (2.758) 419 −1781 83.70 7.19***

(0.028) 11.863*** (1.583) 419 −1540 82.61 15.57***

(5.593) 1420.629*** (310.105) 419 −3762 16.22 4.70**

Notes: AE, adult equivalent; RE, retinol equivalent. Coefficients are shown with robust standard errors in parentheses. *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

Sustainability standards, gender, and nutrition 11 Table 4. Impact of certification duration on calorie and micronutrient consumption (IV models) Calorie consumption Iron consumption Zinc consumption Vitamin A consumption (kcal/AE) (mg/AE) (mg/AE) (μg RE/AE) Number of years certified Male household head (dummy)

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Age of household head (years) Education of household head (years) Household size (AE) Number of rooms (5 years ago) Total land owned 5 years ago (acres) Distance to all-weather road (km) Constant Observations Wald Chi-squared Durbin-Wu-Hausman (Chi-squared)

111.421*

1.530***

1.202***

105.236*

(58.189) −111.271

(0.519) −1.248

(0.302) 0.045

(56.897) −110.718

(164.769) 6.591

(1.404) 0.033

(0.812) 0.012

(167.292) −0.713

(4.945) −27.347

(0.042) −0.284

(0.025) −0.160

(5.104) −35.228**

(19.916) −205.155*** (25.976) 64.004*

(0.176) −1.439*** (0.232) 0.230

(0.105) −0.805*** (0.140) −0.068

(16.489) −37.346* (21.170) 31.655

(38.022) 6.520

(0.359) −0.025

(0.175) 0.057

(28.794) −5.891

(15.166) 10.822*

(0.122) 0.100*

(0.083) 0.064**

(9.816) −1.559

(5.773) 3179.127*** (336.352) 419 76.98 5.351**

(0.051) 23.422*** (2.895) 419 54.19 8.005***

(0.029) 11.257*** (1.670) 419 49.48 15.84***

(4.907) 1366.514*** (370.882) 419 16.54 5.048**

Notes: AE, adult equivalent; RE, retinol equivalent. Coefficients are shown with robust standard errors in parentheses. *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

is not statistically significant. These results suggest that participation in sustainability certification improves food security and dietary quality among coffee farmers in Uganda. The results in Table 4 use the same reduced-form models, but now with the duration of certification as a continuous treatment variable. Each additional year that a household has been certified increases the consumption of calories and all three micronutrients. In these models, the effect for vitamin A is also significant. The results suggest that certification does not only lead to a one-time shift, but to steady improvements in nutrition, pointing at profound behavioural changes within households.

5.2 Robustness Checks and Disaggregation Before moving on to the analysis of impact pathways, we use the reduced-form model for calorie consumption to carry out a few robustness checks. First, we examine whether the results are possibly driven by location fixed effects that we did not control for until now. We include sub-county dummies in addition to the other control variables. The problem is that the sub-county dummies are closely correlated with altitude, which is our IV. Hence, we use an OLS specification to test for location fixed effects. Results are shown in Table A4 in the Online Appendix. The two specifications with and without sub-county dummies produce similar coefficients for certification, suggesting that the results are not driven by location fixed effects. Only the standard error of the treatment estimate increases

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12 B. Chiputwa & M. Qaim with sub-county dummies included, which is due to the regional clustering of certification schemes in Uganda. Second, we test whether cooperative membership plays an important role. As mentioned, all certified farmers are members of farmer cooperatives, while some of the non-certified farmers are not. Controlling through an additional dummy suggests that cooperative membership as such does not have a significant effect on nutrition. At the same time, the certification effect remains significant and even increases somewhat in magnitude (Table A4 in the Online Appendix). Finally, we would like to get a better understanding of possible differences between different types of standards. As described, all three standards considered – Fairtrade, Organic, and UTZ – have social and environmental objectives, but they differ in terms of pricing schemes and other regulations. In their study on income and poverty impacts, Chiputwa et al. (2015) distinguished by type of standard and found that the effects of Fairtrade were larger than those of Organic and UTZ. For the nutrition effects we find a somewhat different pattern: when using three separate treatment dummies in the calorie consumption model, the effect of Organic is larger than that of Fairtrade and UTZ (Table A4 in the Online Appendix). However, these differences in nutrition effects should not be over-interpreted, because the OLS estimates cannot control for unobserved heterogeneity, which we know exists. Using an IV approach to estimate unbiased treatment effects for all three standards would have required three different instruments, which we were unable to identify. Hence, we proceed by combining the three standards in one treatment variable, acknowledging that further disaggregation remains an important issue for follow-up research. 5.3 Impact Pathways We now turn to the analysis of possible impact pathways, estimating the simultaneous equation system discussed in Equations (2) to (5). Again, we use two different treatment variables, namely the certification dummy and the duration of certification as a continuous variable. The main results for the dummy specification are summarised in Table 5 (full results are shown in Table A5 in the Online Appendix). The first two rows in Table 5 show how household expenditure Table 5. Impact pathways of certification status on calorie and micronutrient consumption

Effect on nutrition Per capita expenditure per day (UGX) Male controls revenue (dummy)

Calorie consumption (kcal/ AE)

Iron consumption (mg/AE)

Zinc consumption (mg/AE)

Vitamin A consumption (μg RE/AE)

0.306***

0.002***

0.002***

0.045

(0.034) −664.215***

(0.000) −6.525***

(0.000) −2.346**

(0.034) −557.335***

(1.687)

(0.930)

(198.880)

4521.814*** (544.884)

4546.756*** (544.798)

4496.279*** (544.950)

−0.669*** (0.127)

−0.680*** (0.127)

−0.661*** (0.128)

−0.001*** (0.000)

−0.001*** (0.000)

−0.001*** (0.000)

(198.861) Effect on p.c. expenditure (UGX) Certified (dummy) 4513.056*** (544.917) Effect on male control (dummy) Certified (dummy) −0.657*** (0.127) Effect on certified (dummy) Farm altitude (m) −0.001*** (0.000)

Notes: UGX, Ugandan shillings; AE, adult equivalent; RE, retinol equivalent; p.c., per capita. Coefficients are shown with standard errors in parentheses. Only main variables of interest are shown. Full results are presented in Table A5 in the Online Appendix. *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

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Sustainability standards, gender, and nutrition 13 (income) and gender roles affect calorie and micronutrient consumption. Each additional UGX (Ugandan shilling) of daily per capita expenditure increases calorie consumption by 0.306 kcal/AE. That is, an additional 1000 UGX (about 0.38 US$) increases calorie consumption by 306 kcal per day. Per capita expenditure levels also have a positive impact on iron and zinc consumption, whereas the effect for vitamin A is not statistically significant. Gender roles within the household have a significant effect on all nutrition indicators, including vitamin A. If a male household member controls the revenue from coffee sales (as compared to female or joint control), calorie consumption is reduced by 664 kcal, equivalent to 23 per cent of mean calorie consumption levels. Iron, zinc, and vitamin A consumption are also reduced considerably through male control of coffee revenues. This is consistent with the literature showing that men and women often spend income on different types of goods (Hoddinott & Haddad, 1995; Quisumbing & Maluccio, 2003). The other rows in Table 5 show that certification significantly affects household expenditure (income) and gender roles, confirming the two main hypothesised impact pathways. The estimated effect of certification on expenditure is very large. In all models, the coefficients are in a magnitude of 4500 UGX per AE and day, which would imply an increase of more than 100 per cent through certification. This is higher than what one could reasonably expect and also higher than what Chiputwa et al. (2015) found with their propensity score matching approach, although they also found a significantly positive effect. We tried various alternative model specifications by adding additional controls and changing the functional form through log-transformations of the continuous variables. The marginal effect of certification on expenditure changed somewhat, but it remained large and significant in all alternative models. Perhaps a second instrument, which affects certification but not expenditure, would have been useful. Unfortunately, we could not identify a second instrument that fulfilled all validity criteria. We therefore caution that the exact magnitude of the expenditure effect should not be over-interpreted. Nevertheless, consistent with Chiputwa et al. (2015) we are confident that certification affects household living standards positively. Looking at the gender effect, when a household is certified the probability that a male alone controls coffee revenues is reduced by 0.66. This is also a very strong effect and may be explained by two factors. First, as discussed, some of the sustainability standards promote gender equity through special training, awareness building, and other gender mainstreaming activities. Second, certified coffee production with stricter standards increases the demand for labour, so that female household members become increasingly involved in the coffee crop. More female labour spent on coffee production seems to improve women’s bargaining power and their influence on decision-making. Table 6 summarises the results for the simultaneous equation system using the duration of certification as a continuous treatment variable (full results are shown in Table A6 in the Online Appendix). These estimates are consistent with the findings so far. Each additional year that a household is certified increases per capita expenditure by about 500 UGX per day and reduces the probability of male revenue control by 0.09. These results point at learning effects of producing successfully in certified markets and at a positive trend towards women’s empowerment.

6. Conclusions Global food systems are undergoing a rapid transformation, with voluntary sustainability standards and certification schemes gaining in importance. Smallholder farmers in developing countries may potentially benefit from such standards. Previous research had analysed impacts of smallholder participation in sustainability certification schemes in terms of output prices, profits, and incomes. Impacts on nutrition had hardly been evaluated. We have addressed this shortcoming, using survey data from smallholder coffee farmers in Uganda who participate in Fairtrade, Organic, and UTZ certification schemes. Our contribution to the existing literature is twofold. First, we have analysed impacts on household food security and dietary quality, building on various indicators constructed from comprehensive food consumption data. Second, we have developed and estimated systems of simultaneous equations to analyse impact pathways with a particular focus on income and gender roles

14 B. Chiputwa & M. Qaim Table 6. Impact pathways of certification duration on calorie and micronutrient consumption

Effect on nutrition Per capita expenditure (UGX) Male controls revenue (dummy)

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Effect on p.c. expenditure (UGX) Number of years certified Effect on male control (dummy) Number of years certified Effect on years certified Farm altitude (m)

Calorie consumption (kcal/AE)

Iron consumption (mg/AE)

Zinc consumption (mg/AE)

Vitamin A consumption (μg RE/ AE)

0.310*** (0.034) −665.098*** (198.959)

0.002*** (0.000) −6.573*** (1.688)

0.002*** (0.000) −2.409*** (0.930)

0.043 (0.034) −557.986*** (198.909)

500.387*** (81.421)

501.219*** (81.420)

508.781*** (81.396)

497.385*** (81.408)

−0.090*** (0.020)

−0.093*** (0.020)

−0.095*** (0.020)

−0.089*** (0.020)

−0.012*** (0.002)

−0.012*** (0.002)

−0.012*** (0.002)

−0.012*** (0.002)

Notes: UGX, Ugandan shillings; AE, adult equivalent; RE, retinol equivalent; p.c., per capita. Coefficients are shown with standard errors in parentheses. Only main variables of interest are shown. Full results are presented in Table A6 in the Online Appendix. *, **, *** denote significance at 10 per cent, 5 per cent, and 1 per cent level, respectively.

within farm households. The approaches developed may also be useful for impact evaluation in other contexts, thus contributing to the broader research direction on agriculture-nutrition linkages. The empirical results suggest that sustainability standards in the coffee market have positive impacts on food security and dietary quality for smallholder farmers in Uganda. Controlling for other factors, participation in the certification schemes has increased household consumption of calories and micronutrients. In terms of impact pathways, the results indicate that sustainability certification increases household incomes and improves gender equity. Both these factors contribute to improved nutrition. The gender effects are particularly noteworthy. Agricultural commercialisation often contributes to women losing control of farm production and revenues, sometimes with negative marginal effects for household nutrition. The reason is that women tend to spend a greater share of their income on family nutrition and health than men. Our results suggest that this loss of female control can be prevented and even reversed when measures to promote gender equity are integrated into market-linkage initiatives. Sustainability standards vary in their concrete measures and approaches, but their codes of conduct generally emphasise zero tolerance to discrimination, marginalisation, and unfair treatment of family members and workers employed on certified farms. In addition to the structured survey, we conducted several focus group discussions with certified and non-certified farmers, separately for men and women. These discussions support the results from the quantitative analysis. Spouses of male farmers often stated that intra-household gender relations have changed through certification. Many females had received training courses on coffee production and marketing. Several also reported that certified households had to participate in workshops on gender equity where attendance of both partners was required. During these workshops, couples discussed gender roles in agricultural production and marketing and possibilities to make the division of labour and resources within the household more equitable. Even some of the men reported that these workshops were useful to raise awareness about gender issues. Beyond the specific workshops on gender equity, cooperatives with certification are also hiring more women as extension workers and foster equal representation of women in the leadership structure. In some cases, payments for coffee

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Sustainability standards, gender, and nutrition 15 delivered to the cooperative are only made if both spouses are present. This improves transparency and women’s involvement in decisions on how to spend the income. These results from Uganda should not simply be generalised. As discussed, previous research has shown that sustainability standards have not improved household incomes and women’s rights everywhere (Ruben et al., 2009; Terstappen et al., 2013). Development impacts depend on the particular context. Nevertheless, we cautiously conclude that sustainability standards can contribute to improved livelihoods of smallholder farm households, including higher incomes, better nutrition, and improved gender equity, when the conditions are favourable and the certification schemes are carefully designed. One shortcoming of our analysis is that it builds on cross-section data, so that not all possible biasing factors may have been eliminated completely. To reduce issues of selection bias we used an instrumental variable approach and identified farm altitude as a valid instrument for farmers’ certification decision. While this worked well in the reduced-form regressions, the simultaneous equation model to explain impact pathways would have benefited from a second instrument, which we were not able to identify. Therefore, the exact magnitude of the estimates on impact pathways should be interpreted with caution. Follow-up research with panel data may help to further increase the robustness of the estimates and shed additional light on impact dynamics. Analysis with more comprehensive data would also be useful to evaluate possible differences in nutrition effects between the different certification schemes. While the schemes differ in terms of priorities and policies, we were unable to further disaggregate due to data limitations. One general observation is that certified households in Uganda tend to be better off than noncertified households in terms of farm size, income levels, and infrastructure conditions. While the statistical approaches used reduce possible biases from such heterogeneity, certification schemes still seem to be more difficult to access for marginalised farm households. This would imply that certification may possibly aggravate local inequality, which should also be studied more carefully in follow-up research.

Acknowledgements This research was financially supported by the German Research Foundation (DFG). We thank two anonymous reviewers and the editors of this journal for useful comments. The data used in this research and related details can be made available upon request.

Disclosure statement No potential conflict of interest was reported by the authors.

Notes 1. The data from Uganda are the same as those used by Chiputwa et al. (2015), but the focus and methodological approaches are different. Chiputwa et al. (2015) analysed effects of certification on income and poverty with a propensity score matching approach. They did not look at nutrition and gender, nor did they examine impact pathways with structural models, as we do here. 2. General principles of these three sustainability standards are described in the Online Appendix. 3. In their study on the income and poverty effects, Chiputwa et al. (2015) included a few additional asset and market access variables in a logit model to estimate propensity scores for certification. Here, we concentrate on a smaller set of key variables for two reasons. First, the simultaneous equation system is more sensitive to changes in the specification and less robust when more variables are added. Second, possible issues of endogeneity are more relevant here than they are in logit models used for the calculation of propensity scores. 4. As these assets may be influenced by certification, which could lead to issues of reverse causality, we use values lagged by five years, thus referring to 2007 (the other values refer to 2012 when the survey was conducted). Most households in the sample were not certified before 2007.

16 B. Chiputwa & M. Qaim

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