This article was downloaded by: [22.214.171.124] On: 14 April 2014, At: 01:13 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Canadian Journal of Development Studies / Revue canadienne d'études du développement Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rcjd20 How to do (and how not to do) fieldwork on Fair Trade and rural poverty a a a Christopher Cramer , Deborah Johnston , Bernd Mueller , Carlos a a Oya & John Sender a School of Oriental and African Studies (SOAS), University of London, London, UK Published online: 01 Apr 2014. To cite this article: Christopher Cramer, Deborah Johnston, Bernd Mueller, Carlos Oya & John Sender (2014) How to do (and how not to do) fieldwork on Fair Trade and rural poverty, Canadian Journal of Development Studies / Revue canadienne d'études du développement, 35:1, 170-185, DOI: 10.1080/02255189.2014.873022 To link to this article: http://dx.doi.org/10.1080/02255189.2014.873022 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. 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Terms & Downloaded by [126.96.36.199] at 01:13 14 April 2014 Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions Canadian Journal of Development Studies Revue canadienne d’études du développement, 2014 Vol. 35, No. 1, 170–185, http://dx.doi.org/10.1080/02255189.2014.873022 How to do (and how not to do) ﬁeldwork on Fair Trade and rural poverty Christopher Cramer*, Deborah Johnston, Bernd Mueller, Carlos Oya and John Sender Downloaded by [188.8.131.52] at 01:13 14 April 2014 School of Oriental and African Studies (SOAS), University of London, London, UK ABSTRACT The Fair Trade, Employment and Poverty Reduction (FTEPR) project investigated poverty dynamics in rural Ethiopia and Uganda. When designing ﬁeldwork to capture poor people often missing from standard surveys, several methodological challenges were identiﬁed and, in response, four decisions were made. First, FTEPR focused on wage workers rather than farmers and improved on standard questionnaires when collecting labour market information. Second, researchers adopted contrastive venue-based sampling. Third, sampling was based on clearly identiﬁable “residential units” rather than unreliable ofﬁcial registers of “households”. Fourth, an economic deﬁnition of “household” was used rather than the more common deﬁnition based on residential criteria. RÉSUMÉ Le projet Fair Trade, Employment and Poverty Reduction (FTEPR), qui portait sur la dynamique de la pauvreté dans les régions rurales d’Éthiopie et d’Uganda, a dû relever plusieurs déﬁs pour rejoindre les personnes pauvres échappant aux enquêtes standardisées. Quatre décisions ont été prises à cet égard. Premièrement, le projet a mis l’accent sur les travailleurs salariés plutôt que sur les agriculteurs et il a amélioré les questionnaires habituellement utilisés pour récolter de l’information sur les marchés du travail. Deuxièmement, il a adopté un plan d’échantillonnage raisonné des lieux d’enquête. Troisièmement, l’échantillonnage s’est basé sur des unités résidentielles facilement identiﬁables plutôt que sur les registres ofﬁciels des ménages qui sont peu ﬁables. Enﬁn, les ménages ont été déﬁnis en termes économiques plutôt qu’en fonction du lieu d’habitation. Keywords: methodology; poverty; agriculture; labour markets; Africa Introduction Policy design and debate often draw on a combination of ofﬁcial or administrative data and on evidence generated by speciﬁc or one-off research projects. Bespoke data collection can be a cornerstone for policy: where there is limited ofﬁcial data or where the variables of interest are not routinely collected in ofﬁcial surveys. Further, the ﬁndings of microresearch can inﬂuence adjustments to the questionnaires used in ofﬁcial surveys. While the limitations of ofﬁcial data on employment and agriculture in Africa have been recognised for many years (Sender and Smith 1986, 100; Sender, Cramer, and Oya 2005; Oya 2013), the quality of project-speciﬁc ﬁeldwork evidence is often ignored. Unfortunately, project-speciﬁc ﬁeldwork can share many of the weaknesses of ofﬁcial data collection. This article describes the methods adopted for ﬁeld research in the Fair Trade, Employment and Poverty Reduction in Ethiopia and Uganda project (FTEPR), funded by the UK Department for International Development.1 Contrasting the FTEPR *Corresponding author. Email: [email protected] © 2014 Canadian Association for the Study of International Development (CASID) Downloaded by [184.108.40.206] at 01:13 14 April 2014 Fieldwork on Fair Trade and rural poverty 171 methods with standard approaches to rural development economics research has broader implications for data use and policy debates. This paper highlights four methodological contributions. First, in marked contrast to most rural surveys in Africa and to virtually all previous evaluations of Fair Trade schemes, FTEPR methods were designed speciﬁcally to collect evidence on wage workers rather than producers. It was necessary, therefore, to make an unusually sustained effort to uncover complex, irregular labour market activities, through repeated piloting of re-designed questionnaires and the (re)training of enumerators. Second, the project adopted a form of purposive contrastive venue-based sampling,2 with clear and transparent site selection criteria. Third, within the selected research sites, sampling procedures, aided by the use of GPS devices and handheld computers, were based on clearly identiﬁable “residential units” as opposed to “ofﬁcial” household registers. And, fourth, when constructing a household roster the research used an economic deﬁnition, discussed below, rather than the more common and often misleading residential criteria. The research method reported in this paper is based on a contrastive case study approach that provides a detailed rationale for the purposive selection of different research sites and that generates comparisons within as well as between sites. The purpose of the research itself was to assess the strength of mechanisms connecting agricultural commodity export production with the lives and prospects of poor rural people, particularly those involved in wage employment. Other publications will report on research ﬁndings.3 One purpose of discussing the (soft and hard) technology used in this research is to advocate a very simple reform to the way that rural research on poverty is reported, recommending greater openness on how and why particular research sites are chosen and sampling methods adopted. It is suggested that this kind of microresearch may be an important complement to ofﬁcial census or “nationally representative” survey data, in part because it allows for a more precise focus of policy design on areas of speciﬁc socioeconomic dynamism and in part because it facilitates greater knowledge about the material conditions of poor people, often missing from ofﬁcial data. Neglect of wage work in Fair Trade research A recent review of research on Fair Trade and other ethical labels argues that ﬁeld studies “lack a convincing and consistent methodology” (International Trade Centre 2011, 25). And Fair Trade is just one among many institutional forms of the broader phenomenon of agricultural exports, where it has been acknowledged that too little is known about the labour market and other transmission mechanisms linking agricultural commodity trade and poverty reduction. Collecting more evidence, speciﬁcally on complex rural labour markets, is therefore urgently required.4 The evidence needed is not available because almost all socioeconomic surveys in developing countries fail to capture data on the most vulnerable, poorly educated, casual and seasonal workers, especially temporary migrant workers (Sender, Cramer, and Oya 2005; Pincus and Sender 2008; Sender and von Uexkull 2009, 64–66).5 The key aim of FTEPR research was to provide robust data on wage employment. In contrast, most Fair Trade research has concentrated on producers, idealised as small farm households using family labour to produce certiﬁed crops. One systematic review of the certiﬁcation literature found that “most of the studies reviewed deal with the producer as a self-employed individual and with producer cooperatives” (International Trade Centre 2011, 19). The Fairtrade Foundation commissioned a survey of 33 case studies, which concluded that: “there is limited evidence of the impact on workers of participation in Fairtrade, and more research is required ...” (Nelson and Pound 2009, 35). A recent impact evaluation commissioned by the Fairtrade Foundation of certiﬁed smallholder banana organisations failed to obtain any data at all on workers hired by producers or their organisations in two of the three case studies (Smith 2010, 52). Other research on Downloaded by [220.127.116.11] at 01:13 14 April 2014 172 C. Cramer et al. the impact of Fair Trade certiﬁcation, based on case studies of six rather successful small producer organisations, simply assumes that the landless, women and those with limited education do not beneﬁt from and are “outside the dynamics of Fair Trade labelling” (Laroche and Guittard 2009, 34). The International Initiative for Impact Evaluation (3ie) highlights the problem that “many Fairtrade organisations ... establish a minimum price for producers but do not deal with the conditions of workers that the producers may employ” (3ie 2010, 2).6 A good and especially relevant example is Jena et al. (2012), whose study of the impact on poverty of coffee certiﬁcation in Jimma Zone, Ethiopia, fails to examine wage employment and focuses solely on “smallholder farmers”. Thus, the majority of these studies do not even attempt to construct samples of seasonal and permanent wage workers producing Fair Trade certiﬁed export commodities. On the rare occasions when wage workers are included in Fair Trade research, information on these workers is often collected from lists of wage workers provided, and sometimes selected, by employers or by ofﬁcially sanctioned worker representatives (Barrientos and Smith 2006, 4; Omosa, Kimani, and Njiru 2006, 7; Klier and Possinger 2012; Ruben, Fort, and Zuniga n.d., 23). These lists may well be censored and are certainly unlikely to contain all casual workers, let alone recently dismissed or disgruntled workers. The other main information source is focus groups, with group participation guided by employers’ advice, or over-representing the leaders of the permanent workforce relative to the numbers of illiterate casual (female) wage workers. A convincing rationale for the selection of members of these focus groups is not usually provided (Pound and Phiri 2009). The unrepresentative workers who appear on these lists or in these focus groups are, all too frequently, interviewed on their employer’s premises (Ewert, Martin, and Nelson 2005, 22–23; Barrientos et al. 2009, 27). Such interviews are unlikely to elicit reliable information; workers who are not interviewed in private and with ﬁrm assurances of conﬁdentiality may go to great lengths to avoid the risk of being seen to offend dominant classes. In Nicaragua, for example, some workers for cooperative coffee processing mills were interviewed at their workplace and some while waiting for buses along the roads outside the mills. “Unsurprisingly, workers interviewed outside the mills were more critical of their working place than those interviewed inside. According to these workers, visitors often come to the mill to ask about their working conditions, but they are afraid to say anything negative for fear of losing their job” (Valkila and Nygren 2009, 5).7 While rural wage work is commonplace, especially where high value exports are produced, the ideological and practical factors that tend to limit survey coverage of such workers suggest that rural casual wage workers should be regarded as a vulnerable, “hard-to-reach” or “hidden” population, excluded from many ofﬁcial statistical surveys and rural development programmes. Rigorous epidemiological research on similarly hard-to-reach populations, such as illegal migrants, refugees, commercial sex workers and other groups at high risk of HIV, offers valuable lessons for the design of surveys of rural labour markets in Africa. Indeed, the method adopted in FTEPR research shares many features with venue-based sampling, as developed by the US Centers for Disease Control and Prevention (Muhib et al. 2001; Vermund et al. 2010). As Landry and Shen (2005, 1) explain, excluded populations tend not to be missing at random, so that census or survey data is usually biased, “particularly with respect to minorities, immigrant groups, or the homeless”. The success of the venue-based method depends on the ability of researchers to triangulate limited quantitative data with ethnographic qualitative work to identify speciﬁc geographic areas (or “venues”) with a high density of the target population to be sampled (Singh and Clark 2012). Combined with venue-based sampling, a key decision in this research, given the focus on wage employment, was to invest heavily in enumerator and supervisor training and to develop Fieldwork on Fair Trade and rural poverty 173 a more complex questionnaire module on employment than is typical. Instead of using “main” and “secondary” employment categories, an employment matrix containing an exhaustive list of possible activities was developed and enumerators were also required to describe the full array of activities in different sections of the questionnaire, as well as to probe and repeat questions. Downloaded by [18.104.22.168] at 01:13 14 April 2014 Selecting research sites One unresolved issue in previous Fair Trade research concerns the criteria used in the selection of research sites. Certiﬁed or Fair Trade production takes place in very different contexts, with certiﬁed schemes varying, among other factors, in terms of the level of external subsidy they have received, the number of producers participating, the number of years of operation and the degree of ﬁnancial viability. The range of rural areas participating in Fair Trade schemes (and therefore the range of possible research sites within a country) is very wide, but the reasons for deciding to focus ﬁeldwork in a particular rural area, or on one particular group of certiﬁed producers, have rarely been explained in any detail. For example, in one methodologically ambitious “quasi-experimental” study of the welfare impact of Fair Trade programmes, the only rationale for choosing producers is this brief statement: “The selection of FT organisations for the analysis has been conducted in coordination with Solidaridad” (Ruben, Fort, and Zúñiga-Arias 2009; Ruben, Fort, and Zuniga n.d., 17).8 The study of smallholder banana producer organisations, mentioned above, was carried out in three countries, but provided little discussion of the choice of smallholder organisations or the country context (Smith 2010, 28). Nevertheless, this “Global Assessment of Impact” study did acknowledge that one very important country producing Fair Trade bananas (Colombia) had been excluded for some unstated reason, and that the three banana plantations selected were not at all representative of the majority of Fair Trade certiﬁed plantations (Smith 2010, 33). In particular, two of the smallholder case studies involved organisations that were “larger, more sophisticated and/or more supported by external partners” than other Fair Trade organisations. Further, all three banana case studies were also unrepresentative of the majority of Fair Trade producers in that they sold almost their entire output to the Fair Trade market (Smith 2010, 138). These problems are shared by much economic research in poor rural areas. For example, the most inﬂuential rural surveys conducted in Ethiopia in recent decades have made little effort to justify their selection of sample sites. Debates on rural poverty in Ethiopia very often cite the results of the Ethiopian Rural Household Surveys 1989–2004 (ERHS). These surveys selected only 15 (out of more than 20,000) kebeles in Ethiopia as the sites for data collection.9 The rationale provided for the selection of these kebeles is confusing: initially some were chosen on the grounds that they were typical (in some undeﬁned sense) of areas affected by the 1984–1985 famine; additional kebeles were later selected “to account for the diversity of the farming systems in the country”, and it was then claimed that the households in the survey were “broadly representative of households in non-pastoralist farming systems as of 1994” (Dercon and Hoddinott 2009, 6–8).10 However, 18 agro-ecological zones have been deﬁned for Ethiopia (CSA 2006, 16) and within each of these zones there are many hundreds of kebeles that could have been alternatively selected as research sites. The reasons for selecting the 15 particular kebeles that continue to be the focus of so much research are not discussed. There were plans to study exactly the same 15 kebeles in 2010, not on the old grounds that they are representative of agro-ecological zones, but because they “fall into ﬁve main livelihood categories of broad relevance for policy” (Bevan 2009, 14). However, no less than 175 extremely diverse “livelihood zones” have been identiﬁed and mapped in Ethiopia (LIU 2011). The 15 kebeles selected for the ERHS obviously provide a limited coverage of this wide range of livelihood categories Downloaded by [22.214.171.124] at 01:13 14 April 2014 174 C. Cramer et al. (LIU 2011, 121). The claimed policy relevance of the selected sites is also questionable; they certainly cannot be useful to illuminate some key economic policy debates – about ﬂoriculture, coffee or foreign exchange availability, for example – since they do not represent key areas characterised by agricultural export production.11 By contrast, a much more transparent discussion of the choice of rural research sites for another longitudinal survey in Ethiopia notes that: “budgeting constraints and concerns regarding the long-term sustainability of the study meant that the rural sites selected were located in relatively better accessible areas. This is likely to have resulted in (surveyed) rural households being located in wealthier sites than the typical Ethiopian rural household” (Outes-Leon and Sanchez 2008, 4). The choice of ﬁeldwork site may be expedient and more or less defensible. For example, an authoritarian ruling political party or the Fair Trade certifying body or manipulative cooperative union ofﬁcials may have preselected the area for researchers, discouraging research in other areas; there may be insufﬁcient research funding to travel to more distant research sites; record keeping may be weak at other sites, or production volumes erratic; or local managers and state ofﬁcials may welcome (or refuse) visits from outsiders. These types of practical consideration will always play a role, but it is difﬁcult to make a judgement about the meaning of research results without a detailed discussion of the reasons for the selection of research sites. It is necessary to weigh up and balance complex information about potential sites, since sampling will have little credibility if it appears to have been ad hoc or arbitrary (Wilson, Huttly, and Fenn 2006). There is, therefore, a strong case for more detailed discussions than are typical of the rationale for and methods of site selection in research projects. The purposive selection of subsites in the FTEPR was motivated by an aim to understand complex mechanisms and to accumulate new knowledge about rural development processes through old-fashioned theoretically motivated descriptive research. Such methods have been favourably compared to more fashionable “quasi-experimental” methods promoted by “randomistas” – the advocates of randomised control trials (Deaton 2010). Deaton’s argument is that it is far more important to achieve an improved understanding of how (through what mechanisms), for whom (which speciﬁc population groups) or why the production of export commodities might inﬂuence rural poverty, rather than to answer the narrow question of whether or not signiﬁcant poverty reduction has been achieved on “average” in the “treatment” (the Fair Trade certiﬁed) group. Further, a contrastive case study strategy does not have to make overambitious claims to establish “control” groups, emphasising rather the complexity and ﬂux within speciﬁc rural populations and research sites. A contrastive case study approach can more easily explore and highlight the distributive implications of different institutional arrangements for agricultural export production (for example) than the “randomista” effort to isolate average “treatment” effects. One principle of site selection – though not the only justiﬁable one – is that of contrastive case studies. The point of contrastive research is to explore the factors responsible for differences between phenomena in conditions with some common features: ﬁrst, to establish whether there are contrasts, and what they are (between Fair Trade and non-Fair Trade crop production, or between Fair Trade production and labour in coffee versus ﬂowers, for example); and, second, to try to explain some of these differences (Lawson 2003). FTEPR research adopted a contrastive approach to studying rural employment and poverty dynamics in two very poor sub-Saharan African countries, based on a theoretical interest in the impact of small- and large-scale export crop production certiﬁed and non-certiﬁed production, and production of different commodities. A decision was taken to select two commodities in each country, allowing for further contrasts within and – in the case of one commodity – across the two countries. Thus, coffee and ﬂower production in Ethiopia and coffee and tea production in Uganda were chosen for reasons including: the macro-economic importance of at least two of these commodities in Ethiopia and Uganda; Fieldwork on Fair Trade and rural poverty Table 1. 175 Research sitea selection in Ethiopia and Uganda. Ethiopia Floriculture Fair Trade Non-Fair Trade Coffee Large-scale Small-scale Large-scale Small-scale Tefki Ziway n.a. Holeta n.a. Limu-Kossa Ferro Kochera Uganda Tea Downloaded by [126.96.36.199] at 01:13 14 April 2014 Fair Trade Non-Fair Trade Coffee Large-scale Small-scale Large-scale Small-scale n.a. Ankole Mpanga Kabale n.a. Kaweri Ishaka Masaka Note: aThe names in this table refer to local town and area names. the labour-intensity and contribution to employment of all these commodities; the relatively long history of Fair Trade and other certiﬁcation schemes for these commodities; the dramatic contrast between the dynamism of ﬂoriculture in Ethiopia and the relative stagnation in the production of both coffee and tea in Uganda and in the production of coffee in Ethiopia; and the opportunity to contrast techniques of arabica production (Ethiopia) with those of robusta production (Uganda). Brieﬂy explaining some of the decisions taken in the FTEPR research project helps to illustrate the purposive research site selection method. The contrastive objectives implied that it would be useful to identify several of the most important agricultural commodity exporting sites in each country. As a general rule, within each sample category (certiﬁed/non-certiﬁed, small-scale/largescale production) an effort was made to identify cases exemplifying the “best” producing sites in terms of reputation for quality and technological dynamism, in order to achieve consistency in contrastive exploration. Table 1 indicates the degree to which the objective of studying certiﬁed and non-certiﬁed production on large- and small-scale farms could be combined in each country.12 As can be seen, it was impossible to identify appropriate research sites to complete all of the cells of the simple matrix in Table 1 because, for example, there are no small-scale Fair Trade certiﬁed ﬂoricultural enterprises in Ethiopia and no large-scale Fair Trade certiﬁed coffee estates in either country. This was one reason for the selection of only six research sites in each country, although the constraints imposed by the FTEPR budget also limited the total number of sites. There are clearly difﬁculties involved in this approach. It may be difﬁcult, for example, to attribute outcomes to certiﬁcation as opposed to scale. These challenges may in part be addressed through the detailed evidence and, especially, the qualitative research which was conducted to complement the quantitative survey. Within the boundaries of each of these 12 research sites, it was possible to achieve additional variation, that is, to identify further contrastive opportunities by careful selection of heterogeneous subsites or “venues”, for sampling. For example, some export production subsites are in rural areas of very recent settlement and others in areas where people have been living for many years.13 Selecting research subsites and sampling Beyond the choice of sites, there are important methodological challenges in sampling in order to capture some dynamics of poverty and employment that tend to be disregarded by much socioeconomic research. Pragmatic as well as methodological concerns inﬂuence sample size. Downloaded by [188.8.131.52] at 01:13 14 April 2014 176 C. Cramer et al. Sampling procedures may then depend on how important it is to capture variations within sites and among subgroups in a population. Some over-sampling may be required to capture people typically ignored in socioeconomic research and to gather evidence on, for example, the heterogeneity of poor rural wage workers. Here over-sampling meant stratiﬁed non-proportional sampling. Speciﬁc examples of the application of these methodological principles are discussed in more detail below. For most researchers, practical constraints, including budget limitations, will combine with methodological principles and research objectives to shape samples. The budget for ﬁeldwork was one of the determinants of the overall FTEPR sample size. It was calculated that the ﬁrstround quantitative survey could only afford to interview approximately 750 individual respondents, equivalent to about 120 respondents per site, in each country. Since comparisons between sites are an extremely important part of the FTEPR analysis, there were good arguments for achieving a roughly equal sample size in each site, also considering possible variation within sites (Wilson, Huttly, and Fenn 2006, 357–358). The random sample at each purposively chosen site was large enough to be statistically representative of all female and male adults – aged 14 years or older – resident in the research subsite areas (see Table 2). However, the total sample at each research site was also designed to oversample those adults whose experience has been neglected in previous surveys but is most relevant to FTEPR research – namely, casual wage workers producing the relevant export crop. Qualitative evidence from scoping trips in the preselected sites conﬁrmed that wage workers were a Table 2. Summary of FTEPRPa sample sizes. Survey phase Units measured Phase 1 Total number of GPSRegister all RUs in each site, using registered RUs GPS Phase 2 Number of RUs in Create a stratiﬁed sample frame using quasi-census a brief PDA survey across Number of individuals registered RUs (quasi-census) living in these RUs Sample size of quasicensus (% of total RUs) Average number of adults per RU Extrapolated number of adults in all sites Phase 3 Total number of individuals Conduct main survey using detailed interviewed questionnaires Estimated % of total population interviewed ET ET UG ﬂoriculture coffee tea UG Total coffee 2280 2813 1350 1906 8349 1066 1678 890 1380 5014 2358 4721 2014 2765 11858 46.8% 59.7% 65.9% 72.4% 60.1% 2.21 2.81 2.26 2.00 2.4 5043 7914 3055 3819 19745 572 439 1710 356 7.1% 343 7.2% 11.2% 11.5% 8.7% Note: aThis table uses the following acronyms: ET: Ethiopia FTEPRP: Fair Trade, Employment and Poverty Reduction project, supported by the UK Department for International Development GPS: Global Positioning System PDA: personal digital assistant (handheld computer) RU: residential unit UG: Uganda Downloaded by [184.108.40.206] at 01:13 14 April 2014 Fieldwork on Fair Trade and rural poverty 177 heterogeneous group. Thus, it was considered necessary to create a large enough subsample of wage workers to be able to account for variation and allow comparisons. In other words, selective over-sampling reﬂected the priority given to comparisons among different groups of wage workers. Since the overall site samples were designed to be large enough in absolute terms to be representative of the local adult population, they include and allow for comparisons with both male and female non-wage workers. Moreover, the data from the sampling frame that was constructed in each site also allow for the application of sampling weights whenever inference is designed to apply to the whole population in each site. In addition, the total sample at each research site was designed to ensure some variation in the characteristics of respondents, mainly through the choice of analytically relevant subsites. Several days of qualitative research and discussions with key informants living in each research site provided sufﬁcient information to identify subsites (venues) where there was a relative concentration of residential units (RUs) housing wage workers. For example, in Ziway two distinctive subsites were selected, both of which contain a large proportion of ﬂower wage workers. The ﬁrst was in a well-established part of Ziway town, where most residents originated from Ziway or its immediate surroundings. The other subsite was a very new neighbourhood on the border of the rapidly growing town. This is the area where most newly-arriving migrant workers settle, many of them originating from the Southern Nations, Nationalities and People’s Region (SNNPR) of Ethiopia. Exclusively sampling in only one of the two venues would have resulted in the virtual exclusion of either group of core respondents. The point is that each sampling site is likely to contain quite distinct subsites, because rural areas are rarely homogeneous. Through random sampling in several contrasting, purposively selected subsites it was possible to achieve much more heterogeneous samples – samples that included non-wage workers, non-migrant and food crop wage workers, females, more highly paid, and permanent wage workers, for example. The analysis could thus be based on comparisons of data on very different types of local people, leading to a better understanding of the complex determinants of rural welfare. Ofﬁcial household surveys in Ethiopia and Uganda are based on samples drawn from lists of rural households provided by village-level authorities. Ofﬁcially maintained registers of “households” are often used as the basis for the distribution of scarce resources such as food aid, or subsidised agricultural inputs and credit; thus rural elites are likely to have good reasons for selective editing of the names appearing on lists of potential beneﬁciaries.14 Moreover, ﬁeldwork experience in these two countries, as well as elsewhere in Africa, indicates that these lists are frequently unreliable because, apart from excluding marginalised people who have encroached as squatters and all those living in arrangements that do not correspond to standardised households, the lists are not sufﬁciently up-to-date to include all newly arrived (or departed) residents.15 Handheld computers with global positioning devices allowed FTEPR researchers to obtain sampling lists more accurate than the ofﬁcial registers. The qualitative work discussed above helped identify various subsites. A boundary was drawn around each research subsite with the aid of waypoints deﬁned by the GPS unit.16 The process of listing residential units started with a complete enumeration (census) by the research ofﬁcer and a ﬁeld supervisor of all the RUs observed within the subsite boundaries. An RU was deﬁned as any structure in which at least one person was sleeping. Special care was taken by these senior and experienced members of the research team to record the precise GPS location and to assign an identiﬁer to each RU, including non-conventional RUs such as temporary shacks and the doors of rented rooms where groups of migrants were sleeping. The complete census of a subsite could be accomplished fairly rapidly, since it involved walking up to the door of each RU and entering its position on the handheld computer (personal digital assistant, or PDA).17 Once the preliminary residential census had been completed, ﬁeld teams constructed a more detailed and up-to-date sampling frame of all Downloaded by [220.127.116.11] at 01:13 14 April 2014 178 C. Cramer et al. adult potential respondents living in RUs in the research subsite by conducting a PDA-facilitated survey that included a small number of questions, designed to stratify the selection of respondents for the main paper-based interview. A high proportion of RUs – between 45 and 100 per cent of the total number of RUs identiﬁed in the census – were then randomly selected for the next phase of the sampling procedure, using software installed on the PDA.18 Enumerators were sent to each of the randomly selected RUs (or to all of the RUs19) to make contact with any willing adult who had slept in the RU on the previous night. It was usually easy, with the aid of the GPS device and the locations recorded in the census, for enumerators to ﬁnd these randomly selected RUs and a willing respondent. The interviews were completed electronically, using a short questionnaire installed on the PDA. The PDA survey, equivalent to a quasi-census given the large samples involved, was used to build the ﬁnal sampling frame for each subsite. The key information used to deﬁne strata for the ﬁnal sample of adults concerned labour market participation and migration. For example, adults could be classiﬁed into the following strata: “never worked for wages”; “currently working for (a named certiﬁed or non-certiﬁed export crop enterprise)”; “currently working for wages for another farm”; “currently working for wages for an export crop processing factory”; or “recent migrant”.20 It was easy to conﬁrm that the ﬁnal sample was representative of the large population of adults from which it was drawn – in the sense that the gender, mean age and education of the sample respondents generally closely matched the gender, mean age and education recorded in the population lists. This congruence was expected, since rather high percentages of the individuals in all the strata on the population list were sampled. Finally, GPS identiﬁers helped enumerators to locate the individuals selected for the sample and to locate respondents randomly selected as substitutes by the research ofﬁcer in case the individual originally selected could not be found or did not consent to the interview.21 Deﬁning the members of a “household” roster Ofﬁcial data often import assumptions built into common deﬁnitions of “the household”. But researchers must decide how to deﬁne a household and be clear about the implications of such deﬁnitions. Here the FTEPR project eschewed the misleading and narrow criteria used by many other studies, in favour of an approach that allowed the linkages between geographically distant individuals to be better understood. Almost all socioeconomic surveys in developing countries fail to capture data on the most vulnerable, poorly educated, casual and seasonal workers, especially temporary migrant workers.22 One reason for this failure is that the most inﬂuential of these surveys, the Living Standards Measurement-type household surveys (LSMS) promoted and funded by the World Bank throughout the developing world, rely on an a priori standardised, narrow and inappropriate deﬁnition of “the household” and its “residents”. So, in both Ethiopia and Uganda, the Rural Household Surveys and the National Household Surveys fail to collect detailed information from “non-residents” concerning migration episodes in search of wage employment.23 Important groups of vulnerable wage workers, especially those engaged in seasonal, casual and low-paid jobs outside major urban centres, are frequently not “resident” in households. They live and work for long periods in hostels, labour camps, barracks, construction sites and illegal squatter settlements, or they have been given some space to sleep at their workplace during the harvest season, or while working as domestic servants. These are the “nowhere people”, the uncounted ﬂocks of footloose migrants who “drift in and out of temporary worksites” (Breman 2010, 135). Such migrant workers remit part of their wage earnings to other individuals and these remittances are usually recorded in the LSMS Surveys covering the recipients’ households. Unfortunately these wage earnings are not classiﬁed as wage income in the Downloaded by [18.104.22.168] at 01:13 14 April 2014 Fieldwork on Fair Trade and rural poverty 179 receiving households, leading to a serious underestimate of the degree to which poor rural households depend on income derived from wage employment. FTEPR enumerators completed a long, paper-based questionnaire that provided information not only about the selected individual respondent, but also about a large number of other individuals to whom the respondent was “economically linked”. The concept of a roster of economically linked individuals replaces the more conventional concept of a “household roster” (based on residential criteria), providing additional and extremely useful information on labour market participation and the other characteristics of individuals usually considered “absent” and therefore irrelevant to an analysis of the welfare of rural populations.24 The problems and associated “myths” surrounding the use of conventional (residential) deﬁnitions of the household in rural surveys in Africa have been subjected to extensive debate and criticism (Guyer and Peters 1987; O’Laughlin 1995; Adato, Lund, and Mhlango 2007; Akresh and Edmonds 2010).25 Randall, Coast and Leone (2011, 217) point out that, despite widespread international endorsement of the importance of household surveys as providers of data for development planning, “little attention has been paid to the issue of what ‘household’ means in these surveys: how it is deﬁned for data collection purposes and what the deﬁnition implies for the analysis and interpretation of results”. There are challenges in using a more realistic deﬁnition of economic households, but these are far outweighed by the beneﬁts of not missing relevant linked members and therefore achieving a more solid basis for understanding individual and household welfare. In selecting the FTEPR respondent, it was not necessary to identify or deﬁne a “household head”, and the selected respondent was asked many detailed, repeated and probing questions about their own experience in the labour market. The acknowledged unreliability of standard Household Surveys as sources of accurate labour market data stems partly from reliance on proxy respondents (such as the household “head”), partly from insufﬁcient attention to the structure, order and wording of the questions on employment and partly from the brevity of the labour market module in these questionnaires (Bardasi et al. 2011). FTEPR made great efforts to overcome these sources of error, including a protracted effort to train and retrain enumerators to avoid gender stereotypes, the mechanistic application of either the standard international classiﬁcations of labour market activities and the assumptions of local urban elites about these activities.26 Conclusion This paper has made it clear how fragile the methodological foundations of many rural socioeconomic studies are, especially perhaps those that study the impact of Fair Trade. Lack of clarity about research site selection and ill-considered sampling methods are often compounded by ideological blind spots. It is to highlight the signiﬁcance of these methodological weaknesses and to begin to address them that this paper has summarised the ﬁeldwork methods designed for the FTEPR research. Aside from the aim of ensuring that different types of rural wage workers were included along with non-wage workers, three further methodological choices with broad relevance have been highlighted: a site selection protocol allowing for contrastive explanation; the construction of accurate and up-to-date household lists using GPS technology; and an approach to household membership that emphasises economic linkages rather than short-term residence criteria. The hope is that this generates more accurate evidence on the large number of “hidden” wage workers in rural Africa. Jerven (2013) has shown how choice of base year, somewhat arbitrary ideas about rural economies and choices about what economic activity to include and exclude have had profound effects on African national accounts data. Further debate on methods and assumptions underpinning micro-level economic research in Africa can also help to provide new and fuller information about people frequently missing from national statistics 180 C. Cramer et al. and about the complexity of their labour market activities. This, it is hoped, provides a ﬁrmer foundation for policy design. Aside from the advantages conferred by new technology, FTEPR has also argued for greater transparency and clarity in presenting the choices made in ﬁeldwork – and this applies as much to project-related data as it does to ofﬁcial data sets. Biographical notes Christopher Cramer is Professor of the Political Economy of Development at SOAS. He has worked in and on sub-Saharan Africa for more than 25 years, teaching and conducting research, on rural labour markets, commodity processing and violent conﬂict. Downloaded by [22.214.171.124] at 01:13 14 April 2014 Deborah Johnston is a Reader in Development Economics at SOAS, London. She has worked on subSaharan Africa for over 20 years, researching rural labour markets, poverty, welfare and land. She is coeditor of the Journal of Agrarian Change. Bernd Mueller has been research ofﬁcer in the FTEPR project, for which he spent extended periods in Ethiopia and Uganda on ﬁeldwork missions. His main research interests include rural labour markets and the political economy of rural development. After his departure from FTEPR, he joined the Decent Rural Employment Team at FAO in Rome, as rural employment specialist. Carlos Oya is Senior Lecturer in Political Economy of Development in the Development Studies Department, SOAS, London. He has done primary research in Mozambique, Senegal, Mauritania, Uganda and Ethiopia, focusing on the political economy of agrarian change, capitalist accumulation, rural wage labour and poverty. He is also co-editor of the Journal of Agrarian Change. John Sender is Professor Emeritus of Economics at SOAS, London. He has designed surveys and conducted ﬁeldwork in many rural areas of Africa and Asia over a period of almost 40 years, usually focusing on the development of labour markets. Notes 1. 2. 3. 4. 5. 6. 7. Fair Trade, Employment and Poverty Reduction in Ethiopia and Uganda was a four-year research project (2009–2013) funded by the UK’s Department for International Development (DFID); see http://www.ftepr.org/ Venue-based sampling is a method developed by the US Centers for Disease Control and Prevention (Muhib et al. 2001; Vermund et al. 2010), among others. See http://www.ftepr.org for information on publications. The World Development Report 2008 emphasised, for example, that “stunningly little policy attention has been given to the structure, conduct and performance of rural labour markets and how they ease successful transitions out of agriculture” (World Bank 2007, 221). On the poor coverage of rural wage employment in commonly cited Ethiopian surveys, see speciﬁcally Rizzo (2011). The more general point has been made that “in much of the development literature on pro-poor growth nowadays, little or no attention is paid to the underlying mechanisms that determine the dynamics of income … speciﬁcally, the dynamics of employment growth and of how and to what extent productivity growth translates into the growth in labour earnings is left out of the equation” (Wuyts 2011, 10). Similarly, Amsden (2010, 57) points out: “Despite championing the cause of poor people around the world, and dramatising the human condition, the United Nations’ Millennium Development Goals make not the slightest mention of employment generation as a means to battle poverty.” Important exceptions to this neglect of wage workers include research by Valkila and Nygren (2009), Luetchford (2008), Maertens and Swinnen (2012) and Maertens, Colen and Swinnen (2011). FTEPR ﬁeldwork highlighted precisely that employers, who have close ties to local ofﬁcials, are keen to avoid situations where their workers have the freedom to engage independently and privately with researchers. Local security ofﬁcials and the police in one ﬁeldwork site detained research assistants for several hours and lectured the senior researchers on “proper” research methods, which included asking “the owner” of a large agro-export (multinational) business to select workers and then interviewing only the selected workers at the workplace. Fieldwork on Fair Trade and rural poverty 8. Downloaded by [126.96.36.199] at 01:13 14 April 2014 9. 10. 11. 12. 13. 14. 15. 16. 17. 181 A more recent attempt to assess the impact of Fair Trade on poverty reduction also depended upon the Fair Trade organisations funding the research to select the research sites and the producers organisations to be studied (Klier and Possinger 2012, 4). In Ethiopia, the kebele is the smallest administrative unit. It is broadly comparable to a ward. A similar claim was made concerning the choice of the 36 villages surveyed in an inﬂuential study of poverty in rural Uganda: “The selected villages represent quite well the considerable diversity that exists within the two selected regions.” However, the researchers make it clear that the actual choice of villages was heavily inﬂuenced by the wishes of district-level bureaucrats; the measures, or the relevance to issues of poverty, of the indicators of “diversity” are not discussed (Krishna et al. 2006). Another study in four districts of rural Uganda (of coffee producers) sampled only those producers appearing in the Uganda National Household Survey (UNHS). Unfortunately, the UNHS was not designed to be representative of coffee producers (or of households in each district), so that the sample cannot be considered representative of robusta producers in the districts concerned, let alone of coffee producers in Uganda as a whole. This fundamental problem did not prevent the World Bank-funded researcher from drawing conclusions about “the Ugandan coffee market” and “the majority of coffee grown in Uganda” from unrepresentative data (Hill 2010, 455, 438). The fact that the UNHS speciﬁcally excluded larger scale coffee farmers in Uganda from the survey is another important reason for caution in extrapolating its results to the coffee market as a whole (Ssekiboobo 2008, 7). The broader political relevance of the sample is also questionable. The ERHS is not representative of the ethnic and religious composition of the rural Ethiopian population; for example, Oromos are underrepresented (Kumar and Quisumbing 2012, 2). Further criticisms of World Bank-funded household surveys in other developing economies, focusing on their failure to provide a robust, transparent record of poverty incidence, has been provided by Walters, Marshall and Nixson (2012). For site selection purposes, large-scale farms were deﬁned as enterprises employing at least 75 wage workers; small-scale farms were deﬁned as enterprises that are members of Fair Trade certiﬁed smallholder cooperatives, or as enterprises employing fewer than 75 workers. The Holeta site in Ethiopia was selected not only because it included small-scale ﬂower farms, but also because it was the only area of ﬂoricultural production known to have experienced labour disputes and trade union interventions. After site selection these deﬁnitions were adjusted in line with sector and geographic norms. A more detailed discussion of the selection of the 12 research sites (including maps), showing how contrastive exploration has been operationalised, can be found in Methodological Issues, FTEPR Discussion Paper No.1, “How to do (and how not to do) ﬁeldwork on fair trade and rural poverty” on the project website www.ftepr.org. Ethnographic work in two villages in northeast Ethiopia describes how local ofﬁcials administering the Productive Safety Net Programme constructed lists of households so as to reserve the beneﬁts of the programme for “the more afﬂuent and economically potent households”, excluding “the poorest and chronically food-insecure households”, many of which depended on casual agricultural wage labour (Bishop and Hilhorst 2010). For example, ﬁeldwork in Kabale District in Uganda compared the ofﬁcial list of households maintained by one LC1 Chairman with a careful FTEPR village census (the LC1 is the lowest level of local council, usually a village or neighbourhood). The chairman’s list was found to be grossly inaccurate. There is also evidence, insufﬁciently discussed in the relevant survey documentation, that the lists of households at the kebele level in Ethiopia, which are regularly used as rural sampling frames, are also unreliable. For example, a choice has to be made between alternative lists of households held by the kebele chairman, local health extension workers or development agents; one or more of these lists may well have been amended by the survey team (IFPRI and EEPRI n.d.). It has been admitted that not all villages sampled in the Ethiopian Rural Household Surveys had good lists of registered households (Dercon and Hoddinott 2009, 7). A quantitative survey in the northeastern highlands of Ethiopia, backed up by careful qualitative work, indicated that ofﬁcial kebele lists usually excluded households that did not pay tax, as well as some single-person households and people belonging to “socially marginalised groups” (Sharp, Devereux, and Amare 2003, 36). Epidemiologists have pioneered the use of these technologies for surveys in rural Africa; see for example Vanden Eng et al. (2007). FTEPR beneﬁted from advice and training in the use of handheld computers (or personal digital assistants, PDAs) with GPS provided by Anja Terlouw and James Smedley of the Liverpool School of Tropical Medicine. The census of RUs in rural Ethiopia and Uganda was much less problematic than similar exercises listing unregistered urban populations in China with the aid of PDAs (Treiman et al. 2005, 13). 182 18. 19. 20. Downloaded by [188.8.131.52] at 01:13 14 April 2014 21. 22. 23. 24. 25. 26. C. Cramer et al. Depending on settlement density and topography, the FTEPR research teams might enumerate between 80 and 150 RUs a day in rural settings, while in the more urban settlements of Ziway the number could rise to more than 300 a day. The software used for GPS navigation and the collection of GPS census data was CDCGPS2, developed by a team of researchers at the Centre for Disease Control (freely available at http://ftp.cdc.gov/ pub/gpscs/). The digital questionnaires were designed and programmed using Syware Visual CE (http://www.syware.com/products/visual_ce.php). In some smallholder coffee research subsites in Uganda, all of the RUs identiﬁed in the census, as opposed to a random selection of RUs, were revisited to obtain the expanded sampling frame of individuals. This strategy was adopted in research sites where export crop wage workers lived in scattered RUs interspersed with many other RUs containing no such workers. The aim was to ensure that the population list of individuals contained a sufﬁcient number of the scattered wage workers so that a random sample drawn from the list of individuals was likely to capture respondents with and without relevant labour market experience. The list of possible classiﬁcations of respondents varied across research sites. The electronic questionnaire included additional questions for some research sites, reﬂecting the type of variation that FTEPR hoped to achieve in the context of different crops and production conditions. The site selection and sampling methods and GPS technology allow for a follow-up survey of a subsample of the original respondents. FTEPR research also involves qualitative research methods, including life histories of a small sample of those included in the initial survey. The advantages of “nesting” life histories within larger quantitative surveys are described in Schatz (2012) and in Sender, Oya and Cramer (2006). At the other end of the scale, the largest and richest farmers in a rural area may also be excluded from lists of households or farm households because their farms are not deﬁned as being operated by “households” (Choudhry 2008, 11) or simply because surveys of households usually exclude the top end of the wealth/income distribution (Székely and Hilgert 1999; Deaton 2001; Banerjee and Piketty 2003, 4). The domestic and farm servants living with and working for the rural rich are, therefore, also missing from rural household surveys. Some implications of the failure to collect information on young, mobile rural people who are deﬁned as ‘non-residents’ in conventional household surveys have been quantiﬁed using data from Burkina Faso. Their exclusion has a major inﬂuence on assessments of rural living standards (Akresh and Edmonds 2010). In Vietnam, assessments of rural and urban living standards have been shown to be unreliable for the same reasons (Pfau and Giang 2008). The deﬁnition was designed to include the following four categories of linked individuals: (1) those who live permanently with the principal respondent and who share income and expenditure; (2) those who, even if not sharing residence on a regular basis, make signiﬁcant economic contributions (in cash or in kind) to the expenses of the household/respondent; (3) those who, even if not sharing residence, regularly depend on economic contributions in cash or in kind from the respondent or others in the RU; and (4) those who, even if not resident at all in the same place as the respondent, either can be relied upon by the respondent, or receive contributions from the respondent. Evidence from different disciplines “shows that the household as deﬁned by survey statisticians may bear little resemblance to the social unit in which people live” (Randall, Coast, and Leone 2011, 217). The innovative FTEPR questionnaire is available at www.ftepr.org. References Adato, M., F. Lund, and P. Mhlongo. 2007. “Methodological Innovations in Research on the Dynamics of Poverty: A Longitudinal Study in KwaZulu-Natal, South Africa.” World Development 35 (2): 247–263. Akresh, R., and E. Edmonds. 2010. The Analytical Returns to Measuring a Detailed Household Roster. Discussion Paper No. 4759. Bonn: Institute for the Study of Labor (IZA). Amsden, A. H. 2010. “Say’s Law, Poverty Persistence, and Employment Neglect.” Journal of Human Development and Capabilities 11 (1): 57–66. Banerjee, A. V., and T. Piketty. 2003. Top Indian Incomes, 1956–2000. Department of Economics Working Paper Series, Working Paper 03–32. Cambridge, MA: Massachusetts Institute of Technology (MIT). Bardasi, E., K. Beegle, A. Dillon, and P. M. Serneels. 2011. “Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania.” The World Bank Economic Review 25 (3): 418–447. Downloaded by [184.108.40.206] at 01:13 14 April 2014 Fieldwork on Fair Trade and rural poverty 183 Barrientos, S., J. Anarﬁ, N. Lamhauge, A. Castaldo, A. Akua, and N. Akua. 2009. Social Protection for Migrant Labour in the Ghanaian Pineapple Sector. Thematic working paper no 30. Brighton: Development Research Centre on Migration, Globalisation and Poverty (DRCMGP), University of Sussex. Barrientos, S., and S. Smith. 2006. The ETI Code of Labour Practice: Do Workers Really Beneﬁt? Brighton: Institute of Development Studies, University of Sussex. Bevan, P. 2009. Methodological Approach and Fieldwork Plan. Oxford: Mokoro Limited. Bishop, C., and D. Hilhorst. 2010. “From Food Aid to Food Security: The Case of the Safety Net Policy in Ethiopia”. Journal of Modern African Studies 48 (2): 181–202. Breman, J. 2010. “A Poor Deal.” Indian Journal of Human Development 4 (1): 133–142. Central Statistical Agency (CSA). (2006). Atlas of the Ethiopian Rural Economy. Washington, DC and Addis Ababa: Central Statistical Agency with International Food Policy Research Institute. Choudhry, G. H. 2008. Consultancy Report on the World Bank Mission to Uganda for Developing a Sample Design for the Uganda Agriculture Census and Surveys. Washington, DC: World Bank. Deaton, A. 2001. “Counting the World’s Poor: Problems and Possible Solutions.” The World Bank Research Observer 16 (2): 125–147. Deaton, A. 2010. “Instruments, Randomization, and Learning about Development.” Journal of Economic Literature 48 (2): 424–455. Dercon, S., and J. Hoddinott. 2009. The Ethiopian Rural Household Surveys 1989–2004: Introduction. Oxford: University of Oxford Department of Economics and International Food Policy Research Institute. Ewert, J., A. Martin, and V. Nelson. 2005. Assessment of the Social Impact of Adoption of Codes of Practice in the South African Wine Industry. Washington, DC: Natural Resources Institute and DFID. Guyer, J. I., and P. E. Peters. 1987. “Introduction.” Development and Change 18 (2): 197–214. Hill, R. V. 2010. “Liberalisation and Producer Price Risk: Examining Subjective Expectations in the Ugandan Coffee Market.” Journal of African Economies 19 (4): 433. International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI). n.d. Making Rural Services Work for the Poor and Women: Local Public Investments in Agricultural and Water Services in Ethiopia. Addis Ababa: IFPRI and EDRI. International Initiative for Impact Evaluation (3ie). 2010. Fair and Square: Better Market Share, More Beneﬁts through Fairtrade. New Delhi: International Initiative for Impact Evaluation. International Trade Centre. 2011. The Impacts of Private Standards on Producers in Developing Countries. Doc. No. MAR-11–201.E. Geneva: International Trade Centre. Jena, P. R., B. B. Chichaibelu, T. Stellmacher, and U. Grote. 2012. “The Impact of Coffee Certiﬁcation on Small-scale Producers’ Livelihoods: A Case Study from the Jimma Zone, Ethiopia.” Agricultural Economics 43 (4): 429–440. Jerven, M. 2013. Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It. Ithaca, NY: Cornell University Press. Klier, S., and S. Possinger. 2012. Final Report, Fairtrade Impact Study: Assessing the Impact of Fairtrade on Poverty Reduction through Rural Development. Saarbruecken: Saarland University, Center for Evaluation (CEval). Krishna, A., D. Lumonya, M. Markiewicz, F. Mugumya, A. Kafuko, and J. Wegoye. 2006. “Escaping Poverty and Becoming Poor in 36 Villages of Central and Western Uganda.” Journal of Development Studies 42 (2): 346–370. Kumar, N., and A. R. Quisumbing. 2012. Policy Reform towards Gender Equality in Ethiopia: Little by Little the Egg Begins to Walk. International Food Policy Research Institute (IFPRI) Discussion Paper 01226. Washington, DC: IFPRI. Landry, P. F., and M. Shen. 2005. “Reaching Migrants in Survey Research: The Use of the Global Positioning System to Reduce Coverage Bias in China.” Political Analysis 13 (1): 1–22. Laroche, K., and B. Guittard. 2009. The Impact of Fairtrade Labelling on Small-Scale Producers: Conclusions of the First Studies. Montreuil: Max Havelaar France. Lawson, T. 2003. Reorienting Economics: Economics as Social Theory. London: Routledge. Livelihoods Integration Unit (LIU). 2011. Livelihoods Analysis. Accessed November 13, 2011. http://www. dppc.gov.et/Livelihoods/livelihoodhome.html Luetchford, P. 2008. “The Hands That Pick Fair Trade Coffee: Beyond the Charms of the Family Farm.” In Hidden Hands in the Market: Ethnographies of Fair Trade, Ethical Consumption, and Corporate Social Responsibility, edited by G. De Neve, P. Luetchford, D. Wood, and J. Pratt, 143–169. Bingley: Emerald Group. Downloaded by [220.127.116.11] at 01:13 14 April 2014 184 C. Cramer et al. Maertens, M., L. Colen, and J. F. M. Swinnen. 2011. “Globalisation and Poverty in Senegal: A Worst Case Scenario?” European Review of Agricultural Economics 38 (1): 31–54. Maertens, M., and J. F. M. Swinnen. 2012. “Gender and Modern Supply Chains in Developing Countries.” Journal of Development Studies 48 (10): 1412–1430. Muhib, F. B., L. S. Lin, A. Stueve, R. L. Miller, W. L. Ford, W. D. Johnson, P. J. Smith, and Community Intervention Trial for Youth Study Team. 2001. “A Venue-Based Method for Sampling Hard-toReach Populations.” Public Health Reports 116 (Suppl 1): 216–222. Nelson, V., and B. Pound. 2009. The Last Ten Years: A Comprehensive Review of the Literature on the Impact of Fairtrade. London: Fairtrade Foundation. O’Laughlin, B. 1995. “The Myth of the African Family in the World of Development.” In Women Wielding the Hoe: Lesson from Rural Africa for Feminist Theory and Development Practice, edited by D. F. Bryceson, 63–92. Oxford: Berg. Omosa, M., M. Kimani, and R. Njiru. 2006. The Social Impact of Codes of Practice in the Cut Flower Industry in Kenya. London: Natural Resources Institute and DFID. Outes-Leon, I., and A. Sanchez. 2008. An Assessment of the Young Lives Sampling Approach in Ethiopia. Young Lives Technical Note No. 1. Oxford: Department of International Development, Oxford University. Oya, C. 2013. “Rural Wage Employment in Africa: Methodological Issues and Emerging Evidence.” Review of African Political Economy 40 (136): 251–273. Pfau, W. D., and L. T. Giang. 2008. Groups Excluded from “Representative” Household Surveys: An Analysis Based on Remittances Sent and Received in Vietnam. GRIPS Discussion Paper 01/2008. Tokyo: National Graduate Institute for Policy Studies (GRIPS). Pincus, J., and J. Sender. 2008. “Quantifying Poverty in Viet Nam: Who Counts?” Journal of Vietnamese Studies 3 (1): 108–150. Pound, B., and A. Phiri. 2009. Longitudinal Impact Assessment Study of Fairtrade Certiﬁed Tea Producers and Workers in Malawi. London: Natural Resources Institute and Fairtrade Foundation. Randall, S., E. Coast, and T. Leone. 2011. “Cultural Constructions of the Concept of Household in Sample Surveys.” Population Studies 65 (2): 217–229. Rizzo, M. 2011. Rural Wage Employment in Rwanda and Ethiopia: A Review of the Current Policy Neglect and a Framework to Begin Addressing It. Policy Integration Department Working Paper No. 103. Geneva: International Labour Organisation (ILO). Ruben, R., R. Fort, and G. Zúñiga-Arias. 2009. “Measuring the Impact of Fair Trade on Development.” Development in Practice 19 (6): 777–788. Ruben, R., R. Fort, and G. Zuniga. n.d. Fair Trade Programme Evaluation: Impact Assessment of Fair Trade Programs for Coffee and Bananas in Peru, Costa Rica and Ghana. Nijmegen: Centre for International Development Issues, Radboud University. Schatz, E. 2012. “Rationale and Procedures for Nesting Semi-Structured Interviews in Surveys or Censuses.” Population Studies 66 (2): 183–195. Sender, J., C. Cramer, and C. Oya. 2005. Unequal Prospects: Disparities in the Quantity and Quality of Labour Supply in Sub-Saharan Africa. Washington, DC: World Bank. Sender, J., C. Oya, and C. Cramer. 2006. “Women Working for Wages: Putting Flesh on the Bones of a Rural Labour Market Survey in Mozambique.” Journal of Southern African Studies 32 (2): 313–333. Sender, J., and S. Smith. 1986. The Development of Capitalism in Africa. London: Methuen. Sender, J., and E. von Uexkull. 2009. A Rapid Impact Assessment of the Global Economic Crisis on Uganda. Geneva: International Labour Organisation (ILO). Sharp, K., S. Devereux, and Y. Amare. 2003. Destitution in Ethiopia‘s Northeastern Highlands (Amhara National Regional State), Final Report. Brighton: Institute of Development Studies and Save the Children-UK Ethiopia. Singh, G., and B. D. Clark. 2012. “Creating a Frame: A Spatial Approach to Random Sampling of Immigrant Households in Inner City Johannesburg.” Journal of Refugee Studies 26 (1): 126–144. Smith, S. 2010. Fairtrade Bananas: A Global Assessment of Impact. Brighton: Institute of Development Studies, University of Sussex. Ssekiboobo, A. M. 2008. Practical Problems in the Estimation of Performance Indicators for the Agricultural Sector in Uganda. Kampala: Institute of Statistics and Applied Economics, Department of Planning and Applied Statistics, Makerere University. Székely, M., and Hilgert, M. 1999. What‘s Behind the Inequality we Measure? An Investigation using Latin American Data. Inter-American Development Bank Working Paper No. 340. Washington, DC: World Bank. Downloaded by [18.104.22.168] at 01:13 14 April 2014 Fieldwork on Fair Trade and rural poverty 185 Treiman, D. J., W. M. Mason, Y. Lu, Y. Pan, Y. Qi, and S. Song. 2005. Observations on the Design and Implementation of Sample Surveys in China. California Center for Population Research Working Papers Series CCPR-006-05. Los Angeles, CA: CCPR. Valkila, J., and A. Nygren. 2009. “Impacts of Fair Trade Certiﬁcation on Coffee Farmers, Cooperatives, and Laborers in Nicaragua.” Agriculture and Human Values 27 (3): 321–333. Vanden Eng, J. L., A. Wolkon, A. S. Frolov, D. J. Terlouw, M. J. Eliades, K. Morgah, V. Takpa, A. Dare, Y. K. Sodahlon, Y. Doumanou, W. A. Hawley, and A. W. Hightower. 2007. “Use of Handheld Computers with Global Positioning Systems for Probability Sampling and Data Entry in Household Surveys.” American Journal of Tropical Medicine and Hygiene 77 (2): 393–399. Vermund, S. H., S. L. Hodder, J. E. Justman, B. A. Koblin, T. D. Mastro, K. H. Mayer, D. P. Wheeler, and W. M. E. Sadr. 2010. “Addressing Research Priorities for Prevention of HIV Infection in the United States.” Clinical Infectious Diseases 50 (Supplement 3): S149–S155. Walters, B., R. Marshall, and F. Nixson. 2012. “Consistent and Transparent? The Problem of Longitudinal Poverty Records.” Development and Change 43 (4): 899–918. Wilson, I., S. R. A. Huttly, and B. Fenn. 2006. “A Case Study of Sample Design for Longitudinal Research: Young Lives.” International Journal of Social Research Methodology 9 (5): 351–365. World Bank. 2007. World Development Report 2008: Agriculture for Development. Washington, DC: World Bank. Wuyts, M. 2011. “Growth, Employment and the Productivity–Wage Gap: Revisiting the Growth–Poverty Nexus.” Development and Change 42 (1): 437–447.
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