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How to do (and how not to do)
fieldwork on Fair Trade and rural
Christopher Cramer , Deborah Johnston , Bernd Mueller , Carlos
Oya & John Sender
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
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Canadian Journal of Development Studies
Revue canadienne d’études du développement, 2014
Vol. 35, No. 1, 170–185,
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
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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
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)
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Fieldwork on Fair Trade and rural poverty
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
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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
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
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
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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
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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.
Research sitea selection in Ethiopia and Uganda.
Fair Trade
Non-Fair Trade
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Fair Trade
Non-Fair Trade
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.
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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
Phase 2
Number of RUs in
Create a stratified sample frame using
a brief PDA survey across
Number of individuals
registered RUs (quasi-census)
living in these RUs
Sample size of quasicensus (% of total
Average number of
adults per RU
Extrapolated number
of adults in all sites
Phase 3
Total number of
Conduct main survey using detailed
Estimated % of total
floriculture coffee tea
2765 11858
59.7% 65.9% 72.4% 60.1%
7914 3055
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
GPS: Global Positioning System
PDA: personal digital assistant (handheld computer)
RU: residential unit
UG: Uganda
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Fieldwork on Fair Trade and rural poverty
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
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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
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Fieldwork on Fair Trade and rural poverty
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
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
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
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.
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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.
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
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 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
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
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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
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).
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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
pub/gpscs/). The digital questionnaires were designed and programmed using Syware Visual CE
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).
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