### Problems in employment modules and employment-related questions in conventional household surveys

The paucity of labour surveys and data is compounded by the low quality of the data actually collected. One principal reason is that employment modules in conventional household surveys in Africa are generally weak, partly for some of the reasons stated above. The integrated and income/expenditure surveys promoted by the Bank and other donor agencies use employment modules that are small, very standardised and generally not adequate to capture the complexity and specificity of rural labour relations in developing countries. In fact, even surveys following standard ILO guidelines may not be able to capture complex labour relations, which are common in developing countries. As Standing (2006) argues, the ‘labour force approach’ has dominated the statistical agenda for labour indicators since the 1930s–1940s, when concern over mass unemployment was paramount. The resulting ‘trichotomy’ of ‘employed, unemployed and economically inactive’, which suits industrialised labour markets in high-income societies, is problematic for analyses of labour market and employment dynamics in low-income countries where the ‘standard employment relationship’ is much less well established.8 The standard dichotomy of self- vs. wage-employment is also very problematic. Wuyts (2011, 11) questions the significance and rise of ‘self-employment’ and argues that ‘under conditions of low productivity in the context of the uncertain environment of informality the character of wage labour, and the variety of forms in which it occurs, does not correspond to the conventional or “formal” definition of wage labour’. Furthermore, implemented surveys do not even follow the detailed guidelines produced by the World Bank itself on labour questions (see Grosh and Glewwe 2000, volume 1, 217–250) but focus instead on the details of consumption expenditure. As a result, as. Sender clearly puts it: [In] most developing economies no efforts at all are made to collect time-series data on the wages of those employed in small-scale farm and non-farm rural enterprises, especially on the wages of those who are irregularly, seasonally, or casually employed. In most of these economies, in fact, there is no reliable data on the number of people or households that depend upon earnings in these types of employment; it is simply assumed that the rural poor are, or will become self-employed… (Sender 2003, 414)9 There are various problems with these inadequate employment modules and statistical conventions. First, and perhaps this is the most important problem, employment modules in large-scale household surveys, and even in more carefully designed labour force surveys, rely too much on problematic notions of the ‘main job-holding’. The usefulness of this concept is diminished in contexts of occupation multiplicity, irregularity and strong seasonality, which may lead to respondents' and enumerators' biases. They prevent a mapping of the complete set of economic activities in which individuals engage over an extended period of time (e.g., 12 months) and the relative importance of each of them for their subsistence. This statistical category is particularly misleading in contexts where land is abundant, typically leading either respondents or enumerators to emphasise own-account farming at the expense of other more irregular but perhaps more remunerative activities.10

Second, the excessive reliance on standard questions with a seven-day reference period, which in contexts of strong seasonality, irregularity of activities and occupation multiplicity, introduces potentially damaging statistical biases. Asking a poor rural person what he/she has done in the last seven days is not particularly useful, given structural seasonality, occupation multiplicity and job instability.11 This also biases the responses to questions on ‘main occupation’ since different occupations have very different time patterns.

Third, very crude questions and distinctions on status in employment (with the main distinction between self-employment versus paid employment) may miss out on a range of economic activities which would be classified as paid employment. This applies especially to those of a casual nature that are particularly prevalent, precisely because of their diversity, irregularity and sometimes ambiguous nature. Despite the efforts by the International Conference of Labour Statisticians (that is convened by the ILO) to establish clear distinctions according to the International Classification of Status in Employment (ICSE-93) and, independently from this under the International Standard Classification of Occupations (ISCO-08), their applicability in rural areas of Africa is not always straightforward. The aim of ISCE-93 is to classify jobs ‘with respect to the type of explicit or implicit contract of employment of the person with other persons or organisations’, and the basic distinction is between paid employment and self-employment (under which employers, own-account workers and contributing family workers fall). ISCO-08, which focuses on the tasks and duties undertaken in the job, does not map occupations directly into status in employment.

However, the practice of many enumerators in large-scale data collection processes is often to assume that certain jobs/occupations ‘naturally’ belong to a particular status in employment (self-employment or paid employment). I have observed this in rural surveys where enumerators often assume a self-employment status to typically ‘informal’ occupations (e.g., in trade and transport), without the necessary probing. For example, most jobs listed in Table 3 (in rural Mauritania) were initially considered as ‘self-employment’ by enumerators almost automatically. Once conscientious probing was done, the proportion of paid employment within some of those categories significantly increased to the levels recorded in Table 3. Therefore, the use of supposedly well-defined standard labour statistical categories sometimes becomes a problem and a source of serious biases. Part of the problem lies in the standardisation itself, and the use of general standard questions across widely different contexts. Partly it lies in the extent to which standard labour categories reflect the labour force approach applied to data collection in the particular context of advanced capitalist countries (Standing 2006). Arguably, the notion of ‘self-employment’ may carry different meanings in different places unless a detailed explanation is provided in each case. In fact, as cogently argued by Wuyts (2011), many informal workers usually regarded and classified as ‘self-employed’ simply constitute another form of wage labour that does not conform to standard conventions (see also Breman 2006). Conceptually, they should not be seen as self-employed because they do not own the means of production, their job is highly insecure, and they earn a residual income after the entrepreneur (owner of the means of production) is paid the agreed (rather imposed) profits. This situation is very common in non-farm employment but some may argue that even smallholder contract farming often constitutes another form of ‘disguised wage labour’ especially for the poorest producers (Clapp 1994). Based on more than 10 years of experience in training enumerators for rural labour surveys, I come to conclude that the distinction between self-employment and wage employment in rural settings remains one of the biggest challenges in rural labour survey designs. Enumerators easily make mistakes and have an inclination to classify many activities as ‘self-employed’ without properly probing the central questions of ownership and control over means of production and labour decisions.

<table class="table-figure"><colgroup><col/><col/><col/><col/><col/></colgroup><thead valign="bottom"><tr><th align="left" colspan="1" rowspan="1">Activity</th><th align="center" colspan="1" rowspan="1">Monthly salaried(%)</th><th align="center" colspan="1" rowspan="1">Daily or piecework wage labour(%)</th><th align="center" colspan="1" rowspan="1">Commission or profit share(%)</th><th align="center" colspan="1" rowspan="1">Own-account with own means of production(%)</th></tr></thead><tbody valign="top"><tr><td align="left" colspan="1" rowspan="1">Working in palm tree/date cultivation</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">43</td><td align="char" char="." colspan="1" rowspan="1">14</td><td align="char" char="." colspan="1" rowspan="1">43</td></tr><tr><td align="left" colspan="1" rowspan="1">Mason/brick maker</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">100</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td></tr><tr><td align="left" colspan="1" rowspan="1">Trader/shopkeeper</td><td align="char" char="." colspan="1" rowspan="1">12</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">10</td><td align="char" char="." colspan="1" rowspan="1">78</td></tr><tr><td align="left" colspan="1" rowspan="1">Small itinerant trader</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">5</td><td align="char" char="." colspan="1" rowspan="1">95</td></tr><tr><td align="left" colspan="1" rowspan="1">Processing agricultural commodities or homemade food for sale</td><td align="char" char="." colspan="1" rowspan="1">14</td><td align="char" char="." colspan="1" rowspan="1">9</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">77</td></tr><tr><td align="left" colspan="1" rowspan="1">Teacher (koranic)</td><td align="char" char="." colspan="1" rowspan="1">65</td><td align="char" char="." colspan="1" rowspan="1">35</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td></tr><tr><td align="left" colspan="1" rowspan="1">Hairdressing</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">100</td></tr><tr><td align="left" colspan="1" rowspan="1">Artisan (weaving, dye, tailor, etc.)</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">3</td><td align="char" char="." colspan="1" rowspan="1">97</td></tr><tr><td align="left" colspan="1" rowspan="1">Fishing</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">14</td><td align="char" char="." colspan="1" rowspan="1">0</td><td align="char" char="." colspan="1" rowspan="1">86</td></tr><tr><td align="left" colspan="1" rowspan="1">Source: Author elaboration from Rural Labour Market Survey in Mauritania. Also GIRM and World Bank <a class="xref-link" href="#CIT0074">(2007)</a>.</td></tr></tbody></table>

Fourth, in many parts of rural Africa there are stigmas associated with casual wage employment, especially the most exploitative and degrading forms, which are thus easily under-reported or unreported altogether. Many of the local words used to describe these occupations indeed reflect the social stigma associated with them. Kibarua in Swahili speaking countries (especially Tanzania) is in fact a derogatory term that is used for most forms of casual manual wage labour; it contains reminiscences of slave forms of labour (Mueller 2011, 33). Hill (1968) reported how men in rural Nigeria typically considered wage work for neighbours as a source of shame, as being a sign of desperation. The terms ganho-ganho in Mozambique and ganyu in Malawi, used for various forms of casual labour, are also intrinsically associated with low social status and despair (Bryceson 2006). In addition, patriarchal barriers to women's entry into local wage employment, particularly for demeaning casual jobs, may lead to important biases in reporting women's participation in rural labour markets (Sender 2003; World Bank 2007, 204). These studies show that qualitative research is very useful in helping researchers reveal and understand some of these biases and stigmas, which are hard to capture in quantitative surveys. Ignoring these stigmas and not acting upon them through careful wording and probing can introduce very substantial biases in the estimation of people in wage employment and in the correlation between poverty and status in employment. Therefore, at best, the officially recorded share of ‘employees’ (wage employment) is probably a very low boundary for the total number of workers who engage at least occasionally in wage labour.

Fifth, the terminology is also important. Some analytical and statistical categories may be difficult to comprehend by enumerators and respondents, especially when terms like ‘salary’ or ‘wage’ are associated with formal sector ‘well remunerated’ employment such as civil servant jobs, or teacher jobs. This implies that any question that uses these terms is likely to yield biased responses about participation in local wage labour markets. In general, notions of ‘employment’, ‘gainful activity’ or ‘remunerated activity’ are not unproblematic in rural African contexts (Bardasi et al. 2010, and for urban areas see Luebker 2008).12 Many activities may not yield cash income and some forms of wage labour paid in kind (with food) may typically be regarded as a source of ‘help’, whereas the mention of ‘salary’ or ‘wage’ depending on the local terms may be automatically associated with regular stable wage employment. As Hussmanns et al. (1990, 256) argue, ‘the concept [of employment] is complex and interviewers’ or respondents' own subjective understanding of terms like “economic activity” or “work for pay or profit” may differ from what the concept intends to include'. Besides, there are many specific local terms for various forms of wage tasks which change from place to place. If enumerators don't use these locally recognisable terms and instead rely on literal translations, agricultural wage employment may be seriously under-reported.

Rather than just asking respondents if they have been ‘employed’ during the reference period, it is thus important to operationalise the concept of employment through questions that are easily understood by respondents. For example, in one of the micro-studies referred to in the next section (in Mauritania), the test of alternative methods to estimate labour force participation rates led to disparate results especially for one village. If a question about ‘remunerated employment’ was used (for people aged over 14), the resulting ‘employment-to-population ratios’ were very low: around 50% for two villages and only 26% for the most problematic village, precisely the one where more economic dynamism had been observed by the team. Instead, when ILO best practice was applied and respondents were asked to report and list all the economic activities that brought in some income or that occupied their time on the basis of an activity list (‘did you do any of the following?’ etc.), employment-to-population ratios increased significantly, in the more problematic village (to 70%) and the others (to 85%), more in line with reported labour force participation rates in rural Africa. The results were then triangulated with in-depth qualitative research to understand relevant concepts of labour participation and respondents' perceptions of the meaning of ‘employment’. Qualitative evidence is indeed crucial to help in the design of adequate quantitative labour surveys.

Recent experimental research carried out by the World Bank has provided additional compelling evidence about the sensitivity of labour market statistics, especially labour participation rates and wage employment, to survey design (Bardasi et al. 2010; Dillon et al. 2012). Thus Bardasi et al. (2010), after testing designs with shorter vs longer employment modules, find significant differences across survey designs and conclude that: Our findings suggest that both types of survey design decisions have statistically significant effects on labor statistics. These effects are largest on the measure of labor force participation, but also exist for weekly hours of work, daily earnings, main activity, and type of work… Using the short questionnaire lowers female labor force participation and also affects the distribution of workers across sectors, lowering the share of paid employees among the employed. (Bardasi et al. 2010, 31, emphasis mine)