Data and descriptive statistics

The data we use for our empirical analysis stem from a set of surveys (1-2-3 surveys or Enquêtes 1-2-3) in seven economic capitals of the West-African Economic and Monetary Union (WAEMU) in the early 2000s.³⁹ A 1-2-3 survey is a multi-layer survey organised in three phases and specially designed to study the informal sector.⁴⁰ Phase 1 is a representative labour force survey collecting detailed information on individual socio-demographic characteristics and employment. Phase 2 is a survey which interviews a representative sub-sample of informal production units identified in Phase 1. The focus of the second phase is on the characteristics of the entrepreneurs and their production units, including the characteristics of employed workers. It also contains detailed information on input use, investment, sales, and profits. Phase 3 is a household expenditure survey interviewing (again) a representative sub-sample of Phase 1. The dataset from all three phases are organised in such a way that they can be linked.

For this paper we use data from Phase 2 which is a sample of informal entrepreneurs in urban centres of seven countries in Sub-Saharan Africa (Brilleau et al., 2005). The 1-2-3 surveys define informal enterprises as production units that (a) do not have written formal accounts and/or (b) are not registered with the tax administration. Part (b) of this definition varies slightly between countries, as registration may not always refer to registration with tax authorities. Table 2-1 is based on our sample of 6,521 firms and shows employment by company type. It shows the importance of informal enterprises for employment in urban centres in the seven countries in Sub-Saharan Africa in our sample in the early 2000s.

Averaged over all countries in the sample, private informal firms are responsible for more than 70 percent of employment. In Cotonou and Lomé employment by informal firms exceeds 80 percent of employment. Pure ‘self-employment’ on average accounts for about 70 percent of these workers. The remaining 30 percent

39 These urban economic centres are Abidjan (Cote d’Ivoir), Bamako (Mali), Cotonou (Benin), Dakar (Senegal), Niamey (Niger), Lomé (Togo) and Ouagadougou (Burkina Faso). The surveys were carried out by AFRISTAT and the National Statistical Institutes (INS) with the support of Developpement Institutions &

Mondalisation (DIAL) as part of the Regional Program of Statistical Support for Multilateral Surveillance (PARSTAT) between 2001 and 2003. For a more detailed description of the data see Brilleau, et al. (2005a).

40 See Roubaud (2008) for a detailed assessment of this type of survey instrument.

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are split almost equally between enterprises that employ family workers and non-family workers.

Table 2-1: Employment by sector in seven urban centers in Sub-Saharan Africa (percent) Principal

employment Cotonou Ouaga. Abidjan Bamako Niamey Dakar Lomé Total Public

Source: Brilleau et al. (2005), and authors’ computations based on 1-2-3 surveys (Phase 2, 2001/02, AFRISTAT, DIAL, INS).

Following the model described in Section 2.2 we restrict the sample to households that have exactly two enterprises, one managed by a woman and one by a man. The results will be prone to selection bias as we reduce the representative sample of about 6,521 firms to 922 firms in 461 households. It could be that these households are more entrepreneurial than households with only one enterprise. In such a case we would expect estimates of input factors to be upwardly biased. An upward bias could also be created by the fact that households with more than one enterprise are better able to diversify risks. In addition, the results are prone to downward bias caused by measurement error.

This bias is increased by reducing the sample size. To check for possible selection biases we estimated all specifications for the entire sample and also for sub-samples containing; households with more than 2 enterprises, and; households with enterprises headed by a female or male entrepreneur.⁴¹ All results are robust to this sample variation. Certainly our results should be interpreted with caution due to the relatively small sample size. Nonetheless, the fact that the results are robust across various samples is encouraging.

Profit is defined as value added and is computed as sales minus input costs including expenses for products for re-sale.⁴² Note that labour costs and interest

41 This sub-sample contains about 1,300 firms in about 600 households.

42 Input costs are measured in detail and include the following items: raw materials, materials for re-sale, rent, water, gas, electricity, telephone, fuel, tools, transport insurance, repair costs, taxes, patents and other charges and fees.

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payments are not deducted.⁴³ Capital is measured by the replacement value of capital stock. Labour input is measured in hours worked in the enterprise per month by the owner and all employees. The reference period for all of the variables is one month. All monetary values are in international US Dollars (Int.

USD).⁴⁴ Table 2-2 shows that the average monthly value added of a male entrepreneur in the wholesale or retail sector is 576 Int. USD monthly compared to 329 Int. USD for the average female entrepreneur. The distribution between firms with pure ‘self-employment’ and firms that employ either family or non-family workers remains similar after restricting the sample. The main enterprise characteristics of this sub-sample are shown in Table 2-2.

43 To check for the robustness of our results we also deducted labour costs. When controlling for labour inputs we excluded paid labour hours. The results are robust to this variation. Results are available from the author on request.

44 PPP conversion rates from the World Development Indicators by the World Bank (World Bank, 2013) have been used.

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Table 2-2: Basic enterprise characteristics by gender and sector

Source: Authors’ computation based on 1-2-3 surveys (Phase 2, 2001/02, AFRISTAT, DIAL, INS).

On average the value added of an enterprise headed by a man is about three times as high as the value added in the average female headed enterprise. This ratio is similar when using the median value of value added. Even larger differences can be found when examining the differences in capital stocks. The average capital stock of a male headed enterprise is about 5.5 times bigger than the average

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capital stock of a female headed enterprise. Both distributions of capital stock and value added are heavily skewed. Median values are much smaller than the respective means.

When comparing the labour inputs by the gender of the enterprises owners we cannot detect big differences. Assuming six working days per week, both male and female entrepreneurs work about 8 hours daily. Firm size (which includes the owner) is slightly bigger in male headed enterprises. On average male headed enterprise have 1 employee compared to 0.3 in female headed enterprises. Again, the average is driven by a few firms that have a large number of employees. The majority of the firms do not have employees. One of the most important findings for our analysis, shown in Table 2, is the unequal distribution of female and male headed enterprises by sector. In construction, repair services and transport there are virtually no female enterprise heads whereas the sectors of ‘hotels and restaurants’ and ‘petty trading’ are dominated by female entrepreneurs.

These descriptive statistics show two relevant findings. First, in some capital intensive sectors (e.g. transport) there are virtually no female enterprise owners.

Second, male headed enterprises in all sectors have larger capital stocks and value added, whereas labour inputs by male and female enterprise owners do not differ substantially. This fact, combined with the findings of decreasing marginal returns to capital in all sectors, suggests inefficient resource allocations within households.