in which the household head has not finished primary education, the risk of poverty decreases for households in which the head of household has finished at least primary school and junior high school, in rural as well as urban areas. However, the main reason for the substantial number of poor is probably again the absence of productive employment possibilities. Three main reasons speak for this: First, the probit regressions indicate that the risk of poverty in urban areas depends statistically significantly on the region, even after controlling for education. This indicates that workers with the same level of education face different possibilities for productive employment across the regions. Similarly, rural workers in sectors other than agriculture also face a lower risk of poverty than workers in agriculture. Second, the substantial share of workers who are self‐employed or work as unpaid family helpers points to the absence of more productive employment opportunities. Third, and as mentioned, Table 10 shows that workers of all levels of education are affected by under‐ and unemployment.

**Table 11: Marginal effects for probit regression on the determinants of poverty, urban and rural areas, Togo 2006**

||Urb|an|Ru|ral|
|---|---|---|---|---|
||Coef.|z|Coef.|z|
|primary|‐0.04|‐1.87|‐0.07|‐2.48|
|junior high|‐0.05|‐2.11|‐0.09|‐2.56|
|senior high and above|‐0.11|‐6.09|‐0.38|‐2.79|
|Agriculture|0.07|1.86|||
|Mining|0.02|0.29|‐0.10|‐0.89|
|Manufacturing and utilites|0.00|‐0.07|‐0.01|‐0.19|
|Construction|‐0.01|‐0.43|‐0.12|‐1.33|
|Transport/ communication|‐0.01|‐0.20|‐0.12|‐1.83|
|Commerce and hotelery|||‐0.18|‐3.48|
|Education/ health|0.00|‐0.08|‐0.17|‐2.33|
|Public Administration|0.03|0.71|‐0.21|‐2.17|
|Services/ banks/ insurances|0.02|0.52|‐0.17|‐1.32|
|Other services|0.00|0.04|‐0.11|‐0.86|
|HH size|0.04|7.65|0.10|15.33|
|Lomé|**‐0.53**|**‐7.52**|||
|Maritime|**‐0.08**|**‐3.55**|**‐0.16**|**‐2.66**|
|Plateaux|**‐0.10**|**‐5.75**|**‐0.37**|**‐6.54**|
|Centrale|**‐0.07**|**‐3.28**|‐0.09|‐1.62|
|Kara|‐0.04|‐1.12|‐0.06|‐1.22|
|Age|0.00|3.13|0.00|2.21|
|sick last 4 weeks|0.04|2.03|0.12|5.55|
|underemployed|0.02|1.14|‐0.05|‐2.00|
|Land per capita|||‐1.67|‐1.98|

_Source_ : CWIQ 2006 and author’s calculations.

**Mincerian wage regressions reveal the existence of some premium – as expected ‐ for additional years of education, but these are very low compared to other SSA countries**<sup>**39**</sup> **.** This regression of earnings on a proxy for human capital (the education of the labor force), controlling for a set of individual, community, household and regional, characteristics, shows a positive non‐linear relation between educational attainments and the dependent variable (Figure 43 and Table A4 in Appendix 1). For the average population, the mean marginal return to education stands at 5.2%, which is quite close from the findings of Kuepie et al (2008) who, by means of a similar specification estimated returns to education to be around 5% in Lomé. Figure 43 also shows that wages, given the level of primary education completion rate, are very low in Togo compared to other WAEMU countries further indicating a low demand from firms for more educated work force (See Appendix, Table A4 for summary econometric results from the Mincerian specification).

> 39 There are a number of methodological problems when comparing regressions on returns to education across countries, but Psacharopoulos (1994) can give us a sense that in most SSA countries the return for different levels of education is usually above 15%.

40