Each explanatory variable will now be discussed in turn starting by location and followed by: (1) education, (2) experience, (3) labor market association and status, (4) sector, (5) gender, and (6) social capital.

Rural living is in many ways very different from urban and metropolitan living in Haiti (Table 2). The largest statistical differences in poverty reduction between rural and other areas are found in the effect of education, gender, and social capital.<sup>11</sup> However, as this section shows, not all variables are significant at all five quantiles.

Living in rural areas in Haiti does by itself affect wages for the workers in the first quantile. For workers in quantile 2-5 individual and other characteristics are more important than geographical location. However, rural workers in quantile 1 earn statistically significantly more than their peers in urban areas, controlling for education, skills and other variables. This finding may indicate that migration from rural to urban areas has caused an inflow of workers, maybe creating an excess supply, and therefore forced wages of the poorest workers below wages in rural areas. This is not the case for any other income level.

Human capital has proven to be important in enhancing long term economic growth.<sup>12</sup> A more educated workforce is likely to be more productive, flexible, and innovative, and to facilitate the adoption and use of new technologies. The increasing speed of technological change faced by firms and farms today and international economic integration means that workers need to have more skills at higher levels in order for firms or farms to be competitive. One reason for this is that employees that are more skilled can adjust more easily to changes in their firm’s or farm’s economic and technological environment than less skilled workers.<sup>13</sup> Knowledge about educational income differentials or income gaps serves at least three different purposes. First, income differentials reveal the magnitude of incentives or returns obtained by workers acquiring education, and hence, individual educational demand. Second, knowing the extent of economic returns to human capital makes it possible to assess whether it is worth making this kind of investment instead of others. Third, income differentials disclose how the labor market translates educational inequalities into income inequalities, which is important information in the process of reducing the latter. Furthermore, educational returns link to some extent education to labor productivity and indicate the magnitude of the contribution of education to 11 See Verner (2007).

> 12 See, for example, Barro (1991) and Mankiw, Romer, and Weil (1992).

13 One issue that needs to be mentioned relates to the endogeneity of education in the regressions. There is vast evidence of a positive correlation between earnings and education. However, social scientists are cautious to draw strong inference about the causal effect of education. In the absence of experimental evidence, it is tedious to recognize whether higher earnings observed for better educated employees are caused by their higher level of completed education, or whether employees with greater earnings capacity have chosen to acquire more education. Card (1998) surveys the literature on the causal relationship between education and earnings and finds that the average marginal returns to education is not much below the estimate that emerges from standard human capital earnings function studies.

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