Wages are modeled using log monthly wages as the dependent variable. The general wage model contains explanatory variables in levels and allows for nonlinearities in the data. For example, the log wage equation is found to be nonlinear in education and experience. This way of modeling wages indicates that returns to education and experience are not constant but decreasing over the life cycle. In addition, the model contains dummy variables that take the value one if, for example, a worker is a contract worker, and zero otherwise. Such a dummy variable may reveal whether there is a wage premium related to this kind of employment. We use standard quantiles, throughout this section namely the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> , and 90<sup>th</sup> quantiles.

All included explanatory variables have the expected signs. None of the included variables are not statistically significantly different from zero for all quantiles. Each explanatory variable will now be discussed in turn: (1) education; (2) experience; (3) labor market association; (4) occupation and sector; (5) gender; and (6) rural _versus_ more dispersed rural living and regions.

# **Education**

Human capital has proven to be important in enhancing long-term economic growth.<sup>6</sup> A more educated workforce is likely to increase worker productivity, to be flexible and innovative, and to facilitate the adoption and use of new technologies. The increasing speed of technological change faced by firms today and international economic integration means that workers need to have more skills at higher levels in order for firms to be competitive. One reason for this is that more skilled employees can adjust more easily to changes in their firm’s economic and technological environment than less skilled workers.<sup>7</sup> Hence, low returns are an obstacle to economic growth in rural Mexico.

Knowledge about educational wage differentials or wage gaps serves at least three purposes. First, wage 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 access whether it is worth making this kind of investment instead of others. Third, wage differentials disclose how the labor market translates educational inequalities into wage 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 economic growth. Therefore, it is of interest to estimate the impact of different levels of education and experience on money wages.

- 6 See, for example, Barro (1991) and Mankiw, Romer, and Weil (1992).

> 7 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 tricky 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.

**19**