# **3. Methodology**

Only a few countries in Latin America conduct employment surveys or provide other data sources that follow workers over time (e.g. Brazil, Argentina, and Mexico). However, even in these few cases, the resulting samples suffer from data problems such as attrition. Furthermore, panel data from social security records often does not include data on workers’ educational attainment. This data constraint imposes potential biases in cross-sectional estimates of RTS. In particular, such estimates suffer from the “ability bias” of estimates of RTS that do not control for unobserved innate ability of workers, which can be correlated with both wages and the workers’ level of education.

Consequently, it seems practical to develop an approach to study returns to schooling for a sample of twelve LAC countries with repeated cross sections of workers. Fortunately, there is an econometrics literature that outlines how to use these data for estimating RTS while simultaneously eliminating the ability bias that afflicts standard Mincerian estimates. Specifically, we use a pseudo-panel approach following Deaton (1997) and Deaton and Paxson (1994).

In this pseudo-panel approach, a group (or cohort) of individuals are followed over time. This semiaggregated structure allows us to link different surveys for each country to estimate returns to schooling over time. Moreover, it eliminates the ability bias through averaging across workers of a given cohort while controlling for cohort-specific (or generational) effects.

When we estimate RTS using pseudo-panels, they are decomposed into two parts: a trend and a cyclical component. The cyclical component is used in a second stage to look at the relationship between returns to schooling and foreign demand, and other determinants of the business cycle. As the business cycle might be driven by various shocks, we use a set of four variables representing the types of shocks to analyze the determinants of the cyclical component of the returns to schooling.

# _3.1 Estimation of the Returns to Schooling_

Our data consists of a set of household surveys from 12 Latin American countries (see Table 1). In the pseudo-panel approach, a group of workers (cohort) is followed over time as opposed to an individual. A cohort is formed by those individuals that were born in the same year. The relevant variables are thus averages of each cohort.

As Deaton (1997) points out, pseudo-panels have several advantages over panel data. A pseudo-panel does not suffer from attrition because it is constructed from new samples every year. Furthermore, “it is likely to be less susceptible to measurement error than panel data, because the quantity that is tracked is normally an average and the averaging will nearly always reduce the effects of measurement error" (Deaton 1997, p. 120).

As mentioned above, the pseudo-panel approach also removes ability biases. The ability bias refers to the omission of unobserved worker ability in traditional Mincerian estimates of RTS. Individual ability might be correlated with both the level of education and wages. Its omission (as an explanatory variable) might yield biased estimates of RTS. As explained by Warunsiri _et al._ (2009) and Kaymak (2008), 7