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    • After applying the above mentioned filters to the

    2018-10-30

    After applying the above-mentioned filters to the sample, we divided it into four sub-samples: (i) women with full secondary education, (ii) men with full secondary education, (iii) women with higher education, and (iv) men with higher education. Table 1 provides the proportion of individuals based on historical information about the age at which they started working in each of the nine years analysed here.
    Empirical modeling Heckman\'s procedure (by maximum likelihood) was used to correct the sample selectivity bias in the earnings equations. Sample expansion factors associated to each observation were used and the characteristics of the sampling, designated as complex surveys, were taken into account. The sampling plan applied to the surveys is stratified and clustered and uses unequal selection probabilities. Ignoring the sample design may underestimates the actual variance. The Taylor series linearization method was used to provide correct standard errors. In the basic model, the explanatory variable of interest is the age at which individuals started working. This is done in two ways: (ii) model A – using a dummy variable that assumes value 1 if the individual began working before the age of 16 (15 or less) and 0 if that was not the case (Child labor); and (ii) model B – using a set of dummy variables to distinguish between age intervals at which the individuals entered the labor market (Age started to work, where for high school, the p-gp inhibitors age group is 17 years old and over, and for higher education, the base age is 23 years old and over). In the earnings equation the other control variables are: (a) age of the individual (Age) in years and the square of this variable (Age squared); (b) a dummy variable for the individual\'s genders, which is 1 for males and 0 for females (Man); (c) four dummy variables to distinguish between five skin colors – White (base group), Black, Mulatto, Yellow (Asian) and Indigenous people; (d) a dummy variable for residence location, which is 1 when the individual resides in an urban area and 0 if he or she resides in a rural area (Urban area); (e) a dummy variable for association to a labor union membership, which is 1 if the individual is member and 0 if not (Labor union); (f) five dummy variables to control for possible regional differences: North, South, Mid-West, Northeast, and Southeast (base group); (g) nine dummy variables for year, to control for possible fixed time effects (2001 as the base group). The participation equation contains all regressors cited above, except the labor union membership and age at which he or she began to work because both are only defined in the database if the person worked. We also included other personal and family characteristics: (a) a dummy variable for existence of some non-labor income (e.g., conditional cash transfer programmes), which is 1 if the individual receives and 0 if not; (b) a dummy variable for marital status, which is 1 if the individual is married and 0 if he or she is not; (c) a dummy variable for children living in the same household, which is 1 if there is and 0 if not; (d) a dummy variable for position in the family, which is 1 if the individual is the head of the household and 0 if he or she assumes other positions in the family.
    Results and discussions Table 2 shows estimates of marginal effects in percentage, by gender and level of schooling group. The value are the effect of the age at which Brazilians in the 23–65 age intervals began to work on earnings. Table A.1 shows the results of earnings equations estimated. Since the sample was not restricted to specific schooling-based groups, but rather to groups based on the age at which a person began to work, education was initially measured alternatively in the light of two aspects: (a) years of schooling (S), ranging from 0 for no schooling or less than one year of schooling to 15 for those with 15 years of schooling or more; (b) 15 dummy variables to distinguish 16 years of schooling, based on people without any schooling or who had only studied for less than one year.