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Carneiro and Henley (2001) estimate the wage functions for the formal and the informal sectors.
Statistical estimation of wage functions that seek to represent the relationship between wage rates and a variety of human capital stocks may yield biased estimates of private rates of return from these investments for a variety of reasons.
In single markets, depending on their education level and experience, workers have different wage functions and face differential market imperfections, such as different entry costs, probability of losing their job, and home production productivity.
Although modeling these three channels through matching is a fairly complex process, in practice, the first two channels (occupation upgrading and specialization) are controlled in Mincer-type wage functions by binary variables that identify occupation and field of study fixed effects.
We calculated the following wage functions following the procedure summarized in additional file 1.
The estimation of indirect costs included: 1) Estimation of wage functions; 2) assumption settings of indirect costs; and 3) estimation of workdays lost by patients or family members.
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Although modeling these three channels through matching is a fairly complex process, in practice, occupation upgrading and specialization are controlled in wage earnings functions by binary variables that identify occupation and field of study fixed effects.
One way to address this problem is to control for occupational matching by using NHRIC NVRIC matrices in wage earnings functions, so that we can compare the returns to education in each cell separately.
Labor demand and the wage function are exogenously determined and identical for all individuals.
As such, we guarantee that the respondent is the single wage earner, for whom we can thus estimate a wage function.
A standard object of empirical analysis in labor economics is a modified Mincer wage function in which an individual's log wage is a function of education, experience, and race.
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