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What explains differences in pre-market factors? Three types of inputs are believed to determine the skills agents take to the labor market: ability, family inputs and school inputs. Therefore it is crucial to understand first the importance of each of these inputs. The literature on the production of achievement has not been able to provide an estimation that can take the three factors into account simultaneously at the student level. Chapter 1 attempts to fill this gap by providing an estimation of the production function of achievement where both types of investments (families and schools) are considered in a framework where the inputs are allowed to be correlated with the unobserved term, ability to learn. I do this by applying Olley and Pakes' (1996) algorithm which accommodates for endogeneity problems in the choice of inputs for the production of achievement and by using parents saving for their child's postsecondary education to control for the unobserved component (i.e. ability to learn) in the production of skills. What makes this saving measure informative is the fact that parents decide it at the same time they choose the home and school inputs that will affect the observed test score (the current outcome). However those savings will not affect the current outcome, but instead will affect future labor market outcomes through college choices. The estimates for the role of family inputs are in line with previous findings. Additionally, the estimates of school inputs show that they are also important for the formation of students' skills even after controlling for ability to learn. The estimates of the production funct ion are used to compute counterfactual exercises. In particular, this paper evaluates what would happen if the inputs for black students are reassigned so that their inputs are the actual amount they receive plus the differential that white students receive. This exercise shows that equalizing home inputs would reduce the achievement gap by 15.6 percent while equalizing school inputs would do it by 9.2 percent. If instead inputs are altered only in 12th grade, house and school inputs have a similar impact on students' achievement: school inputs would reduce the gap by 7.2 percent while home inputs would do it by 7.4 percent.

Chapter 2 explores a further area that Chapter 1 does not discuss: whether parents substitute or complement families and school inputs. Parents may alter the investment in their child's human capital in response to changes in schooling inputs. If substitutability between parental and school inputs in the production of achievement is prevalent, then increases in school inputs could crowd out parental inputs. If instead there exist couplementarities between school and parental inputs, then increases in school inputs might increase parental involvement. Chapter 2 studies whether parents react when their child's school inputs decrease by studying out-of-school suspensions and their effect on parental involvement. Because out-of-school suspensions are chosen by the class teacher or the principal of the school and not by the parents, they are a good candidate for exogenous (to parental choice) variation in the level of school resources across students. Out-of-school suspensions are a consequence of student misbehavior, and thus do not occur randomly across students. Therefore, in order to capture the effect of how parents react to the decrease in school inputs, I instrument the number of out-of-school suspensions with measures of "principal's preference toward discipline." The identification comes from the fact that students in schools wi th stricter principals are more likely to be suspended. The estimates show that without controlling for selection, out-of-school suspensions are negatively correlated with the level of parental involvement. Once selection is taken into account, the effect disappears.

Earnings depend not only on pre-market factors but also on the agent's experience in the labor market. That is, it is important which job he gets and how his earnings evolve within and across employers. In their seminal paper, Topel and Ward (1992) estimate that nearly a third of total wage growth in the first 10 years of labor market experience is due to wage jumps at the time of changing a job. Unfortunately, the job ladder model, the workhorse for this literature, cannot explain the big number of wage cuts for workers that change employers (as opposed to those who remain in their job). An extension of the job ladder model that has been proposed to ameliorate this failure is the introduction of a shock to the existing employer-employee match. But such a process has not been identified empirically in the literature yet. Chapter 3 uses a particular feature of the National Longitudinal Survey of Youth (NLSY79) to provide a convincing identification strategy for the wage shock process: two measures of workers' compensation, wages and labor earnings. The first part of the chapter shows that although the dynamics of wages are consistent with a job ladder model, the same is not true for the dynamics of earnings. While relatively large wage increases follow job-to-job transitions. we observe that job-to-job transitions are negatively correlated with hourly earnings. We speculate that this is due to the fact that job-to-job transitions are more likely to follow a large reduction in wages. We find that this result is robust to mis-measurement in the labor supply and disappears for workers paid by the year. The rationale for this last finding is that. workers paid by the year are much less likely to be hit by wage shocks than other workers. Using the multiple measures of workers' compensation and data on employment transition, we calibrate a modified job ladder model that allows for shocks to the employer-employee snatch. We show that the model fits the data well and that a model that does not include this feature would fail to match the data.