Lastly, occupational changes are characterized. The analysis uses the 1-digit occupation codes. Table A1 shows the multinomial Logit results on the initial occupation choice. The estimation includes age started, total household asset value in 2000, mother/guardian's age and years of schooling. The sample consists of those who started their first job at or above age 15. The omitted case is the occupation category 9, and the estimation does not include the category 0 in the Philippine occupation code system (the number of observations was only five in our sample). The results show that years of schooling significantly increase the likelihood of choosing occupations that require relatively high skills (categories 1 to 5). Females tend to choose occupations 4 and 5 (including housekeepers), but less so in the categories 6 to 8. Educational attainment appears to be an important determinant of the first entry point.

Figure 5 plots the relationship between the first and second occupations, measured by the 1 digit occupation codes. The transition path is differentiated by mathematics and English test scores. To predict transition paths, I used non-parametric smoothing. Interestingly, both graphs show that those who started from occupations in 1 to 4 and scored high in grade 6 tend to upgrade their occupations (i.e., occupation codes become smaller). Figure 6 show transitions from the first to the current occupations among those who have ever changed their jobs. Even in this longer dynamics, test scores (as a measure of ability) positively influence occupation upgrading if they entered in the occupations 1 to 4. Good academic performers in grade 6 have a higher tendency to upgrade their occupations, which also increases their wages along their occupational upgrading.

# **5. Empirical Results**

# **5.1 Returns to Schooling and Experience**

This section summarizes the empirical results. In Table 7, I use log earnings of the first and latest jobs as dependent variables. Column 1 shows a benchmark estimate of returns to schooling at the first job, 0.0862. Age is that of the time when students started the first job. In Column 2, tenure of the first job (measured in years, including both completed and incomplete cases) is used in addition to age started in the first job. Note that since by construct, we only observe the average wage for each job (position), the tenure effect could be possibly underestimated, which may offset the usual upward bias in the cross sectional estimates. The estimate of 0.0196 is about 20% of returns to schooling.

Columns 3 and 4 use the latest job. In Column 3, I use current age and its square term to represent experience effects. Signs of the estimates are consistent with the existing literature, i.e., the effect is increasing and concave. Column 4 explicitly uses experience in years (defined in the previous section). Returns to experience is 0.0408, which is more than twice as large as that of the first work tenure, and slightly smaller than a half of returns to schooling. In other words, potential gain from accumulating labor market experience is large.

The above estimates are, however, subject to bias due to unobserved error components that cannot be wiped out in the cross sectional analysis. Next, I conduct a panel analysis of wage growth to resolve the above problem.

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