worker who switched from line A to B and experienced a large change of products produced or the nature of production process in which he/she was engaged, a worker transferred from factory A (location a) to B (location b), and a teenager who started working as a home helper and then found a new job promoting sales at a store. All these were recoded as separate jobs. In each case, a 4-digit occupation code was assigned. A great advantage of the above data is their ability to analyze the wage dynamics associated with work changes, especially since adolescents and young adults frequently change their work (supported by our sample as well as the literature). As described in Section 3, the average observed tenure in this group is 1.53 years (including cases where the employee remains at work).

The empirical analysis shows that, first, returns to experience are relatively large among adolescents. Second, returns to experience are higher if educational attainment and/or test score is higher. Third, ability, measured by test scores, positively influences the upgrading of occupations toward more skilled categories, although educational attainment plays an important role in determining the first occupation. The complementarity between schooling and experience is greater among good performers who showed high test scores; education and ability augment gains from accumulating labor market experiences.

The paper is organized as follows. Sections 2 and 3 describe the data and empirical framework, respectively. Empirical findings are summarized in Sections 4 and 5.

# **2. Data: Student Tracking Survey**

This section describes the data used in our analysis. The data come from the survey conducted in eight education divisions in the Philippines from July 2010 through April 2011. The survey aimed to gather household and individual data to assess the impact of a large school-based intervention implemented in 23 relatively poor divisions in the period 2001 to 2006.<sup>10</sup> For this objective, the survey includes 4 intervention and 4 non-intervention divisions. Each intervention division is paired with an adjacent nonintervention division in the same area so that the pair shares similar socioeconomic conditions. In the above method, our sample is grouped into four areas: (i) Ifugao and Neuva Vizcaya, (ii) Antique and Iloilo, (iii) Negros Oriental and Cebu, and (iv) Leyte and Western Samar. Figure 1 maps the 8 sample provinces in the Philippines.

The following three steps were followed: First, relatively poor municipalities (school districts) in each division were chosen. Municipalities of census-2000 income classes ranked 3 to 5 (where the highest income is rank 1 and the poorest is rank 6) were chosen from the adjacent area (near the division border) of an intervention and a non-intervention division (see Republic of the Philippines, 2001). In one intervention division, all of our school districts were taken from income classes ranked 4 and 5, which created an imbalance with the paired non-intervention division where some of the school districts are ranked 3.

Second, schools were randomly sampled from the list of elementary schools in school year (SY) 2002/03 satisfying three criteria: (i) total enrollment larger than 120, (ii) mono-grade (at least one class for each grade), and (iii) complete (having grades 1 to 6). That is, schools had an average of at least 20 students in

> 10 See Yamauchi and Liu (2013, 2014) for details on the school intervention.

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