opportunity cost (labor market wage).<sup>7</sup> Our unique contribution is two-fold. First, we analyze long-term impacts of the school intervention on subsequent human capital and labor market outcomes. In the developing country context, when assessing impacts of an education intervention, the literature only uses outcomes measured only in education cycles (for example, change in test score at elementary school education), but we go beyond the school education cycles to investigate post-school outcomes such as labor market earnings.<sup>8</sup> The analysis required a unique tracking survey of students who moved out of their original school areas. Second, we show that impacts of an education intervention can depend on the features of labor markets. On this issue, we describe more details below.

This paper pays particular attention to gender, given the fact that females have traditionally been more educated in the Philippines. We focus on the gender gap in returns to schooling as a source of differentiation in the impacts of school quality improvement.<sup>9</sup> Figure 1.1 displays a clear difference in the returns to schooling for females and males, based on our estimation using the October 2009 round of the Philippine Labor Force Survey. Females experience higher (marginal) returns to schooling (that is, the slope of wage profile), especially above high school completion. The return function is steeper for females than males, which creates a greater incentive for females to study.<sup>10</sup> 7 Moreover, the program can have general equilibrium effects. When school quality improves, more children will be enrolled and the dropout rate also decreases, which alters the distribution of student abilities in the school. If low-quality students start to enroll, the average performance of students in school can be worsened. Therefore, the total effect of the intervention can be either positive or negative. 8 See, e.g., Hanushek (2006), Glewwe and Kremer (2006) and Speakman and Welch (2006).

9 See Sakelariou (2004), Schady (2003), Lanzona (1998), and Yamauchi (2005) for existing estimates of returns to schooling in the Philippines. Sakelariou (2004) decomposes gender wage gaps. Both Schady (2003) and Yamauchi (2005) report convexity in the return structure. Yamauchi (2005) shows that higher returns to private school education are spurious in the sense that high-ability students are screened into private schools. Lanzona (1998) points out the importance of migration selectivity. Orbeta (2002) summarizes observations on labor force participation and education in the Philippines.

10 First, Aslam (2009) reports a similar finding on gender gap in returns to schooling in Pakistan. Returns to schooling for females are significantly larger than that for males. On estimation issues of returns to schooling, see also Card (1999, 2001). Second, some studies examined the effects of returns on schooling investments by explicitly incorporating agent’s learning and perceptions. Yamauchi (2007) used a change in returns to schooling due to a new technological opportunity to test social learning in schooling investment decisions. Anderson, King, and Wang (2003) examined inter-generational effects of mother’s returns to schooling on child schooling investments. More recently, Jensen (2010) showed that students at randomly selected schools given information on the higher measured returns completed more years of schooling (given that the perceived returns were lower than the actual market returns).

3