on dubious assumptions.<sup>2</sup> Because of the small sample size, the estimated effects of vocational education in this study are insufficiently precise to rule out large returns.<sup>3</sup> Finally, the analysis does not distinguish between men and women, despite important gender differences in the vocational education curriculum and labor force participation rates.

The mixed conclusions of past studies have contributed to a contentious debate on the validity of standard regression estimates, given that selection of students into vocational and general tracks is not random. Attributes that could influence whether a student chooses one track over the other include scholastic ability, parental education, and location of residence. Failure to control for these variables likely confounds estimates of the returns to vocational education. In developing countries, access to data on these attributes is rare. Although many studies attempt to correct for non-random selection into work, we know of only two studies that address the role of unobserved determinants of school type.<sup>4</sup> In this paper, we use a rich longitudinal household survey from Indonesia to evaluate the outcome of vocational high school graduates relative to general school graduates along four dimensions: earnings, labor market participation, risk of unemployment, and job quality. Unlike the Indonesian labor force survey, retrospective information is available for all levels of school attendance. In addition, the data contain a rich set of control variables that allow us to control for non-random selection more carefully than the vast majority of existing studies. This is important because unobserved determinants of school type may confound the estimates, both through spurious correlations to outcomes and, for some measures, through correlation to the probability of employment. The set of control variables include the district where a person graduated from junior high school, whether they lived in a city, town, or village at age twelve, grade repetition and outside employment during elementary and junior high school, adult height, and the level of parental education. Junior high exit exam scores are not included as a control variable, because they are only available for the youngest cohort.

> 2 The Heckman selection equation is identified by excluding parental education, lagged household income, and junior high test score from the earnings equation.

> 3 In the OLS estimates, the 95 percent confidence interval ranges from 0 to 60 percent of average earnings, while in the IV estimates, the 95 percent confidence interval ranges from -50 to 150 percent of average earnings.

> 4 The one study that uses a plausibly exogenous source of variation in vocational school attendance is Malamud and Pop-Eleches (2008), which employs a regression discontinuity design to evaluate a 1973 policy that promoted general education in Romania. Chen (2009) uses the proportion of schools reported by village households that are vocational as an instrument for school type. This technique is intended to mitigate the estimated vocational penalty on test scores, assuming that vocational students are less academically able in unobserved ways. The use of this instrument, however, nearly triples the estimated negative effect of vocational school on test scores, suggesting that the instrument is negatively correlated to unobserved determinants of test scores and is therefore not valid. Other studies control for observables (Kahyarara and Teal (2008), and Lechner (2000)), or model selection into work rather than school type (El-Hamidi (2006) and Moenjak and Worswick (2003)). In a review of several prominent studies between 1980s and 1990s, Bennell (1996) criticizes many studies’ failure to correct for bias due to choice of school type and participation in work.

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