Ability, Parental Valuation of Education, and the High School Dropout Decision

Abstract

We use a large, rich Canadian micro-level dataset to examine the channels through which family socio-economic status and unobservable characteristics affect children's decisions to drop out of high school. First, we document the strength of observable socio-economic factors: our data suggest that teenage boys with two parents who are themselves high school dropouts have a 16% chance of dropping out, compared to a dropout rate of less than 1% for boys whose parents both have a university degree. We examine the channels through which this socio-economic gradient arises using an extended version of the factor model set out in Carneiro, Hansen, and Heckman (2003). Specifically, we consider the impact of cognitive and non-cognitive ability and the value that parents place on education. Our results support three main conclusions. First, cognitive ability at age 15 has a substantial impact on dropping out. Second, parental valuation of education has an impact of approximately the same size as cognitive ability effects for medium and low ability teenagers. A low ability teenager has a probability of dropping out of approximately .03 if his parents place a high value on education but .36 if their education valuation is low. Third, parental education has no direct effect on dropping out once we control for ability and parental valuation of education. Our results point to the importance of whatever determines ability at age 15 (including, potentially, early childhood interventions) and of parental valuation of education during the teenage years. We also make a small methodological contribution by extending the standard factor based estimator to allow a non-linear relationship between the factors and a covariate of interest. We show that allowing for non-linearities has a substantial impact on estimated effects.

How Skills and Parental Valuation of Education Influence Human Capital Acquisition and Early Labor Market Return to Human Capital in Canada

“…16 Specifically, as described in further detail in the Estimation subsection 3,4 below, we use the result of Ferguson (1983) that a mixture of normal distributions can approximate any distribution. For each of the three competencies, we assume that the given competency is a mixture of two Normal distributions and estimate the standard deviation of each distribution 15 Our measurement equations differ sharply from Foley et al (2014) and Foley (forthcoming) who are just identified, and we overlap with using the PISA reading score, parental question on highest educational attainment and question on the child's perspective of the amount of education their parent hopes they complete. While Foley et al (2014) and Foley (forthcoming) allow for more correlations between the factors across the measurement equation, this relaxation comes at the cost of imposing additional strong covariance restrictions between the residuals in the system of measurement equations for identification.…”

“…For each of the three competencies, we assume that the given competency is a mixture of two Normal distributions and estimate the standard deviation of each distribution 15 Our measurement equations differ sharply from Foley et al (2014) and Foley (forthcoming) who are just identified, and we overlap with using the PISA reading score, parental question on highest educational attainment and question on the child's perspective of the amount of education their parent hopes they complete. While Foley et al (2014) and Foley (forthcoming) allow for more correlations between the factors across the measurement equation, this relaxation comes at the cost of imposing additional strong covariance restrictions between the residuals in the system of measurement equations for identification. For completeness, the variables used as outcomes in the measurement equation system that differ from our own study include self-reported high school GPA, parental savings for child education, and responses to different questions related to effort spent on homework for non-cognitive skills.…”

“…Foley et al (2014) andFoley (forthcoming) also estimate a three-factor model to respectively understand high school dropout decision for males and the gender gap in university enrollment in Canada. Beyond the different outcomes considered, the second estimation step in these papers includes a single choice equation and does not consider potential outcomes.…”

Stepping Stone and Option Value in a Model of Postsecondary Education

“…Another way wealth can affect the optimal policy of students is by introducing credit constraints. In my model, students do not face a credit constraint motivated by the evidence in Heckman, Lochner, and Taber (1998), Cameron and Heckman (2001), Keane and Wolpin (2001), Cameron and Taber (2004), Foley, Gallipoli, andGreen (2009), andNielsen, Sorensen, andTaber (2010), which find no (or little) effect of credit constraints in shaping postsecondary enrollment, and Stinebrickner and Stinebrickner (2008), which finds no effect of credit constraints in explaining dropout behavior. 8 In the end, the combination of exponential utility function and no credit constraints simplifies the model considerably.…”

Schools, ability, and the socioeconomic gradient in education choices

“…Further, Belley et al (2009) show that the impact of parental income on high school completion is lower in Canada than in the US. A recent paper by Foley et al (2009) finds that the socioeconomic gradient of high school dropout propensity in Canada is considerably reduced when controlling for cognitive ability at age 15. In particular, adding a measure of parents value placed on education effectively removes the direct impact of parental education on high school completion.…”