Research

Subjective performance evaluations play a prevalent role across many parts of society, despite concerns regarding bias. Consistently measuring the extent of bias with such evaluations is inherently difficult. We propose a new approach to tackle this longstanding question in a setting where teachers were required to assign students both grades and rankings within each grade. By exploiting the discrete nature of these evaluations, we compare the concentration of student types of adjacent students either side of grade boundaries. Through an application of a local randomization approach, we establish systematic bias favoring higher income female students. We then establish that these grading decisions carry real consequences: students just above the grade threshold are significantly more likely to attend university and secure their first-choice degree.

Recent work has highlighted the significant variation in returns to higher education across individuals. I develop a novel methodology --- exploiting recent advances in the identification of mixture models --- which groups individuals according to their prior ability and estimates the wage returns to a university degree by group, and show that the model is non-parametrically identified. Applying the method to data from a UK cohort study, the findings reflect recent evidence that skills and ability are multidimensional. The flexible model allows the returns to university to vary across the (multi-dimensional) ability distribution, a flexibility missing from commonly used additive models, but which I show is empirically important. Returns are generally increasing in ability for both men and women, but vary non-monotonically across the ability distribution.

Inequality in elite college attendance is a key driver of intergenerational mobility. This paper shifts the focus upstream to examine how elite high school attendance-specifically, enrollment in UK private, fee-paying schools-shapes university destinations across the academic ability distribution. Using linked administrative data, we show that the main advantage conferred by private schools is not that their highachieving students are more likely to access elite degree courses, but rather that their lower-achieving students are more likely to overmatch by attending more selective degree courses than might be expected given their grades. In particular, we show that lower attaining pupils from fee-paying high schools enrol in university courses around 15 percentiles higher ranked than similarly qualified state school students. The greater propensity of private school students to overmatch is driven largely by differences in application behavior, with even the weakest private school students aiming higher than their higher achieving state school peers.

We develop a finite-mixture framework for nonparametric difference-in-differences analysis with unobserved heterogeneity correlating treatment and outcome. Our framework includes an instrumental variable for the treatment, and we prove non- parametric identification. We can thus relax the single index and stationarity as- sumptions of Athey and Imbens (2006) at the cost of adding slightly more structure on unobserved heterogeneity. We apply our framework to evaluate the effect of on-the-job training on wages, using novel French linked employee-employer data. Estimating a parametric version of our model with the help of an EM-algorithm, we find small ATEs and ATTs on hourly wages, around 4% in the year of training, falling to under 2% in the following year.

Why do some people choose to attend university, and enjoy state-subsidised benefits, while others do not? We shed new light on this key issue by comparing and quantifying the roles of earnings and non-pecuniary factors in the educational de- cisions of young people in the UK, exploiting information on young people’s beliefs about the advantages and disadvantages of university. We also investigate changes in these factors over time, and their implications for social mobility. We specify a model of educational choice, explicitly including expectations about earnings, financial, and non-pecuniary factors. Our estimation strategy exploits panel survey data on young people’s expectations about key outcomes both at, and after, university, linked to their realised outcomes. Income maximisation, despite its prevalent role in the literature, is only part of the story: other factors are at least as important as earnings in determining whether someone goes to university. Non-pecuniary factors also play an important role both the SES-gap in educational attainment, and the huge growth in degree attainment between the 1980s and 2010s.