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While it has been shown that university attendance is strongly predicted by parental education, we know very little about why some potential 'first in family' or firstgeneration students make it to university and others do not. This paper looks at the role of non-cognitive skills in the university participation of this disadvantaged group in England. We find that conditional on national, high-stakes exam scores and various measures of socioeconomic background, having higher levels of non-cognitive skills, specifically locus of control, academic self-concept, work ethic, and self-esteem, in adolescence is positively related to intergenerational educational mobility to university. Our results indicate that having higher non-cognitive skills helps potential first in family university students to compensate for their relative disadvantage, and they are especially crucial for boys. The most important channel of this relationship seems to be through educational attainment at the end of compulsory schooling.

The labor market returns to 'first in family' university graduates We examine how first in family (FiF) graduates (those whose parents do not have university degrees) fare on the labor market. We find that among women, FiF graduates earn 7.4% less on average than graduate women whose parents have a university degree. For men, we do not find a FiF wage penalty. A decomposition of the wage difference between FiF and non-FiF graduates reveals two interesting findings. First, two-thirds of the female FiF penalty are explained by certain characteristics, including: having lower attainment in school, attending an elite university, selecting particular degree courses, working in smaller firms, working in jobs that do not require their degree, and motherhood. Second, FiF graduate men also differ in their endowments from non-FiF graduate men; however, FiF men earn higher returns on their endowments than non-FiF men and thus compensate for their relative social disadvantage, while FiF women do not. We also estimate the returns to graduation for potential FiF and non- FiF young people. We find that the wage returns to graduation are not lower among FiF graduates compared to those who match their parents with a degree. The effects of coming from a lower educated family are large and positive for men and large and negative for women in general, irrespective of graduation. We provide some context, offer explanations, and suggest implications of these findings.

While it has been shown that university attendance is strongly predicted by parental education, we know very little about why some potential 'first in family' or first-generation students make it to university and others do not. This paper looks at the role of non-cognitive skills in the university participation of this disadvantaged group in England. We find that conditional on national, high-stakes exam scores and various measures of socioeconomic background, having higher levels of non-cognitive skills, specifically locus of control, academic self-concept, work ethic, and self-esteem, in adolescence is positively related to intergenerational educational mobility to university. Our results indicate that having higher non-cognitive skills helps potential first in family university students to compensate for their relative disadvantage, and they are especially crucial for boys. The most important channel of this relationship seems to be through educational attainment at the end of compulsory schooling.

There has been much interest across the social sciences in the link between young people's socioemotional (non-cognitive) skills and their educational achievement. But much of this research has focused upon the role of the Big Five personality traits. This paper contributes new evidence by examining two inter-related non-cognitive factors that are rarely studied in the literature: ambition and drive. We use unique survey-administrative linked data from England, gathered in the lead-up to high-stakes compulsory school exams, which allow us to control for a rich set of background characteristics, prior educational attainment and, unusually, school fixed effects. Our results illustrate substantial gender and immigrant gaps in young people's ambitiousness, while the evidence for socio-economic differences is more mixed. Conversely, we find a strong socioeconomic gradient in drive, but no gender gap. Both academically ambitious and driven teenagers achieve grades around 0.37 standard deviations above their peers, even controlling for prior academic attainment and school attended.

'Bullshitters' are individuals who claim knowledge or expertise in an area where they actually have little experience or skill. Despite this being a well-known and widespread social phenomenon, relatively few large-scale empirical studies have been conducted into this issue. This paper attempts to fill this gap in the literature by examining teenagers' propensity to claim expertise in three mathematics constructs that do not really exist. Using Programme for International Student Assessment (PISA) data from nine Anglophone countries and over 40,000 young people, we find substantial differences in young people's tendency to bullshit across countries, genders and socio-economic groups. Bullshitters are also found to exhibit high levels of overconfidence and believe they work hard, persevere at tasks, and are popular amongst their peers. Together this provides important new insight into who bullshitters are and the type of survey responses that they provide.

Universities around the world are attempting to increase the diversity of their student population. This includes individuals who are 'first in family' (FiF), those who achieve a university degree, but whose (step) parents did not. We provide the first large scale, quantitative evidence on FiF graduates in England using a nationally representative survey linked to administrative education data. We find that FiF young people make up 18 percent of a recent cohort, comprising nearly two-thirds of all university graduates. Comparing individuals with no parental higher education we show that ethnic minorities and those with higher levels of prior attainment are more likely to experience intergenerational educational mobility and become a FiF. Once at university, those who are FiF are more likely to study Law, Economics and Management and less likely to study other Social Sciences, Arts and Humanities than students whose parents are university graduates. We also find evidence that FiF students are less likely to graduate from elite universities and have a higher probability of dropping out, even after prior educational attainment, individual characteristics and socio-economic status are taken into account.

Universities use 'first in family' or 'first generation' as an indicator to increase the diversity of their student intake, but little is known about whether it is a good indicator of disadvantage. We use nationally representative, longitudinal survey data linked to administrative data from England to provide the first comprehensive analysis of this measure. We employ parametric probability (logit) and non-parametric classification (random forest) models to look at its relative predictive power of university participation and graduation. We find that being first in family is an important barrier to university participation and graduation, over and above other sources of disadvantage. This association seems to operate through the channel of early educational attainment. Our findings indicate that the first in family indicator could be key in efforts to widen participation at universities.

International large-scale assessments such as PISA are increasingly being used to benchmark the academic performance of young people across the world. Yet many of the technicalities underpinning these datasets are miss-understood by applied researchers, who sometimes fail to take into account their complex survey and test designs. The aim of this paper is to generate a better understanding amongst economists about how such databases are created, and what this implies for the empirical methodologies one should or should not apply. In doing so, we explain how some of the modelling strategies preferred by economists is at odds with the design of these studies. In doing so, we hope to generate a better understanding of international large-scale education datasets, and promote better practice in their use. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.

Additional file 1: Table S1. Descriptive statistics for the countries included in the PISA sample. Table S2. Correlations between country-level physical activity outcomes and country-level factors. Table S3. Mean, Median, and SD in physical activity outcomes by country. Table S4. Frequencies of physical activity outcomes by country. Table S5. Tabulations of physical activity outcomes by gender. Table S6. Tabulations of physical activity outcomes by wealth (top and bottom quintile). Table S7. Correlations between socioeconomic disparities in physical activity and income inequality. Figure S1. A flow chart showing derivation of the analytical sample. Figure S2. Gender disparities in adolescents’ (95% CI) physical activity: in school and out of school. Figure S3A. Forest plot of gender differences in physical activity outcomes (vigorous activity outside of school). Figure S3B. Forest plot of gender differences in physical activity outcomes (moderate activity outside of school). Figure S3C: Forest plot of gender differences in physical activity outcomes (in school activity). Figure S4. Socioeconomic (wealth-based) disparities in adolescents’ (95% CI) physical activity: in school and out of school among males. Figure S5. Socioeconomic (wealth-based) disparities in adolescents’ (95% CI) physical activity: in school and out of school among females. Figure S6A. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (vigorous activity outside of school), among males. Figure S6B. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (moderate activity outside of school), among males. Figure S6C. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (in school activity), among males. Figure S7A. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (vigorous activity outside of school), among females. Figure S7B. Forest plot of socioeconomic (wealth) differences in physical activity out-comes (moderate activity outside of school), among females. Figure S7C. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (in school activity), among females.

Additional file 1: Table S1. Descriptive statistics for the countries included in the PISA sample. Table S2. Correlations between country-level physical activity outcomes and country-level factors. Table S3. Mean, Median, and SD in physical activity outcomes by country. Table S4. Frequencies of physical activity outcomes by country. Table S5. Tabulations of physical activity outcomes by gender. Table S6. Tabulations of physical activity outcomes by wealth (top and bottom quintile). Table S7. Correlations between socioeconomic disparities in physical activity and income inequality. Figure S1. A flow chart showing derivation of the analytical sample. Figure S2. Gender disparities in adolescents’ (95% CI) physical activity: in school and out of school. Figure S3A. Forest plot of gender differences in physical activity outcomes (vigorous activity outside of school). Figure S3B. Forest plot of gender differences in physical activity outcomes (moderate activity outside of school). Figure S3C: Forest plot of gender differences in physical activity outcomes (in school activity). Figure S4. Socioeconomic (wealth-based) disparities in adolescents’ (95% CI) physical activity: in school and out of school among males. Figure S5. Socioeconomic (wealth-based) disparities in adolescents’ (95% CI) physical activity: in school and out of school among females. Figure S6A. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (vigorous activity outside of school), among males. Figure S6B. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (moderate activity outside of school), among males. Figure S6C. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (in school activity), among males. Figure S7A. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (vigorous activity outside of school), among females. Figure S7B. Forest plot of socioeconomic (wealth) differences in physical activity out-comes (moderate activity outside of school), among females. Figure S7C. Forest plot of socioeconomic (wealth) differences in physical activity outcomes (in school activity), among females.