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The effects of an information beyond university enrolment: A large-scale field experiment on the choices of high school students

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Abstract

This paper presents a large-scale field experiment assessing the impact of an intervention providing evidence-based information about costs and returns to higher education. Treatment impacts are evaluated through university enrolment, choice of field of study, and performance either at university or in the labour market. Thanks to the large sample size, treatment effects can also be assessed for subgroups (by gender and parental education). We find that treated females from high-educated families chose more economically rewarding fields of study, while treated males from low-educated families were more likely to enter the labour market. Although not necessarily in line with policy goals, choices induced by additional information were not detrimental to students’ opportunities, as treated students displayed a similar academic performance and higher employment rates.

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... The literature has identified nine factors for IPT selection. The first factor is cost, which refers to education costs, study aids and cost of living [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The second is program-specific factors such as programmes offered, accreditation body, study duration, program content and structure [11,[21][22][23]34]. ...
... The first factor is cost, which refers to education costs, study aids and cost of living [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The second is program-specific factors such as programmes offered, accreditation body, study duration, program content and structure [11,[21][22][23]34]. Reputation is another factor considered, which includes image, reputation, and links with foreign universities [6,11,[17][18][19][20][21][22]24,26]. ...
... The second is program-specific factors such as programmes offered, accreditation body, study duration, program content and structure [11,[21][22][23]34]. Reputation is another factor considered, which includes image, reputation, and links with foreign universities [6,11,[17][18][19][20][21][22]24,26]. Next is social factors such as input from family members, peers and social media [6][7][8][9][16][17]19,21,[25][26]. ...
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Based on a growing body of experimental and other studies, two recent economics survey articles claim to find “strong evidence” that women are “fundamental[ly]” more risk-averse than men. Yet, much of the literature fails to clearly distinguish between differences that hold at the individual level (categorical differences between men and women) and patterns that appear only at the aggregate level (statistically detectable differences in men's and women's distributions, such as different means). There is a resulting problem of possible misinterpretation, as well as a dearth of appropriate attention to substantive significance. Additionally, one of the two surveys suffers from problems of statistical validity, possibly due to confirmation bias. Applying appropriate, expanded statistical techniques to the same data, this study finds substantial similarity and overlap between the distributions of men and women in risk taking, and a difference in means that is not substantively large.
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In this paper we seek to provide an explanation of three widely documented empirical phenomena. These are: (i) increasing educational participation rates; (ii) little change in class differentials in these rates; and (iii) a recent and very rapid erosion of gender differentials in educational attainment levels. We develop a formal mathematical model, using a rational action approach and drawing on earlier work that seeks to explain these three trends as the product of individual decisions made in the light of the resources available to, and the constraints facing, individual pupils and their families. The model represents children and their families as acting rationally, i.e. as choosing among the different educational options available to them on the basis of evaluations of their costs and benefits and of the perceived probabilities of more or less successful outcomes. It then accounts for stability, or change, in the educational differentials that ensue by reference to a quite limited range of situational features. So, both class and gender differences in patterns of educational decisions are explained as the consequence of differences in resources and constraints. We do not, therefore, invoke 'cultural' or 'normative' differences between classes or genders to account for why they differ in their typical educational decisions (though we have something to say about the role of norms in such an account). Because the model is presented mathematically, testable corollaries are easy to derive as are other implications of our model for patterns of relevant behaviour.
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Existing empirical work looking at the effects of parental income on IQ, schooling, wealth, race, and personality is only able to explain about half of the observed intergenerational income elasticity. This paper provides a possible behavioral explanation for this elasticity in which heterogeneous agents in sequential generations choose their education levels in the face of loss-averse preferences and weak borrowing constraints. These borrowing-constrained agents make education investment choices in part to avoid consumption losses rather than to maximize lifetime resources. The model generates a positive intergenerational income elasticity even when there are functioning capital markets to finance education investments. I find empirical support for the J-shape education decision rule generated by the model and show that it is mostly successful in matching the asymmetric intergenerational transition rates between income quintiles of white families.
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This paper constructs a simulation approach to estimate the lifetime returns to various college majors. I use data from the 1979 cohort of the National Longitudinal Survey of Youth and American Community Survey to estimate the parameters which form the backbone of the simulation. I address selection into both higher education and specific major categories using measures of cognitive and noncognitive ability. Additionally, I present the lifetime premia under various assumptions regarding the magnitude of unobservable sorting. I find substantial heterogeneity in the returns to each educational outcome, ranging from 700,000forArts/Humanitiesmajorsto700,000 for Arts/Humanities majors to 1.5 million for Science Technology Engineering or Math (STEM) graduates (each premium is relative to high school graduates with no college experience). The differentials are larger when search behavior (allowing for differential unemployment probabilities across majors) is taken into account. Finally, I estimate the major premia separately across three birth cohorts to account for the changing nature of selection into both college and majors over time.
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Recent studies have shown that only about two-thirds of the students from poor, rural areas in China finish junior high school and enter high school. One factor that may be behind the low rates of high school attendance is that students may be misinformed about the returns to schooling or lack career planning skills. We therefore conduct a cluster-randomized controlled trial (RCT) using a sample of 131 junior high schools and more than 12,000 students to test the effects of providing information on returns or career planning skills on student dropout, academic achievement and plans to go to high school. Contrary to previous studies, we find that information does not have significant effects on student outcomes. Unlike information, counseling does have an effect. However, the effect is somewhat surprising. Our findings suggest that counseling increases dropouts and seems to lower academic achievement. In our analysis of the causal chain, we conclude that financial constraints and the poor quality of education in junior high schools in poor, rural areas (the venue of the study) may be contributing to the absence of positive impacts on student outcomes from information and counseling. The negative effects of counseling on dropout may also be due to the high and growing wages for unskilled labor (high opportunity costs) in China’s transitioning economy. It is possible that when our counseling curriculum informed the students about the reality of how difficult were the requirements for entering academic high school, it may have induced them to revise their benefit-cost calculations and come to the realization that they are better off dropping out and/or working less hard in school.
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In this paper we study the effects of educational reforms on school attainment. We construct a dataset of relevant reforms that occurred at the national level over the last century, and match individual information from 24 European countries to the most likely set-up faced when individual educational choices were undertaken. Our identification strategy relies on temporal and geographical variations in the institutional arrangements, controlling for time/country fixed effects, as well as for country specific time trend. By characterizing each group of reforms for their impact on mean years of education, educational inequality and intergenerational persistence, we show an ideal policy menu which has been available to policymakers. We distinguish between groups of policies that are either ‘inclusive’ or ‘selective’, depending on their diminishing or augmenting impact on inequality and persistence. Finally, we correlate these reform measures to political coalitions prevailing in parliament, finding support for the idea that left-wing parties support reforms that are inclusive, while right-wing parties prefer selective ones. — Michela Braga, Daniele Checchi and Elena Meschi
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High school students from disadvantaged high schools in Toronto were invited to take two surveys, about three weeks apart. Half of the students taking the first survey were also shown a 3 minute video about the benefits of post secondary education (PSE) and invited to try out a financial-aid calculator. Most students' perceived returns to PSE were high, even among those not expecting to continue. Those exposed to the video, especially those initially unsure about their own educational attainment, reported significantly higher expected returns, lower concerns about costs, and expressed greater likelihood of PSE attainment.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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In their widely cited study, Shavit and Blossfeld report stability of socioeconomic inequalities in educational attainment over much of the 20th century in 11 out of 13 countries. This article outlines reasons why one might expect to find declining class inequalities in educational attainment, and, using a large data set, the authors analyze educational inequality among cohorts born in the first two-thirds of the 20th century in eight European countries. They find, as expected, a widespread decline in educational inequality between students coming from different social origins. Their results are robust to other possible choices of method and variables, and the authors offer some explanations of why their findings contradict Shavit and Blossfeld's conclusions.
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Early school leaving, despite being quite common in Italy, has not been studied much. There is a lack of data and quantitative research concerning its pattern over time, and its relation to social stratification and inequality. The paper contributes to this topic using the first two waves of the ILFI data (1997, 1999), which contain detailed informations on both educational and occupational careers of a representative sample of Italians. This makes it possible to describe the pattern over time of early school leaving in Italy for each level of schooling. The main aim of the paper is, however, to integrate early leaving in the typical research questions concerning the association between social class of origin, education and occupational attainment. The general problem is: what happens if education is measured taking early leaving into account? More in detail, two research questions are asked: a) does the association among class of origin and education found measuring education according to the highest title achieved still hold when early leaving are considered? in other words: is school leaving class-biased? b) does the picture of the transition from school to work change when early leaving is taken into account? how do early leavers perform in the labour market, with respect to both those who do not even start and those who complete a given educational level?
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Growing concerns about low awareness and take-up rates for government support programs like college financial aid have spurred calls to simplify the application process and enhance visibility. We present results from a randomized field experiment in which low-income individuals receiving tax preparation help were also offered immediate assistance and a streamlined process to complete the Free Application for Federal Student Aid (FAFSA) for themselves or their children. Treated participants were also provided with aid estimates that were compared against tuition cost amounts for nearby colleges. The combined assistance and information treatment substantially increased FAFSA submissions and ultimately the likelihood of college attendance, persistence, and aid receipt. In particular, high school seniors whose parents received the treatment were 8 percentage points more likely to have completed two years of college, going from 28% to 36%, during the first three years following the experiment. Families who received aid information but no assistance with the FAFSA did not experience improved outcomes. The findings suggest many other opportunities for using personal assistance to increase participation in programs that require filling out forms to become eligible. JEL Codes: I2, H4, J24. Copyright 2012, Oxford University Press.
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In this article, we analyze gender differences in college major selection for respondents to the Education Longitudinal Study (2002-2006), focusing on educational pathways through college that lead to science, engineering, or doctoral-track medicine occupations and to non-doctoral track clinical and health sciences occupations. We show that gender differences in college major selection remain substantial, even for a cohort in which rates of enrollment in postsecondary education are more than ten percent higher for young women than for young men. Consistent with other recent research, we demonstrate that neither gender differences in work-family goals nor in academic preparation explain a substantial portion of these differences. However, the occupational plans of high school seniors are strong predictors of initial college major selection, a finding that is revealed only when occupational plans are measured with sufficient detail, here by using the verbatim responses of students. We also find that the association between occupational plans and college major selection is not attributable to work-family orientation or academic preparation. Finally, we find gender differences in the associations between occupational plans and college major selection that are consistent with prior research on STEM attrition, as well as with the claim that attrition also affects the selection of majors that are gateways into doctoral-track medicine. We discuss the implications of the predictive power of occupational plans formed in adolescence for understanding sex segregation and for policies intended to create a gender-balanced STEM and doctoral-level medical workforce.
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In a period of expanding higher education, the field of study becomes a key determinant of university graduates' labour market success. In this paper, by means of multivariate analyses of the quality of graduates' early employment outcomes, we first describe how the effect of different fields of study on the university-to-work transition changed between 1995 and 2004 in Italy. Second, we put forward some alternative hypotheses to interpret the changes observed over time and assess which ones seem to be more consistent with the data.