# Parental Income and Higher Education: Evidence From France

Cécile Bonneau, Sébastien Grobon

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CES

Centre d'Économie de la Sorbonne
UMR 8174

Parental Income and Higher Education Patterns:
Evidence From France

Cécile BONNEAU, Sébastien GROBON

2025.04
Parental Income and Higher Education: Evidence
From France

Cécile Bonneau* Sébastien Grobon†

First version: January 2022

This version: January 2025

Abstract

This paper offers new stylized facts on inequality of enrollment in higher education according to parental income in France. On average, an increment of 10 percentiles in the parental income distribution is associated with a 5.8 percentage-point increase in the proportion of individuals entering higher education. This degree of inequality is strikingly close to that observed in the United States. We identify potential explanatory factors behind this similar degree of inequality in two markedly different institutional contexts. We then explore one consequence of enrollment disparities, assessing the distribution of spending on higher education.

JEL Codes: H52, I2, J62.

**Keywords:** Higher Education, Parental Income, Expenditures, Human Capital, Resource Allocation, France.<fnref n="1" />

<footnote>*Paris School of Economics (PSE) and Ecole Normale Supérieure de Paris (ENS-PSL). Email: ce-cile.bonneau@psemail.eu. Professional address: 48 Boulevard Jourdan, 75014 PARIS, France.</footnote>

<footnote>†Conseil d'Orientation des Retraites (COR) and Centre d'Economie de la Sorbonne (CES). Email: sebastien.grobon@cor-retraites.fr. Professional address: 20, avenue de Segur, 75007 PARIS, France.</footnote>

<footnote n="1">¹This article previously circulated under the title 'Unequal access to higher education based on parental income: evidence from France.' **Declarations of Interest:** None. **Availability of Data:** Restrictions apply to the availability of these data, which were used under license for this study. Data are available from ADISP (*Archives de Données Issues de la Statistique Publique*) for the main databases (*ENRJ 2014* and *ERFS 2014* surveys), from INSEE (*Institut National de la Statistique et des Etudes Economiques*) for additional variables and from the Ministry of Higher Education and Research (*Direction Générale de l'Enseignement Supérieur et de l'Insertion Professionnelle* (DGE-SIP)) for higher education spending (*Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche* project).</footnote>
# 1 Introduction

Since the 1960s the number of university students has soared throughout the developed world. In France it increased eight-fold from 310,000 in 1960 to 2,551,000 in 2015 (MESRI-SIES, 2021). Is this massive increase in higher education enrollment due to mass schooling actually a sign of equal opportunity? This paper reasserts the persistence of marked inequality in higher education attendance patterns, observing it from a fairly unusual perspective, i.e. according to parental income.

Despite the extensive documentation of inequalities stemming from social origin (e.g. parents' education or occupation) that followed pioneering work in sociology in the 1960s in France (Bourdieu and Passeron, 1964; Boudon, 1973), as well as in the United States (Blau and Duncan, 1967), surprisingly few studies have addressed the strictly economic dimension of these inequalities. This perspective has been adopted in recent studies in the United States (Chetty et al., 2014, 2020), Norway (Bütikofer et al., 2019), and Germany (Dodin et al., 2024) but remains rare in most countries.

Inequality according to parental income provides a continuous measure of the effect of social background, which is more suitable for international comparisons. However, enrollment in higher education is rarely studied in relation to parental income for two main reasons. First, administrative information on parental income is rarely available in the educational data, and self-reported income, which is more commonly available, can bias the estimates significantly (Moore et al., 2000). Second, the specific situation of young adults raises difficult issues of survey design, such as multiple residences and varying dependency statuses with respect to their parents – issues that are much less common among minor children. This concern is important, because unrepresentative samples are subject to homogeneity bias and may underestimate the relationship between parental income and children's outcomes (Solon, 1992).

In this article, we use an innovative survey design.<fnref n="2" /> Young adults are surveyed regardless of their housing situation (parents' home, own apartment, student housing), and parental income is retrieved from tax data. This produces new descriptive estimates of inequalities in higher education attendance by parental income. To our knowledge, this is the first comparison between the United States and a European country of the degree of inequality in enrollment in higher education according to parental income, with a representative sample of the entire population and a reliable measure of parental income. We organize our analysis into three parts.

The first part sets out new statistics on attendance patterns in French higher education by parental income. Following the literature, we use the percentile of pre-tax parental household income<fnref n="3" /> to study relative attendance in higher education. We find substantial differences in

<footnote n="2">²The National Survey of Youth Resources or *Enquête Nationale sur les Ressources des Jeunes*, DREES-INSEE, 2014</footnote>

<footnote n="3">³We rank young adults aged 18-24 in 2014 by parents' pre-tax income. The rank specification tends to be preferred to the log of income as a measure of intergenerational mobility because it yields more stable estimates (Chetty et al., 2014). Deciles are calculated for all individuals aged 18 to 24, even though our main sample consists of those aged 19 to 24. This enables us also to conduct analyses on aspirations among the subset of 18- and 19-year-olds.</footnote>
enrollment as a function of parental income: at the bottom, some 35 percent of 19-24-year-olds enroll, compared with over 90 percent at the top. Enrollment in master's and selective programs is even more unequal. In the bottom 40 percent of the income distribution, under 10 percent of individuals enroll in master's degree programs and under 5 percent in selective programs, as against 50 percent at the very top of the income distribution. We then compare our estimates with those obtained in the United States by Chetty et al. (2020), finding that inequality in France is only marginally less than in the United States, despite the great institutional differences. Specifically, a 10-percentile increase in parental income rank is associated with a 5.8 percentage-point (p.p.) increase in participation in higher education in France and a 6.1 p.p. increase in the United States. However, the functional form of the relationship differs: in the U.S. it is linear, whereas in France the effect of parental income on enrollment is much greater in the upper than in the lower half of the distribution: a 10-percentile increase in rank is associated with a 4.9-p.p. increase in attendance in the lower half of the income distribution and an 8.6-p.p. increase in the upper half.

In the second part of the paper, we examine the differences in institutional context between France and the United States in order to shed light on the surprising similarity in degree of higher education inequality. Descriptive statistics compare the two countries, weighing the hypothesis that the comparable inequality in higher education attendance in the two countries results from a mix of institutional factors that exert opposing influences on educational disparities. The higher cost of higher education and the greater income inequalities in the United States would suggest more pronounced inequality; the apparent paradox of a similar degree of inequality in France may be explained by the strong correlation between parents' socio-economic background and academic performance, as well as France's early tracking into different educational pathways (academic, technological, vocational), which comes in the 10th grade.

In the third section we compare the distribution of spending on higher education in France and in the United States. We also combine our estimates of attendance inequality with new data on higher education spending to assess the redistributive profile of expenditure on higher education in France. While disaggregated data for the most expensive elite programs are unfortunately lacking, we still find substantial differences in higher education spending along the parental income distribution. On average, young adults in the bottom third of the income distribution (percentiles 0-30, or P0-P30) receive around €9,000 worth of investment in higher education over six years of studies, compared with €32,000 in the top 10 percent – broken down into €21,000 of public investment and €11,000 of private investment in tuition fees (paid by parents). In France, attendance disparities are the main factor in public spending inequalities according to parental income; differences in the annual cost and duration of studies have a more limited impact.

Our paper is structured as follows. We begin in Section 2 with a review of the literature. Section 3 describes the institutional structure of the higher education system in France. Section 4
describes the data and the main variables. In Section 5, we present the main results as regards
enrollment in higher education according to parental income. Section 6 develops a comparison
of institutional factors in France and the United States, considering how they might contribute
to the observed similarity in higher education inequalities between the two countries. Section 7
examines the income redistribution through public spending on higher education in relation to
these attendance inequalities. Section 8 concludes.

## 2 Related Literature

Our paper builds on several strands of the literature. First, it relates to the extensive work
on intergenerational mobility reviewed by Black and Devereux (2011). Broad and equitable
enrollment in higher education can be seen as a corollary to Rawls' theory of justice (Rawls,
1971) and his second principle, equality of opportunity, which means that individuals' place in
the social hierarchy is not fixed by birth. To date, there is no consensus on how much social
mobility there is in France compared to other countries.<fnref n="4" /> Given this ambiguity, it is crucial to
see how enrollment in higher education – a known driver of social mobility (Blau and Duncan,
1967; Chetty et al., 2020) – varies with parental income in France relative to other countries.

Our paper is more directly related to the literature on the elasticity of educational attainment with respect to parental income (Taubman, 1989; Haveman and Wolfe, 1995; Mayer, 1997; Blau, 1999).<fnref n="5" /> Our estimate of the impact of parental income on enrollment in higher education in France contributes in two ways. First, the great bulk of work on this issue refers to the U.S., and in most European countries the effect of income on university attendance has not been assessed. Yet the magnitude of the effect is likely to depend, at least in part, on country-specific institutional factors such as the cost of higher education, the diversity of higher education programs, the diffusion of information on the returns to higher education, public aid, or the relationship between students' academic performance and their parents' educational attainment. Second, our study spans the entire distribution of parental income and applies to a representative sample of all young adults aged 19-24 in France. This makes our results complementary to the efforts to gauge the causal effect of income on educational attainment using an instrument

<footnote n="4">⁴Some studies, like those by Lefranc and Trannoy (2005) and Alesina et al. (2018), suggest that France has a moderate to high level of social mobility, falling between the U.S. and Nordic countries or even paralleling Sweden. Others, however, such as Corak (2013) and Perez-Arce et al. (2016), suggest that France has less social mobility than one might expect, especially in view of its moderate inequality of income. This inconsistency challenges the commonly accepted "Gatsby curve," i.e. the negative relationship between social mobility and income inequality. Recent findings by Kenedi and Sirugue (2023) and Sicsic (2023) also highlight the persistence of income across generations in France, mirroring trends found in the U.S. by Chetty et al. (2014).</footnote>

<footnote n="5">⁵This literature has not yet produced a consensus on the magnitude of this effect. Taubman (1989) finds that the elasticity ranges from 3 to 80 percent, while Mayer (1997) reports that the average effect of a 10 percent increase in parental income on years of schooling ranges from 0.024 to 0.104 years. For France, Ben-Halima et al. (2014), using a parental income variable estimated on the basis of other observable characteristics (father's occupation and education), show that between 1993 and 2003 the effect of father's income on educational outcomes increased. We extend this work, matching a survey with administrative tax data, which allows direct study of the relationship between parental income and children's education, with no need to estimate income.</footnote>
whose validity is by definition local – i.e. at a specific point in the parental income distribution (Shea, 2000; Acemoglu and Pischke, 2001; Maurin, 2002; Løken, 2010; Hilger, 2016).

Our research also contributes to comparative work on how the characteristics of education systems influence educational inequalities in different countries. We focus on France and the United States, whose higher education systems offer stark contrasts. Key differences include the cost of higher education, the proportion of expenses covered by state subsidies, and the volume of student debt. Based on these criteria alone, one might well expect significantly greater inequality in the United States. However, a thorough understanding of the relationship between higher education participation and parental income requires one to consider additional country-specific factors, such as the correlation between parents' socio-economic status and academic performance, the variety of possible educational pathways, and the distribution of educational resources (Causa and Chapuis, 2011; Chmielewski and Reardon, 2016). From a policy perspective, designing a higher education system capable of accommodating the largest possible number of persons is essential to economic growth and productivity in both the medium and long term, as exemplified by the Morrill Act in the United States (Ehrlich et al., 2018). Nevertheless, comparative research in this domain remains scarce.

The stylized facts developed here also contribute to the literature on the mechanisms characterizing the link between parental income and educational attainment. Two main channels have been identified. The first is parents' direct investment in children's education. Traditional models of human capital investment (Becker and Tomes, 1979) emphasize the importance of credit constraints in explaining low-income families' relative absence from higher education. In France, tuition fees in public programs are very low (see Section 3), but there may still be credit or budget constraints related to living costs for students, especially as most universities are located in large cities where the cost of living is high. One must also consider the opportunity cost incurred by students who opt for education over immediate employment. The second channel consists in long-run factors associated with parental income (parents' education and occupation, transmission of aspirations or risk aversion, etc.), including the intergenerational transmission of academic skills or abilities.<fnref n="6" /> Although our data do not allow us to isolate the specific causal pathways through which parental income affects educational outcomes, the comprehensive nature of the survey facilitates a thorough descriptive examination of multiple contributing factors, including prior academic credentials and aspirations. This kind of analysis can shed light on the possible mechanisms that produce similar levels of educational inequalities in France and the United States, despite their very different institutional frameworks.

Lastly, our paper contributes to the literature on the redistributive impact of public spending on higher education. The empirical question of just how regressive this spending is has

<footnote n="6">⁶The importance of such transmission has been confirmed in France by recent studies (OECD, 2018b; Herbaut, 2019; Barhoumi and Caille, 2020). A key point in this literature is the relative importance of nature and nurture in the intergenerational transmission of abilities. In a study of donor children in Denmark, conceived through egg or sperm donation, Rasmussen et al. (2024) find that, net of genetic skill transfers, only the mother's education matters in explaining the school performance of children.</footnote>
been the subject of intense methodological debate since the 1970s. The early empirical studies conducted in the United States starting in 1969 sparked a controversy between Hansen and Weisbrod (1969), who find higher education spending in California to be regressive – individuals at institutions with the least subsidies being less wealthy on average – and Pechman et al. (1970), who claim that higher education spending in California is progressive – wealthy paying more in taxes than they receive in subsidies, compared with the less affluent. The issue of redistribution via in-kind transfers has received renewed attention recently in distributional accounts research (Piketty et al., 2018; André et al., 2023). Table A1 in the Appendix reviews the findings on the redistributivity of educational spending in different institutional settings and based on different methodological choices. Our study updates and extends the work of Albouy et al. (2002) on higher education spending, making several contributions to the literature. First, it addresses the lack of recent results for the U.S. and Europe (Johnson, 2006) and includes an unprecedented comparison of the distribution of higher education spending in France and in the United States. Next, it explores the situation in a European country marked by low tuition fees but strong educational inequality; and it uses data that, for the first time in France, disaggregate the costs of different higher education programs and fields of study.

## 3 Institutional Background

**The French Higher Education System.** The French higher education system is characterized by significant institutional differentiation and academic hierarchy. There are three main tracks: technical and vocational, non-selective academic (university), and selective academic, i.e. preparatory programs and elite graduate schools (Grandes écoles). Among first-year students in 2014-2015, 35% were enrolled in technical and vocational tracks, 45% were on the university track, and 20% were in selective academic tracks or specialized programs (MENESR-DEPP, 2015). The coexistence of two different academic tracks is a specificity of French higher education. Until 2018, university attendance was formally available to anyone holding the high school graduation exam (Baccalauréat).<sup>7</sup> Since then, some programs have introduced selection criteria based on academic performance in high school. However, most university programs remain under-attended, hence in practice not selective: Bechichi et al. (2021) estimated that since 2018, 84% of university programs are non-selective, in the sense that they reject under 5 percent of applicants. Enrollment in elite graduate schools, on the other hand, is highly selective, admission being based on performance in competitive national exams and candidates being required to take an intensive 2-3-year preparatory course (Classes Préparatoires aux Grandes Écoles) for eligibility to take the exams. Admission to these preparatory courses is already selective.<sup>8</sup>

<footnote n="7"><sup>7</sup>Of students with a high school diploma (academic, technical or vocational) 75% enrolled in higher education; among those with an academic diploma, 99%.</footnote>

<footnote n="8"><sup>8</sup>These students account for just 3.4 percent of total enrollment in higher education (MENESR-DEPP, 2015). In 2014-2015, 39% of the students in preparatory courses had passed their high school graduation exam with highest honors – a GPA of 16/20 or better – as against 3% of technical/vocational students and 6% of university students</footnote>
**Tracking.** The stratification of the French education system begins as early as the 10th grade, when students must select either a vocational, a general academic or a technological track. Stratification continues with the choice of majors within the academic or technological track (before 11th grade). The STEM major is the most prestigious and the one that most often leads to selective programs in higher education, while the vocational tracks are the least likely to lead to enrollment in higher education.

**The Financing of Higher Education.** Most French institutions of higher education are publicly funded, with very low tuition fees. In 2014-2015, annual tuition at public universities was €184 for undergraduate students, €256 for master's students, €391 for Ph.D. students, and €610 for most engineering schools.<fnref n="9" /> The private sector accounted for 18% of total enrollment in higher education (MENESR-DEPP, 2015). All general universities are public; the private sector consists mainly of business schools, a third of engineering schools, plus other specialized schools (journalism, paramedical, social, cultural and artistic, etc.). Tuition at business schools generally ranges from €9,000 to €15,000, at private engineering schools around €7,000.

# 4 Data and Definition of Variables

In this section, we describe our data and the construction of our main sample, present summary statistics, and define the key variables and concepts.

## 4.1 The Data

**Survey of Young Adults and Their Parents.** The main dataset used in our work is the 2014 National Survey of Youth Resources (Enquête Nationale sur les Ressources des Jeunes, ENRJ), administered jointly by the Direction de la Recherche, des études, de l'évaluation et des Statistiques (DREES) and the Institut National de la Statistique et des études économiques (INSEE). The ENRJ surveyed a representative subset of young adults aged 18 to 24 and their parents. The sample is smaller than those of studies using administrative or census data, but our data offer the advantage of combining detailed information on young adults' educational background and the resources they have to live on with the reliability of administrative data on parental income, and a representative sample of young adults.<fnref n="10" /> The parental income data in the ENRJ are sourced directly

<footnote>(Bonneau et al., 2021).</footnote>

<footnote n="9">⁹Order of August 12, 2014, setting tuition rates for public higher education institutions under the authority of the ministry of higher education (https://www.legifrance.gouv.fr/loda/id/JORFTEXT000029409760/).</footnote>

<footnote n="10">¹⁰As highlighted by Dodin et al. (2024), administrative datasets, although vast in sample size and capable of linking parents with their children, are often constrained by a paucity of variables, typically lacking information on other aspects of parental background, such as education and occupation. Census data, such as those used by Dodin et al. (2024) and Hilger (2015), partly address these limitations by offering large samples, but with the significant drawback of covering mostly young adults who are still living with their parents. This is a potential bias, especially as regards enrollment in higher education – a crucial juncture when a substantial share of students leave home, departures that are quite likely to be correlated with parental income.</footnote>
from administrative tax records of the surveyed parents. The final sample includes 5,776 young adults and 6,304 parents; in 5,197 cases responses were received from both the young adult and at least one parent. Survey weights are used to compensate for non-response bias.

**Higher Education Expenditure in France.** The final section of our analysis incorporates supplementary data on French spending for higher education. In the national accounts and in earlier studies, these expenditures are typically grouped into just three categories, irrespective of the specific track or field of study (MESRI-SIES, 2021). Our research instead uses additional data from the "Cost Awareness" project.<fnref n="11" /> This project, carried out from 2016 to 2019 at 65 higher education institutions (enrolling about one-third of all French students), provides a more detailed breakdown of costs across various programs and fields. Notably, it allows for calculating the instructional expenditures on higher education as such, net of research costs, which are present in the more highly aggregated data.

We use the main survey information on individuals' courses of study (ENRJ) to match the two data sources at the level of each track and field of study.<fnref n="12" /> The link with the ENRJ survey data also permits one to gauge private investment in higher education through tuition fees paid by parents, a distinction not generally made in the literature on France.

**Higher Education Cost Data in the United States.** For the United States, the data source is IPEDS 2015 (Integrated PostSecondary Education Data System). IPEDS is a comprehensive survey of higher education institutions that covers areas such as college finance, enrollment, admissions, completions, graduation rates, outcomes, and institutional characteristics. To ensure consistency with the French data, which cover only instruction expenditure, we compute per-student instructional expenditure, i.e. total instructional expenditure<fnref n="13" /> over the institution's total fall semester enrollment.

## 4.2 Sample Definition

The main sample includes young adult survey respondents aged 19 to 24 (n=3,947). We exclude 18-year-olds, as individuals in this age group are likely to still be in high school. When the parents are still living together, only one was interviewed and answered questions about their partner.<fnref n="14" /> When parents are separated (26% of the sample), both were interviewed in 46% of the cases and only one in 54%; in the latter case we have no information about the non-responding parent. With regard to educational spending, we intend to calculate spending across entire trajectories in higher education. We accordingly focus on individuals aged 22 to 24, who are

<footnote n="11">¹¹Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux, 2019).</footnote>

<footnote n="12">¹²See Appendix B.2.2.3 for more details on these new data on higher education costs and Table B5 for annual public spending per student, broken down by field of study and track, i.e. the level of disaggregation used here.</footnote>

<footnote n="13">¹³This comprises the expenses of the colleges, schools, departments, and other instructional divisions, covering costs related to general academic instruction, occupational and vocational instruction, community education, preparatory and adult basic education, and regular, special, and extension sessions.</footnote>

<footnote n="14">¹⁴Some 67% of the young adults in the sample have parents still living together. In 68% of these cases, the mother answered the parents' questionnaire.</footnote>
the most advanced in their studies in our sample. For those aged 22 and 23, we infer their educational trajectories up to the age of 24.

## 4.3 Descriptive Statistics

Table 1 presents some descriptive statistics for our sample of young adults, by quintile of parental pre-tax income. On average, they are 21.5 years old, and 49 percent are women. The average pre-tax household income of parents – the sum of father's and mother's household income, divided by two when there are two distinct households – ranges from €9,400 in the bottom quintile to €92,700 in the top quintile, with a mean of €41,900. The parents of students or former students are more affluent, with average pre-tax income of €50,900. A bit over 10 percent of all the parents have a bachelor's or graduate degree, compared with more than 20 percent of the parents of students or former students. The higher income quintiles tend to have a greater proportion of parents of high socio-economic status (SES) and with advanced degrees: more than 55 percent of individuals in the top quintile have a father of high SES, against just 3 percent in the bottom quintile. About two thirds of the parents are still living together and a quarter are separated. In 7 percent of the cases the father is unknown or deceased; in 2 percent, the mother. As to geographic origin, 17 percent of the individuals in our sample have at least one parent living in the Paris region and another 29 percent in another urban center of at least 100,000 inhabitants. The proportion of individuals in the Paris region or other large cities is greater at the top of the income distribution.

A possible concern here is that the top deciles might concentrate the oldest parents and accordingly also the oldest young adults, which would bias the estimate of inequality in enrollment in higher education. But this is not the case: neither the age of the young people nor that of their parents is statistically different across income groups.

[place Table 1 here]

## 4.4 Definitions of the Variables

In this section, we define the key variables used to measure enrollment in higher education by parental income.

**Enrollment in Higher Education.** We define individuals as enrolled in higher education if they meet any of the following criteria in the year of the survey: enrolled in a higher education program, already having obtained a higher education degree, or having reached a higher education level without obtaining the corresponding degree. That is, in order to be considered as enrolled in higher education, a person must have been enrolled for at least one year in a higher education institution. Similarly, we define attendance in a specific program (master's
degree, medical school, selective higher education, etc.) as being currently enrolled in, having graduated from, or having reached some level in one of these programs, without necessarily graduating.

**Selective Higher Education.** We define selective programs as preparatory courses, elite graduate schools, medical school and Ph.D. programs. A program's degree of selectivity can be assessed by ranking all students on the basis of their performance on the national high school graduation exam. In 2016-2017, the median percentile rank of non-medical students in university was 50.2, that of med students was 68.1, that of students in preparatory courses was 84.9, and that of students in elite graduate schools was 72.8 (Bonneau et al., 2021).

**Parent Income.** Following Chetty et al. (2014) and Chetty et al. (2020),<fnref n="15" /> our preferred specification uses the percentile of parental pre-tax income, defined as the sum of the father's pre-tax household income and the mother's pre-tax household income, divided by two when we have both parents' household income.<fnref n="16" /> Parental income retrieved from income tax returns corresponds to individual income (before transfers) from wages, unemployment benefits, pensions, alimony, disability pensions, life annuities, agricultural income, industrial and commercial income, and non-commercial income. Capital income (property income, rent, finance, and securities) is not included. Taking property and capital incomes into account would mainly affect rankings within the top decile, those in the rest of the distribution only marginally.

To further assess the representativeness of our sample, we compare the distribution of pre-tax incomes of parents in the ENRJ sample with that of individuals aged 42-62 in the ERFS survey, which has a much larger sample (n=50,096) and also draws income data from tax sources. Overall, pre-tax incomes are quite similar in the two samples (Table B3). Parents of young adults (ENRJ sample), had slightly higher disposable income and a slightly lower standard of living than the general population (ERFS sample), owing to the presence of children. Table B4 shows that the average parental income in France is lower than in the United States, being $53,000 compared to $97,000. Moreover, the standard deviation for France is considerably smaller, at $46,000, as opposed to $353,000 in the U.S. (all amounts in 2012 dollars).

**Parental Tax Income Versus Self-Reported Income.** Our data show that self-reported income is correlated quite weakly with parental income obtained from tax administrative sources. The deciles obtained with the two datasets coincide for only 34 percent of the young adults, and the correlation coefficient of the two variables is only 0.67. The gap in enrollment in higher education is significantly underestimated when self-reported income is used instead of administrative income – by 5 to 12 percentage points, depending on the specification – due to underestimation of enrollment rates at the top of the distribution.

**Parents' Occupation.** For occupations, we rely on the Department of Education's statisti-

<footnote n="15">¹⁵The authors show that using a rank specification provides much more stable estimates of intergenerational mobility than using the logarithm of parental income and allows for the inclusion of zeros in parental income.</footnote>

<footnote n="16">¹⁶This household measure of income increases with cohabitation; to ensure that this feature does not generate bias, Appendix C.3.2 presents a robustness test of our main results based on the sum of individual parental income – sum of the father’s and mother’s pre-tax income, regardless of marital status.</footnote>
cal service<fnref n="17" /> classification into four socio-economic status groups (SES). High SES encompasses professionals, managers, CEOs, teachers, professionals and artists. Upper-middle SES corresponds to intermediate occupations, technicians, foremen and supervisors. Lower-middle SES refers to farmers, artisans, shopkeepers and white-collar employees. Low SES is defined as blue-collar workers and non-working people. Table B2 in the Appendix details the four SES categories and the corresponding occupations.

**Parents' Diploma.** The data specify the parents' highest academic degree. We define six categories: elementary school certificate or less, middle school certificate, high school diploma or equivalent, two-year university degree, bachelor's or master's degree, and M.D. or Ph.D.

**Socio-Economic Position Index.** For some analyses, we rely on a composite index of economic, social and cultural status (ESCS), based on OECD PISA 2015 and 2018. This index integrates a range of resources available to students, deriving from parents' education and occupations, as well as an index summarizing numerous household possessions that serve as proxies for material wealth or cultural capital (OECD, 2016).

# 5 Unequal Enrollment in Higher Education by Parental Income

This section sets out our main findings on income-related disparities in enrollment in higher education and different programs in France. We next explore the interplay between attendance disparities, which are linked to parental income, and other parental background characteristics, such as education and occupation. Finally, we compare the correlations between parental income and enrollment in higher education in France and in the United States.

## 5.1 Inequalities in Higher Education Enrollment by Parents' Income

The proportion of 19-24-year-olds enrolled in higher education rises sharply with parental income (Figure 1): around 35 percent of those whose parents are in the bottom income quintile (P0-P20) are or have been students, scarcely a third as many as in the top decile – around 90 percent.

[place Figure 1 here]

Enrollment in master's degrees and selective programs – preparatory courses, elite graduate schools, medical school and Ph.D. – is more unequal still (Figure 2). Up to the 5th income decile for master's degrees and the 9th for selective programs, under 10% of individuals enter these studies. On the other side of the distribution, around 50 percent of individuals whose

<footnote n="17">¹⁷Direction de l’évaluation, de la prospective et de la performance (DEPP).</footnote>
parents are in the top 2 percent of the income distribution (P98-P100) reach the master's level or enter a selective program between the ages of 19 and 24. These results confirm the problem of France's essentially dual system of higher education, in which enrollment in the most prestigious programs is strictly dependent on parental income.

[place Figure 2 here]

**Robustness Checks.** A first concern is possible mis-estimation due to the single-year measure of parental income, which could therefore be affected by transitory fluctuations (Solon, 1992). To test this, we use an alternative measure of parental income: the parents' household taxable income. This administrative variable is available in our sample at the household level for years *t* and *t* − 2. We then compare the attendance differential in these two years, as shown in Table C6. Higher education attendance rates by decile of parental household taxable income in year *t* and in year *t* − 2 do differ somewhat, but the difference is never statistically significant. These results are consistent with the finding that the measure of social mobility is practically unaffected by the number of years for which parental income is measured.<fnref n="18" />

A second concern is that the household measure of income, which we use for consistency with Chetty et al. (2014) and Chetty et al. (2020), increases with cohabitation. We accordingly run a robustness check with individual parental income – the sum of the individual incomes of the mother and father, regardless of marital status – in Appendix C.3.2. Figure C1 shows that, overall, the results for enrollment in higher education are quite close to those for our main specification. However, the non-linearity of the effect of parental income, discussed in a subsequent section, is more pronounced when using individual parental income.

## 5.2 Complementarity of Inequalities Related to Parents' Income, Education and Occupation

Enrollment in higher education is often evaluated in relation to social background factors like parents' educational degree or occupation, rather than purely economic criteria. Our analysis delves into the connections among parental income, education, and occupation as they bear on higher education. We document descriptively that a significant portion, though not the entirety, of the higher education gap correlated with parental income actually relates to parents' education or occupation.

The heatmap in Figure 3 highlights disparities associated with parental income and occupation (Panels a and b) as well as education (Panels c and d). It shows a staircase pattern, suggesting links between these factors. Specifically, within a given parental income bracket, one observes an association between higher parental occupation or educational qualifications and

<footnote n="18">¹⁸See Figure III Panel B of Chetty et al. (2014).</footnote>
enrollment in higher education – and the reverse is also true. This pattern is notably evident for parents' occupation. For parents' educational attainment, there are pronounced discontinuities according to the number of years of education. Interestingly, this marked shift occurs at the 14-year education mark, which corresponds to the point where the parents themselves have attained a higher education degree. For instance, 87 percent of individuals whose mother has a higher educational degree – 14 years of education or more – have enrolled in higher education between the ages of 19 and 24, as against 47 percent of those whose mother does not. This observation aligns with a pattern where the children of parents with higher education qualifications are often found in higher education themselves. Notably, this gap is greatest when the observation involves the education level of the mother, in line with some previous findings in the literature (Currie and Moretti, 2003; Sacerdote, 2007; Chevalier et al., 2013; Rasmussen et al., 2024).

[place Figure 3 here]

More formally, we explore the complementarity of the different dimensions of inequality by tracking the regression coefficient of parental income on enrollment in higher education, successively adding various parental background controls (Table 2).<fnref n="19" /> These descriptive regressions do not aim to establish a causal impact of parental income on enrollment but rather to illustrate the complementarity among inequalities stemming from parents' income, occupation, and education. For a given occupation and educational degree, the higher the income percentile, the greater the proportion of individuals enrolling in higher education and different programs. For example, 19-24-year-olds whose mother's highest degree is a high school diploma – 12 years of education or less – have a 36 percent rate of enrollment in higher education when their parents are in the bottom income quintile and 77 percent when they are in the top quintile.<fnref n="20" /> Conversely, for a given level of parental income, the more privileged the parents' occupation or the higher their educational degree, the higher the proportion of children entering higher education.

Our most comprehensive regression controls not only for parents' education and occupation but also for other observable characteristics likely to be linked to participation in higher education: family structure and the size of the place of residence. After controlling for all these factors, a significant residual effect of parental income remains, albeit only at the lower end of the distribution.<fnref n="21" />

[place Table 2 here]

<footnote n="19">¹⁹See table D9 in the Appendix for the details of all the coefficients on the control variables.</footnote>

<footnote n="20">²⁰Results not reported in the table, available upon request.</footnote>

<footnote n="21">²¹This descriptive analysis does not demonstrate a causal effect of parental income on higher education enrollment but simply indicates that the correlation between parental income and higher education enrollment cannot be fully explained by these observable characteristics alone.</footnote>
We have examined how the differences in enrollment in higher education according to parental income overlap with inequalities related to other factors, such as parental education and occupation, but remain significant after controlling for these observable factors. In the next section, we compare inequalities in higher education attendance in France with those in the United States.

## 5.3 Comparison With the United States

Very few works have studied enrollment in higher education in relation to parental income on a nationally representative basis. One exception is Chetty et al. (2020) in the United States, which studies college enrollment between the years individuals turn 19 and when they turn 22, using extensive administrative data. We compare these estimates with those for our sample, which is representative of individuals aged 19-24.

Surprisingly, given the differences in institutional contexts, the inequality appears to be almost as pronounced in France as in the United States (Figure 4). In the U.S., an increase of one decile in parental income is associated with an increase of 6.1 p.p. in the proportion of individuals entering higher education, compared to 5.8 p.p. in France. We cannot reject the hypothesis that the coefficient in France is similar to that in the U.S.<fnref n="22" /> In both countries, the proportion of individuals in higher education at the bottom of the income distribution is around 35 percent, and at the top around 90 percent.

[place Figure 4 here]

**Linearity of the Income Effect.** Although the relationship between parental income and higher education is broadly similar between the two countries, its functional form differs. The link is linear in the United States, but much higher in the upper than in the lower half of the distribution in France, where an increase of one decile in parental income is associated with an increase of 4.6 p.p. in the proportion of young people entering higher education in the lower half of the income distribution and 9.6 p.p. in the upper half. Although attendance rates are quite similar at the two extremes of the income distribution, these non-linearities imply that the enrollment rate is lower in the middle of the distribution in France than in the U.S.

Table 2 shows the non-linear association in France between income and enrollment in greater detail. The rate of college attendance for young people with parents in the top income quintile is 30 percentage points higher than for those in the middle quintile. Conversely, that of those with parents in the bottom quintile is 18 percentage points lower than that of those in the middle quintile. The stronger correlation between parental income and higher education in the upper half of the income distribution is somewhat counterintuitive. If this effect depended on

<footnote n="22">²²The 95% confidence interval for the coefficient of parent's income percentile on enrollment in higher education in France is [5.3 ; 6.3].</footnote>
credit constraints, one would expect larger gaps at the lower end of the spectrum. This suggests that the income gradient in higher education attendance might be linked to other parental characteristics correlated with income. However, when controlling for parental observable characteristics, the strong association between parental income and enrollment is concentrated in the lower half of the income distribution (Table 2, Column 4). This descriptive pattern suggests that financial constraints might play a more significant role for individuals with lower incomes, while disparities linked to income-correlated characteristics could be more prevalent in the upper half of the distribution.

We have reported novel estimates of inequality in higher education enrollment according to parental income in France – using reliable income data from administrative tax sources – and compared our estimate with that found in the United States. We find similar levels of inequality in France and the United States, which may seem surprising given the very different frameworks of higher education in the two countries. In the next section we present some possible explanations.

# 6 Institutional Factors Related to Education Inequality in France and the U.S.

We analyze the institutional differences between higher education in France and the United States, seeking insights into their comparable levels of inequality of enrollment. Specifically, we explore costs, early tracking, and education disparities prior to the higher education level. The evidence suggests that the inequality in higher education enrollment between France and the United States, though quite comparable, actually stems from different institutional factors.

## 6.1 Cost of Studies and Income Inequalities

First we consider institutional factors related to the cost of higher education and income inequalities, which might suggest lesser enrollment disparities in France than in the United States.

First, higher education is much more expensive in the U.S. In 2014-2015, average tuition was $9,139 at public four-year colleges for in-state students and $31,231 at private nonprofit institutions (The College Board, 2014), incomparably more than the fees of between €184 and €610 for most programs in France (see Section 3 for more details).

Second, income inequality is more pronounced in the U.S. than in France.<fnref n="23" /> Greater income inequality means that the top and bottom deciles differ more markedly in the United States. Some studies indicate that countries or regions with greater income inequality often exhibit greater educational or skill inequality as well (Causa and Chapuis, 2011; Duncan et al., 2017;

<footnote n="23">²³Table B4 details parental income distribution in both countries. In 2012 dollars, the standard deviation of parental household income is $353,430 in the U.S. against $45,640 in France. In 2014, the top income decile accounted for 46% of total income in the U.S. and 33% in France (https://wid.world/data/).</footnote>
Workman, 2022).

Finally, income inequality might have a knock-on effect on educational inequality through the returns to higher education. These are greater in the United States – estimated at about 14%, compared to 10% in France (Psacharopoulos and Patrinos, 2018). Given the significant relationship between parents' and children's education, countries with higher returns to education are likely to exhibit a stronger link between parental income and children's educational outcomes when parental education is not controlled for in the estimation<sup>24</sup>

This section addresses a notable paradox: France and the United States exhibit similar levels of income-related educational inequality, despite stark contrasts in their educational and economic landscapes. In the United States, higher education is substantially more expensive, income inequality is greater, and returns to higher education are significantly higher. The next two sections will examine institutional factors in France that could explain its important enrollment inequalities. These include the strong correlation between parents' socio-economic status and offsprings' academic achievement, as well as early tracking.

## 6.2 Unequal Pre-university Academic Performance

The unequal participation in higher education in France likely reflects an accumulation of inequalities from earlier schooling. The correlation between the parents' socio-economic status and PISA results in math, reading, and science at age 15 is stronger in France than in the U.S. (Figure 5 (a)). According to the 2015 PISA survey, 20 percent of the variation in science performance is explained by socio-economic status in France, as against only 11 percent in the United States and an OECD average of 13 percent (OECD, 2018b). France is one of the OECD countries where educational performance is most closely correlated with social background. Socio-economic disparities in academic performance are found in both primary and secondary schools (Barhoumi and Caille, 2020; Barasz et al., 2023).

[place Figure 5 here]

We employ the decomposition method of Das Gupta (1991) to examine the contribution of prior credentials to the higher education attendance gap between the top and bottom income deciles (Table 3).<sup>25</sup> The middle school certification gap accounts for 21 percent of the total higher

<footnote n="24"><sup>24</sup>This discussion focuses solely on the relationship between parental returns to education and children's enrollment in higher education. However, the returns to education for the children as such could have ambiguous effects: significant returns may encourage all individuals to invest more in higher education, and the impact of these returns on inequalities ostensibly depends on (i) their heterogeneity by social origin (if they are stronger for the less affluent, they should decrease enrollment inequalities) and (ii) the awareness of these returns across the income distribution.</footnote>

<footnote n="25"><sup>25</sup>When the outcome is defined as a product of other variables of interest, this method can calculate the contribution of each to the total gap in the outcome variable between two subpopulations. We disaggregate the disparity in higher education participation between students whose parents are at the top of the income distribution (denoted</footnote>
education enrollment disparity; the high school certification gap, 50 percent. Together, these degrees thus explain around two thirds of the gap in higher education attendance; the remaining third is unexplained by prior credentials. In the United States, disparities in high school graduation rates according to parental income are less pronounced. We compare our data with the analysis conducted by Ziol-Guest and Lee (2016) for the U.S.: the proportion of high school graduates in France is substantially lower in the bottom half of the income distribution than in the U.S., by at least 20 percentage points. Furthermore, in France, in the top half of the income distribution the percentage of high school graduates increases with parental income, whereas in the U.S. the proportion of high school graduates remains consistently above 90% throughout the upper half of the distribution.<fnref n="26" />

[place Table 3 here]

Inequalities likely encompass both academic/cognitive skills and social-behavioral/non-cognitive skills. The disparity between students of high and low socio-economic status, especially in terms of self-perceived ability or academic self-efficacy, is more pronounced in France than in the U.S. France also exhibits a considerably lower average perceived self-efficacy score (Figure 5 (b)). France's lag in incorporating social and behavioral skills into its educational system (Algan and Huillery, 2022) might be an aggravating factor in these socio-economic-based inequalities.

## 6.3 Early Tracking and School Segregation

**Early Tracking.** Early tracking in France might further heighten the strong correlation between socio-economic status and academic performance. From the 10th grade onwards, students and their parents have to make key decisions about their school careers. These decisions are closely correlated with socio-economic status: Herbaut et al. (2019) found that 92% of high school students whose parents have a higher education diploma choose the general and technological track over the vocational track. By contrast, only 52% of the students whose parents have just a high school diploma make the same choice. The authors also show that such decisions significantly impact educational and career trajectories. These differences in orientation according to social origin are only partially explained by differences in previous academic achievement:

$$HE^G = MSC^G \times HSC_{MSC}^G \times HE_{HSG}^G$$

<footnote>T) and those with parents at the bottom (denoted B), by examining prior degree attainment as follows:</footnote>

<footnote>Here, $HE^G$ represents the rate of higher education participation for individuals in group G (where $G \in B, T$). $MSC^G$ represents the rate at which individuals in group G attain the middle school certificate. $HSC_{MSC}^G$ indicates the rate of high school certificates among those in group G who have previously obtained a middle school certificate. Lastly, $HE_{HSG}^G$ specifies the rate of higher education participation for those in group G who possess a high school certificate. See Appendix B.2.3 for more details on this decomposition method.</footnote>

<footnote n="26">²⁶Refer to table 2, p.5, in Ziol-Guest and Lee (2016) for detailed data.</footnote>
for the cohort entering 6th grade in 1995, Ichou and Vallet (2012) found that half of the gap in enrollment in high-school academic tracks between individuals from working-class and upper-class backgrounds depended on differences in educational outcomes, but the other half cannot be so explained.

Earlier tracking leading to separate pathways is generally associated with more pronounced educational inequality (Van de Werfhorst, 2019). The "republican elitism" of France (Baudelot and Establet, 2009) stems from the Napoleonic system, in which schools were expected above all to train the nation's elite. Until the 1970s, both secondary and higher education catered mainly to an upper-class minority.<fnref n="27" /> This socio-economic divide persisted until the introduction of the unified middle school in 1975 (collège unique, Merle (2009)).<fnref n="28" /> In the United States, tracking is much less prevalent through high school (Brunello and Checchi, 2007).

Countries with early tracking also tend to exhibit sharper disparities in educational aspirations based on socio-economic status (OECD, 2017). Our sample of young adults in France shows notable variations in educational aspirations along the parental income distribution. Among 18-19-year-olds,<fnref n="29" /> those in the top income decile aim chiefly for advanced degrees (master's or Ph.D.) – nearly 80%, as against only about 35% from the bottom quintile (Figure 6 (a)). The PISA data on the aspirations of high-achieving students indicate that a significantly higher proportion of disadvantaged French than American students do not expect to succeed in tertiary education (Figure 6 (b)). The correlation between lower aspirations and later educational inequalities has been documented in France by Guyon and Huillery (2021).

[place Figure 6 here]

Students on vocational tracks in France are significantly less likely to enroll in higher education than those on academic tracks: in our sample, 97 percent of individuals with an academic high school diploma enroll in some form of higher education, as opposed to 55 percent of individuals with a technical or vocational diploma. Addressing this issue by ensuring that high-ability students from low socio-economic backgrounds have enhanced access to academic tracks in high school could be an effective way of increasing their enrollment in higher education.

**School Segregation.** The wider use of private schools in France also accentuates socio-economic segregation at school (Boutchenik et al., 2021), potentially reinforcing income-based inequalities. In 2018, only 80% of 15-year-old pupils attended public schools in France, compared with 93% in the United States (OECD, 2020). And the socio-economic disparity between

<footnote n="27">²⁷In 1930, fewer than 7% of individuals aged 11-17 pursued education beyond primary school (Isambert-Jamati, 1970)</footnote>

<footnote n="28">²⁸Before then, one educational track was tailored for the upper classes, offering secondary education, while another track, established in the late 19th century, was designed to provide upper primary education for the intermediate classes. Despite the educational reforms of the late 19th century and the introduction of free secondary education in 1930, participation in higher education remained limited.</footnote>

<footnote n="29">²⁹We focus on the youngest in the sample to minimize the influence of completed schooling on aspirations.</footnote>
students in public and private institutions in France has been widening since the early 2000s (Guillerm and Monso, 2022).

In this section we have outlined institutional factors that could explain the similar levels of enrollment inequality in higher education in France and the U.S. In France, the strong correlation between students' academic performance and their parents' socio-economic status could be mitigated by the lower cost of attending higher education, while in the U.S., these factors might interact in the opposite direction. The following section provides a more detailed analysis of the distribution of higher education spending in France and in the U.S.

# 7 Unequal Public Expenditure for Young Adults

In this last section, we analyze the implications of the unequal enrollment in higher education based on parental income by assessing the redistributive profile of higher education expenditure.

First, we analyze the distribution of public higher education spending for all individuals, regardless of parental income. We also compare the annual distribution of higher education instruction expenditures between France and the United States. We then examine in more detail the distribution of higher education spending by parental income in France. This permits the descriptive decomposition of three factors – attendance disparities, duration of studies, and annual costs – in the inequality in public spending for higher education among individuals from the lowest and highest parental income deciles.

Table A1 in the appendix presents a review of the literature on the redistributivity of education spending, bearing on different institutional settings and applying different methodologies. Usually, spending on K-12 education tends to be redistributive and that on higher education regressive. The table shows that the various studies all make crucial methodological choices, such as how to take into account the socio-economic status of parents, whether or not to take taxes into account,<fnref n="30" /> and in a minority of cases the use of a life-cycle rather than cross-section approach.<fnref n="31" /> This also points out the lack of recent estimates as regard the distribution of higher education expenditure for the two countries.

<footnote n="30">³⁰When taxes are not taken into account, the authors generally find that higher education spending is regressive. Conversely, when all the taxes paid by households are taken into account, the authors generally conclude that higher education spending is progressive, because wealthy people pay more in taxes than they receive in subsidies for public higher education.</footnote>

<footnote n="31">³¹Unequal enrollment in higher education is also associated with inequalities in lifetime earnings, which we approximate using the recent estimates of Garbinti et al. (2023) and differences in the degrees attained by young adults aged 22 or over in our sample. This additional implication of unequal enrollment is explored in Appendix D.4.5.</footnote>
## 7.1 Distribution of Spending on Higher Education in France and the United States

We begin by examining the distribution of spending on higher education in France and in the United States, without relating it to parental income. Spending on higher education is highly concentrated in both countries, as illustrated in Figure 7. In both cases, the bottom half of the expenditure distribution is marginal, mainly due to the fact that an important share of individuals do not pursue higher education and thus have zero spending for higher education. In France, the lower 50% of individuals account for 4.1 percent of the total spending, and in the United States for 3.4 percent. However, the concentration of spending at the upper end is more pronounced in the U.S. Specifically, the top 10 percent of individuals who receive the most spending on higher education account for 45 percent of the total expenditure, as against just 28 percent in France. Similarly, the top one percent in the U.S. receives 16 percent of the total spending, against 5 percent in France.

For France, we are able to map out comprehensive higher education trajectories, focusing on individuals aged 22 to 24. This provides insights into the distribution of higher education expenditures over a span of 6 years, from ages 18 to 24. As presented in Figure D2 in the Appendix, approximately 40 percent of individuals – specifically those who do not pursue higher education – accrue no educational expenditures. Only 10 percent incur an educational cost exceeding €30,000 during their studies, but the top 1 percent accrue €71,000 over their 6-year educational journey: 2.4 times the expenditure on those at the 90th percentile and 9 times that of the median, €7,800.

In the next section we go further in analysing public and private higher education expenditures in relation to parental income in France.

[place Figure 7 here]

## 7.2 Inequality in Spending on Higher Education by Parental Income in France

We find significant disparities in educational spending according to parental income (Figure 8). Those in the bottom 50 percent receive €8,000 to €11,000 worth of investment in higher education between the ages of 19 and 24, compared with €32,000 – of which €21,000 in public funds – for those in the top 10 percent (Figure 8a). The differences in private investment in higher education are more pronounced: parents at the bottom of the income distribution pay an average of just €200 in tuition for their child over six years, compared with more than €10,000 for those at the top. The ratio of higher education spending between the top and bottom income deciles (D10/D1) is 4.0:1, while that of public investment alone is 2.7:1. In Appendix D.4.4, we discuss how taking account of parental taxes to fund public higher education would alter these findings.
Another way of gauging inequalities in public spending for higher education is to consider the correlation between the percentile rank of parental income and that of public spending on higher education (Figure D3). On average, a 1-percentile increase in parental income is correlated with a 0.4-percentile gain in the distribution of public spending on higher education. Individuals whose parents are in the lowest decile of pre-tax income are on average in the 30th percentile of public spending for higher education, while those whose parents are in the top decile are in the 68th percentile.

[place Figure 8 here]

## 7.3 Decomposition of Inequalities in Public Spending

The observed disparities in public higher education expenditure according to parental income could stem from three factors: variations in enrollment rates in higher education, unequal duration of studies, and differential spending across distinct higher education programs, given the existing enrollment disparities. The aim of this section is to delineate the impact of these possible determinants.

In France, the inequalities in public spending appear to derive primarily from differences in attendance: when only higher education students are considered, the inequality is much less pronounced (Figure 8b). Students whose parents are in the highest deciles receive only a slightly higher public investment between the ages of 18 and 24 (about €24,000 in the top third, compared to €20,000 in the bottom two thirds). Those at the very top of the distribution, however, still benefit from a much larger private investment in higher education: about €11,000 over 6 years on average in the top 10%, compared with €500 for those at the bottom of the income distribution and between €1,000 and €3,000 in the rest of the distribution.

We use Gupta's rate decomposition method (Das Gupta, 1991) to further decompose public expenditure inequalities between an extensive margin – enrollment in higher education – and an intensive margin related to the duration of studies and differences in annual cost.<fnref n="32" /> Inequalities in enrollment account for 67 percent of the disparity in spending between the top and

$$PS^G = HE^G \times LS_{student}^G \times AC_{student}^G$$

<footnote n="32">³²Although the method was originally designed to be applied to rates, it can also be applied to differences between two groups, as long as the outcome variable is defined as the product of the dependent variables. We decompose differences in public spending on higher education between individuals in the top (T) and bottom (B) income deciles based on differences in enrollment in higher education, duration of study, and spending on each year of higher education. Formally, public spending on higher education for individuals in the G group (where $G \in B, T$)) can be defined as follows:</footnote>

<footnote>with PS^G public spending on higher education for individuals in the G group, HE^G the participation rate in higher education of individuals in the G group, LS_{student}^G the average duration of study of students in the G group, and AC_{student}^G the average annual public cost of students in the G group. See Appendix B.2.3 for more details on this decomposition method.</footnote>
bottom income deciles (Table 4 and Figure D5). The remainder is almost entirely explained by
differences in the duration of studies – students at the bottom of the income distribution pursue
higher education for an average of 3.3 years, compared with 4.5 years for students at the top
(Figure D4).<fnref n="33" /> In our main specification, there is almost no difference in annual public spending
between students whose parents are at the top and the bottom of the income distribution. This
is primarily because, while high-income students are considerably overrepresented in selective
programs (Figure 2), low-income students more often enroll in vocational programs, which also
benefit from much higher endowment than university programs (Table B5).

[place Table 4 here]

**Robustness Check: Matching Richer Individuals with Better-Endowed Programs.** A potential concern with the finding of equal annual public spending for higher education students relates to the limited ability to match students with the specific programs in which they are enrolled, and the lack of per student cost data on the most selective programs, especially elite graduate schools. We can disaggregate the costs at the level of each track and field of study, but not for specific programs.<fnref n="34" /> Consequently, the contribution of enrollment disparities to inequality in public spending might be overestimated.

We simulate a variant of public spending on higher education to predict how a more uneven
distribution of public spending within each track and field might alter this result (Table 4).
Positing that within a particular field of study and track, the most affluent consistently benefit
from the most expensive programs and from the most expensive elite graduate schools (column
2),<fnref n="35" /> the contribution of the attendance gap declines significantly but still accounts for about half
of the total public expenditure difference between the poorest and the richest individuals.

In this section, we have determined that the discrepancies in higher education spending
in France result chiefly from disparities in enrollment rates, not differences in funding across
programs. In contrast, in the United States the disparities appear to be associated mainly with
differences in funding across programs. Bonneau (2020) posits that given the variance in spend-
ing within a particular educational level in the United States, the degree of inequality would
persist nearly unchanged even if everyone reached the same level of educational attainment. To
draw more conclusive indications, further research would be needed for a more comprehensive
comparison between the two countries.

<footnote n="33">³³To match the public spending on higher education of those in the middle-income bracket (P50-P60), the enrollment rate for students from the bottom third of parental income distribution would need to rise by 19 percentage points. This required increase is only slightly greater than the current enrollment gap of 15 percentage points.</footnote>

<footnote n="34">³⁴See Table B5 for annual public spending per student, divided by field of study and track, the level of disaggregation we use.</footnote>

<footnote n="35">³⁵This specification can be seen as an upper bound of inequalities in educational expenditure. See Appendix C.3.3 for more details on the assumptions made in this counterfactual scenario associating the wealthiest individuals with the best-endowed programs.</footnote>
# 8 Conclusion

Using a detailed survey of young adults together with administrative data on their parents' income, this study examines inequality of enrollment in higher education and selective programs according to parental income. On average, a 10-percentile increase in the parental income distribution is associated with a 5.8-p.p. increase in the proportion of individuals enrolling in higher education. The degree of inequality by income in France is very similar to that in the United States despite much lower tuition fees and less income inequality in France. This is a new result, which we explain by reference to the intergenerational transmission of educational outcomes and early tracking in France. In France, spending on higher education is regressive, owing above all to the unequal participation rate, despite the fact that the concentration of resources among higher education institutions is less uneven than in the United States.

Our analysis reveals a particular disparity in France in the prerequisites for higher education, which in policy terms calls for earlier and more robust skill support of low-income students. Specifically, reducing the attendance gap in high school academic tracks according to socio-economic background could be an effective way of enhancing the enrollment of low-income students. Additionally, the significant disparities in enrollment rates for selective programs among high school graduates highlight the importance of giving disadvantaged students targeted, transparent, and clear information about the various programs available and the returns to them. The effectiveness of need-based scholarships (Fack and Grenet, 2015) as well as marked differences in parental transfers among students (Grobon, 2018) indicate a need for more comprehensive need-based scholarships that truly cover the cost of higher education. This need is particularly acute for students in the middle-income bracket, who benefit the least from the combination of public support and parental tax deductions.

Our research identifies a key divide in higher education enrollment inequalities that depend on parental income. In some countries, these inequalities may stem primarily from financial barriers to entry, while in others they could result from earlier disparities in the accumulation of human capital. Applying this perspective to European or developing countries could offer valuable insights. In Europe, a crucial question is how to reconcile the educational divide with the distinctions between social democratic and liberal welfare state regimes as outlined by Willemse and De Beer (2012) and the disparities in social mobility observed between Nordic and liberal countries. For developing countries, this divide should be considered in relation to the level of public funding for higher education systems. Funding could affect income inequalities through an increase in human capital, as discussed by Ehrlich and Kim (2007).

Such international comparison requires that educational data be supplemented by reliable data on parental income. Detailed comparative data on spending differentials in higher education, especially for selective programs, would contribute to the debate by allowing more systematic estimation of the redistribution effected through public expenditure on higher education.
# Figures

<figure><img src="image_3.png" /><figcaption>Figure 1: Proportion of Individuals Enrolling in Higher Education, by Parents' Pre-Tax Income</figcaption></figure>

*Notes*: This figure displays the proportion of individuals aged 19-24 who are currently enrolled, have enrolled in higher education without necessarily obtaining a degree, or already hold a higher education degree, according to their parents' income. The parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. We further decompose the top 10 percent of the distribution (x-axis). The black line represents the standard errors.

<figure><img src="image_4.png" /><figcaption>Figure 2: Proportion of Individuals Enrolling in Master's Degree or Selective Programs, by Parents' Pre-Tax Income</figcaption></figure>

*Notes*: These figures show the proportion of individuals entering master's degree or selective programs between the ages of 19 and 24, according to parental income. Panel (a) displays the proportion of individuals aged 19-24 currently enrolled in master's programs or having already reached the master's level, without necessarily obtaining the diploma, or already holding a master's degree or higher, by parental income. Panel (b) shows the proportion of 19-24-year-olds currently enrolled in or having obtained a certain level of selective programs without necessarily having graduated, or already holding a degree from a selective program, by parental income. We define selective programs as preparatory courses, elite graduate schools, medical school and Ph.D. programs. The parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. We further decompose the top 10 percent of the income distribution (x-axis). The black line represents the standard errors.
<figure><img src="image_5.png" /><figcaption>Figure 3: Higher Education Attendance by Parents' Pre-Tax Income, Occupation, and Diploma</figcaption></figure>

*Notes*: These heatmaps show the proportion of individuals enrolling in higher education between the ages of 19 and 24, according to parental income (x-axis) and parents' occupation (Panel a and b) or parents' diploma (Panel c and d). The parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. Parental occupations are grouped into four categories – high socio-economic status (SES), upper-middle SES, lower-middle SES and low SES (see Table B2 for more details on this variable). We measure parental education with six diploma categories – primary school or less (9 years of education), middle school diploma (10 years of education), high school diploma (12 years of education), two-year higher education degree (14 years of education), Bachelor's or master's degree (17 years), and M.D. or Ph.D. (20 years).
<figure><img src="image_6.png" /><figcaption>Figure 4: Enrollment in Higher Education in France and in the United States, by Parents' Pre-Tax Income</figcaption></figure>

*Notes:* This figure shows the proportion of individuals enrolling in higher education in France and in the United States, by parental income (for the U.S., based on figures from Chetty et al. (2020)). In both countries, the parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. In the U.S., these figures represent the proportions of individuals who enrolled in higher education at least once between the ages of 18 and 21. In France, we consider individuals aged 19-24 currently enrolled in or having graduated from higher educational institutions, as these individuals are most likely to have entered higher education between the ages of 18 and 21. We exclude the youngest (18-year-olds) from our sample in order to limit the bias stemming from the fact that some are still in high school. We do not include property and capital incomes in the estimate for France because they cannot be identified independently of the incomes of all household members, including the incomes of cohabiting young adults. In any case, taking property and capital incomes into account would mainly affect rankings within the top decile and only marginally those in the rest of the distribution. Chetty et al. (2020) study cohorts born between 1980 and 1982 in the United States, whereas the survey used in our study focuses on cohorts born between 1990 and 1996 in France.<fnref n="36" />

<footnote n="36">³⁶A previous version of our study (see the French Council of Economic Analysis focus https://www.cae-eco.fr/enseignement-superieur-un-acces-inegal-selon-le-revenu-des-parents) used older data from Chetty et al. (2014). The update of Chetty et al. (2020) including Pell Grant recipients who were not counted in the 1098-T data used for Chetty et al. (2014) makes the estimates of higher education attendance by parental income in France even closer to those for the United States.</footnote>
<caption>Figure 5: Correlations Between Academic Skills, Behavioral Skills and Socio-Economic Status in France and in the United States</caption>

<figure><img src="image_7.png" /><figcaption>(a) Socio-Economic Index Increase and Academic Skills Score
(b) Self-Efficacy Score by Socio-Economic Status</figcaption></figure>

Notes: These figures illustrate the correlation between socio-economic background and academic skills (Figure (a)) or self-efficacy score (Figure (b)) in France and the U.S. Figure (a) shows the association between a 1-point increase in the Socio-Economic Index and increase in academic scores. Figure (b) shows the average self-efficacy score and difference between high and low SES groups in this score. Figure (a) is based on 2015 PISA data, Figure (b) on 2018 PISA data. The measure of parental background is derived from the Socio-Economic Index (ESCS) in PISA. This index is a composite measure combining the financial, social, cultural, and human-capital resources available to students to produce a single score. It is calculated from parents' education, occupations, and an index summarizing home possessions that represent material wealth or cultural capital. Self-efficacy (Figure (b)) is defined as the degree to which individuals believe in their own ability to perform specific tasks and engage in activities, particularly in adverse circumstances (Bandura, 1977). In 2018, PISA asked students about their general sense of efficacy or competence, especially in challenging situations. These figures are sourced from the PISA 2015 and 2018 databases (OECD, 2018a, 2021).
<caption>Figure 6: Educational Aspirations in France and the United States</caption>

<figure><img src="image_8.png" /><figcaption>(b) High Achieving Students Not Expecting to Enroll in Tertiary Education, by Socio-Economic Status</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<footnote>Notes: Figure (a) shows the educational aspirations of individuals aged 18 and 19, based on parental income. These educational aspirations are reported by young adults in the ENRJ survey. We restrict the sample to individuals aged 18 and 19 in order to avoid having educational aspirations influenced by years of higher education already completed. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. Figure (b) shows the percentage of high-achieving students in France and the U.S. who do not expect to complete tertiary education. High-achieving students are defined as those who have achieved at least minimum academic proficiency (Level 2) in the three core PISA subjects and are high performers (Level 4) in at least one subject. The data for Figure (b) are sourced from the PISA 2018 database (OECD, 2019).</footnote>
<figure><img src="image_10.png" /><figcaption>Figure 7: Distribution of Higher Education Spending Over One Year in France and in the United States</figcaption></figure>

*Notes:* The figure presents the distribution of higher education expenditure over a single year for France and the United States. Individuals with zero spending are those who do not enroll in higher education. The data for France may appear to be noisier as only 65 higher education institutions are sampled, but in practice these institutions have been demonstrated to be representative of the public higher education institutions overseen by the Ministry of Higher Education (Bennani et al., 2021). The U.S. data are sourced from the 2015 IPEDS dataset. In France, we use data from the "Cost Awareness" project (Boiteau and Jameux, 2019) and MESRI-SIES (2021) and matched with ENRJ individual data at the level of each track and field of study. See Table B5 for annual public spending per student, divided by field of study and track, i.e. the level of disaggregation used for public spending on higher education.
<figure><img src="image_11.png" /><figcaption>Figure 8: Inequalities in Public and Private Spending on Higher Education in France</figcaption></figure>

Notes: These figures display the disparities in public and private spending on higher education according to parental income. Panel (a) shows the total amount of public and private investment in higher education for all individuals up to age 24, as a function of parental income. Panel (b) shows total public and private investment in higher education for students or former students up to that age, by parental income. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22-24-year-olds, while the length of study for 22-23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. Private spending corresponds to tuition paid by the parents and expanded over the 6-year period, taking account of the number of years of education completed by each individual. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition (x-axis). We use the "Cost Awareness" project and MESRI-SIES (2021) data for higher education spending.

Tables
<table><caption>Table 1: Descriptive Statistics</caption><thead><tr><th>Quintile of income</th><th>P0-P20</th><th>P20-P40</th><th>P40-P60</th><th>P60-P80</th><th>P80-P100</th><th>Total</th><th>Students or<br>former students</th></tr><tr><th></th><th>n= 760</th><th>n= 796</th><th>n= 838</th><th>n= 820</th><th>n= 733</th><th>n= 3,947</th><th>n= 2,353</th></tr></thead><tbody><tr><td>Age of young adults</td><td>21.5 (1.7)</td><td>21.6 (1.7)</td><td>21.4 (1.7)</td><td>21.5 (1.7)</td><td>21.3 (1.7)</td><td>21.5 (1.7)</td><td>21.5 (1.7)</td></tr><tr><td>Age of fathers</td><td>55.5 (6.6)</td><td>54.5 (6.8)</td><td>53.3 (5.5)</td><td>53.0 (5.6)</td><td>53.6 (5.9)</td><td>53.9 (6.1)</td><td>53.9 (5.8)</td></tr><tr><td>Age of mothers</td><td>50.6 (6.2)</td><td>49.9 (5.6)</td><td>49.7 (5.0)</td><td>50.3 (4.6)</td><td>51.0 (4.4)</td><td>50.3 (5.3)</td><td>50.8 (5.0)</td></tr><tr><td>Female</td><td>0.53</td><td>0.47</td><td>0.50</td><td>0.49</td><td>0.47</td><td>0.49</td><td>0.53</td></tr><tr><td>Pre-tax household<br>income of parents</td><td>9,360<br>(6,280)</td><td>24,050<br>(3,320)</td><td>36,050<br>(3,490)</td><td>49,690<br>(4,770)</td><td>92,690<br>(60,550)</td><td>41,904<br>(39,173)</td><td>50,869<br>(46,298)</td></tr><tr><td>Father's occupation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low SES</td><td>0.33</td><td>0.40</td><td>0.41</td><td>0.28</td><td>0.06</td><td>0.30</td><td>0.21</td></tr><tr><td>Lower-middle SES</td><td>0.35</td><td>0.28</td><td>0.22</td><td>0.22</td><td>0.14</td><td>0.24</td><td>0.24</td></tr><tr><td>Upper-middle SES</td><td>0.05</td><td>0.11</td><td>0.20</td><td>0.27</td><td>0.21</td><td>0.17</td><td>0.20</td></tr><tr><td>High SES</td><td>0.03</td><td>0.04</td><td>0.06</td><td>0.19</td><td>0.56</td><td>0.17</td><td>0.26</td></tr><tr><td>Mothers's occupation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low SES</td><td>0.27</td><td>0.20</td><td>0.19</td><td>0.13</td><td>0.04</td><td>0.17</td><td>0.10</td></tr><tr><td>Lower-middle SES</td><td>0.58</td><td>0.60</td><td>0.56</td><td>0.45</td><td>0.28</td><td>0.50</td><td>0.44</td></tr><tr><td>Upper-middle SES</td><td>0.05</td><td>0.11</td><td>0.13</td><td>0.27</td><td>0.30</td><td>0.17</td><td>0.23</td></tr><tr><td>High SES</td><td>0.02</td><td>0.04</td><td>0.07</td><td>0.11</td><td>0.37</td><td>0.12</td><td>0.19</td></tr><tr><td>Father's dipoma</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary School or less</td><td>0.21</td><td>0.22</td><td>0.20</td><td>0.08</td><td>0.02</td><td>0.15</td><td>0.10</td></tr><tr><td>Middle School degree</td><td>0.03</td><td>0.05</td><td>0.04</td><td>0.06</td><td>0.04</td><td>0.04</td><td>0.04</td></tr><tr><td>High-School degree</td><td>0.23</td><td>0.43</td><td>0.51</td><td>0.54</td><td>0.29</td><td>0.40</td><td>0.38</td></tr><tr><td>Two-year degree</td><td>0.04</td><td>0.04</td><td>0.06</td><td>0.13</td><td>0.19</td><td>0.09</td><td>0.13</td></tr><tr><td>Bachelor or Master degree</td><td>0.03</td><td>0.02</td><td>0.04</td><td>0.11</td><td>0.32</td><td>0.10</td><td>0.16</td></tr><tr><td>M.D. or Ph.D.</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.10</td><td>0.02</td><td>0.04</td></tr><tr><td>Mother's dipoma</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary School or less</td><td>0.44</td><td>0.29</td><td>0.19</td><td>0.10</td><td>0.02</td><td>0.21</td><td>0.12</td></tr><tr><td>Middle School degree</td><td>0.07</td><td>0.10</td><td>0.09</td><td>0.08</td><td>0.03</td><td>0.07</td><td>0.07</td></tr><tr><td>High-School degree</td><td>0.33</td><td>0.44</td><td>0.50</td><td>0.48</td><td>0.31</td><td>0.41</td><td>0.39</td></tr><tr><td>Two-year degree</td><td>0.04</td><td>0.08</td><td>0.09</td><td>0.19</td><td>0.24</td><td>0.13</td><td>0.19</td></tr><tr><td>Bachelor or Master degree</td><td>0.04</td><td>0.04</td><td>0.08</td><td>0.12</td><td>0.31</td><td>0.12</td><td>0.18</td></tr><tr><td>M.D. or Ph.D.</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.01</td><td>0.06</td><td>0.02</td><td>0.03</td></tr><tr><td>Marital status of parents</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Parents live together</td><td>0.37</td><td>0.55</td><td>0.70</td><td>0.81</td><td>0.87</td><td>0.65</td><td>0.70</td></tr><tr><td>Parents are separated</td><td>0.44</td><td>0.35</td><td>0.23</td><td>0.17</td><td>0.12</td><td>0.26</td><td>0.23</td></tr><tr><td>Father unknown or deceased</td><td>0.16</td><td>0.09</td><td>0.05</td><td>0.02</td><td>0.01</td><td>0.07</td><td>0.05</td></tr><tr><td>Mother unknown or deceased</td><td>0.03</td><td>0.02</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0.02</td></tr><tr><td>Parents unknown or deceased</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Biggest urban center<br>of parent's residence</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Parisian area</td><td>0.14</td><td>0.14</td><td>0.12</td><td>0.15</td><td>0.30</td><td>0.17</td><td>0.20</td></tr><tr><td>More than 100,000 inhabitants</td><td>0.33</td><td>0.29</td><td>0.28</td><td>0.27</td><td>0.29</td><td>0.29</td><td>0.29</td></tr><tr><td>Less than 100,000 inhabitants</td><td>0.51</td><td>0.56</td><td>0.59</td><td>0.57</td><td>0.41</td><td>0.53</td><td>0.49</td></tr></tbody></table>

Notes: The sample is constructed from the ENRJ survey. It comprises only individuals for whom both the young person and at least one parent responded to the questionnaire. The number of observations is not equal for all parental income quintiles because in creating the parental income ranks we apply the survey weights for assessing non-response bias. The mother's occupation is missing for 4 percent of the observations and the father's for 9 percent. The mother's diploma is missing for 4 percent of the observations and the father's for 17 percent. When the parents are separated, both were questioned in 46 percent of the cases and only one in 54 percent. When the two parents do not live in the same location, we take the larger city or town.
<table><caption>Table 2: Enrollment in Higher Education, by Parents' Pre-Tax Income Quintile</caption><thead><tr><th>Enrollment in higher education</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Baseline proba. of enrollment</td><td>0.57</td><td>0.57</td><td>0.57</td><td>0.57</td></tr><tr><td><strong>Parents' income quintile</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Parents' income quintile=1</td><td>-0.18***</td><td>-0.15***</td><td>-0.041</td><td>-0.087***</td></tr><tr><td>Parents' income quintile=2</td><td>-0.073***</td><td>-0.067***</td><td>-0.035</td><td>-0.048*</td></tr><tr><td>Parents' income quintile=3</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Parents' income quintile=4</td><td>0.12***</td><td>0.057**</td><td>0.064***</td><td>0.040</td></tr><tr><td>Parents' income quintile=5</td><td>0.30***</td><td>0.091***</td><td>0.12***</td><td>0.032</td></tr><tr><td><strong>Control variables included</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Father Occupation (26 categories)</td><td></td><td>X</td><td></td><td>X</td></tr><tr><td>Mother Occupation (26 categories)</td><td></td><td>X</td><td></td><td>X</td></tr><tr><td>Father Diploma (6 categories)</td><td></td><td></td><td>X</td><td>X</td></tr><tr><td>Mother Diploma (6 categories)</td><td></td><td></td><td>X</td><td>X</td></tr><tr><td>Family situation</td><td></td><td></td><td></td><td>X</td></tr><tr><td>Urban center size</td><td></td><td></td><td></td><td>X</td></tr><tr><td>Observations</td><td>3,947</td><td>3,616</td><td>3,066</td><td>3,019</td></tr></tbody></table>

Notes: This table presents the estimates of the association between parental income and enrollment in higher education (1), successively adding controls for parental occupation (2), parental education (3), and then all these controls together with family status and urban center size (4). Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Quintiles are based on this definition. We omit the third quintile. The coefficients should thus be interpreted as the difference from the baseline probability of enrollment for individuals with parents in the third income quintile. The potential financial transfer variable represents what young adults would have received from their parents if they were studying, based on their characteristics (indicators for parents' income decile, number of siblings, father's degree, mother's degree, father's occupation, mother's occupation, parents' marital status, and size of urban center where the young adult lives). Parents' occupation is introduced in dummy variables for each of the 26 job categories (Table B2), separately for father and mother. Parents' education is introduced in dummy variables for each of the 6 degree categories (elementary certificate or less, middle school certificate, high school diploma or equivalent, two-year degree, bachelor's or master's degree, and M.D. or Ph.D.), separately for father and mother. Family situation contemplates 5 categories: parents together, separated, father unknown, mother unknown, parents unknown. Urban center size has three categories: Paris metropolitan area, urban center over 100,000, urban center under 100,000 inhabitants. When the two parents do not live in the same town or city, we use the larger of the two.

<footnote>* p < 0.1, ** p < 0.05, *** p < 0.01</footnote>
<table><caption>Table 3: Decomposition of Higher Education Participation Rate Differences by Prior Credentials</caption><thead><tr><th>Contribution of previous credentials<br>to enrollment disparities (in percent)</th><th>Higher<br>education</th><th>Selective<br>programs</th><th>Master's<br>degrees</th></tr></thead><tbody><tr><td>Middle school graduation</td><td>21</td><td>9</td><td>10</td></tr><tr><td>High school graduation</td><td>50</td><td>21</td><td>24</td></tr><tr><td>Enrollment in higher education</td><td>-</td><td>12</td><td>15</td></tr><tr><td>Residual differential enrollment</td><td>29</td><td>58</td><td>51</td></tr></tbody></table>

*Notes:* This table presents the contribution of different credentials – middle school graduation, high school graduation for those who passed the middle school graduation exam, higher education attendance gap for those who passed the high school graduation exam – to the total difference in enrollment in higher education, selective programs, and master's programs between individuals in the top and bottom 10 percent of the parental income distribution (in percent). To decompose the contribution of the various factors, we rely on Gupta's decomposition method (Das Gupta, 1991). See Appendix B.2.3 for more details.
<table><caption>Table 4: Decomposition of Differences in Public Spending on Higher Education</caption><thead><tr><th></th><th>(1)<br>Main specification</th><th>(2)<br>Wealthier individuals<br>attend more costly<br>programs and elite<br>graduate schools</th></tr></thead><tbody><tr><td>Enrollment in higher education</td><td>67</td><td>49</td></tr><tr><td>Duration of study</td><td>23</td><td>17</td></tr><tr><td>Annual public spending</td><td>10</td><td>33</td></tr></tbody></table>

Notes: This table shows the contribution of different factors – enrollment in higher education, differences in duration of study for students, differences in annual public spending for students – to the total difference (in percent) in public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the parental income distribution. Parental income is the sum of the pre-tax income of the father’s household and the mother’s household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22-24-year-olds; the length of study for 22-23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. To decompose the contribution of different factors, we rely on the method of Das Gupta (1991). See Appendix B.2.3 for more details on this method. Column (1) represents our main specification. Column (2) presents a simulation designed to address potential underestimation of inequalities in public spending on higher education in our data. This counterfactual approach involves associating the most expensive courses in their respective fields with the most affluent students and adding consideration for increased spending on elite graduate schools. More specifically, for each combination of tracks and fields we assign the 10th (or 25th) percentile of the distribution of student-weighted costs – or the next higher percentile when we do not have enough observations for the 10th or 25th percentile – to individuals whose parents are in the bottom 10 percent (or 25 percent) of the income distribution and the 75th percentile (or 90th percentile) – or the next lower percentile – to those whose parents are in the top 25 percent (or 10 percent). For elite graduate schools, individuals in the top 5 percent of the income distribution are assumed to enroll in schools with endowments three times the typical endowment, those in the next 5 percent in schools with endowments twice the typical endowment. See Appendix C.3.3 for more details on these simulations.
# Acknowledgements

This work was supported by French National Research Agency Grant ANR-17-EURE-0001. We would like to thank Gabrielle Fack for most helpful and relevant feedback throughout this project, as well as Jérôme Gautié, Julien Grenet, Thomas Piketty and François-Charles Wolff for valuable comments. This paper also benefited from useful suggestions from Guillaume Alègre, Mathias André, José De Sousa, Elvire Guillaud, Elise Huillery, Xavier Joutard, Gustave Kenedi, Chloé Lallemand, David Margolis, Michaël Sicsic, Louis Sirugue, Camille Terrier and Sander Wagner. We would also like to thank participants in the Applied Economics Lunch Seminar and PSIPSE Seminar at PSE, INSEE Internal Seminar, INED Coffee Seminar, IPP Internal Seminar as well as participants in the 69th Congress of the French Economic Association, the World Inequality Lab Conference #2, the 2022 LESE Conference, the 2022 Doctorissime Conference, the 2022 JMA Conference and the 2022 LAGV Conference for useful remarks. We would like to thank Bertrand Garbinti and Vladimir Pecheu for sending us data on lifetime earnings by gender and educational attainment. We also thank the French Council of Economic Analysis and the Ministry of Higher Education and Research for access to data on higher education spending, and in particular Madeleine Péron, Hamza Bennani and Gabriele Dabbaghian for formatting the data and helping to interpret them. Lastly, we would like to thank Roger Meservey for his outstanding copy-editing service.
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# Appendix

## A.1 Inequalities in Education Spending: A Literature Review 43

## B.2 Variables, Data and Methods 44

B.2.1 Grouping of Parental Occupations 44

B.2.2 Data 46

B.2.2.1 Representativeness of the Sample 46

B.2.2.2 Comparison of Parental Income Distribution in France and the United States 46

B.2.2.3 Data on Public Spending on Higher Education in France 47

B.2.3 Das Gupta Decomposition Method 51

## C.3 Robustness Checks 52

C.3.1 Using Multiple Years of Parental Income 52

C.3.2 Using Parents' Individual Income Instead of Parents' Household Income 54

C.3.3 Matching Richer Individuals with Better-Endowed Programs 54

## D.4 Additional Results 58

D.4.1 Parental Income, Education, and Occupation: Patterns in Higher Education Enrollment 58

D.4.2 Distribution of Higher Education Public Spending on Young Adults 60

D.4.3 Decompositions of Inequalities in Public Spending on Higher Education 62

D.4.4 Accounting for Parental Taxes in the Higher Education Spending Distribution 63

D.4.4.1 Methodology 63

D.4.4.2 Results 68

D.4.5 Predicted Lifetime Earnings 71

## E.5 List of Tables and Figures 77
## A.1 Inequalities in Education Spending: A Literature Review

<table><caption>Table A1: Synthesis of the Economic Literature on the Redistributivity of Education Spending</caption><thead><tr><td>Authors and Year</td><td>Country</td><td>Parental Background</td><td>Taxes Taken Into Account</td><td>Higher Education Only</td><td>Results in Terms of Redistributivity</td></tr></thead><tbody><tr><td>Hansen and Weisbrod (1969)</td><td>U.S. (California)</td><td>Average income by university</td><td>Yes</td><td>Yes</td><td>Regressive</td></tr><tr><td>Hansen (1970)</td><td>U.S. (Wisconsin)</td><td>Average income by university</td><td>Yes</td><td>Yes</td><td>Regressive</td></tr><tr><td>Pechman et al. (1970)</td><td>U.S. (California)</td><td>Income deciles</td><td>Yes</td><td>Yes</td><td>Progressive</td></tr><tr><td>Fields (1975)</td><td>Kenya</td><td>Social origins</td><td>No</td><td>Yes</td><td>Regressive</td></tr><tr><td>Hatchuel (1976)</td><td>France</td><td>Social origins (occupation)</td><td>Yes</td><td>No</td><td>Progressive</td></tr><tr><td>Mendès France (1987)</td><td>France</td><td>Social origins (occupation)</td><td>No</td><td>No</td><td>Progressive up to age 15 Higher Ed. regressive</td></tr><tr><td>Lemelin (1992)</td><td>Quebec</td><td>Education level of parents</td><td>No</td><td>Yes</td><td>Regressive</td></tr><tr><td>Tsakloglou and Antoninis (1999)</td><td>Greece</td><td>Income quintiles</td><td>No</td><td>No</td><td>Progressive</td></tr><tr><td>Albouy et al. (2002)</td><td>France</td><td>Standard of living</td><td>Yes</td><td>No</td><td>Progressive up to 8th decile Higher Ed. regressive</td></tr><tr><td>O'Donoghue (2003)</td><td>European Union</td><td>Standard of living</td><td>Yes</td><td>No</td><td>Progressive</td></tr><tr><td>Barbaro (2002)</td><td>Germany</td><td>Standard of living</td><td>Yes</td><td>Yes</td><td>Progressive</td></tr><tr><td>Johnson (2006)</td><td>U.S.</td><td>Income, permanent income, dynastic income</td><td>Yes</td><td>Yes</td><td>Progressive</td></tr><tr><td>Allègre et al. (2012)</td><td>France</td><td>Permanent income (life cycle)</td><td>Yes</td><td>No</td><td>Progressive</td></tr></tbody></table>

<footnote>Notes: This table summarizes the findings of the empirical literature on the progressivity or regressivity of educational expenditures, in various countries and institutional contexts. The "parental background" column specifies which parental social origin variable is used (education level, occupation or income). The column "taxes taken into account" indicates whether the authors deduct the taxes paid by the parents (either partially or entirely) or not. The "higher education only" column specifies whether the analysis is devoted solely to higher education spending or to all education spending.</footnote>
# B.2 Variables, Data and Methods

## B.2.1 Grouping of Parental Occupations

Table B2 below details the four socioeconomic status (SES) categories and the corresponding occupations. The mother's occupation is missing for 4% of the observations, the father's for 9%.
<table><caption>Table B2: Grouping of Occupations</caption><thead><tr><th>Socioeconomic status (SES)</th><th>Corresponding occupation</th></tr></thead><tbody><tr><td><strong>High SES</strong></td><td></td></tr><tr><td>23</td><td>Entrepreneurs with 10 or more employees</td></tr><tr><td>31</td><td>Liberal professions</td></tr><tr><td>33</td><td>Public service executives</td></tr><tr><td>34</td><td>Professors, scientific professions</td></tr><tr><td>35</td><td>Information, arts and entertainment professions</td></tr><tr><td>37</td><td>Administrative and commercial executives of companies</td></tr><tr><td>38</td><td>Engineers and technical company executives</td></tr><tr><td>42</td><td>Teachers and assimilated</td></tr><tr><td>73</td><td>Former executives</td></tr><tr><td><strong>Upper-middle SES</strong></td><td></td></tr><tr><td>43</td><td>Intermediate professions in health and social work</td></tr><tr><td>44</td><td>Clergy, religious</td></tr><tr><td>45</td><td>Administrative intermediary professions of the public service</td></tr><tr><td>46</td><td>Administrative and commercial intermediary professions of companies</td></tr><tr><td>47</td><td>Technicians</td></tr><tr><td>48</td><td>Foremen, supervisors</td></tr><tr><td>74</td><td>Former intermediary professions</td></tr><tr><td><strong>Lower-middle SES</strong></td><td></td></tr><tr><td>11</td><td>Smallholder farmers</td></tr><tr><td>12</td><td>Farmers on medium farm</td></tr><tr><td>13</td><td>Large-scale farmers</td></tr><tr><td>21</td><td>Craftsmen</td></tr><tr><td>22</td><td>Traders and assimilated</td></tr><tr><td>52</td><td>Civilian employees and civil servants</td></tr><tr><td>53</td><td>Police and military</td></tr><tr><td>54</td><td>Administrative employees of companies</td></tr><tr><td>55</td><td>Commercial employees</td></tr><tr><td>56</td><td>Staff in direct personal services</td></tr><tr><td>71</td><td>Former farmer operators</td></tr><tr><td>72</td><td>Former craftsmen, traders, business leaders</td></tr><tr><td>75</td><td>Former employees</td></tr><tr><td><strong>Low SES</strong></td><td></td></tr><tr><td>62</td><td>Skilled industrial type workers</td></tr><tr><td>63</td><td>Skilled artisan type workers</td></tr><tr><td>64</td><td>Drivers</td></tr><tr><td>65</td><td>Skilled workers in handling, warehousing and transport</td></tr><tr><td>67</td><td>Unskilled industrial workers</td></tr><tr><td>68</td><td>Unskilled artisan type workers</td></tr><tr><td>69</td><td>Agricultural workers</td></tr><tr><td>76</td><td>Former workers</td></tr><tr><td>80</td><td>Inactive</td></tr></tbody></table>

<footnote>Notes: This table details the occupations corresponding to the four socio-economic status groups considered in our analyses. This grouping corresponds to that of the Department of Education's statistical service (Direction de l'évaluation, de la prospective et de la performance (DEPP)).</footnote>
## B.2.2 Data

### B.2.2.1 Representativeness of the Sample

<table><caption>Table B3: Comparison of Income Distributions in ENRJ and ERFS Samples</caption><thead><tr><th></th><th>ENRJ (2014)<br>(Parents of<br>young adults)</th><th>ERFS (2014)<br>(One member of<br>the household between)<br>43-62 years old)</th></tr></thead><tbody><tr><td><strong>Mean age</strong></td><td>51.5 (5.8)</td><td>51.6 (6.9)</td></tr><tr><td><strong>Pre-tax income</strong></td><td></td><td></td></tr><tr><td>Mean</td><td>41,904</td><td>43,022</td></tr><tr><td>Median</td><td>35,514</td><td>35,240</td></tr><tr><td>P10</td><td>9,955</td><td>9,470</td></tr><tr><td>P90</td><td>77,556</td><td>78,010</td></tr><tr><td>P99</td><td>162,021</td><td>188,190</td></tr><tr><td><strong>Disposable income</strong></td><td></td><td></td></tr><tr><td>Mean</td><td>48,047</td><td>41,401</td></tr><tr><td>Median</td><td>42,849</td><td>34,690</td></tr><tr><td><strong>Standard-of-living</strong></td><td></td><td></td></tr><tr><td>Mean</td><td>22,692</td><td>24,408</td></tr><tr><td>Median</td><td>19,712</td><td>20,915</td></tr><tr><td><strong>Number of observations</strong></td><td>3,947</td><td>20,293</td></tr></tbody></table>

Notes: This table compares the distribution of different income variables for parents in our database (ENRJ) and individuals aged 42 to 62 in another tax survey with a much larger sample (ERFS). 99% of the parents of young adults aged 18-24 in our ENRJ data are in the age range 42-62. The two samples are relatively close in terms of pre-tax income. Individuals in our sample (parents of young adults) have slightly higher disposable incomes and a slightly lower standard-of-living than individuals in the ERFS sample, due to the presence of children.

### B.2.2.2 Comparison of Parental Income Distribution in France and the United States
<table><caption>Table B4: Parental Income Distribution in France and the United States</caption><thead><tr><th>Income Distribution</th><th>United States</th><th>France</th></tr></thead><tbody><tr><td>Mean</td><td>87,219</td><td>52,030</td></tr><tr><td>Std. Deviation</td><td>353,430</td><td>46,400</td></tr><tr><td>Median</td><td>60,129</td><td>44,580</td></tr><tr><td>Observations</td><td>9,867,736</td><td>3,947</td></tr></tbody></table>

Notes: This table compares the distribution of parental income in France and the United States. The U.S. data come from Table III in the online Appendix of Chetty et al. (2014). In both cases, family income is pre-tax household income, divided by two when both the father's and mother's household incomes are available. We do not include property and capital incomes in France because it is not possible to identify them independently of the incomes of all household members, including the incomes of cohabiting young adults. In any case, taking property and capital incomes into account would mainly affect rankings within the top decile and only marginally those in the rest of the distribution. In the United States, family income is the average household income from 1996 to 2000 because Chetty et al. (2014) studies cohorts born between 1980 and 1982. In France, family income is for 2014 because the survey we use was conducted in 2014. All amounts are in 2012 dollars.

### B.2.2.3 Data on Public Spending on Higher Education in France

**Cost of Studies by Track and Field of Study.** In the French national accounts, higher education expenditures are generally classed in one of just three categories,<fnref n="37" /> regardless of the track or field of study (MESRI-SIES, 2021). Our study draws additional data from a survey of higher education institutions, the "Cost Awareness" project (Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux, 2019)) designed to assess the expenditures of different institutions of higher education. This survey was conducted between 2016 and 2019 at 65 higher education institutions that enroll about a third of all French students. Not all public institutions were surveyed, but those sampled are representative of all public higher education institutions under the supervision of the Ministry of Higher Education (Bennani et al. (2021)). These data allow for a detailed breakdown of costs across various tracks and fields of study. They enable the determination of instructional costs in higher education, excluding research-related expenses, which are counted in more aggregated data. For all courses – including engineering schools, technical training (IUT), bachelor's degrees (professional or academic), and master's degrees – public expenditures are classed in nine fields: Humanities, Arts, Languages; Markets and Organizations; Mathematics, Engineering, Computer Science; Law; Matter, Earth, and Universe Sciences; Social Sciences; Life Sciences; Medical; and Miscellaneous, which includes elements not classifiable in the preceding categories. The data thus allow a more accurate estimate of public spending on higher education for each individual. Annual public spending per student in higher education varies considerably by track and field of study, as shown in table B5. On average, undergraduate students cost €3,600 per year, graduate students €5,300, and engineering students €10,600. The scientific fields – except for medical studies – are much more expensive than the human-

<footnote n="37">³⁷In 2014-2015, on average, vocational programs cost €14,150 per student, preparatory courses €15,620 and university programs €10,980.</footnote>
ities (up to twice as much for a bachelor's degree), because students have more tutorials and practical work, with a much lower student/instructor ratio.

**Elite Graduate Schools and Variance in Spending Within Tracks and Fields of Study.** The new higher education cost data are of high quality; however, they still have some limitations. Public spending cannot be differentiated among elite graduate schools.<fnref n="38" /> We therefore attribute the average cost of engineering schools to the other public elite graduate schools, which might mean underestimating inequalities in public spending. Some of these schools, in fact, have very high per-student expenditures (for example, Berné and Métivier (2015) found that certain elite graduate schools have expenditures of €60,000 per student per year) and an attendance rate that remains very uneven (Bonneau et al. (2021)). More generally, public spending still varies greatly within tracks and fields, which we do not take into account because we cannot link students to their specific university. We address these issues by simulating two variants of public spending on higher education to predict how a more uneven distribution of public spending within each program and field might alter this result (see Appendix C.3.3). Unlike university programs, for vocational programs and preparatory courses we cannot break down the costs by field because we do not have detailed expenditure data for these programs. Since both are national programs and the course load, student/teacher ratio, and teacher salaries are set by the state, we can assume that there is little disciplinary variation in these programs. In the same way that we remove the costs of research in university programs, here we consider only the costs of training: 85.3 percent of total costs for preparatory courses and 86.7 percent for vocational programs (Bennani et al., 2021).

**Integration of Survey and Cost Data.** Our study integrates two key datasets: cost data from the “Cost Awareness” survey and survey data on young adults (ENRJ). The cost data present expenditures for each track and field within public programs. The survey data details individual registrations across these tracks and fields. To ensure a comprehensive analysis, we matched these datasets based on their respective track and field descriptors, creating a consolidated “track × field” data. For a specific breakdown of the cost data granularity used in our analysis, refer to Table B5.

**Construction of Educational Spending Over the Entire Educational Trajectory.** To study inequality along the entire higher education trajectory, we restrict the examination to individuals aged 22 to 24, who are the most advanced in their studies in our sample. The length of study for 22-23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. We retain the higher cost between the highest degree obtained, the highest level of education attained, or ongoing studies. We partially account for reorientations. For example, a student who is enrolled in a master's program but has an engineering degree is assigned the cost of the full engineering school program plus the cost of one year of a master's degree in their field. Lacking complete data on individual trajectories, we do not consider individuals who repeat in the same program. It is likely that this approximation leads us to underestimate the cost of university students who repeat: 27 % of first-year students (MESRISIES, 2020) but also students in preparatory courses who often repeat the second year of the program – 9.1 % in economic preparatory courses, 20 % in humanities preparatory courses and 22.1 % in scientific

<footnote n="38">³⁸The survey is representative of institutions under the supervision of the Ministry of Higher Education and Research, although some institutions, notably certain engineering schools, are under other ministries (Ministry of the Economy, Ministry of the Army, Ministry of Agriculture, etc.). Business schools too are not included. We therefore supplement this survey with specific data on the costs of business school students (https://www.cefdg.fr/fr/ecoles-et-formations-visees). On average, public funding covers about 5 percent of student costs for these private programs.</footnote>
preparatory courses.<fnref n="39" />

**Parents' Private Spending on Higher Education.** We gauge private investment in higher education by tuition paid by parents; to limit measurement error, we average the tuition amounts declared by the students and their parents in the ENRJ survey. Because our cost data represent the full training cost and not public spending alone, in cases where the tuition is less than €650, we subtract the tuition paid by parents from the total cost to get the net public cost. When the tuition is above €7,500, we consider it to be private education and set the net public cost at 5% of the tuition paid. Between these two extremes, which we must set by convention, we consider the public cost data as the public cost and the tuition as the private cost. For individuals who have already graduated from college (16% of our sample and 29% of those aged 22-24), we cannot know whether they were enrolled in a public or a private program, because we do not have information on the tuition paid by their parents. We assume that all these individuals were enrolled in public programs, which in fact make up the vast majority of programs in France. The private sector accounted for just 18% of total enrollment in higher education (MENESR-DEPP (2015)) in 2014.

<footnote n="39">³⁹Bulletin officiel de l'éducation nationale n°18, 3 May 2012 (https://www.education.gouv.fr/pid285/bulletin_officiel.html?pid_bo=26454)</footnote>
<table><caption>Table B5: Per Student Annual Public Spending</caption><thead><tr><th>Track</th><th>Field of study</th><th>Public cost over one year (in euros)</th></tr></thead><tbody><tr><td>Vocational Track (BTS)</td><td></td><td>12,400</td></tr><tr><td>Preparatory Courses (CPGE)</td><td></td><td>13,400</td></tr><tr><td rowspan="7">Bachelor Degree (Non Vocational) (Licence)</td><td>Humanities, Arts, Languages</td><td>3,400</td></tr><tr><td>Markets &amp; Organizations</td><td>3,000</td></tr><tr><td>Math, Engineering, Computer science</td><td>5,300</td></tr><tr><td>Law</td><td>2,500</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>6,200</td></tr><tr><td>Social Sciences</td><td>3,500</td></tr><tr><td>Life Sciences</td><td>4,900</td></tr><tr><td>Various</td><td>3,600</td></tr><tr><td rowspan="7">Bachelor Degree (Vocational) (Licence professionnelle)</td><td>Humanities, Arts, Languages</td><td>4,900</td></tr><tr><td>Markets &amp; Organizations</td><td>5,400</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,700</td></tr><tr><td>Law</td><td>5,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>9,400</td></tr><tr><td>Social Sciences</td><td>5,300</td></tr><tr><td>Life Sciences</td><td>7,000</td></tr><tr><td>Various</td><td>6,400</td></tr><tr><td rowspan="7">Technical Training (IUT)</td><td>Markets &amp; Organizations</td><td>7,500</td></tr><tr><td>Math, Engineering, Computer science</td><td>11,300</td></tr><tr><td>Law</td><td>7,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>11,700</td></tr><tr><td>Social Sciences</td><td>7,500</td></tr><tr><td>Life Sciences</td><td>10,100</td></tr><tr><td>Various</td><td>9,500</td></tr><tr><td rowspan="7">Master Degree</td><td>Humanities, Arts, Languages</td><td>5,400</td></tr><tr><td>Markets &amp; Organizations</td><td>4,800</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,300</td></tr><tr><td>Law</td><td>4,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>8,900</td></tr><tr><td>Social Sciences</td><td>4,700</td></tr><tr><td>Life Sciences</td><td>6,500</td></tr><tr><td>Various</td><td>5,300</td></tr><tr><td rowspan="4">Engineering Schools</td><td>Math, Engineering, Computer science</td><td>10,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>12,000</td></tr><tr><td>Social Sciences</td><td>11,300</td></tr><tr><td>Life Sciences</td><td>11,900</td></tr><tr><td></td><td>Various</td><td>10,600</td></tr><tr><td>First Year of Medical Studies (PACES)</td><td>Medicine</td><td>2,400</td></tr><tr><td>Medical Studies (Except First Year)</td><td>Medicine</td><td>4,400</td></tr><tr><td>Paramedical Trainings</td><td>Medicine</td><td>2,700</td></tr><tr><td>Institute of Political Studies (IEP)</td><td></td><td>4,000</td></tr><tr><td>Business Schools</td><td></td><td>400</td></tr><tr><td>Other Elite Graduate Schools (Grande école)</td><td></td><td>10,600</td></tr></tbody></table>

Notes: This table presents the annual spending on higher education, for different tracks, degrees and fields. These data are from MESRI-SIES (2021) for vocational programs and preparatory courses, from the Commission d'évaluation des formations et diplômes de gestion (CEFDG) for business schools (https://www.cefdg.fr/fr/ecoles-et-formations-visees) and calculated from the "Cost Awareness" project (DGESIP 2016-2019) for all other courses. In calculating the average annual cost per student, we weight the programs by the number of students enrolled.
### B.2.3 Das Gupta Decomposition Method

We decompose both the enrollment gap in higher education and the differences in the cost of public higher education between individuals from the top income decile (T) and those from the bottom decile (B) using a standard rate decomposition method developed by Das Gupta (Das Gupta, 1991). When the outcome is defined as a product of other variables of interest, the method can calculate the contribution of each of the latter to the total gap in the outcome variable between two subpopulations.

We denote the outcome variable of interest as r and the k explanatory factors as $x_1, \dots, x_k$ such that

$$r(x_1, \dots, x_k) = \prod_{n=1}^{k} x_i$$

When $k=2$, the contribution of each factor $C(x_1)$ and $C(x_2)$ can be written as:

$$\begin{cases} C(x_1) = \frac{1}{2}(x_2^T + x_2^B)(x_1^T - x_1^B) \\ C(x_2) = \frac{1}{2}(x_1^T + x_1^B)(x_2^T - x_2^B) \end{cases} \qquad (1)$$

In the general case where $k \ge 3$, the contribution of each factor $C(x_i)$ can be expressed as:

$$C(x_i) = \sum_{j=1}^{k-1} \frac{R(j-1, i)}{k \binom{k-1}{j-1}} (x_i^T - x_i^B) \qquad (2)$$

where $R(j, i)$ is the sum of all possible values of the product of $k-1$ factors (excluding $x_i$), of which $j$ factors are from population T and all other factors from population B (Li, 2017).

For instance, when $k=3$, the contribution of the first factor to inequalities between individuals from top (T) and bottom (B) income deciles is obtained with:

$$C_1 = \left( \frac{x_2^B \times x_3^B}{3} + \frac{x_2^B \times x_3^T + x_3^B \times x_2^T}{6} \right) \times (x_1^T - x_1^B)$$

In our framework, we initially break down the disparity in higher education participation between individuals whose parents are positioned at the top of the income distribution (denoted T) and those with parents at the bottom (denoted B) by examining prior degree attainment. The rate of participation in higher education for each group G (where $G \in (B, T)$) is computed as:

$$HE^G = MSC^G \times HSC_{MSC}^G \times HE_{HSG}^G \qquad (3)$$

Here, $HE^G$ denotes the rate of higher education participation for individuals in group G. $MSC^G$ represents the rate at which individuals in group G achieve the middle school certificate. $HSC_{MSC}^G$ indicates the rate of obtaining a high school certificate among those in group G who have previously achieved the middle school certificate. Lastly, $HE_{HSG}^G$ specifies the rate of higher education participation for those in group G who hold a high school certificate. Refer to Table 3 for the results derived from this decomposition technique.

Second, we decompose the differences in public spending on higher education between individuals in the top (T) and bottom (B) income deciles based on differences in enrollment in higher education, duration of study, and spending for each year. Formally, public spending on higher education for individuals in group G (with $G \in (B, T)$) can be defined as follows:
$$PS^G = HE^G \times LS_{student}^G \times AC_{student}^G$$

With $PS^G$ public spending on higher education for individuals in group G, $HE^G$ the participation rate in higher education of individuals in group G, $LS_{student}^G$ the average duration of study of students in group G, and $AC_{student}^G$ the average annual public cost of students in group G. The contributions of each of these factors to the differences in higher education costs are presented in Table 4.

## C.3 Robustness Checks

### C.3.1 Using Multiple Years of Parental Income
<table><caption>Table C6: Sensitivity of Results to Using Multiple Years of Parental Income</caption><thead><tr><th></th><th colspan="3" style="text-align:center;">Access rate to higher education</th></tr><tr><th></th><th colspan="3" style="text-align:center;">Parental income variable used:</th></tr><tr><th></th><th style="text-align:center;">Household<br>taxable income<br>in year t</th><th style="text-align:center;">Household<br>taxable income<br>in year t-2</th><th style="text-align:center;">Average of household<br>taxable incomes<br>in years t and t-2</th></tr></thead><tbody><tr><td colspan="4" style="text-align:left;"><em>Panel B. Parental income decile</em></td></tr><tr><td style="text-align:left;">P0-P10</td><td style="text-align:center;">0.40<br>(0.49)</td><td style="text-align:center;">0.48<br>(0.50)</td><td style="text-align:center;">0.42<br>(0.49)</td></tr><tr><td style="text-align:left;">P10-P20</td><td style="text-align:center;">0.42<br>(0.49)</td><td style="text-align:center;">0.35<br>(0.48)</td><td style="text-align:center;">0.40<br>(0.49)</td></tr><tr><td style="text-align:left;">P20-P30</td><td style="text-align:center;">0.43<br>(0.50)</td><td style="text-align:center;">0.43<br>(0.50)</td><td style="text-align:center;">0.43<br>(0.50)</td></tr><tr><td style="text-align:left;">P30-P40</td><td style="text-align:center;">0.52<br>(0.50)</td><td style="text-align:center;">0.55<br>(0.50)</td><td style="text-align:center;">0.53<br>(0.50)</td></tr><tr><td style="text-align:left;">P40-P50</td><td style="text-align:center;">0.53<br>(0.50)</td><td style="text-align:center;">0.48<br>(0.50)</td><td style="text-align:center;">0.53<br>(0.50)</td></tr><tr><td style="text-align:left;">P50-P60</td><td style="text-align:center;">0.57<br>(0.50)</td><td style="text-align:center;">0.54<br>(0.50)</td><td style="text-align:center;">0.50<br>(0.50)</td></tr><tr><td style="text-align:left;">P60-P70</td><td style="text-align:center;">0.61<br>(0.49)</td><td style="text-align:center;">0.62<br>(0.49)</td><td style="text-align:center;">0.64<br>(0.48)</td></tr><tr><td style="text-align:left;">P70-P80</td><td style="text-align:center;">0.65<br>(0.48)</td><td style="text-align:center;">0.65<br>(0.48)</td><td style="text-align:center;">0.66<br>(0.47)</td></tr><tr><td style="text-align:left;">P80-P90</td><td style="text-align:center;">0.76<br>(0.43)</td><td style="text-align:center;">0.80<br>(0.40)</td><td style="text-align:center;">0.77<br>(0.42)</td></tr><tr><td style="text-align:left;">P90-P100</td><td style="text-align:center;">0.85<br>(0.36)</td><td style="text-align:center;">0.86<br>(0.35)</td><td style="text-align:center;">0.86<br>(0.35)</td></tr><tr><td colspan="4" style="text-align:left;"><em>Panel B. Regression coefficients</em></td></tr><tr><td style="text-align:left;">Full sample</td><td style="text-align:center;">0.46<br>(0.03)</td><td style="text-align:center;">0.46<br>(0.03)</td><td style="text-align:center;">0.48<br>(0.03)</td></tr><tr><td style="text-align:left;">Lower half</td><td style="text-align:center;">0.31<br>(0.07)</td><td style="text-align:center;">0.16<br>(0.07)</td><td style="text-align:center;">0.32<br>(0.07)</td></tr><tr><td style="text-align:left;">Upper half</td><td style="text-align:center;">0.72<br>(0.07)</td><td style="text-align:center;">0.81<br>(0.07)</td><td style="text-align:center;">0.85<br>(0.07)</td></tr></tbody></table>

Notes: Our main analyses are based on a single year of parental income: namely, the survey year (2014). In this table we present the sensitivity of our results to using multiple years of parental income. The parental income used in our main specification is the sum of the father's household and the mother's household pre-tax income, divided by two when two parental households are identified. To perform this robustness check between year t and year t-2, we use a slightly different concept of income, namely, parents' household taxable income, which is available for both year t and year t-2. The regression coefficients report estimates of a regression of parental income percentile on enrollment in higher education. They should be interpreted as the average change in the participation rate in higher education for an increase of 10 percentile ranks (i.e., one decile) in the parental income distribution. The differences in estimation between our main concept of parental income and this taxable income concept are due to better imputation of missing responses in our main concept and to the fact that some types of income (alimony, retirement savings, and special allowances) are not taxable, thus not included in this income measure. Standard deviations (Panel A) and standard errors (Panel B) in parentheses.
### C.3.2 Using Parents' Individual Income Instead of Parents' Household Income

The parental income variable used in our main analysis is the sum of the father's household and the mother's household pre-tax income, divided by two when two parental households are identified. In this Appendix, we present the results using a slightly different concept of parental income, i.e. the sum of the father's pre-tax income and the mother's pre-tax income, regardless of marital status. This variant differs from our main specification in that it does not take into account the incomes of the new partners of separated parents who have remarried, consistent with the fact that income pooling is less frequent in re-partnered couples (Burgoyne and Morison (1997), Ponthieux (2012)). When parents are separated (26% of our sample of young adults), both were interviewed in 46% of the cases and only one in 54%. To prevent non-response from biasing our income variable, we impute the income of the non-responding parent using the responses of the separated parents who both responded, controlling for the gender and income decile of the responding parent.

<figure><img src="image_12.png" /><figcaption>Figure C1: Proportion of Individuals Enrolled in Higher Education, by the Sum of Individual Pre-Tax Income of Both Parents</figcaption></figure>

*Notes:* This figure displays the proportion of individuals aged 19-24 who are currently enrolled, have enrolled in higher education without necessarily obtaining a degree, or already hold a higher education degree, by parental individual pre-tax income. The parental income used is the sum of the father's pre-tax individual income and the mother's pre-tax individual income, regardless of marital status. Deciles are based on this definition. We further decompose the top 10 percent of the distribution (x-axis). The black line represents the standard errors.

### C.3.3 Matching Richer Individuals with Better-Endowed Programs

There is still variance in within-field and within-track spending that we cannot account for because we are not able to link students to the particular university in which they are enrolled. This could lead to a downward bias in the estimate of the redistribution via higher education spending if wealthier individuals are able to attend better-endowed programs.

We thus perform a robustness check by assigning, for each combination of tracks and fields, the 10th (resp. 25th) percentile of the distribution of student-weighted costs – or the nearest higher percentile
when we do not have enough observations for the 10th (resp. 25th) percentile – to individuals whose parents are in the bottom 10 percent (resp. 25 percent) of the income distribution and the 75th percentile (resp. 90th percentile) – or the nearest lower percentile – to those whose parents are in the top 25 percent (resp. 10 percent). Our main results are relatively close to those found with this assumption of extreme concentration of wealthier individuals in the more expensive programs, meaning that there is not much variation in costs within programs enrolling a large portion of the student body. In our main specification, individuals in the top decile of the income distribution receive 2.7 times more public investment in higher education than those in the bottom decile (Table C7). When richer programs are associated with richer individuals, individuals in the top 10 percent of the income distribution benefit 3.7 times more than those in the bottom decile. This extreme concentration of spending on higher education, wherein the most affluent benefit from the most expensive courses of study, may be regarded as representing the upper bound of inequalities in public spending on higher education.

One of the other limitations of the cost data used is that we are not able to differentiate public spending among the most selective elite graduate schools, which could lead us to underestimate inequities in public spending. Indeed, some of these schools have very high per-student expenditures – for example, Berné and Métivier (2015) found that certain elite graduate schools have expenditures of €60,000 per student per year – and participation rates that remain highly unequal (Bonneau et al., 2021). In a final specification, we assume both that wealthier individuals attend better-endowed university programs, as above, and that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering schools, institutes of political studies, or other specialized schools), attend the better endowed ones. Individuals in the top decile of the income distribution account for about 30 percent of elite graduate school enrollment in our sample. We assume that individuals in the top 5 percent of the income distribution attend schools with endowments three times the typical endowment and those in the next 5 percent enroll in schools with twice the typical endowment. In this latter specification, which is the most comprehensive and included in the main body of the paper, we find that individuals at the top of the income distribution benefit from 4.5 times as much spending as individuals at the bottom (Table C7).

In our main specification, enrollment inequality explains about 70 percent of the inequality in public spending on higher education between individuals in the top and bottom income deciles. When we allocate wealthier individuals to better-endowed programs, the contribution of enrollment inequality to public spending inequality decreases mechanically but remains equal to about half of total spending differences (Table C8).
<table><caption>Table C7: Sensitivity of Results to Student-Program Matching</caption><thead><tr><th></th><th>Decile of parental<br>household income</th><th>(1)<br>Main specification</th><th>(2)<br>Association of<br>wealthier individuals<br>to better-endowed programs</th><th>(3)<br>Assumption (2) +<br>assumptions on the cost of<br>elite graduate schools</th></tr></thead><tbody><tr><td rowspan="10">All individuals</td><td>D1</td><td>7,800</td><td>6,200</td><td>6,200</td></tr><tr><td>D2</td><td>7,600</td><td>6,400</td><td>6,400</td></tr><tr><td>D3</td><td>8,800</td><td>7,800</td><td>7,900</td></tr><tr><td>D4</td><td>9,100</td><td>8,500</td><td>8,600</td></tr><tr><td>D5</td><td>10,000</td><td>9,400</td><td>9,500</td></tr><tr><td>D6</td><td>12,500</td><td>11,200</td><td>11,400</td></tr><tr><td>D7</td><td>12,200</td><td>11,200</td><td>11,300</td></tr><tr><td>D8</td><td>16,800</td><td>16,200</td><td>16,700</td></tr><tr><td>D9</td><td>21,300</td><td>21,700</td><td>22,200</td></tr><tr><td>D10</td><td>21,400</td><td>22,900</td><td>27,900</td></tr><tr><td></td><td>D10/D1 Ratio</td><td>2.7</td><td>3.7</td><td>4.5</td></tr><tr><td rowspan="10">Students or<br>former students</td><td>D1</td><td>22,200</td><td>17,500</td><td>17,600</td></tr><tr><td>D2</td><td>19,200</td><td>16,100</td><td>16,200</td></tr><tr><td>D3</td><td>18,900</td><td>16,700</td><td>16,900</td></tr><tr><td>D4</td><td>19,900</td><td>18,600</td><td>18,700</td></tr><tr><td>D5</td><td>18,500</td><td>17,300</td><td>17,400</td></tr><tr><td>D6</td><td>21,300</td><td>19,000</td><td>19,400</td></tr><tr><td>D7</td><td>23,200</td><td>21,300</td><td>21,500</td></tr><tr><td>D8</td><td>21,300</td><td>20,600</td><td>21,200</td></tr><tr><td>D9</td><td>26,700</td><td>27,200</td><td>27,800</td></tr><tr><td>D10</td><td>23,500</td><td>25,100</td><td>30,500</td></tr><tr><td></td><td>D10/D1 Ratio</td><td>1.1</td><td>1.4</td><td>1.7</td></tr></tbody></table>

Notes: This table presents public spending on higher education by parental income decile under different assumptions. The parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22-24-year-olds, while the length of study for 22-23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. Column (1) represents our main specification. Column (2) shows the results obtained by assigning wealthier individuals to the better-endowed programs in their track and field of study. In this simulation, for each combination of programs and fields we assign the 10th (resp. 25th) percentile of the distribution of student-weighted costs – or the nearest higher percentile when we do not have enough observations for the 10th (resp. 25th) percentile – to individuals whose parents are in the bottom 10 percent (resp. 25 percent) of the income distribution and the 75th percentile (resp. 90th percentile) – or the nearest lower percentile – to those whose parents are in the top 25 percent (resp. 10 percent) of the income distribution. Column (3) presents the same simulation as in column (2) with additional assumptions about the cost of highly selective elite graduate schools: that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering schools, institutes of political studies, or other specialized schools), enroll in the better endowed ones. More specifically, individuals in the top 5 percent of the income distribution are assumed to enroll in schools with endowments three times the typical endowment and those in the next 5 percent to enroll in schools with endowments twice the typical endowment.
<table><caption>Table C8: Decomposition of Differences in Public Spending on Higher Education: Three Variants of Spending</caption><thead><tr><th></th><th>(1)<br>Main specification</th><th>(2)<br>Association of<br>wealthier individuals<br>with better-endowed<br>programs</th><th>(3)<br>Assumption (2) +<br>wealthier individuals<br>attend more costly<br>elite graduate schools</th></tr></thead><tbody><tr><td>Enrollment in higher education</td><td>67</td><td>52</td><td>49</td></tr><tr><td>Duration of study</td><td>23</td><td>18</td><td>17</td></tr><tr><td>Annual public spending</td><td>10</td><td>30</td><td>33</td></tr></tbody></table>

*Notes:* This table shows the contribution of different factors – enrollment in higher education, differences in duration of study for students, differences in annual public spending for students – to the total difference in public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the parental income distribution (in percent). Parental income used is the sum of the pre-tax income of the father’s household and the mother’s household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- to 24-year-olds and the length of study for 22- and 23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. To decompose the contribution of different factors, we rely on Das Gupta (1991) decomposition method. See Appendix B.2.3 for more details on this decomposition method. Column (1) represents our main specification. Column (2) represents the results obtained by assigning wealthier individuals to programs that are better endowed in their track and field of study. In this simulation, for each combination of tracks and fields, we assign the 10th (resp. 25th) percentile of the distribution of student-weighted costs – or the next higher percentile when we do not have enough observations for the 10th (resp. 25th) percentile – to individuals whose parents are in the bottom 10 percent (resp. 25 percent) of the income distribution and the 75th percentile (resp. 90th percentile) – or the next lower percentile – to those whose parents are in the top 25 percent (resp. 10 percent) of the income distribution. Column (3) represents the same simulation as in column (2) with additional assumptions about the cost of highly selective elite graduate schools. We assume that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering, institutes of political studies, or other specialized schools), enroll in better endowed schools. More specifically, individuals in the top 5 percent of the income distribution are assumed to enroll in schools with endowments three times the typical endowment and those in the next 5 percent to enroll in schools with endowments twice the typical endowment.
## D.4 Additional Results

### D.4.1 Parental Income, Education, and Occupation: Patterns in Higher Education Enrollment
<table><caption>Table D9: Enrollment in Higher Education Based on Parents' Income, Occupation and Diploma</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Parent's income percentile rank</td><td>0.58***</td><td>0.55***</td><td>0.22***</td><td>0.27***</td><td>0.14***</td><td>0.100**</td></tr><tr><td>Log of potential financial transfers</td><td></td><td>0.0097***</td><td></td><td></td><td></td><td>0.0028</td></tr><tr><td>Father Primary school</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Father Middle School</td><td></td><td></td><td>0.00035</td><td></td><td>-0.027</td><td>-0.029</td></tr><tr><td>Father High School</td><td></td><td></td><td>0.051**</td><td></td><td>0.032</td><td>0.040*</td></tr><tr><td>Father Two-year degree</td><td></td><td></td><td>0.19***</td><td></td><td>0.13***</td><td>0.13***</td></tr><tr><td>Father Bachelor or Master degree</td><td></td><td></td><td>0.23***</td><td></td><td>0.16***</td><td>0.15***</td></tr><tr><td>Father M.D. or Ph.D.</td><td></td><td></td><td>0.26***</td><td></td><td>0.18***</td><td>0.19***</td></tr><tr><td>Mother Primary school</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Mother Middle School</td><td></td><td></td><td>0.14***</td><td></td><td>0.12***</td><td>0.13***</td></tr><tr><td>Mother High School</td><td></td><td></td><td>0.16***</td><td></td><td>0.13***</td><td>0.14***</td></tr><tr><td>Mother Two-year degree</td><td></td><td></td><td>0.37***</td><td></td><td>0.30***</td><td>0.31***</td></tr><tr><td>Mother Bachelor or Master degree</td><td></td><td></td><td>0.31***</td><td></td><td>0.20***</td><td>0.21***</td></tr><tr><td>Mother M.D. or Ph.D.</td><td></td><td></td><td>0.35***</td><td></td><td>0.22***</td><td>0.21***</td></tr><tr><td>Father Low SES</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Father Lower-middle SES</td><td></td><td></td><td></td><td>0.13***</td><td>0.099***</td><td>0.095***</td></tr><tr><td>Father Upper-middle SES</td><td></td><td></td><td></td><td>0.16***</td><td>0.11***</td><td>0.11***</td></tr><tr><td>Father High SES</td><td></td><td></td><td></td><td>0.24***</td><td>0.14***</td><td>0.15***</td></tr><tr><td>Mother Low SES</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Mother Lower-middle SES</td><td></td><td></td><td></td><td>0.095***</td><td>0.061***</td><td>0.062***</td></tr><tr><td>Mother Upper-middle SES</td><td></td><td></td><td></td><td>0.24***</td><td>0.13***</td><td>0.14***</td></tr><tr><td>Mother High SES</td><td></td><td></td><td></td><td>0.28***</td><td>0.17***</td><td>0.18***</td></tr><tr><td>Parents live together</td><td></td><td></td><td></td><td></td><td></td><td>-</td></tr><tr><td>Separated parents</td><td></td><td></td><td></td><td></td><td></td><td>-0.077***</td></tr><tr><td>Parisian area</td><td></td><td></td><td></td><td></td><td></td><td>-</td></tr><tr><td>Urban unit larger than 100,000 inhabitants</td><td></td><td></td><td></td><td></td><td></td><td>-0.032</td></tr><tr><td>Urban unit smaller than 100,000 inhabitants</td><td></td><td></td><td></td><td></td><td></td><td>-0.055**</td></tr><tr><td>Observations</td><td>3,947</td><td>3,947</td><td>3,066</td><td>3,329</td><td>3,046</td><td>3,013</td></tr><tr><td>R<sup>2</sup></td><td>0.114</td><td>0.116</td><td>0.196</td><td>0.176</td><td>0.214</td><td>0.221</td></tr></tbody></table>

Notes: This table reports the estimates of the effect of parental income percentile, potential financial transfers, parents' education, parents' occupation, family status and urban center size on enrollment in higher education. Parental income is the percentile rank calculated from the sum of the father's household and the mother's household pre-tax income, divided by two when two parental households are identified. The potential financial transfer variable represents what young adults would have received from their parents if they were studying, based on their characteristics (indicators for parents' income decile, number of siblings, father's degree, mother's degree, father's occupation, mother's occupation, parents' marital status, and size of urban center where the young adult lives). For parent's education we define six categories: elementary school certificate or less, middle school certificate, high school diploma or equivalent, two-year degree, bachelor's or master's degree, and M.D. or Ph.D. The mother's degree is missing for 4 percent of observations and the father's for 17 percent. For occupations, we rely on the Department of Education's statistical service classification into four socio-economic status groups (SES). High SES includes professionals, managers, CEOs, teachers, professionals and artists. Upper-middle SES corresponds to intermediate occupations, technicians, foremen and supervisors. Lower-middle SES refers to farmers, artisans, shopkeepers and employees. Low SES is defined as blue-collar workers and non-working people. Table B2 details the four SES categories and the corresponding occupations. The mother's occupation is missing for 4% of the observations and the father's for 9%. When the two parents do not live in the same city, we use the larger of the two.

<footnote>* p < 0.1, ** p < 0.05, *** p < 0.01</footnote>
D.4.2 Distribution of Higher Education Public Spending on Young Adults

In this section we present the distribution of public higher education expenditures dedicated to young adults, regardless of parental income.

Figure D2 shows the distribution of public spending on higher education aggregated over studies (up to age 24). About 40% of individuals have zero spending because they did not enter higher education. Only 10% of receive more than €30,000 of investment in higher education during their school career. The top 1% of individuals with the highest public expenditure on higher education receive on average €70,500 over 6 years of study. They receive 2.4 times more than individuals at the 90th percentile of higher education spending and 8.5 times more than the median (€70,500 versus €8,300 on average).

Figure D3 shows the correlation between the percentile rank of parental income and the percentile rank of public spending on higher education. The correlation is significant. On average, a 1-percentile increase in parental income is correlated with a 0.4-percentile increase within the distribution of public spending on higher education. On average, individuals whose parents are in the lowest decile of pre-tax income are in the 30th percentile of public spending on higher education, while those whose parents are in the highest decile are in the 68th percentile.

<figure><img src="image_13.png" /><figcaption>Figure D2: Distribution of Public Spending for Higher Education</figcaption></figure>

Notes: This figure shows the average public spending on higher education over 6 years (up to age 24), by public spending percentile. 42% of 22-24-year-olds have zero spending because they did not go into higher education at all. Unlike the rest of our analyses, this one does not consider parental income in calculating this distribution. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22-24-year-olds, extending the length of study for 22-23-year-olds by parental income decile based on the continuation rates observed in the sample.
<figure><img src="image_14.png" /><figcaption>Figure D3: Rank-Rank Correlation</figcaption></figure>

*Notes*: This figure plots the correlation between the percentile rank of parental income and that of public spending on higher education. The red line is the linear regression of the percentile rank of the former on the latter. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. The percentiles are calculated based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22-24-year-olds, extending the length of study for 22-23-year-olds by parental income decile based on the continuation rates observed in the sample.
D.4.3 Decompositions of Inequalities in Public Spending on Higher Education

<figure><img src="image_15.png" /><figcaption>Figure D4: Duration of Study</figcaption></figure>

(a) All individuals

(b) Students or former students

Notes: These figures represent the duration of studies for all individuals (Panel (a)) and individuals entering higher education between the ages of 18 and 24 (Panel (b)), as a function of parental income. We restrict the sample to 22-24-year-olds, extending the length of study for 22-23-year-olds by parental income decile based on the continuation rates observed in the sample. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are calculated based on this definition. The black line represents standard errors.

<figure><img src="image_16.png" /><figcaption>Figure D5: Extensive and Intensive Margins</figcaption></figure>

Notes: This figure shows the decomposition of inequality in public spending on higher education between an extensive margin – differences in enrollment in higher education – and an intensive margin – differences in spending on higher education for those who enroll, related to both inequality in duration of study and inequality in annual spending. We restrict the sample to 22-24-year-olds, extending the length of study for 22-23-year-olds by parental income decile based on the continuation rates observed in the sample. The dark curve represents the current level of inequality in public spending on higher education (corresponding to the dark grey in Figure 8a). The left-hand curve represents the level of inequality that would result if, given the current level of spending inequality, all individuals enrolled in higher education. The right-hand curve represents the level of inequality that would result if, given the current level of inequality in participation, all individuals had the same expenditure on higher education. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are calculated based on this definition (x-axis).
D.4.4 Accounting for Parental Taxes in the Higher Education Spending Dis-
tribution

One common objection to the finding of regressive government spending is that wealthier parents con-
tribute more to the funding of higher education through their taxes. We accordingly estimate a net
benefit measuring the degree of redistribution of public spending on higher education, net of the share
of the taxes paid by parents that must be allocated to finance this expenditure. Given our data, we
consider a static framework in which young adults' higher education is funded by a contribution from
all taxpayers, including their parents. Alternative methods might adopt a life-cycle approach, wherein
individuals repay through taxes the benefits they received during their studies. We first outline the
accounting method devised to incorporate parents' taxes into the evaluation of the redistributivity of
higher education spending, followed by a presentation of our findings.

D.4.4.1 Methodology

We assess the redistribution via public spending on higher education and propose a method of account-
ing for parents' taxes that go to finance this particular public expenditure. This section details and
motivates the assumptions concerning the scope of higher education spending, the duration of study or
the taxes considered that underlie our calculation of the degree of redistribution of public spending on
higher education, once parental tax contributions are taken into account.

**Scope of Public Spending on Higher Education.** We restrict our analysis to higher education expenditures alone. We draw upon data from the "Cost Awareness" project<fnref n="40" />, designed to assess the expenditures of various higher education institutions. Conducted between 2016 and 2019 at 65 institutions enrolling a third of all French students, this survey enables us to segregate costs by program and field of study and to discern the cost of higher education net of research costs, which are included in more aggregated data. We use the main survey's information on individual studies (ENRJ) to match the two data sources at the level of each track and field of study.<fnref n="41" /> Consequently, by comparison with more highly aggregated data this data facilitate a more precise estimation of public spending on higher education per individual. For all programs, we count instructional expenditure only.

**Period Considered.** Our main sample comprises young adults aged 19 to 24, spanning a 6-year per-
iod. This range encapsulates the majority of student trajectories, but it omits expenditures on those who
continue their education post-24. We also project fiscal contributions over a 6-year timeframe by mul-
tiplying the year's parental contribution by 6. In order to assess public spending on higher education,
the focus is on individuals aged 22 to 24, those within our dataset who are the most advanced in their
academic journey. The study duration for individuals aged 22 and 23 is extended based on continuation
rates observed in the sample, stratified by parental income decile.

<footnote n="40">⁴⁰Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux 2019).</footnote>

<footnote n="41">⁴¹Refer to Appendix B.2.2.3 for more detail on these new higher education cost data and Table B5 for annual public spending per student, divided by field of study and track, i.e. the level of disaggregation used here.</footnote>
Aggregate public spending on higher education, $S_{all}^{HE}$, is given by:

$$S_{all}^{HE} = \sum_{j=1}^{y} (S_{j}^{HE} \times Weight_{j}) \qquad (4)$$

Where:

* $y$ is the total number of young adults in our sample;

* $S_{j}^{HE}$ denotes the expenditures on completed higher education over a 6-year period for the $j^{th}$ young adult;

* $Weight_{j}$ is the sampling weight allocated to the $j^{th}$ young adult in the ENRJ survey;

* $\sum_{j=1}^{n} Weight_{j} = Y$ ensures that $Y$ corresponds to the total number of young adults in the broader population

**Taxes Financing Higher Education.** The literature on distributed national accounting takes account of all taxes and redistribution that occurs via direct public support and indirect in-kind transfers (e.g., public financing of education or health). In our case, the consideration of taxes to study the redistribution effected by the public financing of higher education is complicated by the fact that we are only interested in one specific item, namely higher education expenditure. In contrast to what has generally been done in the literature, where taxes are considered as a whole, we consider only unearmarked taxes – those that help to finance higher education – and only to the extent that they finance higher education. Deducting only the taxes needed to cover the expenditures under consideration, but extending the contributors to all taxpayers, is more consistent with the fact that: (i) parents of young adults are not the only taxpayers funding higher education, and (ii) not all of their taxes are allocated to higher education. In contrast to life-cycle approaches focused on the individual, we consider a state that balances its higher education budget annually with unearmarked tax funds.

We assume that higher education is funded by households through direct and indirect taxation and by firms through corporate taxes but not through direct contributions (unlike, say, apprenticeships); nor is it funded by public debt. The taxes most likely to contribute to higher education are income tax, VAT, corporate taxes that we allocate to households, and other indirect taxes – tax on energy products (TICPE) and excise duties. We do not take into account social security contributions or taxes dedicated to the financing of social security (CGS and CRDS). Nor do we take account of local taxes, which contribute only marginally to higher education.

Our sample consists of parents of young adults. Since these are not the only people involved in financing higher education, we need to estimate the share of taxes to be considered out of sample. We use the 2014 Tax and Social Income Survey,<fnref n="42" /> which polls a representative sample of all taxpayers in France.

We calculate the total amount of unearmarked taxes paid over six years by French taxpayers as follows:

$$Taxes_{all} = 6 \times \sum_{i=1}^{n} [(IT_{i} + VAT_{i} + CT_{i} + E_{i}) \times Weight_{i}] \qquad (5)$$

<footnote n="42">⁴²Enquête sur les revenus fiscaux et sociaux (ERFS), 2014</footnote>
Where:

* Taxesall represents the total amount of unearmarked taxes paid by all taxpayers over a six-year period;

* n is the total number of observations in the survey;

* ITi is the income tax paid by i;

* VATi the value added tax paid by i;

* CTi the corporate tax paid by i;

* Ei the energy and excise taxes paid by i;

* Weighti the weight assigned to individual i in the ERFS survey;

* $\sum_{i=1}^{n} Weight_{i} = N$ ensures that N corresponds to the total number of taxpayers in the general population.

The total unearmarked taxes paid by all French taxpayers over six years amounts to €1.50 trillion.

**Proportion Needed To Fund Higher Education Expenditures.** We calculate the share of unearmarked taxes needed to fund public spending on higher education as follows:

$$Share\_Taxes^{HE} = \frac{S_{all}^{HE}}{Taxes_{all}} \qquad (6)$$

Where:

* Share_TaxesHE represents the share of taxes devoted to public spending on higher education;

* $S_{all}^{HE}$ is the sum of total public spending on higher education devoted to individuals up to age 24 as derived in Equation 4;

* Taxesall is the sum of all unearmarked taxes paid by all taxpayers over six years as derived in Equation 5.

In total, 4% of all unearmarked taxes paid by taxpayers must go to higher education. This proportion is consistent with official data from national accounts.<fnref n="43" />

**Taxes Paid by Parents.** Our database incorporates administrative records of parents' income tax payments. We calculate the proportion of income allocated to VAT, corporate income tax, and other indirect taxes for each standard-of-living decile based on the national accounts (André et al. (2023)). We use standard-of-living deciles to match the figures in the study cited (André et al. (2023)). This differs

<footnote n="43">⁴³The 2021 Finance Bill indicates an amount of €220 billion for the sum of the taxes we are considering here (VAT, income tax, corporate tax and domestic consumption tax on petroleum and energy products) and €10 billion (4.5% of the tax revenue considered) for higher education excluding university research (see https://www.budget.gouv.fr/budget-etat/ for unearmarked tax amounts and https://www.budget.gouv.fr/budget-etat/ministere?annee=118&loi_finances=50&type-budget=all&ministere=34726&programme=30340&type_donnee-budget=ae&op=Valider for higher education spending). We cannot report these figures for 2014, the year of the survey, because government spending was not reported with the breakdown by spending item at that time.</footnote>
from the rest of the article, where the deciles pertain to parents' pre-tax household income. The tax rates derived for each standard-of-living decile, presented in Table D10, are subsequently applied to the pre-tax income of parents in our database, based on their standard-of-living decile.

<table><caption>Table D10: Taxes as a Proportion of Pre-Tax Income, by Overall Population Standard-of-Living</caption><thead><tr><th rowspan="2">Standard-of-living decile</th><th colspan="3">Distributional national accounts at the macro level</th><th>ENRJ administrative data</th></tr><tr><th>VAT</th><th>Energy and excise taxes</th><th>Corporate tax</th><th>Income tax</th></tr></thead><tbody><tr><td>D1</td><td>0,17</td><td>0,06</td><td>0,02</td><td>0,01</td></tr><tr><td>D2</td><td>0,14</td><td>0,05</td><td>0,01</td><td>0,03</td></tr><tr><td>D3</td><td>0,12</td><td>0,04</td><td>0,01</td><td>0,02</td></tr><tr><td>D4</td><td>0,12</td><td>0,04</td><td>0,01</td><td>0,03</td></tr><tr><td>D5</td><td>0,11</td><td>0,04</td><td>0,01</td><td>0,03</td></tr><tr><td>D6</td><td>0,11</td><td>0,03</td><td>0,01</td><td>0,04</td></tr><tr><td>D7</td><td>0,11</td><td>0,03</td><td>0,01</td><td>0,04</td></tr><tr><td>D8</td><td>0,10</td><td>0,03</td><td>0,02</td><td>0,05</td></tr><tr><td>D9</td><td>0,10</td><td>0,03</td><td>0,02</td><td>0,08</td></tr><tr><td>D10</td><td>0,08</td><td>0,01</td><td>0,11</td><td>0,13</td></tr><tr><td>Total</td><td>0,11</td><td>0,03</td><td>0,04</td><td>0,04</td></tr><tr><td>Public support included in pre-tax parental income</td><td>Yes</td><td>Yes</td><td>No</td><td>/</td></tr></tbody></table>

Notes: This table shows the proportion of pre-tax income that goes to pay various taxes (VAT, energy and excise taxes, corporate taxes, and income taxes) according to the standard-of-living decile of the entire population. For instance, households in the first standard-of-living decile devote 17% of their total pre-tax income (which includes public support) to VAT. The percentage of income allocated to VAT, corporate income tax, and other indirect taxes for each decile is determined by dividing the tax revenue produced by that decile by its collective income, including public support for VAT and excise duties computation. The results are derived from the distributed national accounts (André et al. (2023)). Note that the standard-of-living deciles pertain to the broader population, for consistency with the figures used in André et al. (2023). This differs from the rest of the article where the deciles are based on pre-tax parental income. For the income tax data, we rely on the administrative variable provided in the ENRJ survey.

**Net Benefit From Public Spending on Higher Education.** We then apply the share of taxes dedicated to the public funding of higher education, which comes to 4%, to the unearmarked taxes paid by parents. This calculation yields the estimated portion of taxes from parents that goes to higher education. To ascertain their net benefit, we subtract this tax amount from the higher education spending received by their child. This net benefit can be understood as the advantage parents gain from the public funding of higher education after accounting for the taxes they have paid for this funding.

The mean net benefit for each parental income decile *d* is therefore calculated as follows:

$$Net\_Benefit_d = \frac{1}{n_d} \sum_{i=1}^{n_d} (S_i^{HE} - (0.04 \times Taxes_i \times 6)) \quad (7)$$

Where:
* Net_Benefit<sub>d</sub> denotes the average net benefit for the d<sup>th</sup> parental income decile;

* $n_d$ represents the number of individuals in decile d;

* $S_j^{HE}$ is the higher education spending over a 6-year period for the i<sup>th</sup> individual;

* Taxes<sub>i</sub> denotes the taxes paid by the i<sup>th</sup> individual's parents;

* The factor of 0.04 represents the 4% share of taxes going to higher education, defined above as $Share\_Taxes^{HE}$;

* Multiplication by 6 adjusts the tax amount over the relevant period under study for higher education expenses.

Thanks to the specifics of our survey data, this equation does not need to account for the number of children parents have. We establish deciles based on the parental income of the young adults surveyed, who are representative of individuals aged 18 to 24. In cases where a given demographic group has a larger number of children on average, these offspring will naturally cover a broader spectrum of the parental income distribution. While it is not universally true that one child aged 18-24 equates to one parent observation, this assumption is valid in our survey data, given the sampling method (1 young adult = 1 parental household, or 2 in the case of separated parents). Therefore, in the equation for computing the redistribution of higher education public spending, net of parental tax contributions, we can assume that 1 parental household accounts for 1 young adult, with the survey weights adjusting for any overrepresentation or underrepresentation of certain demographics. This approach would be different in comprehensive administrative data, where all young individuals and their parents are accounted for, allowing multiple young adults in the 18-24 age bracket to be linked to the same parents.
D.4.4.2 Results

In this section, we discuss the redistributive effects of public funding for higher education, considering
the taxes paid by parents specifically for this purpose, as elaborated in the preceding methodological
section.

On average, parents receive a net benefit of €9,600 over a six-year period, as depicted in Figure D6 and Table D11. Interestingly, parents benefit from horizontal solidarity since 76% of the higher education expenses for their young adults are funded by taxpayers who do not have young adult children. However, this average masks variations among households. Individuals with children enrolled in higher education receive a net benefit of approximately €18,100, nearly twice as great as the average; conversely, those without children in higher education experience a net disadvantage of €1,900, as they contribute to funding higher education without directly benefiting from it.

When examining the impact of the taxes funding higher education, it is clear that while this spending
remains regressive, there is a noteworthy shift at the top of the income distribution. The net benefit is
nearly twice as great in the upper third of the distribution as in the lower third (€13,900 versus €6,900).
This benefit rises with parental income, particularly in the upper half of the distribution (from P50 to
P90), peaking at about €17,000, as evidenced in Table D11 and Figure D6. Yet the top decile, owing to
higher taxation and despite their higher enrollment rate, obtain a net benefit a third smaller than that of
the ninth decile (€11,600 vs. €17,000). Nonetheless, this figure still exceeds the net benefit for the bottom
decile (€6,800).
<figure><img src="image_17.png" /><figcaption>Figure D6: Spending for Higher Education, Net of Parental Taxes Funding Higher Education</figcaption></figure>

Notes: This figure plots the distribution of public higher education expenditure on young adults over a 6-year period, according to parents' pre-tax income, taking into account parents' taxes going to fund higher education. The solid line shows public spending on higher education up to age 24. Analysis for educational pathways up to that age targets the 22-24 age bracket, extending study periods for 22-23-year-olds based on observed continuation rates by parental income deciles in the sample. This corresponds to the dark grey bars in Figure 8. The dashed line with empty dots shows the same variable, net of the 4 percent parental tax contribution – corresponding to the share of all taxpayers' unearmarked taxes required for public higher education. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition (x-axis). We use the "Cost Awareness" project and MESRI-SIES (2021) data for higher education spending.
<table><caption>Table D11: Average Public Spending and Fiscal Contribution on Young Adults Over 6-Year Period, by Parents' Pre-Tax Household Income (in Euros)</caption><thead><tr><th></th><th>(1)<br>Public<br>spending<br>for higher<br>education<br>(A)</th><th>(2)<br>4% of fiscal<br>contribution<br>of parents<br>over 6 years<br>(B)</th><th>(3)<br>Net benefit<br>for higher<br>education<br>(A)-(B)</th></tr></thead><tbody><tr><td>P0-P10</td><td>7,800</td><td>1,000</td><td>6,800</td></tr><tr><td>P10-P20</td><td>7,600</td><td>1,300</td><td>6,300</td></tr><tr><td>P20-P30</td><td>8,800</td><td>1,600</td><td>7,200</td></tr><tr><td>P30-P40</td><td>9,100</td><td>1,800</td><td>7,300</td></tr><tr><td>P40-P50</td><td>10,000</td><td>1,900</td><td>8,200</td></tr><tr><td>P50-P60</td><td>12,500</td><td>2,400</td><td>10,100</td></tr><tr><td>P60-P70</td><td>12,200</td><td>2,500</td><td>9,700</td></tr><tr><td>P70-P80</td><td>16,800</td><td>2,800</td><td>14,000</td></tr><tr><td>P80-P90</td><td>21,300</td><td>4,200</td><td>17,100</td></tr><tr><td>P90-P100</td><td>21,400</td><td>9,800</td><td>11,600</td></tr><tr><td>Average</td><td>12,500</td><td>2,800</td><td>9,600</td></tr></tbody></table>

Notes: This table shows the distribution of higher education public expenditures on young adults according to the fiscal contributions of their parents, classed by pre-tax income. Parental income is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. We use the "Cost Awareness" project and MESRI-SIES (2021) data for higher education spending. Values are rounded to the nearest €100. Column (1) details public spending on higher education up to age 24. Analysis by educational pathways up to that age targets the 22-24 age bracket, extending study periods for 22-23-year-olds based on observed continuation rates by parental income deciles in the sample. Column (2) represents 4% of the taxes contributed by parents over six years, reflecting the portion of unearmarked taxes from all taxpayers needed to finance public higher education. This figure is derived from a representative dataset of all taxpayers (ERFS), with indirect and corporate tax imputed on the basis of the distributed national accounts (André et al. (2023)). Column (3) displays the net benefit from higher education, calculated by subtracting the parental tax contribution from the total public expenditure on higher education.
### D.4.5 Predicted Lifetime Earnings

Disparities in higher education enrollment based on parental income lead to differences in educational attainment, which in turn result in disparities in lifetime income, presumably a factor in France's relatively low social mobility (Kenedi and Sirugue (2023)). This Appendix offers a descriptive analysis, specifically examining the implications of our main findings in terms of predicted lifetime earnings.

**Educational Attainment Disparities.** The disparities in attendance highlighted in this study lead directly to significant disparities in educational achievement. For instance, 45% of 22-24-year-olds<fnref n="44" /> whose parents are in the top 5% of the income distribution have obtained a bachelor's degree, in stark contrast to just 10% of individuals from the bottom 10%. This pronounced disparity is further illustrated in Figure D7.

<figure><img src="image_18.png" /><figcaption>Figure D7: Highest Degree Earned According to Parents' Pre-Tax Income</figcaption></figure>

*Notes:* This figure reports the highest diploma obtained by individuals aged 22-24, by parental income, i.e. the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition.

**Prediction of Lifetime Earnings Based on Educational Attainment.** To predict lifetime earnings, we combine the educational attainment of 22-24-year-olds observed in our sample with the first empirical measure of lifetime earnings for France, produced by Garbinti et al. (2023).<fnref n="45" /> In their estimation of lifetime earnings, they use six distinct educational levels. Lifetime earnings are average earnings over 31 working years, from age 25 to 55, focusing on the cohort born in 1962.<fnref n="46" /> Additionally, we included a differential in lifetime earnings between Master's degree holders and elite graduate school graduates, as calculated by Dabbaghian and Péron (2021) using self-reported earnings data. Estimated lifetime earnings by gender and education, which form the basis of our analysis, are plotted in Figure D8.

<footnote n="44">⁴⁴For this analysis, we restrict our sample to individuals aged 22-24, who are the most advanced in their studies within our sample.</footnote>

<footnote n="45">⁴⁵We would like to thank Bertrand Garbinti and Vladimir Pecheu for sending us customized data on lifetime earnings by gender and educational attainment.</footnote>

<footnote n="46">⁴⁶This older cohort was selected for its complete labor market trajectory, providing a comprehensive view of lifetime earnings, which are clearly not yet available for our younger cohort (aged 19-24 in 2014).</footnote>
<figure><img src="image_19.png" /><figcaption>Figure D8: Lifetime Earnings by Gender and Educational Level</figcaption></figure>

*Notes:* This figure displays annualized lifetime earnings in thousands of 2015 euros, as estimated by Garbinti et al. (2023). It is defined as the mean income earned during the 31 potential working years from ages 25 to 55. This calculation is for the latest cohort who completed their careers, born in 1962 and thus turning 25 in 1987.

**Underlying Hypothesis.** In our analysis, we correlate the educational attainment of individuals aged 22 or older with their corresponding average lifetime earnings. This approach allows us to explore how differences in educational achievement relate to variations in lifetime income, under specific assumptions outlined below. First, these estimates are based purely on educational attainment levels, ignoring individual variations in earnings within each level. Second, they draw on data from much earlier cohorts who have completed their careers. Third, we do not account for variations in educational returns influenced by parents' socio-economic backgrounds, due to the lack of substantial evidence for such effects in France (Courtieux (2011)), and the lack of detailed figures on returns by degree and socio-economic backgrounds. Lastly, the analysis is purely descriptive in intent. We describe variations in predicted lifetime earnings among young adults with certain characteristics, rather than attempting to ascertain causal relationships.

**Lifetime Earnings Gap According to Higher Education Participation and Parental Income.** Based on the foregoing assumptions, young adults who have enrolled in higher education are predicted to have lifetime earnings approximately 33% higher than their counterparts with no higher education. This figure is descriptive and encompass both the direct causal effect of higher education and the selection factors involved in determining attendance to higher education. The differences in enrollment outlined in our study suggest a disparity of 1.27 between the predicted lifetime earnings of young adults whose parents are in the bottom and the top 10% income brackets. There is a disparity of 1.19 in predicted lifetime earnings between the bottom and the top 30%.

**Bridging the Gap Between the Bottom and the Middle of the Parental Distribution.** As is reported in our main analysis, there is a notable disparity in higher education participation according to parental income. Participation rates for young adults from the bottom 30% of the income distribution are, on average, 15 percentage points lower than those from the middle bracket (P50-P60). Considering the varying lengths of study correlated with parental income, the participation rate for the bottom third would need to increase by 19 percentage points to achieve a level of public spending on higher education equivalent to that for the middle income group.
We descriptively simulate the extent to which greater participation among the most disadvantaged would affect our lifetime earnings predictions. This simulation operates under the significant assumption that individuals from the most disadvantaged group, upon increased participation, would achieve the same level of educational attainment as current students from the bottom 30% of the income distribution, and would have the same returns.<fnref n="47" /> Under this assumption, our findings suggest that the predicted lifetime incomes for this disadvantaged group would increase by an average of 5.7%, and for young adults overall by 1.6%. Consequently, the earnings gap between the most and the least affluent 30% would decrease from a factor of 1.19 to 1.12. Note that this estimate does not account for general equilibrium effects and assumes that the returns to higher education would remain constant even with expanded participation.

<footnote n="47">⁴⁷Expanding access through a shift in earlier orientation could make this hypothesis plausible. Ichou and Vallet (2012) found that students from less privileged backgrounds would have a 21-p.p. higher enrollment rate in academic and technical high school tracks if they had the same inclination to choose these tracks as upper-class students, after controlling for prior academic achievement.</footnote>
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