# Parental Income and Higher Education Patterns: 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

2022.05RR

Version révisée
CES

Centre d'Économie de la Sorbonne
UMR 8174

Parental Income and Higher Education Patterns:
Evidence From France

Cécile BONNEAU, Sébastien GROBON

2022.05RR

Version révisée
Parental Income and Higher Education Patterns:
Evidence From France

Cécile Bonneau* Sébastien Grobon†

First version: January 2022
This version: December 2023

Abstract

In this paper, we present new stylized facts on inequality of enrollment in higher education by 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 children entering higher education. This overall degree of inequality is surprisingly close to that observed in the United States. We identify potential explanatory factors driving comparable levels of inequality in the two countries with markedly different institutional contexts. Finally, we explore a consequence of enrollment disparities by assessing the distribution of public 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: cecile.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">¹Declarations 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* (DGESIP)) for higher education spending (*Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche* project).</footnote>
Parental Income and Higher Education Patterns:
Evidence From France

Cécile Bonneau* Sébastien Grobon†

First version: January 2022
This version: December 2023

**Abstract**

In this paper, we present new stylized facts on inequality of enrollment in higher education by 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 children entering higher education. This overall degree of inequality is surprisingly close to that observed in the United States. We identify potential explanatory factors driving comparable levels of inequality in the two countries with markedly different institutional contexts. Finally, we explore a consequence of enrollment disparities by assessing the distribution of public 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: cecile.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">¹Declarations 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* (DGESIP)) 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, for instance, it increased eight-fold from 310,000 in 1960 to 2,551,000 in 2015 (MESRISIES, 2021). Is this increase in enrollment in higher education due to mass schooling actually a sign of equal opportunity? In this paper, we reassert the persistence of marked inequality in higher education attendance patterns, observing it from an innovative perspective, namely according to parental income.

Despite the extensive documentation of inequalities based on social origin (e.g. parents' education or occupation), since 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), Chetty et al. (2020)), Norway (Bütikofer et al., 2019), and Germany (Dodin et al., 2021) 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. The first is lack of administrative data on parental income in education data and the presence of significant biases in self-reported income (Moore et al., 2000), which complicates the interpretation of survey results; the second is the uneven representativeness of the surveys themselves. In fact, the specific situation of young adults raises difficult issues of survey design, such as multiple residences. This concern is important, because unrepresentative samples are subject to homogeneity bias and may underestimate the relationship between parental income and child outcomes (Solon, 1992).

In this article, we use an innovative survey design<fnref n="2" /> where young adults are surveyed regardless of housing status (parents' home, own apartment, student housing), and parental income is retrieved from administrative tax data. This produces novel descriptive estimates of inequalities in attendance patterns in higher education by parental income in France. To the best of 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 whole population and a reliable measure of parental income. We organize our analysis into three parts.

The first part presents new statistics on attendance patterns in higher education by parental income in France. Following the literature, we use the percentile rank of parental pre-tax household income<fnref n="3" /> to study relative attendance in higher education. We find large 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 is used more commonly than the log of income to measure intergenerational mobility because it yields more stable estimates (Chetty et al., 2014).</footnote>
# 1 Introduction

Since the 1960s, the number of university students has soared throughout the developed world. In France, for instance, it increased eight-fold from 310,000 in 1960 to 2,551,000 in 2015 (MESRISIES, 2021). Is this increase in enrollment in higher education due to mass schooling actually a sign of equal opportunity? In this paper, we reassert the persistence of marked inequality in higher education attendance patterns, observing it from an innovative perspective, namely according to parental income.

Despite the extensive documentation of inequalities based on social origin (e.g. parents' education or occupation), since 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), Chetty et al. (2020)), Norway (Bütikofer et al., 2019), and Germany (Dodin et al., 2021) 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. The first is lack of administrative data on parental income in education data and the presence of significant biases in self-reported income (Moore et al., 2000), which complicates the interpretation of survey results; the second is the uneven representativeness of the surveys themselves. In fact, the specific situation of young adults raises difficult issues of survey design, such as multiple residences. This concern is important, because unrepresentative samples are subject to homogeneity bias and may underestimate the relationship between parental income and child outcomes (Solon, 1992).

In this article, we use an innovative survey design<fnref n="2" /> where young adults are surveyed regardless of housing status (parents' home, own apartment, student housing), and parental income is retrieved from administrative tax data. This produces novel descriptive estimates of inequalities in attendance patterns in higher education by parental income in France. To the best of 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 whole population and a reliable measure of parental income. We organize our analysis into three parts.

The first part presents new statistics on attendance patterns in higher education by parental income in France. Following the literature, we use the percentile rank of parental pre-tax household income<fnref n="3" /> to study relative attendance in higher education. We find large 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 is used more commonly than the log of income to measure intergenerational mobility because it yields more stable estimates (Chetty et al., 2014).</footnote>
enrollment as a function of parental income: at the bottom of the income distribution, around 35 percent of individuals aged 19-24 enroll in higher education, compared with a more than 90 percent at the top. Enrollment in master's degree and selective programs is even more unequal. In the bottom 40 percent of the income distribution, under 10 percent of children do enroll in master's degree programs and under 5 percent to selective programs, compared with about 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). We find that the degree of inequality in France is only marginally lower than in the United States, despite the important institutional differences between the two countries. Specifically, a 10-percentile increase in the 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 is different: in the U.S. the effect of income on enrollment in higher education is linear, whereas in France it is much greater in the upper than in the lower half of the parental income distribution: a 10-percentile increase in rank is associated with a 4.9 p.p. increase in attendance in higher education in the lower half of the income distribution and 8.6 p.p. in the upper half.

In the second part of the paper, we scrutinize the variations in institutional contexts between France and the United States to shed new light on the surprising similarity in their levels of higher education inequality. We present descriptive statistics comparing the two countries to examine the hypothesis that the comparable level of inequality in higher education attendance patterns in both France and the United States results from a mix of different institutional factors exerting opposing influences on educational disparities. The higher cost of attending higher education as well as greater income inequalities in the United States would suggest stronger higher education inequalities. The paradox of a similar level of inequalities in France can be reconciled if we consider the strong correlation between academic performance of parents socio-economic background, as well as the existence of early tracking.

In the third section, we compare the distribution of higher education spending 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 higher education public spending in France. While disaggregated data for the most expensive elite programs are unfortunately lacking, we find substantial differences in higher education spending along the parental income distribution. On average, children 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 for those in the top 10 percent – broken down into €21,000 of public investment and €11,000 of private investment through the tuition paid by their parents. In France, attendance disparities are the main contributor of public spending inequalities by parental income, while differences in annual cost and duration of studies have a more limited role.

Our paper is structured as follows. We begin in Section 2 by presenting the related literature. Section 3 describes the institutional context of the French higher education system. Section 4
enrollment as a function of parental income: at the bottom of the income distribution, around 35 percent of individuals aged 19-24 enroll in higher education, compared with a more than 90 percent at the top. Enrollment in master's degree and selective programs is even more unequal. In the bottom 40 percent of the income distribution, under 10 percent of children do enroll in master's degree programs and under 5 percent to selective programs, compared with about 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). We find that the degree of inequality in France is only marginally lower than in the United States, despite the important institutional differences between the two countries. Specifically, a 10-percentile increase in the 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 is different: in the U.S. the effect of income on enrollment in higher education is linear, whereas in France it is much greater in the upper than in the lower half of the parental income distribution: a 10-percentile increase in rank is associated with a 4.9 p.p. increase in attendance in higher education in the lower half of the income distribution and 8.6 p.p. in the upper half.

In the second part of the paper, we scrutinize the variations in institutional contexts between France and the United States to shed new light on the surprising similarity in their levels of higher education inequality. We present descriptive statistics comparing the two countries to examine the hypothesis that the comparable level of inequality in higher education attendance patterns in both France and the United States results from a mix of different institutional factors exerting opposing influences on educational disparities. The higher cost of attending higher education as well as greater income inequalities in the United States would suggest stronger higher education inequalities. The paradox of a similar level of inequalities in France can be reconciled if we consider the strong correlation between academic performance of parents socio-economic background, as well as the existence of early tracking.

In the third section, we compare the distribution of higher education spending 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 higher education public spending in France. While disaggregated data for the most expensive elite programs are unfortunately lacking, we find substantial differences in higher education spending along the parental income distribution. On average, children 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 for those in the top 10 percent – broken down into €21,000 of public investment and €11,000 of private investment through the tuition paid by their parents. In France, attendance disparities are the main contributor of public spending inequalities by parental income, while differences in annual cost and duration of studies have a more limited role.

Our paper is structured as follows. We begin in Section 2 by presenting the related literature. Section 3 describes the institutional context of the French higher education system. Section 4
describes the data and the main variables of our analysis. In Section 5, we present the main
results in terms of enrollment in higher education according to parental income. Section 6
delves into a comparison of institutional factors in France and the United States, examining
how they might contribute to the observed similarities in higher education inequalities between
the two countries. Section 7 examines the redistribution that occurs through public spending
on higher education, related to these attendance inequalities in higher education. 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). Ensuring broad and equitable enrollment in higher education can be seen as a corollary of Rawls’ theory of justice (Rawls, 1971) and his second principle, namely equality of opportunity, which means that the assignment of individuals to a place in the social hierarchy is not fixed by birth. There is currently no consensus on the level of social mobility in France compared to other countries.<fnref n="4" /> Given this ambiguity, it is crucial to compare how enrollment in higher education – a known driver of social mobility (Blau and Duncan, 1967; Chetty et al., 2020) – varies by parental income in France relative to other countries.

Our paper refers more directly to the literature directed to estimating the elasticity of ed-
ucational 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 enroll-
ment in higher education in France contributes in two ways. First, the vast majority of studies
on this issue refer to the U.S., whereas in most European countries the effect of income on uni-
versity attendance has not been assessed. And yet, the magnitude of the effect could depend at
least partly on country-specific institutional factors such as higher education cost, the diversity
of higher education programs, information dissemination about the returns to higher educa-
tion, existing public aid or the relationship between students’ academic performance and their

<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. However, other research, such as by Corak (2016) and Perez-Arce et al. (2016), suggests that France has lower social mobility than one might expect, especially given its moderate income inequality. This inconsistency challenges the commonly accepted "Gatsby curve," which typically shows a 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 seen 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 of educational attainment with respect to parental income ranges from 3 to 80 percent, and 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. In France, Ben-Halima et al. (2014) show an increase in the effect of father's income on educational outcomes between 1993 and 2003 using a parental income variable estimated on the basis of other observable characteristics (father's occupation and education). We update and extend this work with a survey matched with tax administrative data, which allows direct study of the relationship between parental income and children's education, with no need to estimate parental income.</footnote>
describes the data and the main variables of our analysis. In Section 5, we present the main
results in terms of enrollment in higher education according to parental income. Section 6
delves into a comparison of institutional factors in France and the United States, examining
how they might contribute to the observed similarities in higher education inequalities between
the two countries. Section 7 examines the redistribution that occurs through public spending
on higher education, related to these attendance inequalities in higher education. 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). Ensuring broad and equitable enrollment in higher education can be seen as a corollary of Rawls’ theory of justice (Rawls, 1971) and his second principle, namely equality of opportunity, which means that the assignment of individuals to a place in the social hierarchy is not fixed by birth. There is currently no consensus on the level of social mobility in France compared to other countries.<fnref n="4" /> Given this ambiguity, it is crucial to compare how enrollment in higher education – a known driver of social mobility (Blau and Duncan, 1967; Chetty et al., 2020) – varies by parental income in France relative to other countries.

Our paper refers more directly to the literature directed to estimating the elasticity of ed-
ucational 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 enroll-
ment in higher education in France contributes in two ways. First, the vast majority of studies
on this issue refer to the U.S., whereas in most European countries the effect of income on uni-
versity attendance has not been assessed. And yet, the magnitude of the effect could depend at
least partly on country-specific institutional factors such as higher education cost, the diversity
of higher education programs, information dissemination about the returns to higher educa-
tion, existing public aid or the relationship between students’ academic performance and their

<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. However, other research, such as by Corak (2016) and Perez-Arce et al. (2016), suggests that France has lower social mobility than one might expect, especially given its moderate income inequality. This inconsistency challenges the commonly accepted "Gatsby curve," which typically shows a 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 seen 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 of educational attainment with respect to parental income ranges from 3 to 80 percent, and 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. In France, Ben-Halima et al. (2014) show an increase in the effect of father's income on educational outcomes between 1993 and 2003 using a parental income variable estimated on the basis of other observable characteristics (father's occupation and education). We update and extend this work with a survey matched with tax administrative data, which allows direct study of the relationship between parental income and children's education, with no need to estimate parental income.</footnote>
parents' educational attainment. Second, our results span the entire distribution of parental in-
come and apply to a representative sample of all young adults aged 19-24 in France. This makes
them complementary to the studies that seek to measure the causal effect of income on educa-
tional attainment using an instrument 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 aims to contribute to comparative studies on how the characteristics of education systems influence educational inequalities in different countries. We focus on France and the United States, which offer stark contrasts in their higher education systems. Key differences include the cost of higher education, the proportion of expenses subsidized by state funding, and the extent of student debt. Based on these criteria alone, one might anticipate significantly higher enrollment inequality in the United States. However, a thorough understanding of the relationship between higher education participation and parental income requires considering additional country-specific factors. These include the correlation between parental socio-economic status and academic performance, the variety of educational pathways available, and how resources are distributed (Causa and Chapuis (2011), Chmielewski and Reardon (2016)). From a policy perspective, fostering a higher education system capable of accommodating and educating the largest possible number of individuals is crucial for development 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 ex-
plaining the link between parental income and educational attainment. Two main channels
have been identified in this literature. The first is parents’ direct investment in children’s edu-
cation. Traditional models of human capital investment (Becker and Tomes, 1979) emphasize
the importance of credit constraints in explaining low-income families’ relative lack of enroll-
ment in higher education. In France, tuition fees in public programs are set at a very low level
(see Section 3); however, there may still be credit or budget constraints related to the cost of stu-
dent living, especially since most universities are located in large cities where the cost of living
is high, plus the opportunity cost incurred by students when they opt for education over im-
mediate 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 to isolate the specific causal pathways through which parental income affects
educational outcomes, the comprehensive nature of the survey facilitates a thorough descrip-
tive examination of multiple contributing factors, including prior academic credentials and as-

<footnote n="6">⁶The importance of this transmission has been confirmed in France by recent studies (OECD (2018b), Herbaut (2019), Barhoumi and Caille (2020)). An important debate in this literature concerns the relative significance of nature and nurture in the intergenerational transmission of abilities. In a study of donor children in Denmark, Lundborg et al. (2021) find that net of genetic skill transfers, only the mother's education matters in explaining the children's school performance.</footnote>
parents' educational attainment. Second, our results span the entire distribution of parental in-
come and apply to a representative sample of all young adults aged 19-24 in France. This makes
them complementary to the studies that seek to measure the causal effect of income on educa-
tional attainment using an instrument 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 aims to contribute to comparative studies on how the characteristics of education systems influence educational inequalities in different countries. We focus on France and the United States, which offer stark contrasts in their higher education systems. Key differences include the cost of higher education, the proportion of expenses subsidized by state funding, and the extent of student debt. Based on these criteria alone, one might anticipate significantly higher enrollment inequality in the United States. However, a thorough understanding of the relationship between higher education participation and parental income requires considering additional country-specific factors. These include the correlation between parental socio-economic status and academic performance, the variety of educational pathways available, and how resources are distributed (Causa and Chapuis (2011), Chmielewski and Reardon (2016)). From a policy perspective, fostering a higher education system capable of accommodating and educating the largest possible number of individuals is crucial for development 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 ex-
plaining the link between parental income and educational attainment. Two main channels
have been identified in this literature. The first is parents’ direct investment in children’s edu-
cation. Traditional models of human capital investment (Becker and Tomes, 1979) emphasize
the importance of credit constraints in explaining low-income families’ relative lack of enroll-
ment in higher education. In France, tuition fees in public programs are set at a very low level
(see Section 3); however, there may still be credit or budget constraints related to the cost of stu-
dent living, especially since most universities are located in large cities where the cost of living
is high, plus the opportunity cost incurred by students when they opt for education over im-
mediate 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 to isolate the specific causal pathways through which parental income affects
educational outcomes, the comprehensive nature of the survey facilitates a thorough descrip-
tive examination of multiple contributing factors, including prior academic credentials and as-

<footnote n="6">⁶The importance of this transmission has been confirmed in France by recent studies (OECD (2018b), Herbaut (2019), Barhoumi and Caille (2020)). An important debate in this literature concerns the relative significance of nature and nurture in the intergenerational transmission of abilities. In a study of donor children in Denmark, Lundborg et al. (2021) find that net of genetic skill transfers, only the mother's education matters in explaining the children's school performance.</footnote>
pirations. Such an analysis can shed light on potential underlying mechanisms that lead to similar levels of educational inequalities in both France and the United States, despite their distinct institutional frameworks in higher education.

Lastly, our paper contributes to the literature on the redistributive impact of public higher education spending. The empirical question of the degree of regressivity of spending on higher education has been the subject of intense methodological debate since the 1970s. The first empirical studies, conducted in the United States starting in 1969, gave rise to 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 people paying more in taxes than they receive in subsidies compared with the less affluent. The issue of redistribution via in-kind transfers has also recently received renewed attention 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, allowing several contributions to this literature. First, it addresses the lack of recent results in the U.S. or Europe (Johnson (2006)) and includes an unprecedented comparison of the distribution of higher education spending between France and the United States. Then, it explores a specific context of a European country with low tuition fees yet high levels of educational inequality; and uses data that, for the first time in France, disaggregates the costs of different higher education programs and fields.

## 3 Institutional Background

**The French Higher Education System.** The French higher education system is characterized by significant institutional differentiation and academic hierarchy among tracks. Higher education is divided into 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 on the technical and vocational track, 45% on the university track, and 20% on the selective academic track (MENESR-DEPP, 2015). The coexistence of two academic tracks is a specificity of the French system. Until 2018, university attendance was formally available to anyone holding the high school graduation exam (Baccalauréat),<fnref n="7" /> but enrollment in elite graduate schools was 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

<footnote n="7">⁷Of students with a high school diploma (academic, technical or vocational) 75% enrolled in higher education; among those with an academic diploma, 99%.</footnote>
pirations. Such an analysis can shed light on potential underlying mechanisms that lead to similar levels of educational inequalities in both France and the United States, despite their distinct institutional frameworks in higher education.

Lastly, our paper contributes to the literature on the redistributive impact of public higher education spending. The empirical question of the degree of regressivity of spending on higher education has been the subject of intense methodological debate since the 1970s. The first empirical studies, conducted in the United States starting in 1969, gave rise to 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 people paying more in taxes than they receive in subsidies compared with the less affluent. The issue of redistribution via in-kind transfers has also recently received renewed attention 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, allowing several contributions to this literature. First, it addresses the lack of recent results in the U.S. or Europe (Johnson (2006)) and includes an unprecedented comparison of the distribution of higher education spending between France and the United States. Then, it explores a specific context of a European country with low tuition fees yet high levels of educational inequality; and uses data that, for the first time in France, disaggregates the costs of different higher education programs and fields.

## 3 Institutional Background

**The French Higher Education System.** The French higher education system is characterized by significant institutional differentiation and academic hierarchy among tracks. Higher education is divided into 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 on the technical and vocational track, 45% on the university track, and 20% on the selective academic track (MENESR-DEPP, 2015). The coexistence of two academic tracks is a specificity of the French system. Until 2018, university attendance was formally available to anyone holding the high school graduation exam (Baccalauréat),<fnref n="7" /> but enrollment in elite graduate schools was 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

<footnote n="7">⁷Of students with a high school diploma (academic, technical or vocational) 75% enrolled in higher education; among those with an academic diploma, 99%.</footnote>
take the exams. Attending these preparatory courses after high school is already selective.<fnref n="8" />

**Tracking.** The stratification of the French education system begins as early as the 10th grade when students must decide between a vocational track and a general or technological track in high school. This stratification continues with the choice of majors within the academic or technological tracks (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 track is the least likely to lead to enrollment in higher education.

**The Financing of Higher Education.** Most French higher education institutions are publicly funded, with tuition set at a very low level. In 2014-2015, tuition in public universities were €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 universities are public, and 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 a year, for private engineering schools around €7,000.

# 4 Data and Variable Definition

In this section, we first 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 National Survey of Youth Resources (Enquête Nationale sur les Ressources des Jeunes, ENRJ) from 2014, collaboratively administered 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). This survey targeted a representative subset of young adults between the ages of 18 and 24, as well as their respective parents. The sample is smaller than in studies based on administrative or census data, but these data offer the advantage of combining detailed questions about young adults' educational background and the resources they have to live on, with the reliability of administrative data on parental income, on a representative sample of the young adult population.<fnref n="10" /> The parental income data within the ENRJ is sourced directly from admin-

<footnote n="8">⁸These 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 – compared with 3% of technical/vocational students and 6% of university students (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. (2021), administrative datasets, despite their vast sample sizes and the ability to link parents with their children, are often constrained by a paucity of variables, typically lacking insights into</footnote>
take the exams. Attending these preparatory courses after high school is already selective.<fnref n="8" />

**Tracking.** The stratification of the French education system begins as early as the 10th grade when students must decide between a vocational track and a general or technological track in high school. This stratification continues with the choice of majors within the academic or technological tracks (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 track is the least likely to lead to enrollment in higher education.

**The Financing of Higher Education.** Most French higher education institutions are publicly funded, with tuition set at a very low level. In 2014-2015, tuition in public universities were €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 universities are public, and 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 a year, for private engineering schools around €7,000.

# 4 Data and Variable Definition

In this section, we first 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 National Survey of Youth Resources (Enquête Nationale sur les Ressources des Jeunes, ENRJ) from 2014, collaboratively administered 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). This survey targeted a representative subset of young adults between the ages of 18 and 24, as well as their respective parents. The sample is smaller than in studies based on administrative or census data, but these data offer the advantage of combining detailed questions about young adults' educational background and the resources they have to live on, with the reliability of administrative data on parental income, on a representative sample of the young adult population.<fnref n="10" /> The parental income data within the ENRJ is sourced directly from admin-

<footnote n="8">⁸These 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 – compared with 3% of technical/vocational students and 6% of university students (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. (2021), administrative datasets, despite their vast sample sizes and the ability to link parents with their children, are often constrained by a paucity of variables, typically lacking insights into</footnote>
istrative tax records of the surveyed parents. The final sample includes 5,776 young adults and 6,304 parents. Out of these, in 5,197 instances, responses were received from both the young adult and at least one parent. Survey weights are used to account for nonresponse bias.

**Higher Education Cost Data in France.** The final section of our analysis incorporates supplementary data on higher education expenditure. In national accounts and in earlier studies focused on France, higher education expenditures are typically grouped into just three categories, irrespective of the specific track or field of study (MESRI-SIES, 2021). Our research utilizes additional data from the "Cost Awareness" project.<fnref n="11" /> This project, conducted from 2016 to 2019 across 65 higher education institutions (representing about one-third of all French students), provides a more detailed breakdown of costs across various programs and fields. Notably, it enables the calculation of the instructional expenditures in higher education, excluding research costs that are present in more aggregated data sources.

We use the information contained in the main survey (ENRJ) on the studies undertaken by individuals 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 makes it possible 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 is sourced from 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 data on France, which only includes instruction expenditure, we compute per student spending for instructional expenditure. This is derived by dividing the total instructional expenditure<fnref n="13" /> by the total fall enrollment of the institution.

## 4.2 Sample Definition

The main sample includes young adult respondents to the survey and aged between 19 and 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

other aspects of parental background, like education or occupation. Census data, such as those used by Dodin et al. (2021) and Hilger (2015), partly address these limitations by offering large samples. Nevertheless, a significant drawback is that they predominantly cover children still living with their parents. This is a potential bias, especially concerning enrollment in higher education—a crucial juncture when an important share of students moves out, with these departures potentially correlating with parental income.

<footnote n="11">¹¹*Connaissance 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 higher education cost data and Table B5 for annual public spending per student, broken down by field of study and track, the level of disaggregation that we are using.</footnote>

<footnote n="13">¹³This expense category includes expenses of the colleges, schools, departments, and other instructional divisions. It covers costs related to general academic instruction, occupational and vocational instruction, community education, preparatory and adult basic education, and regular, special, and extension sessions.</footnote>
istrative tax records of the surveyed parents. The final sample includes 5,776 young adults and 6,304 parents. Out of these, in 5,197 instances, responses were received from both the young adult and at least one parent. Survey weights are used to account for nonresponse bias.

**Higher Education Cost Data in France.** The final section of our analysis incorporates supplementary data on higher education expenditure. In national accounts and in earlier studies focused on France, higher education expenditures are typically grouped into just three categories, irrespective of the specific track or field of study (MESRI-SIES, 2021). Our research utilizes additional data from the "Cost Awareness" project.<fnref n="11" /> This project, conducted from 2016 to 2019 across 65 higher education institutions (representing about one-third of all French students), provides a more detailed breakdown of costs across various programs and fields. Notably, it enables the calculation of the instructional expenditures in higher education, excluding research costs that are present in more aggregated data sources.

We use the information contained in the main survey (ENRJ) on the studies undertaken by individuals 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 makes it possible 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 is sourced from 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 data on France, which only includes instruction expenditure, we compute per student spending for instructional expenditure. This is derived by dividing the total instructional expenditure<fnref n="13" /> by the total fall enrollment of the institution.

## 4.2 Sample Definition

The main sample includes young adult respondents to the survey and aged between 19 and 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

<footnote>other aspects of parental background, like education or occupation. Census data, such as those used by Dodin et al. (2021) and Hilger (2015), partly address these limitations by offering large samples. Nevertheless, a significant drawback is that they predominantly cover children still living with their parents. This is a potential bias, especially concerning enrollment in higher education—a crucial juncture when an important share of students moves out, with these departures potentially correlating with parental income.</footnote>

<footnote n="11">¹¹*Connaissance 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 higher education cost data and Table B5 for annual public spending per student, broken down by field of study and track, the level of disaggregation that we are using.</footnote>

<footnote n="13">¹³This expense category includes expenses of the colleges, schools, departments, and other instructional divisions. It covers costs related to general academic instruction, occupational and vocational instruction, community education, preparatory and adult basic education, and regular, special, and extension sessions.</footnote>
questions about their partner.<fnref n="14" /> When parents are separated (26% of our sample young adults), both were interviewed in 46% and only one in 54% of the cases, in which case we have no information about the non-responding parent. In analysing spending on higher education, we restrict ourselves to individuals aged 22-24, who are the most advanced in their education in our sample and we infer the educational trajectories until 24 for individuals aged 22 and 23.

## 4.3 Descriptive Statistics

Table 1 presents some descriptive statistics of our sample of young adults aged 19-24, by quintile of parental pre-tax income. On average, they are 21.5 years old, and 49 percent are female. The average pre-tax 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 per year 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 on average, with an annual pre-tax income of €50,900. A bit more than 10 percent of all the parents have a bachelor’s or graduate degree, compared with over 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, compared with 3 percent in the bottom quintile. About two thirds of the parents are still living together and a quarter are separated. The father is unknown or deceased in 7 percent of cases, the mother in 2 percent. 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 an urban center of at least 100,000 inhabitants (excluding Paris). The proportion of individuals in the Paris region or other large urban centers is greater at the top of the income distribution.

A concern here could be that the top deciles of the distribution concentrate the oldest parents and accordingly also the oldest young adults in our sample, which would bias the estimate of inequality in enrollment in higher education. However, 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 – aged 19-24 in our sample – as having enrollment in higher education if they meet any of the following criteria in the year

<footnote n="14">¹⁴Some 67% of the young adults in the sample have their parents still living together. In 68% of these cases, the mother answered the parents' questionnaire.</footnote>
questions about their partner.<fnref n="14" /> When parents are separated (26% of our sample young adults), both were interviewed in 46% and only one in 54% of the cases, in which case we have no information about the non-responding parent. In analysing spending on higher education, we restrict ourselves to individuals aged 22-24, who are the most advanced in their education in our sample and we infer the educational trajectories until 24 for individuals aged 22 and 23.

## 4.3 Descriptive Statistics

Table 1 presents some descriptive statistics of our sample of young adults aged 19-24, by quintile of parental pre-tax income. On average, they are 21.5 years old, and 49 percent are female. The average pre-tax 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 per year 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 on average, with an annual pre-tax income of €50,900. A bit more than 10 percent of all the parents have a bachelor’s or graduate degree, compared with over 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, compared with 3 percent in the bottom quintile. About two thirds of the parents are still living together and a quarter are separated. The father is unknown or deceased in 7 percent of cases, the mother in 2 percent. 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 an urban center of at least 100,000 inhabitants (excluding Paris). The proportion of individuals in the Paris region or other large urban centers is greater at the top of the income distribution.

A concern here could be that the top deciles of the distribution concentrate the oldest parents and accordingly also the oldest young adults in our sample, which would bias the estimate of inequality in enrollment in higher education. However, 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 – aged 19-24 in our sample – as having enrollment in higher education if they meet any of the following criteria in the year

<footnote n="14">¹⁴Some 67% of the young adults in the sample have their parents still living together. In 68% of these cases, the mother answered the parents' questionnaire.</footnote>
of the survey: they are enrolled in a higher education program, they have already obtained a higher education degree, or they have reached a higher education level without obtaining the corresponding degree. To be considered as enrolling in higher education, an individual must therefore have been enrolled for at least one year in higher education. 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. The degree of selectivity of each of these programs can be assessed by ranking all students on the basis of their performance on the high school graduation exam. In 2016-2017, the median percentile rank of non-medical students in university was 50.2, that of students in medical studies 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 noncommercial 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 and only marginally those in the rest of the distribution.

To further assess the representativeness of our sample, we compare the distribution of pre-tax incomes of parents in the ENRJ sample and individuals aged 42-62 in the ERFS, another survey with a much larger sample (n=50,096) where income data are taken from tax sources as well. Overall, pre-tax income are quite similar in the two samples (Table B3). Parents of young adults (ENRJ sample), exhibited a slightly higher disposable income and a slightly reduced standard of living when compared to the individuals in the general population (ERFS sample), due 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).

**Parent 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 youths, and the cor-

<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, we present in Appendix C.3.2 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>
of the survey: they are enrolled in a higher education program, they have already obtained a higher education degree, or they have reached a higher education level without obtaining the corresponding degree. To be considered as enrolling in higher education, an individual must therefore have been enrolled for at least one year in higher education. 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. The degree of selectivity of each of these programs can be assessed by ranking all students on the basis of their performance on the high school graduation exam. In 2016-2017, the median percentile rank of non-medical students in university was 50.2, that of students in medical studies 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 noncommercial 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 and only marginally those in the rest of the distribution.

To further assess the representativeness of our sample, we compare the distribution of pre-tax incomes of parents in the ENRJ sample and individuals aged 42-62 in the ERFS, another survey with a much larger sample (n=50,096) where income data are taken from tax sources as well. Overall, pre-tax income are quite similar in the two samples (Table B3). Parents of young adults (ENRJ sample), exhibited a slightly higher disposable income and a slightly reduced standard of living when compared to the individuals in the general population (ERFS sample), due 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).

**Parent 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 youths, and the cor-

<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, we present in Appendix C.3.2 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>
relation 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 statistical service<fnref n="17" /> classification, which classes them in four groups by socio-economic status (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 contains information on the highest degree obtained by parents. 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 data. This index integrates a range of resources available to students. These resources are derived from the education and occupations of their parents, 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 to different programs in France. We then explore the interplay between attendance disparities, which are linked to parental income, and other parental background characteristics, such as education and occupation. Following this, we compare the correlations between parental income and children's enrollment in higher education in France and 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, compared with more than two times as many – around 90 percent – in the top decile.

<footnote n="17">¹⁷Direction de l'évaluation, de la prospective et de la performance (DEPP).</footnote>
relation 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 statistical service<fnref n="17" /> classification, which classes them in four groups by socio-economic status (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 contains information on the highest degree obtained by parents. 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 data. This index integrates a range of resources available to students. These resources are derived from the education and occupations of their parents, 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 to different programs in France. We then explore the interplay between attendance disparities, which are linked to parental income, and other parental background characteristics, such as education and occupation. Following this, we compare the correlations between parental income and children's enrollment in higher education in France and 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, compared with more than two times as many – around 90 percent – in the top decile.

<footnote n="17">¹⁷Direction de l'évaluation, de la prospective et de la performance (DEPP).</footnote>
[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 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 a dual system of higher education in France, in which enrollment in the most prestigious courses of study remains highly dependent on parental income.

[place Figure 2 here]

**Robustness Checks.** A first concern is the possible mis-estimation due to the fact that our parental income is calculated for a single year and so could well 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 between 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 slightly, but the difference is never statistically significant. These results are consistent with the finding that the measure of social mobility is little affected 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 thus perform 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. Figures C1 shows that overall, the results in terms of enrollment in higher education are very close to our main specification. The non-linearity of the effect of parental income on enrollment in higher education, discussed in a subsequent section, is nevertheless 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 based on 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 in relation to

<footnote n="18">¹⁸See Figure III Panel B of Chetty et al. (2014).</footnote>
[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 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 a dual system of higher education in France, in which enrollment in the most prestigious courses of study remains highly dependent on parental income.

[place Figure 2 here]

**Robustness Checks.** A first concern is the possible mis-estimation due to the fact that our parental income is calculated for a single year and so could well 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 between 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 slightly, but the difference is never statistically significant. These results are consistent with the finding that the measure of social mobility is little affected 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 thus perform 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. Figures C1 shows that overall, the results in terms of enrollment in higher education are very close to our main specification. The non-linearity of the effect of parental income on enrollment in higher education, discussed in a subsequent section, is nevertheless 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 based on 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 in relation to

<footnote n="18">¹⁸See Figure III Panel B of Chetty et al. (2014).</footnote>
higher education attendance. We document descriptively that a significant portion of the higher education attendance gap based on parental income is associated with parental education or occupation, though not the entirety.

The heatmap in Figure 3 highlights disparities associated with parental income and occupation (Panels a and b) as well as parental education (Panels c and d). It depicts a staircase pattern, suggesting links between these factors. Specifically, within a given parental income bracket, there's an observed association between higher parental occupation or educational qualifications and increased enrollment in higher education – and the reverse is also true. This stair-step trend is notably evident when observing parental occupation. When considering parental educational attainment, there are pronounced discontinuities based on the number of years of education. Interestingly, this marked shift occurs at the 14-year education mark. This corresponds to the point where parents have attained a higher education degree themselves. For instance, 87 percent of individuals whose mother has a higher educational degree – 14 years of education or more – have enrollment 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 offspring of parents with higher education qualifications are often found in higher education themselves. Notably, this gap is even more pronounced when looking at the mother's education level, underscoring its pivotal role in a child's educational prospects. This is in line with some previous findings in the literature (Currie and Moretti (2003), Sacerdote (2007), Chevalier et al. (2013), Lundborg et al. (2021)).

[place Figure 3 here]

More formally, we explore the complementarity of the different dimensions of inequality by observing the evolution of the regression coefficient of parental income on enrollment in higher education by 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 higher education enrollment but rather to illustrate the complementarity among inequalities stemming from parental income, occupation, and education. For a given occupation and educational degree, the higher the percentile rank of parental income, the greater the proportion of individuals enrolling in higher education and different programs. For example, those 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 between the ages of 19 and 24 when their parents are in the bottom income quintile and 77 percent when they are in the top quintile. 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

<footnote n="19">¹⁹See table D9 in the Appendix for the details of all the coefficients on the control variables.</footnote>
higher education attendance. We document descriptively that a significant portion of the higher education attendance gap based on parental income is associated with parental education or occupation, though not the entirety.

The heatmap in Figure 3 highlights disparities associated with parental income and occupation (Panels a and b) as well as parental education (Panels c and d). It depicts a staircase pattern, suggesting links between these factors. Specifically, within a given parental income bracket, there's an observed association between higher parental occupation or educational qualifications and increased enrollment in higher education – and the reverse is also true. This stair-step trend is notably evident when observing parental occupation. When considering parental educational attainment, there are pronounced discontinuities based on the number of years of education. Interestingly, this marked shift occurs at the 14-year education mark. This corresponds to the point where parents have attained a higher education degree themselves. For instance, 87 percent of individuals whose mother has a higher educational degree – 14 years of education or more – have enrollment 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 offspring of parents with higher education qualifications are often found in higher education themselves. Notably, this gap is even more pronounced when looking at the mother's education level, underscoring its pivotal role in a child's educational prospects. This is in line with some previous findings in the literature (Currie and Moretti (2003), Sacerdote (2007), Chevalier et al. (2013), Lundborg et al. (2021)).

[place Figure 3 here]

More formally, we explore the complementarity of the different dimensions of inequality by observing the evolution of the regression coefficient of parental income on enrollment in higher education by 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 higher education enrollment but rather to illustrate the complementarity among inequalities stemming from parental income, occupation, and education. For a given occupation and educational degree, the higher the percentile rank of parental income, the greater the proportion of individuals enrolling in higher education and different programs. For example, those 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 between the ages of 19 and 24 when their parents are in the bottom income quintile and 77 percent when they are in the top quintile. 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

<footnote n="19">¹⁹See table D9 in the Appendix for the details of all the coefficients on the control variables.</footnote>
education: family structure, and size of residential area. After controlling for all these factors, a residual significant effect of parental income remains, albeit only at the lower end of the parental income distribution. This descriptive analysis does not show a causal effect of parental income on higher education enrollment; rather, it simply indicates that the correlation between parental income and higher education enrollment cannot be fully explained by these observable characteristics alone.

[place Table 2 here]

We have examined how the differences in enrollment in higher education by parental income, can partly overlap with inequalities related to other factors, such as parental education and occupation, but remains significant while controlling for these observable factors. In the next section, we compare the higher education attendance inequalities in France and 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 to this 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 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 young people entering higher education, compared to 5.8 p.p. in France. We cannot reject that the coefficient in France is similar to the coefficient in the U.S.<fnref n="20" /> In both countries, the proportion of individuals in higher education at the bottom of the income distribution is around 35 percent and that at the top is around 90 percent.

[place Figure 4 here]

**Linearity of the Income Effect.** Although the relationship between parental income and children enrollment in 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 as in the

<footnote n="20">²⁰The 95% confidence interval for the coefficient of parent's income percentile rank on enrollment in higher education in France is [5.3 ; 6.3].</footnote>
education: family structure, and size of residential area. After controlling for all these factors, a residual significant effect of parental income remains, albeit only at the lower end of the parental income distribution. This descriptive analysis does not show a causal effect of parental income on higher education enrollment; rather, it simply indicates that the correlation between parental income and higher education enrollment cannot be fully explained by these observable characteristics alone.

[place Table 2 here]

We have examined how the differences in enrollment in higher education by parental income, can partly overlap with inequalities related to other factors, such as parental education and occupation, but remains significant while controlling for these observable factors. In the next section, we compare the higher education attendance inequalities in France and 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 to this 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 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 young people entering higher education, compared to 5.8 p.p. in France. We cannot reject that the coefficient in France is similar to the coefficient in the U.S.<fnref n="20" /> In both countries, the proportion of individuals in higher education at the bottom of the income distribution is around 35 percent and that at the top is around 90 percent.

[place Figure 4 here]

**Linearity of the Income Effect.** Although the relationship between parental income and children enrollment in 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 as in the

<footnote n="20">²⁰The 95% confidence interval for the coefficient of parent's income percentile rank on enrollment in higher education in France is [5.3 ; 6.3].</footnote>
lower half of the distribution in France, where an increase of one decile in parental income is as-
sociated 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 to higher education is lower in the middle of the income distribution
in France than in the U.S.

Table 2 shows in more details the non-linear association in France between income and en-
rollment in higher education. Students with parents in the top income quintile are observed
to attend college 30 percentage points more frequently than those in the middle quintile. Con-
versely, students with parents in the bottom quintile are observed to attend college 18 per-
centage points less often than those in the middle quintile. The stronger correlation between
parental income and children's higher education attendance in the upper half of the income
distribution is somewhat counterintuitive. If this effect were related to credit constraints, one
would expect a larger impact at the lower end of the spectrum, where households are typically
more financially constrained. This suggests that the income gradient in higher education at-
tendance might be linked to other parental characteristics that correlate with parental income.
However, when accounting for parental characteristics, a notable association between parental
income and higher education enrollment emerges primarily in the lower half of the income
distribution (Table 2). 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. This
provides only suggestive evidence on the matter, as these associations are merely descriptive.

In this section we have set out novel estimates of inequality in enrollment in higher educa-
tion according to parental income in France – using reliable income data from administrative
tax sources – and compared our estimate to that observed in the United States. We find similar
levels of inequality in France and the United States, which may seem surprising given the very
different higher education contexts between the two countries. In the following section, we
present some potential explanations for the similar levels of enrollment inequalities observed
between the two countries.

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

We analyze institutional differences between France and the United States to understand their
similar levels of higher education inequality. Specifically, we explore education costs, early
tracking and education disparities before entering higher education. The arguments presented
here suggest that the similar levels of inequality in higher education enrollment observed be-
tween France and the United States can be attributed to different institutional factors. Atten-
lower half of the distribution in France, where an increase of one decile in parental income is as-
sociated 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 to higher education is lower in the middle of the income distribution
in France than in the U.S.

Table 2 shows in more details the non-linear association in France between income and en-
rollment in higher education. Students with parents in the top income quintile are observed
to attend college 30 percentage points more frequently than those in the middle quintile. Con-
versely, students with parents in the bottom quintile are observed to attend college 18 per-
centage points less often than those in the middle quintile. The stronger correlation between
parental income and children's higher education attendance in the upper half of the income
distribution is somewhat counterintuitive. If this effect were related to credit constraints, one
would expect a larger impact at the lower end of the spectrum, where households are typically
more financially constrained. This suggests that the income gradient in higher education at-
tendance might be linked to other parental characteristics that correlate with parental income.
However, when accounting for parental characteristics, a notable association between parental
income and higher education enrollment emerges primarily in the lower half of the income
distribution (Table 2). 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. This
provides only suggestive evidence on the matter, as these associations are merely descriptive.

In this section we have set out novel estimates of inequality in enrollment in higher educa-
tion according to parental income in France – using reliable income data from administrative
tax sources – and compared our estimate to that observed in the United States. We find similar
levels of inequality in France and the United States, which may seem surprising given the very
different higher education contexts between the two countries. In the following section, we
present some potential explanations for the similar levels of enrollment inequalities observed
between the two countries.

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

We analyze institutional differences between France and the United States to understand their
similar levels of higher education inequality. Specifically, we explore education costs, early
tracking and education disparities before entering higher education. The arguments presented
here suggest that the similar levels of inequality in higher education enrollment observed be-
tween France and the United States can be attributed to different institutional factors. Atten-
dance disparities in the U.S. are associated with a higher cost of higher education and more pronounced income inequalities. In France, enrollment in higher education could be hindered by early tracking and academic results that highly correlate with social origin.

## 6.1 Cost of Studies and Income Inequalities

We emphasize the institutional factors related to the cost of education and income inequalities in the two countries, which might suggest a lower level of higher education enrollment disparities in France compared to the United States.

First, higher education is much more expensive in the United States. In 2014-2015, average tuition in the United States 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 cost 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, with greater disparities between the top and bottom deciles.<fnref n="21" /> Greater income inequalities means that the top and bottom income deciles are more different in the United States than in France. Moreover, higher income inequalities could exacerbate skill inequalities (Causa and Chapuis (2011)). Some studies also indicate that countries or regions with higher income inequality often exhibit greater educational inequality (Duncan et al. (2017), Workman (2022)).

Ultimately, greater income inequality has a knock-on effect on educational inequality through the returns to higher education. The latters are greater in the United States – estimated to about 14% as against 10% in France (Psacharopoulos and Patrinos, 2018). Given the significant relationship between parental and child education, countries with higher returns to education are likely to have a stronger link between parental income and children’s educational outcomes.

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 costs substantially more, income inequality is greater, and the financial returns to education are higher compared to France. The next two sections will examine institutional factors in France that could explain the similar levels of enrollment inequalities in higher education. These include the strong correlation between parents’ socio-economic status and children’s academic achievement, as well as early tracking.

## 6.2 Unequal Academic Performance Before Higher Education

The unequal participation in higher education in France likely reflects an accumulation of inequalities from earlier schooling. The link 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.

<footnote n="21">²¹Table 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. compared to $45,640 in France. In 2014, the top income decile accounted for 46% of total income in the U.S. and 33% in France, respectively(https://wid.world/data/).</footnote>
dance disparities in the U.S. are associated with a higher cost of higher education and more pronounced income inequalities. In France, enrollment in higher education could be hindered by early tracking and academic results that highly correlate with social origin.

## 6.1 Cost of Studies and Income Inequalities

We emphasize the institutional factors related to the cost of education and income inequalities in the two countries, which might suggest a lower level of higher education enrollment disparities in France compared to the United States.

First, higher education is much more expensive in the United States. In 2014-2015, average tuition in the United States 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 cost 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, with greater disparities between the top and bottom deciles.<fnref n="21" /> Greater income inequalities means that the top and bottom income deciles are more different in the United States than in France. Moreover, higher income inequalities could exacerbate skill inequalities (Causa and Chapuis (2011)). Some studies also indicate that countries or regions with higher income inequality often exhibit greater educational inequality (Duncan et al. (2017), Workman (2022)).

Ultimately, greater income inequality has a knock-on effect on educational inequality through the returns to higher education. The latters are greater in the United States – estimated to about 14% as against 10% in France (Psacharopoulos and Patrinos, 2018). Given the significant relationship between parental and child education, countries with higher returns to education are likely to have a stronger link between parental income and children’s educational outcomes.

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 costs substantially more, income inequality is greater, and the financial returns to education are higher compared to France. The next two sections will examine institutional factors in France that could explain the similar levels of enrollment inequalities in higher education. These include the strong correlation between parents’ socio-economic status and children’s academic achievement, as well as early tracking.

## 6.2 Unequal Academic Performance Before Higher Education

The unequal participation in higher education in France likely reflects an accumulation of inequalities from earlier schooling. The link 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.

<footnote n="21">²¹Table 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. compared to $45,640 in France. In 2014, the top income decile accounted for 46% of total income in the U.S. and 33% in France, respectively(https://wid.world/data/).</footnote>
(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 school performance are observed in both primary and secondary education (Barhoumi and Caille (2020); Barasz et al. (2023)).

[place Figure 5 here]

We employ Das Gupta (1991) decomposition method to examine the contribution of prior credentials on the higher education attendance gap between the top and bottom income deciles (Table 3).<fnref n="22" /> The middle school certification gap accounts for 21 percent of the total higher education enrollment disparity, while the high school certification gap among middle school graduates contributes 50 percent. These degrees thus explain 71% of the gap in higher education attendance; the remaining 29% being unexplained by prior credentials. In the United States, disparities in high school graduation rates based on 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., with a gap of at least 20 percentage points. Furthermore, in France, the percentage of high school graduates increases with parental income in the top half of the income distribution. In contrast, in the United States, the proportion of high school graduates remains consistently above 90% across all income levels in the upper half of the distribution.<fnref n="23" />

[place Table 3 here]

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

## 6.3 Early Tracking and School Segregation

**Early Tracking.** Early tracking in France might enhance the strong correlation between socio-economic status and academic performance. From the 10th grade onwards, students and their

<footnote n="22">²²See Appendix B.2.3 for more details on this decomposition method.</footnote>

<footnote n="23">²³Refer to table 2, p.5, in Ziol-Guest and Lee (2016) for detailed data.</footnote>
(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 school performance are observed in both primary and secondary education (Barhoumi and Caille (2020); Barasz et al. (2023)).

[place Figure 5 here]

We employ Das Gupta (1991) decomposition method to examine the contribution of prior credentials on the higher education attendance gap between the top and bottom income deciles (Table 3).<fnref n="22" /> The middle school certification gap accounts for 21 percent of the total higher education enrollment disparity, while the high school certification gap among middle school graduates contributes 50 percent. These degrees thus explain 71% of the gap in higher education attendance; the remaining 29% being unexplained by prior credentials. In the United States, disparities in high school graduation rates based on 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., with a gap of at least 20 percentage points. Furthermore, in France, the percentage of high school graduates increases with parental income in the top half of the income distribution. In contrast, in the United States, the proportion of high school graduates remains consistently above 90% across all income levels in the upper half of the distribution.<fnref n="23" />

[place Table 3 here]

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

## 6.3 Early Tracking and School Segregation

**Early Tracking.** Early tracking in France might enhance the strong correlation between socio-economic status and academic performance. From the 10th grade onwards, students and their

<footnote n="22">²²See Appendix B.2.3 for more details on this decomposition method.</footnote>

<footnote n="23">²³Refer to table 2, p.5, in Ziol-Guest and Lee (2016) for detailed data.</footnote>
parents have to make key decisions about their school careers. These decisions are highly cor-
lated with socio-economic status: Herbaut et al. (2019) found that 92% of students with parents
holding a higher education diploma choose the general and technological track in high school
over the vocational track. In contrast, only 52% make the same choice among students whose
parents have just a high school diploma. The authors also show that such decisions signifi-
cantly impact educational and career trajectories. These differences in orientation according
to social origin are only partially explained by differences in previous academic achievement:
for the generation entering 6th grade in 1995, Ichou and Vallet (2012) observed that half of the
academic track attendance gap between the working classes and the upper classes was due to
differences in educational outcomes, while the other half was not explained by previous aca-
demic performance.

When comparing educational systems between countries, earlier tracking leading to sepa-
arate pathways is generally associated with a higher level of educational inequality (Van de
Werfhorst (2019)). The French "republican elitism" (Baudelot and Establet (2009)) stems from
the Napoleonic system, in which schools were expected first and foremost to train the nation's
elite. Until the 1970s, both secondary and higher education catered mainly to an upper-class
minority.<fnref n="24" /> This socio-economic divide persisted after primary school until the 1975 introduc-
tion of an unified middle school (collège unique, (Merle (2009))).<fnref n="25" /> In the United States, tracking
is much less marked up to high school (Brunello and Checchi (2007)).

Countries with early tracking also tend to exhibit more pronounced disparities in educa-
tional 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 dis-
tribution. Among 18-19-year-olds,<fnref n="26" /> those in the top income decile predominantly aim for ad-
vanced degrees (master's or Ph.D.) – nearly 80%. In contrast, only about 35% from the bottom
quintile express the same aspiration (Figure 6 (a)). When comparing the aspirations of high-
achieving students using PISA data, a significantly higher proportion of disadvantaged French
students do not expect to succeed in tertiary education compared to their U.S. counterparts
(Figure 6 (b)).<fnref n="27" />

[place Figure 6 here]

Students on vocational tracks in France are significantly less likely to enroll in higher edu-
cation than students from academic tracks. Addressing this issue by ensuring that high-ability

<footnote n="24">²⁴In 1930, less than 7% of children aged 11-17 pursued education beyond primary school (Isambert-Jamati, 1970)</footnote>

<footnote n="25">²⁵One track catered to the upper classes (secondary education), while another track, established in the late 19th century, targeted the intermediate classes (upper primary education). Notably, restricted higher education participation remained, despite the late 19th-century educational laws and the 1930 provision for free secondary education.</footnote>

<footnote n="26">²⁶We focus on the youngest to minimize the influence of completed schooling on aspirations.</footnote>

<footnote n="27">²⁷The important correlation between lower aspirations and later educational inequalities has been documented in France by Guyon and Huillery (2021).</footnote>
parents have to make key decisions about their school careers. These decisions are highly corre-
lated with socio-economic status: Herbaut et al. (2019) found that 92% of students with parents
holding a higher education diploma choose the general and technological track in high school
over the vocational track. In contrast, only 52% make the same choice among students whose
parents have just a high school diploma. The authors also show that such decisions signifi-
cantly impact educational and career trajectories. These differences in orientation according
to social origin are only partially explained by differences in previous academic achievement:
for the generation entering 6th grade in 1995, Ichou and Vallet (2012) observed that half of the
academic track attendance gap between the working classes and the upper classes was due to
differences in educational outcomes, while the other half was not explained by previous aca-
demic performance.

When comparing educational systems between countries, earlier tracking leading to sepa-
arate pathways is generally associated with a higher level of educational inequality (Van de
Werfhorst (2019)). The French "republican elitism" (Baudelot and Establet (2009)) stems from
the Napoleonic system, in which schools were expected first and foremost to train the nation's
elite. Until the 1970s, both secondary and higher education catered mainly to an upper-class
minority.<fnref n="24" /> This socio-economic divide persisted after primary school until the 1975 introduc-
tion of an unified middle school (collège unique, (Merle (2009))).<fnref n="25" /> In the United States, tracking
is much less marked up to high school (Brunello and Checchi (2007)).

Countries with early tracking also tend to exhibit more pronounced disparities in educa-
tional 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 dis-
tribution. Among 18-19-year-olds,<fnref n="26" /> those in the top income decile predominantly aim for ad-
vanced degrees (master's or Ph.D.) – nearly 80%. In contrast, only about 35% from the bottom
quintile express the same aspiration (Figure 6 (a)). When comparing the aspirations of high-
achieving students using PISA data, a significantly higher proportion of disadvantaged French
students do not expect to succeed in tertiary education compared to their U.S. counterparts
(Figure 6 (b)).<fnref n="27" />

[place Figure 6 here]

Students on vocational tracks in France are significantly less likely to enroll in higher edu-
cation than students from academic tracks. Addressing this issue by ensuring that high-ability

<footnote n="24">²⁴In 1930, less than 7% of children aged 11-17 pursued education beyond primary school (Isambert-Jamati, 1970)</footnote>

<footnote n="25">²⁵One track catered to the upper classes (secondary education), while another track, established in the late 19th century, targeted the intermediate classes (upper primary education). Notably, restricted higher education participation remained, despite the late 19th-century educational laws and the 1930 provision for free secondary education.</footnote>

<footnote n="26">²⁶We focus on the youngest to minimize the influence of completed schooling on aspirations.</footnote>

<footnote n="27">²⁷The important correlation between lower aspirations and later educational inequalities has been documented in France by Guyon and Huillery (2021).</footnote>
students from low socio-economic backgrounds have better access to high school academic tracks present an interesting prospect for 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 French pupils attended public schools, compared to 93% in the United States (OECD, 2020). Moreover, the socio-economic disparity between students in public and private institutions in France has been growing since the early 2000s (Guillerm and Monso, 2022).

In this section, we have outlined institutional factors that could explain the similarly observed levels of enrollment inequality in higher education in both France and the U.S. In France, the pronounced correlation between a student's 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 expenses.<fnref n="28" />

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. Then, we examine the distribution of spending across full higher education trajectories in France. We eventually assess the rank-rank correlation between higher education spending and parental income and examine in more detail the distribution of higher education spending by parental income in France. This approach enables us to decompose descriptively the role of three factors – attendance disparities, duration of studies, and annual costs – on 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 in 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 are based on key 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="29" /> and in some cases the use of a life-cycle approach, although most of the studies are

<footnote n="28">²⁸Unequal enrollment in higher education is also associated with inequalities in lifetime earnings, which we approximate using recent estimates of Garbinti et al. (2023) and differences in the degrees attained by young adults aged 22 or over in our sample. This other implication of the unequal enrollment is explored in Appendix D.4.5.</footnote>

<footnote n="29">²⁹When taxes are not taken into account, the authors generally find that higher education spending is regressive.</footnote>
students from low socio-economic backgrounds have better access to high school academic tracks present an interesting prospect for 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 French pupils attended public schools, compared to 93% in the United States (OECD, 2020). Moreover, the socio-economic disparity between students in public and private institutions in France has been growing since the early 2000s (Guillerm and Monso, 2022).

In this section, we have outlined institutional factors that could explain the similarly observed levels of enrollment inequality in higher education in both France and the U.S. In France, the pronounced correlation between a student's 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 expenses.<fnref n="28" />

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. Then, we examine the distribution of spending across full higher education trajectories in France. We eventually assess the rank-rank correlation between higher education spending and parental income and examine in more detail the distribution of higher education spending by parental income in France. This approach enables us to decompose descriptively the role of three factors – attendance disparities, duration of studies, and annual costs – on 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 in 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 are based on key 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="29" /> and in some cases the use of a life-cycle approach, although most of the studies are

<footnote n="28">²⁸Unequal enrollment in higher education is also associated with inequalities in lifetime earnings, which we approximate using recent estimates of Garbinti et al. (2023) and differences in the degrees attained by young adults aged 22 or over in our sample. This other implication of the unequal enrollment is explored in Appendix D.4.5.</footnote>

<footnote n="29">²⁹When taxes are not taken into account, the authors generally find that higher education spending is regressive.</footnote>
estimated in cross-section. This also points out the lack of recent estimates for France and the United States, and the need to supplement this literature.

## 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 connecting 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 distribution of higher education expenditure 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, while in the United States, they account for 3.4 percent. However, the concentration of spending at the upper end is more pronounced in the U.S. Specifically, in the U.S., the top 10 percent of individuals who receive the highest spending on higher education account for 44.9 percent of the total expenditure. In contrast, in France, this figure stands at 28 percent. Similarly, the top one percent in the U.S. receives 16 percent of the total higher education spending, whereas in France, they receive 5 percent.

For France, we are able to map out comprehensive higher education trajectories, focusing on individuals aged 22 to 24 in our sample. This provides insights into the distribution of higher education expenditures over the 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. Merely 10 percent incur an educational cost exceeding €30,000 during their studies. Furthermore, the top 1 percent accrue €71,000 over their 6-year educational journey. This is 2.4 times the expenditure faced by those at the 90th percentile and 9 times that of the median, which stands at €7,800.

In the subsequent section, we delve deeper into the breakdown of 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 strong inequalities in educational spending according to parental income, suggesting a strong link with the inequalities in participation highlighted above (Figure 8). Those in the

<footnote>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>
estimated in cross-section. This also points out the lack of recent estimates for France and the United States, and the need to supplement this literature.

## 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 connecting 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 distribution of higher education expenditure 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, while in the United States, they account for 3.4 percent. However, the concentration of spending at the upper end is more pronounced in the U.S. Specifically, in the U.S., the top 10 percent of individuals who receive the highest spending on higher education account for 44.9 percent of the total expenditure. In contrast, in France, this figure stands at 28 percent. Similarly, the top one percent in the U.S. receives 16 percent of the total higher education spending, whereas in France, they receive 5 percent.

For France, we are able to map out comprehensive higher education trajectories, focusing on individuals aged 22 to 24 in our sample. This provides insights into the distribution of higher education expenditures over the 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. Merely 10 percent incur an educational cost exceeding €30,000 during their studies. Furthermore, the top 1 percent accrue €71,000 over their 6-year educational journey. This is 2.4 times the expenditure faced by those at the 90th percentile and 9 times that of the median, which stands at €7,800.

In the subsequent section, we delve deeper into the breakdown of 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 strong inequalities in educational spending according to parental income, suggesting a strong link with the inequalities in participation highlighted above (Figure 8). Those in the

<footnote>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>
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 also quite substantial: parents at the bottom of the income distribution pay an average of €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 equal to 4.0:1, while that of public investment alone is 2.7:1. In Appendix D.4.4, we discuss how taking into account 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 increase 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 30rd 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 higher education public spending based on parental income can arise from three potential factors: variations in enrollment rates in higher education, unequal duration of studies, or differential spending across distinct higher education programs, considering existing enrollment disparities. The aim of this section is to delineate the influence of these underlying determinants.

In France, the inequalities in public spending appear to derive primarily from differences in higher education attendance: inequality is much less pronounced when only higher education students are considered (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 lowest two third). 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 to €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="30" /> Inequalities in enrollment account for about 67 percent of the disparity in spending between the top

<footnote n="30">³⁰See Appendix B.2.3 for more details on this decomposition method.</footnote>
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 also quite substantial: parents at the bottom of the income distribution pay an average of €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 equal to 4.0:1, while that of public investment alone is 2.7:1. In Appendix D.4.4, we discuss how taking into account 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 increase 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 30rd 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 higher education public spending based on parental income can arise from three potential factors: variations in enrollment rates in higher education, unequal duration of studies, or differential spending across distinct higher education programs, considering existing enrollment disparities. The aim of this section is to delineate the influence of these underlying determinants.

In France, the inequalities in public spending appear to derive primarily from differences in higher education attendance: inequality is much less pronounced when only higher education students are considered (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 lowest two third). 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 to €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="30" /> Inequalities in enrollment account for about 67 percent of the disparity in spending between the top

<footnote n="30">³⁰See Appendix B.2.3 for more details on this decomposition method.</footnote>
and 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 pursuing higher education for an average of 3.3 years, compared with 4.5 years for students at the top (Figure D4).<fnref n="31" /> 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, although high-income students are largely overrepresented in selective programs (Figure 2), low-income students more often enroll in vocational programs, which also benefit from a much higher endowment than university programs (Table B5).

[place Table 4 here]

**Robustness Check: Matching Richer Individuals to Better-Endowed Programs.** One concern about this finding of equal annual public spending for higher education students may be limited ability to match students with the specific program in which they are enrolled and to disaggregate spending on some of the most selective programs (elite graduate schools in particular). The contribution of enrollment disparities to inequality in public spending would then 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="32" /> the contribution of the attendance gap declines significantly but still accounts for about half of the total difference in public spending for higher education between the poorest and richest individuals.

In this section, we have determined that the discrepancies in higher education spending in France largely result from disparities in enrollment rates, rather than from differences in funding across programs. In contrast, in the United States, disparities in educational spending seem to be primarily associated with differences in funding across programs. Bonneau (2020) posits that, given the variance in spending within a particular educational level in the United States, the degree of educational spending inequality would persist nearly unchanged even if everyone reached the same level of educational attainment. To draw more conclusive remarks on these topics, further research would be needed for a more comprehensive comparison between the two countries.

<footnote n="31">³¹To 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 higher than the current enrollment gap of 15 percentage points.</footnote>

<footnote n="32">³²This 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>
and 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 pursuing higher education for an average of 3.3 years, compared with 4.5 years for students at the top (Figure D4).<fnref n="31" /> 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, although high-income students are largely overrepresented in selective programs (Figure 2), low-income students more often enroll in vocational programs, which also benefit from a much higher endowment than university programs (Table B5).

[place Table 4 here]

**Robustness Check: Matching Richer Individuals to Better-Endowed Programs.** One concern about this finding of equal annual public spending for higher education students may be limited ability to match students with the specific program in which they are enrolled and to disaggregate spending on some of the most selective programs (elite graduate schools in particular). The contribution of enrollment disparities to inequality in public spending would then 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="32" /> the contribution of the attendance gap declines significantly but still accounts for about half of the total difference in public spending for higher education between the poorest and richest individuals.

In this section, we have determined that the discrepancies in higher education spending in France largely result from disparities in enrollment rates, rather than from differences in funding across programs. In contrast, in the United States, disparities in educational spending seem to be primarily associated with differences in funding across programs. Bonneau (2020) posits that, given the variance in spending within a particular educational level in the United States, the degree of educational spending inequality would persist nearly unchanged even if everyone reached the same level of educational attainment. To draw more conclusive remarks on these topics, further research would be needed for a more comprehensive comparison between the two countries.

<footnote n="31">³¹To 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 higher than the current enrollment gap of 15 percentage points.</footnote>

<footnote n="32">³²This 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 within the parental income distribution is associated with a 5.8-percentage-point increase in the proportion of children enrolling in higher education. The level of attendance inequality according to parental income in France is very similar to that in the United States despite much lower tuition fees and income inequalities in France. This is a new result that we interpret by the importance of intergenerational transmission of educational outcomes and early tracking in France. In France, spending on higher education is regressive. This is primarily due to the inequalities in participation that we have highlighted, even though the concentration of resources among higher education institutions is less uneven than in the United States.

Our analysis reveals a disparity in the prerequisites for higher education enrollment, calling for earlier and more robust skill support of low-income students. Specifically, policies aiming at reducing the attendance gap in high school academic tracks based on socio-economic background could be a beneficial strategy to enhance higher education 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 higher education programs available. 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 to adequately cover higher education expenses. 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 based on parental income. In some countries, these inequalities could arise primarily due to 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 be insightful. In Europe, a crucial question is how to align this divide with the differences between social democratic and liberal welfare state regimes, as outlined by Willemse and De Beer (2012), and the contrast in social mobility 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. This funding could affect income inequalities through an increase in human capital, as discussed by Ehrlich and Kim (2007).

This need for international comparison of inequality in enrollment in higher education calls for educational data to be further enriched with reliable data on parental income. Detailed comparative data on spending differentials in higher education, especially for selective programs, would further contribute to the public debate by allowing more systematic estimation of the redistribution occurring through higher education public spending.
# 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 within the parental income distribution is associated with a 5.8-percentage-point increase in the proportion of children enrolling in higher education. The level of attendance inequality according to parental income in France is very similar to that in the United States despite much lower tuition fees and income inequalities in France. This is a new result that we interpret by the importance of intergenerational transmission of educational outcomes and early tracking in France. In France, spending on higher education is regressive. This is primarily due to the inequalities in participation that we have highlighted, even though the concentration of resources among higher education institutions is less uneven than in the United States.

Our analysis reveals a disparity in the prerequisites for higher education enrollment, calling for earlier and more robust skill support of low-income students. Specifically, policies aiming at reducing the attendance gap in high school academic tracks based on socio-economic background could be a beneficial strategy to enhance higher education 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 higher education programs available. 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 to adequately cover higher education expenses. 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 based on parental income. In some countries, these inequalities could arise primarily due to 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 be insightful. In Europe, a crucial question is how to align this divide with the differences between social democratic and liberal welfare state regimes, as outlined by Willemse and De Beer (2012), and the contrast in social mobility 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. This funding could affect income inequalities through an increase in human capital, as discussed by Ehrlich and Kim (2007).

This need for international comparison of inequality in enrollment in higher education calls for educational data to be further enriched with reliable data on parental income. Detailed comparative data on spending differentials in higher education, especially for selective programs, would further contribute to the public debate by allowing more systematic estimation of the redistribution occurring through higher education public spending.
Figures

<figure><img src="image_1.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, by parental 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_2.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 programs 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 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. 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.
Figures

<figure><img src="image_2.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, by parental 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_3.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 programs 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 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. 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_3.png" /><figcaption>Figure 3: Higher Education Attendance by Parents' Pre-Tax Income, Occupation, and Diploma</figcaption></figure>

Notes: These heatmap 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 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 of education), and M.D. or Ph.D. (20 years of education).
<figure><img src="image_4.png" /><figcaption>Figure 3: Higher Education Attendance by Parents' Pre-Tax Income, Occupation, and Diploma</figcaption></figure>

Notes: These heatmap 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 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 of education), and M.D. or Ph.D. (20 years of education).
<figure><img src="image_4.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 education as these individuals are most likely to have entered higher education between the ages of 18 and 21. We exclude the youngest (18-year old individuals) from our sample in order to limit the minor 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 it is not possible to identify them independently of the incomes of all household members, thus also including the incomes of cohabiting young adults. 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="33" />

<footnote n="33">³³A 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 and the United States even more similar.</footnote>
<figure><img src="image_5.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 education as these individuals are most likely to have entered higher education between the ages of 18 and 21. We exclude the youngest (18-year old individuals) from our sample in order to limit the minor 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 it is not possible to identify them independently of the incomes of all household members, thus also including the incomes of cohabiting young adults. 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="33" />

<footnote n="33">³³A 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 and the United States even more similar.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 5: Correlations Between Academic Skills, Behavioral Skills and Socio-Economic Status in France and in the United States</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) is based on 2015 data, while Figure (b) is based on 2018 data. The measure of parental background is derived from the Socio-Economic Index (ESCI) in PISA data. This index is a composite measure combining the financial, social, cultural, and human-capital resources available to students into 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 database (OECD (2018a), OECD (2021)).
<figure><img src="image_6.png" /><figcaption>Figure 5: Correlations Between Academic Skills, Behavioral Skills and Socio-Economic Status in France and in the United States</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) is based on 2015 data, while Figure (b) is based on 2018 data. The measure of parental background is derived from the Socio-Economic Index (ESCI) in PISA data. This index is a composite measure combining the financial, social, cultural, and human-capital resources available to students into 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 database (OECD (2018a), OECD (2021)).
<caption>Figure 6: Educational Aspirations in France and the United States</caption>

<figure><img src="image_6.png" /><figcaption>(b) High Achieving Student Not Expecting to Enroll in Tertiary Education, by Socio-Economic Status</figcaption></figure>

<figure><img src="image_7.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 survey. We restrict ourselves to individuals aged 18 and 19 in order to avoid having educational aspirations influenced by years of schooling completed in higher education. 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. 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 these figures is sourced from the PISA 2018 database (OECD (2019)).</footnote>
<caption>Figure 6: Educational Aspirations in France and the United States</caption>

<figure><img src="image_7.png" /><figcaption>(b) High Achieving Student Not Expecting to Enroll in Tertiary Education, by Socio-Economic Status</figcaption></figure>

<figure><img src="image_8.png" /></figure>

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 survey. We restrict ourselves to individuals aged 18 and 19 in order to avoid having educational aspirations influenced by years of schooling completed in higher education. 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. 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 these figures is sourced from the PISA 2018 database (OECD (2019)).
<figure><img src="image_8.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 both France and the United States. Individuals with zero spending represent those who do not enroll in higher education. The data for France appear noisier due to the sampling of only 65 higher education institutions. However, these institutions have been demonstrated to be representative of 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).
<figure><img src="image_9.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 both France and the United States. Individuals with zero spending represent those who do not enroll in higher education. The data for France appear noisier due to the sampling of only 65 higher education institutions. However, these institutions have been demonstrated to be representative of 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).
<figure><img src="image_9.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, by parental income. Panel (a) shows the total amount of public and private investment in higher education for all individuals until 24, as a function of parental income. Panel (b) shows total public and private investment in higher education for students or former students until 24, by parental income. 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. Private spending corresponds to tuition paid by the parents and expanded over the 6-year period, taking into account the number of years of education completed by each individual. 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 (x-axis). We use the "Cost Awareness" project and MESRI-SIES (2021) data for higher education spending.

Tables
<figure><img src="image_10.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, by parental income. Panel (a) shows the total amount of public and private investment in higher education for all individuals until 24, as a function of parental income. Panel (b) shows total public and private investment in higher education for students or former students until 24, by parental income. 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. Private spending corresponds to tuition paid by the parents and expanded over the 6-year period, taking into account the number of years of education completed by each individual. 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 (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 includes only individuals for whom a young adult 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 and only one in 54 percent of the cases. When the two parents do not live in the same city, we use the larger urban center.
<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 includes only individuals for whom a young adult 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 and only one in 54 percent of the cases. When the two parents do not live in the same city, we use the larger urban center.
<table><caption>Table 2: Enrollment in Higher Education, by Parents' Pre-Tax Income Quintile</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Enrollment in higher education</td><td></td><td></td><td></td><td></td></tr><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>Parents' income quintile</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>Control variables included</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 effect of parental income on enrollment in higher education (1), successively adding controls for parental occupation (2), parental education (3), and all these controls simultaneously with family status and urban center size (4). 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. 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 higher education 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 categories of occupation (Table B2), separately for father and mother. Parents' education is introduced in dummy variables for each of the 6 categories of degree (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.), separately for father and mother. The family situation is introduced in 5 categories: parents together, separated, father unknown, mother unknown or parents unknown. Urban center size is introduced in three categories: Paris metropolitan area, urban center of more than 100,000 inhabitants, urban center of under 100,000 inhabitants. When the two parents do not live in the same urban center, we use the larger of the two.

<footnote>* p < 0.1, ** p < 0.05, *** p < 0.01</footnote>
<table><caption>Table 2: Enrollment in Higher Education, by Parents' Pre-Tax Income Quintile</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Enrollment in higher education</strong></td><td></td><td></td><td></td><td></td></tr><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><strong>Observations</strong></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 effect of parental income on enrollment in higher education (1), successively adding controls for parental occupation (2), parental education (3), and all these controls simultaneously with family status and urban center size (4). 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. 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 higher education 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 categories of occupation (Table B2), separately for father and mother. Parents' education is introduced in dummy variables for each of the 6 categories of degree (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.), separately for father and mother. The family situation is introduced in 5 categories: parents together, separated, father unknown, mother unknown or parents unknown. Urban center size is introduced in three categories: Paris metropolitan area, urban center of more than 100,000 inhabitants, urban center of under 100,000 inhabitants. When the two parents do not live in the same urban center, 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 degrees between individuals in the top and bottom 10 percent of the parental income distribution (in percent). To decompose the contribution of different factors, we rely on Gupta's decomposition methods (Das Gupta, 1991). See Appendix B.2.3 for more details on this decomposition method.
<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 degrees between individuals in the top and bottom 10 percent of the parental income distribution (in percent). To decompose the contribution of different factors, we rely on Gupta's decomposition methods (Das Gupta, 1991). See Appendix B.2.3 for more details on this decomposition method.
<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 public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the 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) 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 the respective field with the most affluent students and adding consideration for increased spending on elite graduate schools. More specifically 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) of the income distribution. Concerning 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 and those in the next 5 percent to enroll in schools with endowments twice the typical endowment. See Appendix C.3.3 for more details on these simulations.
<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 public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the 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) 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 the respective field with the most affluent students and adding consideration for increased spending on elite graduate schools. More specifically 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) of the income distribution. Concerning 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 and those in the next 5 percent to enroll 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 the 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
Allègre, Mathias André, Elvire Guillaud, Elise Huillery, Xavier Joutard, Gustave Kenedi, Chloé
Lallemand, David Margolis, Michaël Sicsic, Louis Sirugue 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 custom data on lifetime earnings by gender and educational
attainment. Lastly, we thank the French Council of Economic Analysis and the Ministry of
Higher Education and Research for giving us 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.