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.
References

Acemoglu, Daron and J. S. Pischke, “Changes in the Wage Structure, Family Income, and Children’s Education,” *European Economic Review*, 2001, 45 (4-6), 890–904.

Albouy, Valérie, François Bouton, and Nicole Roth, “Les transferts en faveur des familles: un bilan statique des transferts liés à l’éducation,” 2002. Communication préparée pour le sémi-naire "les effets redistributifs de l’éducation : les enseignements d’une approche monétaire statique" organisé par le Cerc, le DPD du Ministère de l’Education Nationale et l’Insee.

Alesina, Alberto, Stefanie Stantcheva, and Edoardo Teso, “Intergenerational Mobility and Preferences for Redistribution,” *American Economic Review*, 2018, 108 (2), 521–554.

Algan, Yann and Élise Huillery, *Économie du savoir-être*, Presses de Sciences Po, 2022.

André, Mathias, Jean-Marc Germain, and Michaël Sicsic, “"Do I Get My Money Back?": A Broader Approach to Inequality and Redistribution in France with a Monetary Valuation of Public Services,” *Insee Working Paper N° 2023-07*, 2023.

Bandura, Albert, “Self-Efficacy: Toward a Unifying Theory of Behavioral Change.,” *Psychological review*, 1977, 84 (2), 191.

Barasz, Johanna, Peggy Furic, and Bénédicte Galtier, “Scolarités. Le poids des héritages.,” 2023. Rapport France Stratégie.

Barhoumi, Meriam and Jean-Paul Caille, “Les élèves sortent de l’enseignement secondaire de plus en plus diplômés mais au terme de parcours scolaires encore socialement différenciés,” *Éducation & Formations*, 2020, 101, 323–358.

Baudelot, Christian and Roger Establet, *L’élitisme républicain: l’école française à l’épreuve des comparaisons internationales*, Paris: Seuil, coll. "La République des idées", 2009.

Becker, Gary S. and Nigel Tomes, “An Equilibrium Theory of the Distribution of Income and Intergenerational Mobility,” *Journal of Political Economy*, 1979, 87 (6), 1153–1189.

Ben-Halima, Bassem, Nathalie Chusseau, and Joël Hellier, “Skill Premia and Intergenerational Education Mobility: The French Case,” *Economics of Education Review*, 2014, 39, 50–64.

Bennani, Hamza, Gabriele Dabbaghian, and Madeleine Péron, “Les coûts des formations dans l’enseignement supérieur français: déterminants et disparités,” 2021. Focus du CAE N° 074-2021.

Black, Sandra E. and Paul J. Devereux, “Recent Developments in Intergenerational Mobility,” *Handbook of Labor Economics*, 2011, 4, 1487–1541.
References

Acemoglu, Daron and J. S. Pischke, “Changes in the Wage Structure, Family Income, and Children’s Education,” *European Economic Review*, 2001, 45 (4-6), 890–904.

Albouy, Valérie, François Bouton, and Nicole Roth, “Les transferts en faveur des familles: un bilan statique des transferts liés à l’éducation,” 2002. Communication préparée pour le sémi-naire "les effets redistributifs de l’éducation : les enseignements d’une approche monétaire statique" organisé par le Cerc, le DPD du Ministère de l’Education Nationale et l’Insee.

Alesina, Alberto, Stefanie Stantcheva, and Edoardo Teso, “Intergenerational Mobility and Preferences for Redistribution,” *American Economic Review*, 2018, 108 (2), 521–554.

Algan, Yann and Élise Huillery, *Économie du savoir-être*, Presses de Sciences Po, 2022.

André, Mathias, Jean-Marc Germain, and Michaël Sicsic, “"Do I Get My Money Back?": A Broader Approach to Inequality and Redistribution in France with a Monetary Valuation of Public Services,” *Insee Working Paper N° 2023-07*, 2023.

Bandura, Albert, “Self-Efficacy: Toward a Unifying Theory of Behavioral Change.,” *Psychological review*, 1977, 84 (2), 191.

Barasz, Johanna, Peggy Furic, and Bénédicte Galtier, “Scolarités. Le poids des héritages.,” 2023. Rapport France Stratégie.

Barhoumi, Meriam and Jean-Paul Caille, “Les élèves sortent de l’enseignement secondaire de plus en plus diplômés mais au terme de parcours scolaires encore socialement différenciés,” *Éducation & Formations*, 2020, 101, 323–358.

Baudelot, Christian and Roger Establet, *L’élitisme républicain: l’école française à l’épreuve des comparaisons internationales*, Paris: Seuil, coll. "La République des idées", 2009.

Becker, Gary S. and Nigel Tomes, “An Equilibrium Theory of the Distribution of Income and Intergenerational Mobility,” *Journal of Political Economy*, 1979, 87 (6), 1153–1189.

Ben-Halima, Bassem, Nathalie Chusseau, and Joël Hellier, “Skill Premia and Intergenerational Education Mobility: The French Case,” *Economics of Education Review*, 2014, 39, 50–64.

Bennani, Hamza, Gabriele Dabbaghian, and Madeleine Péron, “Les coûts des formations dans l’enseignement supérieur français: déterminants et disparités,” 2021. Focus du CAE N° 074-2021.

Black, Sandra E. and Paul J. Devereux, “Recent Developments in Intergenerational Mobility,” *Handbook of Labor Economics*, 2011, 4, 1487–1541.
Blau, David M., "The Effect of Income on Child Development," *The Review of Economics and Statistics*, 1999, 81 (2), 261-276.

Blau, Peter M. and Otis D. Duncan, *The American Occupational Structure.*, New York: John Wiley & Sons, 1967.

Boiteau, Jean-Serge and Claude Jameux, "Le projet de connaissance des couts des activités des établissements d'enseignement supérieur et de recherche : retour d'expérience," *Recherches en sciences de gestion*, ISEOR, 2019, 4 (127), 215-240.

Bonneau, Cécile, "The Concentration of Investment in Education in the US (1970-2018)," WID Working Paper n° 2020/04, 2020.

Bonneau, Cécile, Pauline Charousset, Julien Grenet, and Georgia Thebault, "Quelle démocratisation des grandes écoles depuis le milieu des années 2000 ?," 2021. Rapport IPP n° 30.

Boudon, Raymond, *L'inégalité des chances: la mobilité sociale dans les sociétés industrielles*, Paris: Armand Colin, 1973.

Bourdieu, Pierre and Jean-Claude Passeron, *Les héritiers: les étudiants et la culture*, Paris: Les Éditions de Minuit, 1964.

Boutchenik, Béatrice, Pauline Givord, and Olivier Monso, "How Do Urban Segregation and High School Choice Contribute to School Segregation?," *Revue économique*, 2021, 72 (5), 717-747.

Brunello, Giorgio and Daniele Checchi, "Does School Tracking Affect Equality of Opportunity? New International Evidence," *Economic policy*, 2007, 22 (52), 782-861.

Bütikofer, Aline, Erling Risa, and Kjell Salvanes, "Status Traps and Human Capital Investment," 2019. Erling Risa's 2nd Chapter PhD dissertation, NHH Norwegian School of Economics.

Causa, Orsetta and Catherine Chapuis, "Equity in Student Achievement across OECD Countries: An Investigation of the Role of Policies," *OECD Journal: Economic Studies*, 2011, 2010 (1), 1-50.

Chetty, Raj, John N. Friedman, Emmanuel Saez, Nicholas Turner, and Danny Yagan, "Income Segregation and Intergenerational Mobility Across Colleges in the United States," *The Quarterly Journal of Economics*, 2020, 135 (3), 1567-1633.

, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez, "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States," *The Quarterly Journal of Economics*, 2014, 129 (4), 1553-1623.
**Blau, David M.**, "The Effect of Income on Child Development," *The Review of Economics and Statistics*, 1999, 81 (2), 261–276.

**Blau, Peter M.** and **Otis D. Duncan**, *The American Occupational Structure.*, New York: John Wiley & Sons, 1967.

**Boiteau, Jean-Serge** and **Claude Jameux**, "Le projet de connaissance des couts des activités des établissements d'enseignement supérieur et de recherche : retour d'expérience," *Recherches en sciences de gestion*, ISEOR, 2019, 4 (127), 215–240.

**Bonneau, Cécile**, "The Concentration of Investment in Education in the US (1970-2018)," *WID Working Paper n° 2020/04*, 2020.

**Bonneau, Cécile**, **Pauline Charousset**, **Julien Grenet**, and **Georgia Thebault**, "Quelle démocratisation des grandes écoles depuis le milieu des années 2000 ?," 2021. Rapport IPP n° 30.

**Boudon, Raymond**, *L'inégalité des chances: la mobilité sociale dans les sociétés industrielles*, Paris: Armand Colin, 1973.

**Bourdieu, Pierre** and **Jean-Claude Passeron**, *Les héritiers: les étudiants et la culture*, Paris: Les Éditions de Minuit, 1964.

**Boutchenik, Béatrice**, **Pauline Givord**, and **Olivier Monso**, "How Do Urban Segregation and High School Choice Contribute to School Segregation?," *Revue économique*, 2021, 72 (5), 717–747.

**Brunello, Giorgio** and **Daniele Checchi**, "Does School Tracking Affect Equality of Opportunity? New International Evidence," *Economic policy*, 2007, 22 (52), 782–861.

**Bütikofer, Aline**, **Erling Risa**, and **Kjell Salvanes**, "Status Traps and Human Capital Investment," 2019. Erling Risa's 2nd Chapter PhD dissertation, NHH Norwegian School of Economics.

**Causa, Orsetta** and **Catherine Chapuis**, "Equity in Student Achievement across OECD Countries: An Investigation of the Role of Policies," *OECD Journal: Economic Studies*, 2011, 2010 (1), 1–50.

**Chetty, Raj**, **John N. Friedman**, **Emmanuel Saez**, **Nicholas Turner**, and **Danny Yagan**, "Income Segregation and Intergenerational Mobility Across Colleges in the United States," *The Quarterly Journal of Economics*, 2020, 135 (3), 1567–1633.

, Nathaniel Hendren, **Patrick Kline**, and **Emmanuel Saez**, "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States," *The Quarterly Journal of Economics*, 2014, 129 (4), 1553–1623.
Chevalier, Arnaud, Colm Harmon, Vincent O' Sullivan, and Ian Walker, "The Impact of Parental Income and Education on the Schooling of their Children," *IZA Journal of Labor Economics*, 2013, 2 (1), 1–22.

Chmielewski, Anna K. and Sean F. Reardon, "Patterns of Cross-National Variation in the Association Between Income and Academic Achievement," *Aera Open*, 2016, 2 (3), 2332858416649593.

Corak, Miles, "Inequality from Generation to Generation: The United States in Comparison," *IZA Discussion Paper* N° 9929, 2016.

Currie, Janet and Enrico Moretti, "Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings," *The Quarterly Journal of Economics*, 2003, 118 (4), 1495–1532.

Das Gupta, Prithwis, "Decomposition of the Difference Between Two Rates and its Consistency when more than Two Populations Are Involved," *Mathematical Population Studies*, 1991, 3 (2), 105–125.

Dodin, Majed, Sebastian Findeisen, Lukas Henkel, Dominik Sachs, and Paul Schüle, "Social Mobility in Germany," *CESifo Working Paper* N° 9200, 2021.

Duncan, Greg J., Ariel Kalil, and Kathleen M. Ziol-Guest, "Increasing Inequality in Parent Incomes and Children's Schooling," *Demography*, 2017, 54 (5), 1603–1626.

Ehrlich, Isaac, Adam Cook, and Yong Yin, "What Accounts for the US Ascendancy to Economic Superpower by the Early Twentieth Century? The Morrill Act–Human Capital Hypothesis," *Journal of Human Capital*, 2018, 12 (2), 233–281.

— and Jinyoung Kim, "The Evolution of Income and Fertility Inequalities over the Course of Economic Development: a Human Capital Perspective," *Journal of Human Capital*, 2007, 1 (1), 137–174.

Fack, Gabrielle and Julien Grenet, "Improving College Access and Success for Low-Income Students: Evidence from a Large Need-Based Grant Program," *American Economic Journal: Applied Economics*, 2015, 7 (2), 1–34.

Garbinti, Bertrand, Cecilia García-Peñalosa, Vladimir Pecheu, and Frederique Savignac, "Trends and Inequality in Lifetime Earnings in France," *CEPR Discussion Paper* N° 18472, 2023.

Grobon, Sébastien, "Combien coûte un jeune adulte à ses parents?," *Les revenus et le patrimoine des ménages*, Insee References., 2018, pp. 65–81.
Chevalier, Arnaud, Colm Harmon, Vincent O' Sullivan, and Ian Walker, "The Impact of Parental Income and Education on the Schooling of their Children," *IZA Journal of Labor Economics*, 2013, 2 (1), 1–22.

Chmielewski, Anna K. and Sean F. Reardon, "Patterns of Cross-National Variation in the Association Between Income and Academic Achievement," *Aera Open*, 2016, 2 (3), 2332858416649593.

Corak, Miles, "Inequality from Generation to Generation: The United States in Comparison," *IZA Discussion Paper* N° 9929, 2016.

Currie, Janet and Enrico Moretti, "Mother's Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings," *The Quarterly Journal of Economics*, 2003, 118 (4), 1495–1532.

Das Gupta, Prithwis, "Decomposition of the Difference Between Two Rates and its Consistency when more than Two Populations Are Involved," *Mathematical Population Studies*, 1991, 3 (2), 105–125.

Dodin, Majed, Sebastian Findeisen, Lukas Henkel, Dominik Sachs, and Paul Schüle, "Social Mobility in Germany," *CESifo Working Paper* N° 9200, 2021.

Duncan, Greg J., Ariel Kalil, and Kathleen M. Ziol-Guest, "Increasing Inequality in Parent Incomes and Children's Schooling," *Demography*, 2017, 54 (5), 1603–1626.

Ehrlich, Isaac, Adam Cook, and Yong Yin, "What Accounts for the US Ascendancy to Economic Superpower by the Early Twentieth Century? The Morrill Act–Human Capital Hypothesis," *Journal of Human Capital*, 2018, 12 (2), 233–281.

— and Jinyoung Kim, "The Evolution of Income and Fertility Inequalities over the Course of Economic Development: a Human Capital Perspective," *Journal of Human Capital*, 2007, 1 (1), 137–174.

Fack, Gabrielle and Julien Grenet, "Improving College Access and Success for Low-Income Students: Evidence from a Large Need-Based Grant Program," *American Economic Journal: Applied Economics*, 2015, 7 (2), 1–34.

Garbinti, Bertrand, Cecilia García-Peñalosa, Vladimir Pecheu, and Frederique Savignac, "Trends and Inequality in Lifetime Earnings in France," *CEPR Discussion Paper* N° 18472, 2023.

Grobon, Sébastien, "Combien coûte un jeune adulte à ses parents?," *Les revenus et le patrimoine des ménages*, Insee References., 2018, pp. 65–81.
Guillerm, Marine and Olivier Monso, “Évolution de la mixité sociale des collèges,” *Note d'information de la Depp*, 2022, 22 (26).

Guyon, Nina and Elise Huillery, “Biased Aspirations and Social Inequality at School: Evidence from French Teenagers,” *The Economic Journal*, 2021, 131 (634), 745–796.

Hansen, W. Lee and Burton A. Weisbrod, “The Distribution of Costs and Direct Benefits of Public Higher Education: The Case of California,” *The Journal of Human Resources*, 1969, 4 (2), 176–191.

Haveman, Robert and Barbara Wolfe, “The Determinants of Children’s Attainments: A Review of Methods and Findings,” *Journal of Economic Literature*, 1995, 33 (4), 1829–1878.

Herbaut, Estelle, “Inequalities in Access to Higher Education in France. Cumulative Advantage and Compensatory Advantage over the Course of Secondary Education,” *Revue Française de Sociologie*, 2019, 60 (4), 535–566.

—, Carlo Barone, Mathieu Ichou, and Louis-André Vallet, “Tracking and Sorting in the French Educational System,” 2019.

Hilger, Nathaniel G., “The Great Escape: Intergenerational Mobility in the United States Since 1940,” 2015. NBER Working Paper 21217.

—, “Parental Job Loss and Children’s Long-Term Outcomes: Evidence from 7 Million Fathers’ Layoffs,” *American Economic Journal: Applied Economics*, 2016, 8 (3), 247–283.

Ichou, Mathieu and Louis-André Vallet, “Performances scolaires, orientation et inégalités sociales d’éducation. Évolution en France en quatre décennies,” *Éducation et formations*, 2012, 82, 9–18.

Isambert-Jamati, Viviane, *Crises de la société, crises de l'enseignement*, Paris: PUF, 1970.

Johnson, William R., “Are Public Subsidies to Higher Education Regressive?,” *Education Finance and Policy*, 2006, 1 (3), 288–315.

Kenedi, Gustave and Louis Sirugue, “Intergenerational income mobility in France: A comparative and geographic analysis,” *Journal of Public Economics*, 2023, 226, 104974.

Lefranc, Arnaud and Alain Trannoy, “Intergenerational Earnings Mobility in France: Is France More Mobile than the US?,” *Annales d’Économie et de Statistique*, 2005, pp. 57–77.

Løken, Katrine V., “Family Income and Children’s Education: Using the Norwegian Oil Boom as a Natural Experiment,” *Labour Economics*, 2010, 17 (1), 118–129.

Lundborg, Petter, Erik Plug, and Astrid Würtz Rasmussen, “On the Family Origins of Human Capital Formation: Evidence from Donor Children.,” *IZA Discussion Paper No. 14708*, 2021.
Guillerm, Marine and Olivier Monso, “Évolution de la mixité sociale des collèges,” *Note d'information de la Depp*, 2022, 22 (26).

Guyon, Nina and Elise Huillery, “Biased Aspirations and Social Inequality at School: Evidence from French Teenagers,” *The Economic Journal*, 2021, 131 (634), 745–796.

Hansen, W. Lee and Burton A. Weisbrod, “The Distribution of Costs and Direct Benefits of Public Higher Education: The Case of California,” *The Journal of Human Resources*, 1969, 4 (2), 176–191.

Haveman, Robert and Barbara Wolfe, “The Determinants of Children’s Attainments: A Review of Methods and Findings,” *Journal of Economic Literature*, 1995, 33 (4), 1829–1878.

Herbaut, Estelle, “Inequalities in Access to Higher Education in France. Cumulative Advantage and Compensatory Advantage over the Course of Secondary Education,” *Revue Française de Sociologie*, 2019, 60 (4), 535–566.

—, Carlo Barone, Mathieu Ichou, and Louis-André Vallet, “Tracking and Sorting in the French Educational System,” 2019.

Hilger, Nathaniel G., “The Great Escape: Intergenerational Mobility in the United States Since 1940,” 2015. NBER Working Paper 21217.

—, “Parental Job Loss and Children’s Long-Term Outcomes: Evidence from 7 Million Fathers’ Layoffs,” *American Economic Journal: Applied Economics*, 2016, 8 (3), 247–283.

Ichou, Mathieu and Louis-André Vallet, “Performances scolaires, orientation et inégalités sociales d’éducation. Évolution en France en quatre décennies,” *Éducation et formations*, 2012, 82, 9–18.

Isambert-Jamati, Viviane, *Crises de la société, crises de l'enseignement*, Paris: PUF, 1970.

Johnson, William R., “Are Public Subsidies to Higher Education Regressive?,” *Education Finance and Policy*, 2006, 1 (3), 288–315.

Kenedi, Gustave and Louis Sirugue, “Intergenerational income mobility in France: A comparative and geographic analysis,” *Journal of Public Economics*, 2023, 226, 104974.

Lefranc, Arnaud and Alain Trannoy, “Intergenerational Earnings Mobility in France: Is France More Mobile than the US?,” *Annales d’Économie et de Statistique*, 2005, pp. 57–77.

Løken, Katrine V., “Family Income and Children’s Education: Using the Norwegian Oil Boom as a Natural Experiment,” *Labour Economics*, 2010, 17 (1), 118–129.

Lundborg, Petter, Erik Plug, and Astrid Würtz Rasmussen, “On the Family Origins of Human Capital Formation: Evidence from Donor Children.,” *IZA Discussion Paper No. 14708*, 2021.
**Maurin, Eric**, "The Impact of Parental Income on Early Schooling Transitions: A Re-Examination Using Data over Three Generations," *Journal of Public Economics*, 2002, 85 (3), 301–332.

**Mayer, Susan E.**, *What Money Can't Buy: Family Income and Children's Life Chances*, Cambridge, MA.: Harvard University Press, 1997.

**MENESR-DEPP**, *Repères et références statistiques - Édition 2015*, Ministère de l'Éducation Nationale, de l'Enseignement Supérieur et de la Recherche, 2015.

**Merle, Pierre**, *La démocratisation de l'enseignement*, La découverte, 2009.

**MESRI-SIES**, *L'état de l'Enseignement supérieur et de la Recherche en France - Édition 2021*, Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, 2021.

**Moore, Jeffrey C., Linda L. Stinson, and Edward J. Welniak**, "Income Measurement Error in Surveys: A Review," *Journal of Official Statistics*, 2000, 16 (4), 331–361.

**OECD**, "PISA 2015 Results (Volume I): Excellence and Equity in Education," 2016.

—, "PISA 2015 results: Students' Well-Being (Volume III)," 2017.

—, "Equity in Education: Breaking Down Barriers to Social Mobility," 2018.

—, "PISA 2015: Results in Focus," 2018.

—, "PISA 2018 Results (Volume II): Where All Students Can Succeed.," 2019.

—, "PISA 2018 Results (volume V): Effective Policies, Successful Schools," 2020.

—, "OECD Skills Outlook 2021. Country Profiles : France and United States," 2021.

**Pechman, Joseph A., W. Lee Hansen, and Burton A. Weisbrod**, "The Distributional Effects of Public Higher Education in California: A Review Article," *The Journal of Human Resources*, 1970, 5 (3), 361–370.

**Perez-Arce, Francisco, Ernesto F. Amaral, Haijing Huang, and Carter C. Price**, *Inequality and Opportunity: The Relationship Between Income Inequality and Intergenerational Transmission of Income*, Rand Corporation, 2016.

**Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman**, "Distributional National Accounts: Methods and Estimates for the United States," *The Quarterly Journal of Economics*, 2018, 133 (2), 553–609.

**Psacharopoulos, George and Harry Anthony Patrinos**, "Returns to Investment in Education: a Decennial Review of the Global Literature," *Education Economics*, 2018, 26 (5), 445–458.
**Maurin, Eric**, "The Impact of Parental Income on Early Schooling Transitions: A Re-Examination Using Data over Three Generations," *Journal of Public Economics*, 2002, 85 (3), 301–332.

**Mayer, Susan E.**, *What Money Can't Buy: Family Income and Children's Life Chances*, Cambridge, MA.: Harvard University Press, 1997.

**MENESR-DEPP**, *Repères et références statistiques - Édition 2015*, Ministère de l'Éducation Nationale, de l'Enseignement Supérieur et de la Recherche, 2015.

**Merle, Pierre**, *La démocratisation de l'enseignement*, La découverte, 2009.

**MESRI-SIES**, *L'état de l'Enseignement supérieur et de la Recherche en France - Édition 2021*, Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, 2021.

**Moore, Jeffrey C., Linda L. Stinson, and Edward J. Welniak**, "Income Measurement Error in Surveys: A Review," *Journal of Official Statistics*, 2000, 16 (4), 331–361.

**OECD**, "PISA 2015 Results (Volume I): Excellence and Equity in Education," 2016.

—, "PISA 2015 results: Students' Well-Being (Volume III)," 2017.

—, "Equity in Education: Breaking Down Barriers to Social Mobility," 2018.

—, "PISA 2015: Results in Focus," 2018.

—, "PISA 2018 Results (Volume II): Where All Students Can Succeed.," 2019.

—, "PISA 2018 Results (volume V): Effective Policies, Successful Schools," 2020.

—, "OECD Skills Outlook 2021. Country Profiles : France and United States," 2021.

**Pechman, Joseph A., W. Lee Hansen, and Burton A. Weisbrod**, "The Distributional Effects of Public Higher Education in California: A Review Article," *The Journal of Human Resources*, 1970, 5 (3), 361–370.

**Perez-Arce, Francisco, Ernesto F. Amaral, Haijing Huang, and Carter C. Price**, *Inequality and Opportunity: The Relationship Between Income Inequality and Intergenerational Transmission of Income*, Rand Corporation, 2016.

**Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman**, "Distributional National Accounts: Methods and Estimates for the United States," *The Quarterly Journal of Economics*, 2018, 133 (2), 553–609.

**Psacharopoulos, George and Harry Anthony Patrinos**, "Returns to Investment in Education: a Decennial Review of the Global Literature," *Education Economics*, 2018, 26 (5), 445–458.
**Rawls, John**, *A Theory of Justice*, Cambridge, MA.: Harvard University Press, 1971.

**Sacerdote, Bruce**, "How Large are the Effects from Changes in Family Environment? A Study of Korean American Adoptees," *The Quarterly Journal of Economics*, 2007, 122 (1), 119–157.

**Shea, John**, "Does Parents' Money Matter?," *Journal of Public Economics*, 2000, 77 (2), 155–184.

**Sicsic, Michaël**, "Who Climbs Up the Income Ladder? An Analysis of Intergenerational Income Mobility in France," *Economie et Statistique/Economics and Statistics*, 2023, (540), 3–20.

**Solon, Gary**, "Intergenerational Income Mobility in the United States," *American Economic Review*, 1992, 82 (3), 393–408.

**Taubman, Paul**, "Role of Parental Income in Educational Attainment," *American Economic Review*, 1989, 79 (2), 57–61.

**The College Board**, "Trends in College Pricing 2014," 2014.

**Van de Werfhorst, Herman G.**, "Early Tracking and Social Inequality in Educational Attainment: Educational Reforms in 21 European Countries," *American Journal of Education*, 2019, 126 (1), 65–99.

**Willemse, Nienke and Paul De Beer**, "Three Worlds of Educational Welfare States? A Comparative Study of Higher Education Systems across Welfare States," *Journal of European Social Policy*, 2012, 22 (2), 105–117.

**Workman, Joseph**, "Inequality Begets Inequality: Income Inequality and Socioeconomic Achievement Gradients Across the United States," *Social Science Research*, 2022, 107, 102744.

**Ziol-Guest, Kathleen M. and Kenneth T.H. Lee**, "Parent Income-Based Gaps in Schooling: Cross-Cohort Trends in the NLSYs and the PSID," *AERA Open*, 2016, 2 (2), 2332858416645834.
**Rawls, John**, *A Theory of Justice*, Cambridge, MA.: Harvard University Press, 1971.

**Sacerdote, Bruce**, "How Large are the Effects from Changes in Family Environment? A Study of Korean American Adoptees," *The Quarterly Journal of Economics*, 2007, 122 (1), 119–157.

**Shea, John**, "Does Parents' Money Matter?," *Journal of Public Economics*, 2000, 77 (2), 155–184.

**Sicsic, Michaël**, "Who Climbs Up the Income Ladder? An Analysis of Intergenerational Income Mobility in France," *Economie et Statistique/Economics and Statistics*, 2023, (540), 3–20.

**Solon, Gary**, "Intergenerational Income Mobility in the United States," *American Economic Review*, 1992, 82 (3), 393–408.

**Taubman, Paul**, "Role of Parental Income in Educational Attainment," *American Economic Review*, 1989, 79 (2), 57–61.

**The College Board**, "Trends in College Pricing 2014," 2014.

**Van de Werfhorst, Herman G.**, "Early Tracking and Social Inequality in Educational Attainment: Educational Reforms in 21 European Countries," *American Journal of Education*, 2019, 126 (1), 65–99.

**Willemse, Nienke and Paul De Beer**, "Three Worlds of Educational Welfare States? A Comparative Study of Higher Education Systems across Welfare States," *Journal of European Social Policy*, 2012, 22 (2), 105–117.

**Workman, Joseph**, "Inequality Begets Inequality: Income Inequality and Socioeconomic Achievement Gradients Across the United States," *Social Science Research*, 2022, 107, 102744.

**Ziol-Guest, Kathleen M. and Kenneth T.H. Lee**, "Parent Income-Based Gaps in Schooling: Cross-Cohort Trends in the NLSYs and the PSID," *AERA Open*, 2016, 2 (2), 2332858416645834.
# Appendix

<table><tr><td>A.1 Inequalities in Education Spending: A Literature Review</td><td>43</td></tr><tr><td>B.2 Variables, Data and Methods</td><td>44</td></tr><tr><td>B.2.1 Grouping of Parental Occupations</td><td>44</td></tr><tr><td>B.2.2 Data</td><td>46</td></tr><tr><td>B.2.2.1 Representativeness of the Sample</td><td>46</td></tr><tr><td>B.2.2.2 Comparison of Parental Income Distribution in France and the United States</td><td>46</td></tr><tr><td>B.2.2.3 Data on Public Spending on Higher Education in France</td><td>47</td></tr><tr><td>B.2.3 Gupta Decomposition Method</td><td>51</td></tr><tr><td>C.3 Robustness Checks</td><td>52</td></tr><tr><td>C.3.1 Using Multiple Years of Parental Income</td><td>52</td></tr><tr><td>C.3.2 Using Parents' Individual Income Instead of Parents' Household Income</td><td>54</td></tr><tr><td>C.3.3 Matching Richer Individuals to Better-Endowed Programs</td><td>54</td></tr><tr><td>D.4 Additional Results</td><td>58</td></tr><tr><td>D.4.1 Parental Income, Education, and Occupation: Patterns in Higher Education Enrollment</td><td>58</td></tr><tr><td>D.4.2 Distribution of Higher Education Public Spending on Young Adults</td><td>60</td></tr><tr><td>D.4.3 Decompositions of Inequalities in Public Spending on Higher Education</td><td>62</td></tr><tr><td>D.4.4 Accounting for Parental Taxes in Higher Education Spending Distribution</td><td>63</td></tr><tr><td>D.4.4.1 Methodology</td><td>63</td></tr><tr><td>D.4.4.2 Results</td><td>68</td></tr><tr><td>D.4.5 Predicted Lifetime Earnings</td><td>71</td></tr><tr><td>E.5 List of Tables and Figures</td><td>76</td></tr></table>
# Appendix

<table><tr><td>A.1 Inequalities in Education Spending: A Literature Review</td><td>43</td></tr><tr><td>B.2 Variables, Data and Methods</td><td>44</td></tr><tr><td>B.2.1 Grouping of Parental Occupations</td><td>44</td></tr><tr><td>B.2.2 Data</td><td>46</td></tr><tr><td>B.2.2.1 Representativeness of the Sample</td><td>46</td></tr><tr><td>B.2.2.2 Comparison of Parental Income Distribution in France and the United States</td><td>46</td></tr><tr><td>B.2.2.3 Data on Public Spending on Higher Education in France</td><td>47</td></tr><tr><td>B.2.3 Gupta Decomposition Method</td><td>51</td></tr><tr><td>C.3 Robustness Checks</td><td>52</td></tr><tr><td>C.3.1 Using Multiple Years of Parental Income</td><td>52</td></tr><tr><td>C.3.2 Using Parents' Individual Income Instead of Parents' Household Income</td><td>54</td></tr><tr><td>C.3.3 Matching Richer Individuals to Better-Endowed Programs</td><td>54</td></tr><tr><td>D.4 Additional Results</td><td>58</td></tr><tr><td>D.4.1 Parental Income, Education, and Occupation: Patterns in Higher Education Enrollment</td><td>58</td></tr><tr><td>D.4.2 Distribution of Higher Education Public Spending on Young Adults</td><td>60</td></tr><tr><td>D.4.3 Decompositions of Inequalities in Public Spending on Higher Education</td><td>62</td></tr><tr><td>D.4.4 Accounting for Parental Taxes in Higher Education Spending Distribution</td><td>63</td></tr><tr><td>D.4.4.1 Methodology</td><td>63</td></tr><tr><td>D.4.4.2 Results</td><td>68</td></tr><tr><td>D.4.5 Predicted Lifetime Earnings</td><td>71</td></tr><tr><td>E.5 List of Tables and Figures</td><td>76</td></tr></table>
# A.1 Inequalities in Education Spending: A Literature Review

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

*Notes*: This table summarizes the results of the empirical literature on the progressivity or regressivity of educational expenditures, obtained in various countries and institutional contexts. The "parental background" column specifies which parental social origin variable is used in the analysis (education level, occupation or income). The column "taxes taken into account" indicates whether or not the authors deduct the taxes paid by the parents (either partially or entirely) or not. The "higher education only" column specifies whether the analysis is devoted solely to higher education spending or to all education spending.
# A.1 Inequalities in Education Spending: A Literature Review

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

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

### B.2.1 Grouping of Parental Occupations

Table B2 below details the four SES categories and the corresponding occupations. The mother's occupation is missing for 4% of the observations and the father's for 9% of the observations.
## B.2 Variables, Data and Methods

### B.2.1 Grouping of Parental Occupations

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

<footnote>Notes: This table details the occupations corresponding to the four socio-economic status used in our analyses. This grouping corresponds to the official grouping of the Department of Education's statistical service (Direction de l'évaluation, de la prospective et de la performance (DEPP)).</footnote>
<table><caption>Table B2: Grouping of Occupations</caption><thead><tr><th>Socioeconomic statuses (SES)</th><th>Corresponding occupations</th></tr></thead><tbody><tr><td><strong>High SES</strong></td><td></td></tr><tr><td>23</td><td>Entrepreneurs with 10 or more employees</td></tr><tr><td>31</td><td>Liberal professions</td></tr><tr><td>33</td><td>Public service executives</td></tr><tr><td>34</td><td>Professors, scientific professions</td></tr><tr><td>35</td><td>Information, arts and entertainment professions</td></tr><tr><td>37</td><td>Administrative and commercial executives of companies</td></tr><tr><td>38</td><td>Engineers and technical company executives</td></tr><tr><td>42</td><td>Teachers and assimilated</td></tr><tr><td>73</td><td>Former executives</td></tr><tr><td><strong>Upper-middle SES</strong></td><td></td></tr><tr><td>43</td><td>Intermediate professions in health and social work</td></tr><tr><td>44</td><td>Clergy, religious</td></tr><tr><td>45</td><td>Administrative intermediary professions of the public service</td></tr><tr><td>46</td><td>Administrative and commercial intermediary professions of companies</td></tr><tr><td>47</td><td>Technicians</td></tr><tr><td>48</td><td>Foremen, supervisors</td></tr><tr><td>74</td><td>Former intermediary professions</td></tr><tr><td><strong>Lower-middle SES</strong></td><td></td></tr><tr><td>11</td><td>Smallholder farmers</td></tr><tr><td>12</td><td>Farmers on medium farm</td></tr><tr><td>13</td><td>Large-scale farmers</td></tr><tr><td>21</td><td>Craftsmen</td></tr><tr><td>22</td><td>Traders and assimilated</td></tr><tr><td>52</td><td>Civilian employees and civil servants</td></tr><tr><td>53</td><td>Police and military</td></tr><tr><td>54</td><td>Administrative employees of companies</td></tr><tr><td>55</td><td>Commercial employees</td></tr><tr><td>56</td><td>Staff in direct personal services</td></tr><tr><td>71</td><td>Former farmer operators</td></tr><tr><td>72</td><td>Former craftsmen, traders, business leaders</td></tr><tr><td>75</td><td>Former employees</td></tr><tr><td><strong>Low SES</strong></td><td></td></tr><tr><td>62</td><td>Skilled industrial type workers</td></tr><tr><td>63</td><td>Skilled artisan type workers</td></tr><tr><td>64</td><td>Drivers</td></tr><tr><td>65</td><td>Skilled workers in handling, warehousing and transport</td></tr><tr><td>67</td><td>Unskilled industrial workers</td></tr><tr><td>68</td><td>Unskilled artisan type workers</td></tr><tr><td>69</td><td>Agricultural workers</td></tr><tr><td>76</td><td>Former workers</td></tr><tr><td>80</td><td>Inactive</td></tr></tbody></table>

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

### B.2.2.1 Representativeness of the Sample

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

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

### B.2.2.2 Comparison of Parental Income Distribution in France and the United States
## B.2.2 Data

### B.2.2.1 Representativeness of the Sample

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

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

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

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

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

**Cost of Studies by Track and Field of Study.** In the French national accounts, higher education expenditures are generally classed in one of just three categories,<fnref n="34" /> regardless of the track or field of study (MESRI-SIES, 2021). Our study draws additional data from a survey of higher education institutions, the "Cost Awareness" project (Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux, 2019)) designed to assess the expenditures of different higher education institutions. This survey was conducted between 2016 and 2019 at 65 higher education institutions that enroll approximately one-third of all French students. Not all public institutions have been surveyed, but the sampled institutions are representative of all public higher education institutions under the Ministry of Higher Education (Bennani et al. (2021)). These data allow for a detailed breakdown of costs across various tracks and fields of study. They enable the determination of instructional costs in higher education, excluding research-related expenses, which are considered in more aggregated data. For all courses – including engineering schools, technical training (IUT), bachelor's degrees (professional or academic), and master's degrees – public expenditures are categorized into nine fields: Humanities, Arts, Languages; Markets and Organizations; Mathematics, Engineering, Computer Science; Law; Matter, Earth, and Universe Sciences; Social Sciences; Life Sciences; Medical; and Miscellaneous, which includes elements not classifiable in the preceding categories. The data thus allow a more accurate estimate of public spending on higher education for each individual. Annual public spending per student in higher education varies considerably by track and field of study, as shown in table B5. On average, undergraduate students cost €3,600 per year, graduate students €5,300, and engineering students €10,600. The scientific fields – except for medical studies – are much more expensive

<footnote n="34">³⁴In 2014-2015, on average, vocational programs cost €14,150 per student, preparatory courses €15,620 and university programs €10,980.</footnote>
<table><caption>Table B4: Parental Income Distribution in France and the United States</caption><thead><tr><th>Income Distribution</th><th>United States</th><th>France</th></tr></thead><tbody><tr><td>Mean</td><td>87,219</td><td>52,030</td></tr><tr><td>Std. Deviation</td><td>353,430</td><td>46,400</td></tr><tr><td>Median</td><td>60,129</td><td>44,580</td></tr><tr><td>Observations</td><td>9,867,736</td><td>3,947</td></tr></tbody></table>

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

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

**Cost of Studies by Track and Field of Study.** In the French national accounts, higher education expenditures are generally classed in one of just three categories,<fnref n="34" /> regardless of the track or field of study (MESRI-SIES, 2021). Our study draws additional data from a survey of higher education institutions, the "Cost Awareness" project (Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux, 2019)) designed to assess the expenditures of different higher education institutions. This survey was conducted between 2016 and 2019 at 65 higher education institutions that enroll approximately one-third of all French students. Not all public institutions have been surveyed, but the sampled institutions are representative of all public higher education institutions under the Ministry of Higher Education (Bennani et al. (2021)). These data allow for a detailed breakdown of costs across various tracks and fields of study. They enable the determination of instructional costs in higher education, excluding research-related expenses, which are considered in more aggregated data. For all courses – including engineering schools, technical training (IUT), bachelor's degrees (professional or academic), and master's degrees – public expenditures are categorized into nine fields: Humanities, Arts, Languages; Markets and Organizations; Mathematics, Engineering, Computer Science; Law; Matter, Earth, and Universe Sciences; Social Sciences; Life Sciences; Medical; and Miscellaneous, which includes elements not classifiable in the preceding categories. The data thus allow a more accurate estimate of public spending on higher education for each individual. Annual public spending per student in higher education varies considerably by track and field of study, as shown in table B5. On average, undergraduate students cost €3,600 per year, graduate students €5,300, and engineering students €10,600. The scientific fields – except for medical studies – are much more expensive

<footnote n="34">³⁴In 2014-2015, on average, vocational programs cost €14,150 per student, preparatory courses €15,620 and university programs €10,980.</footnote>
than the humanities (up to twice as much for a bachelor's degree), because students have more tutorials and practical work, with a much lower student/teacher ratio.

**Elite Graduate Schools and Variance in Spending Within Tracks and Fields of Study.** Despite their quality, these new higher education cost data still have some limitations. We are not able to differentiate public spending among elite graduate schools.<fnref n="35" /> We therefore attribute the average cost of engineering schools to the other public elite graduate schools, which might lead us to underestimate inequalities in public spending. Some of these schools indeed have very high per-student expenditures (for example, Berné and Métivier (2015) found that some specific elite graduate schools have expenditures of €60,000 per student per year) and an attendance rate that remains very uneven (Bonneau et al. (2021)). More generally, public spending still varies greatly within tracks and fields, which we do not take into account because we cannot link students to the exact university in which they are or were enrolled. We address these issues by simulating two variants of public spending on higher education to predict how a more uneven distribution of public spending within each program and field might alter this result (see Appendix C.3.3). Unlike university programs, we cannot break down the costs of vocational programs and preparatory courses by field because we do not have detailed expenditure data for these programs, which are located in specific high schools. Since both are national programs and the course load, student/teacher ratio, and teacher salaries are set by the state, we can assume that there is little disciplinary variation in these programs. In the same way that we remove the costs of research in university programs, here we consider only the costs of training: 85.3 percent of total costs for preparatory courses and 86.7 percent for vocational programs (Bennani et al., 2021).

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

**Construction of Educational Spending Over the Entire Educational Trajectory.** To study inequality along the higher education trajectory, we restrict ourselves to individuals aged 22 to 24, who are the most advanced in their studies in our sample. 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. We retain the higher cost between the highest degree obtained, the highest level of education attained, or ongoing studies. We partially account for reorientations. For example, someone who is enrolled in a master's program but has an engineering degree has the cost of the full engineering school program plus the cost of one year of a master's degree in their field. In the absence of complete information on individual trajectories in the data, we do not consider individuals who repeat in the same program. It is likely that this approximation leads us to underestimate the cost of university students who repeat: 27 % of first-year students (MESRI-SIES, 2020) but also students in preparatory courses who often repeat the second year of the program

<footnote n="35">³⁵The survey is representative of institutions under the supervision of the Ministry of Higher Education and Research, although some institutions, notably certain engineering schools, are under the supervision of other ministries (Ministry of the Economy, Ministry of the Army, Ministry of Agriculture, etc.). Business schools are not included in the survey either. We therefore supplement this survey with specific data on the costs of business school students (https://www.cefdg.fr/fr/ecoles-et-formations-visees). On average, public funding covers about 5 percent of student costs for these private programs.</footnote>
than the humanities (up to twice as much for a bachelor's degree), because students have more tutorials and practical work, with a much lower student/teacher ratio.

**Elite Graduate Schools and Variance in Spending Within Tracks and Fields of Study.** Despite their quality, these new higher education cost data still have some limitations. We are not able to differentiate public spending among elite graduate schools.<fnref n="35" /> We therefore attribute the average cost of engineering schools to the other public elite graduate schools, which might lead us to underestimate inequalities in public spending. Some of these schools indeed have very high per-student expenditures (for example, Berné and Métivier (2015) found that some specific elite graduate schools have expenditures of €60,000 per student per year) and an attendance rate that remains very uneven (Bonneau et al. (2021)). More generally, public spending still varies greatly within tracks and fields, which we do not take into account because we cannot link students to the exact university in which they are or were enrolled. We address these issues by simulating two variants of public spending on higher education to predict how a more uneven distribution of public spending within each program and field might alter this result (see Appendix C.3.3). Unlike university programs, we cannot break down the costs of vocational programs and preparatory courses by field because we do not have detailed expenditure data for these programs, which are located in specific high schools. Since both are national programs and the course load, student/teacher ratio, and teacher salaries are set by the state, we can assume that there is little disciplinary variation in these programs. In the same way that we remove the costs of research in university programs, here we consider only the costs of training: 85.3 percent of total costs for preparatory courses and 86.7 percent for vocational programs (Bennani et al., 2021).

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

**Construction of Educational Spending Over the Entire Educational Trajectory.** To study inequality along the higher education trajectory, we restrict ourselves to individuals aged 22 to 24, who are the most advanced in their studies in our sample. 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. We retain the higher cost between the highest degree obtained, the highest level of education attained, or ongoing studies. We partially account for reorientations. For example, someone who is enrolled in a master's program but has an engineering degree has the cost of the full engineering school program plus the cost of one year of a master's degree in their field. In the absence of complete information on individual trajectories in the data, we do not consider individuals who repeat in the same program. It is likely that this approximation leads us to underestimate the cost of university students who repeat: 27 % of first-year students (MESRI-SIES, 2020) but also students in preparatory courses who often repeat the second year of the program

<footnote n="35">³⁵The survey is representative of institutions under the supervision of the Ministry of Higher Education and Research, although some institutions, notably certain engineering schools, are under the supervision of other ministries (Ministry of the Economy, Ministry of the Army, Ministry of Agriculture, etc.). Business schools are not included in the survey either. We therefore supplement this survey with specific data on the costs of business school students (https://www.cefdg.fr/fr/ecoles-et-formations-visees). On average, public funding covers about 5 percent of student costs for these private programs.</footnote>
– 9.1 % in economic preparatory courses, 20 % in humanities preparatory courses and 22.1 % in scientific preparatory courses.<fnref n="36" />

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

<footnote n="36">³⁶Bulletin officiel de l'éducation nationale n°18, 3 May 2012 (https://www.education.gouv.fr/pid285/bulletin_officiel.html?pid_bo=26454)</footnote>
– 9.1 % in economic preparatory courses, 20 % in humanities preparatory courses and 22.1 % in scientific preparatory courses.<fnref n="36" />

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

<footnote n="36">³⁶Bulletin officiel de l'éducation nationale n°18, 3 May 2012 (https://www.education.gouv.fr/pid285/bulletin_officiel.html?pid_bo=26454)</footnote>
<table><caption>Table B5: Per Student Annual Public Spending</caption><thead><tr><th>Track</th><th>Field of study</th><th>Public cost<br>over one year<br>(in euros)</th></tr></thead><tbody><tr><td>Vocational Track (BTS)</td><td></td><td>12,400</td></tr><tr><td>Preparatory Courses (CPGE)</td><td></td><td>13,400</td></tr><tr><td rowspan="7">Bachelor Degree (Non Vocational) (Licence)</td><td>Humanities, Arts, Languages</td><td>3,400</td></tr><tr><td>Markets &amp; Organizations</td><td>3,000</td></tr><tr><td>Math, Engineering, Computer science</td><td>5,300</td></tr><tr><td>Law</td><td>2,500</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>6,200</td></tr><tr><td>Social Sciences</td><td>3,500</td></tr><tr><td>Life Sciences</td><td>4,900</td></tr><tr><td>Various</td><td>3,600</td></tr><tr><td rowspan="7">Bachelor Degree (Vocational) (Licence professionnelle)</td><td>Humanities, Arts, Languages</td><td>4,900</td></tr><tr><td>Markets &amp; Organizations</td><td>5,400</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,700</td></tr><tr><td>Law</td><td>5,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>9,400</td></tr><tr><td>Social Sciences</td><td>5,300</td></tr><tr><td>Life Sciences</td><td>7,000</td></tr><tr><td>Various</td><td>6,400</td></tr><tr><td rowspan="7">Technical Training (IUT)</td><td>Markets &amp; Organizations</td><td>7,500</td></tr><tr><td>Math, Engineering, Computer science</td><td>11,300</td></tr><tr><td>Law</td><td>7,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>11,700</td></tr><tr><td>Social Sciences</td><td>7,500</td></tr><tr><td>Life Sciences</td><td>10,100</td></tr><tr><td>Various</td><td>9,500</td></tr><tr><td rowspan="7">Master Degree</td><td>Humanities, Arts, Languages</td><td>5,400</td></tr><tr><td>Markets &amp; Organizations</td><td>4,800</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,300</td></tr><tr><td>Law</td><td>4,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>8,900</td></tr><tr><td>Social Sciences</td><td>4,700</td></tr><tr><td>Life Sciences</td><td>6,500</td></tr><tr><td>Various</td><td>5,300</td></tr><tr><td rowspan="4">Engineering Schools</td><td>Math, Engineering, Computer science</td><td>10,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>12,000</td></tr><tr><td>Social Sciences</td><td>11,300</td></tr><tr><td>Life Sciences</td><td>11,900</td></tr><tr><td></td><td>Various</td><td>10,600</td></tr><tr><td>First Year of Medical Studies (PACES)</td><td>Medicine</td><td>2,400</td></tr><tr><td>Medical Studies (Except First Year)</td><td>Medicine</td><td>4,400</td></tr><tr><td>Paramedical Trainings</td><td>Medicine</td><td>2,700</td></tr><tr><td>Institute of Political Studies (IEP)</td><td></td><td>4,000</td></tr><tr><td>Business Schools</td><td></td><td>400</td></tr><tr><td>Other Elite Graduate Schools (Grande école)</td><td></td><td>10,600</td></tr></tbody></table>

Notes: This table presents the annual spending on higher education, for different tracks, degrees and fields. These data come from MESRISIES (2021) for vocational programs and preparatory courses, from the Commission d'évaluation des formations et diplômes de gestion (CEFDG) for business schools (https://www.cefdg.fr/fr/ecoles-et-formations-visees) and calculated from the "Cost Awareness" project (DGESIP 2016-2019) for all other courses. In calculating the average annual cost per student, we weight each program surveyed by the number of students enrolled.
<table><caption>Table B5: Per Student Annual Public Spending</caption><thead><tr><th>Track</th><th>Field of study</th><th>Public cost over one year (in euros)</th></tr></thead><tbody><tr><td>Vocational Track (BTS)</td><td></td><td>12,400</td></tr><tr><td>Preparatory Courses (CPGE)</td><td></td><td>13,400</td></tr><tr><td rowspan="7">Bachelor Degree (Non Vocational) (Licence)</td><td>Humanities, Arts, Languages</td><td>3,400</td></tr><tr><td>Markets &amp; Organizations</td><td>3,000</td></tr><tr><td>Math, Engineering, Computer science</td><td>5,300</td></tr><tr><td>Law</td><td>2,500</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>6,200</td></tr><tr><td>Social Sciences</td><td>3,500</td></tr><tr><td>Life Sciences</td><td>4,900</td></tr><tr><td>Various</td><td>3,600</td></tr><tr><td rowspan="7">Bachelor Degree (Vocational) (Licence professionnelle)</td><td>Humanities, Arts, Languages</td><td>4,900</td></tr><tr><td>Markets &amp; Organizations</td><td>5,400</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,700</td></tr><tr><td>Law</td><td>5,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>9,400</td></tr><tr><td>Social Sciences</td><td>5,300</td></tr><tr><td>Life Sciences</td><td>7,000</td></tr><tr><td>Various</td><td>6,400</td></tr><tr><td rowspan="7">Technical Training (IUT)</td><td>Markets &amp; Organizations</td><td>7,500</td></tr><tr><td>Math, Engineering, Computer science</td><td>11,300</td></tr><tr><td>Law</td><td>7,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>11,700</td></tr><tr><td>Social Sciences</td><td>7,500</td></tr><tr><td>Life Sciences</td><td>10,100</td></tr><tr><td>Various</td><td>9,500</td></tr><tr><td rowspan="7">Master Degree</td><td>Humanities, Arts, Languages</td><td>5,400</td></tr><tr><td>Markets &amp; Organizations</td><td>4,800</td></tr><tr><td>Math, Engineering, Computer science</td><td>7,300</td></tr><tr><td>Law</td><td>4,000</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>8,900</td></tr><tr><td>Social Sciences</td><td>4,700</td></tr><tr><td>Life Sciences</td><td>6,500</td></tr><tr><td>Various</td><td>5,300</td></tr><tr><td rowspan="4">Engineering Schools</td><td>Math, Engineering, Computer science</td><td>10,400</td></tr><tr><td>Material, Earth and Universe Sciences</td><td>12,000</td></tr><tr><td>Social Sciences</td><td>11,300</td></tr><tr><td>Life Sciences</td><td>11,900</td></tr><tr><td></td><td>Various</td><td>10,600</td></tr><tr><td>First Year of Medical Studies (PACES)</td><td>Medicine</td><td>2,400</td></tr><tr><td>Medical Studies (Except First Year)</td><td>Medicine</td><td>4,400</td></tr><tr><td>Paramedical Trainings</td><td>Medicine</td><td>2,700</td></tr><tr><td>Institute of Political Studies (IEP)</td><td></td><td>4,000</td></tr><tr><td>Business Schools</td><td></td><td>400</td></tr><tr><td>Other Elite Graduate Schools (Grande école)</td><td></td><td>10,600</td></tr></tbody></table>

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

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

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

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

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

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

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

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

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

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

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

In our framework, we initially break down the disparity in higher education participation between individuals whose parents are positioned at the top of the income distribution (denoted T) and those with parents at the income distribution's lower end (denoted B) by examining prior degree attainment. The rate of participation in higher education for each group G (where $G \in (B, T)$) is computed as:

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

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

Second, we decompose differences in public spending on higher education between individuals in the top (T) and bottom (B) income deciles based on differences in enrollment in higher education, duration of study, and spending on each year of higher education. Formally, public spending on higher education for individuals in the G group (with $G \in (B, T)$) can be defined as follows:
### B.2.3 Gupta Decomposition Method

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

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

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

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

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

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

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

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

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

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

In our framework, we initially break down the disparity in higher education participation between individuals whose parents are positioned at the top of the income distribution (denoted T) and those with parents at the income distribution's lower end (denoted B) by examining prior degree attainment. The rate of participation in higher education for each group G (where $G \in (B, T)$) is computed as:

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

Here, 'HEG' denotes the rate of higher education participation for individuals in group G. MSCG represents the rate at which individuals in group G achieve the middle school certificate. Furthermore, HSCGMSC indicates the rate of obtaining a high school certificate among those in group G who have previously achieved the middle school certificate. Lastly, HEGHSG specifies the rate of higher education participation for those in group G who hold a high school certificate. Refer to Table 3 for results derived from this decomposition technique.

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

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

## C.3 Robustness Checks

### C.3.1 Using Multiple Years of Parental Income
$$PS^G = HE^G \times LS_{student}^G \times AC_{student}^G$$

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

## C.3 Robustness Checks

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

Notes: Our main analyses are based on a single year of parental income: the survey year (2014). In this table we present the sensitivity of our results to using multiple years of parental income. The parental income used in our main specification is the sum of the father's household and the mother's household pre-tax income, divided by two when two parental households are identified. To perform this robustness check between year t and t-2, we use a slightly different concept of income, parents' household taxable income that is available for both year t and t-2. The regression coefficients part reports estimates of a regression of parental income percentile on enrollment in higher education. They should be interpreted as the average change in the participation rate in higher education for an increase of 10 percentile ranks (i.e., one decile) in the parental income distribution. The differences in estimation between our main concept of parental income and this taxable income concept are due to better imputation of missing responses in our main concept and the fact that some types of income (alimony, retirement savings, and special allowances) are deducted from taxable income. Standard deviations (Panel A) and standard errors (Panel B) in parentheses.
<table><caption>Table C6: Sensitivity of Results to Using Multiple Years of Parental Income</caption><thead><tr><th colspan="4" style="text-align: center;">Access rate to higher education</th></tr><tr><th colspan="4" style="text-align: center;">Parental income variable used:</th></tr><tr><th></th><th style="text-align: center;">Household<br>taxable income<br>in year t</th><th style="text-align: center;">Household<br>taxable income<br>in year t-2</th><th style="text-align: center;">Average of household<br>taxable incomes<br>in years t and t-2</th></tr></thead><tbody><tr><td colspan="4" style="text-align: left;"><em>Panel B. Parental income decile</em></td></tr><tr><td style="text-align: left;">P0-P10</td><td style="text-align: center;">0.40<br>(0.49)</td><td style="text-align: center;">0.48<br>(0.50)</td><td style="text-align: center;">0.42<br>(0.49)</td></tr><tr><td style="text-align: left;">P10-P20</td><td style="text-align: center;">0.42<br>(0.49)</td><td style="text-align: center;">0.35<br>(0.48)</td><td style="text-align: center;">0.40<br>(0.49)</td></tr><tr><td style="text-align: left;">P20-P30</td><td style="text-align: center;">0.43<br>(0.50)</td><td style="text-align: center;">0.43<br>(0.50)</td><td style="text-align: center;">0.43<br>(0.50)</td></tr><tr><td style="text-align: left;">P30-P40</td><td style="text-align: center;">0.52<br>(0.50)</td><td style="text-align: center;">0.55<br>(0.50)</td><td style="text-align: center;">0.53<br>(0.50)</td></tr><tr><td style="text-align: left;">P40-P50</td><td style="text-align: center;">0.53<br>(0.50)</td><td style="text-align: center;">0.48<br>(0.50)</td><td style="text-align: center;">0.53<br>(0.50)</td></tr><tr><td style="text-align: left;">P50-P60</td><td style="text-align: center;">0.57<br>(0.50)</td><td style="text-align: center;">0.54<br>(0.50)</td><td style="text-align: center;">0.50<br>(0.50)</td></tr><tr><td style="text-align: left;">P60-P70</td><td style="text-align: center;">0.61<br>(0.49)</td><td style="text-align: center;">0.62<br>(0.49)</td><td style="text-align: center;">0.64<br>(0.48)</td></tr><tr><td style="text-align: left;">P70-P80</td><td style="text-align: center;">0.65<br>(0.48)</td><td style="text-align: center;">0.65<br>(0.48)</td><td style="text-align: center;">0.66<br>(0.47)</td></tr><tr><td style="text-align: left;">P80-P90</td><td style="text-align: center;">0.76<br>(0.43)</td><td style="text-align: center;">0.80<br>(0.40)</td><td style="text-align: center;">0.77<br>(0.42)</td></tr><tr><td style="text-align: left;">P90-P100</td><td style="text-align: center;">0.85<br>(0.36)</td><td style="text-align: center;">0.86<br>(0.35)</td><td style="text-align: center;">0.86<br>(0.35)</td></tr><tr><td colspan="4" style="text-align: left;"><em>Panel B. Regression coefficients</em></td></tr><tr><td style="text-align: left;">Full sample</td><td style="text-align: center;">0.46<br>(0.03)</td><td style="text-align: center;">0.46<br>(0.03)</td><td style="text-align: center;">0.48<br>(0.03)</td></tr><tr><td style="text-align: left;">Lower half</td><td style="text-align: center;">0.31<br>(0.07)</td><td style="text-align: center;">0.16<br>(0.07)</td><td style="text-align: center;">0.32<br>(0.07)</td></tr><tr><td style="text-align: left;">Upper half</td><td style="text-align: center;">0.72<br>(0.07)</td><td style="text-align: center;">0.81<br>(0.07)</td><td style="text-align: center;">0.85<br>(0.07)</td></tr></tbody></table>

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

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

<figure><img src="image_10.png" /><figcaption>Figure C1: Proportion of Individuals Enrolled in Higher Education, by the Sum of Individual Pre-Tax Income of Both Parents</figcaption></figure>

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

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

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

We thus perform a robustness check by assigning, for each combination of tracks and fields, the 10th (respectively 25th) percentile of the distribution of student-weighted costs – or the nearest higher per-

Documents de travail du Centre d'Economie de la Sorbonne 2022.05RR (Version révisée)
### C.3.2 Using Parents' Individual Income Instead of Parents' Household Income

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

<figure><img src="image_11.png" /><figcaption>Figure C1: Proportion of Individuals Enrolled in Higher Education, by the Sum of Individual Pre-Tax Income of Both Parents</figcaption></figure>

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

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

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

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

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

In our main specification, enrollment inequality explains about 70 percent of the inequality in public spending on higher education between individuals in the top and bottom income deciles. When we allocate better-endowed programs to wealthier individuals, the contribution of enrollment inequality to public spending inequality decreases mechanically but remains equal to about half of total spending differences (Table C8).
centile when we do not have enough observations to observe the 10th or 25th percentile – to individuals whose parents are in the bottom 10 percent (respectively 25 percent) of the income distribution and the 75th percentile (respectively 90th percentile) – or the nearest lower percentile – to those whose parents are in the top 25 percent (respectively 10 percent) of the income distribution. Our main results are relatively close to those found with this assumption of extreme concentration of wealthier individuals in more expansive programs, meaning that there is not much variation in costs within programs enrolling a large portion of the student body. In our main specification, individuals in the top decile of the income distribution receive 2.7 more public investment in higher education than those in the bottom decile (Table C7). When richer programs are associated with richer individuals, individuals in the top 10 percent of the income distribution benefit 3.7 times more than those in the bottom decile.

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

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

Notes: This table presents public spending on higher education by parental income decile under different assumptions. The parental income used is the sum of the pre-tax income of the father's household and the mother's household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- 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. Column (1) represents our main specification. Column (2) represents the results obtained by assigning wealthier individuals to programs that are better endowed in their track and field of study. In this simulation, we assign for each combination of programs and fields, the 10th (respectively 25th) percentile of the distribution of student-weighted costs – or the nearest higher percentile when we do not have enough observations to observe the 10th or 25th percentile – to individuals whose parents are in the bottom 10 percent (respectively 25 percent) of the income distribution and the 75th percentile (respectively 90th percentile) – or the nearest lower percentile – to those whose parents are in the top 25 percent (respectively 10 percent) of the income distribution. Column (3) represents the same simulation as in column (2) with additional assumptions about the cost of highly selective elite graduate schools. We assume that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering schools, institutes of political studies, or other specialized schools), enroll in better endowed schools. We assume that individuals in the top 5 percent of the income distribution attend schools with endowments three times the typical endowment and that those in the next 5 percent of the income distribution enroll in schools with endowments twice the typical endowment.
<table><caption>Table C7: Sensitivity of Results to the Student-Program Matching</caption><thead><tr><th></th><th>Decile of parental<br>household income</th><th>(1)<br>Main specification</th><th>(2)<br>Association of<br>wealthier individuals<br>to better-endowed programs</th><th>(3)<br>Assumption (2) +<br>assumptions on the cost of<br>elite graduate schools</th></tr></thead><tbody><tr><td rowspan="10">All individuals</td><td>D1</td><td>7,800</td><td>6,200</td><td>6,200</td></tr><tr><td>D2</td><td>7,600</td><td>6,400</td><td>6,400</td></tr><tr><td>D3</td><td>8,800</td><td>7,800</td><td>7,900</td></tr><tr><td>D4</td><td>9,100</td><td>8,500</td><td>8,600</td></tr><tr><td>D5</td><td>10,000</td><td>9,400</td><td>9,500</td></tr><tr><td>D6</td><td>12,500</td><td>11,200</td><td>11,400</td></tr><tr><td>D7</td><td>12,200</td><td>11,200</td><td>11,300</td></tr><tr><td>D8</td><td>16,800</td><td>16,200</td><td>16,700</td></tr><tr><td>D9</td><td>21,300</td><td>21,700</td><td>22,200</td></tr><tr><td>D10</td><td>21,400</td><td>22,900</td><td>27,900</td></tr><tr><td></td><td>D10/D1 Ratio</td><td>2.7</td><td>3.7</td><td>4.5</td></tr><tr><td rowspan="10">Students or<br>former students</td><td>D1</td><td>22,200</td><td>17,500</td><td>17,600</td></tr><tr><td>D2</td><td>19,200</td><td>16,100</td><td>16,200</td></tr><tr><td>D3</td><td>18,900</td><td>16,700</td><td>16,900</td></tr><tr><td>D4</td><td>19,900</td><td>18,600</td><td>18,700</td></tr><tr><td>D5</td><td>18,500</td><td>17,300</td><td>17,400</td></tr><tr><td>D6</td><td>21,300</td><td>19,000</td><td>19,400</td></tr><tr><td>D7</td><td>23,200</td><td>21,300</td><td>21,500</td></tr><tr><td>D8</td><td>21,300</td><td>20,600</td><td>21,200</td></tr><tr><td>D9</td><td>26,700</td><td>27,200</td><td>27,800</td></tr><tr><td>D10</td><td>23,500</td><td>25,100</td><td>30,500</td></tr><tr><td></td><td>D10/D1 Ratio</td><td>1.1</td><td>1.4</td><td>1.7</td></tr></tbody></table>

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

*Notes:* This table shows the contribution of different factors – enrollment in higher education, differences in duration of study for students, differences in annual public spending for students – to the total difference in public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the income distribution (in percent). Parental income used is the sum of the pre-tax income of the father’s household and the mother’s household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- to 24-year-olds and the length of study for 22- and 23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. To decompose the contribution of different factors, we rely on Das Gupta (1991) decomposition method. See Appendix B.2.3 for more details on this decomposition method. Column (1) represents our main specification. Column (2) represents the results obtained by assigning wealthier individuals to programs that are better endowed in their track and field of study. In this simulation, for each combination of tracks and fields, we assign the 10th (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. Column (3) represents the same simulation as in column (2) with additional assumptions about the cost of highly selective elite graduate schools. We assume that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering, institutes of political studies, or other specialized schools), enroll in better endowed schools. More specifically, individuals in the top 5 percent of the income distribution are assumed to enroll in schools with endowments three times the typical endowment and those in the next 5 percent to enroll in schools with endowments twice the typical endowment.
<table><caption>Table C8: Decomposition of Differences in Public Spending on Higher Education: Three Variants of Spending</caption><thead><tr><th></th><th>(1)<br>Main specification</th><th>(2)<br>Association of<br>wealthier individuals<br>with better-endowed<br>programs</th><th>(3)<br>Assumption (2) +<br>wealthier individuals<br>attend more costly<br>elite graduate schools</th></tr></thead><tbody><tr><td>Enrollment in higher education</td><td>67</td><td>52</td><td>49</td></tr><tr><td>Duration of study</td><td>23</td><td>18</td><td>17</td></tr><tr><td>Annual public spending</td><td>10</td><td>30</td><td>33</td></tr></tbody></table>

*Notes:* This table shows the contribution of different factors – enrollment in higher education, differences in duration of study for students, differences in annual public spending for students – to the total difference in public higher education spending between individuals in the top 10 percent and the bottom 10 percent of the income distribution (in percent). Parental income used is the sum of the pre-tax income of the father’s household and the mother’s household, divided by two when there are two different households. Deciles are based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- to 24-year-olds and the length of study for 22- and 23-year-olds is extended by parental income decile based on the continuation rates observed in the sample. To decompose the contribution of different factors, we rely on Das Gupta (1991) decomposition method. See Appendix B.2.3 for more details on this decomposition method. Column (1) represents our main specification. Column (2) represents the results obtained by assigning wealthier individuals to programs that are better endowed in their track and field of study. In this simulation, for each combination of tracks and fields, we assign the 10th (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. Column (3) represents the same simulation as in column (2) with additional assumptions about the cost of highly selective elite graduate schools. We assume that individuals from the top decile of the income distribution, when they enter elite graduate schools (engineering, institutes of political studies, or other specialized schools), enroll in better endowed schools. More specifically, individuals in the top 5 percent of the income distribution are assumed to enroll in schools with endowments three times the typical endowment and those in the next 5 percent to enroll in schools with endowments twice the typical endowment.
## D.4 Additional Results

### D.4.1 Parental Income, Education, and Occupation: Patterns in Higher Education Enrollment
## D.4 Additional Results

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

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

<footnote>* p < 0.1, ** p < 0.05, *** p < 0.01</footnote>
<table><caption>Table D9: Enrollment in Higher Education Based on Parents' Income, Occupation and Diploma</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Parent's income percentile rank</td><td>0.58***</td><td>0.55***</td><td>0.22***</td><td>0.27***</td><td>0.14***</td><td>0.100**</td></tr><tr><td>Log of potential financial transfers</td><td></td><td>0.0097***</td><td></td><td></td><td></td><td>0.0028</td></tr><tr><td>Father Primary school</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Father Middle School</td><td></td><td></td><td>0.00035</td><td></td><td>-0.027</td><td>-0.029</td></tr><tr><td>Father High School</td><td></td><td></td><td>0.051**</td><td></td><td>0.032</td><td>0.040*</td></tr><tr><td>Father Two-year degree</td><td></td><td></td><td>0.19***</td><td></td><td>0.13***</td><td>0.13***</td></tr><tr><td>Father Bachelor or Master degree</td><td></td><td></td><td>0.23***</td><td></td><td>0.16***</td><td>0.15***</td></tr><tr><td>Father M.D. or Ph.D.</td><td></td><td></td><td>0.26***</td><td></td><td>0.18***</td><td>0.19***</td></tr><tr><td>Mother Primary school</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Mother Middle School</td><td></td><td></td><td>0.14***</td><td></td><td>0.12***</td><td>0.13***</td></tr><tr><td>Mother High School</td><td></td><td></td><td>0.16***</td><td></td><td>0.13***</td><td>0.14***</td></tr><tr><td>Mother Two-year degree</td><td></td><td></td><td>0.37***</td><td></td><td>0.30***</td><td>0.31***</td></tr><tr><td>Mother Bachelor or Master degree</td><td></td><td></td><td>0.31***</td><td></td><td>0.20***</td><td>0.21***</td></tr><tr><td>Mother M.D. or Ph.D.</td><td></td><td></td><td>0.35***</td><td></td><td>0.22***</td><td>0.21***</td></tr><tr><td>Father Low SES</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Father Lower-middle SES</td><td></td><td></td><td></td><td>0.13***</td><td>0.099***</td><td>0.095***</td></tr><tr><td>Father Upper-middle SES</td><td></td><td></td><td></td><td>0.16***</td><td>0.11***</td><td>0.11***</td></tr><tr><td>Father High SES</td><td></td><td></td><td></td><td>0.24***</td><td>0.14***</td><td>0.15***</td></tr><tr><td>Mother Low SES</td><td></td><td></td><td>-</td><td></td><td>-</td><td>-</td></tr><tr><td>Mother Lower-middle SES</td><td></td><td></td><td></td><td>0.095***</td><td>0.061***</td><td>0.062***</td></tr><tr><td>Mother Upper-middle SES</td><td></td><td></td><td></td><td>0.24***</td><td>0.13***</td><td>0.14***</td></tr><tr><td>Mother High SES</td><td></td><td></td><td></td><td>0.28***</td><td>0.17***</td><td>0.18***</td></tr><tr><td>Parents live together</td><td></td><td></td><td></td><td></td><td></td><td>-</td></tr><tr><td>Separated parents</td><td></td><td></td><td></td><td></td><td></td><td>-0.077***</td></tr><tr><td>Parisian area</td><td></td><td></td><td></td><td></td><td></td><td>-</td></tr><tr><td>Urban unit of more than 100,000 inhabitants</td><td></td><td></td><td></td><td></td><td></td><td>-0.032</td></tr><tr><td>Urban unit of less than 100,000 inhabitants</td><td></td><td></td><td></td><td></td><td></td><td>-0.055**</td></tr><tr><td>Observations</td><td>3,947</td><td>3,947</td><td>3,066</td><td>3,329</td><td>3,046</td><td>3,013</td></tr><tr><td>R<sup>2</sup></td><td>0.114</td><td>0.116</td><td>0.196</td><td>0.176</td><td>0.214</td><td>0.221</td></tr></tbody></table>

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

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

In this section we present the distribution of public expenditures dedicated to young adults and their parents, regardless of parental income.

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

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

<figure><img src="image_11.png" /><figcaption>Figure D2: Distribution of Public Spending for Higher Education</figcaption></figure>

Notes: This figure shows the average public spending on higher education over 6 years (until 24), by public spending percentile. 42% of 22-24 year olds have zero spending because they did not enter higher education. Unlike the rest of our analyses, we do not consider parental income in calculating this distribution. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- 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.
D.4.2 Distribution of Higher Education Public Spending on Young Adults

In this section we present the distribution of public expenditures dedicated to young adults and their parents, regardless of parental income.

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

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

<figure><img src="image_12.png" /><figcaption>Figure D2: Distribution of Public Spending for Higher Education</figcaption></figure>

Notes: This figure shows the average public spending on higher education over 6 years (until 24), by public spending percentile. 42% of 22-24 year olds have zero spending because they did not enter higher education. Unlike the rest of our analyses, we do not consider parental income in calculating this distribution. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- 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.
<figure><img src="image_12.png" /><figcaption>Figure D3: Rank-Rank Correlation</figcaption></figure>

Notes: This figure plots the correlation between the percentile rank of parental income and the percentile rank of public spending on higher education. The red line is the linear regression of the percentile rank of parental income on the percentile rank of public spending on 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. The percentiles are calculated based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- 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.
<figure><img src="image_13.png" /><figcaption>Figure D3: Rank-Rank Correlation</figcaption></figure>

Notes: This figure plots the correlation between the percentile rank of parental income and the percentile rank of public spending on higher education. The red line is the linear regression of the percentile rank of parental income on the percentile rank of public spending on 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. The percentiles are calculated based on this definition. For a complete analysis of educational pathways up to age 24, we restrict the sample to 22- 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.
D.4.3 Decompositions of Inequalities in Public Spending on Higher Education

<figure><img src="image_13.png" /><figcaption>Figure D4: Duration of Study</figcaption></figure>

Notes: These figures represent the duration of studies for all individuals (Panel (a)) and individuals entering higher education between the ages of 18 and 24 (Panel (b)), as a function of parental income. We restrict the sample to 22- 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. 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 calculated based on this definition. The black line represents standard errors.

<figure><img src="image_14.png" /><figcaption>Figure D5: Extensive and Intensive Margins</figcaption></figure>

Notes: This figure shows the decomposition of inequality in public spending on higher education between an extensive margin – differences in enrollment in higher education – and an intensive margin – differences in spending on higher education for those who enroll, related to both inequality in duration of study and inequality in annual spending. We restrict the sample to 22- 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. The dark curve represents the current level of inequality in public spending on higher education (corresponding to Figure 8a). The left curve represents the level of inequality that would result if all individuals enrolled in higher education with the current level of spending inequality. The right-hand curve represents the level of inequality that would result if all individuals had the same expenditure on higher education with the current level of inequality in participation. 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 calculated based on this definition (x-axis).
### D.4.3 Decompositions of Inequalities in Public Spending on Higher Education

<figure><img src="image_14.png" /><figcaption>Figure D4: Duration of Study</figcaption></figure>

*Notes*: These figures represent the duration of studies for all individuals (Panel (a)) and individuals entering higher education between the ages of 18 and 24 (Panel (b)), as a function of parental income. We restrict the sample to 22- 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. 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 calculated based on this definition. The black line represents standard errors.

<figure><img src="image_15.png" /><figcaption>Figure D5: Extensive and Intensive Margins</figcaption></figure>

*Notes*: This figure shows the decomposition of inequality in public spending on higher education between an extensive margin – differences in enrollment in higher education – and an intensive margin – differences in spending on higher education for those who enroll, related to both inequality in duration of study and inequality in annual spending. We restrict the sample to 22- 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. The dark curve represents the current level of inequality in public spending on higher education (corresponding to Figure 8a). The left curve represents the level of inequality that would result if all individuals enrolled in higher education with the current level of spending inequality. The right-hand curve represents the level of inequality that would result if all individuals had the same expenditure on higher education with the current level of inequality in participation. 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 calculated based on this definition (x-axis).
D.4.4 Accounting for Parental Taxes in Higher Education Spending Distri-
bution

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

D.4.4.1 Methodology

We assess the redistribution that takes place through public spending on higher education and propose
a method of accounting for the taxes paid by parents and used to finance this particular public expen-
diture. This section details and motivates the assumptions concerning the scope of higher education
spending, the duration of study or the taxes considered that underlie our calculation of the degree of
redistribution of public spending on higher education, once parental tax contributions are taken into
account.

**Scope of Public Spending on Higher Education.** We restrict our analysis to higher education expenditures only. We draw upon data from the "Cost Awareness" project<fnref n="37" />, designed to assess the expenditures of various higher education institutions. Conducted between 2016 and 2019 at 65 institutions enrolling roughly one-third of all French students, this survey enables us to segregate costs by program and field of study and to discern the cost of higher education net of research costs, which are included in more aggregated data. We use the main survey's (ENRJ) information on individual studies to match the two data sources at the level of each track and field of study.<fnref n="38" /> Consequently, this data facilitates a more precise estimation of public spending on higher education per individual compared to highly aggregated data. For all programs, we focus solely on instructional expenditure.

**Period Considered.** Our main sample comprises young adults aged 19 to 24, spanning a 6-year period. This range encapsulates the majority of student trajectories. However, expenditures on those continuing their education post-24 are omitted. We also project fiscal contributions over a 6-year timeframe by multiplying the annual parental contribution by 6. For the purpose of assessing public spending on higher education, our focus is on individuals aged 22 to 24, representing the most advanced in their academic journey within our dataset. The study duration for individuals aged 22 and 23 is extended based on continuation rates observed in the sample, stratified by parental income decile.

<footnote n="37">³⁷Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux 2019).</footnote>

<footnote n="38">³⁸Refer to Appendix B.2.2.3 for more details on these new higher education cost data and Table B5 for annual public spending per student, divided by field of study and track, the level of disaggregation we use.</footnote>
D.4.4 Accounting for Parental Taxes in Higher Education Spending Distri-
bution

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

D.4.4.1 Methodology

We assess the redistribution that takes place through public spending on higher education and propose
a method of accounting for the taxes paid by parents and used to finance this particular public expen-
diture. This section details and motivates the assumptions concerning the scope of higher education
spending, the duration of study or the taxes considered that underlie our calculation of the degree of
redistribution of public spending on higher education, once parental tax contributions are taken into
account.

**Scope of Public Spending on Higher Education.** We restrict our analysis to higher education expenditures only. We draw upon data from the "Cost Awareness" project<fnref n="37" />, designed to assess the expenditures of various higher education institutions. Conducted between 2016 and 2019 at 65 institutions enrolling roughly one-third of all French students, this survey enables us to segregate costs by program and field of study and to discern the cost of higher education net of research costs, which are included in more aggregated data. We use the main survey's (ENRJ) information on individual studies to match the two data sources at the level of each track and field of study.<fnref n="38" /> Consequently, this data facilitates a more precise estimation of public spending on higher education per individual compared to highly aggregated data. For all programs, we focus solely on instructional expenditure.

**Period Considered.** Our main sample comprises young adults aged 19 to 24, spanning a 6-year period. This range encapsulates the majority of student trajectories. However, expenditures on those continuing their education post-24 are omitted. We also project fiscal contributions over a 6-year timeframe by multiplying the annual parental contribution by 6. For the purpose of assessing public spending on higher education, our focus is on individuals aged 22 to 24, representing the most advanced in their academic journey within our dataset. The study duration for individuals aged 22 and 23 is extended based on continuation rates observed in the sample, stratified by parental income decile.

<footnote n="37">³⁷Connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche, DGESIP 2016-2019 (Boiteau and Jameux 2019).</footnote>

<footnote n="38">³⁸Refer to Appendix B.2.2.3 for more details on these new higher education cost data and Table B5 for annual public spending per student, divided by field of study and track, the level of disaggregation we use.</footnote>
The aggregate public spending on higher education is given by:

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

Where:

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

* $S_j^{HE}$ denotes the expenditures on completed higher education over 6-year period for the jth young adult;

* $Weight_j$ is the sampling weight allocated to the jth young adult in the ENRJ survey;

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

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

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

Our sample consists of parents of young adults. Since these individuals are not the only ones involved in financing higher education, we need to estimate the share of taxes to be considered out of sample. We use the 2014 Tax and Social Income Survey,<fnref n="39" /> a representative sample of all taxpayers in France, for this purpose.

<footnote n="39">³⁹Enquête sur les revenus fiscaux et sociaux (ERFS), 2014</footnote>
The aggregate public spending on higher education is given by:

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

Where:

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

* $S_j^{HE}$ denotes the expenditures on completed higher education over 6-year period for the jth young adult;

* $Weight_j$ is the sampling weight allocated to the jth young adult in the ENRJ survey;

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

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

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

Our sample consists of parents of young adults. Since these individuals are not the only ones involved in financing higher education, we need to estimate the share of taxes to be considered out of sample. We use the 2014 Tax and Social Income Survey,<fnref n="39" /> a representative sample of all taxpayers in France, for this purpose.

<footnote n="39">³⁹Enquête sur les revenus fiscaux et sociaux (ERFS), 2014</footnote>
We calculate the total amount of unallocated taxes paid over six years by French taxpayers as follows:

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

Where:

* Taxesall represents the total amount of unallocated taxes paid by all taxpayers over a six-year period, that will be contributing to the financing of 6-years of higher education;

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

* ITi is the income taxes paid by i;

* VATi the value added taxes paid by i;

* CTi the corporate taxes paid by i;

* Ei the energy and excise taxes paid by i;

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

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

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

**Proportion Needed To Fund Higher Education Expenditures.** We calculate the share of unallocated taxes needed to fund public spending on higher education with the following ratio:

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

Where:

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

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

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

In total, 4% of all unallocated taxes paid by taxpayers must be devoted to funding the cost of higher education. This proportion is consistent with official data from national accounts.<fnref n="40" />

<footnote n="40">⁴⁰The 2021 Finance Bill indicates an amount of €220 billion for the sum of the taxes we are considering here (VAT, income tax, corporate tax and domestic consumption tax on petro-leum and energy products) and €10 billion (4.5% of the tax revenue considered) for higher education excluding university research (see https://www.budget.gouv.fr/budget-etat/ for unallocated tax amounts and https://www.budget.gouv.fr/budget-etat/ministere?annee=118&loi_finances=50&type_ budget=all&ministere=34726&programme=30340&type_donneebudget=ae&op=Valider for higher education spending). We cannot report these figures for 2014, the year of the survey, because government spending was not displayed broken down by spending item at that time.</footnote>
We calculate the total amount of unallocated taxes paid over six years by French taxpayers as follows:

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

Where:

* Taxesall represents the total amount of unallocated taxes paid by all taxpayers over a six-year period, that will be contributing to the financing of 6-years of higher education;

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

* ITi is the income taxes paid by i;

* VATi the value added taxes paid by i;

* CTi the corporate taxes paid by i;

* Ei the energy and excise taxes paid by i;

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

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

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

**Proportion Needed To Fund Higher Education Expenditures.** We calculate the share of unallocated taxes needed to fund public spending on higher education with the following ratio:

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

Where:

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

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

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

In total, 4% of all unallocated taxes paid by taxpayers must be devoted to funding the cost of higher education. This proportion is consistent with official data from national accounts.<fnref n="40" />

<footnote n="40">⁴⁰The 2021 Finance Bill indicates an amount of €220 billion for the sum of the taxes we are considering here (VAT, income tax, corporate tax and domestic consumption tax on petro-leum and energy products) and €10 billion (4.5% of the tax revenue considered) for higher education excluding university research (see https://www.budget.gouv.fr/budget-etat/ for unallocated tax amounts and https://www.budget.gouv.fr/budget-etat/ministere?annee=118&loi_finances=50&type_ budget=all&ministere=34726&programme=30340&type_donneebudget=ae&op=Valider for higher education spending). We cannot report these figures for 2014, the year of the survey, because government spending was not displayed broken down by spending item at that time.</footnote>
**Taxes Paid by Parents.** Our database incorporates administrative records of parents' income tax payments. We calculate the proportion of income allocated to VAT, corporate income tax, and other indirect taxes for each standard-of-living decile based on results from national accounts (André et al. (2023)). We use standard-of-living deciles to match the figures in the referenced study. This is a deviation from the rest of the article, where the deciles pertain to the pre-tax household income of parents. The derived tax rates for each standard-of-living decile, presented in Table D10, are subsequently applied to the pre-tax income of parents in our database, based on the respective standard-of-living decile to which they belong.

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

*Notes:* This table presents the proportion of pre-tax income that is allocated to various taxes (VAT, energy and excise taxes, corporate taxes, and income taxes) based on the standard-of-living decile of the entire population. For instance, households in the first standard-of-living decile expend 17% of their total pre-tax income (which includes public support) on VAT. The percentage of income allocated to VAT, corporate income tax, and other indirect taxes for each decile is determined by dividing the tax revenue sourced from that decile by its collective income, inclusive of public support for VAT and excise duties computation. The results are derived from the distributed national accounts (André et al. (2023)). It's important to note that the table's standard-of-living deciles pertain to the broader population to maintain consistency with the figures in the referenced studies. This approach differs from the rest of the article where the deciles are based on pre-tax parental income. For the income tax data, we rely on the administrative variable provided in our survey.

**Net Benefit From Public Spending on Higher Education.** We then apply the predetermined share of taxes calculated above, which amounts to 4%, dedicated to the public funding of higher education, to the unallocated taxes paid by parents. This calculation yields the estimated portion of taxes from parents that is allocated to higher education. To ascertain their net benefit, we subtract this tax amount from the higher education spending received by their child. This net benefit can be understood as the advantage parents gain from the public funding of higher education after accounting for the taxes they have paid and dedicated to such funding.
**Taxes Paid by Parents.** Our database incorporates administrative records of parents' income tax payments. We calculate the proportion of income allocated to VAT, corporate income tax, and other indirect taxes for each standard-of-living decile based on results from national accounts (André et al. (2023)). We use standard-of-living deciles to match the figures in the referenced study. This is a deviation from the rest of the article, where the deciles pertain to the pre-tax household income of parents. The derived tax rates for each standard-of-living decile, presented in Table D10, are subsequently applied to the pre-tax income of parents in our database, based on the respective standard-of-living decile to which they belong.

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

Notes: This table presents the proportion of pre-tax income that is allocated to various taxes (VAT, energy and excise taxes, corporate taxes, and income taxes) based on the standard-of-living decile of the entire population. For instance, households in the first standard-of-living decile expend 17% of their total pre-tax income (which includes public support) on VAT. The percentage of income allocated to VAT, corporate income tax, and other indirect taxes for each decile is determined by dividing the tax revenue sourced from that decile by its collective income, inclusive of public support for VAT and excise duties computation. The results are derived from the distributed national accounts (André et al. (2023)). It's important to note that the table's standard-of-living deciles pertain to the broader population to maintain consistency with the figures in the referenced studies. This approach differs from the rest of the article where the deciles are based on pre-tax parental income. For the income tax data, we rely on the administrative variable provided in our survey.

**Net Benefit From Public Spending on Higher Education.** We then apply the predetermined share of taxes calculated above, which amounts to 4%, dedicated to the public funding of higher education, to the unallocated taxes paid by parents. This calculation yields the estimated portion of taxes from parents that is allocated to higher education. To ascertain their net benefit, we subtract this tax amount from the higher education spending received by their child. This net benefit can be understood as the advantage parents gain from the public funding of higher education after accounting for the taxes they have paid and dedicated to such funding.
The mean net benefit for each parental income decile *d* is therefore calculated as follows:

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

Where:

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

* *n*<sub>*d*</sub> represents the number of individuals in decile *d*;

* *S*<sub>*j*</sub><sup>HE</sup> is the higher education spending over a 6-year period for the *i*<sup>*th*</sup> individual;

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

* The factor of 0.04 represents the 4% share of taxes dedicated to higher education, defined above as *Share_Taxes*<sup>HE</sup>;

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

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

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

Where:

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

* *n*<sub>*d*</sub> represents the number of individuals in decile *d*;

* *S*<sub>*j*</sub><sup>HE</sup> is the higher education spending over a 6-year period for the *i*<sup>*th*</sup> individual;

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

* The factor of 0.04 represents the 4% share of taxes dedicated to higher education, defined above as *Share_Taxes*<sup>HE</sup>;

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

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

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

The data reveals that, on average, parents receive a net benefit of €9,600 over a six-year period, as depicted in Figure D6 and Table D11. Interestingly, parents benefit from horizontal solidarity since 76% of the higher education expenses for their young adults are funded by taxpayers who do not have young adults. However, this average masks variations among households. Households with young adults enrolled in higher education experience a net benefit that is nearly double at €18,100, while those without young adults in higher education see a negative value of €1,900, as they participate in higher education funding without benefiting from it.

When examining the impact of taxes used to fund higher education, it becomes apparent that while this spending remains regressive, there is a noteworthy shift at the top of the income distribution. Specifically, the net benefit is roughly twice as high in the upper third of the distribution compared to the lower third (€13,900 versus €6,900). This benefit rises with parental income, particularly in the upper half of the distribution (from P50 to P90), peaking at about €17,000, as evidenced in Table D11 and Figure D6. Yet, the top decile, due to higher taxation and even with their increased enrollment in higher education, has a net benefit of only two-thirds that of the ninth decile (€11,600 compared to €17,000). Nonetheless, this figure exceeds the net benefit seen in the bottom decile (€6,800).
### D.4.4.2 Results

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

The data reveals that, on average, parents receive a net benefit of €9,600 over a six-year period, as depicted in Figure D6 and Table D11. Interestingly, parents benefit from horizontal solidarity since 76% of the higher education expenses for their young adults are funded by taxpayers who do not have young adults. However, this average masks variations among households. Households with young adults enrolled in higher education experience a net benefit that is nearly double at €18,100, while those without young adults in higher education see a negative value of €1,900, as they participate in higher education funding without benefiting from it.

When examining the impact of taxes used to fund higher education, it becomes apparent that while this spending remains regressive, there is a noteworthy shift at the top of the income distribution. Specifically, the net benefit is roughly twice as high in the upper third of the distribution compared to the lower third (€13,900 versus €6,900). This benefit rises with parental income, particularly in the upper half of the distribution (from P50 to P90), peaking at about €17,000, as evidenced in Table D11 and Figure D6. Yet, the top decile, due to higher taxation and even with their increased enrollment in higher education, has a net benefit of only two-thirds that of the ninth decile (€11,600 compared to €17,000). Nonetheless, this figure exceeds the net benefit seen in the bottom decile (€6,800).
<figure><img src="image_15.png" /><figcaption>Figure D6: Spending for Higher Education, Net of Parental Taxes Funding Higher Education</figcaption></figure>

Notes: This figure plots the distribution of higher education public expenditures on young adults over a 6-year period, by pre-tax parental income, taking into account parents taxes funding higher education. The solid line shows public spending on higher education until 24. Analysis for educational pathways up to age 24 targets the 22-24 age bracket, extending study periods for 22- and 23-year-olds based on observed continuation rates by parental income deciles in the sample. This corresponds to the dark grey bars in Figure 8. The dash line with empty dots shows the same variable, net of 4 percent of parental tax contribution – corresponding to the share of all taxpayers’ unallocated taxes needed to fund public higher education. 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.
<figure><img src="image_16.png" /><figcaption>Figure D6: Spending for Higher Education, Net of Parental Taxes Funding Higher Education</figcaption></figure>

Notes: This figure plots the distribution of higher education public expenditures on young adults over a 6-year period, by pre-tax parental income, taking into account parents taxes funding higher education. The solid line shows public spending on higher education until 24. Analysis for educational pathways up to age 24 targets the 22-24 age bracket, extending study periods for 22- and 23-year-olds based on observed continuation rates by parental income deciles in the sample. This corresponds to the dark grey bars in Figure 8. The dash line with empty dots shows the same variable, net of 4 percent of parental tax contribution – corresponding to the share of all taxpayers’ unallocated taxes needed to fund public higher education. 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.
<table><caption>Table D11: Average Public Spending and Fiscal Contribution on Young Adults Over 6-Year Period, by Parents' Pre-Tax Household Income (in Euros)</caption><thead><tr><th></th><th>(1)<br>Public<br>spending<br>for higher<br>education<br>(A)</th><th>(2)<br>4% of fiscal<br>contribution<br>of parents<br>over 6 years<br>(B)</th><th>(3)<br>Net benefit<br>for higher<br>education<br>(A)-(B)</th></tr></thead><tbody><tr><td>P0-P10</td><td>7,800</td><td>1,000</td><td>6,800</td></tr><tr><td>P10-P20</td><td>7,600</td><td>1,300</td><td>6,300</td></tr><tr><td>P20-P30</td><td>8,800</td><td>1,600</td><td>7,200</td></tr><tr><td>P30-P40</td><td>9,100</td><td>1,800</td><td>7,300</td></tr><tr><td>P40-P50</td><td>10,000</td><td>1,900</td><td>8,200</td></tr><tr><td>P50-P60</td><td>12,500</td><td>2,400</td><td>10,100</td></tr><tr><td>P60-P70</td><td>12,200</td><td>2,500</td><td>9,700</td></tr><tr><td>P70-P80</td><td>16,800</td><td>2,800</td><td>14,000</td></tr><tr><td>P80-P90</td><td>21,300</td><td>4,200</td><td>17,100</td></tr><tr><td>P90-P100</td><td>21,400</td><td>9,800</td><td>11,600</td></tr><tr><td>Average</td><td>12,500</td><td>2,800</td><td>9,600</td></tr></tbody></table>

Notes: This table outlines the distribution of higher education public expenditures on young adults against the fiscal contributions of their parents, categorized by pre-tax parental income. 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. Values are rounded to the nearest 100 euros. Column (1) details public spending on higher education until 24. Analysis for educational pathways up to age 24 targets the 22-24 age bracket, extending study periods for 22- and 23-year-olds based on observed continuation rates by parental income deciles in the sample. Column (2) represents 4% of taxes contributed by parents over six years, reflecting the portion of unallocated taxes from all taxpayers needed to finance public higher education. This figure is derived from a representative dataset of all taxpayers (ERFS), with imputations for indirect and corporate tax sourced from the distributed national account (André et al. (2023)). Column (3) displays the net benefit from higher education, calculated by subtracting the parental tax contribution from the total public expenditure on higher education.
<table><caption>Table D11: Average Public Spending and Fiscal Contribution on Young Adults Over 6-Year Period, by Parents' Pre-Tax Household Income (in Euros)</caption><thead><tr><th></th><th>(1)<br>Public<br>spending<br>for higher<br>education<br>(A)</th><th>(2)<br>4% of fiscal<br>contribution<br>of parents<br>over 6 years<br>(B)</th><th>(3)<br>Net benefit<br>for higher<br>education<br>(A)-(B)</th></tr></thead><tbody><tr><td>P0-P10</td><td>7,800</td><td>1,000</td><td>6,800</td></tr><tr><td>P10-P20</td><td>7,600</td><td>1,300</td><td>6,300</td></tr><tr><td>P20-P30</td><td>8,800</td><td>1,600</td><td>7,200</td></tr><tr><td>P30-P40</td><td>9,100</td><td>1,800</td><td>7,300</td></tr><tr><td>P40-P50</td><td>10,000</td><td>1,900</td><td>8,200</td></tr><tr><td>P50-P60</td><td>12,500</td><td>2,400</td><td>10,100</td></tr><tr><td>P60-P70</td><td>12,200</td><td>2,500</td><td>9,700</td></tr><tr><td>P70-P80</td><td>16,800</td><td>2,800</td><td>14,000</td></tr><tr><td>P80-P90</td><td>21,300</td><td>4,200</td><td>17,100</td></tr><tr><td>P90-P100</td><td>21,400</td><td>9,800</td><td>11,600</td></tr><tr><td>Average</td><td>12,500</td><td>2,800</td><td>9,600</td></tr></tbody></table>

Notes: This table outlines the distribution of higher education public expenditures on young adults against the fiscal contributions of their parents, categorized by pre-tax parental income. 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. Values are rounded to the nearest 100 euros. Column (1) details public spending on higher education until 24. Analysis for educational pathways up to age 24 targets the 22-24 age bracket, extending study periods for 22- and 23-year-olds based on observed continuation rates by parental income deciles in the sample. Column (2) represents 4% of taxes contributed by parents over six years, reflecting the portion of unallocated taxes from all taxpayers needed to finance public higher education. This figure is derived from a representative dataset of all taxpayers (ERFS), with imputations for indirect and corporate tax sourced from the distributed national account (André et al. (2023)). Column (3) displays the net benefit from higher education, calculated by subtracting the parental tax contribution from the total public expenditure on higher education.
### D.4.5 Predicted Lifetime Earnings

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

**Educational Attainment Disparities.** The disparities in higher education attendance, highlighted in this study, directly lead to significant disparities in educational achievement. For instance, around 45% of 22- to 24-year-olds<fnref n="41" /> whose parents are within the top 5% of the income distribution have obtained a bachelor's degree. In stark contrast, only about 10% of individuals from the bottom 10% income bracket have achieved this level of education. This pronounced disparity is further illustrated in Figure D7.

<figure><img src="image_16.png" /><figcaption>Figure D7: Highest Degree Earned by Parents' Pre-Tax Income</figcaption></figure>

*Notes:* This figure depicts the highest diploma obtained by individuals aged 22 to 24, 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.

**Prediction of Lifetime Earnings Based on Educational Attainment.** To predict lifetime earnings, we combine the educational attainment of 22-24 year-olds observed in our sample with the first empirical measure of lifetime earnings for France, recently produced by Garbinti et al. (2023).<fnref n="42" /> In their estimation of lifetime earnings, they use a granularity of six distinct educational levels. Lifetime earnings, as defined in the study, represent the average earnings across 31 working years, from age 25 to 55, focusing on the cohort born in 1962.<fnref n="43" /> Additionally, we included a differential in lifetime earnings between Master's degree holders and elite graduate school graduates, as calculated by ? using self-reported earnings data.

<footnote n="41">⁴¹For this analysis, we restrict our sample to individuals aged 22 to 24, who are the most advanced in their studies within our sample.</footnote>

<footnote n="42">⁴²We would like to thank Bertrand Garbinti and Vladimir Pecheu for sending us custom data on lifetime earnings by gender and educational attainment.</footnote>

<footnote n="43">⁴³This older cohort was selected for its complete labor market trajectory, providing a comprehensive view of lifetime earnings, a perspective not yet available for our younger cohort (aged 19-24 in 2014).</footnote>
### D.4.5 Predicted Lifetime Earnings

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

**Educational Attainment Disparities.** The disparities in higher education attendance, highlighted in this study, directly lead to significant disparities in educational achievement. For instance, around 45% of 22- to 24-year-olds<fnref n="41" /> whose parents are within the top 5% of the income distribution have obtained a bachelor's degree. In stark contrast, only about 10% of individuals from the bottom 10% income bracket have achieved this level of education. This pronounced disparity is further illustrated in Figure D7.

<figure><img src="image_17.png" /><figcaption>Figure D7: Highest Degree Earned by Parents' Pre-Tax Income</figcaption></figure>

*Notes:* This figure depicts the highest diploma obtained by individuals aged 22 to 24, 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.

**Prediction of Lifetime Earnings Based on Educational Attainment.** To predict lifetime earnings, we combine the educational attainment of 22-24 year-olds observed in our sample with the first empirical measure of lifetime earnings for France, recently produced by Garbinti et al. (2023).<fnref n="42" /> In their estimation of lifetime earnings, they use a granularity of six distinct educational levels. Lifetime earnings, as defined in the study, represent the average earnings across 31 working years, from age 25 to 55, focusing on the cohort born in 1962.<fnref n="43" /> Additionally, we included a differential in lifetime earnings between Master's degree holders and elite graduate school graduates, as calculated by $\beta$ using self-reported earnings data.

<footnote n="41">⁴¹For this analysis, we restrict our sample to individuals aged 22 to 24, who are the most advanced in their studies within our sample.</footnote>

<footnote n="42">⁴²We would like to thank Bertrand Garbinti and Vladimir Pecheu for sending us custom data on lifetime earnings by gender and educational attainment.</footnote>

<footnote n="43">⁴³This older cohort was selected for its complete labor market trajectory, providing a comprehensive view of lifetime earnings, a perspective not yet available for our younger cohort (aged 19-24 in 2014).</footnote>
Estimated lifetime earnings by gender and education level, which form the basis of our analysis, are depicted in Figure D8.

<figure><img src="image_17.png" /><figcaption>Figure D8: Lifetime Earnings by Gender and Educational Level</figcaption></figure>

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

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

**Lifetime Earnings Gap According to Higher Education Participation and Parental Income.** Based on the assumptions previously outlined, we find that young adults who have enrolled in higher education are predicted to have lifetime earnings approximately 33% higher than their counterparts who did not pursue higher education. This figure considers both the direct causal effect of higher education and the selection factors involved into higher education attendance. The disparities in enrollment in higher education outlined in our study suggest a discrepancy of 1.27 between predicted lifetime earnings of young adults whose parents are in the bottom 10% income bracket and those whose parents are in the top 10%. Additionally, there is a disparity of 1.19 in predicted lifetime earnings between the lowest 30% and the highest 30%.

**Bridging the Gap Between the Bottom and the Middle of Parental Distribution.** As highlighted in our main analysis, there is a notable disparity in higher education participation based on parental income. Specifically, participation rates for young adults from the bottom 30% of the parent income distribution are, on average, 15 percentage points lower than those from the middle income bracket (P50-P60). Considering the varying lengths of study correlating with parental income among those enrolled
Estimated lifetime earnings by gender and education level, which form the basis of our analysis, are depicted in Figure D8.

<figure><img src="image_18.png" /><figcaption>Figure D8: Lifetime Earnings by Gender and Educational Level</figcaption></figure>

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

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

**Lifetime Earnings Gap According to Higher Education Participation and Parental Income.** Based on the assumptions previously outlined, we find that young adults who have enrolled in higher education are predicted to have lifetime earnings approximately 33% higher than their counterparts who did not pursue higher education. This figure considers both the direct causal effect of higher education and the selection factors involved into higher education attendance. The disparities in enrollment in higher education outlined in our study suggest a discrepancy of 1.27 between predicted lifetime earnings of young adults whose parents are in the bottom 10% income bracket and those whose parents are in the top 10%. Additionally, there is a disparity of 1.19 in predicted lifetime earnings between the lowest 30% and the highest 30%.

**Bridging the Gap Between the Bottom and the Middle of Parental Distribution.** As highlighted in our main analysis, there is a notable disparity in higher education participation based on parental income. Specifically, participation rates for young adults from the bottom 30% of the parent income distribution are, on average, 15 percentage points lower than those from the middle income bracket (P50-P60). Considering the varying lengths of study correlating with parental income among those enrolled
in higher education, the participation rate for the bottom third would need to increase by an additional 19 percentage points to achieve a level of public spending on higher education equivalent to that for the middle income group.

We descriptively simulate the extend to which increasing higher education participation among the most disadvantaged would affect our lifetime earnings prediction. This simulation operates under the significant assumption that individuals from the most disadvantaged group, upon increased participation, would achieve the same level of educational attainment as current students from the bottom 30% of the income distribution.<fnref n="44" /> Under this assumption, our findings suggest that the predicted lifetime incomes for this disadvantaged group would increase by 5.7%, and by 1.6% for young adults as a whole. Consequently, the earnings gap between the wealthiest 30% and the least well-off 30% would decrease from a factor of 1.19 to 1.12. It is important to note that this estimate does not account for general equilibrium effects and assumes that the financial return to higher education would remain constant even with expanded participation.

<footnote n="44">⁴⁴Expanding access through a shift in early orientation could make this hypothesis more plausible. Ichou and Vallet (2012) found that students from less privileged backgrounds would have a 21 percentage point higher enrollment rate in academic and technical high school tracks if they had the same inclination to choose these tracks as upper-class students, after controlling for prior academic ability.</footnote>
in higher education, the participation rate for the bottom third would need to increase by an additional 19 percentage points to achieve a level of public spending on higher education equivalent to that for the middle income group.

We descriptively simulate the extend to which increasing higher education participation among the most disadvantaged would affect our lifetime earnings prediction. This simulation operates under the significant assumption that individuals from the most disadvantaged group, upon increased participation, would achieve the same level of educational attainment as current students from the bottom 30% of the income distribution.<fnref n="44" /> Under this assumption, our findings suggest that the predicted lifetime incomes for this disadvantaged group would increase by 5.7%, and by 1.6% for young adults as a whole. Consequently, the earnings gap between the wealthiest 30% and the least well-off 30% would decrease from a factor of 1.19 to 1.12. It is important to note that this estimate does not account for general equilibrium effects and assumes that the financial return to higher education would remain constant even with expanded participation.

<footnote n="44">⁴⁴Expanding access through a shift in early orientation could make this hypothesis more plausible. Ichou and Vallet (2012) found that students from less privileged backgrounds would have a 21 percentage point higher enrollment rate in academic and technical high school tracks if they had the same inclination to choose these tracks as upper-class students, after controlling for prior academic ability.</footnote>
Appendix References

Albouy, Valérie, François Bouton, and Nicole Roth, "Les transferts en faveur des familles: un bilan statique des transferts liés à l'éducation," 2002. Communication préparée pour le séminaire "les effets redistributifs de l'éducation : les enseignements d'une approche monétaire statique" organisé par le Cerc, le DPD du Ministère de l'Education Nationale et l'Insee.

Allègre, Guillaume, Thomas Mélonio, and Xavier Timbeau, "Dépenses publiques d'éducation et inégalités. Une perspective de cycle de vie," *Revue Economique*, 2012, 63 (6), 1055-1079.

André, Mathias, Jean-Marc Germain, and Michaël Sicsic, ""Do I Get My Money Back?": A Broader Approach to Inequality and Redistribution in France with a Monetary Valuation of Public Services," *Insee Working Paper N° 2023-07*, 2023.

Barbaro, Salvatore, "The Distributional Impact of Subsidies to Higher Education - Empirical Evidence from Germany," *FinanzArchiv / Public Finance Analysis*, 2002, 59 (4), 458-478.

Bennani, Hamza, Gabriele Dabbaghian, and Madeleine Péron, "Les coûts des formations dans l'enseignement supérieur français: déterminants et disparités," 2021. Focus du CAE N° 074-2021.

Berné, Olivier and François Métivier, "Inégalités de traitement des étudiants suivant les filières en France," 2015. Science en Marche.

Boiteau, Jean-Serge and Claude Jameux, "Le projet de connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche : retour d'expérience," *Recherches en sciences de gestion*, ISEOR, 2019, 4 (127), 215-240.

Bonneau, Cécile, Pauline Charousset, Julien Grenet, and Georgia Thebault, "Quelle démocratisation des grandes écoles depuis le milieu des années 2000 ?," 2021. Rapport IPP n° 30.

Burgoyne, Carole B. and Victoria Morison, "Money in Remarriage: Keeping Things Simple and Separate," *The Sociological Review*, 1997, 45 (3), 363-395.

Chetty, Raj, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez, "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States," *The Quarterly Journal of Economics*, 2014, 129 (4), 1553-1623.

Courtiox, Pierre, "L'origine sociale joue-t-elle sur le rendement des études supérieures," 2011. Edhec Business School-Pôle de Recherche en Économie.

Das Gupta, Prithwis, "Decomposition of the Difference Between Two Rates and its Consistency when more than Two Populations Are Involved," *Mathematical Population Studies*, 1991, 3 (2), 105-125.

Fields, Gaby S., "Higher Education and Income Distribution in a Less Developed Country," *Oxford Economic Papers*, 1975, 27 (2), 245-259.

France, Bernard Mendès, "Les dépenses publiques d'éducation : les effets redistributifs n'éliminent pas toutes les inégalités," *Économie et Statistique*, 1987, 203, 37-48.
# Appendix References

**Albouy, Valérie, François Bouton, and Nicole Roth,** "Les transferts en faveur des familles: un bilan statique des transferts liés à l'éducation," 2002. Communication préparée pour le séminaire "les effets redistributifs de l'éducation : les enseignements d'une approche monétaire statique" organisé par le Cerc, le DPD du Ministère de l'Éducation Nationale et l'Insee.

**Allègre, Guillaume, Thomas Mélonio, and Xavier Timbeau,** "Dépenses publiques d'éducation et inégalités. Une perspective de cycle de vie," *Revue Economique*, 2012, 63 (6), 1055-1079.

**André, Mathias, Jean-Marc Germain, and Michaël Sicsic,** ""Do I Get My Money Back?": A Broader Approach to Inequality and Redistribution in France with a Monetary Valuation of Public Services," *Insee Working Paper N° 2023-07*, 2023.

**Barbaro, Salvatore,** "The Distributional Impact of Subsidies to Higher Education - Empirical Evidence from Germany," *FinanzArchiv / Public Finance Analysis*, 2002, 59 (4), 458-478.

**Bennani, Hamza, Gabriele Dabbaghian, and Madeleine Péron,** "Les coûts des formations dans l'enseignement supérieur français: déterminants et disparités," 2021. Focus du CAE N° 074-2021.

**Berné, Olivier and François Métivier,** "Inégalités de traitement des étudiants suivant les filières en France," 2015. Science en Marche.

**Boiteau, Jean-Serge and Claude Jameux,** "Le projet de connaissance des coûts des activités des établissements d'enseignement supérieur et de recherche : retour d'expérience," *Recherches en sciences de gestion*, ISEOR, 2019, 4 (127), 215-240.

**Bonneau, Cécile, Pauline Charousset, Julien Grenet, and Georgia Thebault,** "Quelle démocratisation des grandes écoles depuis le milieu des années 2000 ?," 2021. Rapport IPP n° 30.

**Burgoyne, Carole B. and Victoria Morison,** "Money in Remarriage: Keeping Things Simple and Separate," *The Sociological Review*, 1997, 45 (3), 363-395.

**Chetty, Raj, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez,** "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States," *The Quarterly Journal of Economics*, 2014, 129 (4), 1553-1623.

**Courtoux, Pierre,** "L'origine sociale joue-t-elle sur le rendement des études supérieures," 2011. Edhec Business School-Pôle de Recherche en Économie.

**Das Gupta, Prithwis,** "Decomposition of the Difference Between Two Rates and its Consistency when more than Two Populations Are Involved," *Mathematical Population Studies*, 1991, 3 (2), 105-125.

**Fields, Gaby S.,** "Higher Education and Income Distribution in a Less Developed Country," *Oxford Economic Papers*, 1975, 27 (2), 245-259.

**France, Bernard Mendès,** "Les dépenses publiques d'éducation : les effets redistributifs n'éliminent pas toutes les inégalités," *Économie et Statistique*, 1987, 203, 37-48.
Garbinti, Bertrand, Cecilia García-Peñalosa, Vladimir Pecheu, and Frederique Savignac, "Trends and Inequality in Lifetime Earnings in France," CEPR Discussion Paper N° 18472, 2023.

Hansen, W. Lee, "Income Distribution Effects of Higher Education," *The American Economic Review*, 1970, 60 (2), 335-340.

— and Burton A. Weisbrod, "The Distribution of Costs and Direct Benefits of Public Higher Education: The Case of California," *The Journal of Human Resources*, 1969, 4 (2), 176-191.

Hatchuel, Georges, "Les bénéficiaires des dépenses de l'éducation nationale en 1965 et 1970: analyse par catégorie socio-professionnelle," 1976. CREDOC.

Ichou, Mathieu and Louis-André Vallet, "Performances scolaires, orientation et inégalités sociales d'éducation. Évolution en France en quatre décennies," *Éducation et formations*, 2012, 82, 9-18.

Johnson, William R., "Are Public Subsidies to Higher Education Regressive?," *Education Finance and Policy*, 2006, 1 (3), 288-315.

Kenedi, Gustave and Louis Sirugue, "Intergenerational income mobility in France: A comparative and geographic analysis," *Journal of Public Economics*, 2023, 226, 104974.

Lemelin, Clément, "Short-Term Redistributive Effects of Public Financing of University Education in Quebec," *Canadian Public Policy / Analyse de Politiques*, 1992, 18 (2), 176-188.

Li, Jinjing, "Rate Decomposition for Aggregate Data Using Das Gupta's Method," *The Stata Journal*, 2017, 17 (2), 490-502.

MESRI-SIES, L'état de l'Enseignement supérieur et de la Recherche en France - Édition 2020, Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, 2020.

— , L'état de l'Enseignement supérieur et de la Recherche en France - Édition 2021, Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, 2021.

O'Donoghue, Cathal, "The Redistributive Impact of Education in the European Union," 2003. CERC.

Pechman, Joseph A., W. Lee Hansen, and Burton A. Weisbrod, "The Distributional Effects of Public Higher Education in California: A Review Article," *The Journal of Human Resources*, 1970, 5 (3), 361-370.

Ponthieux, Sophie, "La mise en commun des revenus dans les couples," 2012. Insee première N° 1409.

Tsakloglou, Panos and Manos Antoninis, "On the Distributional Impact of Public Education: Evidence from Greece," *Economics of Education Review*, 1999, 18 (4), 439-452.