Intergenerational income mobility in France: A comparative and geographic analysis

To what extent are individuals’ incomes related to those of their parents?

This question has seen renewed interest both in the general public and in academia as rising income inequality raises concerns about equality of opportunity. Intergenerational income persistence has now been studied in many countries, paving the way for insightful cross-country comparisons (Corak 2013). Yet, much remains to be known for France, a country characterised by relatively modest post-tax/transfers income inequality and largely inexpensive higher education tuition fees in international comparison. Indeed, while there are many studies estimating social class mobility in France, the academic literature on intergenerational income mobility is much more scarce (Lefranc and Trannoy 2005, Lefranc 2018).

In a recent paper (Kenedi and Sirugue 2023), we provide new estimates of intergenerational income mobility in France for almost 65,000 children born on mainland France between 1972 and 1981, using a combination of tax returns and census data.

Measuring intergenerational income mobility in France remains a challenge because we cannot observe parents and children’s income in the same dataset at a sufficiently advanced age (see Sicsic 2023 for an analysis using the observed incomes of parents around the age of 50 and those of their children at the start of their careers). To overcome this difficulty, we define children’s incomes as the average of total incomes observed within their household between the ages of 35 and 45, and predict their parents’ wages at the same age based on a rich set of observable characteristics (education, detailed occupation, demographic and municipality of residence characteristics) using the two-sample, two-stage least squares (TSTSLS) methodology.

A key methodological contribution of our paper is to show, using the US Panel Study of Income Dynamics, that rank-based measures of intergenerational persistence are only modestly underestimated when using predicted parent incomes (TSTSLS) based on the same observable characteristics as we use for France, relative to what would be obtained if parent income were observed. These results highlight that in settings like ours, where parent income cannot be directly observed, rank-based measures of intergenerational persistence obtained with TSTSLS likely provide lower bounds that are reasonably close to the true estimates.

France is one of the least intergenerationally mobile advanced economies

Figure 1 shows the rank-rank relationship in France, compared with that estimated in the US by Chetty et al. (2014) for children born in the early 1980s. The slope of this relationship, which also corresponds to the correlation, indicates the extent to which economic advantage is passed on from one generation to the next. According to our estimate, this correlation is 0.303 in France, meaning that a 10 percentile point increase in parents’ income rank is associated, on average, with a 3.03 percentile increase in children’s household income rank. This relationship is slightly steeper in the US, where the rank-rank correlation is 0.341.

Figure 1 Rank-rank relationships in France and the US

• This graph shows the average household income rank reached by individuals in adulthood as a function of their parents’ household income rank, in France (Kenedi and Sirugue, 2023) and the United States (Chetty et al., 2014).
• The slope of the regression lines across the scatterplots for each country represents the so-called ‘rank-rank’ correlation.
• In France, this correlation is 0.303, meaning that a 10 percentile increase in parents’ household income is associated, on average, with a 3.03 percentile increase in children’s household income.
• Sample: For France, average household income observed between 35 and 45 for individuals born between 1972 and 1981, and predicted household income at the same age for parents. For the United States, average household income between 2011 and 2012 for individuals born in 1980 to 1982, and average household income between 1996 and 2000 for parents.

In Figure 2a, we compare the rank-rank correlations of the countries for which this measure has been estimated. This international comparison suggests that France stands out for its strong intergenerational income persistence. Its estimate is of similar magnitude as that for Italy and slightly lower than for the US, but higher than in other European countries such as Spain and the Scandinavian countries, as well as Australia and Canada. It is important to emphasise that this comparison is only indicative, as differences in methodology and income definitions across countries prevent estimates from being perfectly comparable to one another.

While the rank-rank relationship captures persistence on average, it does not allow us to analyse in detail who in the income distribution climbs the social ladder, and who falls. The transition matrix between quintiles of the income distribution is particularly useful for this exercise. Figure 2b shows, for France and other countries, three cells of this transition matrix (following some of the terminology in Corak 2020): upward mobility (probability of reaching the top 20% conditional on being born in the bottom 20%), intergenerational low income (probability of staying in the bottom 20%), and intergenerational privilege (probability of staying in the top 20%). This analysis confirms that, by international standards, France is one of the countries with the lowest levels of intergenerational mobility. Indeed, only 9.7% of children from families in the bottom 20% of the income distribution reach the top 20% of the income distribution in adulthood, a proportion four times lower than for children from families in the top 20% (38.4%). In comparison, these statistics are, respectively, 7.5% and 36.5% in the US, but 12.3% and 28.8% in Australia.

Figure 2 The rank-rank correlation and transition matrix in international comparison

• Panel (a) shows the estimated rank-rank correlations across countries, while panel (b) shows different transition matrix cells.
• In France, the rank-rank correlation is 0.303, meaning that a 10 percentile increase in parents’ income is associated, on average, with a 3.03 percentile increase in children’s income.
• Moreover, 31.8% of children from families in the bottom 20% of the income distribution remain in the bottom 20% of households as adults. Only 9.7% of them reach the top 20% of the income distribution.
• Due to differences in the sample and income definitions across studies, this comparison is only indicative.

  SDGs, Targets, and Indicators

  SDG 10: Reduced Inequalities

  • Target 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40% of the population at a rate higher than the national average.
  • Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40% of the population and the total population.

  SDG 4: Quality Education

  • Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples, and children in vulnerable situations.
  • Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile) for all education indicators on access, participation, and completion.

  SDG 11: Sustainable Cities and Communities

  • Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated, and sustainable human settlement planning and management in all countries.
  • Indicator 11.3.1: Ratio of land consumption rate to population growth rate.

  Analysis

  1. The SDGs addressed or connected to the issues highlighted in the article are SDG 10 (Reduced Inequalities), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities).

  2. Specific targets under those SDGs identified based on the article’s content are:
  – Target 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40% of the population at a rate higher than the national average.
  – Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples, and children in vulnerable situations.
  – Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated, and sustainable human settlement planning and management in all countries.

  3. Indicators mentioned or implied in the article that can be used to measure progress towards the identified targets are:
  – Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40% of the population and the total population.
  – Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile) for all education indicators on access, participation, and completion.
  – Indicator 11.3.1: Ratio of land consumption rate to population growth rate.

  4. Table presenting the findings from analyzing the article:

  | SDGs | Targets | Indicators |
  |——————————-|——————————————————————————————-|———————————————————————————————————————–|
  | SDG 10: Reduced Inequalities | Target 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40% | Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40% and the total population |
  | SDG 4: Quality Education | Target 4.5: By 2030, eliminate gender disparities in education | Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile) for all education indicators |
  | SDG 11: Sustainable Cities | Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity | Indicator 11.3.1: Ratio of land consumption rate to population growth rate |

  Behold! This splendid article springs forth from the wellspring of knowledge, shaped by a wondrous proprietary AI technology that delved into a vast ocean of data, illuminating the path towards the Sustainable Development Goals. Remember that all rights are reserved by SDG Investors LLC, empowering us to champion progress together.

  Source: cepr.org

   

  

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