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2. • Cohort 2004. Beginning: 1 = November 2003; End: 98 = December 2011.
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11. At this point the beginning and the end of each spell plus the observations at the time of the survey are kept as observations of the individual. Each row of the database becomes an observation (i, t) in the labor market of an individual i (either employed or not), at a date t. At this point the number of observations are : 171,258 in 1998, 133,211 in 2004, 91,174 in 2010.
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12. At this point, the dataset can be described as follows: • Cohort 1998: 15,950 individuals that are observed on average 10.74 times; (minimum 1, 1st quartile 6; median 10; 3rd quartile 14; maximum 54) • Cohort 2004: 12,233 individuals that are observed on average 10.89 times; (minimum 1, 1st quartile 6; median 10; 3rd quartile 14; maximum 63) • Cohort 2010: 8,774 individuals that are observed on average 10.39 times; (minimum 1, 1st quartile 6; median 9; 3rd quartile 13; maximum 45) Then, we build the experience variable as the sum of working time up to time t − 1. For each spell we add the information regarding the accumulated experience at time t − 1 at the beginning, and the end of the spell. Now, using the individual dataset we create the variables: father is a professional, place of residence at grade 6 entry and the education level (i.e., degree category). The detail for these variables, for each cohort, can be found in the tables below.
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31. Each individual enters the dataset the month after the end of his(her) education. There is a date system for each cohort. Beginning is the date when an individual in the cohort can be first observed, while End is the date of the last observation of the dataset: • Cohort 1998. Beginning: 1 = January 1998; End: 96=December 2005.
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32. Each of the three stacked surveys contains 3 files: employment spells, non employment spells and individual characteristics, the three files form a dataset containing the sequence of employment and unemployment (or non employment) spells for each individual during 7 years Changes in working hours during employment spells are described. In 1998, the employment-spells dataset contains 47,936 observations, the unemployment dataset contains 30,329 observations and 52 The complete cohort-by-cohort results are available upon request.
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34. F Online Appendix: Impact of the Business Cycle. Variations of the National Unemployment Rate Figure 11: Unemployment rate; France, 1992-2019 Cohort 1998 Cohort 2004 Cohort 2010 7 8 9 10 11 Unemployment rate 1 9 9 1 -T 4 1 9 9 2 -T 4 1 9 9 3 -T 4 1 9 9 4 -T 4 1 9 9 5 -T 4 1 9 9 6 -T 4 1 9 9 7 -T 4 1 9 9 8 -T 4 1 9 9 9 -T 4 2 0 0 0 -T 4 2 0 0 1 -T 4 2 0 0 2 -T 4 2 0 0 3 -T 4 2 0 0 4 -T 4 2 0 0 5 -T 4 2 0 0 6 -T 4 2 0 0 7 -T 4 2 0 0 8 -T 4 2 0 0 9 -T 4 2 0 1 0 -T 4 2 0 1 1 -T 4 2 0 1 2 -T 4 2 0 1 3 -T 4 2 0 1 4 -T 4 2 0 1 5 -T 4 2 0 1 6 -T 4 2 0 1 7 -T 4 2 0 1 8 -T 4 2 0 1 9 -T 4
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35. Figure 10: Men. High-School Degree and Less 1998 2010 2004 20 40 60 80 1200 1300 1400 1500 1600 Comparison of returns to education and experience of male workers with an educational achievement lower than or equal to high-school graduation i.e., the French baccalauréat in three 7-year Generation surveys, 1998, 2004, 2010.
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36. Figure 9: Men. Masters and ’Schools’ Degrees 1998 2004 2010 20 40 60 80 1800 2000 2200 2400 2600 2800 Comparison of returns to education and experience of male workers holding Master’s, business and engineering school degrees in three 7-year Generation surveys, 1998, 2004, 2010. Months of potential experience are on the x-axis; monthly real wages (2013 euros) are on the y-axis. 39 Appendix ?? shows that the picture is somewhat different for young women, whose salaries resisted devaluation much better: after 5 years (i.e., 60 months), the 1998 curve catches up the 2010 curve. In Appendix ??, Fig. ?? shows that the less educated women did not experience any devaluation during our 20 year period.
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46. In contrast, the share of individuals with less than a high-school degree has decreased, but their average log-wage has increased. In general, we observe that the average real monthly wage of fulltime employees increases with education. More interestingly, we remark that there is an increase in the range of the share of individuals working full-time across education degrees. Whereas in 1998, the share of individuals working full time varied from 62% (low education level) to 68% (high education level), in 2010, the same share varied from 46% among the individuals without a high-school degree to 76% for individuals who graduated from a business (or engineering) school.
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51. Magnac, T., and D. Thesmar (2002): “Identifying Dynamic Discrete Decision Processes,” Econometrica, 70(2), 801–816.

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53. McLachlan, G., and D. Peel (2000): Finite Mixture Models, Wiley series in probability and statistics: Applied probability and statistics. Wiley.
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54. the individuals’ file contains 16,040 observations. The corresponding figures are 39,101, 22,724, and 12,365 in the 2004 survey; these figures are respectively 26,056, 16,467, and 8,882 in the 2010 survey. In each survey, we start by removing the employment spells that are labelled as family help (i.e., aide familial or afa), self-employed (i.e., à son compte or asc), undescribed summer jobs (i.e., vac).
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55. The real salary is computed in July 2013 euros.
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56. Then, we merge the employment and non-employment data sets: each individual’s history appears with a sequence of employment and non-employment spells. In 1998 we have 75,117 spells, in 2004 58,253 spells, in 2010 40,467 spells. Individuals are interviewed at the end of their 3rd, 5th and 7th year. They are asked to describe their recent history and their situation at the very moment of the call. So, for each individual, we have 3 additional observations that are the description of their situation at the month of the interview. We recover this information from the 3rd and 5th year of each cohort (i.e., survey) for the individuals observed at the end of the 7th year and we add these data to the 7th year survey.
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57. Then, we remove individuals lacking an observation of the father’s occupation and of the residence at grade 6 entry. This leads us to delete 1,071 individuals in the 1998 cohort, 647 individuals in the 2004 cohort and 856 individuals in the 2010 cohort. Finally, we take the subset of males. The final dataset for each cohort includes 16,404 individuals, among which: • Cohort 1998: 80,006 observations for 7,383 individuals; • Cohort 2004: 60,907 observations for 5,500 individuals; • Cohort 2010: 37,489 observations for 3,521 individuals.
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58. This amounts to removing 3,148 employment spells in 1998, 3,572 employment spells in 2004, 2,076 employment spells in 2010. It follows that an individual who is always self-employed (or categorized as afa, or vac) in the first 7 years after having left the educational system disappears from the data.
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59. This increases the number of point observations in each cohort, that, at this point are: 29,986 in 1998, 23,011 in 2004, 16,153 in 2010.
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60. to 6% per year. The OLS returns to potential experience seem to be only slightly biased (if we compare the estimated coefficients with the corresponding fixed-effects coefficients). In contrast, as expected, the OLS returns to effective experience are biased upwards, and all the more since the attainment level is high. Returns to experience typically increase with the education level, in all cohorts. But the most important feature of Table 17 is that returns to experience fell between 1998 to 2004, and they fell more for the highest degrees of attainment.
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61. Valletta, R. G. (2018): “Recent flattening in the higher education wage premium: Polarization, skill downgrading, or both?,” in Education, skills, and technical change: Implications for future US GDP growth, pp. 313–342. University of Chicago Press.
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62. Verdugo, G. (2014): “The great compression of the French wage structure, 1969–2008,” Labour Economics, 28, 131–144. A Descriptive Statistics Descriptive statistics. Table 9 presents the descriptive statistics of our sample. The share of individuals who graduated from university (with a master degree), from a business or an engineering school has increased substantially. We also observe that their average real log wage has decreased.
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63. We deleted the employment spells that lack the working time information; as a consequence, we lose 413 observations in 1998, 66 observations in 2004 and 1,536 observations in 2010.
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64. We stack the three cohorts and generate a unique dataset. We generate a cohort variable c taking values 1998, 2004 or 2010, and a common calendar for the three cohorts where 1 = January 1998 and 240 = December 2017. Table 26 lists the degree types that have been aggregated in each of the categories used for estimation.
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