Wages and employment of second-generation immigrants in France

Transcription

1 Wages and employment of second-generation immigrants in France Romain Aeberhardt Denis Fougère Julien Pouget Roland Rathelot January 22, 2007 Preliminary version: Do not quote please Abstract Our study contains an econometric decomposition of the wage gap and of the difference in employment probabilities between French workers whose both parents had French citizenship at birth and French workers whose at least one parent had the citizenship of an African country at birth. For that purpose, we use data coming from the Formation Qualification Professionnelle (FQP) survey conducted by Insee (Paris) in To our knowledge, our study is the first to analyze wage differentials between native French workers and children of African migrants. Keywords: discrimination, wage differentials. JEL: C24, J31, J71. The authors thank participants at the Alliance Conference on Group Inequality and Discrimination (Columbia), May 2005, and at the Séminaire Recherche de l Insee (Paris), June 2005, for their stimulating comments. All the remaining errors are ours. Any opinions expressed here are those of the authors and not of any institution. CREST - INSEE (Paris), romain.aeberhardt@insee.fr CNRS, CREST-INSEE (Paris), CEPR (London) and IZA (Bonn), denis.fougere@ensae.fr CREST - INSEE (Paris), julien.pouget@insee.fr CREST - INSEE (Paris), roland.rathelot@insee.fr, INSEE DESE - Timbre G Bld G. Péri - BP Malakoff Cedex - France - Tél. : Fax. :

2 1 Introduction For more than forty years, economists and econometricians, following Becker (1957), Arrow (1973) and Phelps (1972), have developed theoretical and empirical tools to study discrimination in the labour market. The comprehensive survey by Altonji and Blank (1999) presents the main econometric studies dealing with discrimination. There has been a number of empirical studies in which attempts were made to decompose observed employment rates and earnings differentials into human capital and discrimination components. One of the decomposition methods that was most often used was popularised by Oaxaca (1973) and Blinder (1973). Most U.S. studies conclude that although differences in worker-observable characteristics are important factors of the Black-White wage differential, the current labour market discrimination accounts for at least one-third of the overall gap. However, these hypothesized skill and treatment components may lead to difficult interpretations. The so-called treatment or discrimination component may be over-estimated due to unobservable heterogeneity. Another twist in the wage gap decomposition methodology is caused by potential selectivity bias. That is why more general approaches were proposed (see for examples the papers by Oaxaca and Ransom (1994), Neuman and Oaxaca (2004a) and Neuman and Oaxaca (2004b)). Other studies tried to account for the fact that controls for worker productivity may be very inaccurate measures of workers skills. For instance, Neal and Johnson (1996) use the armed forces qualification test as a better measure of skill. This test is taken before entry on the labor market and is therefore less likely to be contaminated by worker s choices or labor market discrimination. A different set of studies, known as audit studies, attempt to place comparable minority and White actors into actual social and economic settings and to measure how each group fares in these settings (see Heckman (1998)). These audit studies provide some of the cleanest non-laboratory evidence of differential treatment by race. Bertrand and Mullainathan (2003) performed such a field experiment to measure racial discrimination in the labour market. In spite of this vast literature on racial discrimination issues, little attention has been devoted to the French case. This lack is partly due to the fact that the French republican and egalitarian political model prevents from defining ethnic statistical categories. However, November 2005 riots, occurring simultaneously in various poor suburbs of large cities where immigrants are over-represented, suddenly highlighted the problem of discriminations in the French labour market. Since 1975, the proportion of immigrants in the population has remained stable in France (7.4% in 1999), but their geographical origin has evolved (Insee, 2005). In 1962, most of them came from Europe (79%), especially from Italy and Spain, and only 15% came from Africa. In 1999, 45% came from Europe and 39% came from Africa, especially from North Africa. Immigrants are more affected by unemployment: their unemployment rate (16.4% in 2002) is twice that 2

3 of non-immigrants (8.2%). They are more often manual workers or employees, especially in unskilled jobs, and are over-represented in industry and construction. People born in France with two immigrant parents represented 5% of the persons aged 66 and less in While 20% of the young aged from 19 to 29 whose parents are not immigrants are unemployed, the unemployment rate is 30% for those with two immigrant parents. Their situation depends on their parents origin: their unemployment rate is nearly 40% if their parents are from Algeria or Morocco, whereas it is slightly under 20% if they are from Southern Europe (Spain, Italy, Portugal). These numbers naturally raise the question of the integration and potential discrimination of immigrants children in the labour market. The situation of the children of North African and African immigrants in the suburbs of French cities is particularly at stake. By analyzing longitudinal data, Fougère and Safi (2005) show that being granted French citizenship has a positive impact on the employment probability of immigrants. This naturalization premium seems particularly important for immigrant groups facing difficulties when entering the labor market, that is, mostly men from sub-saharan Africa and from Morocco, and women from Turkey and from North Africa. Silberman and Fournier (1999), and Meurs, Pailhé, and Simon (2005) suggest that children of immigrants might also suffer from discrimination in the labor market. Pouget (2005) focuses on the difficult access to civil service. Aeberhardt and Pouget (2006) use business survey data: as a result they do not take into account the selectivity bias due to unemployment. They perform a switching regression model of wage determination and occupational employment; the results lead them to favour an interpretation in terms of a certain degree of occupational segregation, rather than mere wage discrimination. Our paper is the first econometric analysis in France that examines empirically the employment and wage differences between French workers with different national origins, using an unique household survey, the Survey Formation Qualification Professionnelle (here and after referred as FQP survey) performed by the French Statistical Institute (Insee) in This survey contains a lot of socio-demographic and economic variables, and even thin information on the living area, especially the so-called Zone Urbaines Sensibles (ZUS) which are poor areas often concentrating much of the migrant population. In order to identify the potential effects of discrimination, we estimate a selection model, allowing for the possible endogeneity of the employment situation. The structure of this paper is as follows. Section 2 presents the methodology. Section 3 provides details on the data. Section 4 outlines the main empirical findings. 2 Methodology Empirical evidence of wage and participation discrimination toward workers of foreign origin is established through the decomposition method initiated by Oaxaca (1973) and Blinder (1973). 3

4 Taking into account selectivity terms was covered by Oaxaca and Ransom (1994), Neuman and Oaxaca (2004a) and Neuman and Oaxaca (2004b). We recall here shortly this decomposition method. We denote ln (w ij ) the log-wage of the individual i, in (demographic) group j. Individuals belonging to group j are assumed to be potentially discriminated. We suppose that the wage is generated by the following model: ln(w ij ) = X ijβ j + u ij (1) where the residual u j is assumed to be zero-mean, homoskedastic with variance V (u ij ) = σu,j 2. Another group of individuals, labeled k, is the reference group. The log-wages of workers belonging to this group are modeled as: ln(w ik ) = X ik β k + u ik (2) where the residual u ik verifies the same properties than u ij. All observations from the same group (j or k) are assumed independant and identically distributed. The difference between expected log-wages can be decomposed in the following way: E[ln(w ij )] E[ln(w ik )] = E[X ij](β j β k ) + ( E[X ij] E[X ik ]) β k (3) The first term of this sum can be interpreted as the wage gap due to discrimination. The second part of the wage gap is due to the average gap in individual characteristics between the two groups. OLS estimations of the parameters β j and β k are potentially biased, since only the participants wages are observed and taken into account in the regression. To get unbiased estimates, one can specify a two-equation model, with a selection equation and a wage equation: for group j: for group k: { Yij = Z ijγ j + ε ij (4) ln(w ij ) = X ijβ j + u ij { Yik = Z ik γ k + ε ik ln(w ik ) = X ik β (5) k + u ik The first equation of the system is generated by a latent random variable that is positive if and only if worker i is employed (and thus if the wage is observed). In other words, worker i is employed if and only if Y ij = 1 (i.e. Yij > 0). She is not employed if and only if (Y ij = 0) or < 0. All this remains true for individuals from group k. We add an assumption on the joint Y ij distribution of residuals u and ε. Vectors (ε ij, u ij ) and (ε ik, u ik ) are assumed to be generated 4

5 by a bivariate normal distribution, ( ε ij u ij ) (( N 0 0 ), [ 1 ρ j σ u,j ρ j σ u,j σ 2 u,j ]) (6) and ( ε ik u ik ) N (( 0 0 ), [ 1 ρ k σ u,k ρ k σ u,k σ 2 u,k ]) (7) Under this set of assumptions, the difference between expected log-wages of employed workers in the two groups can be written as: E[ln(w ij ) Y ij = 1] E[ln(w ik ) Y ik = 1] = E[X ij](β j β k ) + ( E[X ij] E[X ik ]) β k (8) + (ρ j σ u,j λ j ρ k σ u,k λ k ) In this expression, terms λ j and λ k are the inverse Mills ratios, defined as: ( ϕ λ j = Φ ) Z ij γ j ( ) and λ k = Z ij γ j ( ) ϕ Z ik γ k Φ ( Z ik γ ) (9) k Just as before, the second term in decomposition (8) can be understood as the part of the wage gap explained by differences in characteristics, while the first term can be attributed to discrimination. The last term in this expression is attributed to the difference in selectivity terms between the two groups. Parameters of models (4)+(6) and (5)+(7) can be estimated either by a maximum likelihood procedure, or by a two-step consistent method. Selectivity terms are difficult to avoid when a Tobit model is specified but make the results harder to interpret. Neuman and Oaxaca (2004a) and Neuman and Oaxaca (2004b) try to deal with these selectivity terms and to interpret them. They incorporate parts or all of the Mills ratios into the explained part and into the discrimination component so that some or all of the selectivity element vanishes. This approach relies on specific choices we do not want to make. To avoid these drawbacks, we decompose the difference between the unconditional expected log wages E[ln (w ij )] E[ln(w ik )]. Estimating the β s using a Tobit model provides consistent estimates of the unconditional expectation of the log-wage: E[ln(w ij )] E[ln(w ik )] = E[X ij](β j β k ) + ( E[X ij] E[X ik ]) β k A practical problem with this method is that some of the variables in the wage equation (in X) are not observed when the worker is not employed. For example, firm seniority can obviously not be observed for unemployed workers. Some imputation rule has to be chosen for these variables, and we must check that our results are not significantly affected by this method. 5

6 A second drawback of the Oaxaca-Blinder method occurs when sample size of one group is too small to provide precise estimates. In our case, the sample size of the potentially discriminated group is actually quite small. Can we derive an estimator for the discrimination component of the wage gap that would not be dependent of β j? Assume that there exists a population l, with exactly the same observable covariates as population j. But a worker belonging to population l has a participation behavior and a wage driven by the following model: { Yil = Z il γ k + ε il ln(w il ) = X il β (10) k + u il which means that the market returns of her characteristics are the same as those of group k. From this definition, E[ln(w il ) Y il = 1] is the counterfactual we are looking for; namely the expected wage of a worker with covariates of type j whose returns are those of an individual of type k. Consequently, decomposing the conditional wage gap using this counterfactual gives: E[ln(w ik ) Y ik = 1] E[ln(w ij ) Y ij = 1] = E[ln(w ik ) Y ik = 1] E[ln(w il ) Y il = 1] }{{} explained component + E[ln(w il ) Y il = 1] E[ln(w ij ) Y ij = 1] }{{} unexplained component All that remains to be done is estimating the counterfactual term E[ln(w il ) Y il = 1]. If we observe all the X ij, X ik, Z ij and Z ik, then an estimator ŵ l for the counterfactual term is: ŵ l = i [ ] Φ(Z ijˆγ k ) ϕ(z ijˆγ k ) X ij ˆβk + ˆρ kˆσ u,k Φ(Z ijˆγ k ) Φ(Z ijˆγ k ) i where ˆβ k, ˆγ k, ˆσ u,k and ˆρ k are consistent estimates of β k, γ k, σ u,k and ρ k and X j is the sample mean of X in group j. 3 Data Labor force surveys undertaken yearly by the National Institute for Statistics and Economic Studies (Insee - Paris) did not allow, until 2002, to get information on the national origin of the surveyed persons parents. These questions regarding the parents citizenship at birth are extremely important because they are the key to identify the second generation of immigrants. To our knowledge, the FQP survey is the first major survey that collects such valuable information on a representative sample of the French population. 6

7 3.1 The Formation Qualification Professionnelle Survey - FQP 2003 The 2003 FQP survey follows similar surveys conducted in 1970, 1977, 1985 and 1993 by INSEE two, three or four years after a population census. Using a complex sampling design they cover all men and women in metropolitan France with a quite substantial number of individual face-to-face interviews ( in 2003). In France these surveys are usually considered as offering unique information about the returns to education, the efficiency of the educational system, the impact of social origin on academic and professional success, the impact of vocational training on careers, in terms of mobility or earnings. It also enables to conduct studies on specific populations, e.g. the rise of unemployment among high school drop-outs in the nineties. The questionnaire is made of five parts: professional mobility, initial education, vocational training, social origin and earnings. FQP is the only survey that allows to link these five topics and observe their interactions. Many questions in the 2003 survey are the same as in the previous surveys conducted in 1964, 1970, 1977, 1985, and However, the 2003 survey focuses on professional mobility with a particular emphasis on the professional career in the last five years. Special attention was also put upon organizational and technological changes that employees face during their career. The reference population consists in all individuals between 18 and 65 who live in France (metropolitan area) in an ordinary dwelling. Within each dwelling, if there were more than two persons in the scope of the survey, only two were randomly drawn and surveyed. The initial sample comprises dwellings. Due to vacancies and refusal of participation, the final sample had about individuals. The survey is conducted in face-to-face interviews using CAPI (computer assisted personal interviewing). After the description of the household, which takes about 3 minutes, the survey questionnaire takes about 30 minutes per person. The data collection took place between April and July Sample and groups considered for the analysis Scope of the study Given the model and the quality of the data, the sample had to be reduced. First, since one of the equations of the model is a participation equation, we excluded all individuals who do not participate in the labor market, that is those who already retired and the students. These choices can of course be challenged due to the endogeneity of the decisions regarding the length of the studies and the enrollment in early retirement plans, but they seemed to be appropriate for a first approach. The model distinguishes between those who receive wages and those who do not. Therefore we put out of the sample those who receive only non wage compensations (they account for a very small part of the population anyway). Here again, we could have modeled intermediate deci- 7

8 sions, but the quality of the estimates would have probably been very poor given the very small size of this particular sample. We also left aside those who did not answer the wage question and those who said they did not know it. The major problem when selecting the sample comes from the fact that the situation in 2002 is not explicitly known, contrary to the situation in 2003 on which the rest of the survey is highly conditioned. One of the questions allows to know whether the person worked in 2002 and received wages. That gives those who earn non wage compensations. Among those who did not work we need to spot students and retired people. For the students, we use the calendar of the studies. For the retired and early retired people, we consider that retirement is an absorbing state (that is those who retired in 2002 were still retired in 2003). Therefore we consider as retired in 2002, those who were retired in 2003 and who had left their last job in 2001 or before. By doing this, there is a risk that we get rid of those who were unemployed during their last year before retirement. This question could theoretically be assessed using the professional calendar, but so far, the high rate of non-response does not allow to use it efficiently Sub-populations of interest In this paper we focus on wage and unemployment discrimination against the second generation of migrants. We keep thus three sub-populations: first those whose both parents were French at birth and born in France, second those with at least one parent whose citizenship at birth was the one of an African country (Maghreb included), third those with at least one parent whose citizenship at birth was the one of a southern european country (Italy, Portugal, Spain). We excluded those for whom the citizenship at birth of at least one of the parents was unknown, except if only one citizenship was known and belonged to one of the specific foreign countries. The group with the French parents is the reference group, and the other ones corresponds to the groups with potentially discriminated individuals. Since the reference group is relatively large, it allows to impose conditions on both citizenship and country of birth for the parents, which should improve national origin homogeneity. This survey does not allow to go beyond the second generation, but this should already be enough in terms of homogeneity Unemployment in 2002 As explained before, the 2003 FQP survey allows to know accurately the situation at the time of the interview, and a career calendar describes the last five years of professional life. Unfortunately this calendar proved pretty uneasy to use. Here we describe briefly a method of identification of unemployed individuals as opposed to people who were just inactive. We did not use this distinction in our model, but the descriptive statistics may help in the comparisons between the populations. The difficulty is to find among those who did not work in 2002, those who really were unemployed. First we distinguish between the individuals who worked in 2002 and those who did not. Among those who did not, we check if they ever worked before. Among those who never worked, we 8

9 keep only the unemployed who were not students in Among those who had a job in the past, some of them had it less than five years ago and others more than five years ago (which modifies the interview). For the latter, we have only very little information and we consider as unemployed those who were unemployed at the time of the interview. For those whose last job was in the last five years, we have much more information including their current situation and the reason why they stopped their last occupation. We consider as unemployed those who were unemployed when they left their last occupation and were still unemployed at the time of the interview. A few people declared themselves as unemployed when they left their last job but were out of the labor force (retired, back to school or university, or at home) at the time of the interview. And among those who declared themselves as unemployed, some left their job for health or family reasons, i.e. another reason than layoff, resignation or any end of contract. In that case we do not know whether these individuals participated in the labor market in 2002 and we exclude them from the unemployed group. We might therefore slightly underestimate the number of unemployed people by putting some of them into the inactive group. As shown in table 1, individuals originating from Southern Europe have a very similar situation in the labor market to that of the reference population. Those with African origin, on the other hand, are relatively much more numerous in all precarious situations: 9.3% of them were unemployed for twelve months whereas only 3.5% were in that situation in the reference population. They are also much more likely to be inactive or to have worked less than twelve months during the year Variables considered for the analysis The variable of interest is the wage or more precisely the logarithm of the wage. We worked both with real earnings and with the wage in full-time full-year equivalent. Distributions and means of these variables for the different sub-populations are shown in table 5. We created twelve different categories of households crossed with gender, depending on the presence of children and the presence of a working spouse. People with a Southern-European origin are really similar to the reference group, which is not the case for those with African origin. Singles are pretty much in the same proportions except for single women with children who are relatively more numerous among the latter. Women living in couple are less likely to be without children and more likely to have a non working husband. Men in couple with children are relatively more likely to have a non working wife. The distributions of ages are very similar for those with Southern-European origin and those with French origin. On the other hand, for those with African origin, the distribution is shifted to the left, that is, they are more numerous in the younger age groups. There are much more people without any diploma among both subpopulations with foreign 9

10 origin. The rest of the distribution looks the same, except for vocational degrees which are relatively more common among those with Southern-European origin and relatively less common for those with African origin. Concerning the area of residence, once again, there are no significant differences between France and Southern-Europe, except for a slightly larger concentration around Paris for the latter. For those with African origin, on the contrary, there is a huge difference both in terms of concentration around Paris and in the number of people residing in a poor area. Here, we denote as poor area any place belonging to a Zone Urbaine Sensible (ZUS). 10

11 Table 1: Distribution of Observations (Full Sample - Overall) France Africa Southern Europe Number of observations Gender Female 54,2 54,1 54,6 Male 45,8 45,9 45,4 Age less than 20 0,5 1,4 0,3 20 to 29 15,4 24,6 11,2 30 to 39 29,4 35,6 28,3 40 to 49 27,0 25,5 28,1 50 to 59 24,0 10,8 26,5 60 and more 3,7 2,1 5,6 Diploma Graduate 11,7 10,6 8,4 Some College 11,1 9,0 6,8 Completed High School 16,0 13,4 12,9 Vocational Degree 26,4 21,7 28,9 Junior High School 9,5 11,4 9,3 No Diploma 25,3 33,8 33,7 Household Single Man without Children 7,1 6,3 6,0 Single Man with Children 2,1 3,4 1,9 Single Woman without Children 7,1 6,8 5,9 Single Woman with Children 5,5 9,2 6,4 Man with working Spouse with Children 17,0 11,4 17,4 Man with working Spouse without Children 7,3 5,2 6,9 Man with non working Spouse with Children 8,9 18,2 9,5 Man with non working Spouse without Children 3,5 1,4 3,6 Woman with working Spouse with Children 23,2 22,5 22,3 Woman with working Spouse without Children 9,0 4,6 8,4 Woman with non working Spouse with Children 4,2 8,9 4,5 Woman with non working Spouse without Children 5,2 2,1 7,0 Residence Not ZUS Rest of France 81,9 47,1 78,1 Not ZUS Paris and Suburbs 13,3 29,1 16,7 ZUS Rest of France 3,5 15,1 3,6 ZUS Paris and Suburbs 1,2 8,7 1,6 Situation on the Labor Market 12 months FT 59,5 47,7 58,2 12 months PT 9,9 6,0 9,9 12 months FT/PT 1,3 1,1 1,5 12 months unemployed 3,5 9,3 3,8 some work (various situations) 13,7 19,6 12,8 no work (various situations) 11,9 16,2 13,8 Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French individuals whose both parents were French and born in France, 54.2% are Women Source: Formation Qualification Professionnelle (FQP) - Insee

12 Table 2: Distribution of Observations - Comparison Between Working and not Working Individuals not working France Africa Southern Europe working not working working not working Number of observations Gender Female Male Age less than to to to to and more Diploma Graduate Some College Completed High School Vocational Degree Junior High School No Diploma Household Single Man without Children Single Man with Children Single Woman without Children Single Woman with Children Man with working Spouse with Children Man with working Spouse without Children Man with non working Spouse with Children Man with non working Spouse without Children Woman with working Spouse with Children Woman with working Spouse without Children Woman with non working Spouse with Children Woman with non working Spouse without Children Residence Not ZUS Rest of France Not ZUS Paris and Suburbs ZUS Rest of France ZUS Paris and Suburbs working Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French not working individuals whose both parents were French and born in France, 77.0% are Women Source: Formation Qualification Professionnelle Survey (FQP) - Insee

13 Table 3: Workers Occupations France Africa Southern Europe Number of observations Working Time Part Time Full Time Professional Category Craftsman Executive Intermediate Employee Skilled Worker Unskilled Worker Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French workers whose both parents were French and born in France, 82.9% work Full Time Source: Formation Qualification Professionnelle Survey (FQP) - Insee 2003 Table 4: Workers Occupations (ZUS vs non-zus) France Africa Southern Europe non-zus ZUS non-zus ZUS non-zus ZUS Number of observations Working Time Part Time Full Time Professional Category Craftsman Executive Intermediate Employee Skilled Worker Unskilled Worker Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French workers living in a ZUS and whose both parents were French and born in France, 83.4% work Full Time Source: Formation Qualification Professionnelle Survey (FQP) - Insee

14 Table 5: Earnings and Wages France Africa Southern Europe Number of observations Earnings Mean First Quarter Median Third Quarter Wage (Full-Time Full-Year equivalent) Mean First Quarter Median Third Quarter Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French workers whose both parents were French and born in France, 25% earned less than euros in 2002 Source: Formation Qualification Professionnelle Survey (FQP) - Insee 2003 Table 6: Earnings and Wages (ZUS vs non-zus) France Africa Southern Europe non-zus ZUS non-zus ZUS non-zus ZUS Number of observations Earnings Mean First Quarter Median Third Quarter Wage (Full-Time Full-Year equivalent) Mean First Quarter Median Third Quarter Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French workers living in a ZUS, and whose both parents were French and born in France, 25% earned less than euros in 2002 Source: Formation Qualification Professionnelle Survey (FQP) - Insee

15 Table 7: Situation on the Labor Market (ZUS vs non-zus) France Africa Southern Europe non-zus ZUS non-zus ZUS non-zus ZUS Number of observations months FT months PT months FT/PT months unemployed some work (various situations) no work (various situations) Note: All statistics are computed using individual weights. All partial columns sum to 100% Reading: Among French individuals living in ZUS, and whose both parents were French and born in France, 52.3% worked 12 months full time Source: Formation Qualification Professionnelle Survey (FQP) - Insee

16 Table 8: Number of Observations (Full Sample) France Africa Southern Europe Total Number of Observations Gender Female Male Age less than to to to to and more Diploma Graduate Some College Completed High School Vocational Degree Junior High School No Diploma Household Single Man without Children Single Man with Children Single Woman without Children Single Woman with Children Man with working Spouse with Children Man with working Spouse without Children Man with non working Spouse with Children Man with non working Spouse without Children Woman with working Spouse with Children Woman with working Spouse without Children Woman with non working Spouse with Children Woman with non working Spouse without Children Residence Not ZUS Rest of France Not ZUS Paris and Suburbs ZUS Rest of France ZUS Paris and Suburbs Situation on the Labor Market 12 months FT months PT months FT/PT months unemployed some work (various situations) no work (various situations) Note: The figures correspond to the exact number of observations in the sample Reading: Among French individuals whose both parents were French and born in France, there are women and men in the sample Source: Formation Qualification Professionnelle Survey (FQP) - Insee

17 4 Results We start by running ordinary least square estimations for the wage equation on the subsample for which the wage information is available. This leads to a first set of estimates, potentially suffering from a selection bias. However, in a first attempt, these estimates can be used to decompose the raw wage gap into two parts: one resulting from differences in individual observable dotations between the two groups, and the other, labelled as unexplained, that can be interpreted as discrimination. We then estimate by a two-step Heckman-type procedure and a maximum likelihood procedure a Tobit model, in which the equation of interest is the wage equation and the selection equation is the employment equation. Estimation is done for both groups: French individuals whose parents were both French at birth and French individuals who have at least one parent having the citizenship of an African country at birth. We first comment the results of our estimations, before using them for assessing the potential existence of discrimation on the labor market. First, some general characteristics can be presented. Sample sizes drastically vary from one group to the other, bringing about the risk to jeopardize the significativity of our estimates. This leads us to gather men and women in one single sample. Even though, estimation on the sample with African parents are not as reliable as estimation of French parents group. Having a look at the estimated coefficients for the employment equation for each group, we draw a first interpretation, only valid caeteris paribus. These results are available in table 11. In each group, a better diploma increases the probability to be employed. Hardly noticeable is the fact that people without any diploma are less employed in the group in which at least one parent in an African migrant. As we introduce potential experience and squared potential experience, we obtain a concave impact of this variable on participation. Differences between the two groups are not statistically significant. Socio-demographic variables are also important factors which explain the individual probability to be employed. We crossed several variables: gender, marital status, having children, having a spouse that works. Our results are similar to previous studies. Single women with children are less employed than the reference population which includes single men and single women without children. The phenomenon is true for both groups but even more obvious for women whose parents are African migrants. Men with a working spouse and with children are more often employed whereas women in the same situation behave the opposite way: this pattern is shared in the two groups in an equal extent. Men with a working spouse and without children tend to work more than the reference population, but the gap is higher in the African-migrant-parents group. Women with a working spouse behave the same way in the two groups: they are less often employed when they raise children and as employed as the reference when not. The gap of behavior between the two groups increases for women whose spouse does not work. Whereas women with African migrant parents are less often employed when they have children, and their employment probability is significantly different from the 17

18 reference when they do not have children. The area where a person lives has also an impact on her employment probability. We distinguish two dimensions, that we cross: living in the Paris region (called Île-de-France) and living in a poor area (ranked as Zone Urbaine Sensible by public authorities). We take the situation of living neither in Île-de-France nor in a poor area as the reference. For individuals with French born parents, moving from the reference to Île-de-France improves employment whereas moving to a poor area (still outside Île-de-France) drastically diminishes employment. For them, there is no statistically significant difference in participation between a poor area in the Paris region and the reference. The situation is different for individuals with African parents. For them, the only dimension that matters is living in Paris region (which improves employment) or not. We now comment results from the estimation of the wage equation for both groups, reported in table 10. Effects of potential experience and diplomas are the usual ones: hump-shape for potential experience, strict ranking for diplomas. We introduce firm seniority in the equation, even though such a variable may be potentially endogenous. We clearly observe the wage rent for senior workers (more than 5 years). As usual, we also note that women earn less than men. Part-time workers have lower wages than full-time ones, which is consistent with the definition we took for the wage. Interestingly enough, there are no major differences in the coefficients on gender, seniority, experience and education between the two groups. The main differences are on the intercept, the full-time coefficient (which may reflect that part-time workers with African parents make less hours than part-time workers with French parents) and the coefficient from having a college degree compared to a post-graduate degree. Now we get to the main results of our paper: the decomposition of wage and employment gaps between the two groups. These are summed up in table 9. The first line (labelled OLS) of the table gives a decomposition of the wage gap when the wage equation is estimated by ordinary least squares. In this case, almost half of the gap is not explained by the gap between covariates. However, if there exists a selection process correlated with the wage formation process, the OLS estimator is biased. A correction is done by specifying a two stage model, where first a employment equation indicates whether the individual has a job or not and then a wage equation determines its wage. The appropriate model is a Tobit II, that can be estimated through a Heckman-type two-step procedure (H2S) or through maximum likelihood (MLE). Our model is identified thanks to the introduction in the selection equation of variables which are supposed to have an impact on employment but not directly on wage. Socio-demographic variables (living in couple, having children, having a working spouse) seem to be valid instruments, since their impacts on employment are significant. Lines 2 and 3 of table 9 refer to the marginal Blinder-Oaxaca decomposition that requires wage and employment equations on both groups to be estimated. Results obtained through H2S and 18

19 Table 9: Decomposition of the earnings gap and the employment gap between French workers with French parents and French workers with African parents Estimation Wage gap decomposition Participation decomposition log-wage gap explained unexplained participation gap explained unexplained OLS H2S MLE MLE(2) Source: FQP Survey 2003 (Insee). Notes: The three first lines refer to the marginal decomposition, the last one refers to the alternative decomposition. MLE are similar. They contrast from OLS results in that the explained part grows up to almost 75%. Thus the unexplained part is limited to around one quarter of the total wage gap. Line 4 of table 9 refer to our decomposition based only on the estimation of wage and employment equations for the reference group. This decomposition relies only on more precise estimations, as the reference group is larger than the group of interest. The results show an explained part of two third, somewhere between the two other estimates. Bootstrapping on the estimated law of the coefficients, we are able to compute confidence bounds at 95%: between 53% and 72%. Our results all converge to the fact than unobserved factors contribute between one quarter and one half to the wage gap. For the participation, all the decompositions stress that the unexplained part is even higher, between 45% and 50 %. All this tends to prove that there exists a strong difference in the employment patterns between the two groups, that may be partly explained by discrimination. Once hired, there is still some wage gap between the two groups, that could be due to the sector they are enrolled in. 19

20 Table 10: Estimates of wage equation parameters for employed individuals Covariates French parents African parents Intercept OLS H2S MLE OLS H2S MLE (0.021) (0.027) (0.022) (0.114) (0.146) (0.122) Working Time Part Time Ref. Ref. Ref. Ref. Ref. Ref. Full Time (0.011) (0.011) (0.011) (0.069) (0.068) (0.068) Gender Men Ref. Ref. Ref. Ref. Ref. Ref. Women (0.008) (0.012) (0.009) (0.050) (0.078) (0.058) Residence Location Not poor area out of Paris region Ref. Ref. Ref. Ref. Ref. Ref. Paris region not poor area Poor area out of Paris region Poor area in Paris region Experience in Labor Force Experience Squared (0.011) (0.022) (0.035) (0.001) (0.003) (0.011) (0.023) (0.035) (0.002) (0.005) (0.011) (0.023) (0.035) (0.002) (0.003) (0.055) (0.076) (0.088) (0.007) (0.018) (0.058) (0.075) (0.090) (0.011) (0.024) (0.055) (0.075) (0.087) (0.008) (0.020) Firm Seniority Less than 1 year Ref. Ref. Ref. Ref. Ref. Ref. 1 to 5 years (0.015) 5 to 10 years (0.016) More than 10 years (0.016) (0.015) (0.016) (0.016) (0.015) (0.016) (0.016) (0.069) (0.085) (0.085) (0.068) (0.084) (0.084) (0.068) (0.084) (0.084) Diploma University Degree Ref. Ref. Ref. Ref. Ref. Ref. College Completed High School Vocational Degree Junior High School No diploma (0.015) (0.014) (0.014) (0.017) (0.015) (0.015) (0.014) (0.015) (0.018) (0.018) (0.015) (0.014) (0.014) (0.017) (0.015) (0.100) (0.090) (0.083) (0.098) (0.081) (0.099) (0.090) (0.086) (0.109) (0.105) (0.099) (0.089) (0.083) (0.100) (0.087) Nobs Source: FQP Survey 2003 (Insee). Notes: 1 star means 90%-significant, 2 stars means 95%-significant and 3 stars means 99%-significant. Standard errors are in parentheses. 20

21 Table 11: Estimates of the employment equation parameters Covariates French parents African parents Intercept H2S MLE H2S MLE (0.046) (0.046) (0.239) (0.239) Family Situation Single Men and Single Women w/o children Ref. Ref. Ref. Ref. Single women with children Men with working spouse with children Men with working spouse without children Men with not working spouse with children Men with not working spouse without children Women with working spouse with children (0.050) (0.050) (0.056) (0.049) (0.064) (0.035) Women with working spouse without children (0.044) Women with not working spouse with children Women with not working spouse without children (0.053) (0.049) (0.050) (0.050) (0.056) (0.049) (0.064) (0.035) (0.044) (0.053) (0.049) (0.203) (0.244) (0.345) (0.180) (0.506) (0.163) (0.259) (0.206) (0.349) (0.203) (0.245) (0.345) (0.180) (0.506) (0.163) (0.259) (0.209) (0.349) Residence Location Not poor area out of Paris region Ref. Ref. Ref. Ref. Paris region not poor area Poor area out of Paris region (0.035) (0.054) Poor area in Paris region (0.095) Experience in Labor Force Experience Squared (0.003) (0.008) (0.035) (0.054) (0.095) (0.003) (0.008) (0.127) (0.153) (0.194) (0.014) (0.039) (0.127) (0.153) (0.194) (0.014) (0.039) Diploma University Degree Ref. Ref. Ref. Ref. College (0.053) Completed High School Vocational Degree Junior High School No diploma (0.046) (0.043) (0.051) (0.043) (0.053) (0.046) (0.044) (0.051) (0.043) (0.262) (0.237) (0.222) (0.240) (0.209) (0.262) (0.237) (0.222) (0.240) (0.210) Nobs Source: FQP Survey 2003 (Insee). Notes: 1 star means 90%-significant, 2 stars means 95%-significant and 3 stars means 99%-significant. Standard errors are in parentheses. 21

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