# The impact of territories on the salaries of young individuals

P. Caro, A. Checcaglini, J.-P. Guironnet

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P. Caro, A. Checcaglini, J.-P. Guironnet. The impact of territories on the salaries of young individuals. Papers in Regional Science, 2025, 104 (3), pp.100098. 10.1016/j.pirs.2025.100098. hal-05353033

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# The impact of territories on the salaries of young individuals

P. Caro<sup>a</sup>, A. Checcaglini<sup>b</sup>, J.-P. Guironnet<sup>c,*</sup>

## ARTICLE INFO

JEL classification in this article:

C24

J01

R12

Keywords:

Local socio-economic dynamism

Employment

Salaries

Mobility

Territorial inequalities

Employment zone

## ABSTRACT

What influence do the past and present locations of young workers have on their professional careers? To answer this question, Céreq's Génération 2010 survey was used to examine the impact of Employment Zone (EZ) characteristics on wages, particularly through interviews conducted three and seven years after these individuals left the school system. An estimate based on a multi-level model confirms that individual characteristics largely determine the level of pay. However, the characteristics of the EZ also significantly influence pay trends, with the effect of the area being much greater for mobile individuals. In addition, individuals' career paths within territories leave a lasting impact on their salary progression: the characteristics of the EZ in which they resided in 2013 still exert influence on their salaries in 2017. The proposed analysis also reveals distinct effects based on qualifications and mobility. Territorial inequalities are more pronounced for less mobile graduates, whereas the most mobile graduates can leverage these differences to maximise the returns on their education. However, using data spanning 7 years, it becomes evident that this effect diminishes with repeated territorial mobility. The uniqueness of this article is further underscored by the presentation of results through the cartographic transposition of econometric findings.

## 1. Introduction

Many spatial fractures mark the entire French territory, and the characteristics of the locations where individuals reside and work inherently exhibit structural inequalities. A vast literature has explored the potential connections between employment and regional characteristics. Studies have demonstrated that various regional demographic, economic, and geographical factors influence access to employment (Duguet et al., 2008) and the integration of young people. Similarly, the geography of integration emphasises that regional effects can either facilitate or impede the integration process (Baron and Perret, 2005, 2006, 2008; Boudesseul et al., 2018). Using multilevel modelling, Dupray and Gasquet (2004) highlighted a direct effect of the region of education on the professional integration (i.e., the start of an individual's professional career) of young people, three years after they left the school system in 1998. In other words, the probability of employment is influenced by the region of education (Minni and Vergnies, 1994).

While this study aims to analyse, firstly, the occupational integration of school leavers, our work primarily focuses on determining the factors

influencing pay differentials between individuals, both at the beginning of their working life and seven years after leaving the education system. This area of analysis is of relative importance, as the early stages of a professional career can significantly impact the trajectory of future careers, particularly with stigmatizing effects that may persist throughout one's career (the 'Hysteresis' effect, Phelps, 1972). These stigmatizing effects can also arise from territorial inequalities, as macroeconomic territorial characteristics can influence the level of individuals' pay (Cipollone and D'Ippoliti, 2011). Card et al. (2024) also show that salaries are mainly influenced by individual characteristics (i.e., education, experience, etc.), but that the geographical characteristics of the job can also influence salary determination (i.e., the concentration of certain sectors of activity in the area under consideration). In this way, individuals' initial choices of job location can potentially influence the rest of their professional careers through a hysteresis effect. Thus, in this work, individual characteristics will be considered as control variables in order to correctly evaluate the effect of the territory on an individual's professional career. Since the choice of location for an individual's job can influence career paths, it is essential to identify these potential territorial inequalities for individuals with identical characteristics, as

<footnote><sup>a</sup> University of Normandy, UniCaen, Céreq & UMR ESO, France</footnote>

<footnote><sup>b</sup> IGR in Economy, Céreq et CNRS UMR ESO, France</footnote>

<footnote><sup>c</sup> University of Normandy, UniCaen, UMR CREM, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: patrice.caro@unicaen.fr (P. Caro), agnes.checcaglini@unicaen.fr (A. Checcaglini), jean-pascal.guironnet@unicaen.fr (J.-P. Guironnet).</footnote>
previously suggested by Groshen (1991): "Is it who you are, what you do, or where you work?" which explains salaries.

Additionally, the literature on agglomeration economies demonstrates that proximity, the density of economic activities and jobs, as well as the concentration of skilled workers, can lead to gains in productivity and/or pay (cf. for example Duranton and Puga, 2004; De la Roca and Puga, 2017). Thus, factors influencing wages can be located at multiple levels, as Groshen points out (1991). While several studies simultaneously analyse the impact of individual and geographical characteristics on workers' wages (Combes et al., 2008; Dauth et al., 2002; De la Roca and Puga, 2017), we choose to follow the approach of Card et al. (2024) by adopting a hierarchical structure on French data that distinguishes between individual and geographical effects. In addition to this recent study, we also pay particular attention to the link between geographical space and professional integration, as well as the potential influence of local difficulties in professional integration on the career trajectories of workers. Thus, in this work, we will consider the following territorial characteristics: the dominant industrial sectors, the unemployment rate, the proportion of young people, and median incomes (Van Der Velden and Wolbers, 2003; Nolte and Ostermeier, 2017; Auber et al., 2006; Ostroff and Atwater, 2003). Additionally, based on the literature, we will explore potential interaction effects between these factors and individual characteristics, aiming to answer the following question: Are certain individual characteristics more stigmatising in specific types of locations?

This article is based on data from Céreg's Génération 2010 survey. The Generation survey is a French public statistical survey that holds the label of general interest and statistical quality. It is designed to study the relationship between initial training and the conditions of professional integration for young people, regardless of their level of education or whether they graduated or not. Using geolocalised data from the Génération 2010 survey at 7 years, we investigate whether certain territorial characteristics can influence the pay of recent leavers of the educational system, whom we well refer to as "young workers". This geographical position will also allow us to distinguish between mobile (the population of workers who relocated, altering EZ between 2013 and 2017) and non-mobile workers.

The goal of this paper is twofold: to comprehend the territorial drivers influencing wage trends at the onset of careers and to present a map of territorial influences on inequalities in France. The objective is also to determine whether territorial characteristics influence career entry and wage trends 7 years later. In other words, the goal is to identify territories that offer the most or least opportunities for pay and are more or less promising for the first seven years of the careers of young people who left the school system in 2010.

Analysis of the effects of the region on wages cannot ignore the importance of the influence of certain individual characteristics (e.g., qualifications) on wages and their potential spatial autocorrelation. To study these factors at different levels of aggregation simultaneously, multi-level modelling has been chosen as the most appropriate method for identifying the impact of the region.

The most appropriate way of dividing the French territory was to use the Employment Zone (EZ), a zoning system developed by INSEE (National Institute of Statistics and Economic Studies of France) for local labour market studies as a geographical area within which most of the working population lives and works. This choice of a territorial unit for analysing residences is also justified by the fact that it groups together enough individuals to guarantee the robustness of the results. Work by Charruau and Epaulard (2017) on the entire working population shows that wage differences between Employment Zones (EZs) are due to the density of economic activities, the concentration of graduates, and metropolitan influences.

## 2. Data from Génération Céreg surveys and INSEE censuses

This article uses original data from the 'Génération 2010' survey,

which tracked 8892 individuals leaving the education system in 2010 over a span of 7 years. The Generation surveys series offers comprehensive information on individual characteristics such as education, family, career paths, place of residence, and employment.

The survey questions a sample of young people about their educational careers and their initial steps into professional life. All young people from the same "Generation" leave the education system during or after the end of the same school year. As a result, they enter the labor market under identical macroeconomic conditions, and all are surveyed using the same protocol and questionnaire. In 2017, the young people were surveyed for the third time, with the first two surveys conducted in 2013 and 2015.

The respondent characteristics are representative of the population. The sampling frame is composed of the files of sampled establishments representative of all training courses; it included 1, 147, 640 individuals. There were 38, 594 individuals who responded in 2013. During the third survey, there were 8, 898 respondents. Particular attention was given to the weighting, and the weighting of the 2017 survey includes a non-response coefficient. These weights have been calibrated on survey indicators for 3 years and 5 years.

The geographical data contained in the Generation survey was complemented with territorial characteristics extracted from INSEE data. According to the spatial unit of our analysis, the French territory is divided into 317 EZs (a division redefined in 2010 by INSEE). The matching of Insee and Generation 2010 data resulted in a final sample of 304 EZs (i.e., 13 EZs with no school leavers or too few school leavers)¹². To distinguish the EZs where young workers reside, this spatial grouping was characterised by territorial indicators proposed by the Observatoire des Territoires (https://www.observatoire-des-territoires.gouv.fr), for 2013 and 2017. These territorial indicators are calculated based on various surveys conducted by INSEE, and they are therefore exogenous to the Génération data. In this approach, a series of economic and demographic indicators per EZ were considered, including median income, unemployment rate, proportion of part-time work, proportion of employment sectors (agricultural, tertiary, industrial, construction), and an indicator of the area's youth. All the variables used for our analysis, at both individual and geographical levels, are detailed in appendix 1 of this document.

The goal of our study is to identify the potential impact of territories on individuals' salaries. As a result, individuals who did not provide the EZ of their place of residence were not considered, leading to an initial sample of 7761 geolocated observations in France. A second sample was then created, including only those who were employed in 2017. Civil servants, with a predetermined salary level unaffected by location, were excluded from this sample. Consequently, our second analysis focuses on graduates employed in the private sector in 2017, comprising a sample of 4614 observations. Lastly, to assess the influence of the region on salary trends, a third sample was constructed, consisting of young graduates employed in 2013 and 2017, representing a sample of 1215 observations.<fnref n="3" />

The 2017 survey, conducted at 7 years, which we have chosen includes 80 % of individuals working on permanent contracts and 86 % on

<footnote n="1">¹ Socio-demographic characteristics could have been considered; however, either they are not available for the French case, or they have proven to be insignificant (such as the proportion of women in employment areas, which is almost invariable at the national level in France).</footnote>

<footnote n="2">² The 13 EZs not included in this analysis are, as expected, among the least densely populated. However, there are still 262 EZs in the sample with a low population density, similar to the 13 EZs not included. The various maps extrapolating our econometric results in this work, therefore, remain highly relevant (although a slight underestimation of the effects may be considered for certain EZs).</footnote>

<footnote n="3">³ The weighting system used in the Generation 2010 survey ensures that this set of observations is representative of a cohort of over 100,000 school leavers in 2010.</footnote>
a full-time basis (see the table in appendix 1). Consequently, our study primarily analyses the sustainable integration of young graduates.

Thanks to this original data, individuals are dispersed across numerous spatial units with a significant degree of heterogeneity throughout the French territory. To analyse individual location choices or the impact of choices in this context of spatial heterogeneity, multi-level modelling was selected (refer to the following sections).

### 3. The relevance of multi-level modelling

This type of modelling enables us to understand wage trends by considering both the intrinsic characteristics of individuals that influence their integration and wages, as well as territorial factors shared by groups of individuals (depending, for example, on the EZ in which they are situated). The inclusion of a significant number of binary variables,<fnref n="4" /> which describe the specific characteristics of the EZs, can introduce a specification bias. This bias can be mitigated by employing modelling techniques (Bryk and Raudenbush, 1992). Multilevel analysis thus enables us to mitigate bias when measuring the impact of territorial factors on wage trends at the beginning of one's career. Our econometric specification builds upon the ground breaking research of Mincer (1954) by estimating an earnings equation with the natural logarithm of wages as the dependent variable. In line with the literature on hierarchical models, individual characteristics (i.e., education, gender, etc.) will be considered level 1 variables, while EZ characteristics (i.e., local unemployment rates, EZ average wages) will be considered level 2 variables.

With this in mind, we initially estimate a random effects model with a simple constant, as follows:

$$y_{ij} = \beta_{0j} + u_{ij} \quad (1)$$

with $\gamma$ being the logarithm of net monthly earnings in 2017, $i$ representing the workers, $j$ indicating the EZs considered, $\beta$ representing the average earnings in group $j$ considered, and $u$ representing the residuals. $\beta$ is assumed to be expressed as a linear combination of the specific deviations $\epsilon$ of group $j$ and a residual constant $\gamma$ of group $j$ such that $\beta_{0j} = \gamma_{00} + \epsilon_{0j}$.

The term $\epsilon_{0j}$ is assumed to have a zero mean and is independent of $u_{ij}$. This model decomposes the variance such that:

$$\mathrm{Var}(y_{ij}) = \mathrm{Var}(\gamma_{00} + \epsilon_{0j}) = \tau_{00} + \sigma_{0j} \quad (2)$$

with $\sigma_{0j}$ the variance within group $j$ and $\gamma_{00}$ the inter-group variance. The intra-class correlation within an EZ $j$ is written as:

$$\rho_j = \frac{\tau_{00}}{\tau_{00} + \sigma_{0j}^2} \quad (3)$$

In relation (1), we can introduce the set of individual variables, $X_{ij}$:

$$y_{ij} = \beta_{0j} + \beta_{1j}(X_{ij}) + \epsilon_{ij} \quad (4)$$

For Eq. (4), it is necessary to centre the variables $X_{ij}$. According to the chosen specification, only one variable is affected by possible centring: the professional experience of individuals. For this variable, we have opted to centre it relative to the overall average, rather than centring it based on the average within each group (by EZ), which can be more challenging to interpret (Hox, 2002).

The next step is to explain the variations in $\beta_{0j}$ and $\beta_{1j}$ through the hierarchical structure, i.e. specifically the level 2 variables:

$$\beta_{0j} = \gamma_{00} + \gamma_{01}(\mathrm{EZCharacteristics}) + \epsilon_{0j} \quad (5)$$

$$\beta_{1j} = \gamma_{10} + \gamma_{11}(\mathrm{EZCharacteristics}) + \epsilon_{1j} \quad (6)$$

The estimated average effects $\gamma$ are assumed to be constant across different groups $j$, while $\beta_{0j}$ and $\beta_{1j}$ vary based on the characteristics of EZ $j$, which is a common factor for all individuals residing in the same EZ. However, this assumption can be relaxed by including variables that span both level 1 and level 2.

To characterise the EZs (level 2), we adopted the empirical approach used in several studies (cf. for example Bunel and Guironnet, 2017; Ruijter and Huffman, 2003). This approach involves categorising the EZ types based on the average of the factor under consideration (cf. appendix 1). We will apply this approach to different sub-populations and create corresponding cartographic representations.

The model presented above is applied to the data from the Generation 2010 survey in order to address our main question: do the characteristics of the EZs have an influence on the initiation of individuals' salary evolution or not?

### 4. Territory and professional integration

The analysis of earnings is always sensitive to the traditional selection bias: the sample consists solely of individuals who are employed, and as a result, the estimation of coefficients is biased by the characteristics<fnref n="5" /> of the unemployed population. Consequently, we first study the determinants of occupational integration. Secondly, the traditional "Inverse Mills Ratio" (IMR) correction term is introduced into the wage regressions, corresponding to the classic Heckman (1979) model. This selection bias correction term (IMR) is estimated from the determinants of employment to account for the characteristics of unemployed individuals, who are not randomly selected from the population.

Table 1 presents the determinants of employment, estimated using a Probit model. No hierarchical structure is retained for this estimation, as

<table><caption>**Table 1**
Determinants of employment in 2017. Source: Calculations by the authors.</caption><caption>Probability of employment in 2017</caption><thead><tr><th>Variables</th><th>Probit</th></tr></thead><tbody><tr><td>Constant</td><td>-0.653 * **</td></tr><tr><td><strong>Individual characteristics</strong></td><td></td></tr><tr><td>Men</td><td>0.017 * **</td></tr><tr><td>Lives with parents</td><td>-0.169 * **</td></tr><tr><td>Foreign origin</td><td>-0.121 * **</td></tr><tr><td>Child(ren)</td><td>-0.122 * **</td></tr><tr><td>Exact Sciences</td><td>0.148 * **</td></tr><tr><td>Social Sciences</td><td>0.060 * **</td></tr><tr><td>Baccalaureate</td><td>0.159 * **</td></tr><tr><td>Long higher education studies</td><td>0.355 * **</td></tr><tr><td>Short Higher education studies</td><td>0.398 * **</td></tr><tr><td>Time to first job</td><td>-0.005 * **</td></tr><tr><td><strong>EZ characteristics</strong></td><td></td></tr><tr><td>Youth Index (2017)</td><td>0.001 * **</td></tr><tr><td>Indus share in % (2017)</td><td>0.022 * **</td></tr><tr><td>Share tertiary sector in % (2017)</td><td>0.014 * **</td></tr><tr><td>Share Building and Public Works in % (2017)</td><td>0.006 * **</td></tr><tr><td>Unemployment rate in the EZ (2017)</td><td>-0.022 * **</td></tr><tr><td>N</td><td>7761</td></tr><tr><td>AIC (complete model)</td><td>702971.06</td></tr><tr><td>% match</td><td>61.1</td></tr></tbody></table>

Note: * ** denotes significance at the 1 % level, * * at the 5 % level and * at the 10 % level. Details of the variables can be found in the appendix.

<footnote n="4">⁴ In this case, a maximum of 304 EZs are considered based on the samples, along with a certain number of binary variables that characterise the individuals (see appendix 1).</footnote>

<footnote n="5">⁵ The same holds true for individuals' education, as certain qualifications increase the likelihood of finding employment and also result in higher wages. This correlation between the integration process and wage determinants needs to be considered in our econometric modelling.</footnote>
the inter-EZ correlation was found to be insignificant: the influence of individual factors does not differ based on the characteristics of the EZ to which young graduates belong in 2017. The econometric specification includes several factors that can potentially influence employment. In particular, the unemployment rate in the area can affect the integration of current school leavers into the labour market (Van Der Velden and Wolbers, 2003). Types of industries can also impact professional integration, especially in the agricultural sector, where job creation is limited and can have a significant impact from a regional perspective rather than on a national level (Nolte and Ostermeier, 2017). Just as the concentration of young people can have a positive influence on innovation (Auber et al., 2006) and a negative influence on wages (Ostroff and Atwater, 2003), a local labour market dominated by young people could positively affect employment through increased dynamism from innovation potential and a lower level of wage reservation.

All the individual characteristics exhibit the expected estimated coefficients. However, it is worth noting that the probability of employment significantly increases when an individual possesses a higher education qualification. In the two-stage Heckman model, we require an instrumental variable that explains job entry without influencing pay. Keeping this in mind, following the approach of Bunel and Guironnet (2017), we have considered whether the individual has at least one child or not as a retained factor.

In terms of EZ characteristics, spatial units with low unemployment rates, high youth indices, and dominance in the tertiary and industrial sectors have an advantage in terms of professional integration (Map 1), compared to more rural areas dominated by the agricultural industry. As mentioned by Greenstone et al. (2010), a concentration of industrial companies increases the demand for employment. The indicator used to generate this map was calculated from the linear combination of EZ characteristics' values and the corresponding coefficients estimated by the Probit model (see Table 1).

In contrast to the analysis of earnings (see the following sections), unemployment plays a crucial role in the local employment integration process within the EZs.

The EZs with high levels of tertiary and industrial employment and/ or low levels of unemployment in the centre-west, the central Paris basin, and the eastern and central-eastern fringes appear to be favourable for professional integration. These areas are centred on metropolises and regional capitals of varying sizes.

All the favourable areas in mainland France can be categorised into three types of predominantly urban areas in terms of employment: numerous industrial clusters (areas with clusters of small and medium-sized industries -SMIs- centred on the same type of production, creating productive jobs due to proximity interactions that foster innovation and development); certain metropolises with hyper-diversified employment, such as Paris, Toulouse, or Lyon; and eastern border areas that host tourist basins with high presential employment (spending by tourists, retirees, and secondary residents), which can be combined with the establishment of an industrial cluster. All these areas share the same logic of strong functional specialisation, whether they are industrial clusters, hyper-tourist areas (presential jobs), or certain metropolises characterised by the greatest variety of productive and presential jobs (Carroué, 2013, Beucher et al., 2014, Bost, 2014). Many of these areas experienced employment and labour force growth rates higher than the median for employment areas during the period 2006–2017 (Bouvart and Donne, 2020). The main hypothesis to explain this finding is that the areas most specialised in certain tertiary and industrial functions create jobs and are therefore very attractive; the renewal of their growing population enhances the area’s youth index. As a result, they generate the most wealth, and consequently, the highest salaries, making them particularly attractive to young workers. The production of wealth depends on the area’s job specialisation: its age, type (Benko and Lipietz, 1992; Datar, 2002), and dynamism in terms of job creation. The analysis of the nature of jobs in these advantageous areas aligns with the work of Davezies and Talandier (2014) on the

emergence of productive-residential systems located in specific employment areas (cf. the notion of productive systems in geography, Carroué 2013; Damette and Scheibling, 1995, 2003; Woessner, 2014). These are the same employment areas highlighted in the typology of employment areas by Baccàini et al. (2017) concerning demographics, the labour market, and employment trends. It therefore becomes evident that certain areas, previously identified in other studies, exert dynamic and positive (or conversely, penalizing) effects on the early careers of young people. This is why this article is innovative and complementary to previous publications in the field under study.

## 5. The impact of the region varies according to young workers' qualifications and mobility levels

The territorial effects must be considered in separate models depending on whether young people remained in the same employment zone for the entire period or whether they moved between 2013 and 2017, i.e., between 3 and 7 years after leaving the education system. Among the factors influencing spatial mobility, the territorial anchoring of individuals is likely to exert a force of retention or recall towards the area of origin, working against mobility strategies that would seek to break free of these limits. This anchoring is constructed differently depending on biographical factors (family roots, in particular, etc.), the abundance of social relationships formed, the narrowness of the geographical perimeter in which they operate, and the possession of a more or less developed capital of autochthony (Renahy, 2010, Coquard, 2019, Amsellem-Mainguy, 2021), or local social capital (David et al., 2010).

This section explores the results derived from the series of multi-level specifications. First, we present the estimate of the log wage for workers residing in the same EZ between 2013 and 2017, referred to as sedentary. Secondly, this approach is applied again to the population of workers who relocated, altering EZ between 2013 and 2017, referred to as mobile workers. For all of these econometric analyses, to obtain robust standard deviations, we employed the sample replication method based on the residuals of the model (Hox, 2002), and thus conducted 1000 sample replications.

### 5.1. Analysis of individuals employed and non-mobile between 2013 and 2017

Model 0 is the hierarchical estimation of a simple constant and serves as the reference specification. Model 1 introduces level 1 variables into the explanation of the logarithm of wages. Model 2 simultaneously estimates level 1 and level 2 characteristics. Model 3 represents the most comprehensive specification, incorporating both hierarchical levels and the correction term (IMR).<fnref n="6" />

According to Table 2, the intra-class correlation is low, indicating that despite a relatively fine spatial unit, there is heterogeneity among workers in each EZ. Based on this specification, the differences between EZs (cf. inter-EZ variable) explain just over 0.5 % of the variance in the 2017 earnings of employed individuals. The influence of the region is, therefore, weak but still significant in explaining 2017 wages. Model II introduces level 2 variables to characterise the differences between EZs, such as territorial factors that can influence the level of individuals' pay as previously mentioned (Cipollone and D'Ippoliti, 2011). For this purpose, we include two control variables (Paris and Median Income for areas with a higher average wage than the national case; see appendix 1 for a complete description of these variables) for particularly dynamic areas (which provide higher productivity and a higher cost of living). As mentioned by Du Caju, et al. (2010), the wage differentials between

<footnote n="6">⁶ Only Model 3 accounts for the selection effect of the unemployed population. All estimates from these models are based on the employed population in 2017.</footnote>
<figure><img src="image_3.png" /><figcaption>Map 1. EZ conditions in 2017 more or less favourable for professional integration, Sources: Insee territorial indicator data and Génération 2010 data (Céreq).</figcaption></figure>

industries are significant and persist over time in the European countries. Consequently, since wage differentials between graduates are positively correlated with their educational level (Lemieux, 2006) and the required skills for a job vary across industries, we have also included some interaction variables between industries and levels of education. The inclusion of these factors reduces the unexplained part of the territorial differences from 0.5 % to 0.4 %.

According to the estimates of model I, individual characteristics exhibit the expected relationships. Only the 'foreign origin' factor appears to have a more specific effect, as second-generation migrants (children of at least one parent of foreign nationality) seem to enjoy a wage advantage. However, this result aligns with several previous studies based on the 'Generation' surveys (Bunel and Guironnet, 2017; Céreq, 2008). This outcome reflects the effect of the traditional selection bias: individuals of non-French origin must possess better characteristics (such as education, experience, etc.) to access employment in order to enter the labour market, all other things being equal. After selection, this explains the superior performance of these individuals in employment, resulting in a wage gain. According to the estimated IMR coefficient, neglecting the potential influence of our variables on the process of professional integration underestimates the impact of individual factors (e.g., diplomas) and the geographical characteristics of the EZs. Furthermore, not accounting for differences in characteristics between EZs overestimates the returns to qualifications, highlighting that spatial factors are correlated with education.<fnref n="7" />

For this sample of young sedentary individuals, only one variable has a consistent influence on all participants, with Paris serving as a control variable to ensure that our significant results do not arise solely from differences between the capital and other regions. Thus, workers experience a wage gain of 0.1 % in employment zones where the median income is above the national average, compared to workers in employment zones below the national average. It is noteworthy that, for

this population of sedentary workers, being located in Paris provides a significant salary advantage of around 9 %, as previously found by Bunel and Guironnet (2017). While this difference is not unique to young people, it should be highlighted that even at the beginning of their careers, young individuals are not exempt from this phenomenon, and Paris offers a salary advantage.

Four other characteristics of EZs exerted influence, albeit with variations depending on the workers' level of qualification. Specifically, the presence of younger workers in the EZ has a significant, positive, and decreasing effect based on the level of qualification. The decreasing effect in relation to educational attainment suggests that this positive impact on wages likely diminishes in relation to professional hierarchy, given that managerial positions often require a minimum level of professional experience. The proportion of the industrial<fnref n="8" /> and service sectors in the EZs appears to have a similar positive impact. The larger these sectors, the more their effect is positively correlated with education. One possible explanation for this is likely the increase in task complexity in jobs within these sectors, leading to higher qualification requirements. Another possible explanation is that investment in human capital by companies within the same sector (agglomeration economy) increases the productivity of all workers in that sector (Autant-Bernard et al., 2011). In contrast, an inverse relationship was observed for the agricultural and construction sectors. Hence, EZs with a high proportion of these two sectors are likely to have more offers for lower-skilled and consequently lower-paid jobs, which may be less advantageous for those with higher skills.

It also appears that for this sedentary population in employment, the risk of unemployment has no influence on workers' wages in 2017. These individuals likely have more stable careers and, therefore, are less sensitive to local variations in unemployment.

<footnote n="7">⁷ For instance, some EZs that provide a higher level of education are also those with a more dynamic local labour market.</footnote>

<footnote n="8">⁸ This positive impact has also been observed in the industrial sector (Greenstone et al., 2010).</footnote>

<footnote n="9">⁹ The sector represents our reference in the specification to avoid collinearity between different sectors of activity in an EZ.</footnote>
<table><caption>**Table 2**
Sample of individuals residing in the same location between 2013 and 2017.</caption><thead><tr><th colspan="5">Estimate of ln(salaries) in 2017</th></tr><tr><th>Variables</th><th>Model 0</th><th>Model I</th><th>Model II</th><th>Model III</th></tr></thead><tbody><tr><td>Inter-EZ (2017)-(τ<sub>00</sub>)</td><td>0.043 ***</td><td>0.020 ***</td><td>0.019 ***</td><td>0.019 ***</td></tr><tr><td>Residual Variance-(σ<sup>2</sup><sub>0j</sub>)</td><td>9.3 ***</td><td>5.0 ***</td><td>4.9 ***</td><td>4.9 ***</td></tr><tr><td>Intra-class correlation-(ρ<sub>j</sub>)</td><td>0.5 %</td><td>0.4 %</td><td>0.4 %</td><td>0.4 %</td></tr><tr><td colspan="5"><strong>Level 1 (individual characteristics)</strong></td></tr><tr><td>IMR</td><td>-</td><td>-</td><td>-</td><td>0.140 ***</td></tr><tr><td>Constant</td><td>7.365 ***</td><td>6.734 ***</td><td>6.380 ***</td><td>6.233 ***</td></tr><tr><td>Men</td><td>-</td><td>0.112 ***</td><td>0.112 ***</td><td>0.115 ***</td></tr><tr><td>Lives with parents</td><td>-</td><td>-0.043 ***</td><td>-0.041 ***</td><td>-0.051 ***</td></tr><tr><td>Father and/or Mother executive</td><td>-</td><td>0.074 ***</td><td>0.073 ***</td><td>0.073 ***</td></tr><tr><td>Foreign origin</td><td>-</td><td>0.029 ***</td><td>0.032 ***</td><td>0.022 ***</td></tr><tr><td>School Exchange</td><td>-</td><td>0.036 ***</td><td>0.035 ***</td><td>0.002 ***</td></tr><tr><td>Human Sciences</td><td>-</td><td>-0.098 ***</td><td>-0.097 ***</td><td>-0.104 ***</td></tr><tr><td>Social Sciences</td><td>-</td><td>0.050 *</td><td>0.001 ***</td><td>0.000</td></tr><tr><td>Baccalaureate</td><td>-</td><td>0.050 ***</td><td>0.018 ***</td><td>0.039 ***</td></tr><tr><td>Short higher education studies</td><td>-</td><td>0.154 ***</td><td>0.094 ***</td><td>0.125 ***</td></tr><tr><td>Long higher education studies</td><td>-</td><td>0.419 ***</td><td>0.336 ***</td><td>0.368 ***</td></tr><tr><td>Full time</td><td>-</td><td>0.266</td><td>0.267 ***</td><td>0.269 ***</td></tr><tr><td>Permanent contract</td><td>-</td><td>0.056 ***</td><td>0.055 ***</td><td>0.055 ***</td></tr><tr><td>Seniority</td><td>-</td><td>0.001 ***</td><td>0.001 ***</td><td>0.001 ***</td></tr><tr><td>Professional experience</td><td>-</td><td>0.003 ***</td><td>0.002 ***</td><td>0.002 ***</td></tr><tr><td colspan="5"><strong>Level 2 (EZ characteristics)</strong></td></tr><tr><td>Paris (2017)</td><td>-</td><td>-</td><td>0.090 ***</td><td>0.092 ***</td></tr><tr><td>Median income (2017)</td><td>-</td><td>-</td><td>0.001 ***</td><td>0.001 ***</td></tr><tr><td colspan="5"><strong>Diploma*Level 2</strong></td></tr><tr><td>Baccalaureate *Youth (2017)</td><td>-</td><td>-</td><td>0.058 ***</td><td>0.058 ***</td></tr><tr><td>Short higher education studies *Youth (2017)</td><td>-</td><td>-</td><td>0.040 ***</td><td>0.048 ***</td></tr><tr><td>Long higher education studies *Youth (2017)</td><td>-</td><td>-</td><td>0.006 ***</td><td>0.010 ***</td></tr><tr><td>Baccalaureate *PartIndus (2017)</td><td>-</td><td>-</td><td>0.001 *</td><td>-0.005 ***</td></tr><tr><td>Short higher education studies * ShareIndus (2017)</td><td>-</td><td>-</td><td>0.013 ***</td><td>0.008 ***</td></tr><tr><td>Long higher education studies * ShareIndus (2017)</td><td>-</td><td>-</td><td>0.055 ***</td><td>0.050 ***</td></tr><tr><td>Baccalaureate *Tertiary share (2017)</td><td>-</td><td>-</td><td>-0.052 ***</td><td>-0.059 ***</td></tr><tr><td>Short higher education studies * Tertiary share (2017)</td><td>-</td><td>-</td><td>0.006 ***</td><td>0.012 ***</td></tr><tr><td>Long higher education studies tertiary share (2017)</td><td>-</td><td>-</td><td>0.084 ***</td><td>0.076 ***</td></tr><tr><td>Baccalaureate *BTP (2017)</td><td>-</td><td>-</td><td>0.052 ***</td><td>0.055 ***</td></tr><tr><td>Short higher education studies* ShareBTP (2017)</td><td>-</td><td>-</td><td>0.043 ***</td><td>0.044 ***</td></tr><tr><td>Long higher education studies * ShareBTP (2017)</td><td>-</td><td>-</td><td>0.004 ***</td><td>0.006 ***</td></tr><tr><td>N (*1000 for replications) (2017)</td><td>4614</td><td>4614</td><td>4614</td><td>4614</td></tr><tr><td>EZ (2017)</td><td>304</td><td>304</td><td>304</td><td>304</td></tr><tr><td>AIC</td><td>4757.4</td><td>1849.1</td><td>1817</td><td>1816</td></tr></tbody></table>

Note: * * * denotes significance at the 1 % level, * * at the 5 % level and * at the 10 % level.

Source: Calculations by the authors.

According to model 3, the IMR correction term is positive and significant, indicating that unobservable characteristics influencing the probability of being in employment are positively correlated with earnings. Compared to the specification in model 2, the introduction of IMR primarily affects the returns to qualifications, with their magnitude increasing. Neglecting the selection bias from the unemployed population underestimates the returns to education.

To convey these econometric results for the entire France, we present an original cartographic synthesis. The indicator used to generate Maps 2, 3, and 4 was calculated through a linear combination of the values of the EZs' characteristics and the corresponding coefficients estimated based on model 3 in Table 2. The three maps can be distinguished by considering only the estimated coefficients for the relevant diploma (see Diploma*Level 2 in Table 2). In this approach, the thresholds employed to differentiate localities with the most advantageous dimensions are identical to those used in prior econometric studies (see appendix 1), i. e., EZs that fall "above" or "below" the average for the respective factor.

Territorial disparities arising from variations in age structure (youth index) and employment sectors (proportion of industry, tertiary sector, construction) among employment zones impact salary trends for young sedentary workers based on their qualifications (Maps 2, 3, and 4). The applied multi-level modelling exposes distinct cross-effects contingent on both qualification and territorial context. These territorial disparities exert a more pronounced influence on wage trends for individuals with lower formal education levels (secondary education or below), compared to those with short tertiary qualifications, and even less so for those holding long tertiary qualifications.

For higher education graduates, no area can be described as unfavourable. The number of favourable areas increases sharply as the level of qualifications rises, both in mainland France and in the French overseas departments and territories. In other words, for sedentary young people, territorial inequalities that impact salary trends decrease as qualifications rise.

Three types of areas exert influence: metropolitan areas, industrial clusters, and tourist regions. Certain areas, comprising medium-sized towns and prefectures with elevated levels of public and private sector employment, seem unfavourable for salary growth among young sedentary individuals with lower levels of education (Map 2). However, these areas prove favourable for those with higher education qualifications, as depicted in Maps 3 and 4. Other regions, housing the dynamic industrial clusters mentioned earlier (Map 1), are favourable regardless of the qualification level of sedentary workers. The favourable scenario in France for individuals with short higher education qualifications aligns with what is depicted on Map 1: industrial clusters, certain border areas, and regions with high levels of residential employment. In the case of long higher education graduates, a few metropolises and large urban areas are even more favourable in a France that is already mostly favourable or neutral.

This cartographic analysis tends to indicate that having a higher level of education enables individuals to opt to stay in their native or emotionally connected region, with no resulting impact on their pay. Conversely, for the least qualified individuals, spatial inequality plays a more significant role. Salaries tend to increase less, holding other factors constant, if young workers choose to remain in a less dynamic region during their first 7 years of professional life. Consequently, certain areas might pose risks of 'territorial traps' for wage growth.

## 5.2. Analysis of employed and mobile individuals from 2013 to 2017

After analysing the profile and geographical distribution of favourable areas for sedentary young people, it is essential to adopt a similar approach for mobile young individuals. Spatial mobility, aligned with
<figure><img src="image_4.png" /><figcaption>Maps 2, 3, and 4. EZs in 2017 more or less favourable to the remuneration of young sedentary workers depending on their qualifications. Sources: Insee territorial indicator data and Génération 2010 data (Céreq).</figcaption></figure>

the theory of the competitive market, is considered by some as a method to address spatial mismatches between the available labour supply and the jobs vacancies. Additionally, it can also be related to wage opportunities (Eyméoud, Wasmer, 2016). The challenge here is to move beyond labour market theories and ascertain whether territorial contexts also impact wage trends.

Of particular interest is the study of the mobile population that no longer resides in the same EZ in 2017 as in 2013. It should be noted that this reduces the sample size and the number of spatial units, but the results nevertheless remain robust. These are individuals who have been mobile over the period, allowing us to analyse the influence of the characteristics of their different EZs in dynamic terms, thanks to the longitudinal nature of the data spanning 7 years. This type of analysis could not have been conducted on the non-mobile population (as discussed in the previous section), as the differences in the characteristics of the localities considered over time exhibit a certain inertia. This would have led to a strong correlation of the fixed effects of the EZs considered for the entire sample in the previous section (including non-mobile individuals). Table 3 summarises the main differences between mobile and sedentary workers.

As expected, individuals who have changed their location exhibit a slightly higher level of education. Conversely, the mobile population represents workers whose working conditions are somewhat inferior to those of non-mobile workers, characterised by slightly lower average

salaries and a lower proportion of permanent contracts. There are two opposing effects at play: a better educational record for mobile individuals and an incentive for those who perceive themselves as being underpaid relative to their level of training to seek employment elsewhere. Meanwhile, better-paid workers are likely to be motivated to remain in their integration EZ.

The models estimated in Tables 4 and 5 are similar to those used in the previous analysis, except for specific level 1 variables that are no longer significant for the mobile population. More specifically, in 2017, "having a managerial parent" and seniority no longer exert a significant influence on net monthly salaries. The influence of parents is therefore limited to their area of residence, and mobile individuals have inevitably changed jobs, resulting in greater professional experience but at a lower level of seniority compared to the non-mobile sample.

For the mobile population, the heterogeneity of residential EZs in 2013 and 2017 explains 4.1 % of the variance in wages in 2017. The characteristics of previous EZs thus exert a significant influence on current wages. This effect is likely indirect, as the spatial characteristics of 2013 must impact pay in that year, subsequently influencing pay in 2017. Additionally, mobility is a process that magnifies the influence of geographical characteristics on pay, compared to the findings of the previous section. This phenomenon can be attributed to forced mobility for the less qualified vs. voluntary mobility for the more qualified. The latter group benefits from a broader array of job opportunities, enabling them to fully exploit the advantages of specific localities. For mobile workers, "where you come from" and "where you go" are phenomena that significantly affect pay. Conversely, this time, mobility has diminished the salary advantage of working in Paris.

Concerning education, the level of study is more advantageous, yielding higher returns to diplomas compared to the sedentary population (refer to Table 2; Di Cintio, Grassi, 2013; Lehmer, Ludsteck, 2011). Moreover, the failure to account for differences in characteristics between EZs underestimates the returns to diplomas. This highlights that the mobile population is not random; *ceteris paribus*, these individuals likely achieved better academic results and possess superior intrinsic unobservable characteristics.

The introduction of employment zone characteristics seems to explain the variance in wages less effectively than our previous analysis

<table><caption>**Table 3**

Descriptive statistics for mobile and sedentary samples.</caption><thead><tr><th>Variables</th><th>Sedentary<br>individuals</th><th>Mobile<br>individuals</th></tr></thead><tbody><tr><td>Salary 2013</td><td>1827.23</td><td>1817.74</td></tr><tr><td>Permanent contract 2013</td><td>79.71</td><td>73.05</td></tr><tr><td>H</td><td>51.89</td><td>53.80</td></tr><tr><td>CAP (Vocational qualification) /BEPC<br>(General certificate of secondary education)</td><td>14.97</td><td>11.12</td></tr><tr><td>Baccalaureate</td><td>29.38</td><td>32.37</td></tr><tr><td>Short Higher education studies</td><td>24.58</td><td>26.06</td></tr><tr><td>Long higher education studies</td><td>18.07</td><td>18.69</td></tr></tbody></table>

Source: Calculations by the authors.
<table><caption>**Table 4**
Sample of mobile individuals from 2013 to 2017.</caption><thead><tr><th colspan="5">Estimate of ln(salaries) in 2017</th></tr><tr><th>Variables</th><th>Model 0</th><th>Model I</th><th>Model II</th><th>Model III</th></tr></thead><tbody><tr><td>Inter-EZ (2017)-(τ<sub>00</sub>)</td><td>0.117 ***</td><td>0.066 ***</td><td>0.066 ***</td><td>0.066 ***</td></tr><tr><td>Inter-EZ (2013)-(τ<sub>00</sub>)</td><td>0.082 ***</td><td>0.050 ***</td><td>0.050 ***</td><td>0.050 ***</td></tr><tr><td>Residual Variance-(σ<sup>2</sup><sub>0</sub>)</td><td>4.728 ***</td><td>2.615 ***</td><td>2.536 ***</td><td>2.536 ***</td></tr><tr><td>Intra-class correlation-(ρ<sub>1</sub>)</td><td>4.1 %</td><td>4.2 %</td><td>4.5 %</td><td>4.5 %</td></tr><tr><td colspan="5"><strong>Level 1 (individual characteristics)</strong></td></tr><tr><td>IMR</td><td>-</td><td>-</td><td>-</td><td>0.302 ***</td></tr><tr><td>Constant</td><td>7.398 ***</td><td>6.638 ***</td><td>6.678 ***</td><td>6.495 ***</td></tr><tr><td>Men</td><td></td><td>0.085 ***</td><td>0.089 ***</td><td>0.094 ***</td></tr><tr><td>Lives with parents</td><td></td><td>-0.069 ***</td><td>-0.072 ***</td><td>-0.091 ***</td></tr><tr><td>School Exchange</td><td></td><td>0.072 ***</td><td>0.070 ***</td><td>0.070 ***</td></tr><tr><td>Foreign origin</td><td></td><td>0.014 ***</td><td>0.002 ***</td><td>-0.014 ***</td></tr><tr><td>Human Sciences</td><td></td><td>-0.076 ***</td><td>-0.081 ***</td><td>-0.033 ***</td></tr><tr><td>Social Sciences</td><td></td><td>-0.031 ***</td><td>-0.030 ***</td><td>-0.094 ***</td></tr><tr><td>Baccalaureate</td><td></td><td>0.109 ***</td><td>0.121 ***</td><td>0.149 ***</td></tr><tr><td>Short higher education degree</td><td></td><td>0.208 ***</td><td>0.214 ***</td><td>0.269 ***</td></tr><tr><td>Long higher education degree</td><td></td><td>0.403 ***</td><td>0.429 ***</td><td>0.490 ***</td></tr><tr><td>Permanent Contract</td><td></td><td>0.121 ***</td><td>0.119 ***</td><td>0.117 ***</td></tr><tr><td>Full time</td><td></td><td>0.284 ***</td><td>0.289 ***</td><td>0.294 ***</td></tr><tr><td>Professional experience</td><td></td><td>0.003 ***</td><td>0.003 ***</td><td>0.004 ***</td></tr><tr><td colspan="5"><strong>Level 2 (EZ characteristics)</strong></td></tr><tr><td>Paris (2017)</td><td>-</td><td>-</td><td>0.034 ***</td><td>0.034 ***</td></tr><tr><td>Paris (2013)</td><td></td><td></td><td>-0.013 ***</td><td>-0.009 ***</td></tr><tr><td>Partial share (2017)</td><td></td><td></td><td>-0.077 **</td><td>-0.085 **</td></tr><tr><td colspan="5"><strong>Diploma*Level 2</strong></td></tr><tr><td>Baccalaureate*Unemployment (2017)</td><td>-</td><td>-</td><td>-0.184 ***</td><td>-0.085 ***</td></tr><tr><td>Baccalaureate*Unemployment (2013)</td><td>-</td><td>-</td><td>0.168 ***</td><td>0.171 ***</td></tr><tr><td>Long higher education degree * Median income (2017)</td><td>-</td><td>-</td><td>0.016 ***</td><td>0.014 ***</td></tr><tr><td>Long higher education degree * Median income (2013)</td><td>-</td><td>-</td><td>-0.058 ***</td><td>-0.060 ***</td></tr><tr><td>N (*1000 for replications)</td><td>1215</td><td>1215</td><td>1215</td><td>1215</td></tr><tr><td>EZ (2013&amp;2017)</td><td>498</td><td>498</td><td>498</td><td>498</td></tr><tr><td>EZ (2013)</td><td>250</td><td>250</td><td>250</td><td>250</td></tr><tr><td>EZ (2017)</td><td>248</td><td>248</td><td>248</td><td>248</td></tr><tr><td>AIC</td><td>-331.45</td><td>-366.66</td><td>-366.66</td><td>-368.45</td></tr></tbody></table>

Note: * ** denotes significance at the 1 % level, * * at the 5 % level and * at the 10 % level.

Source: Calculations by the authors.

<table><caption>**Table 5**
Situations with a significant effect.</caption><thead><tr><th>Sample</th><th>Comments</th><th>Inter-EZ (2013)</th><th>Inter-EZ (2015)</th><th>Inter-EZ (2017)</th></tr></thead><tbody><tr><td>No mobility since 6th grade</td><td>2796</td><td>-</td><td>-</td><td>0.039 ***</td></tr><tr><td>Mobile only between 2015 and 2017</td><td>697</td><td>-</td><td>0.179 ***</td><td>0.153 ***</td></tr><tr><td>Mobile between 2013 and 2015 then between 2015 and 2017</td><td>176</td><td>0.004 ***</td><td>0.003 ***</td><td>0.002 ***</td></tr></tbody></table>

Source: Calculations by the authors.

in Table 2. However, the proportion of part-time work is a factor that appears to exert a negative and uniform influence on all workers: being employed in an EZ with greater economic uncertainty results in a loss of pay. This time, only two variables seem to have distinct effects based on the level of qualification. Therefore, we observe a traditional result in the literature on interregional mobility (Franck et al., 2017): the least qualified are only influenced by the unemployment rate of the EZs (i.e., the probability of finding a job), and the most qualified are influenced by the average income of the EZs. More precisely, for the least qualified, leaving an EZ with a high unemployment rate and moving to a locality with a high level of employment is particularly beneficial for their professional careers. A similar rationale can be applied to the most highly educated individuals and the median income in the EZs. They are not constrained by the risk of unemployment and are primarily seeking to maximise their income by relocating from EZs offering modest salaries to EZs with higher median pay. Furthermore, working in more

urban EZs enables workers to accumulate more experience and provides greater wage benefits to workers with superior intrinsic unobservable characteristics (De la Roca and Puga, 2017). These decompositions also allow us to observe that certain mobilities should be preferred over others.<fnref n="10" /> For instance, it does not appear advantageous to leave an EZ with a high median income for a locality with similar characteristics since, according to our estimates, the final net effect would be -4.2% (1.6%-5.8%). The initial positive effect is, in fact, attributed to the migration of long-term higher education graduates from poorer areas to wealthier EZs. A similar rationale can be applied to baccalaureate holders: leaving an EZ with a high unemployment rate for an EZ with equally challenging entry conditions results in a negative final effect on 2017 wages.

Following the specification of model 3, the IRM is once again positive and significant, but this time of greater magnitude than for the sedentary sample (see Table 2). The impact of this term is consequently more pronounced, resulting in variations in the estimated coefficients (in comparison to model 2) of greater magnitude, particularly for the return on diplomas. The correction for selection bias is therefore more crucial than in the case of the sedentary sample: mobile individuals likely possess more favourable unobservable characteristics, exerting a more substantial positive influence on occupational integration and pay.

The linear combination of results obtained in model 3 has been mapped (Maps 5 and 6) to extend the findings to the entirety of France and to emphasise the territorial disparities influencing the initiation of

<footnote n="10">¹⁰ Card et al. (2024) find similar results in the United States, with mobility being more advantageous when the destination zone has a high average salary and less advantageous when the employee arrives in a geographical zone with a lower average salary profile.</footnote>
Graduates with a high school diploma or less Graduates with a long higher education degree

<figure><img src="image_5.png" /></figure>

**Maps 5 and 6.** depict EZs in 2017 that varied in their favourability towards the pay of young mobile workers, depending on their qualifications. Sources: Insee territorial indicator data and Génération 2010 data (Céreq). The indicator used to generate these maps was calculated through a linear combination of the values of the characteristics of the EZs and the corresponding coefficients estimated according to model 3 in Table 4. The two maps can be differentiated by focusing solely on the estimated coefficients related to the specific diploma (refer to Diploma*Level 2 in Table 2). To accurately represent the effects of the EZs in 2017, we exclude the influence of the EZ in 2013, as the latter does not align with that of 2017.

careers. Areas where migration is unfavourable for young mobile individuals with secondary education or lower qualifications (Map 2) are characterised by a significant imbalance between job supply and demand, resulting in unemployment rates for individuals aged 15–64 often exceeding the area average (13 % in 2018, Bouvart and Donne, 2020). Regardless of whether the area is predominantly urban or rural, regional effects (such as unbalanced regional economies) account for the concentration of disadvantaged areas, known for their high unemployment rates.

Conversely, metropolitan areas like Paris seem to have a more neutral impact on the salary development of young mobile individuals with a secondary education or less. This is attributed to the lower unemployment rates characterising the employment zones within these areas. However, they become favourable for the salary development of highly educated individuals. Regardless of the qualification level, the Paris region is the most favourable for mobile individuals compared to other regions.

The France most favourable to wage growth is urban, centred on the Paris basin, encompassing nearly all employment zones influenced by the Paris metropolis. This area also comprises regions with prefectures and regional capitals. In contrast, other regions, where the proportion of part-time work exceeds the average, prove unfavourable for the salary development of young mobile individuals with lengthy higher education qualifications. The profile of these regions is twofold: some, more rural in nature, are characterised by a significant presence of residential employment in general and tourism in particular. Other areas are included in the category of unemployment in France, as previously indicated on Map 2.

In contrast to the sedentary population, the mobility of the highly educated allows them to leverage the returns on their qualifications (Pekkala, 2002) and fully exploit spatial disparities in economic dynamism, whereas this effect is less pronounced for those with lower levels of education.

### 5.3. Successive Territories and Mobilities between 2013 and 2017

The longitudinal nature of the data from the Generation 2010 survey allows for the identification of various stages in individuals’ mobility, starting from their situation at the beginning of their schooling in 6th grade (equivalent year 7 in GB) and examining different types of mobility after they leave the school system. The mobile population can, therefore, be analysed from multiple perspectives. As a result, we replicate the

analysis and specifications of model I using various combinations of time cut-offs. The estimated inter-EZ coefficients are presented in the table below, summarising all situations exhibiting a significant effect.

While acknowledging potential biases associated with each estimate, one result appears to emerge: repeated moves gradually diminish the impact of characteristics from previous locations. Successive migratory movements enable individuals to increasingly influence their salary conditions, irrespective of their destination region. Nevertheless, the original location still appears to exert a significant influence. Favourable or unfavourable geographical conditions can impact individuals throughout their career. In contrast to the risks of 'territorial traps' mentioned at the beginning of the article, one can envision a virtuous circle where certain individuals are fortunate enough to begin their journey in a locality that offers high-quality education and initial professional integration. This sets the stage for more opportunities and repeated mobility. Conversely, other individuals may not have the chance to initiate such a cycle.

## 6. Conclusion

The areas that generate the most wealth and create the most jobs among production-residential systems are the most favourable for young entry-level workers in terms of pay. Three types of area stand out: metropolitan areas, industrial clusters, and tourist areas. High median incomes, the share of employment sectors corresponding to a degree of functional specialisation of the territory, low unemployment, and the youthfulness of the areas of residence linked to a renewal of the population generated by the attractiveness of the area are all territorial inequalities that exert a positive influence on the salary trajectories of beginners.

Thus, median earnings, employment sector shares, and the youth of EZs explain 0.4 % of the wage variation in 2017 for sedentary workers. While this influence may be small, its impact is likely more significant when considering the longitudinal aspect of a professional career. On the one hand, this spatial influence pertains to monthly wages, which accumulate when considering an employee’s entire year. On the other hand, this effect likely influences the worker’s future wage negotiations throughout their professional career.

This influence is also ten times greater for mobile individuals, and furthermore, the EZ characteristics from 2013 continue to exert an influence on wage variations in 2017. In simpler terms, the mobility process amplifies the impact of EZ characteristics, and the territorial
path chosen by individuals leaves a lasting imprint on workers' wage trajectories. In this article, the original use of longitudinal data from the Generation survey has allowed us to highlight dynamic phenomena during the first seven years of working life. Distinct effects are also discerned based on qualifications; the unemployment rate in the EZs only impacts the least qualified, whereas for the most qualified, only the median income in the EZs holds significance. Furthermore, a comprehensive analysis of all our findings reveals that sedentary and mobile workers do not exhibit the same profiles: mobile individuals are likely those who have achieved the best educational results. This outcome is further corroborated by incorporating a correction term for the selection bias of unemployed individuals, as it emerges as a more predominant factor in the sample of mobile individuals. Without employing the selection bias treatment method, the return on diplomas would have been more underestimated in correlation with the increased mobility of individuals.

In the end, the region's characteristics have a significantly different impact on individuals' professional careers, contingent upon their education level and involvement in the mobility process. While the influence of the region is generally weaker for sedentary individuals, it is the less educated who bear the brunt of disparities in regional economic dynamism, unlike their more educated counterparts who can maintain their earnings in less dynamic areas. On the other hand, the decision to relocate may prove to be more critical for highly qualified individuals, as it enables them to leverage their level of qualification and exploit variations in regional dynamism, which subsequently plays a more pivotal

role in explaining salaries. These initial relocations are particularly significant as the influence of the region diminishes with repeated moves. The cartographic representation of econometric results also offers an original summary of the findings, using maps for the entire France at the EZ level to illustrate territorial inequalities in pay increases at the start of a career. Taken together, these results shed light on the crucial aspects of people's initial job choices and the locations of these jobs. As a result, localities now have insights into the factors influencing the attractiveness of their employment areas, since employment (including pay and accessibility) is a key factor in the appeal of a geographical area (Sen, 2000).

Extensions to this work could be envisaged, particularly through longitudinal studies that analyze the potential persistence of geographical factors' influence over individuals' working careers.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Guironnet reports was provided by University of Caen, Normandy. Guironnet reports a relationship with University of Caen, Normandy that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

<table><caption>Appendix 1. Descriptive statistics Full sample (mobile&sedentary-5829 observations). Sources: "Génération 2010" survey (Céreq) & Observatoires des Territoires</caption><thead><tr><th>Variables</th><th>Description</th><th>Average</th><th>Standard deviation</th></tr></thead><tbody><tr><td>Men</td><td>Coded 1 if the individual is male, 0 otherwise.</td><td>0.53</td><td>0.50</td></tr><tr><td>Children</td><td>Coded 1 if the individual has at least one child</td><td>0.34</td><td>0.22</td></tr><tr><td>Lives with parents</td><td>Coded 1 if the place of residence is that of the parents, 0 otherwise.</td><td>0.21</td><td>0.41</td></tr><tr><td>Foreign origin</td><td>Coded 1 if one of the parents is not of French nationality, 0 otherwise.</td><td>0.13</td><td>0.33</td></tr><tr><td>Father and/or Mother executive</td><td>Coded 1 if one of the parents has a managerial job, 0 otherwise.</td><td>0.34</td><td>0.47</td></tr><tr><td>School Exchange</td><td>Coded 1 if the individual participated in a school exchange abroad during their training, 0 otherwise.</td><td>0.06</td><td>0.24</td></tr><tr><td>Human Sciences</td><td>Coded 1 if the individual has a background in the humanities, 0 otherwise.</td><td>0.06</td><td>0.23</td></tr><tr><td>Social Sciences</td><td>Coded 1 if the individual has a social science background, 0 otherwise.</td><td>0.36</td><td>0.48</td></tr><tr><td>Baccalaureate</td><td>Coded 1 if the individual's highest level of education is the baccalaureate, 0 otherwise.</td><td>0.23</td><td>0.42</td></tr><tr><td>Short Higher Education degrees</td><td>Coded 1 if the individual's highest qualification is a Bachelor's Degree, 0 otherwise.</td><td>0.28</td><td>0.45</td></tr><tr><td>Long higher education degrees</td><td>Coded 1 if the individual's highest level of education is higher than a Bachelor's Degree, 0 otherwise.</td><td>0.31</td><td>0.46</td></tr><tr><td>Permanent contract</td><td>Coded 1 if the worker has a permanent contract, 0 otherwise.</td><td>0.80</td><td>0.40</td></tr><tr><td>Professional Experience</td><td>Number of months in employment (excluding the current job).</td><td>74.17</td><td>17.86</td></tr><tr><td>Seniority</td><td>Number of months in the current job.</td><td>45.33</td><td>30.31</td></tr><tr><td>Full time</td><td>Coded 1 if the individual works full-time, 0 otherwise.</td><td>0.86</td><td>0.35</td></tr><tr><td>Paris (2013)</td><td>Coded 1 if the individual lived in Paris in 2013, 0 otherwise.</td><td>0.06</td><td>0.24</td></tr><tr><td>Paris (2017)</td><td>Coded 1 if the individual lives in Paris in 2017, 0 otherwise.</td><td>0.06</td><td>0.24</td></tr><tr><td>Unemployment (2013)</td><td>Coded 1 if, in 2013, the individual lived in an EZ with unemployment higher than the national average, 0 otherwise.</td><td>0.34</td><td>0.47</td></tr><tr><td>Unemployment (2017)</td><td>Coded 1 if, in 2017, the individual lived in an EZ with unemployment higher than the national average, 0 otherwise.</td><td>0.33</td><td>0.47</td></tr><tr><td>Median income (2013)</td><td>Coded 1 if the individual lived in an EZ with a median income above the national average in 2013, 0 otherwise.</td><td>0.46</td><td>0.50</td></tr><tr><td>Median income (2017)</td><td>Coded 1 if the individual lived in an EZ with a median income above the national average in 2017, 0 otherwise.</td><td>0.45</td><td>0.50</td></tr><tr><td>Youth (2017)</td><td>Coded 1 if, in 2017, the individual lived in an EZ with a youth indicator higher than the national average, 0 otherwise.</td><td>0.48</td><td>0.50</td></tr><tr><td>PartIndus (2017)</td><td>Coded 1 if the individual lived in an EZ in 2017 with a share of the industrial sector higher than the national average, 0 otherwise.</td><td>0.45</td><td>0.50</td></tr><tr><td>Tertiary share (2017)</td><td>Coded 1 if, in 2017, the individual lived in an EZ with a tertiary sector share higher than the national average, 0 otherwise.</td><td>0.55</td><td>0.50</td></tr><tr><td>Building and public work share (2017)</td><td>Coded 1 if, in 2017, the individual lived in an EZ with a share of building and public work above the national average, 0 otherwise.</td><td>0.54</td><td>0.50</td></tr><tr><td>Agricultural share (2017)</td><td>Coded 1 if, in 2017, the individual lived in an EZ with a share of the agricultural sector higher than the national average, 0 otherwise.</td><td>0.39</td><td>0.49</td></tr></tbody></table>
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