INSTITUT D'ETUDES POLITIQUES DE PARIS Ecole doctorale

Dynamic Barriers to Entry, the Far-Right and Discrimination in the Labor

Market

Dylan Glover

Mémoire présenté pour le Master en

Economie

Ph.D. Track

Directeur du mémoire : Yann Algan

Année académique

2012-13

Dynamic Barriers to Entry, the Far-Right and

Discrimination in the Labor Market

Dylan Glover∗

Masters Dissertation, Department of Economics, Science Po, Ecole Polytechnique

May 21, 2013

Abstract Using yearly variation in barriers to entry in the French retail sector, I exam-

ine the effect of competition on unemployment rates of different demographic groups under

varying levels of discrimination as measured by the far-right vote share. The effect of exoge-

nous changes in the level of competition has ambiguous impacts. It is only when competition

is interacted with discrimination that clear results become apparent. I find that decreases

in barriers to entry unambiguously reduce unemployment in the upper distribution of the

far-right vote for women, women of African origin and women who reside in areas categorized

as Zone Urbaine Sensible (ZUS). There is also strong evidence that beneficial effects exist for

immigrants and people of African origin, in general. These findings are in line with Becker’s

theory of taste discrimination in which increased competition will decrease manifestations

of labor market discrimination only in economies where employers have a strong taste for

discrimination.

∗I thank Frederic Cochinard and Axelle Charpentier for their invaluable help in constructing the CDACdata set, Yann Algan for his insight and avenues to explore, and Victor Pouliquen and Adrien Bougen fortheir helpful comments. Lastly and most importantly, I thank Johana Carrier for her understanding andsupport during the long hours spent on this paper.

1

1 Introduction

There is a vast literature on the economics of discrimination that begins with Gary

Becker’s 1957 book The Economics of Discrimination. Economic models of discrimination

can be divided into two groups, competitive and collective models. Competitive models

focus on classic individual maximizing behavior that incorporates discrimination, whereas

collective models focus on groups acting collectively against one another. The large majority

of the economic literature focuses on competitive models which, in turn, can be divided

into two categories: taste-based and statistical discrimination.1 Taste-based discrimination

models are based on the assumption that a taste for discrimination is built into an individual’s

utility function. In the labor market, this means that employers have a distaste for certain

types of workers regardless of their productivity, thus employing a less preferred worker

type reduces the employers utility. Less preferred types must compensate the employers

by either being more productive for a given wage, or accept a lower wage for identical

productivity. Statistical discrimination developed first by Phelps (1972) and Arrow (1973),

on the other hand, views discrimination as simply a market imperfection. Employers have

trouble assessing worker productivity at the point of hire because full information is not

available. This causes them to use observable characteristics such as race, origin or gender

to get an idea of expected productivity. If the employer receives a noisy signal of productivity

from the worker and also has a prior about the productivity of the type of worker, say mean

productivity, the employer will place weight on the signal and the mean.2

The taste-based discrimination framework is relevant because it provides a testable pre-

diction: free entry should eliminate manifestations of discrimination in the labor market.

This is because non-discriminating employers will have an incentive to enter, hire equally

productive, less preferred workers at lower wage rates, in-turn raising relative demand for

these less preferred types, and thus eliminating any wage or hiring differential. The impor-

tant thing to note is that Becker’s model predicts that reduced barriers to entry will only

reduce the wage or employment gap if discrimination is really contributing to the differential.

In the absence of discrimination, increased competition should not have an effect on the gap

in employment outcomes between groups. Using this insight, I examine whether dynamic

changes in barriers to entry in the French retail sector, as measured through yearly retail

zoning authorization rates, have differential effects on the employment outcomes of different

demographic groups. Furthermore, I test whether these differential outcomes are linked to

the distribution of discrimination as measured by the vote share for the far-right.

1David Autor’s lecture series on the economics of discrimination provides an excellent overview andanalysis of the current state of the literature.

2For more on statistical discrimination start with Aigner and Cain (1977).

2

Exploiting panel data on barriers to entry and unemployment rates from 2007-2001, I

find that a marginal decrease in barriers to entry leads to statistically significant and large

reductions in the unemployment rates for persons of sub-Saharan African origin, women who

reside in Zone Urbaine Sensible (ZUS), direct immigrants and women in general, one to two

years later.3 Importantly, these reductions are only significant in areas where the vote share

of the far-right is above the mean. I estimate that a 1% reduction in barriers to entry in the

retail sector leads to a 0.29% drop in the unemployment rate for women who live in ZUS in

areas where the far-right vote share is 1 standard deviation over the mean. This percentage

drop increases to 0.431% at 2 standard deviations above the mean. Similar results are found

for other disadvantaged demographics. Interestingly, the effect for men of French origin is the

opposite. Reductions in barriers to entry may actually be detrimental to their employment

status. This may be evidence of a displacement or destruction effect whereby new firm entry

reduces men’s labor market power and the rent previously held under higher regulation.

The paper is organized as follows. In the remaining introduction subsections, I contex-

tualize barriers to firm entry in France and highlight the literature that examines discrimi-

nation and more broadly its relationship to competition, offering evidence that taste-based

discrimination is a real phenomenon in France. Section two describes the data. Section

three provides the theoretical framework. Section four explains the empirical methods and

presents estimation results. Section five discusses and offers further analysis of the results.

Section six concludes.

1.1 Barriers to entry in France

Several studies have highlighted strong entry regulation in the French economy. Simeon

et al. (2002) note that starting a business in France took up to 66 days and 16 different

administrative steps just several years ago. One industry in France has been particularly

marked by a specific type of regulation. In 1974, France enacted legislation aimed at protect-

ing small retail businesses from the growing emergence of large retail chains. The Loi Royer

(Law Royer) stipulated that proposed retail establishments must be reviewed by a local

zoning commission to obtain authorization. Before this law, the largest obstacle in starting

a new business was obtaining a building permit. This authorization process was and still

applies to all construction, transfer or extension of retail space of a certain size. Bertrand

and Kramarz (2002) were the first to examine this entry regulation as a factor for the slow

employment growth experienced in Europe in the last quarter of the 20th century (1974 to

1998), arguing that there is indeed a negative relationship between the authorization rate

3ZUS are defined by the French government as disadvantaged areas or neighborhoods in which socialaction is a priority.

3

and unemployment. I take this idea further to measure the authorization rate’s dynamic

effect on the employment rate differential between groups.

The current appellation of this commission is the “Commission departemental

d’amenagement commerical” (CDAC), loosely translated as “State Commission for Retail

Zoning” or “State retail zoning board.” Apart from a change in the square meter exemption

level in 2009, the composition and process of the commission has remained the same for

my panel period. The commission is at the departement level, a geographic and political

equivalent to something larger than a U.S. county, but smaller than a state. There are

96 departements in metropolitan France and thus 96 commissions.4 The commissions are

composed of nine members: the departement prefect, five elected officials and three special-

ists. The five elected officials include the mayor of the town on which the application is

pending, the mayor of the most populated town other than the town of implantation, the

president of the council of “inter-town cooperation,” the president of the regional council

and the president of the regional labor union. In addition, the commission includes three

people considered specialists in the retail industry and sustainable development. To obtain

authorization, a simple majority is required with votes being transparent.5 For the first 3

years of the panel, the decision to authorize had to happen within 4 months of submission.

After 2009, the delay was reduced to 2 months.6

1.2 Discrimination in France and evidence for taste-based discrim-

ination

The literature exploring discrimination at the moment of hiring has been almost exclu-

sively based on audit studies. These studies involve sending resumes with names that connote

the race, origin and/or gender of the applicant. In France, several of these studies have found

substantial evidence that discrimination at the point of hiring is rampant in France. Cediey

and Foroni (2007) show that when French employers are faced with choosing between two

applicants, they choose the majority applicant over the minority 4 out of 5 times and 3 out

of 4 times in the retail industry, specifically. More striking is that when faced with choosing

between a majority employee and a black employee they choose the majority employee 4

out 5 times regardless of the sex of the applicant. Duguet et al. (2010) find that for an

accountant position, applicants with Arabic sounding names need to send out 10 times as

4Corsica is split into 2 distinct departements.5If the application is refused, an appeal can be made to the national commission (CNAC). The decision

of the CNAC is final.6If an application is not reviewed within the required delay, authorization is implicit. These deadline

requirements require the CDAC to meet several times per year, thus providing continual variation in barriersto entry.

4

many identical resumes to get comparable call-back rates to applicants of French sounding

names.

Outside of France, the literature on discrimination at the point of hiring has focused on

the U.S. labor market. Arguably the most famous of these studies is Bertrand and Mul-

lainathan (2004) who randomly assign African American-sounding or white sounding-names

to identical resumes and send them to employers. Similar to France, they find that call-back

rates for African American sounding names are 50 percent lower than those for whites. The

authors argue that a taste-based effect best describes the lack of variation of discrimination

across sectors, finding that employers based in African American neighborhoods appear to

discriminate less. In addition, a larger skill update in the information set (information on the

resume) of African American candidates does not improve their chance for a call back. This

is at odds with a statistical theory of discrimination which predicts that the cost to employ-

ers to indulge their discrimination tastes should increase as African American qualifications

increase.

In another audit study, Pager et al. (2009) also provide evidence of the existence of

taste-based discrimination by comparing the call back rates of minority applicants to white

applicants just released from prison. In an elaboration of the basic audit study, real actors

are randomly assigned to apply for randomly selected, low-skilled jobs. The authors find

large differences in call-back rates. On average, white applicants with a criminal record do

as well as minority applicants with no criminal record. The fact that employers are given

supplemental negative information about white applicants and still have comparable call-

back rates gives credence to the existence of a taste-based effect. They also present strong

evidence that minorities are channeled to more low-skilled jobs than whites.

1.3 The link between competition and discrimination

Several important papers studying the relationship between competition and discrimi-

nation have found substantial evidence for the existence of a taste-based effect in the U.S.

Black and Strahan (2001) find that when the U.S. started deregulating the banking indus-

try in the 1970s, average compensation for employees fell in the sector, implying that the

rents gained from regulation were shared with labor. More importantly, they find that male

wages fell much more sharply than female wages, suggesting that the rents due to regulated

competition were shared mostly with men. Closely related to this study (and also using a

discontinuity design methodology), Levine et al. (2013) use cross-state and cross-time varia-

tion to investigate the effects of the banking deregulation on the aggregate black-white wage

gap. Importantly, they use a robust data set to control for differences in individual Mince-

5

rian characteristics. Consistent with a taste-based phenomenon, they find that reductions in

barriers to entry for banks reduced the wage gap by more than one-fifth, principally through

spill-overs into other industries. That is, the bank deregulation lowered entry barriers for

nonfinancial firms, thus driving down the wage gap throughout the economy. Importantly

from a taste-based perspective, they find that this effect is localized only in states with high

degrees of racial bias.

Likewise, Charles and Guryan (2008) show that the wage gaps within U.S. states be-

tween minority and majority workers is much more strongly correlated to the prejudice of

the marginal employer (the most prejudiced employer in the distribution with whom minor-

ity workers come into contact). A standard deviation increase in the level of prejudice is

associated with 23 percent of the wage gap across states.

One study that does focus on employment differentials and not wages (and is not an

audit study) comes from Heywood et al. (1994). They find that deregulation in the U.S.

trucking sector had a positive effect on African American employment, concluding that limits

on a competitive market provided by regulation “allowed managers latitude to discriminate

which...resulted in under representation.”

In another interesting audit study in France, Berson (2012) studies the relationship be-

tween competition and hiring discrimination. She tests callback rates for resumes randomly

sent to employers while using regional Herndahl-Hirschman indices for variation in intra-

sector competition, assuming the indices are exogenous. She finds callback rates for men

are systematically lower than callback rates for women and that only the male callback

rate declines in the competition index. For other demographics, she finds no evidence of a

differential effect of competition on labor market outcomes.

Audit studies are informative and provide strong evidence of the existence of taste dis-

crimination, but there is a drawback. Heckman (1998), particular, has criticized audit studies

of discrimination because they assume that workers randomly apply to jobs and do not select,

and therefore avoid biased employers. Examining the link between real unemployment rates,

competition and discrimination allows me to get around this critique while not focusing on

wages.7

This paper contributes to the literature by using dynamic panel data in which barriers to

entry vary year-to-year to examine the relationship between competition, discrimination and

real employment outcomes of different demographic groups. To my knowledge, it is also the

first to explore how a voting distribution can be interacted with a measure of competition

to analyze the significance and magnitude of a taste-based effect.

7Wages are often fixed at levels such as the minimum wage, or standardized within companies and thisis not widely discussed in the literature linking discrimination in wage rates.

6

2 Data and descriptive statistics

The zoning commission (CDAC) data was constructed by “hand” through cutting and

pasting data on decisions from 2006 to mid 2008 from the website of LSA (Libre Service

Actualites), a weekly review for professionals working in the retail sector.8 The data base

contains the departement and town, type of application (creation, extension, or transfer),

meters squared applied for, existing surface area (if extension or transfer), the retail cate-

gory, the store name and the date of the commission’s meeting. Commission decisions from

late 2008 through 2010 were obtained directly from the French ministry’s website (Direction

generale de la competitivite de l’industrie et des services) where decisions have been aggre-

gated in pdf format. These pdfs were converted to excel using special software, cleaned and

then appended to the LSA data. The CDAC data set thus contains an exhaustive list of

decisions from 2006 through 2010 at the town level. In total, 8,723 commission decisions,

concerning 2,872 towns, were compiled between 2006 to 2010. The second column in table

1 gives the proportion of retail store types where we see that superstores (Hypermarches),

supermarkets, discount supermarkets and home improvement chains account for almost 50%

of the applications. This town level data was then aggregated at the departement level and

provides my independent variable of interest, the authorization rate per department per year,

as well as important control variables.

Specific demographic unemployment rates were obtained using the INSEE’s (Institut

national de la statistique et des etudes economiques) Enquete Emploi data sets (French

Labor Force Survey) from 2007 to 2011.9 The labor force survey is an ongoing survey

conducted in all French departements in which strats of households are surveyed for six

consecutive quarters before being renewed with a new, randomly selected strat. This data

set is primairily used for the calulation of employment statistics in line with International

Labor Organizatioin and Eurostat standards. The “production and research” version of the

Enquete Emploi data set is special in that it contains information on the birth place of the

respondent and also the birthplace of the respondent’s parents. This has allowed me to

calculate unemployment rates of specific demographic groups such as direct immigrants or

children of immigrants versus children of non-immigrants.10 I first create indicators for origin,

sex and employment status. I then aggregate the data at the departement level by year and

8LSA allows access to their CDAC decisions database online, but only 5 decisions are available perwebpage. I contacted both LSA and the French Ministry by telephone and email respectively, in order toobtain a usable version of the data set. Unfortunately they were unwilling or unable to provide a copy.

9Emploi (en continu) - serie 2003 - 2011 (version production et recherche), INSEE, Centre MauriceHalbwachs (CMH).

10Note that collecting data on race or ethnicity is illegal in France and thus variables that directly indicateskin color are not available.

7

Table 1: Sector composition of retail applications.

Number ProportionDiverse food 168 2.18Diverse non-food 240 3.11Specialized non-food 445 5.77Home improvement 787 10.21Culture 112 1.45Home appliance 194 2.52Mall 328 4.26Shopping center 187 2.43Discount food 894 11.60Superstore 574 7.45Home and garden 372 4.83Toy store 130 1.69Discount clothing 16 0.21Furniture and home appliance 73 0.95Furniture and decoration 375 4.87Unknown 5 0.06Clothing, jewlers, opticien 853 11.07Sports store 264 3.43Garage and gas station 357 4.63Superette 9 0.12Supermarket 1280 16.61Frozen food 44 0.57Observations 7707

Note: For 1,016 observations at the town level, information on the sector was not available.

Table 2: Commission trends2006 2007 2008 2009 2010

mean sd mean sd mean sd mean sd mean sdAutorization rate t-1 (β) 0.83 0.15 0.81 0.14 0.84 0.18 0.89 0.12 0.90 0.10Total Autorizations t-1 13.66 9.84 27.45 17.35 11.88 8.82 11.86 8.23 13.67 8.26Number of applications t-1 16.88 11.81 33.80 19.69 13.83 9.56 13.46 9.08 15.31 9.00Observations 89 95 90 96 94

calculate unemployment rates per demographic category. The demographic categories are as

follows: Direct immigrants to France (Imm.), individuals for whom at least one parent was

born in North Africa (N. Afr. Origin), individuals for whom at least one parent was born in

sub-Saharan Africa (SS. African Origin) and women of these two categories (Fem. N. Afr.

Origin and Fem. SS. African Origin). I calculate aggregate female (Fem.) and male (Male)

unemployment rates as well as those living in ZUS and women living in ZUS (Fem. Zus).

Finally, I distinguish the counter factual as individuals for whom both parents were born in

France for both men and women (Male French Origin and Female French Origin). There

are large disparities in the unemployment rates across the different demographic groups as

can be seen in table 3. The rates for people of African origin and those residing in ZUS

8

exhibit the highest unemployment rates and the most volatility. These rates are about three

times as high as people of French origin. These disparities may reflect discrimination in the

labor market, but also average gaps in skills between demographics and, as we will see in

the theory section below, it will be important to account for any skill gap when comparing

outcomes across demographics.

Using the fourth quarter survey data from the Enquete Emploi allows me to fully exploit

the 2011 unemployment data given that I have commission decisions up until the end of

2010. The employment rate is calculated at the 4th quarter of each year so the lag on

the authorization rate represents the commission authorization rate in the past one to two

years.11 The combined panel is unbalanced due to the fact that certain small departements

Table 3: Unemployment trends for demographic groups2007 2008 2009 2010 2011

mean sd mean sd mean sd mean sd mean sdImm. 0.154 0.115 0.174 0.137 0.174 0.107 0.173 0.118 0.174 0.124N. Afr. Origin 0.138 0.096 0.168 0.150 0.173 0.113 0.165 0.109 0.183 0.117SS. Afr. Origin 0.206 0.251 0.221 0.286 0.201 0.218 0.200 0.221 0.205 0.217Fem. N. Afr. Origin 0.155 0.157 0.161 0.152 0.152 0.134 0.185 0.135 0.209 0.151Fem SS. Afr. Origin 0.203 0.283 0.286 0.350 0.188 0.235 0.221 0.257 0.206 0.246Fem. 0.085 0.035 0.089 0.035 0.098 0.034 0.100 0.032 0.099 0.034Male 0.073 0.043 0.075 0.035 0.099 0.039 0.090 0.032 0.088 0.032ZUS 0.208 0.138 0.223 0.158 0.233 0.163 0.263 0.144 0.262 0.154Fem. ZUS 0.217 0.212 0.198 0.165 0.220 0.191 0.276 0.212 0.271 0.181Female French Origin 0.073 0.041 0.076 0.034 0.086 0.035 0.084 0.035 0.084 0.037Male French Origin 0.058 0.035 0.061 0.035 0.084 0.035 0.076 0.035 0.073 0.032Overall unemployment rate 0.078 0.034 0.082 0.028 0.098 0.032 0.095 0.026 0.093 0.027Observations 89 95 95 96 95

do not have commission decisions for certain years. The departements in question concern

the Lozere, Haute Corse, Corse Sud, Allier, Alpes de Haut Provence, and the Hautes Alpes.

Unfortunately, I am unable to clarify if this is the result of missing data or due to the

fact that, for certain years, these departements had no applications above the square meter

threshold which would require review by the commission.

Finally, in an effort to find instrumental variables to ensure exogenous variation in the

authorization rate, I created several complementary variables and data sets in order to test

first stage correlations with the authorization rate. Unfortunately, analysis of the first stage

estimates using these IVs (and their linear combinations) resulted in unsatisfactory F stats

11Inspection of the data and qualitative investigation on the length of start up provides evidence that theoptimal lag is currently t− 1 in order to capture the largest effect on firm entry. A lag of larger than t− 2would also be prohibitive to this study given I was only able to collect zoning board decisions from 2006 to2010 and the latest available data set from the Enquete Emploi is 2011.

9

and second stage estimates which were highly variable to the combination of instruments. I

believe that these instruments satisfied the exclusion restriction and several exhibited sta-

tistically significant correlations, but were much too weak to be used in the final analysis.

Nevertheless, I believe insight can be taken from the hypotheses these instruments are based

on. Descriptions and results using these variables can thus be found in the appendix.

3 Theoretical Framework

Simply put, Becker’s taste-based model assumes that employers have a distaste, or an-

imus, for certain workers built into their utility function and that this distaste can create

hiring or wage differentials between “preferred” and “less preferred types.” Because of this

employer distaste, less preferred types must compensate for the employer’s distaste by being

more productive or accept lower wages for identical productivity to preferred types. We can

formalize Becker’s model as follows. Firms maximize,

Uk = pF (Na +Nb)− waNa − wbNb −DNb,

where p is price a good, F the production function of that good, N the number of workers

of group d = {a, b} and w the wage paid to the worker. D is the taste parameter of the

employer or what Becker called the “coefficient of discrimination.” Firms find the solution

to the optimal number of workers hired,

pF ′(Na) = wa

pF ′(Nb) = wb +D,

where we see that employers who are prejudiced (D > 0) will consider the wage of less

preferred workers, b, as wb + D and thus will only hire these workers if if wa ≥ wb +

D. A hiring differential between preferred and less preferred workers occurs only if the

fraction of biased employers is large enough that the demand for less preferred workers when

wa = wb is less than supply. If there is a sufficiently large number of non-discriminating

employers then discrimination is competed away because less preferred workers do not work

for discriminating employers. However, if there is a sufficiently large number of biased

employers, or biased employers hold market power in the sector, then minority workers will

be hired less (or be forced to work for biased employers with a wage differential (wb < wa)).

Thus it is the magnitude of the discrimination, D, that determines this employment gap.

We note, that with free entry non-discriminating firms will enter and expand to arbitrage

10

away any hiring or wage differential.

We can summarize the results of Becker’s model in three points. One, in partial equilib-

rium minority workers must compensate biased employees through accepting lower wages or

providing higher productivity than majority workers in order to be hired. Two, tastes create

an incentive for labor market segregation so that no one bears the cost of distaste. Three,

in general equilibrium, tastes can only be indulged at a cost to the biased employer unless

this equilibrium is characterized by barriers to free entry. Thus, given market imperfections,

Becker’s theory implies that taste-based discrimination may produce lasting gaps in the

employment rate between preferred and less preferred demographics and that reducing the

imperfections should reduce any employment gap between identically productive workers.

This third point is the key theoretical result that I test empirically. As barriers to entry

diminish, we should see that minority employment increases as either the market segregates,

biased employers are driven out of the market as new firms enter and produce with wages

(wb < wa) or these biased employers accept minority workers in order to stay competitive

and pay cost D of their own distaste.

I now formalize how we can isolate differences in the effects of discrimination from average

differences in the skill set between types, adapting the framework of Levine et al. (2013) in

their analysis of the effects of banking deregulation on the wage differential between white

and black workers in the U.S. I decompose the employment differential between preferred and

less perferred types, a and b, into an explained and residual component where the residual

component is the employment gap. The mean employment rate e, in departement i at time

t for type a workers can be written as,

eait = Xitθat +Ra

it, (3.1)

and likewise for type b workers,

ebit = Xitθbt +Rb

it, (3.2)

where Xit is composed of observable aggregate worker characteristics correlated with employ-

ment status in departement i in year t, such as education or experience. I define parameters

θat and θbt so that E[Rait|X

ait] = E[Rb

it|Xbit] = 0, where Xa

it and Xbit are the means of Xit of type

a and b workers, respectively. Average employment rates for type a and b workers are now

simply eait = Xaitθ

at and ebit = Xb

itθbt . I now define the employment differential in departement

11

i in time t as,

ebit − eait = Xbitθ

bt −Xa

itθat

= Xbitθ

bt −Xa

itθat +Xb

itθat −Xb

itθat

= (Xbit −Xa

it)θat +Xb

it(θbt − θat ),

rewritten as,

ebit − eait = ∆Xitθat +Xb

it∆θt, (3.3)

where ∆Xit = Xbit −Xa

it and ∆θt = θbt − θat .

The first term on the r.h.s. of (3.3) is the explained part of the employment differential

that arises from differences in average observed characteristics. These skills are valued using

the returns that average preferred worker a receives from these characteristics, θat . The

second term on the r.h.s. is the residual employment gap. This term captures the gap in

employment rates not associated with average skill differences between worker types. The

residual, thus, captures the mean employment differential between the two types of workers

with identical observable characteristics where the employment gap is driven by the differing

returns to these characteristics, ∆θt = θbt − θat .

I now add an innovation to the model. Assume that Xbit − Xa

it = ci for t ∈ [1, 5], that

is the average skill gap between the two worker types is constant within departements for

relatively short time intervals, say five years. The differential in mean employment rates can

now be written as,

ebit − eait = µi +Xbit∆θt, (3.4)

where µi is now simply a departement fixed effect. This constant term captures the part

of the employment differential associated with type’s observable characteristics within de-

partements. Using standard econometric techniques to account for this fixed effect, I can

now isolate the term Xbit∆θt which captures the effect of labor market discrimination on dif-

ferent types of workers, in addition to possible unobserved productivity differences between

types. This greatly simplifies the analysis because I am no longer required to account for

the observable characteristics for demographic groups that cause a part (and possibly a large

part) of the employment differential. Using variation in barriers to firm entry across time

and departements and variations in the taste for discrimination, I can identify the effect

of competition on the manifestation of discrimination in the labor market for workers of

different demographic backgrounds with equal observed productivity.

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4 Empirical strategy and results

I model the relationship between specific demographic unemployment and the barriers

to firm entry as follows,

ydit =γydit−1 + β(autrate)it−1 + φ(unemp)it + ψ(unemp)it−1

+Kit−1δ + η(autrate)it + µi + Tt + ǫit, (4.1)

where ydit is the unemployment rate in departement i at time t for a given demographic

category, d and ydt−1 its lag. β is the parameter of interest that captures the effect of entry

regulation on the employment rate through the zoning board authorization rate at t − 1.

φ and ψ account for the correlation between the aggregate unemployment rate at time t

and t − 1 and the specific demographic’s unemployment rate. K is a matrix of control

variables related to the zoning commission decisions at t − 1 that include total number of

authorizations, total number of applications and total square meters authorized. η captures

any effect of the contemporaneous authorization rate. Finally, µi captures department fixed

effects including average skill differences between demographics as highlighted above. T are

year dummies to control for a trend and ǫit is an error term that is possibly serially correlated

between periods.

4.1 OLS estimates

I begin my analysis with a regression of unemployment rates on the authorization rate

at t − 1 abstracting from the possibility of department level fixed effects. Including the

lagged dependent variable in all specifications is particularly important for two reasons: 1)

there is undoubtedly significant first order autocorrelation within panels and the lag helps

control for this. For example, Shimer (2005) highlights that detrended unemployment ex-

hibits considerable persistence with an autocorrelation of around 0.94 for the U.S.12 2) we

should take very seriously the possibility that previous unemployment predicts the autho-

rization rate. We can think of this as a case of “Ashenfelter Dip” (Ashenfelter and Card,

1985) whereby the commission induces their population into “treatment” by increasing (or

decreasing) authorizations as a function of the employment rate.

12A test for autocorrelation using Wooldrige’s test for serial correlation highlighted in Drukker (2003)rejects the null for no first order autocorrelation for all demographics. Assuming that we have heteroskedas-ticity across panels and autocorrelation in the error term, estimation using feasible GLS is most efficient[Greene (2003), Wooldridge (2002)]. Unfortunately including a lagged dependent variable prohibits use ofFGLS. I have chosen to use OLS with a lag over FGLS because of the importance of the possible predictivepower of the lag and because FGLS has poor small sample properties.

13

I start with comparing the evolution of β for 11 different demographic categories. Re-

sults from these regressions can be found in tables 12 through 22 in the appendix. All

standard errors are clustered at the departement level to help control for any remaining

serial correlation within departements in addition to heteroskedasticity and heterogeneity

across departements. In examining these results we see no statistically relevant results for

the effect of the authorization rate, β, for any demographic in the fully specified model (7).

Secondly, the sign of the coefficient on the authorization rate is always negative for every

demographic except for Female and French female unemployment, which are positive and

French male which is quasi-null. Third, the systematic addition of controls appears to pri-

marily affect β for individuals with diverse backgrounds, i.e. parents born outside of France,

immigrants or people who reside in ZUS. We also note that the coefficient on the lagged

dependent variable, unemployment in the previous period is usually large, highly significant

and positive across demographics, indicating that unemployment is indeed persistent. Inter-

estingly, the persistence seems much higher for individuals of only French parentage. Finally,

we note that for the models for people of African origin or ZUS, we have significantly fewer

observations. Examination of the INSEE data set indicates that this is likely an artifact of

the INSEE sampling strategy, thus we may consider differences in the number of observations

as random.

If we were to make a naive causal inference based simply on the signs and magnitude

of the coefficient of interest, these estimates would fit nicely with a model of taste-based

discrimination against ethnic minorities. We would have evidence that a reduction in the

barriers to entry, or an increase in market competition, reduces minority unemployment

which contrasts with its effects on majority unemployment rates.

Before we go further into a more robust analysis it is prudent to discuss the importance of

the covariates in the fully specified model. Controlling for total number of authorizations and

total square meters authorized is necessary because it distinguishes the effect of aggregate

creation volume from the level of barriers to entry captured by the authorization rate. Indeed,

we need to distinguish between two cases of an authorization rate of 50% where one is 5/10

and another 50/100 for a departement in a given year. Likewise, controlling for the total

number of applications helps capture variation associated with an increase or decrease in

entrepreneurship which may covary significantly with more “macro-level” changes in the

economy.

As highlighted above, one of the biggest threats to a causal interpretation of the autho-

rization rate is the strong possibility of reverse causality. It is intuitive to imagine that when

mayors know that they have high unemployment within their town they are more likely to

authorize retail space in an effort to drive down future unemployment. Thus, in addition to

14

the lagged dependent variable, I include the lagged aggregate unemployment rate. If mayors

do behave in this way, it is most likely driven by aggregate unemployment and not a specific

demographic’s unemployment rate, hence inclusion of this variable is vital.

I also include the aggregate unemployment rate at time t. This term helps captures

residual variation in the components of unemployment not explained by terms lagged at

t − 1. We see in the OLS regressions in the appendix that inclusion of this term is of

particular importance for female and ZUS unemployment and for the unemployment rate of

people of North African heritage.

Finally, it is important to discuss why I have included (autrate)it to complete the fully

specified model. Indeed it may capture the effect of new competition through new businesses

that start very quickly, but it is also useful in that it may capture some lingering endogeneity

related to the departement’s authorization rate trend.

4.2 Fixed effects

Having discussed the importance of the controls, we now turn to analysis of a model that

accounts for departement fixed effects. In the theory section, it became evident that control-

ling for a fixed effect is important is we assume that skill differentials between demographic

groups are relatively static for our panel period. Controlling for average skill differentials

through the departement fixed effect allows us to better isolate the effect of competition on

discrimination as we will now be comparing individuals of equal observed productivity.

To eliminate the fixed effect, I first difference the equation to obtain,

∆ydit =γ∆ydit−1 + β∆(autrate)it−1 + φ∆(unemp)it + ψ∆(unemp)it−1

+∆(K)it−1δ + η∆(autrate)it +∆Tt +∆ǫit, (4.2)

Wooldridge (2002) notes that in the presence of serial correlation in the error terms, the first

difference estimator will be more efficient than the fixed effect estimator. It is easy to see

why taking first differences should control for part of the remaining autocorrelation in the

errors as we are taking the difference in unobserved components from the previous period,

∆ǫit = [ǫi,t − ǫi,t−1]. With the addition of a fixed effect, identification for the effect of the

authorization rate is now defined as,

E[yit|µi, yit, Z, (autrate)it−1] = E[yit|µi, yit, Z], (4.3)

where Z now stands for the matrix of controls above. We must note that estimates of (4.2)

will be systematically biased because ∆ǫit is necessarily correlated with our lag, ∆ydit−1,

15

due to the fact that Cov(yi,t−1, ǫi,t−1) 6= 0 by definition of the model. Following Anderson

and Hsiao (1981) and later Arellano and Bond (1991), I use ydit−2 to instrument ∆ydit−1

because it is necessarily correlated with ∆ydit−1 but is excluded from (4.2) by construction.13

Under the assumption that our autocorrelation is sufficiently controlled for with the lag,

differencing the data and clustering at the departement level, this model will give consistent

estimates of the effect of authorizations on the unemployment rate given that authorization

rates at t − 1 are exogenous conditional on the additional controls and elimination of the

departement fixed effect.

The first differenced estimates can be found in table 4. We see that the F statistic for

the instrument ydit−2 is consistently large and always above the threshold of the 5% Wald

test, so we can reject the null that the instrument is weak, as expected. We turn now

to the effects of the authorization rate. The first sign of a statistically significant effect

emerges for women who reside in ZUS. An augmentation in the authorization rate in the

departement reduces this demographics unemployment and the effect is significant at 5%.

We can interpret this as an increase of 1% in the authorization rate at t− 1 leads to a drop

of 0.2% in the unemployment rate at time t for women residing in ZUS. This is a large effect,

but it’s important to highlight that the 95% confidence interval on this estimate is very

large [-.47, -.0025]. Also, the coefficients on French male, female, and people whose parents

were born in sub-Saharan Africa have switched signs in comparison to the OLS estimates

that don’t take into account a fixed effect, though they are not significantly different from

zero. Remember that we are controlling for the observable skill differential through the

departement fixed effect. Hence, we are now comparing the effects of competition on

people with equal observable productivity. In addition, the fixed effect controls for time

invariant characteristics that my be correlated with the error in (4.1) such as the size of the

departement and long lasting structural differences in types of jobs and sectors, in addition

to stable differences in demographic composition.

13If we have higher order serial correlation we find ourselves in a difficult situation because this instrumentwill not pass the exclusion restriction. Using deeper lags reduces substantially the number of observations inthe sample so is not optimal, but one positive point that emerges from this exercise is that the explanatorypower of a t−3 lag to predict ∆ydit−1 is very low indicating that the autocorrelation may indeed be boundedat one period.

16

Tab

le4:

First

difference

estimationsinstrumenting∆ydit−1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

-0.134

-0.130

0.047

-0.071

0.028

-0.005

0.005

-0.143

-0.239

∗∗

-0.005

0.030

t-1(β)

[0.094]

[0.122]

[0.221]

[0.122]

[0.266]

[0.016]

[0.016]

[0.101]

[0.121]

[0.018]

[0.022]

Con

trols

inadditionto

timetren

d

Laggeddep.var.

t-1

-0.061

0.031

-0.003

-0.065

0.009

0.110

0.182

0.253

0.088

0.026

0.291∗

[0.102]

[0.119]

[0.205]

[0.111]

[0.197]

[0.118]

[0.132]

[0.170]

[0.159]

[0.088]

[0.171]

Total

Autorization

s0.005

0.007

-0.010

0.003

-0.010

-0.000

0.000

0.012∗

0.014∗

∗-0.001

-0.001

t-1

[0.004]

[0.004]

[0.009]

[0.005]

[0.013]

[0.001]

[0.001]

[0.007]

[0.006]

[0.001]

[0.001]

Total

meterssq.

0.054

-0.003

-0.007

-0.029

0.021

-0.004

0.005

-0.048

0.168∗

0.001

0.001

authorized

t-1

[0.040]

[0.037]

[0.059]

[0.060]

[0.108]

[0.007]

[0.007]

[0.094]

[0.093]

[0.009]

[0.009]

Number

of-0.006

∗-0.005

0.007

-0.000

0.010

0.000

-0.000

-0.011

∗∗

-0.014

∗∗∗

0.001

0.000

application

st-1

[0.003]

[0.004]

[0.009]

[0.005]

[0.012]

[0.001]

[0.001]

[0.005]

[0.005]

[0.001]

[0.001]

Overallunem

ployment

0.245

-0.022

1.441∗

0.449

0.876

-0.230

∗∗

-0.098

-1.767

∗∗∗

-0.724

-0.105

-0.233

rate

t-1

[0.378]

[0.432]

[0.847]

[0.645]

[1.014]

[0.104]

[0.157]

[0.610]

[0.578]

[0.111]

[0.194]

Overallunem

ployment

2.018∗

∗∗

2.252∗

∗∗

0.221

2.091∗

∗∗

0.148

0.972∗

∗∗

1.025∗

∗∗

1.506∗

∗∗

1.396∗

0.803∗

∗∗

0.705∗

∗∗

rate

t[0.512]

[0.332]

[0.910]

[0.507]

[1.206]

[0.080]

[0.074]

[0.436]

[0.732]

[0.086]

[0.102]

Authorizationrate

t-0.053

-0.008

0.145

-0.081

0.089

-0.022

0.020

-0.082

-0.042

-0.016

0.021

[0.070]

[0.061]

[0.166]

[0.101]

[0.169]

[0.014]

[0.013]

[0.081]

[0.142]

[0.013]

[0.018]

Observations

268

267

204

252

160

273

273

166

163

273

273

F-statof

firststage

142

128

153

192

123

7244

4696

108

49Sizeof

nom

inal

5%Waldtest

(10%

)16.430

16.430

16.430

16.430

16.430

16.430

16.430

16.430

16.430

16.430

16.430

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

Note:

IVestimationmethod

is2SLSforthis

just

iden

tified

model

withstan

dard

errors

clustered

atthedepartem

ent.

Estim

ationusingLIM

Ldoes

not

substantively

chan

getheestimates.

17

4.3 Instrumenting the authorization rate

Under the hypothesis that the authorization rate is still not exogenous given our con-

trols, the instrumented lag and elimination of departement fixed effects, I now instrument

authorization rates directly. As underlined in the data section, the instrumental variable

search was extensive. Though several instruments were statistically significant predictors

of the authorization rate these instruments lack the explanatory power necessary for IV re-

gression.14 Thus, the IV strategy adopted here follows directly from above, using the lags

of the authorization rate, (autrate)it−2 and (autrate)it−3 to instrument ∆(autrate)it−1. The

gain from instrumenting the authorization rate with its lags is that if there are shocks in-

fluencing the authorization rate at t − 1 at the town level that covary with employment

outcomes at t, instrumenting with earlier values will eliminate this endogeneity. Obviously,

it is important to clarify the purpose of this instrumental variable estimation. I am using

this IV strategy to disprove the previous first differenced results. If we find substantive dif-

ferences with the previous estimates where I only instrument ∆ydit−1, this would be reason

for concern. On the other hand, if we are able to confirm the previous results in directly

instrumenting the authorization rate at t− 1, and the first stage statistics and tests for over

identifying restrictions are satisfactory, it will make it easier to accept that the fixed effect

model with an instrumented lagged dependent variable has stripped the authorization rate

of its endogeneity, allowing for causal inference.

14See appendix for first and second stage results using these instruments.

18

Tab

le5:

First

difference

estimationsinstrumenting∆(autrate) it−

1an

d∆ydit−1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

-0.133

-0.066

0.229

-0.024

0.340

-0.014

0.014

-0.207

-0.391

∗∗

-0.019

0.060∗

∗

t-1(β)

[0.103]

[0.154]

[0.307]

[0.226]

[0.356]

[0.020]

[0.020]

[0.126]

[0.158]

[0.023]

[0.027]

Con

trolsin

additionto

timetrend

Laggeddep.var.

t-1

-0.031

-0.042

-0.021

-0.086

-0.003

0.079

0.159

0.250

0.095

-0.010

0.273∗

[0.104]

[0.118]

[0.203]

[0.110]

[0.193]

[0.122]

[0.133]

[0.172]

[0.166]

[0.093]

[0.153]

Total

Autorization

s0.004

0.005

-0.016

0.000

-0.020

0.000

-0.000

0.015∗

∗0.020∗

∗∗

-0.000

-0.002

∗

t-1

[0.004]

[0.005]

[0.012]

[0.008]

[0.015]

[0.001]

[0.001]

[0.007]

[0.006]

[0.001]

[0.001]

Total

meterssq.

0.059

-0.000

-0.007

-0.029

0.026

-0.005

0.005

-0.043

0.178∗

0.001

-0.000

authorized

t-1

[0.041]

[0.036]

[0.061]

[0.060]

[0.110]

[0.007]

[0.007]

[0.095]

[0.094]

[0.009]

[0.009]

Number

of-0.005

-0.004

0.012

0.002

0.017

0.000

-0.000

-0.013

∗∗

-0.019

∗∗∗

0.000

0.002∗

application

st-1

[0.004]

[0.005]

[0.011]

[0.007]

[0.013]

[0.001]

[0.001]

[0.005]

[0.005]

[0.001]

[0.001]

Overallunem

ployment

0.157

-0.031

1.382

0.364

0.676

-0.197

∗-0.082

-1.787

∗∗∗

-0.779

-0.061

-0.199

rate

t-1

[0.399]

[0.441]

[0.853]

[0.675]

[1.047]

[0.116]

[0.155]

[0.601]

[0.590]

[0.117]

[0.179]

Overallunem

ployment

2.339∗

∗∗

2.147∗

∗∗

0.345

2.277∗

∗∗

0.160

0.998∗

∗∗

0.994∗

∗∗

1.555∗

∗∗

1.463∗

0.823∗

∗∗

0.675∗

∗∗

rate

t[0.461]

[0.333]

[0.991]

[0.546]

[1.260]

[0.084]

[0.078]

[0.456]

[0.761]

[0.091]

[0.098]

Authorizationrate

t0.008

-0.017

0.245

-0.019

0.249

-0.022

0.020

-0.116

-0.116

-0.022

0.040∗

∗

[0.071]

[0.074]

[0.183]

[0.111]

[0.206]

[0.015]

[0.014]

[0.087]

[0.141]

[0.014]

[0.018]

Observations

260

258

200

243

158

264

264

161

158

264

264

F-statof

firststage

4340

5060

4022

1515

3032

172S

LSsize

ofnom

inal

5%Waldtest

(10%

)13.430

13.430

13.430

13.430

13.430

13.430

13.430

13.430

13.430

13.430

13.430

P-val.of

overidentifyingrest.test

(Sargan)

0.913

0.142

0.190

0.838

0.282

0.201

0.211

0.816

0.720

0.140

0.703

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

Note:

IVestimationmethodis

2SLSwithstan

dard

errors

clustered

atthedepartem

ent.

Instruments

are(autrate) it−

2,(autrate) it−

3an

dydit−2.Estim

ationusingGMM

orLIM

Lgive

nearlyidenticalresults.

19

Table 5 presents results from additionally instrumenting ∆(autrate)it−1. The results are

qualitatively similar to the previous results when we assume exogeneity of ∆(autrate)it−1

conditional on the controls and instrumented ∆ydit−1. The signs on all of the coefficients (β)

remain unchanged. Additionally the magnitude of the coefficients remain relatively stable

and within the confidence intervals of the previous estimates in table 4. The authorization

rate is now statistically significant for French males, but this estimate confirms the sign and

is within the confidence interval of the previous result. The F stats for the 3 IVs (ydit−2,

(autrate)it−2, (autrate)it−3) in the first stage are above the standard of 10 for all models.

Additionally, and perhaps most importantly all models easily pass a Sargan test for over

identifying restrictions at the 10% level.

To summarize, the estimates when instrumenting the authorization rate confirm the

previous first differenced estimates in sign and in magnitude. We can, thus, be confident in

causally interpreting the effect of the authorization rate using the benchmark model. Yet,

up to this point we have been just been looking at effects of an exogenous change in the

level of competition on different demographic groups. Though changes in the authorization

rate may indeed have differential effects on different demographic groups, we are still not

getting at the underlying theory: We now are ready to ask the question, “What is the

effect of a change in barriers to entry under varying levels of discrimination?”, which is

a much more interesting question. I now adapt the benchmark model by interacting the

authorization rate with the level of far right vote within the departement. This will allow

us to examine the differential effects of competition over a distribution of discrimination.

It will tell us, first, if there is a relationship between competition and discrimination and,

second, who benefits and who loses out.

4.4 The relationship between competition and discrimination

Uncontrolled immigration is a source of tension in a Republic that is unable to assimilate

new French citizens. Ghettos, ethnic conflict, communitarian demands and politico-religious

provocations are the direct consequence of massive immigration. It destroys our national

identity and leads to an Islamization, with it parade of demands, that is becoming more and

more apparent. Communitarianism is poisoning our national cohesion. - Front National15

If we really want to examine the prediction of the taste discrimination model, we must

find an indicator of discrimination and explore its interaction with barriers to entry. The

15Public stance on immigration quoted from the Front National website May, 2013. http://www.

frontnational.com/le-projet-de-marine-le-pen/autorite-de-letat/immigration/

20

results up to this point have not been entirely clear cut. We have seen that an exogenous

reduction in barriers to entry has a potential beneficial effect on the employment outcomes

for women who reside in ZUS and possibly an opposite effect for French males, yet we

have been examining these effects in isolation of the term that competition is supposed to

mitigate. Bertrand and Kramarz (2002) (BK) use the political flavor of the departement as

an instrument for authorizations.16 Their data set spanned the years 1974-1998 and we start

by investigating whether the vote share of the departement still predicts the authorization

rate in the more up-to-date, albeit shorter, panel. In their analysis, they found that the

right wing vote predicted less authorizations. I examine the first stage results of a 2SLS

regression from the baseline model using the vote share for the two major parties in the 2007

legislative elections, Socialist (majority-left) and UMP (majority-right), as an instrument for

authorizations and find that the correlations are very small and not statistically significant

at any reasonable level. For the Socialist vote the first stage coefficient is -0.0007 with a t

stat of -0.99.17 For the UMP, the coefficient is 0.0005 with a t stat of 0.78. Finally, I test the

first stage with the Front National (far-right) vote share. The coefficient is -0.003 with a t

stat of -0.95. We discover two important things from reexamining the BK IV strategy. First,

using the vote share as an instrument for authorizations would not work in the current data

set due to a clear lack of first stage correlation. Second, this lack of correlation between the

far-right vote and the authorization rate allows me to use it in a much more interesting way:

as a proxy for discrimination.

I add the interaction term to the model as follows,

∆ydit =γ∆ydit−1 + β∆(autrate)it−1 + φ∆(unemp)it + ψ∆(unemp)it−1

+∆(K)it−1δ + η∆(autrate)it + λ[∆(autrate)it−1 ∗ (farright)it]︸ ︷︷ ︸

+ τ(farright)it +∆Tt +∆ǫit, (4.4)

where λ now captures the interaction between a change in the level of competition and the

percent of far-right vote in legislative elections in departement i. We can now define the

effect of a marginal change in (autrate)it−1 on a demographic’s unemployment rate as,

∂∆ydit∂∆(autrate)it−1

= β + λ(farright)it. (4.5)

16They justify this instrument by arguing that the commissions are composed of elected officials, meaningthat the decision is a political process, decided by the median voter and thus can be predicted by thelegislative composition of the departement. The idea that political composition only affects unemploymentthrough its effect on entry deterrence is indeed a strong assumption.

17The interpretation is a 1% increase in the vote for the Socialist party reduces the authorization rate by0.0007%.

21

As one can see, β now denotes the effect of authorizations when the vote share for the far-

right is zero. This is informative, but not what we are after. We are interested in the partial

effect of the authorization rate at increasing values of the far-right vote share and we can

achieve this by plugging in different values of the vote share and rerunning the regression.

But first we must test the joint hypothesis, H0 : β = 0, λ = 0 to investigate whether there is

indeed a statistically significant relationship between competition and the far-right vote that

affects the labor outcomes of our different demographics. Table 6 presents the coefficients

and the results of the joint F test using the fully specified model.18 It is important that we

not read too much into the standard errors of the individual coefficients. For instance in table

6, if we were to simply look at the coefficients and standard errors for women who reside in

ZUS, we would erroneously conclude there is no relationship. In fact, the F tests of this joint

hypothesis show that we have statistically significant interactions (at least 5%) for women

whose parents were born in sub-Saharan Africa, for women and men in general, for women

who reside in ZUS and finally for French males (10%).19 Note that the coefficient, λ, on the

interaction term is negative for all demographics except men and French men, meaning that

the effect of increases in competition at driving down unemployment for minority groups

becomes greater as we move to the right of the far-right vote distribution.

18Additionally instrumenting the authorization rate would require instrumenting for ∆(autrate)it−1,∆(autrate)it−1 ∗ (farright)it and (farright)it. I do not have an instrument for the vote share of thefar-right thus it is not possible to appropriately instrument the interaction term.

19We note that there may be interactions for immigrants and people of African parentage in general,where the p-value< 0.2.

22

Tab

le6:

FrontNational

(far-right)

interaction-Resultsof

jointF

tests

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

0.110

-0.365

0.864

0.140

1.523∗

∗∗

0.048∗

-0.046

-0.230

0.232

0.013

0.009

t-1(β)

[0.220]

[0.309]

[0.576]

[0.236]

[0.555]

[0.028]

[0.029]

[0.296]

[0.401]

[0.031]

[0.053]

(Autrate*FarRight)

-0.054

0.051

-0.168

∗-0.045

-0.290

∗∗∗

-0.012

∗∗

0.012∗

∗0.016

-0.088

-0.004

0.005

(λ)

[0.044]

[0.050]

[0.092]

[0.044]

[0.095]

[0.005]

[0.005]

[0.044]

[0.067]

[0.006]

[0.009]

Far-rightvote

share

0.003

-0.007

∗∗

0.011

0.001

0.007

0.001∗

∗-0.001

∗∗

0.001

0.011

0.001

0.000

[0.004]

[0.003]

[0.009]

[0.005]

[0.013]

[0.001]

[0.001]

[0.004]

[0.007]

[0.001]

[0.001]

Obs.

268

267

204

252

160

273

273

166

163

273

273

P-val.F-test-H0:β=λ=

00.191

0.452

0.114

0.516

0.009

0.020

0.017

0.328

0.027

0.701

0.081

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

∆ydit−1instrumen

tedwithydit−2using2S

LS.Resultsofthecontrolvariablesha

vebeen

supp

ressed

forease

ofexposition

.

Tab

le7:

UMP(m

ajority-right)

interaction-Resultsof

jointF

tests

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

0.493

-0.133

0.385

1.205∗

∗∗

-0.193

0.165∗

∗-0.152

∗∗

-0.545

0.716

0.080

-0.079

t-1(β)

[0.481]

[0.589]

[1.000]

[0.447]

[1.433]

[0.071]

[0.069]

[0.618]

[0.783]

[0.078]

[0.117]

(Autrate*Right)

-0.014

0.000

-0.007

-0.029

∗∗∗

0.005

-0.004

∗∗∗

0.004∗

∗0.009

-0.021

-0.002

0.002

(λ)

[0.011]

[0.012]

[0.019]

[0.010]

[0.028]

[0.001]

[0.001]

[0.012]

[0.017]

[0.002]

[0.002]

Rightvote

share

0.000

-0.001

-0.001

-0.001

0.002

0.000

-0.000

0.001

0.003

-0.000

-0.000

[0.001]

[0.001]

[0.002]

[0.001]

[0.003]

[0.000]

[0.000]

[0.001]

[0.002]

[0.000]

[0.000]

Obs.

268

267

204

252

160

273

273

166

163

273

273

P-val.F-test-H0:β=λ=

00.184

0.423

0.928

0.022

0.975

0.013

0.016

0.302

0.056

0.458

0.053

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

∆ydit−1instrumen

tedwithydit−2using2S

LS.Resultsofthecontrolvariablesha

vebeen

supp

ressed

forease

ofexposition

.

23

I replicate the same exercise with the vote shares of the majority-right, majority-left and

far-left vote share. If we find contradictory results from interacting with the majority parties

and the far-left, this would make it more difficult to argue the key point: that increases in

competition only reduces labor market discrimination when there is a sufficient amount of

discrimination. As tables 7 and 8 demonstrate, the interaction effect diminishes as we move

to the left of the political spectrum. When interacted with the UMP vote, we can no longer

reject the joint hypothesis for women of African parentage as the p-value jumps to 0.899

for the joint F test. We still reject the null of no joint effect for women, men and women

residing in ZUS and the signs of the coefficients on the interaction term are the same. But

importantly, the size of these coefficients are much smaller in magnitude. This indicates that

that the effect is much less dramatic than when the term is interacted with the far-right vote.

Interestingly, we see that the interaction term is now statistically significant for women of

North African heritage. The fact that the interaction term is negative is in line with the

results for women of sub-Saharan African parentage, but the magnitude of the interaction

term is small. This means that even in the extreme upper distribution of the UMP vote,

authorizations may still not be beneficial to this group (though they will be “less bad”).20

In inspecting the interaction with the Socialist vote share, we see that we can still reject

the null of the joint F test at 10% for the women from ZUS, but the sign of the coefficient

on the interaction term is now positive, meaning that the beneficial effect of increases in

competition is decreasing as we move higher in the distribution of the left-wing vote share.

This is consistent with the results using the right wing vote share.

For both results using the interactions with the majority-left and far-left vote share, the

joint F test for French males remains significant at the 10% level. Interestingly, the sign

of the coefficient on the interaction term has become negative. How do we interpret this?

It appears that in higher levels of the distribution of the left-wing vote share, French men

begin to benefit from lower entry barriers in the same way that the previous disadvantaged

demographics benefited at high levels of the right-wing vote.

20This can be seen in table 24 in the appendix

24

Tab

le8:

Socialist(m

ajority-left)

interaction-Resultsof

jointF

tests

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

-0.161

-0.421

-0.093

-0.123

0.086

-0.039

0.036

0.128

-0.425

∗-0.017

0.147∗

∗∗

t-1(β)

[0.230]

[0.286]

[0.253]

[0.223]

[0.424]

[0.041]

[0.039]

[0.126]

[0.256]

[0.037]

[0.044]

(Autrate*Left)

0.001

0.016

0.008

0.003

-0.003

0.002

-0.002

-0.017

∗0.010

0.001

-0.006

∗∗∗

(λ)

[0.009]

[0.011]

[0.018]

[0.009]

[0.025]

[0.002]

[0.002]

[0.010]

[0.014]

[0.002]

[0.002]

Leftvote

share

-0.000

-0.000

-0.001

0.001

0.002

0.000

-0.000

-0.001

-0.003

∗0.000

0.000

[0.001]

[0.001]

[0.002]

[0.001]

[0.003]

[0.000]

[0.000]

[0.001]

[0.002]

[0.000]

[0.000]

Obs.

268

267

204

252

160

273

273

166

163

273

273

P-val.F-test-H0:β=λ=

00.290

0.309

0.906

0.837

0.975

0.560

0.603

0.191

0.062

0.893

0.003

Standarderrors

clustered

atthedepartementlevelin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

∆ydit−1instrumen

tedwithydit−2using2S

LS.Resultsofthecontrolvariablesha

vebeen

supp

ressed

forease

ofexposition

.

Tab

le9:

Com

munist(far-left)

interaction-Resultsof

jointF

tests

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

-0.110

-0.169

-0.148

-0.080

0.411

-0.011

0.006

-0.091

-0.323

0.000

0.064∗

∗

t-1(β)

[0.157]

[0.213]

[0.309]

[0.174]

[0.339]

[0.024]

[0.024]

[0.134]

[0.227]

[0.023]

[0.028]

(Autrate*FarLeft)

-0.007

0.014

0.065

0.004

-0.114

0.002

-0.000

-0.017

0.027

-0.002

-0.012

∗

(λ)

[0.033]

[0.043]

[0.071]

[0.035]

[0.081]

[0.005]

[0.005]

[0.036]

[0.057]

[0.005]

[0.006]

Far-leftvote

share

0.004

0.001

-0.001

0.001

0.009

-0.000

0.000

0.001

-0.000

-0.001

∗∗

0.000

[0.002]

[0.002]

[0.005]

[0.002]

[0.006]

[0.000]

[0.000]

[0.002]

[0.004]

[0.000]

[0.000]

Obs.

268

267

204

252

160

273

273

166

163

273

273

P-val.F-test-H0:β=λ=

00.265

0.522

0.638

0.848

0.338

0.891

0.946

0.369

0.121

0.843

0.075

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

∆ydit−1instrumen

tedwithydit−2using2S

LS.Resultsofthecontrolvariablesha

vebeen

supp

ressed

forease

ofexposition

.

25

We now want to get an idea of the effect size at different levels of discrimination.

The theory tells us that we should see higher returns to competition for discriminated-

against groups when the level of discrimination is high. For each demographic I re-

run the regression, plugging in values of the far-right vote from the mean to 3 standard

deviations above the mean, thus replacing the term ∆(autrate)it−1 ∗ (farright)it with

∆(autrate)it−1 ∗ [(farright)it − (farright+ x ∗ s.d.)], where farright is the sample mean of

the far-right vote share and x ∈ [0, 3]. In these regressions β can now be interpreted as the

differential effect of competition at different levels of the distribution of the vote share. Table

10 exhibits the results from these regressions where the coefficients of the control variables

have been suppressed to facilitate exposition of the principal effect. We see that the effect

of the increase in the authorization rate has the most profound and statistically significant

effects for persons of African heritage, particularly women, women from ZUS and women

in general. We also not that increases in the authorization rate also lead to statistically

significant (at 10%) reductions in the immigrant unemployment rate. These results are con-

centrated in the upper distribution of the far-right vote share. The curve is the opposite for

French men, and men in general as increases in authorizations increases their unemployment

rate. This detrimental effect for men is increasing in the share of far-right vote. We see, as

the theory predicts, that the effect of an increase in competition has its strongest effects in

improving labor market outcomes for disadvantaged groups when the level of discrimination

is high.21

21I also tested a specification including a quadratic of the interaction term, hypothesizing that the inter-action may have non-linear effects on the unemployment rate. I found no statistically significant effect fromthe quadratic term for any of the demographics except immigrants (at 5%).

26

Table 10: Front National (far-right): Effects of authorizations over upper distribution of vote

Mean +1 s.d. +2 s.d. +3 s.d.

Imm. : Autorization rate t-1 (β) -0.122 -0.210∗ -0.298∗ -0.387∗

[0.098] [0.117] [0.167] [0.230]

Observations 268 268 268 268

N. Afr. Origin : Autorization rate t-1 (β) -0.146 -0.062 0.021 0.105

[0.117] [0.078] [0.108] [0.173]

Observations 267 267 267 267

SS. Afr. Origin : Autorization rate t-1 (β) 0.147 -0.127 -0.401∗∗ -0.675∗∗

[0.223] [0.152] [0.204] [0.324]

Observations 204 204 204 204

Fem. N. Afr. Origin : Autorization rate t-1 (β) -0.051 -0.124 -0.197 -0.270

[0.121] [0.133] [0.177] [0.234]

Observations 252 252 252 252

Fem SS. Afr. Origin : Autorization rate t-1 (β) 0.288 -0.184 -0.655∗∗ -1.126∗∗∗

[0.213] [0.178] [0.257] [0.385]

Observations 160 160 160 160

Fem. : Autorization rate t-1 (β) -0.005 -0.025∗ -0.045∗∗∗ -0.065∗∗∗

[0.013] [0.013] [0.017] [0.024]

Observations 273 273 273 273

Male : Autorization rate t-1 (β) 0.005 0.025∗∗ 0.044∗∗∗ 0.064∗∗∗

[0.013] [0.012] [0.016] [0.022]

Observations 273 273 273 273

ZUS : Autorization rate t-1 (β) -0.164 -0.138 -0.113 -0.088

[0.130] [0.093] [0.104] [0.152]

Observations 166 166 166 166

Fem. ZUS : Autorization rate t-1 (β) -0.144 -0.288∗∗ -0.431∗∗∗ -0.575∗∗

[0.155] [0.118] [0.166] [0.254]

Observations 163 163 163 163

Female French Origin : Autorization rate t-1 (β) -0.006 -0.013 -0.020 -0.027

[0.017] [0.019] [0.025] [0.032]

Observations 273 273 273 273

Male French Origin : Autorization rate t-1 (β) 0.031 0.040∗∗ 0.048∗∗ 0.057∗

[0.023] [0.018] [0.023] [0.034]

Observations 273 273 273 273

Standard errors clustered at the departement in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: I have omitted the estimates for the control variables to save space.

The magnitudes of the coefficients are striking. For example, at two standard deviations

above the mean of the far-right vote, a one percent increase in the authorization rate leads

to a 0.655 percent drop in the unemployment rate for women of sub-Saharan African origin.

This drop increases to over 1 percent at 3 standard deviations above the mean. We find

27

comparable magnitudes for women living in ZUS, direct immigrants and both men and

women of African origin. In contrast, the coefficients for women and men and men of French

origin are much smaller in magnitude. This may reflect the type of sector that we are

dealing with. Retail may disproportionately attract disadvantaged groups in comparison

with the population in general. We will discuss this in the next section. Nevertheless, the

fact that these effects emerge only in the upper distributions of the far-right vote supports

the underlying theory.

One plausible critique for using the vote share for the Front National as a proxy for

discrimination is that it does not really capture discriminatory attitudes, instead measuring

the level of discontent with the majority parties. Areas that exhibit high levels of far-

right vote share may be disenfranchised with the current political dynamic. The interaction

may then simply be capturing the relationship between changes in barriers to entry and

disenfranchisement. To test this hypothesis, I repeat the same exercise this time using the

vote share for the far left (Communist party). If it is unobserved discontent with the current

system then both the far-left and far-right proxy for this. Table 11 presents the results

from these regressions. The results contrast sharply with the Front National vote share

results. We have no statistically significant results from this interaction. Thus, it does not

appear that a rejection of the current establishment is being measured by using the extreme

parties’ vote share. Also, results for β using the majority vote shares interacted with the

authorization rate can be found in tables 24 and 25 in the appendix.

28

Table 11: Communist (far-left): Effects of authorizations over upper distribution of vote

Mean +1 s.d. +2 s.d. +3 s.d.

Imm. : Autorization rate t-1 (β) -0.141 -0.154 -0.166 -0.178

[0.087] [0.107] [0.145] [0.190]

Observations 268 268 268 268

N. Afr. Origin : Autorization rate t-1 (β) -0.110 -0.087 -0.065 -0.042

[0.099] [0.117] [0.165] [0.225]

Observations 267 267 267 267

SS. Afr. Origin : Autorization rate t-1 (β) 0.129 0.234 0.340 0.446

[0.234] [0.295] [0.382] [0.481]

Observations 204 204 204 204

Fem. N. Afr. Origin : Autorization rate t-1 (β) -0.064 -0.059 -0.053 -0.047

[0.120] [0.142] [0.180] [0.226]

Observations 252 252 252 252

Fem SS. Afr. Origin : Autorization rate t-1 (β) -0.075 -0.260 -0.445 -0.630

[0.297] [0.375] [0.477] [0.590]

Observations 160 160 160 160

Fem. : Autorization rate t-1 (β) -0.002 0.001 0.005 0.008

[0.015] [0.018] [0.023] [0.030]

Observations 273 273 273 273

Male : Autorization rate t-1 (β) 0.005 0.004 0.004 0.003

[0.016] [0.019] [0.024] [0.031]

Observations 273 273 273 273

ZUS : Autorization rate t-1 (β) -0.162 -0.190 -0.217 -0.244

[0.118] [0.158] [0.206] [0.259]

Observations 166 166 166 166

Fem. ZUS : Autorization rate t-1 (β) -0.206∗ -0.162 -0.118 -0.074

[0.124] [0.182] [0.260] [0.346]

Observations 163 163 163 163

Female French Origin : Autorization rate t-1 (β) -0.009 -0.012 -0.016 -0.019

[0.018] [0.022] [0.027] [0.033]

Observations 273 273 273 273

Male French Origin : Autorization rate t-1 (β) 0.014 -0.005 -0.025 -0.044

[0.020] [0.025] [0.032] [0.041]

Observations 273 273 273 273

Standard errors clustered at the departement in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: I have omitted the estimates for the control variables to save space.

Another possible way in which these results might be spurious may be that the Front

National vote covaries positively with minority populations. That is, people vote far-right

when they are in contact with large numbers of immigrants or people of immigrant origin.

It could then be that the far-right vote is proxying for the presence of certain demographic

29

populations. If this is true then it may be that the proportion of the demographic in the

population is driving the effect. To address this concern, I add the aggregate population of

active persons in the departement from each demographic into equation (4.4). The addition

of this control should disentangle any potential correlation between the vote share and the

proportion of the demographic in the departement. Adding this control has no substantive

effect on the estimations, supporting the hypothesis that the Front National vote share

captures discriminatory attitudes and it is these discriminatory attitudes that interact with

the level of competition.

5 Discussion

If we follow the theory of the taste-based model, employment differentials will arise only

if the fraction of biased employers (D > 0) is sufficiently large that the demand for less

preferred workers is less than supply. In moving towards a free entry condition we effectively

increase the share of non-discriminating employers entering who expand to arbitrage the

hiring differential which was previously borne by the less preferred workers. This means that

labor market manifestations of discrimination are effectively being competed away. Indeed,

in line with the empirical findings of Charles and Guryan (2008) and Levine et al. (2013),

I find that it is the distribution of discrimination that determines the differential effects of

competition on unemployment rates in France: we see that the beneficial effect of increases

in competition for disadvantaged groups only emerges at higher levels of the far-right vote

share. If we take the vote share of the far-right as a proxy for discrimination, these findings

strongly support the taste-based theory.

Interpreting these results requires us to contextualize the work of the zoning commissions.

Commission decisions concern the French retail sector which may explain why the effects are

centered principally around female labor outcomes. It may be that the retail sector, with

its high demand for cashiers attracts female employees disproportionately. Yet, this does

not diminish the impact of the findings because I explicitly model the effect of competition

interacted with a measure of discrimination. In addition, the lack of effect seen on French

female unemployment rates would be hard to reconcile with the idea that the type of sector

is driving the results.

This last point speaks to the other interesting finding in this paper. The effect on men

and French men in particular is the opposite of the other demographic groups. Black and

Strahan (2001) found that deregulation in the U.S. banking sector diminished the wage

rents that men were able to obtain in a highly regulated market. I find evidence of the

same phenomenon in that the decreases in barriers to entry increase the unemployment

30

rate for men in areas of high discrimination. In areas of high discrimination where the

barriers to entry in the retail sector are high, French men hold an “employment rent” in

that they exploit the discrimination of the employer (possibly themselves) to maintain their

employment status. Reducing barriers to entry in these areas my have a two-fold effect.

1) new firms enter and use minority labor with a wage initially inferior to that paid to

French men, thus driving some discriminating employers out of business. 2) the remaining

discriminating employers begin paying the cost of their own distaste to stay competitive

by hiring minority labor paid at its marginal product, meaning that French men are being

displaced by other demographics.

The public policy implications are unclear. We saw that in interacting the effect of the

the authorization rate with the majority vote shares, the direction of the interaction term

remains consistent with the theory: decreased barriers to entry have the most positive effects

(or least bad) for disadvantaged groups the higher the vote share for the right wing, and vice

versa for the left wing. Yet the sign of the point estimates vary considerably depending on the

interaction. In areas where discrimination is not highly prevalent the effect of lower barriers

to entry is much more ambiguous for the employment outcomes of disadvantaged groups

(see tables 24 and 25 in the appendix). This may be evidence that competition destroys

as much as it creates. Furthermore, imposing higher levels of free entry in Front National

strongholds would be democratically and socially unfeasible. Finally, we must remember

that competition only reduces the manifestations of discrimination in the labor market. It

does not reduce discrimination, per se. To reduce discrimination itself, we most certainly

have to look outside the market for an answer.

6 Conclusion

To my knowledge, this is the first paper to exploit dynamic barriers to entry to measure

competition’s effect on the manifestation of discrimination in the labor market. This paper

also demonstrates a novel way to account for observable skill differences between demograph-

ics in order to isolate the effect of discrimination on employment outcomes if panel data is

available. The data allow me to interact barriers to entry with discrimination in order to

test the predictions of Becker’s taste-based model. In line with the predictions of the theory,

increased competition improves labor market outcomes for disadvantaged groups, but only

in the upper percentiles of the discrimination distribution, as measured by the vote share for

the far-right. Specifically, I find that, in the presence of high discrimination, a marginal de-

crease in barriers to entry in the French retail sector reduces women’s overall unemployment

rate by as much as 0.065 percent. Women who reside in Zone Urbaine Sensible and women

31

of sub-Saharan African origin see much larger returns, with a marginal decrease in barriers

to entry reducing their unemployment rates by between 0.5 and 1 percent. The employment

status of direct immigrants to France and people of sub-Saharan African origin, in general, is

also improved by increased competition in areas of high discrimination. The effect on Male

and particularly French male unemployment is the opposite. I find that, in high levels of the

discrimination distribution, increases in competition are detrimental to their employment

status, suggesting that competition eliminates their labor market power. At average or low

levels of discrimination the effect of competition on employment is ambiguous.

32

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7 Appendices

7.1 OLS estimations

Estimation results of equation (4.1) for all demographic categories not accounting for

a potential fixed effect. Controls are added sequentially to examine the evolution of the

coefficient on the authorization rate, β.

Table 12: OLS estimates - Dependent variable: Immigrant unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.071 -0.085 -0.192∗∗ -0.193∗∗ -0.136 -0.142

[0.057] [0.056] [0.089] [0.088] [0.095] [0.096]

Controls in addition to time trend

Lagged dep. var. t-1 0.186∗∗ 0.178∗∗ 0.171∗∗ 0.141∗ 0.187∗∗∗ 0.189∗∗∗

[0.075] [0.075] [0.075] [0.074] [0.062] [0.064]

Total Autorizations t-1 -0.000 0.008∗∗ 0.008∗∗ 0.006 0.006∗

[0.001] [0.004] [0.004] [0.003] [0.003]

Total meters sq. approved t-1 0.054 0.051 0.046 0.035 0.040

[0.038] [0.039] [0.039] [0.035] [0.035]

Number of applications t-1 -0.007∗∗ -0.007∗∗ -0.005∗ -0.006∗

[0.003] [0.003] [0.003] [0.003]

Overall unemployment rate t-1 0.290 -0.662∗∗ -0.594∗∗

[0.248] [0.263] [0.265]

Overall unemployment rate t 1.979∗∗∗ 1.934∗∗∗

[0.378] [0.382]

Authorization rate t -0.011

[0.060]

Observations 368 368 368 368 368 362

Adj. R sqd. 0.026 0.030 0.041 0.043 0.198 0.189

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

38

Table 13: OLS estimates - Dependent variable: N. African origin unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.045 -0.050 -0.128 -0.130 -0.068 -0.081

[0.070] [0.073] [0.103] [0.105] [0.079] [0.078]

Controls in addition to time trend

Lagged dep. var. t-1 0.123 0.113 0.112 0.049 0.072 0.080

[0.093] [0.091] [0.090] [0.099] [0.084] [0.085]

Total Autorizations t-1 -0.000 0.005 0.005 0.002 0.004

[0.001] [0.004] [0.004] [0.003] [0.003]

Total meters sq. approved t-1 0.044 0.042 0.035 0.020 0.022

[0.030] [0.030] [0.029] [0.024] [0.024]

Number of applications t-1 -0.005 -0.005 -0.002 -0.003

[0.003] [0.003] [0.002] [0.002]

Overall unemployment rate t-1 0.613 -0.394 -0.501

[0.400] [0.316] [0.308]

Overall unemployment rate t 2.285∗∗∗ 2.388∗∗∗

[0.326] [0.327]

Authorization rate t -0.069

[0.071]

Observations 369 369 369 369 369 363

Adj. R sqd. 0.008 0.007 0.011 0.027 0.245 0.257

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 14: OLS estimates - Dependent variable: Female N. African origin unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.072 -0.082 -0.087 -0.095 -0.046 -0.058

[0.057] [0.055] [0.091] [0.091] [0.092] [0.092]

Controls in addition to time trend

Lagged dep. var. t-1 0.149∗∗ 0.144∗∗ 0.144∗∗ 0.109∗ 0.108∗ 0.103

[0.061] [0.061] [0.062] [0.066] [0.062] [0.065]

Total Autorizations t-1 0.001 0.001 0.001 -0.001 0.000

[0.001] [0.004] [0.004] [0.004] [0.004]

Total meters sq. approved t-1 -0.017 -0.017 -0.028 -0.034 -0.027

[0.034] [0.034] [0.032] [0.030] [0.031]

Number of applications t-1 -0.000 0.000 0.002 0.001

[0.004] [0.003] [0.003] [0.003]

Overall unemployment rate t-1 0.640 -0.160 -0.126

[0.395] [0.372] [0.407]

Overall unemployment rate t 1.791∗∗∗ 1.861∗∗∗

[0.337] [0.346]

Authorization rate t -0.053

[0.064]

Observations 350 350 350 350 350 344

Adj. R sqd. 0.032 0.029 0.027 0.040 0.130 0.131

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

39

Table 15: OLS estimates - Dependent variable: SS. African origin unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.048 -0.052 -0.037 -0.043 -0.034 -0.025

[0.098] [0.101] [0.143] [0.144] [0.146] [0.148]

Controls in addition to time trend

Lagged dep. var. t-1 0.138 0.134 0.134 0.127 0.131 0.127

[0.099] [0.101] [0.101] [0.099] [0.102] [0.101]

Total Autorizations t-1 0.001 0.000 0.000 -0.000 -0.001

[0.001] [0.007] [0.007] [0.007] [0.007]

Total meters sq. approved t-1 -0.054 -0.054 -0.063 -0.065 -0.061

[0.055] [0.055] [0.056] [0.055] [0.056]

Number of applications t-1 0.001 0.001 0.001 0.002

[0.006] [0.006] [0.006] [0.007]

Overall unemployment rate t-1 0.480 0.286 0.412

[0.544] [0.602] [0.664]

Overall unemployment rate t 0.387 0.266

[0.585] [0.622]

Authorization rate t 0.064

[0.111]

Observations 297 297 297 297 297 295

Adj. R sqd. 0.004 -0.000 -0.004 -0.003 -0.005 -0.008

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 16: OLS estimates - Dependent variable: Female SS. African origin unemployment

rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.129 -0.132 -0.205 -0.225 -0.224 -0.225

[0.129] [0.129] [0.188] [0.188] [0.187] [0.189]

Controls in addition to time trend

Lagged dep. var. t-1 0.192∗ 0.188∗ 0.189∗ 0.182∗ 0.181∗ 0.182∗

[0.097] [0.099] [0.100] [0.099] [0.099] [0.100]

Total Autorizations t-1 0.001 0.006 0.006 0.006 0.005

[0.002] [0.009] [0.009] [0.009] [0.009]

Total meters sq. approved t-1 -0.064 -0.064 -0.075 -0.075 -0.070

[0.064] [0.065] [0.065] [0.065] [0.066]

Number of applications t-1 -0.004 -0.004 -0.004 -0.004

[0.008] [0.008] [0.008] [0.008]

Overall unemployment rate t-1 0.799 0.719 0.744

[0.603] [0.789] [0.792]

Overall unemployment rate t 0.119 0.085

[0.749] [0.766]

Authorization rate t 0.049

[0.123]

Observations 245 245 245 245 245 244

Adj. R sqd. 0.036 0.031 0.028 0.030 0.026 0.023

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

40

Table 17: OLS estimates - Dependent variable: ZUS unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) 0.019 0.008 -0.031 -0.031 -0.060 -0.056

[0.058] [0.057] [0.088] [0.088] [0.084] [0.082]

Controls in addition to time trend

Lagged dep. var. t-1 0.542∗∗∗ 0.546∗∗∗ 0.546∗∗∗ 0.552∗∗∗ 0.528∗∗∗ 0.528∗∗∗

[0.078] [0.078] [0.079] [0.069] [0.060] [0.060]

Total Autorizations t-1 0.002 0.004 0.004 0.004 0.005

[0.001] [0.005] [0.005] [0.005] [0.005]

Total meters sq. approved t-1 -0.061 -0.061 -0.061 -0.056 -0.054

[0.051] [0.051] [0.051] [0.051] [0.051]

Number of applications t-1 -0.002 -0.002 -0.002 -0.003

[0.004] [0.004] [0.003] [0.003]

Overall unemployment rate t-1 -0.070 -1.363∗∗∗ -1.358∗∗∗

[0.328] [0.286] [0.291]

Overall unemployment rate t 2.449∗∗∗ 2.451∗∗∗

[0.367] [0.382]

Authorization rate t -0.045

[0.060]

Observations 240 240 240 240 240 238

Adj. R sqd. 0.292 0.296 0.294 0.291 0.402 0.393

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 18: OLS estimates - Dependent variable: Female ZUS unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) 0.028 -0.001 -0.062 -0.062 -0.083 -0.082

[0.096] [0.094] [0.130] [0.131] [0.118] [0.119]

Controls in addition to time trend

Lagged dep. var. t-1 0.387∗∗∗ 0.385∗∗∗ 0.385∗∗∗ 0.380∗∗∗ 0.358∗∗∗ 0.357∗∗∗

[0.095] [0.093] [0.094] [0.099] [0.103] [0.103]

Total Autorizations t-1 0.000 0.004 0.004 0.004 0.004

[0.002] [0.006] [0.006] [0.005] [0.006]

Total meters sq. approved t-1 0.077 0.076 0.075 0.081 0.084

[0.071] [0.071] [0.073] [0.075] [0.076]

Number of applications t-1 -0.004 -0.004 -0.004 -0.004

[0.005] [0.004] [0.004] [0.004]

Overall unemployment rate t-1 0.120 -0.782 -0.766

[0.511] [0.553] [0.560]

Overall unemployment rate t 1.721∗∗∗ 1.723∗∗∗

[0.458] [0.462]

Authorization rate t -0.019

[0.086]

Observations 236 236 236 236 236 234

Adj. R sqd. 0.176 0.181 0.180 0.176 0.209 0.199

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

41

Table 19: OLS estimates - Dependent variable: Female unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) 0.002 -0.001 -0.007 -0.007 0.014 0.013

[0.014] [0.015] [0.022] [0.022] [0.013] [0.013]

Controls in addition to time trend

Lagged dep. var. t-1 0.302∗∗∗ 0.301∗∗∗ 0.299∗∗∗ 0.286∗∗∗ 0.221∗∗∗ 0.227∗∗∗

[0.076] [0.075] [0.076] [0.103] [0.066] [0.067]

Total Autorizations t-1 0.000 0.001 0.001 -0.000 -0.000

[0.000] [0.001] [0.001] [0.001] [0.001]

Total meters sq. approved t-1 -0.001 -0.001 -0.001 -0.009 -0.008

[0.009] [0.008] [0.009] [0.006] [0.006]

Number of applications t-1 -0.000 -0.000 0.000 0.000

[0.001] [0.001] [0.000] [0.001]

Overall unemployment rate t-1 0.017 -0.341∗∗∗ -0.332∗∗∗

[0.129] [0.069] [0.074]

Overall unemployment rate t 1.035∗∗∗ 1.044∗∗∗

[0.055] [0.057]

Authorization rate t -0.004

[0.009]

Observations 375 375 375 375 375 368

Adj. R sqd. 0.101 0.100 0.099 0.096 0.683 0.687

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 20: OLS estimates - Dependent variable: Male unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.012 -0.015 -0.034 -0.034 -0.014 -0.012

[0.017] [0.017] [0.026] [0.026] [0.012] [0.012]

Controls in addition to time trend

Lagged dep. var. t-1 0.409∗∗∗ 0.402∗∗∗ 0.400∗∗∗ 0.162 0.238∗∗∗ 0.252∗∗∗

[0.067] [0.066] [0.067] [0.103] [0.072] [0.074]

Total Autorizations t-1 -0.000 0.001 0.001 0.000 0.000

[0.000] [0.001] [0.001] [0.001] [0.001]

Total meters sq. approved t-1 0.018∗∗ 0.018∗∗ 0.017∗∗ 0.009∗ 0.009

[0.008] [0.008] [0.008] [0.005] [0.005]

Number of applications t-1 -0.001 -0.001 -0.000 -0.000

[0.001] [0.001] [0.000] [0.000]

Overall unemployment rate t-1 0.336∗∗ -0.142 -0.176∗

[0.128] [0.095] [0.096]

Overall unemployment rate t 0.977∗∗∗ 0.972∗∗∗

[0.053] [0.055]

Authorization rate t 0.001

[0.008]

Observations 375 375 375 375 375 368

Adj. R sqd. 0.237 0.242 0.245 0.262 0.742 0.735

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

42

Table 21: OLS estimates - Dependent variable: French origin female unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) 0.006 0.002 -0.004 -0.004 0.015 0.018

[0.014] [0.015] [0.021] [0.020] [0.014] [0.014]

Controls in addition to time trend

Lagged dep. var. t-1 0.340∗∗∗ 0.340∗∗∗ 0.339∗∗∗ 0.330∗∗∗ 0.265∗∗∗ 0.272∗∗∗

[0.073] [0.073] [0.074] [0.100] [0.079] [0.083]

Total Autorizations t-1 0.000 0.001 0.001 -0.000 -0.000

[0.000] [0.001] [0.001] [0.001] [0.001]

Total meters sq. approved t-1 -0.000 -0.000 -0.001 -0.008 -0.009

[0.009] [0.009] [0.009] [0.007] [0.007]

Number of applications t-1 -0.000 -0.000 0.000 0.001

[0.001] [0.001] [0.001] [0.001]

Overall unemployment rate t-1 0.015 -0.286∗∗∗ -0.314∗∗∗

[0.100] [0.077] [0.079]

Overall unemployment rate t 0.879∗∗∗ 0.883∗∗∗

[0.074] [0.073]

Authorization rate t 0.019

[0.012]

Observations 375 375 375 375 375 368

Adj. R sqd. 0.130 0.134 0.132 0.129 0.518 0.520

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table 22: OLS estimates - Dependent variable: French origin male unemployment rate(1) (2) (3) (4) (5) (6)

Autorization rate t-1 (β) -0.003 -0.004 -0.019 -0.018 -0.003 -0.000

[0.012] [0.012] [0.017] [0.017] [0.016] [0.016]

Controls in addition to time trend

Lagged dep. var. t-1 0.435∗∗∗ 0.434∗∗∗ 0.432∗∗∗ 0.359∗∗∗ 0.325∗∗∗ 0.325∗∗∗

[0.058] [0.059] [0.059] [0.077] [0.066] [0.069]

Total Autorizations t-1 -0.000 0.001 0.001 0.000 0.000

[0.000] [0.001] [0.001] [0.001] [0.001]

Total meters sq. approved t-1 0.007 0.007 0.005 -0.000 -0.000

[0.008] [0.008] [0.008] [0.007] [0.007]

Number of applications t-1 -0.001 -0.001 -0.000 -0.000

[0.001] [0.001] [0.001] [0.001]

Overall unemployment rate t-1 0.114 -0.164∗ -0.173∗

[0.099] [0.089] [0.098]

Overall unemployment rate t 0.757∗∗∗ 0.749∗∗∗

[0.066] [0.066]

Authorization rate t -0.002

[0.010]

Observations 375 375 375 375 375 368

Adj. R sqd. 0.238 0.235 0.236 0.238 0.533 0.520

Standard errors in brackets clustered at the departement. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

43

7.2 Instrumental variable search

Bertrand and Kramarz (2002) (BK) use the political persuasion of the departement as an

instrument for the authorization rate, finding (somewhat surprisingly) that there is negative

correlation between departements that vote right in legislative elections and authorization

rates. Yet, one can think of a number of ways in which politics directly affect employment

policies and thus satisfying the exclusion restriction seems difficult to argue. Nevertheless,

I test the first stage from BK, using the 2007 legislative results and find no meaningful

correlation. Yet going more in depth with this strategy, I also compiled town (commune) level

data from the 2001 and 2008 municipal elections. If BK were right, and commission decisions

hinge on the median voter then matching the commission decisions directly to the mayors

that sit on the commission may have provided a more robust instrument. I thus matched

each town with the political nuance of the mayor who sits on the commission, hence creating

a variable indicating if the two mayors on the commission are of opposite political parties. I

hypothesized that this variable may measure either strife in the authorization process, thus

reducing authorizations through antagonism or cause higher levels of authorization due to

close electoral competition in the region. In the author’s opinion, this variable would be

more exogenous than the departement legislative vote shares.

Also in-line with exploiting the composition of the commission, I compiled the GPS

coordinates of all towns included in the CDAC data set as well as the GPS coordinates of the

most populous town in the “arrondissement” at the date of the commission.22 Remember

from above that the two key members of the commission are the mayor of the town in

which the application is pending and the mayor of the most populous town within the

arrondissement. I, thus, hypothesized that there could be a strong negative correlation in

the distance between the two towns and the authorization rate, the logic being that the

mayor of the most populous town is more willing to vote in favor of authorization the closer

the proposal is to his or her own town.

Using the dates available in the CDAC data set, I also tested the first stage robustness of

the day of the week as an instrument on which the commission was held. I found that Tuesday

exhibited the highest correlation with the authorization rate. Bryson and Forth (2007) note

that the day of the week is correlated with productivity and that Tuesday is the most

productive day. I also hypothesized that the day of the week may have psychological effects

in that members of the commission are more or less positive on certain days, influencing

authorizations, but exogenous to employment outcomes at time t.

Another hypothesis, was that the number of authorizations is increasing in the number of

22Departements are cut into several political jurisdictions called arrondissement.

44

applications reviewed on a given day. Remember from the introduction that if an application

is not reviewed within the deadline, the authorization is implicit. Thus, if the commission

did not have time to review each application this would provide exogenous increases in the

authorization rate.

Finally, under the hypothesis that weather conditions may influence the mobility or

behavior of commission participants I matched average daily weather conditions with the

location and date of the commission. This data was collected from the website of the National

Climate Data Center which has daily historical data on weather conditions from over 9000

weather stations. Using the GPS coordinates of the weather station and town, I matched

weather variables to the date and location of the commission. Perhaps unsurprisingly, I found

little to no correlation between variables such as precipitation, temperature or visibility level

with the authorization rate in first stage analysis, nor with these variables’ deviations from

the seasonal average.

Table 23 presents first and second stage results using a linear combination of these in-

struments. In the top half of the table are the correlations and standard errors for the

instruments used to instrument the authorization rate. The bottom half gives the second

stage estimates of the effect of authorizations on the different demographics unemployment

rates along with the standard statistics.

45

Tab

le23:First

andsecondstage2S

LSestimates

-Instrumentedvariab

le:∆(autrate) it−

1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Orig.

SS.Afr.Orig.

Fem

.N.Afr.Orig.

Fem

.SS.Afr.Orig.

Fem

.Male

ZUS

Fem

.Zus

Fem

.French

Orig.

MaleFrench

Orig.

Distance

(<10

km

b/w

towns)

0.084∗

∗0.070∗

∗0.064∗

0.081∗

∗0.082∗

∗0.063∗

0.063∗

0.096∗

∗0.092∗

∗0.068∗

0.066∗

[0.034]

[0.033]

[0.035]

[0.034]

[0.032]

[0.035]

[0.035]

[0.038]

[0.040]

[0.035]

[0.034]

Implicitau

thorization(com

mission

overload

)-0.006

∗∗

-0.006

∗∗

-0.005

∗-0.006

∗∗

-0.004

-0.005

∗∗

-0.005

∗∗

-0.004

-0.004

∗-0.006

∗∗

-0.006

∗∗

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

[0.003]

Com

mission

meets

onTuesday

0.062∗

∗∗

0.060∗

∗∗

0.072∗

∗∗

0.077∗

∗∗

0.098∗

∗∗

0.059∗

∗∗

0.058∗

∗∗

0.042

0.040

0.054∗

∗∗

0.057∗

∗∗

[0.019]

[0.019]

[0.024]

[0.019]

[0.021]

[0.019]

[0.019]

[0.027]

[0.027]

[0.018]

[0.018]

Political

difference

b/w

mayors

0.028

0.033

0.034

0.034

0.060∗

∗0.030

0.029

0.062∗

0.055∗

0.031

0.031

[0.034]

[0.034]

[0.033]

[0.038]

[0.029]

[0.032]

[0.033]

[0.032]

[0.032]

[0.033]

[0.032]

Observations

240

240

201

228

161

244

244

153

151

244

244

Adjusted

R2

0.654

0.649

0.686

0.658

0.741

0.637

0.637

0.674

0.668

0.637

0.635

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Imm.

N.Afr.Origin

SS.Afr.Origin

Fem

.N.Afr.Origin

Fem

SS.Afr.Origin

Fem

.Male

ZUS

Fem

.ZUS

Fem

aleFrench

Origin

MaleFrench

Origin

Autorization

rate

0.053

-0.639

∗-0.108

-0.277

-1.669

∗∗∗

-0.035

0.026

-0.695

∗-0.565

-0.017

0.013

t-1(β)

[0.271]

[0.345]

[0.700]

[0.382]

[0.647]

[0.058]

[0.055]

[0.387]

[0.452]

[0.071]

[0.077]

Observations

240

238

189

226

145

244

244

147

144

244

244

F-statof

firststage

4.808

5.141

7.222

5.277

5.092

3.658

3.670

3.553

3.705

3.705

3.720

2SLSsize

ofnom

inal

5%Waldtest

(10%

)19.45

19.45

19.45

19.45

19.45

19.45

19.45

19.45

19.45

19.45

19.45

P-val.of

overidentifyingrest.test

(Sargan)

0.01

0.49

0.31

0.97

0.97

0.64

0.61

0.02

0.01

0.82

0.55

Standarderrors

clustered

atthedepartementin

brackets.

∗p<

0.10,∗∗p<

0.05,∗∗∗p<

0.01

Note:

IVestimationmethodis

2SLS.Resultsforthecontrolshave

been

suppressed.

46

7.3 Effect of authorizations at levels of vote distribution for major

parties

Table 24: UMP (maj.-right): Effects of authorizations over upper distribution of vote

Mean +1 s.d. +2 s.d. +3 s.d.

Imm. : Autorization rate t-1 (β) 0.432 0.409 0.386 0.363

[0.435] [0.418] [0.401] [0.384]

Observations 268 268 268 268

SS. Afr. Origin : Autorization rate t-1 (β) 0.354 0.342 0.330 0.318

[0.919] [0.888] [0.857] [0.827]

Observations 204 204 204 204

Fem. N. Afr. Origin : Autorization rate t-1 (β) 1.083∗∗∗ 1.036∗∗∗ 0.989∗∗∗ 0.942∗∗∗

[0.404] [0.388] [0.372] [0.356]

Observations 252 252 252 252

Fem SS. Afr. Origin : Autorization rate t-1 (β) -0.173 -0.166 -0.158 -0.150

[1.314] [1.269] [1.224] [1.179]

Observations 160 160 160 160

Fem. : Autorization rate t-1 (β) 0.149∗∗ 0.143∗∗ 0.137∗∗ 0.131∗∗

[0.064] [0.062] [0.060] [0.057]

Observations 273 273 273 273

Male : Autorization rate t-1 (β) -0.137∗∗ -0.131∗∗ -0.125∗∗ -0.120∗∗

[0.063] [0.061] [0.059] [0.056]

Observations 273 273 273 273

Fem. ZUS : Autorization rate t-1 (β) 0.626 0.591 0.556 0.522

[0.714] [0.687] [0.661] [0.635]

Observations 163 163 163 163

Female French Origin : Autorization rate t-1 (β) 0.072 0.069 0.066 0.063

[0.071] [0.069] [0.066] [0.063]

Observations 273 273 273 273

Male French Origin : Autorization rate t-1 (β) -0.069 -0.065 -0.061 -0.057

[0.107] [0.104] [0.100] [0.096]

Observations 273 273 273 273

Standard errors clustered at the departement in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: I have omitted the estimates for the control variables to save space.

47

Table 25: Socialist (maj.-left): Effects of authorizations over upper distribution of vote

Mean +1 s.d. +2 s.d. +3 s.d.

Imm. : Autorization rate t-1 (β) -0.155 -0.153 -0.150 -0.148

[0.194] [0.180] [0.167] [0.154]

Observations 268 268 268 268

SS. Afr. Origin : Autorization rate t-1 (β) -0.059 -0.046 -0.033 -0.021

[0.204] [0.190] [0.180] [0.174]

Observations 204 204 204 204

Fem. N. Afr. Origin : Autorization rate t-1 (β) -0.110 -0.104 -0.099 -0.094

[0.191] [0.179] [0.168] [0.158]

Observations 252 252 252 252

Fem SS. Afr. Origin : Autorization rate t-1 (β) 0.075 0.070 0.066 0.062

[0.343] [0.317] [0.294] [0.275]

Observations 160 160 160 160

Fem. : Autorization rate t-1 (β) -0.031 -0.028 -0.025 -0.022

[0.034] [0.031] [0.029] [0.026]

Observations 273 273 273 273

Male : Autorization rate t-1 (β) 0.028 0.025 0.023 0.020

[0.033] [0.030] [0.028] [0.026]

Observations 273 273 273 273

Fem. ZUS : Autorization rate t-1 (β) -0.383∗ -0.367∗∗ -0.351∗∗ -0.335∗∗

[0.204] [0.186] [0.169] [0.154]

Observations 163 163 163 163

Female French Origin : Autorization rate t-1 (β) -0.014 -0.013 -0.012 -0.011

[0.030] [0.028] [0.026] [0.024]

Observations 273 273 273 273

Male French Origin : Autorization rate t-1 (β) 0.119∗∗∗ 0.109∗∗∗ 0.098∗∗∗ 0.088∗∗∗

[0.035] [0.032] [0.029] [0.027]

Observations 273 273 273 273

Standard errors clustered at the departement in brackets. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: I have omitted the estimates for the control variables to save space.

48