Mathieu Bunel
email: mathieu.bunel@univ-nc.nc

Samuel Gorohouna
email: samuel.gorohouna@univ-nc.nc

Yannick L'horty
email: yannick.lhorty@u-pem.fr.

Pascale Petit
email: pascale.petit@u-pem.fr.

Catherine Ris
email: catherine.ris@univ-nc.nc

Ethnic Discrimination in the Rental Housing Market: An Experiment in New Caledonia

Keywords: Discrimination, Rental Market, Field experiment, New Caledonia JEL Classification: J15 R23 C93

This study focuses on the links between ethnic discrimination, housing discrimination, and the ethnic composition of neighborhoods at a specific spatial level, that of the city quarter. Our goal is to determine whether discrimination exacerbates residential segregation. We measure discrimination and access to housing in Greater Nouméa, the capital of New Caledonia, by ethnic background, distinguishing between the people of Kanak (the indigenous people) and those of European descent. Between October 2015 and February 2016, four applicants individually responded to 342 real estate rental ads, made a total of 1,368 responses. Two of the applicants made their Kanak origin known through their surnames, while two others similarly made their European origin known. In each pairing, an applicant signaled financial and professional stability by explicitly indicating that he was a civil servant. A particularity of the study was to analyze this data statistically by crossing it with the ethnic distribution of neighborhoods. Severe discrimination regarding access to private rental housing for Kanak applicants in all neighborhoods was found. Signaling stability strongly reduced discrimination against Kanak applicants. This discrimination is linked to the behavior of landlords and, to a lesser extent, to the actions of real estate agencies. The difficulties accessing housing are solely due to discrimination linked to the social precariousness of Kanaks in neighborhoods where Kanaks are most represented. They are also linked to ethnic discrimination against Kanaks in neighborhoods dominated by Europeans. Housing providers thus play an active role in residential segregation.

Introduction

Works on urban segregation generally pay little attention to the issue of discrimination in terms of access to housing. However, while urban segregation is often a consequence of residential choices of populations, segregation is problematic when it is an outcome of housing market discrimination. Understanding the processes of discrimination in the housing market is useful both to analyze the determinants of spatial fractures that organize the territorial separation of social groups and also to define the most relevant public policies. 6 Discrimination in terms of access to housing has been widely studied in the United States and more recently in various European countries and beyond. 7 All works highlight that religious or ethnic minorities are victims of differences in treatment by the real estate market. Overall, applicants from these minorities are less frequently contacted and the number and quality of properties presented to them is lower.

Research indicates that the extent of discrimination in the housing market varies according to time and place. It can depend on the characteristics of the lessor, the type of property being rented, the neighborhood where the property is located, and the integration and/or economic stability signal transmitted by the applicant. However, as of yet, little work has focused on the spatialized aspect of discrimination in terms of access to housing by studying the role, in a local context, played by the real estate market.

Is there a link between the ethnic composition of a particular neighborhood and discrimination and access to housing on the basis of origin? Do lessors in the most segregated neighborhoods welcome minority applicants? If there is a link between the ethnic composition of a neighborhood and who lessors select? How should this link be interpreted?

In the United States, the first studies undertaken that took into account the ethnic environment at a disaggregated level are those by [START_REF] Yinger | Measuring Racial Discrimination With Fair Housing Audits: Caught In The Act[END_REF], [START_REF] Page | Racial and Ethnic Discrimination in Urban Housing Markets: Evidence from a Recent Audit Study[END_REF], and [START_REF] Roychoudhury | Evidence of Racial Discrimination in Different Dimension of Owner-Occupied Housing Search[END_REF]. Since then, [START_REF] Ondrich | Now You See It, Now You Don't: Why Do Real Estate Agents Withhold Available Houses From Black Customers?[END_REF], followed by [START_REF] Hanson | Field Experiment Tests for Discrimination against Hispanics in the U.S. Rental Housing Market[END_REF], using larger samples, have clarified the relationship between the proportion of white people in a neighborhood and the extent of discrimination. The results obtained by 6 Kain (1968) "The means by which racial segregation in housing has been maintained are amply documented. They are both legal and extra-legal; for example: racial covenants; racial zoning; violence or threats of violence; preemptive purchase; various petty harassments; implicit or explicit collusion by realtors, banks, mortgage lenders and other lending agencies." 7 For the United States, see in particular publications by [START_REF] Yinger | Measuring Racial Discrimination With Fair Housing Audits: Caught In The Act[END_REF], [START_REF] Choi | Do rental Agents Discriminate Against Minority Customers? Evidence from the 2000 Housing Discrimination Study[END_REF], [START_REF] Page | Racial and Ethnic Discrimination in Urban Housing Markets: Evidence from a Recent Audit Study[END_REF], [START_REF] Choi | Do rental Agents Discriminate Against Minority Customers? Evidence from the 2000 Housing Discrimination Study[END_REF], [START_REF] Ondrich | Now You See It, Now You Don't: Why Do Real Estate Agents Withhold Available Houses From Black Customers?[END_REF], Hanson and[START_REF] Hanson | Do Landlords Discriminate in the Rental Housing Market? Evidence from an Internet Field Experiment in US Cities[END_REF]2014); for Sweden, those by [START_REF] Ahmed | Discrimination in the Rental Housing Market: A Field Experiment on the Internet[END_REF]2010), [START_REF] Bengtsson | Gender and Ethnic Discrimination in the Rental Housing Market[END_REF], and [START_REF] Carlsson | Discrimination in the Rental Market for Apartments[END_REF]; for France [START_REF] Bonnet | Better Residential than Ethnic Discrimination! Reconciling Audit and Interview Findings in The Parisian Housing Market[END_REF]; for Spain, the publication by [START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF]; for Italy, the publication by [START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF]; for Greece, the publication by [START_REF] Drydakis | Ethnic Discrimination in the Greek Housing Market[END_REF]; for Belgium, publications by Heylen et al. (2015aHeylen et al. ( , 2015b)); for Australia, the publications by [START_REF] Nelson | Ethnic Discrimination in Private Rental Housing Markets in Australia[END_REF] and [START_REF] Galster | What Explains the Differential Treatment of Renters Based on Ethnicity? New Evidence from Sydney[END_REF]; and for India, those by Thora et al. (2015) and [START_REF] Datta | For Whom Does The Phone (Not) Ring?: Discrimination In The Rental Housing Market In Delhi, India[END_REF].

these studies seem to indicate the existence of local threshold effects in the distribution of discrimination which leads to a tipping point on the effect of neighborhood composition on discrimination. [START_REF] Ondrich | Now You See It, Now You Don't: Why Do Real Estate Agents Withhold Available Houses From Black Customers?[END_REF] demonstrate that discrimination is lower in neighborhoods where minorities are overrepresented, and [START_REF] Hanson | Field Experiment Tests for Discrimination against Hispanics in the U.S. Rental Housing Market[END_REF] show that discrimination is most prevalent in relatively mixed neighborhoods. In a compilation, [START_REF] Oh | What Have We Learned from Paired Testing in Housing Markets?[END_REF] emphasize that real estate agents play a central role in the racial and ethnic steering of applicants towards neighborhoods where their ethnic group is most represented.

In Europe, this neighborhood issue was addressed by [START_REF] Bengtsson | Gender and Ethnic Discrimination in the Rental Housing Market[END_REF] in the Greater Stockholm Area and by [START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF] in various Italian regions. In France, [START_REF] Acolin | A Field Study of Rental Market Discrimination Across Origins in France[END_REF] found that there is more discrimination in areas with more immigrants. These results, which are based on highly aggregated data, show that the share of minorities in the surroundings of the offer tested increases in proportion to the extent of discrimination. However, these results remain tenuous because they do not take into account neighborhood tipping and modifiable areal unit8 that must be identified and controlled.

In Australia, [START_REF] Galster | What Explains the Differential Treatment of Renters Based on Ethnicity? New Evidence from Sydney[END_REF] assessed whether variation in discriminatory incidences across agents, home seekers, dwellings, and neighborhoods in Sydney was similar to that previously observed in the U.S. and Western European markets. They found that differences in geographic context and in the circumstances surrounding the home seekers and the dwellings sought jointly contributed to the likelihood that Anglos will receive favorable treatment from agents.

In this study, we propose to explore the relationship between discrimination and access to housing and local environment based on an experimental measure of discrimination in housing in a French territory according to community of belonging. We use a signal of stability, which makes it possible to distinguish discrimination based on information from discriminations based on preferences. Moreover, we take into account the local socio-ethnic composition at a specific level, that of the city quarter.

From October 2015 to February 2016, we conducted an access to private rental housing test in the Greater Nouméa area (the main agglomeration of New Caledonia) using a discrimination test method for two ethnic groups: Kanaks and Europeans. These communities represent respectively 26% and 33% of the population in the area studied. 9The high level of segregation in the Nouméa agglomeration means that it is perfectly suited for studying the link between ethnic environment and discrimination. In the southern part of this agglomeration, the share of the Kanak population is only 5%, whereas in the northern part of the agglomeration, which is only 10 kilometers away, this proportion reaches 50%.

Moreover, as in this geographical area the two ethnic groups studied are non-minorities, it is possible to clearly distinguish two phenomena: applying for housing as an applicant from an ethnic minority and applying for housing in a neighborhood in which one's ethnic group is underrepresented.

Beyond this spatial dimension, our study protocol also identifies the origin of this discrimination by distinguishing between discrimination resulting from the preferences of the housing providers and that which stems from the social situation and stereotypes regarding tenants' ability to pay. To this end, two profiles for each community were created, i.e., those who sent a signal of financial stability and those who did not. Lastly, in line with the work by [START_REF] Zhao | Does the Number Of Houses A Broker Shows Depend On A Homeseeker's Race?[END_REF], [START_REF] Zhao | Why Do Real Estate Brokers Continue To Discriminate?[END_REF], [START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF]Heylen and[START_REF] Heylen | Discrimination and Selection in the Belgian Private Rental Market[END_REF], our analysis clarifies the impact of real estate agents on the process of discrimination and steering of applicants.

The results show that there is strong discrimination regarding access to housing against Kanak applicants in all neighborhoods of the agglomeration of Nouméa. However, sending a stability signal greatly reduces discrimination against them. The most notable result is that the community environment where housing is being offered strongly affects the scale and especially the nature of the discrimination. In districts where Kanaks are most represented, the difficulties of access to housing are essentially linked to discrimination stemming from their social precariousness, while such difficulties are linked to ethnic discrimination against Kanaks in neighborhoods highly dominated by Europeans. Housing providers thus play an active role in residential segregation.

The structure of the article is as follows. The first section contextualizes the social environment of the Nouméa agglomeration. The second provides an overview of the literature on discrimination in terms of access to housing, focusing in particular on interactions between ethnic environments and the extent of discrimination. The third describes the method and construction of data. The fourth presents the results of testing and discusses the links between discrimination and urban segregation.

Segregation and the housing market in the Nouméa agglomeration

Located in the Pacific, New Caledonia is a French territory that enjoys a special status of broad autonomy, which is very different from the other French overseas communities. This island territory is characterized by severe inequalities based on ethnic background. Despite a proactive and relatively old policy aimed at promoting a more equitable distribution of resources, opportunities, and powers between communities, i.e., the so-called rebalancing (rééquilibrage ) policy pledged in the Matignon Agreements (1988) and strengthened by the Nouméa Agreements (1998), Kanaks, the indigenous people, remain socially disadvantaged [START_REF] Gorohouna | Decomposing Differences in Employment Outcomes Between Kanak and Other New Caledonians: How Important is the Role of School Achievement?[END_REF]. While these inequalities have tended to decline in recent years, they remain significant in many socio-economic areas.10 Table A3 in the annex provides, based on data from the 2014 census, the differences in average socio-professional status by community of membership for the chosen field in our study (men aged 25-55 from the south province). It appears that the proportion of unemployed Kanaks out of the job market is double that of Europeans and their unemployment rate is seven times higher. Among the jobs held by Kanaks, 16.9% have a fixed term contract; 70% have a permanent contract in the private sector; and 13.1% a permanent contract in the public sector. For Europeans, these percentages are respectively 9.8%, 58.7%, and 31.9%. In terms of socio-professional situations, the discrepancies are also very large, as only 14.9% of employed Kanaks hold managerial positions or are mid-level professionals, while 48.1% of Europeans fall into this category.

Beyond these inequalities, New Caledonia is also characterized by a high level of ethnic and social segregation, in particular in the Greater Nouméa agglomeration, which includes the four most populated municipalities of New Caledonia's south province: Nouméa, Mont Dore, Païta, and Dumbéa. Some 60% of the population and 73% of the jobs in the archipelago are concentrated in this area. The southern neighborhoods, predominantly European and residential where well-to-do populations are clustered, sharply contrast with the northern neighborhoods, which are predominantly Kanak, poorer, and sometimes trapped in a process of ghettoization11 [START_REF] Pestana | Kanak and the City: Being of the Place, Expressing It, Urban Issues[END_REF]. To measure the degree of segregation, we applied the usual Duncan and Duncan index of dissimilarity for the two groups, European and Kanak, in the urban area of Nouméa. Based on the census and by using a division of 78 neighborhoods, we obtained an index of 50.8. That means one have to change of neighborhoods more than 50% of Kanak in order to produce a distribution that matches that of the European. According to [START_REF] Lichter | Residential Segregation[END_REF], in the US the estimated segregation indexes vary substantially across metropolitan areas (13) ranging from 32.8 to 54.5. In Europe, this index ranges from 19 to 31 for four capitals.

The agglomeration's housing stock can be described using census data. In 2014, Greater Nouméa had more than 60,000 housing units. Among them, 51% were occupied by their owners, 26% by tenants of private housing, 18% by tenants of social housing, and 5% could be characterized as unplanned housing (slums). There are notable differences between the city of Nouméa and the other areas in the agglomeration. In the latter, nearly two-thirds of the population own their lodgings compared to two-fifths for Nouméa. Social housing is concentrated mainly in the neighborhoods of Nouméa and Dumbéa, where they represent 21% of the housing stock, as opposed to 8% in other areas. There are notable spatial differences in the city of Nouméa depending on the neighborhoods. The northern neighborhoods are characterized by a relatively high proportion of unsanitary housing 12 and social housing. 13 Conversely, in the southern neighborhoods of the city, only a very small proportion of housing is social housing and rental housing is of very good quality and significantly more expensive. 14

Discrimination regarding access to housing and the ethnic environment: an overview of the literature

The testing method most commonly used to measure both discrimination and access to housing and access to employment is also the method that is predominant in the international literature [START_REF] Bertrand | Field Experiments on Discrimination[END_REF]. In the United States, following the Fair Housing Act (1968), a great number of studies used this methodology to analyze discrimination and access to housing. A number of testing campaigns were carried out in more than 30 American agglomerations over several successive waves (1977, 1989, 2000, and 2012) under the auspices of the Department of Housing and Urban Development. Experimental evidence of discriminatory behavior against Black, Hispanic, Asian, and Native Americans minorities was found to be widely practiced (see [START_REF] Oh | What Have We Learned from Paired Testing in Housing Markets?[END_REF] for a recent overview of these studies). In European countries, these tests, although more recent, also exist and confirm the presence of ethnic discrimination. 15 In France, Alcolin et al. (2017), after having tested 300 offers, found strong evidence of ethnic discrimination in the housing market, while [START_REF] Bonnet | Better Residential than Ethnic Discrimination! Reconciling Audit and Interview Findings in The Parisian Housing Market[END_REF], from a telephone test responding to 250 real estate ads, showed that being of North African origin had no significant discriminatory effect when applicants first indicated that they lived in a deprived suburb.

12 According to the IEOM (2008), 4.2% of the housing units of the downtown neighborhoods are unsanitary; this rate reaches 9% in the Vallée du Génie, Quartier Latin, Vallée du Tir, Nouville, and Presqu'île de Ducos neighborhoods. 13 The Tina sur mer neighborhood located north of Nouméa is an exception. 14 According to a 2007 study, prices per m2 of apartments available for rent are 50% higher in the southern neighborhoods of Nouméa than in the rest of the agglomeration (IEOM, 2008). 15 See note 7.

Three variants of discrimination tests coexist: a test based solely on an item of correspondence (a request for information sent by e-mail); a test based on a telephone call; and a test involving the physical presence of potential tenants. 16 Articles by [START_REF] Heckman | Detecting Discrimination[END_REF] and [START_REF] Heckman | The Urban Institute Audit Studies: Their Methods and Findings[END_REF] identified and discussed the limitations associated with this third method, several of which may also apply to the telephone method.

Overall, the objective of these three methods is to compare the success rates of fictitious applicants who are meant to be similar in all aspects with the exception of the characteristic tested. For each of the preceding methods, two variants may exist: the first aims to randomly select a single profile [START_REF] Carpusor | Rental Discrimination and Ethnicity in Names[END_REF] for each offer tested, while the second is based on sending all profiles to the same ad [START_REF] Ahmed | Discrimination in the Rental Housing Market: A Field Experiment on the Internet[END_REF]. The advantage of the first strategy is to reduce the risk of being detected, but its main disadvantage is that it does not control the effect of unobservable fixed variables on the response rate. The second strategy offers the opposite advantages and disadvantages.

The results obtained by these various studies converge to emphasize that ethnic and religious minorities are discriminated against in most of the countries and cities studied. However, these studies demonstrate, given their great heterogeneity, that differences in response rates to the same housing offer vary between 0 and 30 percentage points. This is why most recent studies attempt to characterize and explain the elements that affect these differences.

In order to identify the role played by statistical discrimination (as Arrow, 1973, did) a number of authors crossed the ethnic or religious signal with a signal suggesting a high social level (speaking in a standardized, formal manner), a high level of integration (mastery of the language), or greater employment stability (elevated professional status). Overall, regardless of its form, a signal of quality greatly reduces the extent of discrimination without completely eliminating it [START_REF] Massey | Use of Black English and Racial Discrimination in Urban Housing Markets: New Methods and Findings[END_REF][START_REF] Ahmed | Can Discrimination in the Housing Market be Reduced by Increasing the Information about the Applicants?[END_REF][START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF][START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF]; Drydakis 2011).

The role played by real estate agencies is more ambiguous. According to a test conducted in Belgium by [START_REF] Heylen | Discrimination and Selection in the Belgian Private Rental Market[END_REF] that measured the proportion of agencies agreeing to exclude applicants of foreign origin when renting housing, real estate agencies seem to be a major channel of their clients' discrimination. Previous results by [START_REF] Ahmed | Discrimination in the Rental Housing Market: A Field Experiment on the Internet[END_REF] confirm this conclusion for Sweden. However, the findings of [START_REF] Carpusor | Rental Discrimination and Ethnicity in Names[END_REF] for the city of Los Angeles, indicate that agencies do not discriminate more than private owners, while those of [START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF] regarding different Spanish cities demonstrate that agencies can in fact have a moderating influence in this regard. For Australia, [START_REF] Galster | What Explains the Differential Treatment of Renters Based on Ethnicity? New Evidence from Sydney[END_REF] investigated the relationship between discrimination and neighborhood crime and demonstrated that rental agents in Sydney, regardless of their ethnicity, exhibited the most variation in discriminatory behaviors when the dwellings they were trying to rent were located in neighborhoods classified by differences in safety and tenure; agents were substantially more likely to favor Anglos in neighborhoods that were more desirable. They found that discriminatory behavioral patterns for Anglo and non-Anglo agents often were diametrically opposite in their responses to identical contexts.

One point that is little explored is the link between discrimination and the local ethnic environment of the housing offer. The question is whether discrimination against a given population segment can be linked to the size of this group within the local populace. This is an important question to address in order to understand the urban segregation process and its persistence over time. If the populations targeted for discrimination are a local minority, discrimination in the real estate market amplifies and maintains the phenomena of urban segregation, whereas if the populations represent a majority, the opposite is the case.

The studies do not seem to come to clear conclusions regarding the United States. Works by [START_REF] Yinger | Measuring Racial Discrimination With Fair Housing Audits: Caught In The Act[END_REF], conducted in the city of Boston, by [START_REF] Page | Racial and Ethnic Discrimination in Urban Housing Markets: Evidence from a Recent Audit Study[END_REF], in 25 American cities, and by [START_REF] Roychoudhury | Evidence of Racial Discrimination in Different Dimension of Owner-Occupied Housing Search[END_REF], in Detroit, directly or indirectly examined the link between ethnic environment and the extent of discrimination. However, due to the size of their samples and/or the small differences in existing ethnic distributions in the areas studied, these studies were limited to a pair of neighborhood categories or to considerations at the level of neighborhoods.

Using larger bases (about 2,000 housing offers), [START_REF] Ondrich | Now You See It, Now You Don't: Why Do Real Estate Agents Withhold Available Houses From Black Customers?[END_REF], [START_REF] Hanson | Do Landlords Discriminate in the Rental Housing Market? Evidence from an Internet Field Experiment in US Cities[END_REF], and [START_REF] Hanson | Field Experiment Tests for Discrimination against Hispanics in the U.S. Rental Housing Market[END_REF] produced more sophisticated analyses. They studied the extent of discrimination against Blacks and Hispanics based on the proportion of white people in the neighborhood of the offer in dozens of areas. They showed a nonlinear relation between these two variables that vary according to the cities considered. An agglomeration effect and a neighborhood effect coexist and remain to be characterized.

To our knowledge, in Europe, four studies integrate this dimension: [START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF] in their study of different Italian regions, [START_REF] Bengtsson | Gender and Ethnic Discrimination in the Rental Housing Market[END_REF] in the Stockholm area, [START_REF] Carlsson | Discrimination in the Rental Market for Apartments[END_REF] in various Swedish regions, [START_REF] Acolin | A Field Study of Rental Market Discrimination Across Origins in France[END_REF] in various French regions. These four studies seem to indicate that discrimination is only significant in areas with a larger concentrated share of immigrants (the northern regions of Italy for the first, the suburbs of Stockholm for the second, metropolitan areas for the third, and the Northeast, Southeast and Paris regions for the fourth). However, their results need to be confirmed because they may be sensitive to a modifiable areal unit problem or a fixed effect.

The objective of this article is to provide an in-depth study of the relationship between ethnic discrimination and ethnic segregation of a given neighborhood based on a geographically limited area characterized by major differences in ethnic distribution.

Experimental protocol and data collection

We conducted a test to determine access to a private housing unit for rent in Greater Nouméa. The protocol did not cover access to social housing, achieved through very specific circuits, or access to private homes, which are rare in this area. Data collection took place from October 2015 to February 2016. We tested all ads for apartments for rent in Greater Nouméa published on the reference site http://www.immobilier.nc/ which centralizes all real estate offers in New Caledonia. 17 The applicants for rental housing were men making their Kanak or European origin known through their surnames and first names. The choice of the surnames and first names correspond to credible and realistic identities. They are among the most common first names for this age group and for the community to which they belong. Surnames unambiguously indicate that a person is of Kanak or European background. 18 The order of the response of each type of applicant was randomly changed daily throughout the data collection period. The test consisted of sending short and interrogative emails in response to ads published with a view to requesting a housing unit visit. The following messages were sent:

Dear Sir or Madam: Your ad fits the requirement that I am seeking. I would like to arrange a visit to see the apartment. What documents do you require? Thank you, First Name and Surname (our translation)

These messages were deliberately short, being a first contact. Questions were asked in order to elicit a reaction from the recipient. The surname indicated the ethnicity of the applicant. A specific database was created, listing for each offer the date of publication; the 17 The internet is not the only channel through which housing offers are advertised. Other channels such as newspapers and social networks were not taken into account in this study. In addition, in the case of real estate agencies, we retained only one offer per agency. 18 For European surnames, we used names that are among the most common in metropolitan France. For Kanak surnames, we used common names that identify the person as being a member of a tribe of the south province of New Caledonia, that is to say, in the province where Greater Nouméa is located. Note that New Caledonia is a small archipelago and that Kanak family names are very characteristic and well known. The Kanak names used for our test are relatively widespread and associated with the great tribes of Grand Noumea (mainly in Mont-Dore and Païta) in the South Province. We chose two Kanak names from the Greater Noumea tribes to forestall a potential reaction from landlords who would be less inclined to rent to members from remote tribes (especially those from the Northern provinces and the Loyalty Islands). The landlords' concern would be that extended families (family in the broad sense that it may include several clans or tribes) would come regularly to stay in the future rented accommodations because return trips to and from their homes can be costly. This is an argument often used when deciding not to rent to a Kanak family. Choosing names from adjacent tribes limited this possibility because the extended family would have fewer reasons to stay with the renters, as round trips from their homes can be made in less than an hour.

geographical location; the rental price per month; the size of the unit, both in terms of the number of rooms and square meters; the gender and status of the landlord (real estate agencies or individuals) ; and finally the nature of the response provided to the applicant. Certain sprawling and sparsely populated neighborhoods of Greater Nouméa were not sampled. In the end, 31 neighborhoods were tested, sometimes with a very low number of ads (Map 1). In the spatial exploitation of results, a regrouping of neighborhoods into three areas was carried out (Map 2):

• group 1: neighborhoods in which Kanaks are a small minority (fewer than 15% of residents), located in the south of Nouméa • group 2: mixed neighborhoods, located in Nouméa, where Kanaks represent 15% to 25% of the population • group 3: neighborhoods comprising more than 25% of Kanaks, located in the north of Nouméa and in the areas of Païta, Dumbéa, and Mont-Dore, regardless of the percentage of Kanaks19

In total, 342 real estate ads were tested, including 118 in group 1 (34.5%), 156 in group 2 (45.6%), and 68 in group 3 (19.9%).

Potential differences in the spatial treatment of applicants may come from statistical discrimination or discrimination based on preferences. In order to identify these two sources of discrimination, as was done by [START_REF] Massey | Use of Black English and Racial Discrimination in Urban Housing Markets: New Methods and Findings[END_REF], [START_REF] Ahmed | Can Discrimination in the Housing Market be Reduced by Increasing the Information about the Applicants?[END_REF]), Bosh et al. (2010), [START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF][START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF][START_REF] Drydakis | Ethnic Discrimination in the Greek Housing Market[END_REF], [START_REF] Galster | What Explains the Differential Treatment of Renters Based on Ethnicity? New Evidence from Sydney[END_REF], we crossed the ethnicity signal with a signal concerning the financial and professional stability of the applicant.

The following sentence was added to certain messages sent by our fictitious applicants: "I am a civil servant and I have just moved to Nouméa." This unambiguously signaled employment stability and on average a higher salary, given the indexation policy that exists in this area. 20As such, four rental applicant profiles were created (A-European, B-European civil servant, C-Kanak, D-Kanak civil servant). In the end, our base contained 1,368 responses to 342 real estate ads (4 × 342).

We chose to send the four messages in response to each ad to ensure that each of the fictitious individuals was confronted with the same characteristics of the agents (observable and unobservable). In this way, we could compare their chances of success for the same providers. If we randomly sent a single fictitious individual's response to each announcement, we would not be able to ensure that each candidate was confronted on average with the same characteristics of the landlords as the other three individuals, given the restricted size of testing samples.

Notice that this perfect control for all the characteristics of the landlords has as a counterpart which is a higher risk of detection, which had to be considered. We believe that we minimized this risk, as well as its possible impact, in the following way. First, we only responded to ads uploaded three days before and, in most cases uploaded on the same day. The most recent ads were the ones that received the most responses, all other things being equal. We therefore made the reasonable assumption that other applicants' requests would be interspersed between our four messages, especially since we allowed at least one hour to elapse between the two messages addressed to the same ad. Furthermore, the messages were not "identical" but "similar," that is to say in terms of substance and form they resembled each other, but notable elements of differentiation in terms of formulation were introduced. We also noted that the websites to which we responded to the ads encouraged interested people to contact the offeror through that channel to ask for additional information or to request a visit to the apartment. In other words, it is common for landlords to receive this type of solicitation when they are posting a rental announcement online.

However, there was a concern that these precautions might not totally avoid the risk of detection. It was then necessary to examine what the reactions of a potential landlord would be in case of detection. Since discrimination is punishable, the landlords who realizes that they are being tested have an interest in not discriminating, i.e., not responding to any of the four solicitations or, in contrast, to respond in a similar manner to the four solicitations. Keep in mind that the order in which the four messages were sent to the same ad was random. Overall, none of the four fictitious individuals could therefore be penalized more than the others when the landlords finally realized their mistake. Assuming that some providers detected our test, we would have to minimize the extent of discrimination in our data. This amounted to providing a low range of extent of discrimination. However, even under this hypothesis, we nonetheless showed significant discrimination. Overall high response rates and the fact that 45% of landlords responded positively to all four ads might suggest no detection at all. By comparing the results obtained for each of these profiles, our protocol evaluated the effect of ethnic background, the employment stability conditional, the effect of stability conditional on ethnic background, as well as cross effects, which combined the two aspects (diagram 1).

Finally, we noted that among the ads tested 87% were published by individuals and 13% by real estate agencies. Testing ads placed both by individuals and by real estate agencies enabled us to determine whether potential discrimination was linked to the personal preferences of the real estate agents and lessors and/or the characteristics of the neighborhoods or even whether the agencies played an active role in steering applicants, all hypotheses that are widely discussed in the literature [START_REF] Choi | Do rental Agents Discriminate Against Minority Customers? Evidence from the 2000 Housing Discrimination Study[END_REF][START_REF] Oh | What Have We Learned from Paired Testing in Housing Markets?[END_REF].

Diagram 1. Evaluated effects

European Kanak

European civil servant

Kanak civil servant

Ethnic background effect

Stable situation effect

Stable situation effect

Cross effect Ethnic background effect

Test results

Of the 342 ads tested, 274 obtained at least one answer and in 258 cases these responses were positive. The response rate, calculated by dividing the number of positive responses by the total number of ads, exceeded 75% 21 (Table 1).

Source: DALTON database

We will first present the aggregated results for all of Greater Nouméa before presenting the results by neighborhood groups.

Aggregated results

The applicant who was most frequently contacted by lessors was the European applicant who signaled stability by mentioning that he was a civil servant (Table 2). He was contacted 67.5% of the time in response to his requests. Next was the European applicant who did not signal stability (65.2%), followed by the Kanak civil servant applicant (63.5%), and finally the Kanak applicant who did not signal stability (52.0%). The latter was systematically the least often contacted, regardless of the total number of responses by the lessor. Based on these results, the European applicant who signaled stability sent out 14.8 applications to receive 10 positive responses, while the Kanak applicant who did not signal stability sent 19.2 applications. 21 The response rates per applicant were slightly higher in our study than in those conducted in the United

States [START_REF] Carlsson | Discrimination in the Rental Market for Apartments[END_REF], in Italy [START_REF] Baldini | Ethnic Discrimination in the Italian Rental Housing Market[END_REF], in Sweden [START_REF] Ahmed | Can Discrimination in the Housing Market be Reduced by Increasing the Information about the Applicants?[END_REF], in Spain [START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF], or in Australia [START_REF] Galster | What Explains the Differential Treatment of Renters Based on Ethnicity? New Evidence from Sydney[END_REF], which vary between 40% and 70%. When comparing applicants with different statuses, discrepancies between the two European applicants were statistically insignificant, indicating that the stability signal of being a civil servant is not valued by lessors for a European applicant (Table 3 andTable 4). On the other hand, the difference between the two Kanak applicants was significant, indicating this time that the stability signal of being a civil servant is highly valued by a lessor when it comes to a Kanak applicant (an 11 point difference in this respect).

As a result of the bonus access, the status of the Kanak civil servant did not differ significantly from that of the two European applicants, at a threshold of 5%. On the other hand, the difference between the European applicants and Kanaks of the same status, who either identified as civil servants or were without apparent status, was significant (10% for civil servant applicants alone). These results suggest the coexistence of two sources of discrimination: information discrimination, or statistical discrimination, vis-à -vis the Kanak applicant who did not signal stability, as evidenced by the differences between the Europeans and the Kanaks, which dwindled sharply when the latter signaled employment stability, such as being a civil servant and discrimination based on preferences, of a lesser magnitude, revealed by the fact that significant differences existed between the European and Kanak civil servants (a discrepancy of 4 percentage points).

Thus, a Kanak who does not signal stability is the victim of double discrimination, being targeted for his or her ethnic background and because of real or presumed social precariousness. Taken together, the two elements result in a penalty of about 15 percentage points in the response rate upon initial contact with the lessor. Compared to the applicant of European origin, this corresponds in relative value to a total discrepancy of nearly 30%, a very considerable figure indeed. et al. (2005), [START_REF] Ahmed | Discrimination in the Rental Housing Market: A Field Experiment on the Internet[END_REF], [START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF]Heylen and[START_REF] Heylen | Differential Treatment of Rental Home Seekers According to their Sociodemographic and Economic Status by Real Estate Agencies in Belgium[END_REF], and contrary to [START_REF] Carpusor | Rental Discrimination and Ethnicity in Names[END_REF], there are differences between private owners and agencies. Agencies respond more often to all applicants and discriminate less often (Table 5). 22 Discrimination against Kanaks is more pronounced with an individual than a professional landlord (-4.7 vs.-1.5 and -14.1 vs. -9.0).

This is an interesting result because it invalidates the hypothesis that housing discrimination is essentially driven by the preferences of real estate agents. While we found real estate agents to be involved in the process of discrimination as regards such access, we are not in a position to determine whether they acted based on their own preferences or if they responded to requests expressed more or less explicitly by those whom they represented. Given the size of our sample, we were unable to explore this link any further.

Disaggregated results by group of neighborhoods

The high level of ethnic segregation in the Nouméa agglomeration makes it possible to identify the effect of socio-ethnic concentration of neighborhoods on the extent and nature of discrimination in the same area. Tables 6 and7 present the results obtained for the three types of neighborhoods we selected: strongly segregated (group 1), segregated (group 2), slightly segregated (group 3).

The results obtained point to two elements: on one hand, there are fairly clear differences in treatment depending on the type of neighborhood and, on the other, this discrimination stems from different sources depending on the neighborhood. More specifically, we believe that statistical discrimination and discrimination based on preferences can be attributed differentially between neighborhoods.

In neighborhoods with the highest Kanak concentration, housing providers strongly value the stability signal from Kanak applicants who indicate that they are civil servants. The probability of being called back increases by 20.7 percentage points. Aside from this aspect, there are no statistically significant differences related to the ethnic community that housing-seekers belong to. The difficulties that Kanaks experience in accessing housing in these neighborhoods, which are very real, constitute statistical discrimination related to social and employment instability, perceived as excessive by providers.

In intermediate neighborhoods, we found evidence of statistical discrimination against Kanaks: an employment stability signal significantly increases their chances of accessing housing (by nine points). There is also evidence of ethnic discrimination in the narrow sense of the term: compared with European applicants, a Kanak's chances of obtaining housing drops by 11.6 points if they exhibit no sign of stability and by 5.8 points if they indicate that they are employed in the civil service.

Ethnic discrimination is most evident in neighborhoods with the fewest Kanak residents, i.e., below 15%. Their chances compared to those of Europeans are reduced by 17.4 points if they show no signs of financial stability and by 6.7 points in the case of civil servants. In addition, there are similar levels of statistical discrimination in the southern neighborhoods of Nouméa. A stability signal increases a Kanak's chances in these areas by 11.6 points, similar to the neighborhoods mentioned above.

Econometric confirmation

Finally, Tables 8 and9 present the results obtained using a random effects Probit model. This model estimates the probability of obtaining a positive response conditional on observed characteristics of offers by assuming that part of the variance of the error term is common to all tested offers.

The 0 model estimates the model without a characterization variable for offers. It confirms the existence of two sources of discrimination: income, with an estimated absence of stability signal (NON-STABLE) and ethnic background (KANAK) and the cross effect of NON-STABLE X Kanak obtained in previous tables. This effect exceeds in absolute value the stability effect, which indicates the coexistence of two sources of discrimination, based on preference and on information.

Model 1 adds a large number of control variables related to neighborhood of residence, apartment size, the gender of the lessor, and the nature of the lessor (individual or agency). The coefficients of the variables of interest hardly change in response to the addition of these numerous controls, thereby confirming the coexistence of two difficulties faced by Kanaks in matters of access to housing: the absence of stable income, on the one hand, and ethnic discrimination on the other.

It should be noted that for models 0 and 1, the intra-class correlation is very important, i.e., 0.86, meaning that 86% of the variance of the probability of any of the four fictitious applicants being contacted by the lessor was due to the behavior of the landlords.

The computation of the marginal effects associated with the employment stability signal effect and the ethnic signal effect obtained using model 2 confirms the aforementioned results (Table 9). In addition, when estimating these effects in relation to the three categories of neighborhoods, we obtain a non-significant effect for the ethnic signal in mixed neighborhoods (the coefficient of which is twice or three times lower), whereas in the two highly segregated neighborhoods it is significant and very negative. By contrast, the stability signal's importance in mixed neighborhoods is two-fold compared with segregated neighborhoods. In these areas, where rent cost is lower, landlords must expect a lesser degree of self-selection among tenants and accept a greater risk of default of payment on their part. This is why they are more sensitive to the financial stability signal. The objective of this test was to measure discrimination based on ethnic background by comparing the responses given to Kanak and to European rental housing-seekers. We added a financial stability signal indicating explicitly that the rental applicant was a civil servant in order to distinguish between discrimination based on background or ethnicity (preferences) and statistical discrimination (information). The latter may be associated with social precariousness which, as of June 2016, has become the 21 st legally prohibited ground for discrimination in French legislation.

We found that Kanak applicants are doubly penalized, first as a result of their less favorable, real or assumed, professional and financial situations, and, secondly, because of the ethnic preferences of the housing lessors. The latter are more marked when providers are individuals as opposed to real estate agencies.

The originality of this paper consists of underscoring the fact that discrimination on ethnic grounds varies depending on the neighborhood. In neighborhoods with a significant number of Kanak residents, social precariousness is the main obstacle to accessing housing for members of this ethnic community. However, a stable financial and occupational status increases the chances of finding rental housing for a Kanak applicant. The stability signal is an advantage in other neighborhoods, but to a lesser extent. Ethnic discrimination is concentrated in a smaller space: this is mostly in neighborhoods with the weakest representation of Kanaks and, to a lesser degree, in intermediate neighborhoods. Such discrimination is not statistically significant in areas with the highest number of Kanak residents.

These results suggest that discrimination regarding access to housing plays an active role in residential segregation in the Nouméa metropolitan area. Population distribution in the city depends both on residents' choices and provider, or real estate agency, offers. We have excluded residents' choice from the scope of this study, but we observe that provider offers play an active role in residential segregation in Nouméa.

The main limitation of this study is that its scope does not extend beyond the first step to finding housing, i.e., obtaining an appointment with the lessor. However, our findings are sufficiently robust to warrant a call for policy action. Whereas social diversity in neighborhoods is a stated government objective in France and in New Caledonia and despite the strict prohibition of discrimination on ethnic grounds, the finding that strong discrimination of this kind exists regarding access to housing in New Caledonia touches on important public policy issues. This study's findings are an invitation to serious reflection regarding the regulation of the real estate market and the means that may be implemented in order to counter housing discrimination. Public policy tools range from legislation to concrete measures aimed at giving teeth to the law. Actions to combat social precariousness, in particular through access to subsidized housing or financial security mechanisms aimed at providing guarantees to private lessors, should find a prominent place among these measures.

Annex 1

Table 1 . Distribution of the number of positive responses by offer

 1

	Number of positive responses by	Number of cases	%
	offer
	None	84	24.6%
	1 and more	258	75.4%
	Number of applicants contacted in the event of a positive response
	1	22	8.5%
	2	36	14.0%
	3	45	17.4%
	4	155	60.1%

Table 2 . Non-null response rates by type of applicant

 2

	Type of applicant	Non-null	Response rate as a condition of the total number of responses
		response rate	1			2	3		At least 1
		N	%	N	%	N	%	N	%	N	%
	European civil servant	231	67.5%	13	52.0% 24 66.7%	39	86.7%	231	89.5%
	European	223
			65.2%	3	12.0% 26 72.2%	39	86.7%	223	86.4%
	Kanak civil servant	217	63.5%	5	20.0% 17 47.2%	40	88.9%	217	84.1%
	Kanak	178	52.0%	1	4.0%	5	13.9%	17	37.8%	178	69.0%
	Number of offers		342	25		36	45		258
	Source: DALTON database

Table 3 . Gross rate of non-null responses for the same advertisements (in %)

 3

	Type of applicant	Non-null response rate	Standard deviation	Student T	Confidence interval of 90%
					Lower limit	Upper limit
	European civil servant	67.5***	2.53	26.72	63.3	71.7
	European	65.2***	2.58	25.27	61.0	69.4
	Kanak civil servant	63.5***	2.57	24.65	59.2	67.7
	Kanak	52.1***	2.66	19.55	47.7	56.4
	Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates
	*** Significant at a threshold of 1%
	Source: DALTON database

Table 4 . Differences in the success rates of fictitious applicants for the same offers according to the applicant's work situation and background (in % points) 2 on 2 comparisons for the same offers Deviation (in % points) Student

 4

	Confidence interval
	of 90%
	Lower	Upper
	limit	limit

of stability signal (reference applicant: no stability signal)

	All	6.9***	5.04	4.7	9.1
	Europeans	2.4	1.21	0.84	5.54
	Kanaks	11.4***	5.52	8.01	14.78
	Ethnic background signal effect
	(European reference applicant)
	All	-8.6***	-5.00	-11.4	-5.8
	Employment stability signal	-4.1*	-1.85	-7.71	-0.46
	No stability signal	-13.2***	-5.75	-16.95	-9.42

Cross effect (European reference applicant with stable employment)

	Kanak whose work situation was not disclosed	-15.51***	-6.55	-19.39	-11.62
	Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates
	*** Significant at the 1%, ** 5%, * 10% threshold
	Source: DALTON database

Table 5

 5 Presents the detailed results of the tests and distinguishes between them according to the origin of the offers, whether they came from an individual or a real estate agency. Similar to Choi

Table 5 . Differences in the success rates of fictitious applicants for the same offers according to the applicant's work situation and background based on the status of the offerer (in % points) 2 on 2 comparisons for the same offers Deviation (in % points) Student

 5

	Confidence interval
	of 90%
	Lower limit Upper limit

Offers by agencies Stability signal effect (reference applicant: no stability signal)

	All	6.1	1.31	1.5	13.7
	Europeans	3.1	0.83	3.0	9.1
	Kanak	10.5**	2.01	1.9	19.1
	Ethnic background signal effect
	(European reference applicant)
	All	-5.3	-1.18	-12.6	2.0
	Employment stability signal	-1.5	-0.31	-9.7	6.6
	No stability signal	-9.0	-1.62	-18.1	0.1
	Cross effect (

European reference applicant with stable employment)

	Kanak whose work situation was not disclosed	-12.0**	-2.07	-21.5	-2.5

Offers by individuals Stability signal effect (reference applicants: no stability signal)

	All	4.5***	2.56	1.6	7.4
	Europeans	2.2	0.97	1.5	5.9
	Kanak	11.6***	5.22	8	15.2
	Ethnic background signal effect
	(European reference applicant)
	All	-9.4***	-5.09	-12.5	-6.4
	Employment stability signal	-4.7*	-1.89	-8.8	-0.6
	No stability signal	-14.1***	-5.61	-18.3	-10.0

Cross effect (European reference applicant with stable employment)

	Kanak whose work situation was not disclosed	-16.3***	-6.24	-20.5	-12.0
	Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates
	*** Significant at the 1%, ** 5%, * 10% threshold The p-value is 0.1023
	Source: DALTON database

Table 6 . Raw rate of non-null responses to the same advertisements (in %)

 6

	Type of applicant	Rate of non-null responses	Standard deviation	Student T	Confidence interval of 90%
				Lower limit	Upper limit
		Neighborhoods with under 15% Kanak residents (group 1)
	European civil servant	68.6***	4.31	15.93	61.5	75.7
	European non civil servant	69.5***	4.28	16.25	62.5	76.5
	Kanak civil servant	63.5***	4.41	14.41	56.3	70.8
	Kanak non civil servant	51.6***	4.66	11.09	44.0	59.3
	Neighborhoods with 15% to 25% Kanak residents in Nouméa (group 2)
	European civil servant	68.6***	3.76	18.26	62.4	74.8
	European non civil servant	65.4***	3.76	17.37	59.2	71.6
	Kanak civil servant	62.8***	3.89	16.17	56.4	69.2
	Kanak non civil servant	53.8***	4.00	13.45	47.2	60.3
	Neighborhoods with over 25% Kanak residents in and outside Nouméa (group 3)
	European civil servant	63.2***	5.9	10.8	53.5	72.8
	European non civil servant	57.4***	5.9	9.7	47.6	67.1
	Kanak civil servant	64.7***	5.8	11.2	55.1	74.2
	Kanak non civil servant	48.4***	6.1	7.9	38.4	58.5
	Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates
	*** Significant at the threshold of 1%, ** of 5%, * of 10%
	Remark: The % of Kanaks per neighborhood is drawn from Pestana et al. (2015) using 2009 ISEE census data
	Source: DALTON database

Table 7 . Differences in the success rate of fictitious applicants responding to the same advertisements according to their work situation and background based on the ethnic environment of the housing location (in percentage points) 2 on 2 comparisons for the same offers Deviation (in % points) Student

 7

	Confidence interval
		of 90%
	Lower limit	Upper limit

background signal effect (European reference applicant)

	All	-12.0***	-4.32	-16.5	-7.4
	Employment stability signal	-6.7*	-1.72	-13.1	-0.3
	No stability signal	-17.4***	-4.53	-23.6	-11.1
	Cross effect (European reference applicant with stable employment)
	Kanak whose work situation was not disclosed	-18.2***	-4.31	-25.1	-11.3
	Neighborhoods with 15%

to 25% Kanak residents in Nouméa (group 2) Stability signal effect (reference applicant: no stability signal)

	All	3.2	1.28	0.9	7.3
	European	3.2	1.29	0.9	7.2
	Kanak	9.0***	2.80	3.7	14.3
	Ethnic

background signal effect (European reference applicant)

	All	-8.7***	-3.42	-12.8	-4.5
	Employment stability signal	-5.8*	-1.89	-10.8	-0.8
	No stability signal	-11.6***	-3.34	-17.2	-5.9

Cross effect (European reference applicant with stable employment)

	Kanak whose work situation was not disclosed	-14.7***	-4.23	-20.5	-9.0

Neighborhoods with over 25% Kanak residents in and outside Nouméa (group 3) Stability signal effect (reference applicant: no stability signal)

	All	17.2***	2.21	4.5	30.0
	European	6.7	1.00	-4.3	17.8
	Kanak	20.7***	2.78	8.5	32.9
	Ethnic

background signal effect (European reference applicant)

	All	0.0	0.00	-10.6	10.6
	Employment stability signal	7.0	0.83	-6.8	20.7
	No stability signal	-6.9	-1.01	-18.0	4.3
	Cross effect (European reference applicant with stable employment)
	Kanak whose work situation was not disclosed	-13.7*	-1.69	-27.0	-0.4
	Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates
	*** Significant at the threshold of 1%, ** of 5%, * of 10%
	Remark: The % of Kanaks per neighborhood is drawn from Pestana et al. (2015) using 2009 ISEE census data
	Source: DALTON database

Table 8 . Random effects Probit model

 8 This study draws upon the results of a discrimination test regarding access to private sector rental accommodation carried out between October 2015 and February 2016 in the Greater Nouméa area. The testing consisted of sending out responses to 342 ads on behalf of four applicants, i.e., 1,368 responses to real estate advertisements.

	Model 0	Model 1

Table A1 :

 A1 Summary of principal works on discrimination in the housing market, where ethnic composition of the neighborhood is directly or indirectly insinuated

	Authors	Method	Location	Weight of minority in	Estimated effect
		Output		total population (1)
	United States
	Yinger (1986)	Audit	United States	25%	Discrimination against Blacks
	AER	Number of homes	Boston		and Hispanics is very
		presented to	312 offers in 1981		significant in neighborhoods
		Black/White			dominated by Whites (-30%). It
		applicants			is practically absent in less
					segregated neighborhoods.
	Pager (1995) JUE Audit	United States	At least 12% Black and	Discrimination varies between -
		Number of homes	25 American cities	7% or more Hispanic	10% and -30% depending on
		presented to a	3,500 offers for		the area.
		Hispanic and Black	rentals and for sales	Analysis of a threshold	The ethnic environment
		applicant	in the first half of	effect with a	significantly affects
			1989	neighborhood	discrimination for sales but not
				concentration of at least	for rentals.
				20% Black or Hispanic
	Roychoudhury	Audit	United States	Two types of	Discrimination is lower by
	and Goodman	Black/White	Detroit	neighborhoods, one	black real estate agents and in
	(1996) REE	applicant	319 tests from 1980	60% or more Black and	neighborhoods with a higher
			to 1990	one less than 60%	proportion of Blacks.
				Black (on average, 80%
				of Detroit's population
				is Black)
	Ondrich et al.	Audit	United States	At least 12% Black and	Discrimination is lower for
	(2003) RES	Black/White	25 American cities	7% or more Hispanic	properties located in areas
		applicant	2,465 offers for		where Blacks are over-
		Hispanic/White	rentals and for sales		represented.
		applicant	from May to August
			1989
	Carpusor and	Random testing using	United States	9.8% Afro Americans,	Deviation of 23 (Arabs) and 33
	Loges (2006)	-name	Los Angeles	0.7% Arab, 48.7%	percentage points (Blacks) in
	JASP	-email	1,100 offers in	white	responses.
		Afro-American /	March 2003		No analysis on the effect of the
		Arab /white			ethnic environment.
	Hanson and	Testing	United States	Cities between 3% and	5-point deviation in the
	Santas (2014)	Matching	21 American cities	44% Hispanic	southwest and not significant
	SEJ	Assimilated	3,072 offers		elsewhere. Not directly related
		Hispanics and less			to the % of Hispanics in the
		assimilated			area.
		immigrants (level of
		English)
	Hanson and	Testing	United States	Variation from 0% to	Deviation of 5 percentage
	Hawley (2011 and	Matching	Atlanta, Boston,	100% depending on the	points. Stronger discrimination
	2014) JUE and	Africans using their	Chicago, Dallas,	neighborhood	in mixed neighborhoods.
	RSUE	name	Washington, D.C,
			Houston, Los
			Angeles, New York,
			Seattle, and San
			Francisco local
			July to October 2009
			2,010 offers tested
	Ewens et al.	Random testing using	United States	Variation from 0% to	Deviation of 5 percentage
	(2014) RES	Africans using their	34 cities	100% depending on the	points. Lack of linear relation
		name	14,237 offers tested	neighborhood	between % of Blacks in the area
					and racial discrimination.

Table A3 :

 A3 Characteristics of offers

		In % of offers	Positive response rate Per offer
	Characteristics of the offer and the company
	Size of apartments
	F1	15.5%	66.0%
	F2-LOFT	38.6%	74.2%
	F3	30.1%	81.6%
	F4-F5	15.8%	75.9%
	Characteristics of lessor
	Woman	49.2%	78.9%
	Type of lessor
	Agencies	19.3%	83.3%
	Individuals	80.7%	73.6%
	Location of offers
	Less than 15% (group 1)	34.5%	76.3%
	Between 15% and 25% in Nouméa (group 2)	45.6%	74.4%
	More than 25% and outside of Nouméa (group 3)	19.9%	76.5%
	Average price per m 2
	CFP franc average		-
	(standard deviation)	2,165.2 (549.7)
	Type of game
	A	55.9%	75.9%
	B	44.2%	74.8%
	TOTAL	100%	75.4%
	Number of observations

The modifiable areal unit problem (MAUP) is a source of statistical bias that can alter the results of statistical hypothesis tests. When point-based measures of spatial phenomena are aggregated into districts, the resulting summary values can be influenced by both the shape and scale of the aggregation unit.

In New Caledonia as a whole, Kanaks and Europeans represent respectively 39% and 27% of the population.

According to the Institut de statistique de New Caledonia (New Caledonia Institute of Statistics, or ISEE ), the employment rate was 49% for Kanakss and 71% for non-Kanakss; unemployment according to the census was 27% for Kanakss as opposed to 10% for other natives; 5% of Kanakss have a higher education degree, as opposed to 28% of non-Kanakss; conversely, only 3% hold managerial positions and 16% are mid-level professionals, as opposed to respectively 7% and 24% for other natives.

This is especially the case for the Rivière Salée neighborhood.

[START_REF] Thorat | Urban Rental Housing Market Caste and Religion Matters in Access[END_REF] and[START_REF] Bosch | Information and Discrimination in the Rental Housing Market: Evidence from a Field Experiment[END_REF] are among the few references to combine two approaches simultaneously. The former demonstrated that when fictitious applicants are sent by email, discrimination is stronger than when initial contact is made by telephone. The latter demonstrated that there are no differences between emailed applications and making contact by telephone.

Ethnic disparities by neighborhood in the areas of Païta, Dumbéa, and Mont-Dore, disparities that also exist but in a markedly less pronounced manner than in Nouméa, could not be taken into account because the housing offers in these areas were too low.

The salaries of civil servants in the area considered are multiplied by 1.73 compared to the French metropolitan grid. According to ISEE data, in New Caledonia, civil servants have higher incomes on average than people working in the private sector.

We also tested whether the gender of the lessor influences the response rate and level of discrimination. As with[START_REF] Ahmed | Discrimination in the Rental Housing Market: A Field Experiment on the Internet[END_REF], this dimension did not significantly affect the results.

Source: DALTON database

This research was supported by the The French National Research Agency -Grant DALTON ANR-15-CE28-0004) and by the University of New-Caledonia. We are grateful to Tuamanaia FOIMAPAFISI for her assistance.

Map 1: Location and number of housing offers tested in Greater Nouméa

(1) Data in article or census data