metadata: authors: 0: author: Simon Martin,Abel
count: South Africa
docna: 0: docna: Labor market discrimination and sorting :
            evidence from South Africa
docty: Policy Research Working Paper
subtopic: Human Rights,Indigenous Peoples,Indigenous Peoples Law,Labor Markets,Educational Sciences,Gender and Development,Indigenous Communities,Rural Labor Markets
abstracts: cdata!: This paper collects a unique data set of
            classified ads and exploits quasi-random variation in the
            applicant pool composition to test for hiring discrimination
            against immigrants in South Africa's informal sector.
            Consistent with a tournament models in which immigrants are
            penalized, the analysis finds that foreigners and natives
            benefit from being pooled with foreign job seekers. Next,
            the paper tests whether discrimination affects search
            behavior. Controlling for location fixed effects, the
            analysis finds suggestive evidence for sorting: immigrants
            search further away and higher discrimination in the
            residential area is positively correlated with the decision
            to search in different suburbs.
geo_reg_key: 517195,517191
repnme: repnme: Labor market discrimination and sorting :
            evidence from South Africa
display_title: Labor market discrimination and
            sorting : evidence from South Africa
listing_relative_url: /research/2017/09/27959769/labor-market-discrimination-sorting-evidence-south-africa
topicv3: Labor Markets m1326488 855,Indigenous Peoples m1326947 749,Education m1327048 470,Gender m1327621 615,Discrimination m1505707 3379
publishtoextweb_dt: 2017-09-06T00:00:00Z
no_of_pages: 42
model_extractions: WPS8180 Policy Research Working Paper 8180 Labor Market Discrimination and Sorting Evidence from South Africa _Martin Abel_ Jobs Group & Africa Gender Innovation Lab September 2017
Policy Research Working Paper 8180

# **Abstract**

This paper collects a unique data set of classified ads and exploits quasi-random variation in the applicant pool composition to test for hiring discrimination against immigrants in South Africa’s informal sector. Consistent with a tournament models in which immigrants are penalized, the analysis finds that foreigners and natives benefit from being pooled with foreign job seekers. Next, the paper tests whether discrimination affects search behavior. Controlling for location fixed effects, the analysis finds suggestive evidence for sorting: immigrants search further away and higher discrimination in the residential area is positively correlated with the decision to search in different suburbs.

This paper is a product of the Jobs Group and Africa Gender Innovation Lab. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mabel@middlebury.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Labor Market Discrimination and Sorting: Evidence from South Africa Martin Abel<sup>_∗†_</sup>

> _∗_ Middlebury College. Email: mabel@middlebury.edu

> _†_ Acknowledgements: this paper greatly benefited from discussions with and comments from Willa Brown, Rulof Burger, Adi Eyal, Rema Hanna, Lawrence Katz, Asim Khwaja, Sendhil Mullainathan, Devah Pager, Dorit Posel, Vimal Ranchod, Roland Rathelot, Jann Spiess and seminar participants Harvard University, University of Cape Town, Stellenbosch University and the New England Development Conference. Simeon Abel provided excellent research assistance. All errors and omissions remain fully my responsibility. This paper is a product of the Job Group and the Africa Region Gender Innovation Lab. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
# **1 Introduction**

It is notoriously difficult to study labor market discrimination. In recent years, a large literature has emerged in which researchers send fictitious resumes to estimate discrimination along many characteristics including race, gender, age, and sexual orientation (for recent reviews see Bertrand and Duflo (2016); Neumark (2016); Rich (2014)). While these correspondence studies have important advantages over observational and audit studies, they can only be used for select types of vacancies (Pager (2007)) and have several methodological limitations (Heckman (1998)).

In particular, audit studies have been applied almost exclusively in the formal sector of developed countries.<sup>1</sup> Yet, a minority of the global labor force works in these markets and these are precisely the settings in which employees enjoy the most comprehensive legal protection from discrimination. This study provides evidence for a very different setting: the situation of immigrants working in the informal sector in South Africa. Obtaining evidence on discrimination through audit studies is difficult in these markets as employers typically neither advertise job vacancies nor use formal hiring processes that include systematic screening of job seekers.<sup>2</sup> Pager (2007, p.111) therefore concludes that in this context “ _any study would require in-person application procedure_ ”. I will argue that the observational data collected in this study allows me to not only apply many of the methodological advantages of audit studies, but also address some of the key methodological limitations.

I collect a unique data set of 5,500 job seeker advertisements in the domestic work, nanny and general work sector collected from South Africa’s largest classified ad website, which caters mainly to the informal sector<sup>3</sup> . When employers search for workers in a particular sector and region they are presented with a list of truncated summaries of job seeker profiles. Based on the information visible at the search stage employers decide whether to click on the full profile, which provides additional information and contact details of the job seeker. Importantly, the profile page reports how many people previously clicked on the profile which serves as a measure of employer demand. This setting mimics the ideal experiment in that, similar to correspondence studies, researchers observe exactly

> 1Bertrand and Duflo (2016) note, for example, that there is not a single audit study conducted in Africa.

> 2Moreover, ethical concerns have been raised about conducting audit studies that involve sending thousands of fictitious CVs in labor markets with mass unemployment as this would further impede the hiring process.

> 3According to the ILO, one of the defining characteristics of the informal sector is that “ _Labour relations are based mostly on casual employment, kinship or personal and social relations rather than contractual arrangements with formal guarantees._ ” According to the data I collect, 80% of job seekers employed through the website are agreed on the basis of a verbal agreement rather than a written contract. It is predominantely used by private households looking to hire: among job seekers who receive a response to their advertisement, 95% report being contacted by a private household.

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the same information as the potential employer at the time of her decision whether to further screen a job seeker. I find that after controlling for other covariates, stating that one is an immigrant is associated with receiving about 10-20% fewer employer visits to the profile page than job seekers who state they are South African.

This unique data set also allows me to at least partly address previous studies’ limitation that the pool of applicants from which employer can hire (in addition to the fictitious applicant) is not observed. This is an important concern as the size, qualification and racial composition of the applicant pool affect the cost of discrimination. Intuitively, the fewer and relatively less qualified applicants of the preferred race, the more costly are discriminatory hiring practices.<sup>4</sup> For the classified ad market I investigate, we observe the full set of available candidates presented to employers in her local labor market. What is more, the nationality composition presented to employers is quasi-random as the order is determined by the time of the day that job seekers post their profile. Consistent with theoretical predictions from a tournament model in which employers discriminate against foreigners, I find that being pooled with a larger share of immigrants benefits _both_ foreign and South African job seekers. Employing an estimator that soley exploits variation in the composition of the choice set is a methodological contribution that may be applied to estimate preferences in otthe scenarios.

Exisiting audit studies have been criticized on the ground that minority workers may be able to identify discriminating employers, which would alleviate the effects of employer discrimination (Heckman (1998). In an extension of Becker (1957), Arrow (1973) shows that the wage differential between groups can disappear if there are a sufficient number of non-discriminating employers. In equilibrium, this may result is a (partially) segregated employment without (wage) discrimination. The extent to which (wage) differentials disappear depends on how well minorities can target non-discriminating employers in their job search (Black (1995); Lang and Lehmann (2012)). On the website, I observe both where job seekers live and the location where they search for work. These data allow me to shed some light on whether minority applicants adjust their search behavior in response to discrimination. Employing a residential location fixed effect strategy, I find that immigrants search 21% further away and suggestive evidence that higher levels of discrimination faced in the area of residence induces immigrants to search for work in a different suburb. Geographic sorting in response to discrimination

> 4A related challenge to interpreting audit studies is that results are consistent with a world in which employers’ racial preferences are proportionate to the population’s racial composition but in which the share of minority applicants is disproprtionately high.

2
presents an important additional cost of labor market discrimination to job seekers, especially in a setting with high transport costs like South Africa.<sup>5</sup> These findings complement recent evidence from the U.S. by Pager and Pedulla (2015) who document that perceived discrimination among African Americans is associated with applying to a wider range of jobs.

Last, this study hopes to contribute to the literature on the nature of discrimination.<sup>6</sup> Altonji and Pierret (2001, AP henceforth) aim to distinguish between statistical and taste discrimination by testing how the effect of observable characteristics on wage develop over time. According to the statistical discrimination theory, observable characteristics such as education and race should explain less of wage levels over time as employers learn about harder to observe determinants of productivity.<sup>7</sup> Building on the AP employer learning model, I find that employer demand _diverges_ between South Africans and foreigners once more information is revealed on the applicants’ profile. This is consistent with a model in which employers _positively_ statistically discriminate against foreigners. Results from an anonymous survey I conduct with 208 domestic workers supports this conclusion: I find that immigrants have characteristics (e.g. age, education) favored by employers.

There are other differences between South African and foreign workers which may explain the results. Employers may refrain from selecting foreigners due to uncertainty of their legal status. However, I find that among foreign job seekers, those that indicate they have a work permit receive about 8% _fewer_ profile clicks. The South African government suspects that some employers prefer hiring undocumented foreigners because they have less bargaining power and are thus more exploitative (DoL 2007). Alternatively, revealing that one has a work permit may also signal a higher reservation wage to employers. Results from the survey paint a subtle picture: undocumented immigrants do not have lower reservation wages or are paid lower wages than documented immigrants or South

> 5Kerr (2015) estimates that household spend on average 11% of their income on transport cost and that modes like trains or buses impose an effective tax rate of 25-30% on hourly wages.

> 6The theory of statistical discrimination was pioneered by Phelps (1972) who reasoned that in a world of asymmetric information, employers assess the expected productivity of workers according to the average of the population with similar observable characteristics. Labor market discrimination can result when observable characteristics like race or gender are used by employers to infer information about productivity. By contrast, according to Gary Becker’s model of taste-based discrimination, employers have discriminatory tastes and are thus willing to pay in order to not hire certain groups (Becker (1957)). While existing audit studies (Bertrand and Mullainathan (2004); Oreopoulos (2011)) credibly show that discrimination exists in the screening process, they do not provide conclusive evidence on the source of discrimination (Lang and Lehmann (2012)). For example, a lower return to more credentials is at odds with both the taste and statistical discrimination approach (Bertrand and Mullainathan (2004)).

> 7The authors find little support for a model of statistical discrimination by race: while the coefficient on education falls, the negative coefficient for black race persists. One concern with this interpretation is that the discriminated group may face additional forms of discrimination such as being omitted in promotion decisions due to reasons of statistical discrimination, which may explain the persistent negative effect on race.

3
Africans. They are, however, less likely to know about or be willing to utilize the CCMA, a widely used labor arbitration institution available to employees to take employers to court.

In sum, foreigners receive fewer profile clicks despite positive statistical discrimination and lower risks of being taken to court, pointing to the importance of taste discrimination. Consistent with this interpretation, I find that discrimination increases in the intimacy of the employer-worker relationship: it is highest in the nanny and lowest in the gardening sector.

One limitation of this research design is that, similar to most audit studies, we only observe the first step in the hiring process. Faced with a large number of applications, firms may commonly use heuristics in screening applicants and stop reading once they see a certain signal, in our case foreign nationality (Bertrand and Mullainathan (2004)). In fact, a review by Riach and Rich (2002) concludes around 90% of discrimination occurs at this first selection stage. Kuhn and Shen (2013) develop a screening cost model and show that narrow search strategies that pre-screen applicants are more commonly used in low-skilled sectors with large applicant pools. A coarse job screening strategy is thus highly relevant for the high unemployment, low-skill sectors context of this study (Bartos et al. (2016)).<sup>8</sup> A second limitation is that while we observe the same information as employers, we may fail to notice subtle differences between profiles that differ between natives and immigrants. I try to address this by controlling for a rich set of profile characteristics including the number of words, spelling and grammar mistakes, the time and day of the posting, and a set of variables for information such as experience, age, and qualification. I also find that results are robust to controlling for job seeker characteristics _unobservable_ to employers at the screening stage.

This study adds to a growing literature on online labor markets. These markets, which are predicted to increase rapidly in the future (Horton (2010)), are particularly prone to information asymmetries since, especially at an early stage in the hiring process, employers have very limited information of the job seeker (Pager (2007)). The study is most similar to Kuhn and Shen (2013, 2014) who use data from an online labor market in Xiamen, China to test how employer call back rates differ for job seekers with varying observable characteristics like age, gender, or education. This study also contibutes to a larger literature that uses data from online markets to estimate preferences. For example, Pope and Sydnor (2011) analyze data from propsper.com, a peer-to-peer lending website

> 8In addition, social-psychology theories of _unconscious_ bias predict that discrimination is most pronounced when firms have limited information on job applicants (Arrow (1998); Pager (2007)).

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in which borrowers create a loan listing with unverified personal information and pictures in order to request funding. The authors find that signals about the lenders’ age, race, and gender from the advertised profile significantly affect the likelihood to receive loans and the interest rates paid by borrowers.

The paper proceeds as follows. Section 2 reviews the evidence on labor market discrimination and (undocumented) immigration in the South African context. Section 3 summarizes data collected from an online labor market for domestic workers. Section 4 discusses the identification strategy, reports the main results and offers model extensions that explore the role of the applicant pool composition and market thickness. Section 5 explores mechanisms and Section 6 tests if spatial sorting of job seekers is linked to employer preferences. Section 7 concludes.

# **2 Background: Immigration and discrimination in the South African labor market**

Racial discrimination has played an important role in South Africa’s history, both before and during _apartheid_ . The post- _apartheid_ regime implemented legislation to improve the economic situation of previously disadvantaged groups, most notably the Employment Equity Act (Act 55 of 1998) and the broad-based Black Economic Empowerment (BEE) Act (Act 53 of 2003). Yet, the legacy of half a century of racial segregation and discrimination under apartheid has persistent effects even more than 20 years after the democratic transition.<sup>9</sup> Firstly, while BEE created a small class of highly successful workers, the majority of businesses are still owned by the white minority. Secondly, racial tension and discrimination exists between previously disadvantaged population groups, partly as the result of the apartheid’s policies of racial division and the selective lifting of restrictions for Coloureds<sup>10</sup> and Asians in the 1980s (Seekings and Nattrass (2008)). Thirdly, it has been argued that apartheid’s racist immigration laws, class division, de-sensitization to violence, and attitude of superiority towards the rest of Africa are partly to blame for South Africa’s high degree of xenophobia that resulted in violent riots in 2008 and 2015 (Crush (2008)).

> 9For investigations of labor market discrimination between South African race groups see Burger and Jafta (2006).

> 10The term ‘ _Coloured’_ refers to a heterogeneous ethnic group with ancestry from Europe, local tribes, West Africa, Mozambique and various places in Asia including India, Indonesia, and Malaysia. Coloureds tend to have lighter skin than the black population group and make up 9% of the population in South Africa and are mainly concentrated in the Western Cape (Seekings and Nattrass (2008)).

5
This paper focuses on the situation of immigrants in the South African labor market. At the end of _apartheid_ , South Africa opened up to migration. The total number of documented immigrants increased by 60% from 1994 to 2003 and the share of Africans among immigrants increased from 25% to almost 50% over this time. In 2002, amidst increasing unemployment rates, the government passed the Immigration Act to facilitate easier access for South African employers to foreign skills while limiting labor market access for unskilled and semi-skilled immigrants. Employment opportunities in the formal sector became scarcer for immigrants due to “South Africans first” legislation, most notably in the mining sector. As a result, the proportion of foreign miners fell from 51% in 1997 to 38% in 2006 (DOL (2007)). Yet, rather than deterring immigration, many believe that the new migration policy led to a shift from legal to undocumented migration and employment of immigrants in the informal rather than formal sector. Undocumented immigration in recent years was further spurred by the political and economic crisis in neighboring countries, most notably Zimbabwe.

It is difficult to measure the number of immigrants living in South Africa. According to the 2001 census, 688,000 foreigners lived in South Africa, yet the number of irregular immigrants is likely much higher. The government estimated the number of undocumented immigrants to be around 600,000 but they cannot rule out that the true figure might be as high as three million (DOL (2007)). The government’s response to the influx of undocumented immigrants was increased deportation: more than 3 million undocumented immigrants have been deported since 1990 (97% from SADC countries including 90% from Zimbabwe and Mozambique) and the annual number of cases tripled between 2002 and 2007 (Crush (2011)).

Large immigration flows combined with high unemployment rates have fostered xenophobic attitudes among South Africans as revealed in a survey of South Africans’ attitudes towards migrants and refugees (Crush (2008)).<sup>11</sup> Yet, the South African Department of Labor (DoL) concludes that “ _although a systematic survey of employers has not been conducted, case study evidence suggests that there is widespread preference for non-South African workers._ ” (DOL (2007)). If true, this poses a puzzle: Do employers prefer hiring immigrants despite widespread xenophobic attitudes? What role does the legal status and exploitability of immigrants play? And how does discrimination vary be-

> 1184% of respondents believe that South Africa lets too many foreigners into the country, 74% favor deporting people that are not contributing economically, and two-thirds agree that rights to legal protection and police protection should never be granted to undocumented immigrants. Hostile attitudes are routed in beliefs that foreigners pose a criminal threat (48%), compete for jobs (37%), and bring diseases (29%). Xenophobic attitudes only differ slightly by employment status and tend to be somewhat stronger among whites across all income brackets (Crush 2010).

6
tween occupations? This paper hopes to shed light on these questions using data from the domestic worker, nanny, and general work sector in South Africa.

# **3 Data**

I collect data on job seekers that use the website www.gumtree.co.za, South Africa’s most widely used website for classified job advertisements.<sup>12</sup> Gumtree allows job seekers to post ‘job wanted’ advertisements for free in sectors ranging from housekeeping to engineering.<sup>13</sup> People specify the suburb where they search for employment, include a text with information about themselves, provide their home address and the position they are looking for and may upload a picture. Employers can either post ‘job offered’ advertisements or search for people by sector, region and suburb. Search results provide a list of matched profiles that include a truncated version of the text (see Figure 1). By clicking on the truncated profile of a job seeker, the employer is directed to the individual profile site that contains the full text and contact information. The profile page also has information on the number of people that previously visited this site which will serve as the main outcome variable of interest in the analysis of employer demand.

I collect advertisements posted by job seekers in the housekeeping, general work, and nanny sector between October 2012 and January 2013 in the Cape Town region.<sup>14</sup> Data were captured twice a day for all job seeker ads posted 12 to 24 hours before to alleviate potential selection problems as profiles of people successfully hired may be taken off the website.<sup>15</sup> I collect all posted information, the time of the posting and number of visits for more than 5,400 advertisements. I use only the text visible to employers on the _search result_ site to encode characteristics provided by the job seeker including nationality, age and gender as well as qualification information such as whether the person has job experience or certificates, holds a driver’s license, and is willing to live in the employer’s

> 12Over all provinces, Gumtree has more than 30,000 advertisements posted by employers. Comparable numbers for the next most popular online sites and newspapers are 9,000 by Job mail, 1,000 by Career junction, and 14,000 by JunkMail.

> 13Gumtree also gives the option to pay for priority advertisements that are posted at the top of the search results. These advertisements are excluded from the analysis as this service is mainly used by agencies that act as brokers.

> 14I focus on Cape Town as it is the region most highly on the job website. Other cities and more rural areas are unsuitable for this analysis as they have a much lower frequency of advertisement postings.

> 15Job seekers have the option of re-posting an advertisement once a month. This may potentially bias results as employers may not click on a profile that they recognize from a previous search. In practice, less than 5% of the profiles are re-posted using the identical advertisement and I exclude these from the analysis. More frequently, job seekers post the same profile in a different suburb. This is less of a problem since employers are likely only looking for people searching in their specific suburb.

7
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migrants (Dinat and Peberdy (2007)).

Table A.1 provides summary statistics for the gumtree domestic work sample divided by the main nationality groups. As the website does not systematically collect information of job seekers, all data reported are derived from the profile text visible in the search result. For example, the age mean is calculated from the 28.3% of Gumtree job seekers that provide information on their age. There is thus the possibility of reporting bias - an issue I will discuss below.<sup>16</sup> For a first descriptive summary of employer demand across population groups, Figure A.1 plots the distribution of profile clicks for South Africans, Malawians, Zimbabweans and people who do not state their nationality. The figure shows that the profile clicks follow a normal distribution for each population group. South Africans get more profile clicks than profiles without nationality information which in turn get more clicks than profiles of Zimbabweans and Malawians. The order of employer preference seems robust to outliers; in fact, the ranking of distributions (almost) follows a pattern of statistical dominace. However, these differences may be due to differences in profile characteristics between population groups documented in Table A.1. The next section offers two empirical strategies to estimate the causal effect of nationality on employer demand.

# **4 and Results**

## **4.1 Selection on Observables**

### **4.1.1 Domestic Work Sector**

As discussed in section 3, I observe exactly the same information as the employer at the time she makes the decision whether to screen an applicant’s profile. While employment studies using observational data often suffer from the omitted variable problem, this study design guarantees that the observable data are orthogonal to the error term (assuming that I correctly codify the profile

> 16While this study focuses on domestic workers, I will also compare results to the nanny and general work sector (which includes gardening) to test how employer preferences vary between sectors. Table A.2 compares summary statistics across sectors. 75% and 80% of job seekers revealing their gender in the housekeeper and nanny sector, respectively, are women, whereas the general work sector is dominated by men. The share of Malawians is disproportionately high in the general work sector and Zimbabweans are overrepresented in the nanny sector. While job seeker characteristics such as age, experience, and the share holding work permits are relatively similar across sectors, I can reject a test of equal means for almost all variables.

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information). The first empirical strategy is thus to estimate what information revealed in the search result determines the decision of the employer to click on the full profile page. I estimate the following specification: I regress the log number of profile clicks _yi_ on a vector of control variables ( _X_ ) which includes covariates listed in Table A.1 such as gender, age, experience, and references. I also control for a set of suburb dummies _φj_ and a vector of variables _timet_ that control for the day and time at which the profile was posted and the number of hours for which the profile was online by the time of data collection. The covariate vector _langi_ controls for the number of spelling, grammar and punctuation mistakes in the profile.

This paper’s main focus is on the role of nationality. I include dummies capturing if the applicants are South African ( _SA_ ) or a foreigner ( _For_ ). I will also estimate specifications with a set of nationality dummies to account for the fact that employers may have differential preferences for applicants from different African countries. The omitted category in these specifications is the group of people providing no information on nationality. The parameters of interest _βi_ thus capture the difference in profile clicks among profiles with the same observable characteristics _X_ , posted at the same time for the same suburb but with varying nationality.

Column 1 in Table 1 shows that profiles of South Africans get about 7.5% more clicks compared to profiles without nationality information whereas profiles of Zimbabweans and Malawians get 3.4% and 10.9% fewer clicks, respectively. The difference in coefficients for natives and foreigners are statistically significant. Once I control for language mistakes, the coefficient on the Zimbabwe dummy becomes more negative (Column 2). This reflects that Zimbabweans tend to make fewer language mistakes. Estimates are unchanged when I control for a set of suburb dummies which suggests that differences in employer demand are not due to differences in where people search for jobs (Column 3). In Column 4, I control for the full set of control variables. The nationality coefficients stay qualitatively unchanged and coefficients on most of the control variables have the expected sign which is reassuring. For example, male job seekers receive fewer profile clicks and people with more years of experience or who are willing to live with employers receive more clicks. I estimate the effect 10
Table 1: Housekeeper Analysis <u>(y:</u> Log Profile Clicks)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|South African|0.0755|0.0306|0.0195|0.0353|0.0327|
||(0.0476)|(0.0395)|(0.0398)|(0.0410)|(0.0410)|
|Zimbabwian|-0.0337|-0.0413<sup>_∗_</sup>|-0.0463<sup>_∗∗_</sup>|-0.0409<sup>_∗_</sup>||
||(0.0237)|(0.0214)|(0.0212)|(0.0225)||
|Malawian|-0.109<sup>_∗∗∗_</sup>|-0.105<sup>_∗∗∗_</sup>|-0.104<sup>_∗∗∗_</sup>|-0.0846<sup>_∗∗∗_</sup>||
||(0.0201)|(0.0185)|(0.0191)|(0.0199)||
|Foreign|||||-0.0689<sup>_∗∗∗_</sup>|
||||||(0.0180)|
|Male||||-0.0572<sup>_∗∗∗_</sup>|-0.0639<sup>_∗∗∗_</sup>|
|||||(0.0216)|(0.0213)|
|Female||||0.0176|0.0220|
|||||(0.0174)|(0.0174)|
|Age: <25||||0.0332|0.0386|
|||||(0.0332)|(0.0331)|
|Age: 26-30||||-0.0217|-0.0161|
|||||(0.0226)|(0.0226)|
|Age: 31-35||||-0.0222|-0.0125|
|||||(0.0345)|(0.0342)|
|Age: 35-60||||-0.0557|-0.0455|
|||||(0.0384)|(0.0378)|
|Experience (yr)||||0.0129<sup>_∗_</sup>|0.0132<sup>_∗_</sup>|
|||||(0.00715)|(0.00715)|
|Reference||||-0.0327<sup>_∗_</sup>|-0.0325<sup>_∗_</sup>|
|||||(0.0171)|(0.0171)|
|Employer posts||||0.00163|0.00187|
|||||(0.0467)|(0.0467)|
|Refer. phone||||0.0993<sup>_∗∗∗_</sup>|0.100<sup>_∗∗∗_</sup>|
|||||(0.0320)|(0.0321)|
|Picture||0.446<sup>_∗∗∗_</sup>|0.455<sup>_∗∗∗_</sup>|0.438<sup>_∗∗∗_</sup>|0.442<sup>_∗∗∗_</sup>|
|||(0.0449)|(0.0453)|(0.0448)|(0.0445)|
|Punctuation||-0.0282|-0.0241|-0.0238|-0.0203|
|||(0.0217)|(0.0219)|(0.0219)|(0.0218)|
|Wrong grammer||-0.0663<sup>_∗∗_</sup>|-0.0676<sup>_∗∗_</sup>|-0.0583<sup>_∗_</sup>|-0.0586<sup>_∗_</sup>|
|||(0.0337)|(0.0338)|(0.0341)|(0.0341)|
|Nr Mistakes||-0.0137|-0.0140|-0.0182|-0.0150|
|||(0.0247)|(0.0246)|(0.0252)|(0.0252)|
|Profle Syntax|N|Y|Y|Y|Y|
|Suburb|N|N|Y|Y|Y|
|Control Var|N|N|N|Y|Y|
|_R_<sup>2</sup>|0.011|0.198|0.204|0.216|0.217|
|N|3218|3218|3217|3217|3217|
|p-value: Zim=SA|0.028|0.083|0.115|0.050||
|p-value: Mal=SA|0.000|0.001|0.002|0.003||
|p-value: For=SA|||||0.012|

_Notes:_ standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 ,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Base group for nationality are job seekers who do not reveal their nationality. _Employer posts_ refers to whether a former employer posts on their behalf. _Reference phone_ captures whether a phone nr of a reference is provided. _Reference_ captures any other forms of references mentioned.

11
of age non-parametrically by including dummies for four age groups which roughly correspond to age quartiles. I find a monotonic decline of clicks in age indicating that employers prefer young domestic workers. Overall, it is reassuring that nationality coefficients remain very stable when controlling for demographic and socio-economic variables.<sup>17</sup>

# **4.1.2 Cross-sector comparison**

As described in section 3, I collect data from three sectors: domestic work (Dom), nanny (Nan) services, and general work (Gen). These professions are very different with respect to the frequency and intimacy of interaction. Domestic workers have access to the personal living space and often live with the employer. The relationship with nannies is even more intimate as they take care of the employer’s children. If employers dislike being around foreigners, we would expect discrimination to be largest in the nanny sector and smallest in the general work sector. To test how employer demand for foreigners varies, I estimate equation 1 separately for each sector. Coefficients reported in Table 2 provide support for this hypothesis. The effect of being Malawian or Zimbabwean is about 50% and 200% larger in the domestic work and nanny sector, respectively, compared to the general work sector. However, this test remains inconclusive since there may be other unobservable differences like reputation of different nationality that vary across sectors.

The coefficients on adding a picture to the profile also show interesting cross-sectoral differences. While the effect is large and highly significant in each sector, it is 30% and 40% larger for the domestic work and nanny sector, respectively, compared to the general work sector. Taken at face value, this indicates that the increased level of familiarity and trust that pictures evoke are more important in sectors with a more intimate employer-employee relationship.<sup>18</sup>

> 17 While we focus on the immigrant status, there are additional interesting results, e.g. on references. 31% of profiles in the domestic work sector mention a reference in the search result. I divide these profiles into three groups: ads that were posted by the employer on behalf of their current worker ( _Employer posts_ ), ads that list the phone number of a reference ( _Refer.phone)_ and those that only lists having a reference ( _Reference_ ). Results in Column 4 show that just mentioning a reference is associated with 3.5% _fewer_ profile clicks whereas providing a reference without a number has no effect. By contrast, job ads posted by current employers receive about 10% more clicks. These results suggest that employers tend to be suspicious and that references need to be credible in order to be effective Abel et al. (2017). In addition, many job seekers claim on their profiles to be ‘hardworking’, ‘reliable’ and ‘trustworthy’. The effect of these claims on the number of clicks is very small and statistically insignificant (results not reported), possibly because these traits are not easily verifiable and may thus be regarded as ‘cheap talk’.

> 18In a seperate analysis, I asked South Africans to rate these pictures according to two metrics: i) physical attractiveness and ii) to what extent the picture reflects the image of a domestic worker. The phycial attractiveness measure was a significant positive determinant of profile clicks whereas the second measure was negatively correlated. While these results warrant further investigation they suggest that some people may review job postings for reasons unrelated to hiring workers.

12
Table 2: Cross Sector Analysis

||Full Sample<br>(1)|Housekeeper<br>(2)|Nanny<br>(3)|General<br>(4)|
|---|---|---|---|---|
|South African|0.0335|0.0324|0.0688|0.00791|
||(0.0337)|(0.0411)|(0.0814)|(0.0830)|
|Foreign|-0.0753<sup>_∗∗∗_</sup>|-0.0670<sup>_∗∗∗_</sup>|-0.135<sup>_∗∗∗_</sup>|-0.0252|
||(0.0141)|(0.0180)|(0.0322)|(0.0397)|
|Male|-0.0120|-0.0648<sup>_∗∗∗_</sup>|0.0672|0.0415|
||(0.0163)|(0.0213)|(0.0446)|(0.0344)|
|Female|-0.00235|0.0224|-0.0682<sup>_∗∗_</sup>|-0.0126|
||(0.0143)|(0.0174)|(0.0318)|(0.0650)|
|Age: <25|0.0408|0.0374|-0.001000|0.0470|
||(0.0249)|(0.0332)|(0.0454)|(0.0745)|
|Age: 26-30|-0.0145|-0.0172|0.0190|-0.0384|
||(0.0195)|(0.0226)|(0.0423)|(0.0595)|
|Age: 31-35|-0.0155|-0.00951|0.0138|-0.118|
||(0.0282)|(0.0341)|(0.0556)|(0.103)|
|Age: 36-60|-0.0295|-0.0443|-0.0410|0.0960|
||(0.0307)|(0.0376)|(0.0522)|(0.147)|
|Experience (yr)|0.00138|0.0131<sup>_∗_</sup>|-0.00586|-0.0148|
||(0.00545)|(0.00716)|(0.00957)|(0.0144)|
|Live w Employer|0.0661<sup>_∗∗∗_</sup>|0.0848<sup>_∗∗∗_</sup>|0.0388|0.0303|
||(0.0185)|(0.0223)|(0.0351)|(0.0870)|
|Workpermit|-0.0714<sup>_∗∗_</sup>|-0.0537|-0.0720|-0.0323|
||(0.0295)|(0.0390)|(0.0615)|(0.0652)|
|Urgency|0.102<sup>_∗∗_</sup>|0.169<sup>_∗∗∗_</sup>|0.0919|0.0163|
||(0.0501)|(0.0607)|(0.0950)|(0.112)|
|Picture|0.427<sup>_∗∗∗_</sup>|0.442<sup>_∗∗∗_</sup>|0.478<sup>_∗∗∗_</sup>|0.341<sup>_∗∗∗_</sup>|
||(0.0258)|(0.0444)|(0.0405)|(0.0505)|
|_R_<sup>2</sup>|0.266|0.217|0.320|0.191|
|N|5338|3217|1133|988|
|p-value: For=SA|0.002|0.014|0.019|0.740|

Notes: Robust standard errors parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 , _p <_ 0 _._ 05 , _p <_ 0 _._ 01

# **4.1.3 Robustness Test: Controling for Unobservables**

While I observe the exact information as employers by the time they decide whether to click on a profile, one may be concerned that I fail to codify all relevant information as job seeker texts are multidimensional. To address this concern, I take advantatge of the fact that I observe _more_ information than the employer by the time she makes the screening decision. Specifically, I observe the residential location of 2,120 job seekers (which is revealed on the profile page). Given South Africa’s history of spatial segregation, residential locations are correlated with socio-economic variables. By observing 13
where job seekers live, I can therefore control for factors unobserved by the employer. The rationale for this robustness test is as follows: assume that the residential location ( _zi_ ) is correlated with the number of profile clicks after controlling for other covariates, i.e. it was previously subsumed in the error term ( _ei_ = _vi_ + _zi_ ). Instead of specification (1), I now control for the residential location ( _zi_ ) non-parametrically by including dummies for each 0.5x0.5 grid cell: This unobservable variable explains an additional 20% of variation in profile clicks compared to specification 1. The identification concern was that _E_ ( _For ei_ ) = 0 leading to biased estimates of _β_ 2. Comparing how much coefficient _β_ 2 changes as one reduces the error term by controlling for _zi_ provides an indication to what extent initial results suffered from omitted variable bias. Estimating (1) and (2) separately and testing for equal coefficients, I cannot reject that the coefficients are identical (p-value: 0.52, results not reported). This supports the validity of identification strategy (1), although it is of course still possible that _E_ ( _For vi_ ) = 0.

# **4.2 Identification from Applicant Pool Composition**

## **4.2.1 Framework**

One common criticism of audit studies is that they cannot provide information on the effect of employer discrimination in equilibrium (Heckman (1998)). If the share of discriminating firms is small relative to the share of the minority, than the differences in callback rates observed in these studies may only have a muted effect in equilibrium. As observed by Arrow (1973), it is the attitude of the _marginal_ employer that may determine first order effects of discrimination. Charles and Guryan (2008) attempt to test the effect of discrimination on the marginal by measuring regional racist sentiments with data from the General Social Survey.

In order to derive predictions on the role of the applicant pool composition in determining the cost and prevalence of discrimination, I propose a simple framework in which the employer hires the applicant with the highest expected productivity. This framework builds on tournament models 14
developed by Rosen (1981) and Lazear and Rosen (1981) and is most closely related to Charles and Guryan (2008).<sup>19</sup> The contribution of this study is to provide, to my knowledge, the first direct empirical test of the role of the applicant pool composition on discrimination.

Workers can be of type _s_ (South African) or _f_ (foreigners) and firms derive negative utility _λ_ from employing type _f_ . Let’s assume that there is a fixed market wage _w_ ̄ _s_ = _w_ ̄ _f_ = _w_ ̄ and that ability of all workers follows a uniform distribution with _a ⊂_ [ _<u>a,</u>_ ̄ _a_ ]. Within each population type, the firm can rank applicants according to ability _a_ . I denote the ability level of the highest ranked applicant of each type as _a_<sup>_f∗_</sup> and _a_<sup>_s∗_</sup> . It is straightforward to see that the probability of both South Africans and foreigners to be highest ranked, _P_ [ _a_<sup>_s_</sup> _i_<sup>_>max_(</sup><sup>_as_</sup> _j_<sup>_∗, af_</sup> _j_<sup>_∗_</sup> _− λ_ )] _, ∀j_ = _i_ and _P_ [ _a_<sup>_f_</sup> _i_<sup>_−λ>max_(</sup><sup>_as_</sup> _j_<sup>_∗, af_</sup> _j_<sup>_∗_</sup> _− λ_ )] _, ∀j_ = _i_ respectively, increases in the share of foreigners, denoted _θFor_ , in the applicant pool.

# **4.2.2 Empirical Test**

This framework offers an indirect test of discrimination. If employers penalize foreigners, then _both_ immigrants and South Africans would benefit from being pooled with more foreigners and fewer South Africans. I construct _θFor_ by measuring the nationality composition of the five applicants posting directly before and the five posting after a given profile in the same suburb<sup>20</sup> (see Figure A.3) and estimate the following model: The predictions are that both _δ_ 1 and ( _δ_ 1 + _δ_ 2) are positive. It is a priori unclear if foreigners or natives benefit more from being pooled with foreigners, i.e. _δ_ 2 ≶ 0.

To discuss the identification assumption, imagine three job seekers ordered by the time they posted on the website.

> 19Cornell and Welch (1996) argue that labor market discrimination may be the result of employers’ ability to extract productivity signals more accurately for applicants of their own type. By contrast, results from the present study suggest that employers display a taste for hiring natives. However, the implications of the following simple framework do not depend on the nature of the preference for a certain group.

> 20Computing the share _θ_ off the previous and following five profiles is based on the fact that a total of about 10 profiles are shown on a typical computer screen. Given that only a small percentage of job seekers explicitly state they are South African and that employer preference did not differ significantly between South Africans and those not stating their nationality (Table 2), I pool these two groups for the analyses (and refer to them as _SA_ ) in this section to increase precision.

15
_yi−_ 1 = _α_ + _βFori−_ 1 + _γXi−_ 1 + _ei−_ 1 Identifying discrimination solely from the pool composition requires that the nationality status of job seekers who just posted before or after person _i_ is orthogonal to _i_ ’s error term, i.e. _E_ [<sup>�</sup> _j_ = _i_<sup>_Forjei_] = 0.</sup> This identification is more likely to be valid than the selection on observable strategy which requires _E_ [ _Foriei_ ] = 0 as the neighboring profiles are determined by who posted a profile minutes earlier or later.<sup>21</sup> Regressing the share of foreigners _θFor_ on a host of covariates provides support for the validity of the identification assumption: of the 21 covariates collected, only one (ability to drive) is significant at the 5% level. This suggests that the immigrant share displayed just above and below a profile is quasi-random.

Table 3 reports results of equation (3) estimated across the three sectors. Looking at the pooled sample, we see that job seekers indeed benefit from being in a pool with more foreigners (column 1) and that South Africans benefit more, although the difference is not statistically significant (2). Being pooled only with foreigners increases the number of profile clicks for natives by about 12.2% and for immigrants by 5.6%. While the results are generally consistent across sectors, the relative gain for natives and foreigners varies (4,6,8).

Last, it is interesting to interpret the sign of the coefficients on the foreigner main effect ( _β_ 1). This estimate measures the effect of being the only foreign job seekers that the employer observes in the local applicant pool. Across sectors this coefficient is negative and statistically significant in the nanny and housekeeper sector. This finding is inconsistent with a model of heterogeneous race preferences within the employer population. For example, if 25% of employers had preferences for immigrants, we would expect a foreign job seeker who is pooled only with natives to receive _more_ profile clicks. Taken at face value, the finding provides evidence that employer preferences with regards to nationality are not heterogeneous.

Empirical strategy 3 offers a robustness test for results from specification 2 as preference for South Africans is a necessary condition for the composition of the applicant pool to have an effect. A

> 21While, in theory, job seekers could post their profiles at strategic times (e.g. when they see few natives), this is practically infeasible as there is a delay of about 6 hours between submitting and posting of ads.

16
Table 3: Pool Composition

||Full S|ample|Housek|eeper|Na|nny|Genera|l Work|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Foreign|-0.0882<sup>_∗∗∗_</sup>|-0.0554|-0.0764<sup>_∗∗∗_</sup>|0.0361|-0.135<sup>_∗∗∗_</sup>|-0.191<sup>_∗∗_</sup>|-0.0403|0.0450|
||(0.0139)|(0.0324)|(0.0171)|(0.0555)|(0.0321)|(0.0669)|(0.0382)|(0.0652)|
|Foreign Share (_θF or_)|0.0899<sup>_∗∗_</sup>|0.122<sup>_∗∗_</sup>|0.0144|0.124|0.107|0.0456|0.0639|0.166|
||(0.0334)|(0.0458)|(0.0472)|(0.0718)|(0.0900)|(0.117)|(0.0733)|(0.104)|
|Foreign x Foreign Share||-0.0636<br>(0.0551)||-0.185<sup>_∗_</sup><br>(0.0856)||0.167<br>(0.160)||-0.202<br>(0.127)|
|Profle Syntax|Y|Y|Y|Y|Y|Y|Y|Y|
|Suburb|Y|Y|Y|Y|Y|Y|Y|Y|
|Control Var|Y|Y|Y|Y|Y|Y|Y|Y|
|_R_<sup>2</sup>|0.265|0.265|0.211|0.212|0.316|0.316|0.182|0.184|
|N|5338|5338|3217|3217|1133|1133|988|988|

_Note:_ Robust standard errors in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05 , _p <_ 0 _._ 01 , _p <_ 0 _._ 001 second necessary condition is that employers compare applicants locally, i.e. the ones presented to them at a given time on the computer screen. One implication of this assumption is that the share of foreigners presented on a previous or following search result page should matter less than the share presented on the same screen ( _θFor_<sup>1</sup><sup>_−_5).Itestthisbyestimatingequation3withtheimmigrantshare</sup> of the applicants posted 6 to 10 places above and below a given profile ( _θFor_<sup>6</sup><sup>_−_10).The coefficient drops</sup> from 0.089 to 0.024 and is not statistically significant (not reported, p-value: 0.525).

# **5 Mechanisms**

What may explain the preference for South African job seekers? There are at least three channels: i) employers risk paying a fine if they are caught hiring an immigrant who is undocumented, ii) employers may receive disutility from interacting with foreigners, and iii) employers may believe that South Africans are more productive with respect to unobservables (Appendix C offers a simple theoretical framework). To shed light on these questions I administered an anonymous phone survey with 208 domestic workers in Cape Town who posted a profile on gumtree (for more information on survey procedures, see Appendix D).

17
# **5.1 Legal Status and Bargaining Power**

Employing an immigrant entails an expected cost as employers get fined if the employment contract is monitored and the immigrant does not have a valid work permit.<sup>22</sup> The data allows to explicitly test for the role of the legal status as about 8% of immigrants state in the search result that they have a work permit. One straightforward prediction of this mechanism is that within the group of immigrants, those with work permits receive more profile clicks. I test this hypothesis by including a dummy for workpermit ( _permit_ ) in specification (1).

Column 1 in Table 2 shows that among foreigners having a work permit in significantly _negatively_ correlated with the number of profile clicks in the pooled sample. Stating that one posesses a work permit reduces the number of profile clicks by about 7 percent.<sup>23</sup> While these findings are surprising at first glance, they are consistent with a theory that employers prefer job seekers that are exploitable. The DoL (2007) speculates about the reasons for anecdotal evidence showing lower unemployment among immigrants: “ _The usual advantages of irregular employment (low wages, vulnerability, exploitative conditions) may be at the core of this preference_ [for non-South African workers]”.

Consistent with this exploitation hypothesis is the finding that job seekers stating that they are willing to live with employers or expressing that they need a job urgently receive more profile clicks (Table 2). An alternative plausible theory is that employers infer from reading that somebody holds a work permit that the person demands a higher wage and reading that somebody is ‘urgently’ looking for work may signal that she is less likely to shirk on the job. However, results from the anonymous phone survey do not support this explanation. Undocumented immigrants do not have lower reservation wages are not willing to work longer hours than documented immigrants (Table 4).

Results in Table 4 point to an alternative explanation for the preference of undocumented over documented immigrants: bargaining power. South Africa instituted the Commission for Conciliation, Mediation and Arbitration (CCMA), an administrative tribunal at which employers can bring cases against employer mistreatment at no charge. If disputes are not resolved and no arbitration is reached, cases are referred to labor courts. About 70% of the formal workforce fall under the juristriction

> 22As per the Labour Act (Section 49), “ _anyone who knowingly employs an illegal foreigner or a foreigner in violation of this Act shall be guilty of an offence and liable on conviction to a fine or to imprisonment not exceeding one year_ .”

> 23This finding also provides evidence against the explanation that employers prefer South Africans because they are more likely to remain in the Western Cape area since, controlling for other factors, immigrants with a work permit can be expected to be more established and thus stay in South Africa for longer.

18
of the CCMA. The CCMA is very visible in the media and widely used with about 120,000 cases per year, of which about 70% are about unfair dismissal (Bhorat et al. (2009)). Undocumented immigrants are significantly less likely to know about the CCMA (62.8% vs. 78.9%) and less willing to take their employer to court (57.7% vs. 70.1%) than documented immigrants.<sup>24</sup> Table 4: Domestic Worker Phone Survey Results, Cape Town

||N||Sample|Means|||p-v|alues||
|---|---|---|---|---|---|---|---|---|---|
|||Pooled|SA|D|U|S=F|S=D|S=U.|D=U|
|Age (yrs.)|206|32.102|38.2|31.1|29.6|0|0|0|.098|
|Education (yrs.)|201|11.06|10.46|11.45|11.01|.013|.001|.091|.071|
|Max hours willing to work|202|40.9|41.5|41.2|40.4|.588|.897|.531|.526|
|Wage, daily (ZAR)|191|181.3|179.4|181.2|182.3|.704|.816|.694|.867|
|Wage, hour (ZAR, imputed)|189|23.11|23.97|22.57|23.15|.479|.336|.567|.636|
|Reservation wage, daily|200|185.9|185.6|185.5|186.5|.975|.981|.903|.878|
|Contract|197|.228|.333|.213|.188|.084|.173|.091|.691|
|Treated well: 0=Never..3=Always|197|2.59|2.71|2.58|2.57|.257|.25|.338|.834|
|How not treated well?||||||||||
|.... lower pay than agreed|208|.038|0|.052|.047|.004|.044|.044|.887|
|.....employer rude|208|.077|.043|.091|.082|.329|.292|.364|.848|
|.....had to work more hours|208|.048|.022|.078|.035|.286|.138|.648|.248|
|Can ask for time of|192|.958|.949|.96|.962|.741|.79|.76|.961|
|Paid overtime|198|.47|.476|.432|.5|.924|.653|.804|.401|
|Heard about CCMA|197|.756|.93|.789|.628|0|.023|0|.027|
|Take employer to CCMA|176|.705|.947|.701|.577|0|0|0|.131|
|Know about minwage law|197|.33|.395|.355|.269|.285|.669|.167|.252|
|Should earn minwage|168|.792|.846|.8|.75|.277|.55|.232|.5|
|Ever negotiated wage|196|.464|.524|.453|.443|.376|.469|.402|.899|
|N|||46|77|85|||||

Notes: The table reports means of responses from a phone surveys, divided by legal and immigrant status. P-values report tests of equal means between South Africans (S), Foreigners (F), Documented (D) and Undocumented (U).

Interestingly, this lower bargaining power does not translate into lower actual daily or hourly wages. It is also notable that undocumented and documented immigrants do not report to be treated differently by employers. And while immigrants are more likely than South Africans to report incidents of lower than agreed payments and rude behavior from employers they report positive overall treatment by employers (2.5 on a 0-3 scale), low prevalences of maltreatment (5-10%), and feel comfortable to ask for time off if sick (96%).

However differences in bargaining power cannot explain the overall preference for South Africans.

> 24It is unclear whether undocumented immigrants have access to the CCMA. The case of Discovery Health Ltd v CCMA & others sided with an immigrant whose work permit renewal process was pending.

19
They are significantly more likely to know about the CCMA (93%) and take their employer to court (95%), are more likely to hold a have a written contract (33%) and have negotiated wages (52.4%, not significant). The next section will explore whether the results can be explained by different _expectations_ about the productivity of native domestic workers.

# **5.2 Statistical vs. Taste**

To shed light on the nature of discrimination, I build on insights by Farber and Gibbons (1996) and Altonji and Pierret (2001): if firms use nationality as a proxy for productivity, then the importance of nationality should decrease as other predictors of productivity become available and are factored into employer beliefs. By contrast, if employers have the same productivity expectation of South Africans and foreigners but prefer hiring locals due to _taste_ -based discrimination, we would expect the revelation of additional information to have no effect on the foreign-national gap in hiring decisions. (For a formal employer learning model see Appendix D.)<sup>25</sup> I first predict the number of clicks (ˆ _yi_ ) in the pooled sample using covariate vector _Xi_ . The residualized numbrt of profile clicks ( _yi − y_ ˆ _i_ ) is regressed on nationality dummies interacted with a measure of the number of relevant information ( _I_ ) that the search result includes<sup>26</sup> : The intuition for this test is as follows: imagine there are two binary characteristics ( _X_ 1 and _X_ 2) that equally determine productivity. Individual A has a positive signal for _X_ 1 and a negative for _X_ 2 while person B does not provide any information on _X_ 1 and _X_ 2. While both may have the same predicted productivity, person A’s profile is more informative. If employers statistically discriminate we would expect employer demand to converge as more information ( _I_ ) becomes available. Given that the previous analysis found that foreigners receive fewer clicks than South Africans, we would

> 25Similar tests of the nature of discrimination have been conducted through audit studies, e.g. by Baert and De Pauw (2014). Neumark (2016) points out that it is unclear whether the information content that is varied is relevant to employers.

> 26All variables are categorized to be relevant that have a an absolute t-value>1 in specification (1). Relevant information includes gender, age, experience, references, pictures, workpermit, drivers license, and whether a person is willing to sleep in. The average profile has information on 2.9 of these variables. While applicants who do not provide information on their nationality reveal on average 2.6 characteristics, more information is revealed by South Africans (3.3), Zimbabweans (3.4) and Malawians (3.1).

20
thus expect _δ_ 2 to be positive and _δ_ 1 to be negative. Table 5 shows results of specification (4) estimated for the pooled sample and each sector separately.

Table 5: Statistical Discrimination analysis

||Full S|ample|House|keeper|Nan|ny|General|Work|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|South African|0.558|-0.935|0.246|-2.031<sup>_∗∗_</sup>|1.929|-8.333<sup>_∗_</sup>|-0.815|-0.0466|
||(0.369)|(0.946)|(0.269)|(0.748)|(1.085)|(3.941)|(0.518)|(2.292)|
|Zimbabwian|-0.538<sup>_∗∗∗_</sup>|-0.0695|-0.0247|0.0429|-1.671<sup>_∗∗∗_</sup>|-0.903|0.625|0.338|
||(0.160)|(0.436)|(0.180)|(0.410)|(0.300)|(0.910)|(0.708)|(2.459)|
|Malawian|-0.841<sup>_∗∗∗_</sup>|-0.302|-0.265|0.0770|-1.843<sup>_∗∗∗_</sup>|-0.656|0.437|0.1000|
||(0.141)|(0.346)|(0.154)|(0.404)|(0.361)|(1.100)|(0.377)|(0.875)|
|Information (_I_)||0.197<sup>_∗∗_</sup>||0.0695||0.237||-0.0494|
|||(0.0710)||(0.0954)||(0.131)||(0.157)|
|SA x Inform (_I_)||0.269||0.458<sup>_∗∗_</sup>||2.494<sup>_∗_</sup>||-0.176|
|||(0.211)||(0.175)||(1.065)||(0.556)|
|Zim. x Inform (_I_)||-0.194||-0.0468||-0.279||0.103|
|||(0.107)||(0.114)||(0.216)||(0.677)|
|Mal. x Inform (_I_)||-0.210<sup>_∗_</sup>*||-0.112||-0.359||0.105|
|||(0.0957)||(0.118)||(0.242)||(0.250)|
|Profle Syntax|Y|Y|Y|Y|Y|Y|Y|Y|
|Suburb|Y|Y|Y|Y|Y|Y|Y|Y|
|Control Var|Y|Y|Y|Y|Y|Y|Y|Y|
|_R_<sup>2</sup>|0.008|0.010|0.001|0.003|0.035|0.050|0.003|0.003|
|N|5340|5340|3219|3219|1133|1133|988|988|
|p-value: _Zim x I_=_SA x I_||0.044||0.002||0.012||0.806|
|p-value: _Mal x I_=_SA x I_||0.028||0.001||0.009||0.637|

_Notes:_ Robust standard errors in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05 , _p <_ 0 _._ 01 , _p <_ 0 _._ 001 Results are consistent across most specifications. The interaction term of nationality and information ( _I_ ) is positive for South Africans and negative for Malawians and Zimbabweans (columns 2, 4, 6, 8). While providing more information is beneficial for job seekers without nationality information and especially South Africans, immigrants do not benefit (sum _ρ_ and _δi_ ): at the average level of profile information, job seekers without nationality information and South Africans receive an additional 0.57 (7.7%) and 1.35 (18.2%) clicks, respectively, while demand for immigrants is unaffected. The p-values reported show that these differences in coefficients ( _δ_ 1 and _δ_ 2) are statistically significant at the 5% level in all but the general work specification. These estimates suggest that the number of profile clicks is _diverging_ between South Africans and foreigners as more information becomes available to employers. Results are consistent with a model in which employers believe that foreigners are on average more productive than natives. However, in the aggregate this _positive_ statistical 21
discrimination is offset by the effect of taste-based discrimination.

This conclusion is supported by previous studies concluding that immigrants are _more_ employable (Crush (2008)) and by evidence from the domestic worker survey: immigrants are more educated and younger than South Africans. (Section 4 found that employers prefer younger job seekers.)

# **6 Supply Side Response to Discrimination: Spatial Analysis**

Models of discrimination and job search can be divided into discrimination with random search and discrimination with targeted search. In random search models, the discriminated groups have lower reservation wages and accept jobs with lower match quality given that they expect to receive fewer job offers. In models of targeted search, job seekers decide where to apply after observing firms’ wage offers which provide information on the level of discrimination. The question whether job seekers adjust their search strategy in response to discriminatory practices of firms has important implications for how to interpret empirical results in the existing literature. In particular, Heckman (1998) points out that audit studies make the critical assumption that workers employ random search strategies. If job search is costly and workers are able to direct applications to non-discriminatory firms, this should increase their reservation wage, improve match quality and thus reduce racial wage gaps in equilibrium.

Partly due to a lack of data, there is very little evidence whether discrimination induces people to direct their job search. South Africa provides a context conducive to testing these questions. One of the legacies of apartheid is that workers tend to live far away from where jobs are located.<sup>27</sup> The average distance of African townships from the central business districts (CBDs) of the seven largest South African cities is 28 km. Combined with relatively high costs of public transport, this results in high search and commuting costs both monetary and time-wise.<sup>28</sup>

> 27In an attempt to claim city centers and marginalize the black and coloured population, the apartheid regime forcefully removed large parts of the urban population to homelands or townships outside urban areas; the most famous cases include Sophiatown in Johannesburg and District 6 in Cape Town. Townships suffered from poor infrastructure and provided few employment opportunities as they were located far from business and industry.

> 28Recent evidence shows that people with employment spend on average R215 (7.3% of net salary) per month on transport to and from work (SALDRU (2009)). The mean amount spent by the actively searching unemployed on transport costs related to the job search was R105.75 in the previous week. 42.5% of the unemployed refrain to local job search and report not spending anything.

22
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with the population density of the urban area (with the notable exception of the poorer Cape Flats area), which lends support to the claim that the website is widely used. Second, there is no clear spatial pattern of nationality. However, one may still be concerned that difference in search behavior between immigrants and natives is linked to unobservable differences that are correlated with where people live. For example, if natives live in more central parts of the city, they would mechanically have shorter commuting distances. Conversely, if natives have better access to public transport we would expect them to search further away. These concerns are particularly important in places with large informal settlements like Cape Town where the residential location of people is linked to socio-economic factors (Hellerstein and Neumark (2008)).<sup>30</sup> To address these concerns and control for unobservable differences correlated with the residential location, I draw a 0.25 x 0.25 degree gridnet (approximately 2.5 x 2.5km) and employ an empirical strategy using job seeker location fixed effects following Black et al. (2013) (Figure 2). Coefficients are estimated from variation in nationality within a grid cell with standard errors clustered at the grid cell level. First, I compare how the distance between residential location and the centroid of the suburb where job seekers look for work varies between nationalities (Panel A, Table 6). Columns 1 and 2 indicate that immigrants search for work in places that are significantly further away from their residential area: target suburbs of foreigners are on average 2.2km further compared to the pooled group of South Africans and those that do not indicate their nationality.<sup>31</sup> These differences are economically meaningful given the average search distance of 10.4km. When I control for location fixed effects, coefficients decrease in magnitude but remain statistically significant. One caveat with the location fixed effect strategy is that this limits the sample used to estimate effects. I find that results are robust to extending grid cells to 0.5 x 0.5 degrees which the sample used for identification (results not reported).

> 30Previous studies pointed out that employers may discriminate based on the location where job seekers are living (Bertrand and Mullainathan (2004); Rathelot (2014)). The previous results cannot be explained by this as the location of the job seeker is only revealed once employers click on their profile.

> 31These findings are consistent with Pager and Pedulla (2015) who find that African American job seekers search broader. Of course this correlation could also result from selection as immigrants are widely believed to be more mobile.

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Table 6: Spatial Job Search Analysis Panel A: **Job Search** **_Distance_**

|||y = distance to targ|et suburb (in m)||y=log(d|istance)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Malawian|2850.2<sup>_∗∗∗_</sup>||1159.8<sup>_∗∗_</sup>||0.0769||
||(997.2)||(485.2)||(0.0554)||
|Zimbabwian|955.2||679.3<sup>_∗_</sup>||0.103<sup>_∗_</sup>||
||(700.7)||(383.1)||(0.0538)||
|Foreigner||2243.3<sup>_∗∗∗_</sup>||958.8<sup>_∗∗∗_</sup>||0.0902<sup>_∗∗_</sup>|
|||(724.7)||(343.6)||(0.0437)|
|Covariates|Y|Y|Y|Y|Y|Y|
|Location F.E.|N|N|Y|Y|Y|Y|
|_R_<sup>2</sup>|0.043|0.037|0.440|0.440|0.658|0.658|
|N|2984|2984|2984|2984|2984|2984|
|Mean (natives)|10391|10391|10391|10391|||

Panel B: **Job Search and** **_Home Market Discrimination_**

||y: 1=Search o|utside home suburb|marginal efects|
|---|---|---|---|
||(6)|(7)|(8)|
|Suburb Discrimination|0.875***|0.824<sup>_∗∗∗_</sup>|0.831**|
||(0.299)|(0.314)|(0.373)|
|Covariates|N|Y|Y|
|N|2940|2940|2940|
|Mean (natives)|0.281|0.281|0.281|

_Notes:_ Standard erros (clustered at the grid cell level) in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1 , _p <_ 0 _._ 05 , _p <_ 0 _._ 01 Panel B estimates standard errors through bootstrapping (200 repetitions) to account for the fact that the ’suburb discrimination’ variable is estimated.

One explanation is that job seekers may decide to search further away if they are faced with discrimination in their suburb. Location subgroup analysis of specification (1) suggests that levels of discrimination vary considerably by suburb - both towards immigrants in general and between immigrant groups in a given suburb. For example, in the Northern Suburbs, Malawian profiles receive 19.3% fewer clicks than those not revealing their nationality compared to 1.6% fewer clicks for Zimbabweans. Conversely, Zimbabweans get 9% fewer clicks in Somerset West compared to 2% for Malawians. This may be the result of spatial stratification within the white population and the large historic, political and cultural differences between whites from Dutch and English descent.<sup>32</sup> This raises the question whether discrimination is one of the factors that determine search patterns and

> 32English-speaking whites live predominantly in the southern suburbs while Afrikaans-speakers more frequently live in the northern suburbs. The imaginary dividing line between northern and southern suburbs is referred to by locals as the ’ _boerewors curtain_ ’ in reference to the Afrikaans word for sausage. Furthermore, spatial stratification is reflected by the fact that the most popular newspaper is the “Die Burger” in the northern suburbs and “The Cape Argus” in the southern suburbs.

25
could explain why immigrants search in places further away.

To shed light on this question I next test whether nationality-specific discrimination faced in the suburb of residence affects search behavior. I estimate the following regression: _yi_ is an indicator variable capturing whether a workers searches for work outside her suburb of residence. Variable _σ_ ˆ _j,n_ measures the suburb and nationality specific coefficients of discrimination estimated from equation 1. It is normalized so a higher value presents more discrimination and measures how many fewer percent profiles of nationality _n_ receives in suburb _j_ . To account for the facct that _σ_ ˆ _j,n_ is a generated regressor, I compute standard errors (clustered at the grid cell level) through bootstrapping.

Results reported in Panel B provide suggestive evidence that discrimination in the home suburb induces migrants to search in other areas. The coefficient _δ_ is positive and significant in column 6 and 7. Column 8 reports marginal effects estimated from a probit model. To interpret the magnitude of the coefficients, it helps to keep in mind that the aggregate coefficient of _σn_ is around 0.1 (Section 4). Discrimination faced on the home market thus increases the probability that immigrants search in different suburbs on average by about 8 percentage points which is sizable given the mean value of 28.1% among natives.

At face value, estimates provide some of the first evidence that a group adjusts job search behavior in response to facing discrimination by employers. This has important implications for how to measure the cost discrimination. In particular it would imply that estimates of discrimination observed in Section 4 would underestimate the total cost of discrimination since they are estimated in equilibrium after immigrants adjusted their search behavior. The additional cost of discrimination stems from the transport cost of searching and working in places further away. However, several important caveats should be kept in mind when interpreting these results. First, coefficients (ˆ _σj,n_ ) are estimated from samples of only a few hundred job seekers. Secondly, location choices are endogeneous and native and migrant job seekers living in the same location may differ along unobservable characteristics correlated with job search behavior. Rathelot (2014) notes that if immigrants are penalized on the 26
housring or labor market, the marginal immigrant to participate in the labor force should be more qualified than the marginal native. However, this should lead to _lower_ commuting rates among immigrants as they have an advantage over native competitors in the same market which should reduce their inclination to search in suburbs.

Can job seekers target suburbs with lower discrimination? To shed light on this question, I look at the subset of 1,073 commuters and measure the difference in discrimination between the home and target suburb. I find that on average levels of discrimination in the home suburb are only marginally higher (difference: 0.005 or 0.07 standard deviations, not significant). Taken at face value, this suggests that people have information on discrimination in their place of residence but that this is more difficult to assess for suburbs where one is searching for jobs.

# **7 Concluding Remarks**

This study uses a unique data set of job seekers using a free job advertisement website in South Africa’s Western Cape province. This is to my knowledge the first study of this kind in Africa, a continent in which the rapidly increasing internet access offers new opportunities to create and facilitate markets. In particular, internet fora like gumtree offer an opportunity to match employers with job seekers in spatially segregated urban areas and reduce the reliance of firms to hire through social networks, currently the most common form of hiring in most developed countries (Beaman and Magruder (2012)).

One may wonder why such a large share of job seekers reveal that they are immigrants on their profile page if they get penalized by employers. One explanation is that employers can easily verify job seekers’ nationality (as they typically require to see the national ID card). It is therefore beneficial for immigrants to reveal their nationality upfront to avoid going through the hiring process with discriminating employers.

One limitation of the study is that we know relatively little about the identity of potential employers using the website. From the survey we know that 96% were employed by a private household. In the Western Cape, these employers are predominantely White (63%) compared to Blacks (27%) and Coloured (10%). Whether different race groups have differential preferences over hiring immigrants 27