Detecting Gender and Racial Discrimination in Hiring Through Monitoring Intermediation Services: The Case of Selected Occupations in Metropolitan Lima, Peru

World Development Vol. 40, No. 2, pp. 315–328, 2012� 2011 Elsevier Ltd. All rights reserved

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www.elsevier.com/locate/worlddevdoi:10.1016/j.worlddev.2011.05.003

Detecting Gender and Racial Discrimination in Hiring Through

Monitoring Intermediation Services: The Case

of Selected Occupations in Metropolitan Lima, Peruq

MARTIN MORENOPenn State University, USA

HUGO NOPOInter-American Development Bank, USA

JAIME SAAVEDRAWorld Bank, USA

and

MAXIMO TORERO *

IFPRI, USA

Summary. — Inspired by audit studies methodology, we monitored a job intermediation service in Peru to detect gender and racial dis-crimination in hiring. We capture individual racial information using the approach of Nopo, Saavedra, and Torero (2007), enabling aricher exploration of racial differences. Overall, the study finds discriminatory treatment in hiring only when comparing groups withextremely different observable racial characteristics. We detect discriminatory treatment for female Indigenous applicants in secretarialpositions. In terms of aimed wages, females tend to ask for wages 7% below those of males with comparable skills (although this has nonegative impact on wages at hiring).� 2011 Elsevier Ltd. All rights reserved.

Key words — field experiments, discrimination, occupational segregation

q Previous versions of this paper circulated as “Gender and RacialDiscrimination in Hiring: A Pseudo-Audit Study for Three SelectedOccupations in Metropolitan Lima.”* We thank the Inter-American Development Bank and the Global Devel-

opment Network for their financial support in a substantial component of

this project. The comments of Jeff Carpenter, Alberto Chong, Loujia Hu,

Jacqueline Mazza, Andrew Morrison, Claudia Piras, and Chris Taber, as

well as the assistance of Sebastian Calonico, Deidre Ciliento, Cristina

Gomez, and Lucas Higuera are greatly acknowledged. Final revision acc-epted: May 10, 2011.

1. INTRODUCTION

Despite social advances and a movement toward moderniza-tion of labor markets, especially in Latin America, substantialdifferences in earnings and opportunities for individuals fromdifferent gender and racial groups persist. A simple observa-tion of job openings posted in local newspapers reflects theexistence of occupations for which employers request onlymale or only female employees. In other postings, the euphe-mism “good presence” is used to refer to employers’ specificracial preferences for particular job openings. Occupationaldifferences that are linked to racial characteristics continuedue to the existence of stereotypes and prejudices. These biasesare reinforced by differences in individuals’ access to educationand other assets. Furthermore, cultural differences, often ob-servable through behavior and mode of speech, should beadded to the differences that are based on phenotypic charac-teristics. Sometimes employers make their decisions using ra-cial and ethnic characteristics as proxy measures forattributes that they seek but that are harder to observe in ajob interview. As a result, some employers discriminate againstindividuals on the basis of their racial characteristics, but notbecause they have a “taste for discrimination.” Instead, theseemployers discriminate because they use race as a signaling de-vice, which is representative of statistical discrimination(Coate & Loury, 1993; Yinger, 1998).

Gender differences in occupations in Latin America are veryhigh when compared to the rest of the world (Blau & Ferber,1992; Deutsch, Morrison, Piras, & Nopo, 2002). More specif-

315

ically, in Peru, there are substantial differences in occupationsamong gender and racial groups; and these differences accountfor earnings and wealth differentials (Nopo et al., 2007). How-ever, analyzing figures on segregation is not sufficient in iden-tifying whether this is a discriminatory outcome or not. Thisstudy attempts to isolate and explore discrimination inemployers’ hiring decisions. It analyzes the hiring processesfor specific occupations using information from the job inter-mediation service of the Peruvian Ministry of Labor andEmployment Promotion. The study focuses on three types ofoccupations: salespersons, secretaries, and accounting andadministrative assistants. For this purpose, the experiment in-volved monitoring the job postings in these occupations andall corresponding processes in filling the vacancies (pre-screen-ing of candidates, job interviews, and the final hiring deci-sions). For each job posting information was collected ongender and racial characteristics of all applicants, as well as

http://dx.doi.org/10.1016/j.worlddev.2011.05.003

316 WORLD DEVELOPMENT

the attributes that would make them employable for the occu-pations for which they were applying. This study shares manycharacteristics present in traditional audit studies, but it sur-mounts some of the critiques highlighted by Heckman(1998), although at a cost.

Following this brief introduction, the next section presentssome stylized facts about the labor markets in Peru, emphasiz-ing occupational segregation, both by gender and race. Inaddition, it introduces methodological considerations thatare necessary to take into account when analyzing racial differ-ences in a post-colonial society like the Peruvian in which ra-cial mix prevails and traditional ways of measuring race fail tocapture the vast heterogeneity behind this concept. Later,Section 3 outlines the basic aspects of the audit studies meth-odology, as well as its main criticisms, highlighting the similar-ities and differences between that methodology and the oneemployed in this study. Section 4 describes the dataset, show-ing the main characteristics of the sample of applicants and thesample of firms. Results of the experiment are presented inSection 5, which is followed by a conclusion in Section 6,discussing the study’s scope in the understanding of discrimi-nation in the developing world.

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7 8 9 10

Prop

ortio

n

Indigenous White

Figure 1. Proportion of White collars by racial intensities. Note: The

horizontal axis captures the “racial differences” resulting from a score-based

procedure carried out by pollsters (see Torero et al. (2004); Nopo et al.

(2007) for details). Each individual received an independent score of 0–10

from a pollster in each of four categories: Asian, White, Indigenous, and

Black—groups that people readily recognize as distinct racial groups—with

zero indicating no physical characteristics that resembled a specific race and

10 indicating most features of that group. For example, an individual with

intensities 2 (White), 8 (Indigenous), 0 (Black), and 1 (Asian), would be

considered predominantly Indigenous. Thus, the horizontal axis captures the

proportion of individuals within each indigenous intensity that where

White collar.

2. OCCUPATIONAL SEGREGATION BY GENDERAND RACE IN PERU

Measured by the Duncan Index, occupational segregationby gender in Latin America is high relative to the rest of theworld; and Peru is not different from those of the region. 1

Using data from the Peruvian National Household Surveyfrom the year 2000 (ENAHO 2000), with a classification of se-ven occupational groups, 2 the segregation index betweenmales and females reaches 0.3265; meaning that it would benecessary for at least 32.65% of the working females to switchoccupations into those that have higher male representation,or vice versa, in order to achieve a non-segregated workforce. 3 But, high occupational segregation in Peru occursnot only by gender, but also by race. Just like the existenceof male-dominated and female-dominated occupations, thestudy provides evidence on the existence of White-dominatedand Indigenous-dominated occupations.

The Peruvian ENAHO 2000 used in this study to show theDuncan Index for gender segregation was complemented by amodule of ethnic and racial characteristics. Nopo et al. (2007)show a description of such module and explore the role of ra-cial mixing on earnings in urban Peruvian labor markets. Aninnovative element of the racial module is the way in whichinformation on race was captured. Instead of using the tradi-tional classification in which each individual falls into one andonly one racial category, the module proposes the use of inten-sities along two basic “dimensions” of race that are prevalentin Peru (White/Caucasian and Indigenous). The intensity scaleranges from 0 to 10 and corresponds to a series of observableindividual characteristics that make them resemble a particu-lar racial group. The higher the intensity, the higher the resem-blance of an individual with a typical member of a racialgroup. For example, an individual with intensities 9 and 1 inthe corresponding White/Caucasian and Indigenous dimen-sions would resemble a typical White individual; analogously,an individual with intensities 1 and 8 would represent a typicalIndigenous, whereas an individual with intensities 4 and 6, forexample, would resemble a typical Mestizo. 4

Next, we use racial intensities along the two basic dimen-sions to illustrate some of the patterns of racial differences inoccupations. Figure 1 shows the proportion of White-collar

workers, by racial intensity, of the urban Peruvian populationin 2000. As individuals are categorized with higher intensitiesin the White dimension, the likelihood that they are employedin White-collar occupations rapidly increases. Only one out offive individuals, with White intensity 0, works in White-collaroccupations, while nearly 100% of individuals with Whiteintensity 10 are employed in such occupations. The relation-ship is the opposite in the case of Indigenous dimension: thehigher the intensity, the lower the proportion of white-collarworkers.

Analyzing the seven occupational categories describedabove, the study computes the Duncan Indices that result fromthe comparison of those individuals with racial intensity zerowith different groups of higher intensities, separating the com-parison by gender. The results of comparison in the Indigenousdimension are presented in Figure 2.

Differences in individuals’ racial characteristics and occupa-tions increase simultaneously. Moreover, this result is morepronounced among females than males. In order to achieve anon-segregated work force, it would be necessary for at least16% of females reporting Indigenous intensity 1 switch theiroccupations with the group that has Indigenous intensity 0.The equivalent figure for males is 6%. A similar White dimen-sion analysis produces results that show that the levels of occu-pational segregation increase relatively more when comparingindividuals that report characteristics making them be perceivedas indisputably White. These results are reported in Figure 3.

In summary, national statistics report that Peruvian labormarkets are segregated, by both gender and race. From theinformation provided so far, it is not possible to distinguishwhether these results are the outcome of a series of individualdecisions of self-segregation, or the result of employers’ dis-criminatory practices of either statistical or taste-based nature.The following section presents the methodology employed inthis study to unravel possible explanations behind these na-tional outcomes.

3. DESIGN OF THE PSEUDO-AUDIT STUDY

According to the taxonomy of experiments proposed byHarrison and List (2004), this study could be classified as a

00.05

0.10.150.2

0.250.3

0.350.4

1 2 3 4 5 6 7 to 10Intensities

Dun

can

Inde

x

Females Males

Figure 3. Duncan Index of occupational segregation by intensities in the

White dimension (base group = intensity 0). Note: The Duncan Index was

calculated for each race intensity for males and females within the White

dimension. Each individual received an independent score of 0–10 from a

interviewer in each of four categories: Asian, White, indigenous, and

black—groups that people readily recognize as distinct racial groups—with

zero indicating no physical characteristics that resembled an specific race

and 10 indicating most features of that group. Thus, the horizontal axis

captures the Duncan Index for each level of intensity for the White

category. For example, a female with intensity 4 (White) will have a

Duncan Index of 0.24.

00.05

0.10.150.2

0.250.3

0.350.4

1 2 3 4 5 6 7 to 10Intensities

Dun

can

Inde

x

Females Males

Figure 2. Duncan Index of occupational segregation by intensities in the

indigenous dimension (base group = intensity 0). Note: The Duncan Index

was calculated for each race intensity for males and females within the

Indigenous dimension. Each individual received an independent score of 0 to

10 from a interviewer in each of four categories: Asian, White, indigenous,

and black—groups that people readily recognize as distinct racial groups—

with zero indicating no physical characteristics that resembled an specific

race and 10 indicating most features of that group. Thus, the horizontal axis

captures the Duncan Index for each level of intensity for the indigenous

category. For example, a female with intensity 4 (indigenous) will have a

Duncan Index of 0.27.

GENDER AND RACIAL DISCRIMINATION IN HIRING THROUGH MONITORING INTERMEDIATION SERVICES 317

Natural Field Experiment (NFE). It is a NFE and not a tradi-tional Field Experiment (FE) in the sense that we did not havethe ability to exogenously randomize treatments across thepopulation under study, it just naturally happened from thefunctioning of the market, as it will be shown next in this sec-tion. Our approach to detect discrimination in hiring practiceswas inspired by the audit studies literature. 5 The audit studiestry to verify the hypothesis of discriminatory behavior of adecision-maker, simulating the interviews of a group ofobservably similar applicants called auditors. The simulationis repeated for many decision makers, and when the outcomestatistically favors, or hurts, individuals with a particular set ofcharacteristics the conclusion is that individuals who showsuch characteristics are discriminated in favor, or against.

As mentioned earlier in this paper, the audit methodologyhas received some criticisms. 6 Generally, the auditors are indi-viduals hired for the purposes of the study; they are mostlycollege students that view their participation as a source of in-come. They arrive at job interviews with similar resumes thatare specifically tailored for the study; therefore, auditorsapplying for the same position present comparable informa-tion to decision makers. They are trained to show up for inter-views and pretend to be interested in getting a job. In addition,they are required to act as if they have the education and expe-rience that their resumes claim. In order to keep to a minimumthe possible differences in observable characteristics, the occu-pations examined in these studies typically require minimalskills. Finally, the designers of the study find the job openingsin newspapers.

These characteristics of the audit studies imply the followingproblems:

(1) An auditor does not necessarily put in the same level ofeffort to get a job as a real job-seeker would. Also, it is notpossible to ensure that the auditor will experience the samepressure and anxiety that would be present in a real jobinterview.(2) The auditor knows the purpose of the study and, as it isdocumented in experimental psychology literature, thismay generate incentives—conscious or not—to skew theresults toward the desired outcome of the researchers(Lindzey & Aronson, 1975; Rosenthal, 1976).(3) Descriptions of job requirements that appear in news-papers are rarely exhaustive. Therefore, the role of unob-servable characteristics, that would be of interest toemployers during interviews, but that designers of auditstudies do not take into account when forming auditorgroups, can be important.

Hence, there are some reasons to be suspicious about the re-sults that come from audit studies, as there are many sourcesof statistical noise that could challenge the results. Some of theproblems mentioned above are addressed during the auditwith a slight modification of the approach: using the resumesof fictitious job applicants instead of having face-to-face (ortelephone) interviews. The drawback of this strategy is theinability to test for the final hiring decisions. It only allowsus to test for hypothetical hiring decisions of the employers(van Beek, Koopmans, & van Praag, 1997) or for a one-stepadvancement of the applicants in the hiring process (Bertrand& Mullainathan, 2004), although perhaps the most importantone (Zegers de Beijl, 2000).

This study overcame some of these critiques by designing aNFE in which, instead of hiring auditors to go to the job inter-views, we selected them from a pool of applicants at the jobintermediation service of the Ministry of Labor and Employ-ment Promotion in Lima, Peru, the CIL-PROEMPLEO net-work. 7 With more than 10 years of functioning, thisnetwork is the biggest public job intermediation service inLima, Peru. It receives around 500 job seekers per day, inter-mediating approximately 40,000 positions per year. It has awell established reputation among firms for the speed andquality of the services they provide. A brief description of itsfunctioning follows.

Every morning, the intermediation specialists interview jobapplicants at the offices of CIL-PROEMPLEO. After eachinterview the information of the applicant is entered into adatabase and remains there for a time-window of up to3 months. If at the moment of the interview there is avacancy for which the applicant is qualified, he/she isimmediately sent to the firm that posted the vacancy, with

Table 1. Entire sample

Total samplea Valid sampleb Constrained samplec

Applications 2650 1713 760Individuals 1557 882 565Postings 435 292 113Firms 202 146 91

a Includes applicants sent to postings that were canceled by the firm or topostings with only one applicant.b Includes postings for which we have information about all the applicantssent.c Includes postings for which we have information about all the applicantsand the interviewers.

Table 2. Sample by occupations

Frequency % Accumulated

Salespersons 227 29.9 29.9Accounting/administrativeassistant

183 24.1 54.0

Secretary 350 46.1 100.0Total applications 760 100


a letter of recommendation from the intermediation service,for a job interview at the firm. If there is no suitable vacancyat the moment, the applicant is asked to go home and waituntil one appears. When such vacancy appears, the applicantis called by phone and asked to show up at the CIL-PRO-EMPLEO offices to receive the recommendation letter andgo to her/his job interview. The vacancies are received on acontinuous basis, online or by phone, by the same intermedi-ation specialists and immediately entered into the database(which is shared by all intermediation specialists). The dat-abases of applicants and vacancies are linked using softwarethat helps the intermediation specialists in their matchingtasks. Such software also facilitates the monitoring of thefirms and applicants matching processes such that the inter-mediation system always has up-to-date information aboutopen and closed vacancies, and active and inactive job seek-ers. Note that one firm may post more than one vacancy onthe system at the same time. Additionally, one applicant canapply to more than one posting, as long as s/he satisfies therequirements of each posting.

With the permission from the Ministry of Labor, a pool ofmonitors at the CIL-PROEMPLEO offices was installed forthe purposes of this study, so that before the applicants weresent to the firms for their final job interviews, they were inter-viewed for this project. Such interviews made it possible to col-lect complementary information about applicants, in additionto what was already registered in the administrative databasesof the intermediation service. The applicants were asked aboutadditional labor and socio-demographic characteristics(including their aimed wages at the job for which they wereapplying), a picture of them was taken and their racial inten-sities were registered in our database. Such racial intensitieswere based on observable characteristics, such as the skin col-or, hair color, and shape and color of eyes, among others (seeTorero, Saavedra, Nopo, & Escobal (2004) for details on themethodology to measure racial intensities). The monitors weretrained to homogenize the criteria used to translate the obser-vable characteristics into intensities in the four-dimensionalscale. 8 In the cases in which the applicant was sent to a jobinterview from an office other than the headquarters, the poolof monitors visited the applicant at home.

The monitors were also sent to the firms that posted thevacancies in order to apply a questionnaire to the job inter-viewers. Under the guise of conducting a survey about thequality of services of CIL-PROEMPLEO, the monitors ob-tained information about personal characteristics of interview-ers, such as schooling, tenure, and age. This informationcomplemented the one that CIL-PROEMPLEO already hadabout firms’ and vacancies’ characteristics.

Therefore, as opposed to taking the demand side of thelabor market as a given and simulating the supply side withauditors, this study monitored both sides of the market.There was no need to hire a pool of auditors, only a poolof monitors. This is crucial in avoiding problems relatedto points 1 and 2 mentioned earlier regarding audit studies.On the other hand, since the CIL-PROEMPLEO networkhas direct contact with the firms that post the job openings,they have near perfect information of the requirements at-tached to each job posting. The information related to theobservable characteristics that firms require is more com-plete than the information that one could obtain from read-ing a job posting in the newspaper. By having a richer set ofinformation about observable characteristics, the room forthose that are unobservable, in the sense enunciated in point3 above, is substantially smaller. Hence, CIL-PROEMPLEOcan send homogeneous groups of applicants to interviews,

more than what could have been expected from a traditionalaudit study (Table 1). 9

4. SAMPLE CHARACTERISTICS

This study examines three occupations with the highest vol-umes of intermediation through CIL-PROEMPLEO: account-ing and administrative assistants, secretaries, andsalespersons. 10 There were 1,557 applicants interviewed be-tween September 2002 and March 2003, who submitted 2,650applications for 435 job postings offered by 202 different firms.On average, each individual applied to 1.7 openings. The infor-mation for 43% of the applicants was not used because the ini-tial postings were canceled by the firm, either because the firmhired someone from outside of the system or because the open-ing was closed without any hiring. Also, for some other post-ings, CIL-PROEMPLEO sent only one applicant to the firm,either by the request of the firm or because there were no otherqualified applicants at the moment of the posting. These obser-vations were also left out because it is not possible to detect dis-crimination when an applicant has no competitors. For thosereasons, the number of observations was initially reduced to882 applicants, 1713 applications and 292 postings (referredas the Valid Sample in Table 2). An additional group of 55of the 202 surveyed firms were excluded from the study becauseof missing observations for one or more of the applicants. Fi-nally, combining the restrictions imposed on the data, therewere 91 firms left, 113 postings, 565 applicants, and 760 appli-cations (referred as constrained sample in Table 2).

(a) The sample of applicants

The sample of individuals for this study was composed oftechnicians and professionals from the middle and lower in-come classes of metropolitan Lima. They were relativelyyoung and generally had more than a high school education.The average number of years of schooling was 13.6, with astandard deviation of 1.9. Only 20% of the individuals didnot study after high school while 23% graduated from a pri-vate high school. Their parents’ education was on average lessthan their own. A majority of the parents only finished highschool.


Almost all the applicants had some work experience, 87%worked during the last twelve months as a dependant and50% were self-employed. This overlap reveals the presence ofindividuals with secondary occupations. Among those whoworked as dependants, the average monthly earnings in theirlast occupation exceeded the minimum wage by 50% and wereclose to the average monthly earnings in metropolitan Lima.The average unemployment spell of applicants was 3.5 months.Of all applicants, 36% reported having the required on-the-jobexperience for the position, and those figures were substantiallyhigher among secretaries and accounting assistants.

Two thirds of the applicants were females. In the sample, fe-males were, on average, one year younger than males andcame from families with a higher average income, althoughthis difference is not significant. The percentage of femaleswho experienced at least one unemployment period duringthe last 12 months was smaller than the percentage of males.Household and per-capita income were generally higher forindividuals who attended high school at a private institution,had done some technical or professional studies at a univer-sity, and whose parents received post-high-school diplomas,either at universities or vocational institutions. The individualswho attended private institutions for their technical or profes-sional degrees had earnings that, on average, were not sub-stantially above the earnings of those who had attendedpublic institutions for the same degree. 11

Regarding racial characteristics, individuals with high inten-sities along the Indigenous dimension are prevalent amongboth applicants and interviewers. Table 3 illustrates the distri-bution of the applicants’ racial intensities.

In order to make the racial categorizations somewhat com-parable to those found in traditional surveys, it is necessary todefine a criterion to classify the population. As a result, thesample is divided into three racial groups: Indigenous, White,and Mestizo. Considering that the population under studyrepresents a particular segment of the national population, arelative cut-off criterion was used. The cut-off is defined usingthe distribution of racial characteristics of the sample in thefollowing way:

(1) If an individual’s Indigenous intensity variable has avalue greater than a cut-off “c” and her/his White intensityvariable is smaller than the same cut-off “c”, s/he will beconsidered Indigenous.(2) Similarly, if an individual’s White intensity variable hasa value greater than a cut-off “c” and her/his Indigenousintensity variable is smaller than the same cut-off “c”, s/he will be considered White.(3) An individual that is considered neither Indigenous norWhite will be considered Mestizo.

For the main results, the medians of distributions of raceintensities in the White and Indigenous dimensions are used,respectively, as cut-off points, “c.” Further, Section (c) willanalyze the sensitivity of these results for other possible cut-off points. Using the median cut-off, 45% of the population

Table 3. Percentage of application

Total applications

Total Salesperson Secretaries Assista

Indigenous 45.3 39.6 33.9 54.9Mestizo 10.1 5.7 13.1 11.4White 44.6 54.6 53.0 33.7Total 100 100 100 100

can be classified as Indigenous, 10% as Mestizo, and 45% asWhite. There was a prevalence of Indigenous applicants apply-ing for accounting and administrative assistant positions and aprevalence of White individuals applying for salespersons andsecretarial positions (Table 4). Figure 4 presents a set of indi-vidual and family characteristics for males and females, as wellas for the three occupations under study. For most variables, acomparison of applicants from different gender groups doesnot denote the existence of clearly defined patterns. However,the study found some differences in the asset ownership of thehouseholds, the applicants’ type of education (public/private)and the parents’ schooling for different racial groups (notshown in the table).

(b) The sample of interviewers

The main demographic, education, and labor characteristicsof the interviewers that were surveyed, suggest that they consti-tute a relatively homogeneous group. However, some differ-ences in these characteristics were linked to the size of thefirm for which they worked. Overall, interviewers were equallysplit by gender, but as the firm size increased, the prevalence ofmales increased as well (60% in large firms as compared to 40%in small firms). The average age showed a similar pattern. Theaverage age in the sample was 40 years; however, the averageage of males was higher than that of females, 42 and 35 years,respectively. In small firms the average age of interviewers washigher than the average age of those in medium and largefirms. Furthermore, 80% of the interviewers received a collegedegree and this percentage was higher among large firms. Thedistribution of professional degrees varied by firm size. Insmall firms the interviewers’ area of expertise coincided withareas for which they required applicants: accounting, adminis-tration, economics, or engineering. Meanwhile, in large firms,the interviewers’ area of expertise was related to positions thatwere typically in charge of personnel selection processes: psy-chologists or industrial relations professionals. Males hadlonger job tenure (7 years) and experience (5 years) than fe-males (5 and 4 years, respectively). Finally, the interviewers’racial intensities in the Indigenous dimension are concentratedaround six, while in the White dimension they are around three(Table 5). Applying the same cut-off criterion that was used toclassify the applicants, the sample of interviewers was dividedinto three groups: 13% of the interviewers were Mestizo, 46%White, and 41% Indigenous, as seen in Table 5.

5. RESULTS OF THE NATURAL FIELD EXPERIMENT

(a) Gender and racial differences in hiring

Out of 760 applications in the “constrained sample,” 127individuals who were interviewed for jobs were hired. This istranslated into a success rate of 16.71%. Looking separatelyat the three occupations under consideration, the study can

s and individuals by race group

Total individuals

nts Total Salesperson Secretaries Assistants

45.1 42.0 32.3 55.58.0 5.9 11.3 7.946.9 52.2 56.4 36.6100 100 100 100

Table 4. Household and individual characteristics of the applicants by gender and occupations

Gender of the applicant Occupations

Total Male Female Salesperson Secretary Accounting/administrativeassistants

Demographic characteristicsFemales 71% 0% 100% 57% 100% 69%Age (in years) 28.01 29.21 27.52 27.04 28.54 28.61Migratory experience

Born in Lima 75% 71% 77% 75% 77% 77%Born in Lima and never migrated 68% 62% 70% 66% 68% 73%

Socio-economic characteristicsHousehold size (persons) 4.94 4.57 5.09 4.88 4.87 5.31Monthly household income (S/.) 1339.27 1237.31 1379.13 1125.33 1421.43 1496.84Monthly household income, per-capita (S/.) 298.35 297.21 298.80 252.03 327.99 307.61Household assets

Microwave oven 20% 20% 20% 15% 28% 18%Washing machine 33% 36% 32% 27% 46% 30%Dryer 3% 2% 3% 2% 5% 4%Car 17% 13% 18% 14% 16% 19%

Educational background (individual)Years of education 13.56 13.71 13.50 12.46 12.61 14.93Attended high school in a

Public institution 77% 81% 75% 79% 69% 79%Private institution 23% 19% 25% 21% 31% 21%

Pursued superior studies inPublic University 16% 21% 14% 7% 3% 35%Private University 17% 19% 16% 12% 9% 23%Public Technological Institute 13% 8% 15% 8% 8% 22%Private Technological Institute 28% 31% 27% 27% 38% 19%Others 5% 6% 4% 9% 12% 0%Without superior studies 21% 16% 23% 37% 31% 2%

Educational background (family)Father’s educational level

Elementary 24% 24% 24% 20% 20% 26%High school 45% 45% 45% 50% 44% 41%College or higher 31% 31% 31% 30% 36% 33%

Mother’s educational levelElementary 34% 31% 35% 28% 32% 37%High school 46% 47% 45% 50% 43% 46%College or higher 21% 22% 20% 22% 25% 16%

Labor historyHas worked once in his life 99% 100% 99% 100% 99% 99%Labor experience (in years) 3.85 4.34 3.66 3.44 4.49 3.80Worked as a dependent during the last 12 months 87% 82% 90% 85% 88% 88%Monthly Earnings in their last dependent occupation 645.07 704.45 622.87 547.97 675.57 708.76Last employment spell (years) 13.41 14.66 12.95 11.78 13.99 12.69Worked as a self-employed in the last 12 months 49% 61% 44% 53% 37% 54%

Job searchingUnemployment spell (months) 3.64 4.03 3.49 4.06 3.21 3.22Months looking for a job 2.22 2.56 2.09 2.53 1.77 2.02Applications sent to PROEMPLEO 1.35 1.26 1.38 1.22 1.83 2.33Has prior experience at the job 34% 26% 37% 17% 46% 43%Hired (%) 18% 15% 19% 15% 20% 16%

Number of observations 565 163 402 227 183 350


report success rates of 14.54% for the salespersons positions,20.22% for the secretaries, and 16.29% for the assistants. 12

The design of the field experiment requires that all appli-cants who are sent to the same job interview satisfy a minimumset of requirements, which are established by the posting firmand verified by CIL-PROEMPLEO. However, it is not possi-ble to ensure that all the applicants that go to the same inter-

view have exactly the same set of observable characteristics. Ifapplicants for the same position differ in observable character-istics, it is necessary to control for them. For that purpose, thestudy estimates discrete models (logit) where the explainedvariable is whether the individual was hired or not. This hiringoutcome is explained by a set of observable characteristics:applicant’s sex and race, interviewers’ characteristics (race,

Applicants Indigenous

0

100

200

300

1 2 3 4 5 6 7 8 9Intensity

Num

ber

of c

ase

White

0

100

200

300

1 2 3 4 5 6 7 8 9Intensity

Num

ber

of c

ase

Interviewers Indigenous

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9Intensity

Num

eber

of

case

Num

eber

of

case

White

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9Intensity

Figure 4. Racial intensity distributions of applicants and interviewers. Note: The horizontal axis reflects the racial intensity that each individual received. The

intensity varies from an independent score of 0–10 from an interviewer in each of two categories: White and Indigenous. Zero indicates no physical

characteristics that resembled a specific race and 10 indicating most features of that group.

Table 5. Number of interviewers by race group

Total interviewers

Total Salespersons Secretaries Assistants

Indigenous 83 22 26 31Mestizo 26 7 5 13White 93 32 25 33Total 202 61 56 77


gender, age, educational level, and job experience), humancapital characteristics of the applicant (educational level), per-sonal characteristics of the applicant (age, marital status, adummy indicating whether the applicant is head of a house-hold or not, and another dummy for migratory status), labormarket characteristics of the applicant (employment status,unemployment spell, and occupational experience), firms’characteristics (number of workers and economic sector) andapplicant’s family controls (education of their parents).

In addition, in an attempt to capture unobservable charac-teristics related to the productivity and expectations of theindividuals, the analysis incorporated two variables: the loga-rithm of their wages at their last job and the logarithm of theiraimed wages. It is understandable that these last two variablesare possibly highly correlated, there was no attempt to inter-pret the resulting logit coefficients for these two variables,and they are included in the regressions merely as additionalcontrols. Also, in an attempt to account for the characteristicsof the competitors that every individual faced at each jobopening, the following controls were added: the percentageof male competitors and the percentage of White competitors(at the job posting). Table 6 show the marginal effects for se-lected coefficients of the estimations for different models com-bining the set of characteristics listed above.

The effects attributable to the gender of the applicants aswell as to the gender and racial characteristics of the interview-ers, although relevant in expected value, do not seem to be sta-tistically different from zero and these results are consistentacross the eight specifications. As more controls are incorpo-rated into the logit regressions, the logarithm of aimed wagesshows a higher effect on hiring, up to the point when it getsstatistically significant for the last specification. Nonetheless,the study refrains from interpreting such coefficients in an iso-lated way. The estimators for the effects of having more Whiteor male competitors are not statistically significant. As onewould expect that the presence of more White or male compet-itors in the job search process would affect negatively hiringchances of an individual, this can be thought of as first evi-dence of the lack of discriminatory practices among employ-ers.

The results shown in Table 6 correspond to the pool ofoccupations that were studied (salespersons, secretaries andassistants). One of these occupations is clearly female-dominated, so it deserves special attention. Table 6 reportsthe equivalent econometric analysis with eight specificationsby occupation. Two results deserve special attention. Onone hand, there seems to be a certain positive discriminationof females among the applicants for salespersons. On theother hand—and this is independent of the regression spec-ification—among applicants for secretarial positions, beingindigenous perfectly predicts failure in getting the job (seefootnote (g) in Table 7). This reveals some sort of discrim-ination among indigenous females when applying to anoccupation for which the euphemism “good presence” isregularly a requisite for the position. It is interesting tonote, however, that interviewer characteristics (includingtheir race and gender) do not seem to play an importantrole on the explanation of the success of failure ofapplicants. 13

Table 6. Marginal effects on hiring for all the occupations. Selected coefficients

(1) (2) (3) (4) (5) (6) (7) (8)

Mestizo applicant 0.067 0.067 0.067 0.07 0.061 0.062 0.06 0.082(1.25) (1.28) (1.27) (1.33) (1.15) (1.15) (1.13) (1.66)

White applicant 0.08 0.08 0.071 0.077 0.06 0.062 0.063 0.108(0.98) (0.99) (0.87) (0.93) (0.75) (0.77) (0.78) (1.18)

Female applicant 0.024 0.024 �0.002 0.002 0.008 0.004 0.001 0.004(0.64) (0.68) (0.04) (0.04) (0.17) (0.08) (0.02) (0.07)

LN last earnings at the main occupation 0.038 0.037 0.035 0.025 0.039 0.035(1.52) (1.40) (1.18) (0.77) (1.23) (1.05)

LN aimed wages 0.064 0.093 0.081 0.088 0.097 0.126(1.05) (1.39) (1.21) (1.28) (1.41) (1.94)

% of male competitors �0.058 �0.061 �0.046 �0.047 �0.034 �0.035(0.83) (0.88) (0.67) (0.69) (0.53) (0.56)

% of White competitors �0.041 �0.059 �0.071 �0.074 �0.052 �0.051(0.50) (0.72) (0.93) (0.97) (0.69) (0.68)

Observations 760 760 732 732 726 721 720 720

Additional controls in the regressions

Occupation type controls Yes Yes Yes Yes Yes Yes Yes YesInterviewer characteristicsa No Yes Yes Yes Yes Yes Yes YesHuman capital controlsb No No No Yes Yes Yes Yes YesPersonal characteristicsc No No No No Yes Yes Yes YesLabor market experienced No No No No No Yes Yes YesFirm controlse No No No No No No Yes YesApplicant’s family controlf No No No No No No No Yes

Absolute value of z statistics in parentheses.Clustered standard errors (at the job posting).*Significant at 5%.**Significant at 1%.Notes:a Interviewer characteristics: race, gender, age, educational level and job experience.b Human capital controls: level of education attained by the applicant.c Personal characteristics: age, marital status, head of household, migration status.d Labor market experience: unemployment spell, occupational status at the time of interview and experience as a self-employed worker.e Firm controls: firm size and economic sector of the firm.f Applicant’s family controls: level of education attained by both applicant’s parents.


(b) Race, gender, and aimed wages

One of the questions that the pool of monitors asked the jobapplicants, after they knew the position for which they wereapplying, and the firm’s identity and location, was about thewages they expected to get at the new job. These are not nec-essarily the wages that they would get if they were offered thejob, nor are they would be their reservation wages; however,the information about aimed wages provides a good indicatorabout expectations in the labor market.

It is possible to think of a situation in which agents in thissystem (employers, employees and job seekers) make theirdecisions based on the assumption that there is some sort ofstatistical discrimination in the market. Then, at the equilib-rium, it would not be surprising to find differences in the dis-tribution of wages offered by gender and race. The individualsthat belong to a discriminated group, anticipating differenti-ated treatment, adjust their beliefs and as a consequence goon their job search processes by choosing reservation wagesthat are below those of the non-discriminated group. On theother side of the market, employers assume the same priorcommon beliefs, and know that individuals from the discrim-inated groups are willing to accept lower wages. In such a way,when equilibrium is achieved, the beliefs of the individuals areconfirmed ex-post creating a self-fulfilling prophecy.

The data obtained from this study enable exploring, at leastpartially, some of the complex relationships outlined above.

Information about the individuals’ wages in their previous jobsas well as their aimed wages for the jobs for which they wereapplying was collected. Table 8 shows the results from regres-sions for the aimed wages of the individuals on a set of individ-ual characteristics, including gender and race. With thepurpose of reducing some possible statistical noises, the analy-sis was restricted to those individuals with no more than 12months of unemployment and who did not work as self-em-ployed in their last job. The controls in bold have statisticallysignificant impacts in determining aimed wages. The studyfound differences in aimed wages by gender, but not by race.Males ask for wages that are approximately 7% higher thanthose of females, other things held equal. Overall, occupationalexperience, college degree, and age of the applicants, as well asmother’s education are important explanatory variables. Thoseapplying for positions of secretaries and assistants have aimedwages above than that of those applying for salespersons. Ta-ble 9 disaggregates the results by occupation and show thatmost of the gender differences in aimed wages occur amongthe applicants for salespersons positions. For that group malestend to ask for wages that are around 16% above than that offemales, other things held equal.

(c) Sensitivity analysis for aimed wages and hiring outcomes

The analysis of previous sections was carried out using thepartition criterion of racial groups with cut-offs on the 50th

Table 7. Marginal effects on hiring for each occupation. Selected coefficients

(1) (2) (3) (4) (5) (6) (7) (8)

Salespersons sample

Mestizo applicant 0.069 0.048 0.028 0.019 �0.049 �0.002 �0.028 �0.032(0.70) (0.64) (0.34) (0.23) (0.42) (0.02) (0.22) (0.24)

White applicant 0.042 0.003 �0.029 �0.031 �0.071 �0.04 �0.048 �0.047(0.28) (0.03) (0.31) (0.35) (0.96) (0.36) (0.57) (0.57)

Female applicant 0.106 0.064 0.133 0.106 0.099 0.101 0.089 0.08(1.74) (1.22) (1.82) (1.50) (2.01)* (1.95) (2.28)* (1.95)

LN last earnings at the main occupation �0.021 �0.025 �0.004 0.006 0.019 0.008(0.79) (0.87) (0.20) (0.28) (1.10) (0.39)

LN aimed wages 0.206 0.202 0.114 0.056 0.017 0.045(2.30)* (2.32)* (1.77) (0.80) (0.31) (0.65)

% of male competitors 0.076 0.047 0.086 0.09 0.1 0.101(0.73) (0.46) (1.22) (1.27) (1.72) (1.66)

% of White competitors 0.149 0.095 0.035 0.018 0.046 0.036(1.33) (0.77) (0.54) (0.26) (0.97) (0.73)

Observations 227 227 214 214 212 211 210 210

Secretaries sampleg

White applicant 0.034 0.044 0.035 0.03 �0.004 0.004 0.002 �0.035(0.50) (0.64) (0.50) (0.42) (0.06) (0.05) (0.03) (0.54)


LN aimed wages �0.006 0.028 0.051 0.076 0.099 0.162(0.04) (0.17) (0.32) (0.47) (0.60) (0.97)


Observations 177 177 169 169 169 161 161 161

Assistants sample

Mestizo applicant 0.037 0.037 0.032 0.036 0.027 0.039 0.047 0.079(0.60) (0.57) (0.49) (0.58) (0.42) (0.62) (0.77) (1.61)

White applicant 0.062 0.061 0.049 0.056 0.044 0.061 0.07 0.174(0.59) (0.57) (0.48) (0.53) (0.43) (0.54) (0.60) (1.23)

Female applicant �0.039 �0.044 �0.048 �0.04 �0.022 �0.029 �0.028 �0.021(1.02) (1.18) (0.79) (0.67) (0.37) (0.46) (0.45) (0.36)


LN aimed wages 0.065 0.104 0.096 0.114 0.121 0.176(0.79) (1.11) (1.07) (1.18) (1.23) (1.98)*

% of male competitors �0.058 �0.046 �0.035 �0.033 �0.041 �0.025(0.83) (0.65) (0.49) (0.46) (0.61) (0.37)


Additional controls in the regressions

Interviewer characteristicsa No Yes Yes Yes Yes Yes Yes YesHuman capital controlsb No No No Yes Yes Yes Yes YesPersonal characteristicsc No No No No Yes Yes Yes YesLabor market experienced No No No No No Yes Yes YesFirm controlse No No No No No No Yes YesApplicant’s family controlf No No No No No No No Yes

Absolute value of z statistics in parentheses.Clustered.*Significant at 5%.**significant at 1%.Notes:a Interviewer characteristics: race, gender, age, educational level and job experience.b Human capital controls: level of education attained by the applicant.c Personal characteristics: age, marital status, head of household, migration status.d Labor market experience: unemployment spell, occupational status at the time of interview and experience as a self-employed worker.e Firm controls: firm size and economic sector of the firm.f Applicant’s family controls: level of education attained by both applicant’s parents.g Since being indigenous perfectly predicts failure, we calculated this panel of the table using only the sample of Mestizos and Whites, so in this caseMestizo is the base category.


Table 8. Determinants of the aimed wages

Without

interactions

With

interactions

Mestizo applicant �0.011

(0.31)

White applicant �0.006

(0.14)

Male applicant 0.069

(2.67)**

Male and Indigenous applicant 0.008

(0.10)

Male and Mestizo applicant 0.023

(0.29)

Male and White applicant 0.067

(0.78)

Female and Mestizo Applicant �0.045

(0.62)

Female and White Applicant �0.048

(0.66)

Human capital controls

Incomplete non-college post-secondary studies �0.01 �0.01

(0.31) (0.31)

Non-college post-secondary degree �0.042 �0.043

(1.60) (1.66)

Incomplete college studies 0.013 0.014

(0.48) (0.50)

College degree 0.162 0.163

(5.83)** (5.85)**

Personal characteristics

Age 0.059 0.058

(4.35)** (4.24)**

Age^2 �0.001 �0.001

(3.60)** (3.54)**

Single 0.018 0.017

(0.77) (0.73)

Household head 0.003 0

(0.10) (0.02)

Applicant’s family controls

Mother has a high-school degree 0.058 0.059

(3.43)** (3.47)**

Mother has a non-college post-secondary degree 0 0

0.00 (0.02)

Mother has a college degree 0.04 0.04

(1.00) (0.96)

Labor market experience

Occupational experience 0.036 0.036

(4.11)** (4.01)**

Occupational experienced �0.002 �0.002

(3.46)** (3.33)**

Unemployment spell (top-coded at 12 months) �0.001 �0.001

(0.14) (0.20)

Was fired from his last job 0.278 0.242

(5.44)** (3.62)**

Quit from his last job 0.243 0.204

Left his last job for other reasons 0.225 0.187

(4.77)** (2.75)**

Had a job at the moment of the application �0.014 �0.011

(0.20) (0.15)

Worked as a dependant during the last 12 months �0.05 �0.048

(1.21) (1.15)

Occupation type controls

Occupation (secretaries) 0.171 0.175

(5.73)** (5.85)**

Occupation (assistants) 0.144 0.147

(4.91)** (4.95)**

Constant 4.901 4.987

(23.73)** (21.18)**

Observations 736 736

R-squared 0.39 0.39

Robust t statistics in parentheses.*Significant at 5%.** Significant at 1%.


percentile. However, there are many other interesting candi-dates for cut-off values. This sub-section is devoted to the sen-sitivity analysis. Here the results for aimed wages and hiringfor different cut-off points are replicated. The higher (lower)the cut-off point the more (less) pronounced the racial differ-ences between the Indigenous and White groups. But at thesame time, the higher (lower) the cut-off points the more likelyit is to have fewer (more) observations in the Indigenous andWhite groups. This fact imposes some restrictions on the rangeof possible values for cut-off points. For instance, using the95th percentile as a cut-off value leads to a partition in whichthe White group represents only 1% of the sample. On theother hand, using the 14th percentile as cut-off value leadsto a partition in which there is no individual left within theWhite group. For this reason, the sensitivity exercise is carriedout only for a set of possible cut-off points. The interval thatgoes from the 15th percentile to the 94th is selected exploringall possible integer percentiles.

For each possible racial group partition the analysis repli-cates the hiring regressions reported in Figure 5 (only the lastspecification: the one that controls for occupation, labor mar-ket experience, human capital, personal and familial charac-teristics of the applicants, as well as firm characteristics).Figure 5 provides the marginal effect of being Mestizo and thatof being White, computed at the mean (for the pooled sample).

The x-axis of each graph represents the set of possible cut-off values and the y-axis represents the effect of the corre-sponding dummy variable on aimed wages. The bold lineshows the average effect while the doted lines illustrate a95% confidence interval for the estimators. The results showthat discriminatory behavior does not prevail in hiring out-comes for most of the cut-off values. Nonetheless, when fairlyhigh cut-off values are used, the study finds evidence of statis-tically different hiring outcomes between Indigenous and Mes-tizos (and also between Indigenous and Whites). Since thehigh cut-off values are linked to more pronounced differencesin racial intensities between the Indigenous and White groups,findings in this study suggest that there is some evidence of dis-crimination only at the extremes of the racial diversity spec-trum for this sample of job seekers in Lima, Peru. With acut-off at the 90th percentile or above, those labeled as Whiteare about 100% more likely to get a job than those labeled asIndigenous. Also, individuals labeled as Mestizos are about30% more likely to get a job than those labeled as Indigenous.

A similar exercise for the aimed wages regression reported inTable 6 (pooling the three occupations, only the specificationwithout race–gender interactions) was also performed. Fig-ure 6 shows these results. There are no statistically significantdifferences in aimed wages among White, Mestizos, and Indig-enous, except for the partitions at the highest percentiles. Inthose cases, White and Mestizo individuals have aimed wagesthat are around 20% lower than that of comparable Indige-nous individuals.

6. CONCLUSIONS

This study explores the role that gender and race play in thejob search and hiring process. This is done by using informa-tion on real applications and job interviews obtained from theCIL-PROEMPLEO network, the job intermediation system ofthe Ministry of Labor and Employment Promotion in Lima,Peru. The study analyzes the relative performance in the jobseeking process of comparable individuals of different gendersand observable racial characteristics. Applicants for

Table 9. Determinants of the aimed wages for each occupation

Salespersons Secretaries Assistants

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

Mestizo applicant 0.003 �0.095 �0.036(0.06) (0.78) (0.75)

White applicant 0.007 �0.115 �0.019(0.14) (0.93) (0.34)

Male applicant 0.152 �0.001(5.93)** (0.04)

Male and Indigenous applicant 0.132 �0.127(1.76) (1.16)

Male and Mestizo applicant 0.145 �0.102(2.54)* (0.97)

Male and White applicant 0.135 �0.179(1.59) (1.24)

Female and Mestizo applicant �0.014 �0.118(0.21) (1.08)

Female and White applicant �0.001 �0.087(0.02) (0.80)

Human capital controls

Incomplete non-college post-secondary studies

0.005 0.007 �0.074 0.001 0.024

(0.11) (0.15) (1.14) (0.00) (0.10)Non-college post-secondarydegree

0.049 0.053 �0.015 �0.17 �0.175

(0.83) (0.93) (0.33) (3.03)** (2.93)**

Incomplete college studies 0.037 0.037 �0.086 �0.023 �0.024(0.84) (0.83) (1.34) (�0.38) (0.37)

College degree 0.156 0.155 0.057 �0.076 0.07(2.15)* (2.11)* (0.64) (1.51) (1.35)

Personal characteristics

Age 0.037 0.037 0.052 0.072 0.073(0.92) (0.91) (2.66)* (1.54) (1.57)

0 0 �0.001 �0.001 �0.001Age^2 (0.71) (0.71) (2.34)* (1.20) (1.29)

�0.037 �0.036 0.019 0.04 0.044Single (1.01) (0.95) (0.39) (0.77) (0.81)Household head �0.048 �0.046 0.115 �0.046 �0.04

(0.90) (0.88) (2.73)** (1.34) (1.15)

Applicant’s family controls

Mother has a high-school degree 0.046 0.048 0.076 0.066 0.07(2.09)* (2.24)* (2.40)* (2.18)* (2.39)*

Mother has a non-college post-secondary degree

0.112 0.116 �0.054 �0.041 �0.042

(2.27)* (2.31)* (1.24) (0.93) (0.95)Mother has a college degree 0.13 0.134 0.156 �0.036 �0.037

(3.07)** (3.15)** (2.19)* (0.57) (0.59)

Labor market experience

Occupational experience 0.048 0.048 0.008 0.014 0.017(3.32)** (3.35)** (0.39) (1.11) (1.37)

Occupational experience^2 �0.004 �0.004 0 0 0(3.32)** (3.36)** (0.24) (0.66) (0.69)

Unemployment spell (top-codedat 12 months)

�0.009 �0.009 �0.007 �0.013 �0.014

(1.55) (1.55) (1.02) (2.01)* (2.08)*

Was fired from his last job 0 0 0.052 0.38 0.463(.) (.) (0.91) (4.12)** (3.74)**

Quit from his last job �0.054 �0.054 �0.012 0.336 0.415(1.33) (1.30) (0.23) (4.56)** (3.72)**

Left his last job because thecontract finished

�0.031 �0.029 0.016 0.329 0.41

(0.55) (0.53) (0.29) (4.96)** (3.87)**

Left his last job for other reasons �0.055 �0.053 0 0.315 0.403(continued on next page)


Table 9—Continued

Salespersons Secretaries Assistants

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

(1.76) (1.62) (.) (4.23)** (3.36)**

Had a job at the moment of the application 0.128 0.129 0.139 �0.08 �0.06(1.14) (1.16) (1.01) (0.64) (0.47)

Worked as a dependant during the last 12 months 0.112 0.114 0.044 �0.147 �0.137(1.60) (1.66) (0.63) (2.20)* (1.99)

Constant 5.317 5.325 5.474 5.002 4.999(9.61)** (9.73)** (15.15)** (7.10)** (6.83)**

Observations 213 213 178 345 345R-squared 0.43 0.43 0.22 0.35 0.35

Robust t statistics in parentheses.Clustered standard errors (at the posting).* Significant at 5%.

** Significant at 1%.


accounting and administrative assistants, salespersons, andsecretaries were the focus of this study. The racial heterogene-ity of the sample enabled grouping of individuals into three ra-cial categories: White, Mestizo, and Indigenous. The creationof these groups was based on a cut-off criterion.

Inspired by audit studies, this paper controls for differ-ences in observable characteristics in a detailed way, notonly during the fieldwork through the formation of thegroups of applicants per job posting, but also in the econo-metric models. However, as in most natural field experi-ments, these gains come at a cost: external validity. Onehas to be cautious before trying to extrapolate the findings

Mestizo Ap

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Cut-off

White App

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Cut-off

Figure 5. Marginal effects on hiri

of this study to the whole Peruvian, or even Limenian, labormarkets. Nonetheless, the findings are interesting, thoughtprovoking, and consistent with a story of discriminatoryoutcomes that arise as a result of information failures.

This study led to two sets of findings: one regarding hiringoutcomes and the other on expectations of applicants. First,the study found evidence of differences in treatment basedon gender and based on racial characteristics, but only whenusing the partition criterion that compared groups with great-er differences in observable racial characteristics. Mestizosseem to perform better in the segment of the labor marketstudied here. In terms of specific occupations analyzed in

plicant

60 70 80 90 100

Value

licant

60 70 80 90 100

Value

ng for different cut-off values.

Mestizo Applicant

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0 10 20 30 40 50 60 70 80 90 100

Cut-off Value

Mar

gina

l Eff

ect

White Applicant

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0 10 20 30 40 50 60 70 80 90 100

Cutt-off Value

Mar

gina

l Eff

ect

Figure 6. Marginal effects on aimed wages for different cut-off values.


metropolitan Lima, there was evidence of positive discrimina-tion for females in salespersons occupations (although notrobust to all specifications) and negative discrimination forindigenous females when applying to secretarial positions(with the basic partition criterion, being indigenous perfectlypredicted a failure in the hiring regressions). Second, the studyfound gender differences in expectations, measured by aimed

wages. When job applicants were asked, “How much wouldyou like to earn in the position for which you are applying?”,females’ responses were on average 7% below those of compa-rable males, after controlling for a rich set of observable char-acteristics of the applicants and the firms. Even though, therewere no differences in wage expectations according to differentracial groups.

NOTES

1. This index is interpreted as the minimum percentage of individuals ofone of the comparing groups that should change their occupations inorder to equalize the distributions of individuals across occupations forboth groups. See Fluckiger and Silber (1999) for a detailed description ofthe segregation measures, including the Duncan Index.

2. These groups are: Professionals and Technicians, Managers, Admin-istrative Personnel, Merchants and Salespersons, Service Workers, Agri-cultural Workers and Non-Agricultural Workers.

3. In this case, a non-segregated work force is defined as a situation inwhich the distributions of males and females across occupations are thesame.

4. “Mestizo” is a term that refers to those of “mixed race” and hence isnot considered as one of the basic dimensions.

5. The literature on audit studies is vast. See Riach and Rich (2002) foran interesting survey of this literature in different market settings. Alsorefer to Goldin and Rouse (2000) on the impact of blind auditions.

6. See Heckman (1993, 1998) and Heckman and Siegelman (1993) for acritical description of the results from a detailed analysis of theidentification assumptions behind the audit study model.

7. CIL stands for Centro de Intermediacion Laboral (Center of JobIntermediation) for further details see (http://www.sil.org.pe).

8. In addition, to validate the pollsters’ racial scores, a random samplewas taken from the pool of applicants. The pictures of these individualswere independently scrutinized by three other equally-trained pollsters.The score comparison of the original pollsters and the control pollstersshowed no differences.

9. However, all these advantages come at a cost: selection. On one hand,the firms and job seekers participating at the intermediation service couldbe a non-random sample of the population; and on the other hand, the jobseekers send to the final job interview could be a non-random sample of allthe applicants. While the first type of selection somewhat limits theexternal validity of our results, the second type of selection can not happenbecause the public intermediation system is explicitly prohibited (by law)to perform any type of discrimination.

http://www.sil.org.pe


10. Initially, data-entry assistants were also considered for selection, butthe number of posted job openings into the system was too small to beconsidered.

11. This is related in the fact that the public and private institutions thatthis segment of the population typically attend have similar qualities.Graduates from elite universities do not use the services of PROEMPLEOin their job search processes.

12. The standard errors for these success rates are 2.3%, 3.0%, and 2.0%for salespersons, secretaries, and assistants, respectively.

13. In other specifications, we also included interactions of the appli-cants’ and interviewers’ gender and racial characteristics and found nosignificant coefficients in the hiring regressions.

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Deutsch, Ruthanne, Morrison, Andrew, Piras, Claudia, & Nopo, Hugo(2002). Working within confines: Occupational segregation by gender inthree Latin American countries (Inter-American Development BankSustainable Development Department Technical Papers Series No. 126).Washington DC: Inter-American Development Bank.

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Detecting Gender and Racial Discrimination in Hiring Through Monitoring Intermediation Services: The Case of Selected Occupations in Metropolitan Lima, Peru