Adverse Selection in Recruitment Agencies:

Evidence from Sri Lankan Migration

A. Nilesh Fernando and Niharika Singh ⇤

PRELIMINARY VERSION. DO NO CITE.

Abstract

Abuse and contractual breach are persistent features of international migrant labor. This paperinvestigates the role of adverse selection in migrant recruitment agencies in explaining the incidence ofmalpractices. To study an otherwise opaque supply chain, we matched over 2 million migrant recordsto foreign intermediaries, local recruitment agencies, and labor complaints registered at over 70 SriLankan consulates in the last decade. We show that there is considerable agency-level variation inthe incidence of complaints. Next, we use a difference-in-difference strategy to estimate the effect ofan agency ratings program. Preliminary results suggest that the program increased foreign demandfor high quality agencies and reduced it for low quality agencies. Second, we find that low qualityagencies experience a differential increase in the share of migrants going into low-skill jobs. Theprogram reduces estimated revenues for all eligible agencies, but this decrease is attenuated for badagencies. Finally, we find the migrants are differentially less likely to make complaints against high-quality agencies after the program and these agencies are considerably less likely to exit the market.Our findings suggest that rewarding agency quality can induce good agencies to furtherimprove theirservice quality and, in so doing, improve migrant outcomes.

⇤University of Notre Dame, and Harvard University. Email: nilesh.fernando@nd.edu and niharikasingh@g.harvard.edu.We thank the Sri Lanka Bureau of Foreign Employment (SLBFE), the Ministry of Foreign Employment Promotion andWelfare, Azam Bakeer Markar and the Hon. Min. Thalatha Atukorale for facilitating this research partnership. We aregrateful to Alison Lodermeier for diligent research assistance. The usual caveat applies.

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1 Introduction

International labor migration provides access to vast opportunities but is often fraught with risk. Supplychains for migrant labor consist of a complex set of actors involving local and foreign intermediaries,governments and foreign employers. Perhaps as a consequence of the legal and cultural factors migrantsmust navigate, malpractices such as abuse and breach of contract are commonplace with a recent HumanRights Watch report on the planned Qatar 2022 Football world cup documenting ‘pervasive employerexploitation and abuse of workers’ made possible by an ‘inadequate legal and regulatory framework’HRW (2012). In addressing these malpractices, civil society organizations have focused on reforminginstitutional features of destination countries such as the Kafala labor system.1 Comparatively lessattention has been paid to the dynamics of the labor recruitment industry in sending countries and howthis may contribute to the incidence of malpractices.

Sending countries may have little scope to influence institutional features in destination countriesand are often faced with two options: ban migration or regulate it. This paper focuses on the effec-tiveness of the latter option. Poorly informed migrants may be unable to observe the ‘quality’ of localrecruitment agencies, resulting in adverse selection of low-quality recruitment agencies. Autor (2008)provides an analytical framework for understanding the role of labor market intermediaries (LMIs) suchas recruitment agencies. While recruitment agencies may serve to reduce the considerable informationcosts of matching migrants to foreign employers, adverse selection may exist among LMI’s as well. Whileprior literature has focused on providing information to migrants on employment risks (Shrestha, 2017),we instead focus on firm side adverse selection in local recruitment agencies.

We ask two main questions: first, to what extent do recruitment agencies vary in quality? Second,how effective is an ‘agency rating’ program by the regulator? A considerable difficulty in studyingmarkets for migrant labor and associated malpractices is the opacity with which recruitment occurs, thehaphazard reporting and politically sensitive nature of labor abuse in Gulf countries, and the logisticalrequirements of surveying across multiple field sites. We overcome these issues by leveraging a richdataset that tracks over 2 million Sri Lankan migrants to over 70 countries over the period 2005-2015.Importantly, this dataset can be matched to foreign agents who issue a ’job order’ to a local Sri Lankanconsulate seeking labor and over 1,500 licensed local recruitment agencies who work on their behalf.In addition, the individual Sri Lankan consulates maintain a series of databases containing 127,238complaints about malpractices ranging from physical abuse to breach of contract which can be matchedto individual migrants, and, consequently, to the agencies that recruited them in Sri Lanka.

First, we document that there is considerable variation in the complaint rate (i.e. the numberof complaints issued divided by the number of migrants recruited) for local recruitment agencies inthe top five migrant destinations: Qatar, Saudi Arabia, Kuwait, Jordan and the U.A.E. This analysisprovides evidence of variation in the quality of recruitment agencies, in that it may imply that localrecruitment agencies themselves have information on foreign agents that is correlated with the incidenceof malpractices.

Next, we ask what sending countries can do to address firm-side adverse selection in recruitmentagencies. We analyze the impact an agency rating system devised by the regulator in Sri Lanka. Theprogram rated agencies on several criteria, including total recruitment, the skill intensity of jobs matchedand pending criminal cases against agencies. In order to be eligible for rating, the agencies should haverecruited at least 100 migrants over the period 2009-2010 (hereafter, eligible agencies). We use this cut

1The Kafala labor system is, or used to be, practiced in Lebanon, Bahrain, Iraq, Jordan, Kuwait, Oman, Qatar, SaudiArabia, and the UAE and consist of individual employers ’sponsoring’ migrant workers for visas and giving them exclusiverights to their labor.

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off to assign ’eligibility for being rated’ and then use a difference in difference strategy to estimate thecausal effect of the program. The identifying assumption is that agencies above and below the cut-offwould follow similar trajectories in the absence of the program.

To support the validity of our identifying assumptions, we use data prior to the program to assigna ‘placebo’ treatment and test differential trends for the full sample and a sub-sample of agencies thatrecruited between 50 and 150 migrants in the assessment period (hereafter, ‘sub-sample’). We findlimited evidence for differential trends aside from in the number of migrants recruited in the full sample.In all subsequent regressions, we control for the number of migrants recruited at baseline.

Using the full sample of agencies, we find that the program on average reduced the percentage oflow-skilled migrants recruited by eligible agencies, reduced estimated revenues and increased job orders.The sub-sample has the effects in reverse, although the coefficients are not estimated precisely. Weinterpret the latter as being the comparison between similar agencies assigned with a poor rating versusno rating.2 Eligible agencies had a lower complaint rate, but those in the sub-sample had a highercomplaint rate. Eligible agencies were also less like to exit the market in both samples, although eligibleagencies in the full sample were 5 times less likely to exit, relative to agencies that were not eligible forrating.

To investigate the heterogeneous effects of the policy by agency quality, we use complaints madeagainst agencies in the period prior to the program to designate ‘high’ and ‘low’ agencies.3 Using thefull sample, we find that bad agencies are less likely to recruit migrants (not significant) and are morelikely to recruit low-skill migrants. ‘Good’ agencies, on average, receive more foreign job orders afterthe policy and bad agencies receive differentially fewer job orders (not significant). Surprisingly, wesee that bad agencies experience a smaller decrease in estimated revenues, relative to good agencies.4

Finally, we find that bad agencies are more likely to receive a complaint and exit the market relative togood agencies, but neither of these effects are statistically significant. Collectively, these results suggestproviding incentives to reward agency quality may further improve service quality, improve the skillcomposition of placements and the probability with which good agencies remain in the market.

We know of two studies in this domain. In the first study, Benson et al. investigate the value ofemployer ratings on an online labor market, Amazon’s Mechanical Turk; their preliminary results showthat employers with exogenously assigned ’good’ reputations attract workers at nearly twice the raterelative to those with exogenously assigned ’bad’ reputations. The second study, which closely resemblesour setting, is Lee (2007). Lee examines the impacts of the rise of public employment agencies in theU.S. between 1890-1930 on the low-quality private employment agencies that prevailed during that time.As in our setting, private agencies during this time in the U.S. appear to have exploited the lack ofinformation workers had about potential employers to send them to locations where there was no workor send unsuspecting female job-seekers to brothels or other abuses. Lee shows that the creation of publicrecruitment agencies was a feasible policy response to increase competition with private agencies andthis competition drove many low-quality private agencies out of business. In contrast to this paper, ourstudy considers how the government may exploit reputational mechanisms towards similar regulatoryends.

In evaluating the impact of firm reputational mechanisms, our study also contributes to existingliterature on the potential value of information provision in markets. There are a range of studies acrosssectors that explore the effects of increased information on consumers and firms. Jin and Leslie (2003)

2Note, agencies just above the 100 thresholds were eligible to be rated.3Specifically, we use complaints made against agencies during period 2005-2007 to assign quality. A ‘bad’ firm is one

that has an above median rate of complaints during this period.4Revenues are estimated using the sum of all de jure salaries of migrants recruited.

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look at the effects of the introduction of restaurant hygiene cards in Los Angeles on customer demand,while Luca (2016) investigates the impact of Yelp reviews on consumer demand and restaurant revenues.There are similar studies in the health and education markets (see (Dranove and Jin, 2010) for a review,and Andrabi et al, 2017). To our knowledge, ours is the first paper to investigate at the firm-side adverseselection in the context of international labor migration.

The remainder of the paper is structured as follows: Section 2 describes the background of ourstudy including patterns on international labor migration in Sri Lanka, the regulatory framework andprocesses for recruitment and the regulatory policy examined in this paper. Section 3 describes ourdata and empirical methods. Section presents our results, and Section 4 offers a discussion on the pathforward.

2 Background

2.1 International Labor Migration in Sri Lanka

An estimated 1.7 million Sri Lankan workers, roughly 24% of the labor force, were employed abroadin 2009. In recent years, more than 200,000 individuals each year have moved abroad for employment.Given these magnitudes, remittances represent a large fraction (XX%) of Sri Lanka’s GDP and was ashigh as $5.1 billion dollars in 2011.

Historically, overseas labor migration accelerated in Sri Lanka in the 1970s on account of the rise inlabor demand due to high oil prices in the Middle East. This demand included all types of skilled andunskilled work, though the bulk of the demand is for unskilled and domestic work. As a result, overseaslabor migration is quite feminized with women employed in the domestic work and textile industries.Indeed, women composed the majority of Sri Lankan migrants in the 1990s and 2000s. In the MiddleEast, which remains the top destination for Sri Lankan migrants accounting for over 90% of migrants,there is still exceptionally high demand for domestic workers; the majority of Sri Lankan female migrantworkers are housemaids and employed in the Middle East. Migrants are also typically young, between25-29 years of age. Due to risks around human trafficking, the mandated minimum age for migrantworkers is 21 years.

2.2 A Brief History of SL Recruitment Regulation

State regulation of international migration in Sri Lanka tracks the transformation of labor migrationfrom an informal enterprise undertaken through personal social networks to a formal recruitment indus-try. The first regulation was enacted in 1956 called the "Fee charging Employment Agency Act No. 37"and included the simple provision that a fee charging employment agency could not recruit for foreignemployment without written permission from the labor authority. Until the opening of foreign employ-ment opportunities in the Gulf countries in the 1970s, migration appears to have been limited to skilledprofessionals such as doctors or engineers, at least according to official statistics (ILO, 2013).

The labor demand from the Gulf countries in the 1970s covered all skill categories, including do-mestic work, and contracts were typically of a short-term nature. These changes meant an increasedparticipation of unskilled workers and the rise of recruitment agencies to facilitate the labor migrationprocess. The state responded to these developments by increasing oversight of the agencies through theForeign Employment Agency Act No. 32 in 1980. Agencies were now required to have service contractsbetween foreign employers and employees and penalties were established for local agency violators. The

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rapid growth in scope and scale of the foreign recruitment industry and malpractice concerns led to thecreation of Sri Lanka Bureau of Foreign Employment (SLBFE) under the Ministry of Labor in 1985.This agency was given legal authority to (i) enter into agreements with foreign countries, employers andagencies; (ii) to enact programs offering technical assistance to migrant workers and their families; and(iii) to regulate local recruitment agencies.

In the decades since the creation of the SLBFE, there have been many amendments to the 1985Act and other operational changes in the functioning of the agency. These include standardization ofcontracts, creation of monitoring units at airports, and regulation of recruitment fee structure amongmany others. The fee structure in particular was often the subject of changes, but by 2009, an amend-ment granted legal authority to recruitment agencies to charge the actual cost of recruitment, providedthey were reasonable. The government also become actively involved in recruitment by setting up a’Recruitment Division’ within the SLBFE initially, eventually creating a separate body in 1996 calledthe Sri Lanka Foreign Employment Agency Private Ltd (SLFEA). Despite the creation of this publicforeign employment agency, private recruitment agencies dominate the overseas recruitment industry interms of migrant outflows.

In terms of direct assistance to migrants, the SLBFE has implemented a series of policies whichinclude registration, pre-departure training and dispute resolution. Since 1996, migrant registration withSLBFE was made compulsory. The government also runs pre-departure training programs, which aremandatory for some occupations such as domestic workers. In 2008, the government also established a24-hour hotline to address complaints from migrant workers. Workers or their family members couldsubmit complaints through this hotline; workers could visit the foreign mission in the destination countryin person. Given the feminized nature of overseas labor migration, it is unsurprising that a majority ofregistered complaints are from female domestic workers.

2.3 Current Recruitment Process

The current process for recruiting workers is summarized in Figure 2. This process typically takes 2months to complete. To participate in this process, the foreign recruitment agency or employer mustregister with the SLBFE via the Sri Lankan Mission in the foreign country and the local SL recruitmentagency must have a valid license. If these requirements are met, then there is a 13-step process from thepoint an employment is initiated to the final approval for migrant worker departure. In the first step, theforeign agency or employer creates a "job order," which describes the labor requirements and associatedsalaries and benefits, including coverage of food, accommodation, air travel, or medical facilities. TheForeign Mission will authenticate the job order and then the local agency will receive the job order. Theforeign employer may send the same job order to upto 5 local agencies. The local agency must submit thejob order to SLBFE for a "First Approval" along with some other documents. The First Approval formincludes some additional information relative to the original job order, namely the share of vacanciesthat are approved. Importantly, we observe data from this first approval form in our study. The SLBFEthen reviews the job order.

If the job order is approved, the local agency may advertise in the SLBFE job bank or throughlocal media. Potential migrants are then interviewed and selected. Selected individuals must pass amedical examination, a background check by the Sri Lankan police, and undergo a basic training. Thelocal agency sends prospective worker information to the foreign agent or employer who then obtainsvisa clearance for the migrant, and the local agency obtains visa and air tickets. If at this stage, theprospective migrant clears all the requirements, the job order is sent for final approval to the SLBFE. In

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the final step, the SLBFE grants final approval and stamps the migrant worker’s passport.

2.4 Regulatory Action by SLBFE

As a part of the regulatory structure for local recruitment agencies, the Sri Lankan government employsmultiple strategies that provide public information on agency quality. This information is availableto local competitors, foreign agencies or employers as well as potential migrants. Revealing agencyquality to the market may have important effects; it could improve overall market efficiency by allowinghigher-quality firms to expand and lower-quality firms to close down or improve service quality. Theformalization of quality revelation could also influence new entrants in the market. The extent of theseeffects will depend on the accessibility of these ratings to the various market actors.

Agency ratings

The SLBFE devised an agency rating program to provide information to market actors on the quality ofagencies. The government has rated local agencies twice thus far, in 2012 and 2014. In 2012, the SLBFEawarded a performance rating between 0-5 stars based on their performance in years 2009 and 2010; theratings in 2014 are based on performance in years 2011 and 2012. Only agencies that meet the followingcriteria are eligible to be ranked: (i) recruited more than 100 workers during the assessment period;(ii) not engaged in underage recruitment; (iii) not convicted of submitting false documents; or (iv) nothave more than 10 pending legal cases. The star-ranking is based on a points system, a maximum of 35points can be awarded. Points are awarded for a range of criteria including skill or market diversification,number of migrants recruited, successful resolution of disputes, educational qualifications of the agencystaff, and up-to-date documentation. These points are converted to integer-based star rankings for publicrelease.

Based on only the size cutoff, 303 out of 800 agencies of operational agencies in 2012 received apositive rating; in 2014, 253 out of 915 agencies were eligible to be ranked. Ranked agencies receivebenefits from the government in the form of preference in overseas promotions, access to marketingprograms, and participation in international conferences and workshops. Figure 3 shows how localagencies may use this ranking in advertising for their services. These rankings are publicly availableonline and at the time of initial release were accompanied by a government award ceremony.

3 Data and Empirical Methods

3.1 Data Sources

We employ data from two sources in our analysis. We have access to unique administrative data thatallow us to link foreign job postings to local recruitment firms to migrants characteristics and outcomes.We also obtain publicly available data on agency quality through the SLBFE website. These data aredescribed below in detail.

Migrants We observe the full universe of legal work migrants (1,369,894 unique individuals) from SriLanka to foreign destinations between 2005-2015 recruited through local agencies. This dataset includesrecruiting firm identifiers, migrant characteristics (age, sex, education, ethnicity, hometown, and maritalstatus), departure information (date, destination), and employment information (salary and work sector).

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In addition to this migrant-trip data, we observe any complaint a migrant or their family member mayhave made in regards to their employment abroad during the years 2005-2016, and the resolution of thecomplaint. There are 127,238 complaints from 112,000 unique individuals who were recruited through1,361 unique local agencies. Complaints may range from breach of contract to sexual harassment todeath. Notably, 80% of all complaints are made by female migrant workers.

Local firms We observe all SLBFE registered agencies until 2016. This dataset provides the licensenumber of the agency (the unique agency identifier), the location and the year of registration of theagency. The total number of agencies observed is 2,752. Agency entry and exit are common phenomena;between 2005-2016, an average of 44 new licenses were issued. We combine this data with publiclyavailable agency quality measures- namely we compile a full list of blacklisted firms and firm ratings in2012 and 2014. The salary information from the migrant data can be used to understand the revenuepath of firms; while the level information may not be what is actually earned by firms (i.e. firms maynot change the full salary amount or may charge additional fees), this estimate provides a reasonableproxy for firm revenues based on the number and salary earned by migrants.

Foreign job postings We observe all job orders that are filed by the local agency to the SLBFEfor first approval between 2006-2016. There are 68,064 unique job orders linked to 1528 local agencies.These data include information on vacancies requested by the foreign employer or agency by occupationwith details on the skill requirements and the compensation package, including salary and benefits (food,accommodation or travel allowance). Merging this data with the migrant data allows us to understandthe anticipated working conditions of the migrant at time of departure (anticipated because workingconditions upon arrival at foreign employer could be different).

3.2 Empirical Methods

We use a difference-in-difference strategy to estimate the effects of the agency ratings policy. At present,we focus on the 2012 ratings5 for which the assessment period is 2009-10. Our strategy relies on the firmsize eligibility criteria in the ratings policy to create a plausible counterfactual. In particular, firms thatsent fewer than 100 migrants during 2009-10 are our ‘control’ group, whereas firms that sent equal orgreater than 100 migrants during the assessment period are considered the ‘treatment’ group. We poolour data from years 2009-15 and estimate the following specification:

Yjt = �t + ↵j + �1Treat ⇤ Postjt + �Xjt + ✏jt

In this regression, Yjt is our outcome variable measured for firm j in year t. We include yearfixed-effects, �t, and firm fixed-effects, ↵j . The inclusion of firm fixed-effects means we do not requirethe average outcomes of the treatment and control groups to be the same in the absence of the ratingspolicy; rather, we leverage variation within firms before and after 2012, comparing treatment group firmsto control group firms. Our key variable of interest is the interaction term, Treat ⇤Postjt, where Treatj

is the treatment indicator for firms that placed greater than 100 migrants in 2009-10 and Postt is a yearindicator that turns on for treatment years. Thus, �1 gives us the overall impact of the ratings policyon the treated firms. We also include additional controls Xjt such as firm age and mean number ofmigrants prior to the assessment period. We cluster standard errors at the agency level to account forserial correlation in our panel data.

5A future version will incorporate both the 2012 and the 2014 ratings periods.

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Table 2 shows the sample sizes of our treatment and control groups by firm size. Overall we have 450firms in treatment and 609 firms in our control group. Firms on either side of the 100-migrant thresholdare likely to be very similar; however, as we move farther away from the 100-migrant threshold in eitherdirection, firms may begin to vary significantly in their underlying characteristics. Given this concern,we will both estimate impacts on the full sample as well as on our sub-sample of firms that recruitedbetween 50 and 150 migrants in the assessment period. The restriction will substantially diminish oursample size and raise power issues.

4 Results

4.1 Summary Statistics

Table 1 provides an array of descriptive measures of our data across 2005-2015. Panel A shows annualmeans for a number of important variables. In an average year, 783 firms send migrants abroad. Thenumber of new entrants in a given year is an average of 72, which stands at 9 percent of operationalfirms in a given year. Over 150,000 migrants are recruited every year from 6,048 job orders and 754,177vacancies. These numbers suggest a large and competitive recruitment market.

Panel B displays summary statistics at the firm level. We note that the median firm is quitesmall with a recruitment of 25 migrants, while the average firm sends 135 migrants which points to thepresence of very large firms in the right tail. Women and low-skilled migrants make up the majority ofthe migrants, and Saudi Arabia is the top destination. There is quite a bit of variation in complaintrates across firms: the average complaint rate is 0.06 while the 95th pctl is 0.20. Given that complaintsfiled likely represent a lower bound of incidents, these numbers suggest that an average of at least 6 outof every 100 migrants that use an agency file a complaint.

4.2 Placebo Tests

To support the validity of our identifying assumption, we use data prior to the program, years 2005-2008, to assign a ‘placebo’ treatment and test differential trends for the full sample and the sub-sampleof agencies. We assume that years 2005 and 2006 are assessment years and ratings are released in year2007. Table 3 reports these results for the key outcomes in our study. In the full sample, Panel A, wefind some evidence for differential trends in the number of migrants recruited and the number of joborders. In all subsequent regressions, we control for the number of migrants recruited at baseline. Inthe sub-sample in Panel B, we do not find any evidence of differential trends across any of the variables.The limited evidence of differential trends lends further credibility to our research design.

4.3 Recruitment, Revenue, and Demand

Table 4 estimates the effect of the program on recruitment, revenue, and demand. Using the full sample ofagencies, we find that the program on average reduced the percentage of low-skilled migrants recruitedby eligible agencies by 3.2% and reduced estimated revenues by more than 50% . The program alsoincrease the number of job orders received by eligible agencies by 2.1 (signifcant at the 1% level). Incontrast, in the sub-sample, we see that eligible agencies recruit more low-skilled migrants, have higherrevenues and fewer job orders, although none of these effects are estimated precisely. One interpretationof this sign reversal is that the sub-sample compares similar agencies assigned with a poor rating versus

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no rating. In this case, we are estimating the effects ofthe revelation of a low quality rating.

4.4 Complaints and Firm Exit

Table 5 estimates the effect of the program on the rate of complaints made against agencies by migrantsrecruited after the program and, separately, on firm exit. Eligible agencies had a 1% lower complaintrate, but those in the sub-sample had a 2.5% higher complaint rate. Given an overall complaint rate of6% these effect represent large changes. Eligible agencies in the full-sample were 55.4% less likely to exitthe market after 1 year, whereas those in the sub-sample were only 10.6% less likely to exit. These effectscould be interpreted as good firms seeing additional incentives to improve service quality and remain inthe market.

4.5 Heterogeneous Effects by Firm Quality

To investigate the heterogeneous effects of the policy by agency quality, we use complaints made againstagencies during period 2005-2007 to assign quality. A ‘bad’ agency is one that has an above median rateof complaints during this period and a ‘good’ agency has the opposite. We run an identical empiricalspecification as above, except all terms are now interacted with a dummy for being a bad agency.

4.5.1 Recruitment, Revenue, and Demand

Table 6 estimates the heterogeneous effects of the program on recruitment, revenue, and demand byagency quality. We see that bad agencies are differentially less likely to recruit migrants overall, relativeto good agencies, although this is not signficant. Good agencies are less likely to recruit low-skill migrants,but bad agencies are differentially more likely to recuit them (significant at the 1% level). Good agencies,on average, receive more foreign job orders after the policy and bad agencies receive differentially fewerjob orders (not signficant). Surprisingly, we see that bad agencies experience a smaller decrease inestimated revenues, relative to good agencies.6

4.5.2 Complaints and Firm Exit

Table 7 estimates the heterogeneous effects of the program on the rate of complaints and exit by agencyquality. We find that that good agencies are 1.4% less likely to receive a complaint from migrantsrecruited after the program and bad agencies are differentially more likely to receive a complaint thoughtthis coefficient is not statistically different from zero. Second, we find that good agencies are likely toreceive a complaint and exit the market relative to good agencies are nearly 58% less likely to exit theindustry after one year. Bad agencies are more likely to quit, but this effect is not statistically significant.

5 Discussion

Labor abuse among international migrants is a ubiquitous phenomenon, yet it is understudied in theeconomics literature. This, in part, owes to the politically sensitive nature of the topic and the difficultyof collecting reliable data on migrant outcomes. We leverage a rich dataset that allows us to link

6Revenues are estimated using the sum of all de jure salaries of migrants recruited.

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individual migrants to their contracts, local recruiters, foreign employers and, ultimately, whether or notthey sought assistance from a Sri Lankan consulate for an employment-related dispute.

Using migrant-recruitment agency matched data, we show that there is considerable variation inthe intensity of complaints made against local agencies, suggesting that adverse outcomes may in partbe due to firm-side adverse selection. Next, we estimate the effect of a regulatory policy of rating localrecruitment agencies. Our preliminary findings suggest that rewarding high quality agencies may haveinduced them to improve their service quality, recruit more highly skilled workers, remain in the marketlonger and receive more foreign demand. These findings suggest the importance of a research agendafocusing on how to improve migrant outcomes.

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References

Autor, D. (2008). The economics of labor market intermediation: an analytic framework. Technicalreport, National Bureau of Economic Research.

Dranove, D. and Jin, G. Z. (2010). Quality disclosure and certification: Theory and practice. Journal

of Economic Literature, 48(4):935–963.

HRW (2012). Building a better world cup protecting migrant workers in qatar ahead of fifa 2022.

Jin, G. Z. and Leslie, P. (2003). The effect of information on product quality: Evidence from restauranthygiene grade cards. The Quarterly Journal of Economics, 118(2):409–451.

Lee, W. (2007). Private deception and the rise of public employment offices in the united states, 1890-1930. Technical report, National Bureau of Economic Research.

Luca, M. (2016). Reviews, reputation, and revenue: The case of yelp. com.

Shrestha, M. (2017). Get rich or die tryin’: perceived earnings, perceived mortality rate and the valueof a statistical life of potential work-migrants from nepal.

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Figure 2: This diagram shows the labor recruitment process for a Sri Lankan worker by a foreign employer/agent, which starts with the foreign employer registering a request with the Sri Lankan Mission and ends with a final approval for departure by the Sri Lanka Bureau of Foreign Employment (SLBFE).

Figure 3: These are two advertisements from local Sri Lankan agencies for foreign employment. The top advertisement from Al Qareem Agency (Pvt) Ltd makes no mention of a ranking. This firm was given a two-star rating in 2012 but did not meet eligibility criteria in 2014. Meanwhile, the advertisement on the right by Al Akeem Enterprises (Pvt) Ltd in 2015 prominently displays their awards from 2012 and 2014 at the bottom next to its name.

Table 1: Summary Statistics, 2005-2015

Variable Mean SD Median 75th pctl 95th pctl N

Panel A: Annual means

No of agencies (>0 migrants) 782.8 123.3 720.0 938.0 982.0 11No of new agencies 72.8 25.8 64.0 93.0 126.0 11No of migrants 151,064.4 18,921.1 155,311.0 167,767.0 171,256.0 11Complaint rate 0.06 0.01 0.07 0.07 0.07 11No of job orders 6,048.3 3,841.2 5,781.0 9,253.0 13,116.0 11No of vacancies 754,177.4 419,524.4 896,613.0 1,054,350.0 1,235,871.0 11

Panel B: Firms means

Number of migrants 135.4 412.2 25.0 119.0 597.0 12,277Female migrants 78.2 196.7 10.0 67.0 392.0 12,277Low-skill migrants 101.8 266.7 16.0 90.0 484.0 12,277Migrants to Saudi Arabia 50.1 181.2 0.0 0.0 319.0 12,277Revenues (USD) 53,703.3 2,132,538.8 5,680.0 27,265.3 132,901.5 12,277No of job order 6.5 12.6 3.0 7.0 26.0 10,168No of vacancies 815.9 1,412.6 335.5 1,050.0 3,163.0 10,168Number of complaints 11.3 30.3 1.0 9.0 56.0 12,277Complaint rate 0.06 0.09 0.04 0.10 0.20 12,277Notes:

a) Panel A shows annual means and Panel B shows firm-level means over the data period, 2005-2015.

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Table 2: Treatment by Firm Size

Control TreatmentMigrants sent No rating Rating No rating Rating Total0-49 506 0 0 0 50650-99 103 0 0 0 103100-149 0 0 60 4 64150-199 0 0 41 14 55200-299 0 0 38 48 86300-399 0 0 13 36 49400 and above 0 0 19 177 196Total 609 0 171 279 1059

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Table 3: Placebo period 2005-8

(1) (2) (3) (4) (5)Migrants Revenues (Levels) Revenues (Logs) No job orders Complaint rate

Panel A: Full Sample (Obs=3204)

Treat * Post -54.923*** -1811.201 -0.114 -1.476*** -0.001(18.64) (15548.16) (0.26) (0.33) (0.01)

Panel B: Restricted Sample 50-150 (Obs=641)

Treat * Post -5.821 -4098.203 -0.957 -0.004 -0.028(8.91) (4434.53) (1.14) (0.88) (0.02)

Notes:

* p<0.1, ** p<0.05, *** p<0.01

a) Regressions include year and firm fixed effects and controls for the number of migrants in 2009-10 and firm age.

b) Standard errors are clustered at the agency level.

c) Placebo treatment is assigned in year 2007; there are two years of pre-data and post-data.

d) Data on job orders is unavailable for year 2005 and so observations will be lower than other columns.

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Table 4: Difference-in-Difference Estimates on Firm Growth and Demand

Migrant Recruitment Est. Revenue (USD) Job Orders

(1) (2) (3) (4) (5) (6)Number % Female % Low-skill Level Logs Number

Panel A: Full Sample (N=6797)

Treat * Post 48.095 -0.013 -0.032* 124942.442 -0.511*** 2.178***(33.42) (0.02) (0.02) (96506.37) (0.17) (0.55)

Panel B: Restricted Sample, 50-150 (N=1029)

Treat * Post -53.769 0.052 0.053 -16664.413 0.421 -0.288(51.63) (0.04) (0.05) (15301.45) (0.46) (0.85)

Notes:

* p<0.1, ** p<0.05, *** p<0.01

a) Regressions include year and firm fixed effects and controls for the number of migrants in 2009-10 and firm age.

b) Standard errors are clustered at the agency level.

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Table 5: Difference-in-Difference Estimates on Complaints and Firm Exit

Complaint Rate Firm Exit 1 yr

(1) (2) (3) (4)All 50-150 All 50-150

Treat * Post -0.010*** 0.025*** -0.554*** -0.106*(0.00) (0.01) (0.02) (0.06)

Observations 6797 1029 1942 294Notes:

* p<0.1, ** p<0.05, *** p<0.01

a) Regressions include year and firm fixed effects and controls

for the number of migrants in 2009-10 and firm age.

b) Standard errors are clustered at the agency level.

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Table 6: Effects on Firm Growth and Demand by Median Firm Quality (Full Sample)

Migrant Recruitment Est. Revenue (USD) Job Orders

(1) (2) (3) (4) (5) (6)Total % Female %Low-Skill Level Logs Number

Panel A: Full Sample (N=6426)

Treat * Post 52.691 -0.038* -0.098*** -9970.623 -0.984*** 2.523**(33.00) (0.02) (0.03) (34049.09) (0.25) (0.99)

Treat * Post * Bad -37.082 0.049 0.122*** 222733.906 0.816** -0.685(31.40) (0.03) (0.03) (232087.48) (0.35) (1.18)

Panel B: Restricted Sample, 50-150 (N=966)

Treat * Post -10.266 0.033 0.019 -5245.380 0.508 0.009(16.09) (0.06) (0.07) (4789.85) (0.66) (1.17)

Treat * Post * Bad -81.228 0.034 0.087 -20414.917 -0.138 -0.843(79.51) (0.09) (0.09) (23519.24) (0.97) (1.57)

Notes:

* p<0.1, ** p<0.05, *** p<0.01

a) Regressions include year and firm fixed effects and controls for the number of migrants in 2009-10 and firm age.

b) Standard errors are clustered at the agency level.

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Table 7: Effects on Complaints and Firm Exit by Median Firm Quality

Complaint Intensity Firm Exit 1 yr

(1) (2) (3) (4)All 50-150 All 50-150

Treat * Post -0.014*** 0.036*** -0.575*** -0.110(0.00) (0.01) (0.04) (0.10)

Treat * Post * Bad 0.009 -0.020 0.063 0.027(0.01) (0.02) (0.05) (0.13)

Observations 6426 966 1836 276Notes:

* p<0.1, ** p<0.05, *** p<0.01

a) Regressions include year and firm fixed effects and controls

for the number of migrants in 2009-10 and firm age.

b) Standard errors are clustered at the agency level.

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