Death scares: How potential work-migrants infer mortality rates from migrant deaths

Journal of Development Economics 141 (2019) 102368

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Death scares: How potential work-migrants infer mortality rates from migrant deaths☆ Maheshwor Shrestha The World Bank, USA

A R T I C L E

I N F O

JEL Codes: F22 D83 O15 O12 Keywords: Migration Migrant mortality Temporary international migration The law of small numbers Nepal

A B S T R A C T

I analyze the migration response to incidents of migrant deaths to understand how potential work migrants infer underlying mortality rates. In the context of migrant workers from Nepal to Malaysia and the Persian Gulf countries, I find that incidents of migrant deaths in district-destination cell lower migration outflows in subsequent months, but do not affect prices or job characteristics. Furthermore, the migration response is stronger when there have been streaks of migrant deaths in recent months. Learning about unobserved costs of migration through migrant deaths, primarily via learning mortality rate abroad, becomes an important part of migration decision in this context. I find that the observed migration response implies a large change in perceived mortality rate in response to a migrant death. Models of learning fallacies, such as the belief in the ‘law of small numbers’, better explain the observed responses than a standard model of rational Bayesian learning.

1. Introduction Media reports abound on numbers of migrants dying on their way to the destination countries or during their stay abroad. For instance, The Guardian reports that almost 1000 legal migrant workers from Nepal, India and Bangladesh, died in Qatar in 2012 and 2013 (Gibson, 2014).1 With such reports, a pertinent policy question is whether potential migrants take their migration decisions knowing the mortality risks. If they do not, then how do they infer the mortality rates from incidences of migrant death that they observe around them? If potential migrants have full information about the risk of death, or if they believe so, then their decision to migrate will have already factored in the (perceived) probability of death during migration. They will take realizations of death as conveying zero information and will not change their migration decision in response to incidences of death.2 This presents a simple test of whether potential migrants are fully informed about the risk of death upon migration: if migration deci-

sion responds to death incidents, then potential migrants are not fully informed about the risk of death from migration. The context of work-related temporary migration from Nepal to Malaysia and the Persian Gulf countries is ideally suited to test this hypothesis. First, the volume of migrant outflow is large and is absorbed by a few destination countries which makes it easier to detect small effects. Second, these migrants are mostly men, typically aged 18–45, who work in low-skilled manual occupations – arguably a homogeneous group facing similar risks. Third, novel administrative data provide accurate counts of the flow of migrants as well as incidences of migrant deaths at a sub-national level. Such sub-national data makes it possible to distinguish impacts from national trends in migration. Fourth, the underlying mortality rates in the destination countries are stable during the study period. Furthermore, the occupations, as well as the work and living environment of these workers are very different from those in Nepal, making the role of active learning even more important.

☆ I am extremely grateful to Esther Duflo, Abhijit Banerjee, and Benjamin Olken for their advice and guidance. I am grateful to the Department of Foreign Employment and Foreign Employment Promotion Board in Nepal for providing me access to their database. E-mail address: [email protected]. 1 The death toll for illegal migrants in other migration corridors are much higher. For instance, in 2016, over 5000 migrants died in the Mediterranean Sea while on their way to Europe (International Organization for Migration, 2017). In 2014, about 445 people died while trying to cross the US-Mexico border (Carroll, 2015). 2 With the obvious caveat that the deceased do not affect the ability of the potential migrants to undertake migration (that is, the deceased are not family or close relatives).

https://doi.org/10.1016/j.jdeveco.2019.07.001 Received 22 December 2017; Received in revised form 28 May 2019; Accepted 9 July 2019 Available online 15 July 2019 0304-3878/© 2019 Published by Elsevier B.V.

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Journal of Development Economics 141 (2019) 102368

In this context, I find that the death of a migrant worker significantly lowers migration. Specifically, I examine the effect of a migrant death in a district-destination cell on migration flows from the district in the subsequent months. After controlling for potential confounds with districtdestination fixed effects, district-month fixed effects and destinationmonth fixed effects, one migrant death reduces monthly migrant flow from that district to the same destination (as the deceased migrant) by 1.2 percent for up to a year after the death. This indicates that potential migrants do not consider themselves fully informed (and rational) on the mortality risk abroad. I also find some increase in migrant flow from the district to other destination countries. However, the amount of substitution to other destination does not fully offset the negative effect. Overall, one migrant death (in any destination) in a district reduces total monthly migration outflow from that district (to any destinations) by 0.9 percent for the subsequent year. Interestingly, I observe no changes in recruitment fees, migrant wages, or occupational composition in response to a migrant death. The evidence is not consistent with employers lowering their demand or raising prices in response to migrant deaths. It is also inconsistent with local officials taking actions to drastically reduce migration (by imposing a ban or black-listing certain local recruiting agents) in response to migrant deaths from their communities. This indicates that the migration response is driven by potential migrants’ decision not to migrate. The net reduction of migrant supply is costly to Nepal. Simple calculations show that, in a year, Nepal could be losing over US$ 40 million (about 0.2 percent of GDP) in foregone earnings because of potential migrants not choosing to migrate in response to migrant deaths. I find that a rational Bayesian model of learning cannot rationalize the magnitude of the migration response observed in the data. I outline a simple framework where potential migrants make their migration decision based on observed costs and benefits of migration as well as unobserved costs. Learning about mortality rate abroad constitutes an important part of the unobserved cost which includes the risk of death as well as other associated disutilities. Migrant deaths serve as signals for potential migrants to infer the underlying mortality rate abroad. Direct evidence of such learning in this context comes from Shrestha (2019) (hereafter, simply “S19”), which finds that information on migrant deaths indeed changes perceived mortality rate as well as the subsequent migration decision among potential migrants. Under the assumption that potential migrants learn about unobserved costs directly or indirectly through learning about mortality rate abroad, I compute the change in perceived mortality rate induced by a migrant death. To achieve this, I combine the estimate of migration elasticity of perceived mortality rate from S19 with the migration response to migrant death events from this study. I find that a single migrant death increases the perceived two-year mortality rate by 5.3 per thousand. This amount of updating is several times higher than that generated by a model of rational Bayesian observing signals (migrant deaths) generated by an underlying independent and identically distributed (iid) mortality rate. Furthermore, the pattern of migration response also rejects a rational Bayesian learning in this context. I find that the migration response to a migrant death in a district-destination cell is much larger if there have been more deaths in the past 6 months in the cell. In fact, the migration response is entirely driven by district-destination cells which have experienced more than 1 deaths in the past six months. Such sequence dependence of response is inconsistent with rational Bayesian learning as the migrant deaths are generated by an iid process. The exaggerated response to migrant deaths and the sequence dependence indicate a learning fallacy. One possibility is that potential migrants are using some heuristic rule to form their beliefs (Kahneman and Tversky, 1974; Tversky and Kahneman, 1973). It could be that they think recent deaths represent the scenario more accurately, or that the past few months of migrant deaths is what is on the top

of their minds while making their migration decision. Another possibility, not necessarily mutually exclusive, is that the potential migrants expect the probability rules to apply exactly even for the ‘small’ samples of migrants from a district (Tversky and Kahneman, 1971; Rabin, 2002). Indeed, in his mathematical formulation of this fallacy, Rabin (2002) shows that such individuals, even when they are Bayesian in updating their beliefs, tend to over-infer from short sequence of signals and conclude that the true rate generating the sequence of signals to be more extreme than the truth. Consistent with this, I also find that migration response to a current death depends upon whether there have been streaks of migrant deaths or streaks of no migrant deaths in the past few months. The high expected mortality rate among potential migrants observed in S19 also fits the result from Rabin’s model. This paper contributes to the literature in three areas. First, it demonstrates a setting where learning fallacy can have large welfare consequences. Most of the empirical literature on the ‘law of small numbers’ or closely related gambler’s fallacy examines behavior in gambling or laboratory settings where stakes are low (see Suetens et al., 2016; Benjamin et al., 2013; Asparouhova et al., 2009, for recent examples). In two notable exceptions, Chen et al. (2016) and Simonsohn and Gino (2013), have examined real world settings where gambler’s fallacy can have high-stakes consequences. In addition to being related to the ‘law of small numbers’ rather than gambler’s fallacy, this paper differs from these in three other key aspects. First, these studies only observe judgments or decisions made by decisionmakers, whereas, closer to the theoretical models, I observe the underlying signals. Second, not only do I examine the (migration) decision response to these signals, but also link it back to the changes in beliefs implied by those decisions. And third, in these studies, the fallacies are committed by decisionmakers (judges, loan officers, baseball umpires, and admission committee members) which have large consequence for others. This paper is, to the best of my knowledge, the only study where such fallacy hurts the decisionmakers themselves. This fallacy prevents many people from migrating abroad for work, which would have been an opportunity to drastically improve their living standards (as seen in Bryan et al., 2014 and McKenzie et al., 2010). Second, this paper highlights a previously unexplored determinant of migration decision. A rich literature examines the role of wage differentials, incomes, liquidity constraints, travel costs, credit access, insurance, and risk aversion in shaping migration decision (Harris and Todaro, 1970; Kennan and Walker, 2011; Bazzi, 2017; Morten and Oliveira, 2018; Bryan and Morten, 2018; Banerjee and Newman, 1998; Munshi and Rosenzweig, 2016; Bryan et al., 2014, among many others). While there exists literature that explores the role of occupational risk on occupation choice, the role of these hazards in influencing migration decision is limited to the role of amenities, often in conjunction with labor and rental markets (see, Roback, 1982 for foundational framework, and Chen and Rosenthal, 2008 for recent empirical example). While such amenities would, in principle, include mortality hazards, it is impossible to identify specific determinant in those contexts. This paper highlights the importance of mortality rates in destinations in influencing migration decisions.3 Third, I present a setting where there is a specific type of market failure due to incomplete information on migrant deaths which could potentially be solved with an information intervention. Information interventions designed to affect migration decision rarely find an impact (for instance, Bryan et al., 2014; Beam, 2016; Beam et al., 2016). One explanation is that the supply of information may not match what potential migrants need to make their decision. Indeed, motivated by

3 There is some literature that associates out-migration with high mortality rates at origin (Deschenes and Moretti, 2009), or, finds higher mortality as a consequence of migration in specific contexts (Black et al., 2015).

2

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Journal of Development Economics 141 (2019) 102368

Table 1 International migration from Nepal and remittance income. Year

1961 1981 1991 2001 2011

Migrant/Population share

Remittance Income % of GDP

All

India

Non-India

3.49 2.68 3.56 3.41 7.43

2.48 3.17 2.61 2.80

0.19 0.37 0.78 4.63

1.5 2.4 22.4

Note: This table shows the migrant to population share for each of the census years since 1961. It also shows the share broken down by destination. The last column shows the personal remittance income as a share of national GDP for the years available. Source: Migrant/Population share from the Census reports for respective years, Remittance as a share of GDP from the World Development Indicator database (The World Bank)

this study, S19 finds that providing simple information on mortality incidences can change potential migrants’ perceptions as well as actual migration decision. This suggests that information interventions can be effective if they address a demonstrated market failure. The remainder of the paper is organized as follows: Section 2 describes the migration process in the context of Nepal and the data sources, Section 3 outlines the empirical strategy, Section 4 discusses the effect of migrant deaths on subsequent changes in migrant flows and prices, Section 5 calculates the change in perceived mortality rate induced by a migrant death and compares it with the updating behavior of a rational Bayesian, and Section 6 concludes. 2. Context and data 2.1. Migration from Nepal Historically, migration of Nepali workers outside the country was low and was limited mostly to India with which Nepal maintains an open border (Table 1). Migrating to other destinations for work has been, however, quite restricted historically. Being recruited to the Indian or British army was one of the very few options available to Nepali commoners to migrate abroad. Only since the mid-1990s, the Government of Nepal allowed private recruitment of workers to certain countries upon clearance from the Ministry of Labor. Work-related migration to destinations outside India surged after 2001. Between 2001 and 2011, the share of non-India migrants exploded six-fold with only a small change in the share of migrants to India (Table 1). This surge was driven by migration of Nepali workers, almost all male, to Malaysia and the Persian Gulf countries, particularly Qatar, Saudi Arabia, United Arab Emirates, Bahrain, and Kuwait. By 2011, these six countries alone hosted 0.9 million male Nepali workers, which is 90 percent of all work-related male migration from Nepal to destinations outside India. The outflow of male Nepali workers has continued to increase in the recent years. Fig. 1 shows that in 2013 alone, over 0.4 million Nepali worker received permits from the Government of Nepal to work in these destination countries. This number represents 7 percent of the adult working age (15–45) male population in the country. Consequently, Nepali workers became one of the largest suppliers of low-skilled labor to these destination countries. In 2013, Nepali workers were 17 percent of the total population in Qatar, making it the second largest population group behind Indian workers.4 Similarly, Nepali workers are expected to be one of the largest minorities in other destination countries as well. As a result, remittance income from abroad has become extremely important to the Nepali economy. Remittance

4

Fig. 1. Permits granted by DoFE for work abroad. Note: This figure shows the number of work-permits issued by DoFE for work abroad by year and destination country. Source: Author’s calculation on the data provided by Department of Foreign Employment (DoFE).

income as a share of national GDP increased from a mere 2.4 percent in 2001 to an overwhelming 32 percent by 2015 (The World Bank). Migration of Nepali workers to these destination countries is different from typical international migration. This type of migration is meant to be strictly temporary, and work is arranged before migrant departure. Migrants to Malaysia usually have an employment contract for 3 years, whereas typical employment contract duration for migrants to the Persian Gulf countries is 2 years. Their visa is always tied to work, and in many cases to a specific employer. Family members do not accompany the migrants unless they have a work-visa of their own. It is rare for these migrants to settle permanently in the destination countries. The process of finding jobs in these destination countries is heavily intermediated. Potential migrants typically contact (or are contacted by) independent local agents that link them to recruitment firms, popularly known as “manpower companies”, in Kathmandu. These local agents are fellow villagers with contacts to the manpower companies and gather people for foreign employment from their own or neighboring villages. In addition, most of them also help potential migrants obtain passports and other related travel documents. The manpower companies in Kathmandu receive job vacancies from firms (or employment agencies) abroad. The manpower companies are responsible for screening (if at all) and matching individuals with demands for workers from abroad, processing contracts, obtaining medical clearances, arranging for travel, visa and other paperwork including obtaining nec-

http://www.bqdoha.com/2013/12/population-qatar. 3

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Journal of Development Economics 141 (2019) 102368

Fig. 2. Total permits issued by DoFE for top destination countries. Note: This figure shows the number of work permits issued every month for the period of the study (2009–2013) to the top six destination countries. This does not include migration flows to India. Source: Author’s calculations from DoFE database

essary clearances from the Department of Foreign Employment (DoFE) for employment abroad.

responsible for sending 95 percent of the migrants. The average manpower company has recruited workers from 59 out of the 75 districts of Nepal; the median recruited from 63. Indeed, the median district of the country was served by 691 different recruitment companies (88 percent), and 522 of the 534 top recruitment companies during this period. This is consistent with manpower companies using independent local agents from all over the country bringing potential migrants to the manpower companies. The structure of intermediation between the independent local agents and the migrants themselves, however, are not observable in the data. For analysis, I aggregate this data up to the level of districtdestination-month cells by taking the counts of migrants (for total outflow), and means for wages, fees, and shares of occupation categories of the migrants. As Table 2 shows, the average migrant outflow per district per month per destination is 50. As expected, the migrant outflows have increased steadily during this period, tripling between 2009 and 2013. Throughout the years, Malaysia is the largest destination, followed by Saudi Arabia and Qatar. The average reported monthly wage in the data is US$ 220 with average fees paid to intermediaries of US$ 580. Wages rose during the period of study, whereas recruitment fees fell. The data on migrant wages and recruitment fees could be subject to reporting biases. DoFE has imposed ceilings on recruitment fees and imposed floors on migrant wages, which could result in misreporting of these numbers. Compared to other sources, the earnings and fees reported here are underestimates.6 While under-reporting of fees could be an understandable response to price ceilings, underreporting of wages seems to have stemmed from the discrepancy between contractual wages in the DoFE database and actual earnings reported in surveys. In this context, it is extremely common for workers to work more hours than stipulated in the contract and have higher earnings (variation in working hours and incomes higher than contractual wages is also documented in Joseph et al., 2018, for migrant workers in the UAE). Moreover, the structure of fees and

2.2. Data on migration flows The data on migration outflow was provided by the Department of Foreign Employment (DoFE) that keeps a record of all permits issued for work abroad.5 DoFE, a department under the Ministry of Labor, was established in December 2008 specifically to handle the processing and issuing of labor permits for Nepali workers with an employment contract in these destination countries. As discussed above, obtaining labor permits is mandatory for every work migrant from Nepal to these destination countries. I have individual level data (without personal identifiers) on all these registered work-migrants from 2009 to 2013. I observe the date of permit, district of residence, destination country, age, gender, contracted wages, fees paid, occupation, manpower company used, and name of employer for each permit issued. I restrict my analysis to the 6 top destination countries (Malaysia, Qatar, Saudi Arabia, United Arab Emirates, Kuwait and Bahrain) that cover more than 98 percent of all the permits issued by DoFE. The sample I use consists of 1.34 million permits issued from January 2009 to December 2013. Over 97 percent of these migrants are male. Average age is 27 years with 99 percent of migrants aged 18–45. During this period, Malaysia led other countries as the most popular destination choice with over 9000 permits being issued every month. As Fig. 2 shows, outflow of migrants is increasing in recent years for most destinations. There are no seasonal patterns in overall migration outflows. By 2013, over 1100 migrants were leaving the country per day with 40 percent going to Malaysia, 25 percent to Qatar and 20 percent to Saudi Arabia. The data also suggests a reasonable degree of competition among manpower companies in the recruitment process. The 1.34 million permits were intermediated through 786 manpower companies. The top thirty percent of the recruitment companies handled recruitment of 73 percent of the migrants. The top 534 recruitment companies were

6

In S19, I find that returnees from these destinations earned over US$ 300 per month and paid around US$ 1000 on recruitment fees. Migrants who had never migrated before, expect to earn higher and pay higher recruitment fees. Higher recruitment fees were also documented in The World Bank (2011).

5 The administrative data was provided specifically for this study and is not in public domain.

4

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Table 2 Summary statistics. Total Outflow mean/(sd) (1)

Number of Deaths mean/(sd) (2)

5

Wage (in US $) mean/(sd) (3)

Fee (in US $) mean/(sd) (4)

Labor jobs mean/(sd) (5)

Construction jobs mean/(sd) (6)

Other jobs mean/(sd) (7)

A. Overall means (means per district per destination per month) Mean 49.639 0.102 SD (90.956) (0.353)

219.490 (44.362)

584.901 (211.749)

0.542 (0.218)

0.087 (0.096)

0.371 (0.190)

B. By year (means per district per destination country per month) 2009 25.093 0.057 (52.254) (0.251) 2010 46.247 0.093 (114.468) (0.338) 2011 42.534 0.105 (65.368) (0.359) 0.116 2012 58.658 (86.307) (0.369) 2013 75.664 0.142 (111.321) (0.423)

188.403 (35.974) 186.340 (30.804) 202.503 (35.603) 214.284 (33.066) 263.646 (27.596)

682.020 (119.131) 694.603 (132.663) 608.127 (201.702) 541.484 (211.156) 506.243 (236.144)

0.496 (0.214) 0.612 (0.234) 0.543 (0.251) 0.529 (0.211) 0.524 (0.184)

0.129 (0.128) 0.060 (0.095) 0.096 (0.108) 0.083 (0.086) 0.087 (0.078)

0.375 (0.187) 0.328 (0.193) 0.361 (0.212) 0.388 (0.186) 0.389 (0.173)

C. By Destination Country (means per district per month) Malaysia 124.452 0.218 (152.036) (0.508) Qatar 56.187 0.141 (79.923) (0.402) Saudi Arabia 72.869 0.178 (88.556) (0.458) UAE 34.804 0.050 (41.604) (0.227) Kuwait 7.037 0.018 (10.687) (0.136) Bahrain 2.485 0.010 (4.113) (0.101)

202.659 (41.994) 236.061 (36.301) 209.728 (30.198) 260.580 (36.715) 268.333 (55.334) 260.270 (90.858)

759.645 (64.391) 300.828 (187.505) 517.820 (121.198) 542.652 (139.585) 658.555 (188.967) 606.718 (200.396)

0.741 (0.090) 0.485 (0.114) 0.454 (0.100) 0.185 (0.104) 0.259 (0.205) 0.281 (0.292)

0.011 (0.020) 0.160 (0.083) 0.163 (0.077) 0.086 (0.088) 0.049 (0.099) 0.098 (0.198)

0.248 (0.084) 0.355 (0.112) 0.383 (0.101) 0.728 (0.130) 0.692 (0.216) 0.621 (0.317) Journal of Development Economics 141 (2019) 102368

Note: This table shows the means and standard deviations of the outcome variables. The column variables indicate the outcome variables. An observation in the dataset used to compute the summary statistics is a district-destination-month cell. Outcomes are first aggregated up to the cell level from the data provided by DoFE and FEPB. Wages and fees are converted to USD using the monthly exchange rate between USD and Nepali Rupee. Panel A shows the average of the outcome in a district-destination-month cell. Panel B shows the average outcome in a district-destination-month cell in each year. The average is taken over all districts, destinations, and months in the year indicated in the corresponding row. Panel C shows the average outcome in a district-destination-month cell for each destination country. The average is taken over all districts and months. Averages for the outflow and deaths are unweighted cell averages, whereas other variables are weighted by the migrant outflow in the cell. Source: Author’s calculations on migrant registration database of DoFE and migrant death database of FEPB.

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Journal of Development Economics 141 (2019) 102368

Fig. 3. Monthly deaths in top destinations. Note: This figure shows the number of deaths of registered Nepali migrants every month for the period of the study (2009–2013). Source: Author’s calculations from FEPB database

wages across destinations qualitatively matches other sources.7 I proceed with these variables recognizing that wages are contractual wages, what migrants see before they leave, and that reported fees could be underreported. A large share (54 percent) of the migrants reported their occupation as ‘labor’ – a term used to refer to low-skilled manual work in presumably manufacturing and construction sectors. Apart from the ‘labor’ occupations, other popular occupations are related to construction (9 percent), operators (7 percent), helper (5 percent), guards (3 percent), and drivers (3 percent).8 I define two additional categories of jobs aggregated from the reported occupations. ‘Definitely not construction’ (21 percent) category includes all jobs that are definitely not in the construction sector. ‘Physical occupations’ (75 percent) includes ‘labor’ workers, as well as those in construction and other physically demanding occupations. As Table 2 shows, there are large variations across destinations on how labor, construction, and other occupations are coded.

insurance policy that migrant workers must purchase to obtain permits from the DoFE. The FEPB provides compensation for any kind of death if the migrant had obtained permits from DoFE, died within the duration of his/her contract, and the family files a claim within a year of the date of death with the certificate of death issued in the destination countries and other necessary documents. The necessary documents would typically be issued by local authorities in Nepal after verifying that the claimant is indeed related to the deceased migrant worker. The records kept at FEPB is widely considered to be comprehensive counts of the deaths of registered migrant workers. First, life insurance purchase is a mandatory condition for migrant workers to obtain permits from DoFE, and hence the payouts are applied universally to all migrant deaths. Second, FEPB is extensively involved in repatriating the bodies of deceased migrant workers from abroad. Third, FEPB also assists in transporting the bodies to various districts outside the capital. Because of these activities at various stages after a migrant death, family members are likely to be in contact with FEPB and made aware of the compensation they could claim. The data used in this paper contains all such claims made with FEPB for migrant deaths that occurred from January 2009 to December 2013. For each deceased (anonymized), I observe the date of death, the country of death, district of residence in Nepal and the cause of death reported in the death certificate. Fig. 3 shows the total number of deaths for every month in each of the major destination countries. The figure shows an increase in the reported number of deaths in most destinations akin to the increase in migrant outflow. Because migrant deaths are rare events, the numbers look sporadic for countries with smaller migrant flows, and therefore a small migrant stock. I aggregate this data up to the district-destination-month cell for analysis.9 Each district-destination cell experienced 0.1 deaths in a month, with

2.3. Data on migrant deaths The data on migrant deaths were provided by the Foreign Employment Promotion Board (FEPB) which keeps administrative records of all migrant deaths. FEPB is a government body established to make foreign migration safe and organized. One of its main tasks include providing financial support to the family of the deceased and helping them retrieve bodies of the deceased workers. When a registered (received labor permits from DoFE) migrant worker dies abroad, his family is eligible to receive compensation (of over US$ 1500 for the study period) through the FEPB. This compensation is part of the mandatory life

7 For instance, S19 and The World Bank (2011) both find slightly lower wages and higher recruitment fees for Malaysia. Typically, contracts to Malaysia are one year longer than elsewhere which could be a reason behind higher fees. 8 Except for construction, other categories are as reported in the database. Construction workers include workers in scaffolding, masonry, and other related occupations.

9

The data from FEPB contains all records of claims filed till end of June 2014. I exclude year 2014 from the analysis to allow for a reporting lag between the time of death and the time a claim is filed with FEPB. The key results are also robust to excluding year 2013 from the results. 6

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Fig. 4. Official cause of migrant deaths by destination. Note: This figure shows the official cause of deaths of Nepali migrants for each of the major destination countries. Source: Author’s calculations from FEPB database

stock in each destination for each month.11 The estimated overall twoyear mortality rate is 1.3 per thousand migrant workers in 2013, which is slightly higher than the rate of 1.16 per thousand workers in 2010. Though the estimated magnitude of the rate is slightly higher in 2013 compared to 2010, the plot of smoothened death rates suggests that they remained stable throughout the study period (Fig. 5).12 The figure also shows that death rates in Malaysia and Saudi Arabia are slightly higher than death rates in Qatar and the UAE.

the variation across destinations resembling the migrant stocks in the destination countries (Column 2, Table 2). Though considered an accurate source of information on the number of migrant deaths, the quality of data on the causes of death is a suspect. The cause of death in the FEPB database is as per the death certificates issued in the destination countries. During qualitative consultations, experts suggested that the causes may have been tampered with to avoid insurance and other hassles (legal hassle for employers, detailed medical procedure required to determine the exact cause, both of which will lead to delays in dead body repatriation for the family of the deceased). For instance, the incidence of natural deaths (coded as ‘natural’, cardiac arrest, or heart attacks), which can be expected to have uniform risk factor for all migrants, is much smaller in Malaysia compared to other destinations (Fig. 4). The large variation of the ‘other’ causes of death, as well as predominance or absence of a particular cause in some countries (traffic accidents in Saudi Arabia, for instance) further add to the unreliability of the cause data.10 Hence, any analysis done with the cause of death using this data needs to be interpreted with caution.

2.4. Other data sources In addition to the data sources mentioned above, I also use data generated from the public use Census micro-data for 2001 and 2011. I use the 2001 census data to compute district level migration rates, TV prevalence, average years of education, population density, and share of upper-caste, which I use for heterogeneity analysis in Section 4.4. The poverty estimates for the same section is produced with small area estimation of poverty on Census 2011 applied using the Nepal Living Standards Survey of 2010 (WB and CBS, 2013). I use the 2011 Census data to compute the stock of migrants in the destination countries as described above.

2.3.1. Mortality rates of Nepali workers abroad The data suggest low and stable mortality rate of Nepali workers abroad. I compute death rate by using the counts from the FEPB database and estimated migrant stock in each month in the destination countries. To estimate the migrant stock, I assume that migrants to the Persian Gulf countries return after 2 years and migrants to Malaysia return after 2.5 years. Using this rule in the migrant outflow database from DoFE, I obtain net flow of migrants to each destination in each month of the study period. The census of 2011 provides a snapshot of the migrant stock for June of 2011 in each of these destinations. This, along with the net flow of migrants provides an estimate of the migrant

3. Empirical strategy I use a triple difference estimator to estimate the effect of death of a migrant worker from origin district o in destination country d in month 11 See Appendix A for details of this estimation. This estimation assumes that no workers return before the expiration of their contract or over-stay their contract illegally. Since it is impossible to get the data on such incidences, I make no adjustments for them. The effects of such irregularities in the stock of migrants, and hence the death rates, are likely to be negligible. Appendix A also compares this rate with mortality rate of average men in Nepal and elsewhere and shows that, at least in absolute terms, the mortality rate of migrant workers is lower than average men in Nepal and elsewhere. 12 Migrant flow data before the formation of DoFE in 2008 is less reliable. Since migrant stock is calculated using outflow of migrants two years ago, this figure only begins in 2010.

10 In data collected for S19, the causes of deaths reported by returnees who have witnessed deaths of fellow workers are not different by destination country.

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Journal of Development Economics 141 (2019) 102368

Fig. 5. Deaths rates over time for top destination countries. Note: This figure shows the smoothened monthly death rates (per 100,000 migrants) of Nepali workers in the top destination countries. Locally linear regression with epanechnikov kernel and bandwidth of 4.5 used for smoothing. Thick lines show the point estimates whereas the light dashed lines around the thick lines show 95% confidence intervals. Source: Author’s calculations from FEPB database and the 2011 Population and Housing Census Public Use Microdata Sample

t on the outflow of migrants (and other outcomes) from the district to the same destination in the months following month t. Specifically, I estimate yodt ,x = 𝛽 Dodt + 𝛿od + 𝛾ot + 𝜉dt +𝜖odt

construction occupations in Table 2, as well as the underlying death rates in Fig. 5 show, occupational composition and mortality risks are stable during the study period. Moreover, the destination-month fixed effects would also absorb any trends in underlying mortality rates or changing occupations. Though Equation (1) can be used to estimate the effects of death on migration flows to the same destination, as well as other destinations, it is not straightforward to compute the net effect of deaths on migration. To directly estimate the overall effect of deaths on migration (irrespective of destination countries), I estimate

(1)

where yodt,x is the outcome for origin (district) - destination (country) cell for the next x months after month t, the month in which deaths are measured. Dodt indicates the number of migrant deaths in origindestination cell in month t. Note that this specification uses all o − d cells for each of month without over-weighting any particular cell. 𝛿 od captures origin-destination fixed effects, 𝛾 ot captures time (month) fixed effects for each district, and 𝜉 td captures destination country specific time fixed effects. I cluster standard errors at the district level allowing for arbitrary correlation in errors across months and destinations for a district. Equation (1) allows a natural way to extend the specification to explore heterogeneity of the effects of migrant death. To explore the heterogeneity by a characteristic X, I estimate yodt ,x = 𝛽 Dodt + 𝛼 (Dodt × Xodt ) + 𝜁 Xodt + 𝛿od + 𝛾ot + 𝜉dt +𝜖odt

yot ,x = 𝛽 Dot + 𝛿o + 𝛾t + 𝜈ot

(3)

where yot,x represents the migration flow measure from district o in the x months following month t, Dot is the number of deaths of migrants from district o that occurred in month t, 𝛿 o and 𝛾 t represent the district and month fixed effects and 𝜈 ot represents the error term. I allow for arbitrary correlation in error terms between any two periods for a district. To explore heterogeneity, I estimate

(2)

Here, 𝛼 denotes the marginal increase in the effect of death on outcome y with an increase in characteristics X. In all these specifications, the identifying variation comes from three sources. Within each origin-destination cell, the variation comes from different months having different number of migrant deaths. Within each origin-month cell, the variation comes from different destination countries having different number of deaths. Within each destinationmonth cell, the variation comes from different districts having different numbers of deaths. All other sources of variations are subsumed by the fixed effects. The central identifying assumption is that the incidences of migrant deaths are uncorrelated with other determinants of migration (or other outcomes) after controlling for all the two-way fixed effects as specified in Equation (1). One implication of this assumption is that death incidences are not serially correlated within origin-destination cells. Indeed, I fail to reject the null of no autocorrelation using Wooldridge and Arellano-Bond tests (Wooldridge, 2002; Arellano and Bond, 1991).13 Furthermore, as the composition of non-labor non-

yot ,x = 𝛽 Dot + 𝛼 (Dot × Xot ) + 𝜁 Xot + 𝛿o + 𝛾t + 𝜈ot

(4)

where Xot represents the variable of interest.

4. Results and discussion In this section, I present the results of estimating the equations on various measures of migration, prices, and job composition. As Sections 2.3 and 3 show, mortality rates abroad are stable and death incidences are consistent with being generated by an iid process. If migrants are fully informed about the mortality rate in the destination countries, then each death is only a realization of the underlying risks and should not elicit a migration response. The first part of this section tests for this and finds that migrant outflow responds to death incidences. The second part establishes that the response is driven by the supply side factors – potential migrants choosing not to migrate – rather than other confounding factors in the demand or recruitment side. The third part then tests whether the results are consistent with a model of Bayesian trying to learn about underlying mortality rate where deaths are generated by an iid process.

13 Stata commands xtserial and xtabond2 used for the tests. The p-values for the tests are 0.43 and 0.80 respectively.

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Fig. 6. Effect of a migrant death on migration flows in months before and after migrant deaths. Note: This figure shows the relationship between migrant death and migrant flows in the months preceding and following the deaths. Each figure plots point estimates (in red) of 𝛽 s from Equation (1), where the outcome is a measure of migration and x ranges from −5 to 12. That is, each point is a separate estimate of the equation with outcomes in the months indicated by the horizontal axis. The measure of migration flows is indicated on the y-axis of each plot. The plot on the top left shows the effect on the logarithm of migration flow from the same district to the same destination as the death event. The plot on the top right shows the effect on the logarithm of migration flow from the same district to other destinations as the death event. The plot on the bottom left shows the effect on the logarithm of migration flow from neighboring district to the same destination as the death event. The plot on the bottom right shows the effect on the logarithm of migration flow from neighboring district to other destinations as the death event. The vertical line at 0 indicates the month in which migrant deaths are measured. In each plot, the blue lines denote the 95% confidence intervals. Robust standard errors are clustered at the district level. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

4.1. Are potential migrants fully informed about mortality rates abroad?

departure may be busy with the logistical arrangements or are away from their districts as many of the visa and work permit related processes require them to be at the capital. It could also be more difficult to back out of the decision once all the payments have been made, and work-permits, visas and travel arrangement have been finalized. There is also evidence of some substitution to other destinations in the subsequent months (Fig. 6, top right plot). Here as well, I cannot reject the null that migration flows to destinations other than that of migrant death do not exhibit a trend in the preceding months. As the plot shows, migration flows to other destinations start to rise after the migrant death. The magnitude of these effects (expressed in logarithm of migration outflow per district per destination per month) in this case are, however, lower than the effect on the same destination. The average monthly migration outflows to other destinations increase by 0.2–0.3 percent for every migrant death in a cell for the subsequent 6–12 months (columns 4, 5, and 6 in panel A of Table 3). Migrant death in a district has spillover effects to neighboring districts as well (Fig. 6, bottom two plots). The migration flows in neighboring districts (expressed in logarithm of flow per district per destination per month) respond in similar way to a migrant death in an origin district - destination cell but with smaller magnitudes. That is, in response to a migrant death in an origin district - destination cell, migrant flows from neighboring districts to the same destination fall and migration flows from neighboring districts to other destinations slightly increase. As panel B of Table 3 shows, migration flows for neighboring districts to the same destination falls by 0.7 percent and to other destination increases by 0.2 percent. Migration from second

Migrant death in an origin district - destination cell lowers migration flow in the same cell in the subsequent months. The top left plot in Fig. 6, shows the coefficients of estimating Equation (1) on the natural logarithm of migration in the same cell ranging from 5 months prior to 12 months after the death measure.14 As expected, migrant deaths do not affect the migration flows in the preceding months. I cannot reject the null that migrant flows in the preceding 12 months are jointly zero. However, migration flow starts to drop in subsequent months. The drop increases over the next 6 months, stabilizes for the next few months, and comes back close to its initial levels in 12 months. A single migrant death in the district lowers the migration to the same destination (as the deceased migrant’s) in the next 6–12 months by about 1.2 percent per month (columns 1, 2 and 3 in panel A of Table 3).15 A few reasons could explain why the impact keeps increasing over the first six months of migrant deaths. Primarily, it could take time for the news of the death to spread around in the district, particularly if the news is transmitted by word-of-mouth. Migrants who are close to

14 Unless otherwise specified, I add 1 to the migration numbers before taking logarithms to adjust for cells with zero migration. Robustness exercise discussed later shows that using the inverse hyperbolic sine transformation yields almost identical results. 15 exp(−0.012) − 1 = − 0.012. Adjusting for the 1 added to the migration does not change the number when evaluated at the level of average migration flows in the district.

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Table 3 Effect of migrant deaths on district-destination level migration flows. To same destination 6 months after death (1) A. log(migration from district) −0.012∗∗ Deaths in month (0.005) Obs 27,000 Adj R2 0.979 B. log(migration from neighboring districts) −0.007∗∗∗ Deaths in month (0.003) Obs 27,000 Adj R2 0.989

To other destinations 9 months after death (2)

12 months after death (3)

6 months after death (4)

9 months after death (5)

12 months after death (6)

−0.012∗∗∗

−0.012∗∗∗

(0.004) 27,000 0.984

(0.004) 27,000 0.987

0.002 (0.001) 27,000 0.998

0.003∗ (0.001) 27,000 0.998

0.003∗∗ (0.001) 27,000 0.998

−0.008∗∗∗

−0.007∗∗

(0.003) 27,000 0.991

(0.003) 27,000 0.993

0.002 (0.001) 27,000 0.998

0.002 (0.001) 27,000 0.998

0.002 (0.001) 27,000 0.998

0.001 (0.001) 27,000 0.999

0.001∗ (0.001) 27,000 0.999

0.001 (0.001) 27,000 0.999

−0.000

−0.001

−0.001

(0.000) 27,000 0.999

(0.000) 27,000 0.999

(0.000) 27,000 0.999

C. log(migration from 2nd degree neighboring districts) −0.004∗∗ −0.005∗∗ Deaths in month (0.002) (0.002) Obs 27,000 27,000 Adj R2 0.993 0.995

(0.002) 27,000 0.996

D. log(migration from far neighboring districts) Deaths in month 0.001 0.001 (0.001) (0.001) Obs 27,000 27,000 Adj R2 0.999 0.999

0.001 (0.001) 27,000 0.999

−0.004∗∗

Note: This table shows the effect of a death of a migrant in a district-destination cell on logarithm of migrant flows, estimated using Equation (1). The first 3 columns show the effect on the logarithm of subsequent migration flows to the same destination as the deceased migrant. Columns (1), (2), and (3) show the estimates for subsequent flow in the 6, 9, and 12 months respectively. The last 3 columns show the effect on the logarithm of subsequent migration flows to destinations other the country of migrant death. Columns (4), (5), and (6) show the estimates for subsequent flow in the 6, 9, and 12 months respectively. Panel A shows the effect on migration flows from the same district as the migrant death. Panel B shows the effect on migration flows from neighboring districts. Neighboring districts share a border with the district of migrant death. Panel C shows the effect of a migrant death on migration flows from second degree neighboring districts. Second degree neighbors are separated from the district of migrant death by one district. Panel D presents the effects on migration flows from districts that are from the district of migrant death. These districts are separated from the district of migrant death by at least 3 districts in between. Each column in each panel is a separate regression. For all specifications, standard errors are reported in parenthesis and are clustered at the district level. ∗∗∗ ∗∗ ∗ ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

degree neighbors (not adjoining) also responds in similar manner, but with much smaller magnitudes. Migrant deaths in an origin district destination cell have no effects on migration outflows from districts that are far (panel D, Table 3). This phenomenon is consistent with word-of-mouth transmission of information on migrant deaths conveyed through potential migrants’ social networks. Potential migrants are more likely to know of the deaths of migrants from their own district or in their own social network which are also likely to be geographically proximate.16 However, the increase in migration flow to other destinations does not fully offset the negative effect in the same origin district -destination cell. Table 4 shows that total migration from the district falls by 0.9–1.2 percent in the subsequent 6–12 months for every migrant death in the district (panel A, columns 1, 2, and 3). There are large spillovers of migrant death to the neighboring district, but the spillover is limited to immediate neighbors only (panel B). Migrant death does not have significant impact on the migration outflows from districts that are further away (panels C and D).

of the fixed effects chosen in Equation (1). Instead of natural logarithm, I use the inverse hyperbolic sine transformation which naturally allows for zeros and has similar interpretation of the coefficients. The results are virtually identical (column 4 of Table B.1).17 Instead of using the two-way fixed effects, another possibility is to use only the origin-destination fixed effects and allow each origin-destination cell to have a linear and a quadratic time trend to capture trends in districtspecific migrant demands from employers as well as destination-specific migrant supply from each district. The results are also robust to these alternative empirical specifications for the measures of migration I use, as well with the inverse hyperbolic sine transformation (columns 2, 3, 5, 6, of Table B.1).18 4.2. Potential demand side response to migrant deaths If the supply of migrant workers falls in response to migrant death, then employers could offer higher compensating wages, or intermediaries could charge lower fees to entice more workers to migrate. Note that, to be detected by Equation (1), these responses have to be within the origin-destination cells. Any changes made by the employers to all

4.1.1. Robustness of main results I explore whether the results are driven by the specific functional forms (taking natural logarithm of 1 + migration flows), or the nature

17 The results are robust to alternative measures of migration (for example, likelihood of a cell having higher than average migrant flows). 18 The results remain similar if, instead, the specification allows for each district to have its own migrant supply trend (independent of the destination) and allows for each destination country to have its own migrant demand trend (independent of district of origin of migrants).

16 One test of this mechanism would be to see whether the intensity of the impact decreases with distance even within a district. Unfortunately, neither the migration data, nor the death data have sub-district identifiers.

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Table 4 Effect of migrant deaths on district level migration outflow. Flow in the next 6 months (1) A. log(total migration from district) −0.012∗∗ All deaths in month (0.005) Obs 4499 Adj R2 0.967

9 months (2)

12 months (3)

−0.010∗∗

−0.009∗∗

(0.005) 4500 0.973

(0.005) 4500 0.976

B. log(total migration from neighboring district) −0.015∗∗∗ −0.013∗∗∗ All deaths in month (0.004) (0.004) Obs 4500 4500 Adj R2 0.962 0.965 C. log(total migration from 2nd degree neighbors) −0.002 −0.001 All deaths in month (0.003) (0.003) Obs 4500 4500 Adj R2 0.957 0.962 D. log(total migration from far neighbors) All deaths in month −0.000 (0.001) Obs 4500 Adj R2 0.991

−0.013∗∗∗ (0.004) 4500 0.968

−0.000 (0.003) 4500 0.967

−0.001

−0.001

(0.001) 4500 0.992

(0.001) 4500 0.992

Note: The table shows the effect of a death of a migrant from a district on the logarithm of migration flows, estimated using Equation (3). Columns (1), (2), and (3) show the effect on flows in the subsequent 6, 9, and 12 months respectively. Panel A shows the effect of a migrant death on flows from the same district. Panel B shows the effect on flows from neighboring districts. Neighboring districts share a border with the district of migrant death. Panel C shows the effect on flows from second degree neighboring districts. Second degree neighbors are separated from the district of migrant death by one district. Panel D shows the effect on flows from districts that are far from the district of migrant death. These districts are separated from the district of migrant death by at least 3 districts in between. Each column in each panel is a separate regression. For all specifications, standard errors ∗∗∗ ∗∗ are reported in parenthesis and are clustered at the district level. ∶ p < 0.01; ∶ ∗ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

Nepali workers will be subsumed by the destination-month fixed effects. The same applies to any nationwide changes to the recruitment fees as well. If this specification finds an effect, it will be capturing the local response, within district-destination cells, to the migrant deaths. There is no evidence of such response in terms of wages and fees in response to a migrant death in the origin-destination cells. As seen in the first two plots of Fig. 7, wages and fees do not respond to migrant deaths in the preceding as well as following months. Not only are the results statistically insignificant, the point estimates are also extremely low. The standard errors suggest that the specifications have the power to detect an effect of 0.16 percent in wages and 2.8 percent in fees (first two rows of Table 5). The larger standard errors for fees are suggestive of measurement errors in reported fees.19 The effects are, indeed, precisely estimated zeros. Another margin of response to migrant deaths could be that local recruitment agents target different demographics of potential migrants or offer different types of jobs. Due to the highly informal nature of intermediation in this context, no data exists to examine the agent-

worker relationship in depth. However, if these agents are targeting different demographics, then it should be reflected in the gender composition or the age of migrants. As Table 5 and Fig. 7 (plot C) show, the demographics of the migrants do not change in response to migrant deaths. If the local recruitment agents are offering different types of jobs, then the occupation shares of the migrants would change as well. As the plots in Fig. 7 shows, the occupation shares do not appear to change. In fact, out of the 91 coefficients estimated across seven mutually exclusive occupation categories (plots O1 - O7) for the subsequent months (0–12 months), only 5.5 percent are significant at 95 percent significance level, and only 9.9 are significant at 90 percent significance level, exactly what would be expected by random coincidence. The same holds true for the aggregated occupation categories (‘definitely not in construction’, and ‘physical’ jobs) as well. The aggregated point estimates in Table 5 also confirm that occupational composition of migrants does not change in response to migrant deaths. Another way to check for response from the demand side is to see directly how the employers respond to migrant deaths in a particular district. Employers could offer compensating wages to the migrants from the district with a recent migrant death, or, anticipating higher wage demand (if any) from the potential migrants avoid recruiting from the district. The former response should translate in higher wages for the migrants whereas the latter response would lead to a reduction in shares of migrants from the district recruited by the employers. During

19 If fees are under-reported by a factor of 𝜈 i for each cell (that is, measured log(𝜈i ),Di ) yi = 𝜈i yi⋆ , with 𝜈 i ≤ 1) then plim𝛽̂ = 𝛽 + Cov(Var . That is, if deaths are (D ) i

uncorrelated with measurement errors, then the estimated coefficient is a consistent estimate of 𝛽 . If, on the other hand, higher death lowers measurement error (increases 𝜈 i ), then the estimated positive coefficient is an over-estimate. 11

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Fig. 7. Effect of a migrant death on wages, fees, and job composition in months before and after migrant deaths. Note: This figure shows the relationship between migrant death and migrant wages, fees and job composition in the months preceding and following the deaths. Each figure plots point estimates (in red) of 𝛽 s from Equation (1), where the outcome is a measure of migration and x ranges from −5 to 12. That is, each point is a separate estimate of the equation with outcomes in the months indicated by the horizontal axis. The title of each plot denotes the dependent variable. Plots A and B show the impact of migrant death on the logarithm of monthly contractual wage and the logarithm of migration costs in the o − d cell. Plot C shows the impact on average age of the migrants in the o − d cell. Plots O1-O7 show the impact on shares of 7 mutually exclusive and exhaustive occupation categories of the migrants. Plot O8 shows the impact on jobs that are definitely not in construction. Plot O9 shows the impact on jobs that are physically demanding. See the text for definitions. The vertical line at 0 indicates the month in which migrant deaths are measured. In each plot, the blue lines denote the 95% confidence intervals. Robust standard errors are clustered at the district level. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

the study period, over 21,000 employers hired migrant workers from Nepal with a mean recruitment of 63 workers per firm. I estimate a specification similar to Equation (1) at the origin district - employer quarter level on the top 50 percent of the employers that are responsible for 95 percent of all recruitment over this period.20 Since I do not know the employer of the deceased migrant, I simply use the number of deaths in the district-destination cell for the quarter. I find that neither the share of migrants originating from the district, nor the wages respond to deaths of migrants from the district in the destination country where the employer is located. As seen in Appendix Table B.2, I cannot reject the null that for an employer, the share of migrants originating from a particular district is not affected by the number of migrant deaths (columns 1 and 2). The point estimates have the expected negative sign only for the top 1 percent or larger employers. Yet, there is a reduction in migrant flow in response to migrant deaths, particularly for larger employers (column 3). These two findings suggests that while migrant flow falls in response to migrant deaths, employers are not be taking a deliberate action to avoid recruiting workers from the district of migrant death. Moreover, the contrac-

tual wages of the migrant workers do not respond to the deaths. This does not indicate any compensating wage offer from the employers (column 5). The only significant result, for the top 0.25 percent of employers, goes in the opposite direction as compensating wage offer and is actually an artifact of how data are coded when there is no migrant flow in the cells. To preserve the structure of the panel, I code cells without a migrant flow as having zero wages. Hence, the extensive margin impact on migration (column 4) is reflected in the wage coefficient as well. The migration response to a migrant death without a wage response is apparent when I aggregate the data up to employer-quarter level. In Appendix Table B.3, I investigate the impact of migrant deaths (from anywhere in Nepal) in destination countries on migrant flows and wages for each employer. As expected, I find that migration to employers falls in response to death of a migrant worker in the destination country. The magnitudes of the fall also similar for the top 50 percent of the employers to the top 0.25 percent (columns 1 and 2). The migration response is mostly at the extensive margin (employers stop recruitment) for small employers whereas the response is at the intensive margin for larger employers (column 3). In samples where the migration response is at the intensive margin, I cannot reject the null of no wage impacts of migrant deaths (column 4).21 Even in samples where I reject the null, the coefficients on wages are actually opposite to that of a compensating wage offer by the employers.

20 I restrict employers to the top 50 percent and use quarters instead of months to reduce the total number of cells in the estimation. I further restrict the sample to the top 10, 5, 1, 0.5, and 0.25 percent of the employers that recruited 64, 49, 22, 15, and 10 percent of the migrant workers respectively. These samples recruited an average of 120, 400, 620, 1,420, 1,880, and 2450 workers respectively during the study period.

21

12

Here as well, contractual wages are set to zero in cells with zero migration.

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Table 5 Effect of migrant deaths on job-composition and prices. To same destination 6 months after death (1) A. Wages, fees, and demographics −0.0000 Log(contractual wage) (0.0010) Log(fee) 0.0022 (0.0016) Share female −0.0002 (0.0004) −0.0212 Average age (0.0402) B. Share of occupations Labor Construction Helper Guard Driver Operator Other occupations Physical occupations Def not construction

−0.0004 (0.0011) −0.0011 (0.0007) 0.0009 (0.0006) 0.0004 (0.0007) −0.0002 (0.0005) −0.0003 (0.0004) −0.0003 (0.0010) −0.0023 (0.0014) −0.0011 (0.0011)

To other destinations 9 months after death (2)

12 months after death (3)

6 months after death (4)

9 months after death (5)

12 months after death (6)

−0.0001

−0.0000

−0.0003

−0.0005

−0.0007

(0.0010) 0.0021 (0.0014) 0.0000 (0.0003) −0.0393 (0.0399)

(0.0008) 0.0017 (0.0014) 0.0003 (0.0003) −0.0515 (0.0347)

(0.0007) −0.0001 (0.0008) −0.0001 (0.0001) 0.0354 (0.0357)

(0.0007) −0.0003 (0.0006) −0.0001 (0.0001) 0.0513 (0.0407)

0.0003 (0.0009) −0.0011∗ (0.0006) 0.0006 (0.0004) 0.0004 (0.0006) −0.0002 (0.0004) −0.0006 (0.0004) −0.0005 (0.0008) −0.0020 (0.0012) −0.0014 (0.0010)

−0.0004

−0.0008∗

−0.0009∗∗

−0.0007∗∗

(0.0009) −0.0008 (0.0005) 0.0007∗ (0.0003) 0.0001 (0.0005) −0.0005 (0.0004) −0.0000 (0.0003) 0.0002 (0.0008) −0.0018 (0.0011) −0.0014 (0.0009)

(0.0004) 0.0002 (0.0002) −0.0002∗ (0.0001) −0.0002 (0.0002) 0.0001 (0.0001) −0.0000 (0.0001) −0.0003 (0.0003) −0.0005 (0.0007) −0.0001 (0.0003)

(0.0004) 0.0001 (0.0002) −0.0001 (0.0001) −0.0003∗ (0.0001) 0.0000 (0.0001) 0.0000 (0.0001) −0.0004 (0.0003) −0.0008 (0.0006) −0.0001 (0.0003)

(0.0003) 0.0001 (0.0001) −0.0001 (0.0001) −0.0002 (0.0001) 0.0001 (0.0001) −0.0000 (0.0001) −0.0006∗∗ (0.0003) −0.0007 (0.0005) −0.0000 (0.0003)

(0.0008)

−0.0003 (0.0006)

−0.0002∗∗ (0.0001) 0.0537 (0.0327)

Note: This table shows the effect of a death of a migrant in a district-destination cell on job composition, logarithm of contracted wages, and logarithm of fees paid to intermediaries, estimated using Equation (1). The first 3 columns show the effect on the outcomes for the same destination as the destination of the deceased migrant. Columns (1), (2), and (3) show the effect for the subsequent 6, 9, and 12 months respectively. The last 3 columns show the effect on the outcomes for migrants going to destinations other the country of migrant death. Columns (4), (5), and (6) show the effect for subsequent the 6, 9, and 12 months respectively. Panel A shows the effect on the logarithm of average contractual wages and fees paid by migrants, as well as on gender and age composition of the migrant pool. Panel B shows the effect on shares of various occupations. Each column in each row represents a separate regression. In all cases, standard errors are reported in ∗∗∗ ∗∗ ∗ parenthesis and are clustered at the district level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

cators of drastic drops. Instead, it affects the likelihood of drops from top 25th percentile to bottom 50th percentile (column 5). The evidence suggests that local responses, at least the ones that drastically curtail the level of migration, are unlikely to be large factors in driving the migration results in Section 4.1.

4.3. Response from local bodies One potential mechanism that could be driving the result is that local bodies (such as government officials, civil society organizations, or victims’ groups) respond to migrant deaths by banning or black-listing local recruiters rather than potential migrants themselves choosing not to migrate. Since these responses are local and in response to deaths in particular destination, the empirical specification cannot directly account for such mechanisms. While I cannot completely rule out such responses, I hypothesize and test for certain patterns through which these responses could manifest. These local responses, if present, are likely to lead to a drastic drop in flow of migrants following migrant incidents, potentially for a shorter period of time. To capture this, I look at how migrant death affects the fluctuation in migrant flows in an o-d cell within a 4-month window around the time of migrant death. In Table B.4, I report the impact of migrant death on four such measures. The first measure (column 1) indicates whether migrant flow in the o-d cell dropped to zero in the subsequent 4-months (post period) from any migration level in the 4-months prior (pre period) to migrant death. The second measure (column 2) indicates whether migrant flow dropped to zero in the post period from o-d cells at the top 75th percentile of destination specific migrant flow in the pre-period. The third measure (column 3) indicates whether the flow dropped to the bottom 10th percentile in the post-period from o-d cells that were at the top 75th percentile in the pre-period. Similarly, the fourth measure (column 4) indicates whether the flow dropped to bottom 10th percentile from the top 50th percentile. Migrant death has no impact of these indi-

4.3.1. How much does the country lose from the migration response to migrant deaths? The evidence presented thus far shows that migrant deaths elicit a migration response but not a price response. The observed responses are consistent with McKenzie et al. (2014), who study the impact of GDP growth in destination countries on migrant outflows from Philippines. The authors argue that, due to price rigidities, the observed effects are driven solely by changes in migrant demand. In this case, as argued above, the responses are unlikely to be driven by changes in migrant demand for a few reasons. First, if Nepali migrant deaths lower the demand for Nepali workers, then one would also expect a change in job composition as it would lower the demand more in riskier occupations. The results do not show such effects. Second, and more importantly, increased migration to other destinations as well as the lack of response from faraway districts are inconsistent with reduced demand for Nepali workers after incidents of migrant deaths. The results imply a net reduction in migrant supply from Nepal in response to death of migrant workers abroad. The estimated migration impact is, hence, a welfare loss for Nepal. Estimates from Tables 3 and 4 show a net negative impact on migration outflows. The net negative migration impact is also confirmed by 13

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Journal of Development Economics 141 (2019) 102368

Table 6 Heterogeneous effect of deaths on migration flows. Flow in the next

6 months (1)

A. log(migration to same destination) Deaths in month 0.001 (0.007) −0.007∗∗∗ x Deaths in past 6 months (0.002) x > 1 deaths in past 6 months Deaths in past 6 months

9 months (3)

(4)

(5)

(6)

0.001 (0.006)

−0.001

−0.001

−0.002

−0.003

(0.007) −0.006∗∗∗ (0.002)

(0.005)

(0.007) −0.005∗∗ (0.002)

(0.005)

−0.031∗∗∗

−0.025∗∗∗

(0.008)

(0.008)

−0.016∗∗∗

−0.015∗∗∗

(0.004)

> 1 deaths in past 6 months

B. log(migration to other destinations) −0.000 Deaths in month (0.002) x Deaths in past 6 months 0.001∗ (0.001) x > 1 deaths in past 6 months Deaths in past 6 months

> 1 deaths in past 6 months

12 months

(2)

−0.021∗∗ (0.008)

−0.014∗∗∗

(0.004)

(0.004)

−0.034∗∗∗

−0.031∗∗∗

−0.028∗∗

(0.012)

(0.012)

(0.011)

−0.001

0.001 (0.002) 0.001∗ (0.001)

(0.002)

0.007∗∗ (0.003)

−0.000 (0.002)

0.002 (0.002) 0.001 (0.001)

0.006∗∗∗ (0.002)

0.003∗∗ (0.002)

0.004∗∗ (0.002) 0.008∗ (0.004)

0.006∗∗ (0.002) 0.004∗∗ (0.002)

0.008∗ (0.004)

C. log(migration from neighbors to same destination) −0.006 0.000 Deaths in month (0.004) (0.004) x Deaths in past 6 months −0.001 (0.002) x > 1 deaths in past 6 months −0.018∗∗∗ (0.006) Deaths in past 6 months −0.012∗∗∗ (0.003) > 1 deaths in past 6 months −0.028∗∗∗ (0.008)

0.001 (0.002)

0.008∗ (0.004)

−0.007∗

−0.002

−0.007∗

−0.002

(0.004) −0.001 (0.001)

(0.003)

(0.004) −0.001 (0.001)

(0.003)

−0.015∗∗∗

−0.014∗∗

(0.005)

−0.011∗∗∗

(0.005)

−0.010∗∗∗

(0.003)

(0.003)

−0.025∗∗∗

−0.023∗∗∗

(0.008)

(0.008)

Note: The table shows how the migration effect of a death of a migrant in a district-destination cells changes with the number of deaths in the district-destination cell in the past six months. The estimates reported are 𝛽 and 𝛼 coefficients from Equation (4). All specifications control for the effect of the interaction between current death and actual underlying death rates in the destination countries. The first two columns present the estimates for migrant outflow in the subsequent 6 months, columns (3) and (4) present the estimates for migrant outflow in the subsequent 9 months, and columns (5) and (6) present the estimates for migrant outflow in the subsequent 12 months. Odd numbered columns show the interaction with the number of deaths in the district-destination cell in the past 6 months. Even numbered columns show the interact with whether there has been more than one death in district-destination cell in the past 6 months. Panel A shows the effect on migration outflow from in the same district-destination cells as the migrant death. Panel B shows the effect on migration outflow to destinations other than that of the migrant death. Panel C shows the effect on migration outflow from neighboring districts to the same destination as the migrant death. ∗∗∗ ∗∗ ∗ For all panels, standard errors are reported in parenthesis and are clustered at the district level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

Appendix Table B.3. During the period of analysis, the total monthly outflow of migrants from each district was 300. The total effect of 0.9 percent reduction for 12 months (Table 4) means 32.4 fewer migrants migrate over a year after the death of a single migrant from this district. If these individuals had migrated, they would have earned US$ 5250 from a migration episode that lasts 2.21 years.22 I assume that if they stayed back, they would earn US$ 2900 in 2.21 years, which is the average income per employed male.23 With these assumptions, a single death represents a loss of US$ 76,000 in forgone earnings over the 2.21-year long migration episode. During the period of the study, an average of 550 migrant workers died annually reducing the migration outflow by 17.8 thousand over the course of a year. This led to a total forgone income of US$ 42 million which translates to

0.24 percent of the average annual GDP, and 1 percent of the remittance received during this period.24 Accounting for spillover effects of death to neighboring districts makes the income loss at 2 percent of GDP (8 percent of total remittance receipt). Note that, however, these calculations do not adjust for cost of living without migration, or for other monetary and non-monetary costs associated with migration. 4.4. Heterogeneity in migration response 4.4.1. Can the cause of death shed light to the migration response? The negative migration response to deaths suggest that potential migrants are using the migrant deaths as a signal conveying information about the mortality rates abroad. One plausible indicator of such information is the cause of death. However, as discussed in Section 2.3, the data on the cause of death are unreliable. Nevertheless, I investigate the migration impacts by different types of death in Appendix Table B.5.

22 Based on earnings and fees reported in Table 2. As discussed in Section 2.2, both of these numbers are likely to be underestimates of actual earnings and fees. Expectations data in S19 suggests returnee migrants expect to make US$ 9600 net from migration. The estimates presented here are, hence, likely to be a lower bound on the monetary impacts of migrant deaths. 23 Author’s calculations from the Nepal Living Standards Survey-III, of 2010.

24

14

GDP and remittance numbers taken from The World Bank.

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Journal of Development Economics 141 (2019) 102368

Table 7 Effect of deaths in any destination on total migration outflow. Flow in the next 6 months (1) A. log(total migration from district) All deaths in month 0.007 (0.008) −0.026∗∗∗ x > 3 deaths in past 6 months (0.009) > 3 deaths in past 6 months −0.019 (0.028) Obs 4499 Adj R2 0.967 B. log(total migration from neighboring district) −0.018∗ All deaths in month (0.010) 0.004 x > 3 deaths in past 6 months (0.011) > 3 deaths in past 6 months −0.068 ∗∗∗ (0.024) Obs 4500 Adj R2 0.962 C. log(total migration from 2nd degree neighbors) All deaths in month −0.003 (0.006) x > 3 deaths in past 6 months 0.002 (0.008) > 3 deaths in past 6 months −0.026 (0.016) Obs 4500 Adj R2 0.957

9 months (2)

12 months (3)

0.009 (0.007) −0.026∗∗∗ (0.009) −0.013 (0.025) 4500 0.973

0.009 (0.007) −0.025∗∗∗ (0.008) −0.012 (0.024) 4500 0.976

−0.013

−0.013

(0.010) 0.000 (0.012) −0.058∗∗ (0.022) 4500 0.966

(0.010) 0.001 (0.013) −0.050∗∗ (0.022) 4500 0.969

−0.001

−0.001

(0.006) 0.000 (0.007) −0.017 (0.014) 4500 0.962

(0.005) 0.000 (0.007) −0.009 (0.013) 4500 0.967

Note: The table shows how the migration effect of a death of a migrant in district changes with whether there has been many (> 3) migrant deaths in the district in the past six months. The estimates reported are 𝛽 and 𝛼 coefficients from Equation (4). Columns (1), (2), and (3) present the estimates for the migrant outflow in the subsequent 6, 9, and 12 months. Panel A shows the effect on migration outflow from in the same district as the migrant death. Panel B shows the effect on migration outflow from neighboring districts. Panel C shows the effect on migration outflow from 2nd degree neighboring districts. For all panels, standard errors are reported in parenthesis and are clustered at the district ∗∗∗ ∗∗ ∗ level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

3, and 5). If anything, the point estimate of the response is positive. But for every additional migrant death in the cell in the past six months, the effect of a current death in the cell on the migration flows falls by 0.5–0.7 percentage points. Put it differently, if there have been one or zero deaths in the origin district - destination cell in the past six months, a current death in the cell does not affect migration flow. But, if there have been more than one migrant deaths in the cell, death in the current month reduces migration flows in the cell by about 2–3 percent for the subsequent 6–12 months (panel A, columns 2, 4, and 6). That is, the entire effect of migrant death on migrant flows is driven by origin district - destination cells which have experienced more than 1 migrant death in the past 6 months. The interaction of the effects with recent deaths persists for migration to other destinations, as well as to migration from neighboring districts (Table 6, panels B and C). As the table shows, migration responses to deaths are larger when there have been more deaths in the recent six months. In all of the specifications, the effect of a current death in cells in which there have been zero deaths in the past 6 months is not significantly different from zero. Table 7 shows that the interactions with recent deaths are important for effects on overall migration from the district. The table shows that in districts where 3 or fewer deaths occurred in the past six months, a current death does not change migration outflows. But if there have been more than 3 migrant deaths in the districts, then the migration flow falls by an additional 2.5 to 2.6 percentage points (panel A). The interaction effect is, however, limited to the same district as the migrant death (panels B and C).

The poor quality of data on the cause of death makes it difficult to interpret the migration response by various causes. For instance, in countries where natural deaths (those coded as ‘natural’, or as ‘cardiac arrests’, or as ‘heart attacks’) are likely to be under-reported (e.g. Malaysia), other causes of deaths seem to be driving the results. In Qatar, where the prevalence of deaths from natural causes is high, it is the natural causes that are driving the results. Hence, I interpret these patterns as being driven by how data are coded in the death certificates rather than conveying substantive information. 4.4.2. Does the recent history of deaths matter? Migration responses to death events generated by an iid mortality rates in the destination countries suggest that potential migrants do not treat death events as a mere realization of a known underlying risk. One possibility is that potential migrants are learning about the underlying iid death rate, treating incidents of migrant deaths as signals. If that is the case, a rational, or a Bayesian, response (as discussed later in Section 5.2), would depend on the signal, but not on the distribution of the signal. That is, the migration response to a migrant death in the current month should not depend upon the number of deaths that have happened in the district in the recent past. However, I find evidence that migration response to a current death depends upon realizations of migrant deaths in the recent past. Table 6 shows that when there have been no deaths in the past six months in the origin district - destination cell, the migration flow does not respond significantly to a current migrant death in the cell (panel A, columns 1, 15

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4.4.3. Heterogeneity by district and destination characteristics One of the concerns with the observed effects could be that the effects are driven by districts or destinations with particular characteristics. For instance, increases in the underlying migrant death rates in the destination could elicit a higher response. Or, districts that have higher shares of ‘risky’ jobs could be driving the results. Similarly, districts with higher ‘historic’ migration rates could elicit a higher response as potential migrants may see larger number of migrant deaths. Or, on the contrary, the effect could be lower as potential migrants in those districts have better information about mortality rates abroad. Districts that may have better communication media may have larger response as news of migrant deaths spread faster in those districts. Similarly, wealthier districts could elicit a larger response if the perceived risks conveyed by the deaths make migrating a poorer choice. If reporting of migrant deaths (to the FEPB) are more difficult for districts that are further from the capital (those in Mid- and Far- Western Regions), then those that do get reported might be well publicized within those districts, which could lead to a larger response. Or, those districts could have larger measurement errors and hence, a more muted effect. However, as Appendix Table B.6 shows, heterogeneity along many of these dimensions are absent in the data.25 The underlying death rates in the destination has no differential impact on migration flows (column 1 in panel A and B). This suggests that the underlying mortality rate has no additional impact in the migration response beyond that of migrant deaths generated by the underlying rate. Migration responses to a death are not different in districts with higher, or lower, share of migrants in ‘risky’ (construction and ‘labor’) occupations (columns 2 and 3). Similarly, migration responses are not different for districts with higher, or lower, migration rates in 2001 (column 4). This suggests that a history of migration does not necessarily convey the information on underlying mortality rates effectively. However, the levels of migration were much lower in 2001 relative to the study period. Similarly, the share of non-minorities in the district, poverty rate, as well as remote Mid-Western and Far-Western regions do not significantly change the migration responses to a migrant death (columns 8, 9, and 10). On the other hand, the migration response to migrant deaths is stronger in districts with characteristics that could be suggestive of better flow of information on migrant deaths. For instance, a one-standard deviation increase in the average years of schooling of the district almost doubles the migration impact of a migrant death to the same destination as well as to other destinations (column 6, panels A and B, Appendix Table B.6). Potential migrants in more educated districts may be better at knowing about the migrant deaths (through greater participation in social media, for instance) and, hence, respond strongly. Consistent with this, districts with greater population density and, to some extent, higher prevalence of TV (columns 5 and 7) also see larger migration response to migrant deaths. It might be easier for death information to spread by word-of-mouth in denser districts compared to sparsely populated ones.

5.1. Theoretical framework Potential migrants have observed benefit of migration B and costs of migration C. They decide to migrate if the net observed benefit exceeds the expected unobserved costs of migration E[ ]. That is, they choose to migrate when B − C > E[ ]. One simple way to express the expected unobserved costs is through mortality risks, E[ ] = d · V, where d is the expected mortality rate abroad and V is the utility lost due to mortality rate. If potential migrants treat the mortality rate as conveying information purely about mortality risks and nothing else, then V is the value of statistical life for potential migrants. If mortality rates convey information about other unobserved risks, then V is the total utility cost arising from expected mortality rate. A crucial assumption here is that both d and V are uncorrelated with the observed benefits and costs, as well as other unobserved costs of migrations not driven by mortality rate. Fig. 5 shows the underlying death rate to be stable. I fail to reject the null of no linear, no quadratic, and no cubic trends in mortality rates with p-values of 0.15, 0.31, and 0.44 respectively. Furthermore, tests for autocorrelation of deaths in Section 3 also provide evidence consistent an underlying iid mortality rate. In this framework, learning about mortality rate abroad becomes an important determinant of the migration decision. That is, migration decision is given by m(B, C, d · V). With the assumption on d and V, this expression further simplifies to m(d · V). I further assume that potential migrants learn about mortality rate based on the information they have on migrant deaths D. That is, migration decision is given by m(V · d(D)) where migrant deaths influences the migration decision via changes in expectations about mortality rates abroad. In this context, there is some direct evidence, as studied in S19, of learning about mortality rates from information on migrant deaths. As summarized in Appendix C, I randomly vary the nature of information provided to a group of potential migrants in Nepal, and subsequently elicit their beliefs on mortality rates, and track their migration decisions few months later. I find that, for the potential migrants who have never migrated before, information on deaths, a statistic on the number of migrants that died in the past year from a reference district, changes their beliefs about mortality rates abroad. While the experimental setup does not allow me to infer how expected mortality changes with information, i.e. 𝜕𝜕Dd = 𝛿 , it provides a strong evidence that potential migrants in this context update their beliefs on mortality rate abroad based on information about migrant deaths. In the remainder of this section, I compute how the expectation of mortality rate changes with incidents of migrant deaths under two scenarios. First, I assume that potential migrants are Bayesian learners who infer the underlying mortality rate from the signals of migrant deaths. I then calibrate the model to calculate 𝛿 , the changes in perception induced by a migrant death, implied by the model. Second, I combine empirical estimates from previous section and those from S19 to compute 𝛿 implied by actual response of the migrants. I then propose an alternative model of learning that can better explain the observed magnitude and patterns of observed migration response.

5. A plausible mechanism of learning from migrant deaths Section 4 establishes that migrants respond to death events by lowering their migration. In this section, I argue that the effect is channeled through learning about mortality rate abroad.

5.2. Belief updating for a rational (iid) Bayesian learner In this part, I characterize how beliefs about mortality rate would evolve if potential migrants are Bayesian in nature who believe that migrant deaths are generated by an underlying iid process. Specifically, they believe that the number of migrant death in their district every month is generated by a binomial distribution B (N , d) where N is the stock of migrants abroad and d is the true but unknown mortality rate. For purposes of simplicity, I assume that N is fixed and remains the same every period. Individuals’ priors follow a beta distribution,  (a0 , b0 ) where a0 and b0 are the parameters of this distribution. Given

25 The characteristics in Appendix Table B.6 are standardized so that the coefficient on migrant death is the impact when the heterogeneity characteristic is at its mean, and the coefficient on the interaction is the additional impact of the death when the characteristic increases by one standard deviation unit. Also note that the direct effect of the characteristic on migration flows is absorbed by the fixed effects.

16

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the binomial signal generating process, the priors a0 and b0 have the interpretation of their prior exposure to migrant deaths, and migrant survivals respectively before the Bayesian learning begins. As I assume a fixed stock of migrants, this simplifies to b0 = N − a0 with the prior expectation of mortality rate of aN0 . In each period, indicated by t, individuals observe the number of migrant deaths in their district, st , which is drawn from the binomial distribution. In period 1, after they observe s1 , their posterior belief on the mortality rate follows a beta distribution given by  (a0 + s1 , N − a0 + N − s1 ). In general, in period n, after observing s1 , s2 , … , sn , their posterior distribution follows a beta distribution given by ) ( n n ∑ ∑ st , (n + 1) N − a0 − st  a0 + t =1

the setting of the experiment in S19 and the data in Section 4. Other estimates of the elasticity of migration to mortality beliefs are nonexistent for this context, and rare in the broader literature, to empirically test the assumption. That being said, this assumption is likely to hold if potential migrants treat the information on migrant death provided in the experiment in the same way as they treat information of actual migrant deaths in their districts while making their migration decisions. Second, I assume that migrant death only affects migration decision through changes in mortality beliefs. That is, migrant deaths do not affect migration decision on their own. This assumption does not necessarily rule out the possibility of potential migrants making inferences on, for instance, risk of injury abroad based on information on migrant deaths. But it imposes a strong structure on how that can happen. If expected mortality rate is proportional to the expected risk of injury, then an induced change in mortality rate leads to a proportional change in the risk of injury which is accounted for by V. However, this assumption is violated if the changes in mortality rate leads to disproportional changes in other dimensions that affect their migration decision. Such violation would lead to an overestimation of 𝛿 from this exercise. While I do not have data to test for these violations, the absence of impacts on occupational composition as well as on prices (in Section 4.2) suggest that changes in other dimensions might not be too large. With these assumptions and caveats, I proceed with the calculations. In Section 4.1, I present the estimates of the effect of a migrant death M 27 D on migration flows M, 𝛽 = 𝜕 log . Each death reduces migrant flow 𝜕D by 1.2 percent for 12 months. This represents a total of 14 percent reduction in monthly migrant flow (albeit over a year) in response to a single migrant death (replicated in column 1 of Table 8).28 In S19, summarized in Appendix C, I use experimental(variation)in information

i=1 ∑ a + n

s

with expected mortality rate of 0(n+1t=)N1 t . Note that when the number of periods, n, is large, the expected mortality rate limits to the true mortality rate p. This model gives a simple prediction on the relationship between the signal at time t, st , and their posterior belief after time t. A regression of their posterior beliefs and the signal in period t produces an estimated coefficient of 𝛿̂t = (t +11)N . The same relationship holds for all periods t from 1 through n. Therefore, a regression of their posterior beliefs and the signal in all periods produces an estimated coefficient given by 1 ∑̂ 1∑ 𝛿̂ = 𝛿t = n

n t =1

n

H −1 1 = n+1 n t =1 (t + 1) N nN

(5)

where Hk represents the k-th Harmonic number.26 Note that the estimated coefficient falls with the size of migrant stock N and the period of observation n, but, obviously, does not depend upon the distribution of past signals. This formula provides a way to quantify the change in priors in response to one migrant death. In the study, we observe each district for 60 periods. A district had an average of 13,300 migrants during the period of this study. Plugging these numbers into the formula above yields a regression coefficient implying an increase in posterior twoyear mortality belief (or, 𝜕𝜕Dd ) of 0.111 per thousand migrants. That the iid Bayesian observes all the signals for 60 periods may be an unrealistic assumption. If we relax this assumption and assume that the Bayesian updates based only on recent 6 months of signals, then this model predicts a coefficient of 0.479. The largest amount of updating this model can generate is when the myopic Bayesian observes only one month of signal. With this extreme restriction, the model generates a coefficient of 0.9.

𝜕m about migrant deaths to estimate 𝛾 = 𝜕 log = d · 𝜕𝜕md , the relationd ship between migration probability, m, and perceived mortality rate, d. An increase in perceived mortality rate of 1 percent reduces migration probability by 0.23 percentage points (column 2, Table 8). Given these estimates, the change in perceptions, induced by a migrant death, 𝛿 is given by:

𝛿=

𝜕d 1 =𝛽 · ·m·d 𝜕D 𝛾

(6)

Plugging in those numbers and the average values of m and d in the experiment, I find that in response to one migrant death, perceived mortality rate increases by 5.3 per thousand migrants. This level of average updating of beliefs in response to a single migrant death is quite large. In particular, the increase in perceived mortality rate is 4 times the actual mortality rate of 1.3 per thousand migrant workers in 2013. More importantly, the implied change in perceived mortality rate in the data is much larger than suggested by the iid Bayesian updating model. It is 47 times higher than if the Bayesian observes all 60 periods of data. It is still 6 times higher than the most myopic Bayesian who only observes 1 period of data. Even if one of the assumptions behind these calculations, that migrant death has a direct impact on migration decision which is disproportional to mortality rate, does not hold true, the indirect effect would need to be several times higher than the direct effect in order for the Bayesian learning model to explain the migration response.

5.3. Belief updating implied by the data The theoretical framework in Section 5.1 implies that the migration response to migrant death is given by:

𝜕 m(V · d(D)) 𝜕 m 𝜕 d = · 𝜕D 𝜕d 𝜕D I use this expression to compute the amount of belief updating,

𝛿 , implied in the data. The empirical estimate of migration response to death comes from Section 4. The empirical estimate of migration response to mortality expectations comes from S19 where I use randomly provided information (on migrant deaths and migrant earnings) as instruments for expected mortality rate and expected earnings of potential migrants. To do this calculation, I make couple of assumptions. First, I assume that the migration response to expected mortality rate is the same across

27

Migration probability m, and migrant number M are related by m =

logm = logM − logP), where population, P, is a constant. Hence, 𝛽 = 𝜕 log m 𝜕D

=

1 𝜕m . m 𝜕D

M P

(or,

𝜕 log M 𝜕D

=

28 Note that the estimating equation here is linear in D. This specification was chosen in Equation (1) as there are no deaths in 91 percent of the cells, and most cells which had a death event had one. However, once the fixed effects have been partialled out, I fail to reject the null that the quadratic and cubic terms are zero. This suggests that the assumption of linearity is justified in this case.

26 At large values of x, Hx = 𝛾 + log (x) where 𝛾 is some constant (EulerMascheroni constant).

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Table 8 Implied impact of migrant deaths on perceptions.

Deaths

𝛽

𝛾

(1)

(2)

m (3)

d (4)

𝛿 (5)

−0.139∗∗∗ (0.042)

Log(expected mortality per 1000)

−0.223∗∗ (0.101) 0.308∗∗∗ (0.037)

Sample mean (experiment)

27.570∗∗∗ (3.973)

Sample mean (experiment)

5.315∗ (3.070)

Implied change in d

Column 1 replicates the estimate of 𝛽 from Table 3 multiplied by 12 to capture the impact over 12 months after a migrant death. Column 2 presents the 2-SLS estimate of 𝛾 from Equation (6) using the experiment data. The dependent variable is an indicator for migration, and the instruments are the randomized information treatments. Columns 3 and 4 present the average migration rate and the average expected mortality in the same data. Column 5 presents the estimate for 𝛿 from Equation (6). Standard error for Column 5 computed using the delta-method assuming a normal distribution of the individual components ∗∗∗ ∗∗ ∗ and no correlation between them. Standard errors are reported in parenthesis. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database, and experiment data collected in S19 (see Appendix C for a description).

5.4. A potential explanation: the ‘law of small numbers’

death in current month. Doing the same exercise as in Section 5.3 shows that the implied change in expected mortality rate in response to a death following a no-death streak period is only 1.75 per thousand (a third of the average effect) whereas the implied change following a death streak is 24.7 per thousand, almost five times the average effect. This pattern holds for migration response to other destinations, as well as for overall migration response to any migrant deaths in the district (panels B and C of Table 9). In addition to the over-inference and streak results, the context also matches another result of the Rabin model. In the model, individuals end up having a belief about the underlying rate which are closer to 0.5 than the true rate. The beliefs of high mortality rates among potential migrants in S19 is also consistent with this result.30

The evidence presented thus far shows that a rational Bayesian model of learning cannot rationalize the migration response to migrant deaths. Two particular features of the response suggest a particular type of learning fallacy. First, Section 5.3 shows that potential migrants overinfer mortality rates from migrant deaths. Second, Table 7 shows that the migration response, and possibly the inference, depends upon the clustering of realized deaths in the recent past. One learning fallacy that generates these two features is the law of ‘small’ numbers (as coined by Tversky and Kahneman, 1971). Here, individuals fallaciously update their beliefs because they expect the probability rules to hold exactly even in ‘small’ samples. The context of this study is aptly suited for individuals to commit this fallacy. Potential migrants do not know the underlying mortality rate and do not have access to the necessary information to compute the underlying rate. Hence, they have to infer it from the migrant deaths that they observe around them. Therefore, they are likely to make inferences based on the sample at their disposal and the migrant deaths that they observe, and hence commit the fallacy of believing in the law of ‘small’ numbers. One of the key results from the mathematical formulation of this fallacy in Rabin (2002) is that such individuals tend to over-infer from short sequences of signals. That is, if they are uncertain about the underlying rate, they interpret a streak of a positive signal as being suggestive of a higher underlying rate. In the context of this study, if potential migrants observe a streak of migrant deaths, they are likely to believe that the underlying mortality rates are much higher. A direct consequence of that is the migration response to a current death is much higher if there have been streaks of migrant deaths in the recent past. Indeed, migration response to streaks of deaths is consistent with overinference. As discussed above, clustering of deaths in the recent past matter in this context (Tables 6 and 7). In Table 9, I specifically examine the role of streaks in the past three months.29 In districtdestination cells without a streak in the past three months, one migrant death reduces subsequent migrant flows by 1.6–2 percent (panel A). However, in cells with a death streak, subsequent migration falls by an additional 4 percentage points. On the contrary, in cells with a no-death streak, subsequent migration does not change in response to a migrant

5.4.1. Other potential explanations Key features of the migration response – particularly, the dependency on past signals – can rule out a few classes of explanations. For instance, the large migration response to rare migrant deaths cannot be solely explained by potential migrants assigning larger decision weights to small probabilities (as in Kahneman and Tversky, 1979). Neither can it be explained by high levels of risk aversion.31 These explanations, on their own, would not generate the dependence of response on past signals. Moreover, these explanations are related to the mapping between perceived (mortality) risks and migration decision, whereas the learning fallacy maps signals to beliefs. In this context, the former is captured by the migration elasticity of perceptions estimated in S19, and the latter is computed in Section 5.3 ( m𝛾 and 𝛿 in Table 8). Even with the levels of risk-aversion, or of higher decision weight placed on mortality rate (as captured by the elasticity), the levels of belief updating from migrant death is high. That is, if belief updating in response to a death was at the level of the most myopic Bayesian, then the elasticity would need to be 6 times higher to generate the same migration response.32 The Rabin (2002) model, on the other hand, qualitatively matches the sequence dependence and over-inference results seen in the data.

30 An average inexperienced potential migrant has expected mortality of 27.6 per thousand, and an average experienced potential migrant has expected mortality rate of 16.4 per thousand (S19, and Table 8). 31 For example, Bryan et al. (2014) estimate large risk aversion among households in rural Bangladesh where small transfers generated large migration response. 32 In Equation (6), m𝛾 and 𝛿 are inversely related.

29

A streak is either a death streak in which death has occurred in the cell in each of the past three months, or a no-death streak in which no death has occurred in the cell in any of the past three months. Similar specification involving streaks is also used in Chen et al. (2016) as a test for gambler’s fallacy. 18

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Journal of Development Economics 141 (2019) 102368

Table 9 Impact of migrant death streaks on migration. Flow in the next 6 months (1)

9 months (2)

12 months (3)

A. log(migration from district to same destination) −0.020∗∗∗ Deaths in month (0.006) × death streak in the last 3 months −0.039∗∗ (0.019) × no-death streak in the last 3 months 0.020∗∗ (0.008) −0.044∗∗ Death streak in the last 3 months (0.018) No-death streak in the last 3 months 0.006 (0.007) Obs 27,000 Adj R2 0.979

−0.017∗∗∗

−0.016∗∗∗

(0.006) −0.042∗∗ (0.017) 0.013∗ (0.008) −0.029∗ (0.015) 0.009 (0.007) 27,000 0.984

(0.005) −0.039∗∗ (0.018) 0.013 (0.008) −0.029∗ (0.015) 0.008 (0.007) 27,000 0.987

B. log(migration from district to other destinations) Deaths in month 0.003 (0.002) × death streak in the last 3 months 0.015∗ (0.008) × no-death streak in the last 3 months −0.002 (0.002) Death streak in the last 3 months −0.001 (0.009) No-death streak in the last 3 months −0.002 (0.002) Obs 27,000 Adj R2 0.998

0.003 (0.002) 0.015∗∗ (0.007) −0.001 (0.002) −0.002 (0.007) −0.004∗ (0.002) 27,000 0.998

0.003 (0.002) 0.014∗∗ (0.006) −0.001 (0.002) 0.002 (0.007) −0.005∗∗ (0.002) 27,000 0.998

C. log(total migration from district) All deaths in month

−0.012∗

−0.010∗

−0.010

(0.007) −0.013∗ (0.007) 0.037∗∗ (0.014) −0.024 (0.017) −0.038∗ (0.020) 4499 0.967

(0.006) −0.012∗∗ (0.006) 0.035∗∗∗ (0.013) −0.020 (0.016) −0.035∗∗ (0.017) 4500 0.973

× death streak in the last 3 months × no-death streak in the last 3 months Death streak in the last 3 months No-death streak in the last 3 months Obs Adj R2

(0.006)

−0.011∗∗ (0.005) 0.034∗∗∗ (0.011) −0.020 (0.016) −0.036∗∗ (0.015) 4500 0.976

Note: The table shows how the migration effect of a death of a migrant in district changes with whether there have been any streaks of migrant deaths or streaks of no-migrant deaths in the past 3 months. A ‘death streak’ means that the cell has experienced at least one migrant deaths in each of the past three months. A ‘no-death streak’ means that the cell has not experienced any migrant deaths in the past three months. The estimates reported are 𝛽 and 𝛼 coefficients from Equations (2) and (4). Columns (1), (2), and (3) present the estimates for the migrant outflow in the subsequent 6, 9, and 12 months. Panels A and B show the effect on migration outflows in the district-destination cells. Panel C shows the effect on total migration outflows from the district. For all panels, standard errors are reported in parenthesis and are ∗∗∗ ∗∗ ∗ clustered at the district level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1. Source: Author’s calculations from the dataset constructed from the FEPB database and the DoFE database

Another key result from this model is that the individuals end up having a belief about the underlying rate which are closer to 0.5 than the true rate. The beliefs of high mortality rates among potential migrants in S19 is also consistent with this result.33 This, however, is does not exclude the role of other channels. For instance, it could be possible that one or many of the channels discussed here are at play alongside the belief in the law of ‘small’ numbers. For instance, it is plausible that potential migrants are also myopic and only use a few months of deaths in their districts to form their perceptions about mortality rate abroad. Indeed, with the implied change

in perception computed in Table 8, a potential migrant with a prior of zero mortality in the destinations can have a posterior mortality rate of 27.6 (the average among inexperienced potential migrants) by observing only 5.3 deaths in his district. With the frequency of migrant deaths in 2013, this will only take 6.2 months on average to realize 5.3 deaths. Hence, the belief held by inexperienced potential migrants is consistent with an updating behavior where they only look at a few months of migrant deaths and believe in the law of ‘small’ numbers to form their beliefs. 6. Conclusion

33 An average inexperienced potential migrant has expected mortality of 27.6 per thousand, and an average experienced potential migrant has expected mortality rate of 16.4 per thousand (S19, and Table 8).

This paper demonstrates two key features of how potential migrants respond to deaths (in the destination countries) of migrants from their

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Journal of Development Economics 141 (2019) 102368

district. First, the negative migration response is large. A single migrant death in an origin district-destination cell reduces migration flow by 1.2 percent for 12 subsequent months. Though there is some offsetting increase to other destinations, the net effect is still negative. Furthermore, migrant deaths do not seem to elicit a demand side response, nor a change in prices or types of jobs available for migrant workers. This suggests that migrant deaths lower the supply of migrant workers in the net. Back of the envelope calculation suggests that the annual loss for Nepal from the under-migration can be at least 0.24 percent of the national GDP. Second, the migration response appears to be generated by a learning fallacy. In this context, potential migrants are trying to learn about the unobserved costs of migration through migrant deaths. If such learning takes place primarily in the form of learning the underlying mortality rate, then the change in expected mortality rate implied by the migration response to a migrant death is too large to be generated by a model of rational Bayesian facing an iid mortality rate. One migrant death from a district increases the perceived mortality rate among potential migrants by 5.3 per thousand migrants which is several times higher than the level of updating generated by a Bayesian. Furthermore, the migration response depends upon the sequencing of migrant deaths in the recent past. More deaths in the recent past generates larger migration response to a current migrant death. The peculiar features of the response, particularly the sequence

dependence of the response, rules out a few classes of explanations. One particular fallacy, the belief in the law of ‘small’ numbers, as modeled in Rabin (2002), qualitatively matches these features. Furthermore, the overinference result from the model is also consistent with the data. Potential migrants could, indeed, observe a few months of migrant deaths in their districts and, given the large updating response to migrant deaths, end up with very high perceived mortality rates. Some of the limitations of this study could highlight potential areas for future research. One area of research could explore how news about locally adverse events, such as migrant deaths, propagate through word-of-mouth and other channels. Another could explore determinants of migration related to mortality risks, such as risks of debilitating injuries and poor working conditions. Relatedly, understanding how individuals (potential migrants) in information scarce environments update their beliefs on multiple dimensions based on information on a particular dimension would be interesting research agenda on its own. Finally, this paper presents a real world setting in which a learning fallacy can have large welfare consequences for potential migrants. Incidences of deaths, which should convey zero information to a fully informed potential migrant, seems to thwart migration. Policies designed to make individuals less ‘scared’ of migrant deaths abroad could potentially have large welfare effects.

Appendices A. Calculation of mortality rates A.1. Mortality rate of Nepali migrants For each of the destination countries d ∈ {Malaysia, Qatar, S. Arabia, U.A.E, Kuwait, Bahrain}, the DoFE data provides the outflow of migrants to these destination countries in each month. Denote this by INd,t . I assume that the migrants come back in 30 months if the destination is Malaysia, and in 24 months if the destination was one of the other countries. That is, { INd,t −30 if d = Malaysia OUTd,t = INd,t −24 otherwise and the net increase in stock of Nepali migrant workers in destination d given by NETd,t = INd,t − OUTd,t . The Census 2011 gives the count of the stock of Nepali workers in each of these destinations for June of 2011 (denote this by CENSUSd,t for t = 201, 106).34 Hence, the stock of Nepali migrant workers in each of the destinations is calculated as follows: ⎧CENSUSd,201106 ⎪ Sd,t = ⎨Sd,t +1 − NETd,t ⎪ ⎩Sd,t −1 + NETd,t −1 ∑

if t = 201106 if t < 201106 if t > 201106

The number of deaths of migrant workers from the FEPB data is then used to produce the mortality rates, with MRd,t =

d DEATHd,t

∑

d Sd,t

DEATHd,t Sd,t

and MRt =

giving destination specific and overall mortality rates used in Fig. 5 and Section 2.3.

A.2. Mortality rates of men in Nepal, U.S. and Canada I calculate age-specific mortality rate for Nepali men from the Public Use Census Microdata for 2011. The mortality rates for U.S. men come from Period life table (Table 4.C6), 2014 published in Annual Statistical Supplement to the Social Security Bulletin, 2016.35 The mortality rates for Canadian men come from Life Tables published by Statistics Canada.36 Using this age specific mortality rates, I compute weighted average mortality rates where the weights are the population of male out-migrants of each age in the DoFE database. With this, the average two-year mortality rate for Nepali men of the same age distribution as the migrants is 4.6 per thousand. The two-year mortality rate of US and Canadian men with the same age distribution as migrant Nepali men is 2.87 and 1.64 per thousand respectively. These numbers are higher than the two-year mortality rate of 1.3 per thousand for Nepali migrants as reported in Section 2.3. A.3. Why are migrant mortality rates lower than average mortality rates? Obviously, the comparison of mortality rates between Nepali migrant workers and other population is not meant to be causal. First, compared to Nepali men in Nepal, migrant workers could be positively selected in terms of health outcomes (for example, Halliday and Kimmitt, 2008,

34 35 36

Date format YYYYMM used for compactness. https://www.ssa.gov/oact/STATS/table4c6.html. Accessed October 2017. https://www.statcan.gc.ca/pub/84-537-x/84-537-x2017001-eng.htm. Accessed October 2017. 20

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Journal of Development Economics 141 (2019) 102368

also document higher mobility among migrants in the United States). At the least, they passed a medical screening test for fitness, and intend to spend at least a few years doing physically demanding work. Second, the occupation and lifestyle of these men are very different. Migrant workers are engaged in occupations that could be riskier, but most of them live in labor camps and do not interact with the wider society as much. For instance, the most common cause of death for Nepali men are: unknown causes, accidents unrelated to transport, suicides, and transport related accidents. In the US, top causes are: unintentional injuries (includes traffic and other accidents), suicides, and homicide. Whereas in Canada, they are: unintentional injuries (includes traffic and other accidents), cancer, and suicide. While occupational injury and mortality rates may be higher for migrant workers, their exposure to deaths from traffic accidents and homicide would be much lower. This could explain why the average mortality rates for Nepali migrant workers are lower compared to average men elsewhere. B. Robustness and sensitivity checks Table B.1 Robustness to functional forms and controls. log(migration flow +1) (1)

(2)

asinh(migration flow) (3)

(4)

(5)

(6)

−0.007∗∗

−0.012∗∗∗

−0.008∗∗

−0.008∗∗

(0.003)

(0.004)

(0.004)

(0.003)

0.004∗ (0.002)

0.003∗∗ (0.001)

0.004∗ (0.002)

0.004∗ (0.002)

−0.007∗∗

−0.007∗∗

−0.009∗∗

−0.007∗∗

(0.003)

(0.003)

(0.003)

(0.003)

Migration from neighboring district to other destinations Deaths in month 0.002 0.002 0.003 (0.001) (0.003) (0.002)

0.001 (0.002)

0.003 (0.003)

0.004 (0.002)

−0.006∗

−0.004∗∗

−0.007∗∗

−0.006∗

(0.003)

(0.002)

(0.003)

(0.003)

0.004∗ (0.002)

0.001 (0.001)

0.004∗ (0.002)

0.004∗ (0.002)

−0.006∗∗

0.001 (0.001)

−0.008∗∗∗

−0.006∗∗

(0.003)

(0.002)

−0.001

0.004 (0.002) Y

0.003 (0.002) Y

Y

Y Y

Migration from district to same destination −0.012∗∗∗ −0.007∗∗ Deaths in month (0.004) (0.003) Migration from district to other destinations 0.004∗ Deaths in month 0.003∗∗ (0.001) (0.002) Migration from neighboring district to same destination −0.007∗∗ −0.009∗∗ Deaths in month (0.003) (0.003)

Migration from 2nd deg. neighbor to same destination Deaths in month −0.004∗∗ −0.007∗∗ (0.002) (0.003) Migration from 2nd deg. neighbor to other destinations Deaths in month 0.001 0.004∗ (0.001) (0.002) Migration from far neighbors to same destination Deaths in month 0.001 −0.008∗∗∗ (0.001) (0.002) Migration from far neighbors to other destinations −0.001 0.004 Deaths in month (0.000) (0.002) o-d FE Y Y o-m FE Y d-m FE Y o-d FE × t Y o-d FE × t2

(0.002)

0.003 (0.002) Y

Y Y

(0.000) Y Y Y

Note: This table shows the robustness of the results to alternative functional forms and controls. The first three column uses the preferred measure of migration outflow: the natural logarithm of migration outflow (with one added to the flow to account for cells with zero migration). The last three column uses the inverse hyperbolic sine of the migration flow. Columns (1) and (4) have the preferred set of controls which include all two-way fixed effects between origin, destination, and time. Columns (2) and (5) have the origin-destination fixed effects, and linear time trends for each origin-destination cell. Columns (3) and (6) add a quadratic time trends for each origin-destination cell. Panel headings indicate the specific measure of migration. Each row in each column is a separate regression. For all estimations, standard errors are reported in parenthesis and are clustered at the district level. ∗∗∗ ∶ p < 0.01;∗∗ ∶ p < 0.05; ∗ ∶ p < 0.1.

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Journal of Development Economics 141 (2019) 102368 Table B.2 Effects of deaths in districts on recruitment by major employers. Share from district 2 quarters after (1)

Chars 3-Q after 3 quarters after (2)

Top 50 percent of employers (95 percent Deaths in o-d-q 0.0001 (0.0001) Obs 15,738,000 Adj R2 0.152 # Employers 10,492

of recruitment) 0.0001 (0.0001) 15,738,000 0.194 10,492

Top 10 percent of employers (64 percent Deaths in o-d-q 0.0001 (0.0001) Obs 3,169,500 Adj R2 0.203 # Employers 2113

of recruitment) 0.0001 (0.0001) 3,169,500 0.251 2113

Top 5 percent of employers (49 percent of recruitment) Deaths in o-d-q 0.0000 0.0000 (0.0001) (0.0001) Obs 1,578,000 1,578,000 Adj R2 0.234 0.285 # Employers 1052 1052 Top 1 percent of employers (22 percent of recruitment) Deaths in o-d-q −0.0001 −0.0002∗ (0.0001) (0.0001) Obs 315,000 315,000 Adj R2 0.318 0.373 # Employers 210 210 Top 0.5 percent of employers (15 percent of recruitment) −0.0001 −0.0002 Deaths in o-d-q (0.0001) (0.0001) Obs 157,500 157,500 Adj R2 0.347 0.403 # Employers 105 105 Top 0.25 percent of employers (10 percent of recruitment) Deaths in o-d-q 0.0000 −0.0002 (0.0002) (0.0002) Obs 81,000 81,000 Adj R2 0.365 0.429 # Employers 54 54

Log migration (3)

Has migration (4)

Log wage (5)

−0.0001

0.0004 (0.0004) 15,738,000 0.443 10,492

0.0049 (0.0041) 15,738,000 0.443 10,492

0.0006 (0.0005) 3,169,500 0.519 2113

0.0066 (0.0054) 3,169,500 0.520 2113

(0.0010) 1,578,000 0.601 1052

0.0005 (0.0006) 1,578,000 0.553 1052

0.0058 (0.0058) 1,578,000 0.554 1052

−0.0061∗∗∗

−0.0013

−0.0118

(0.0019) 315,000 0.675 210

(0.0009) 315,000 0.607 210

(0.0090) 315,000 0.608 210

−0.0094∗∗∗

−0.0015

−0.0136

(0.0027) 157,500 0.709 105

(0.0014) 157,500 0.625 105

(0.0138) 157,500 0.625 105

−0.0119∗∗∗

−0.0042∗∗

−0.0400∗∗

(0.0042) 81,000 0.738 54

(0.0019) 81,000 0.638 54

(0.0185) 81,000 0.638 54

(0.0003) 15,738,000 0.516 10,492

−0.0007 (0.0008) 3,169,500 0.569 2113

−0.0011

Note: This table presents the results of estimating the following variant of Equation (1) modified for individual employers. yoeq,x = 𝛽 Dodq + 𝛿eo + 𝛾oq + 𝜉eq +𝜖oeq where y is the outcome for origin (district) - employer - quarter (o-e-q) cell, measured x quarters after the migrant death, and migrant death is the number of migrant deaths from the district in the quarter in the destination country. The column headings indicate the outcomes used in the estimation. Share from a district variable in the first two columns is defined as the number of migrants from a district recruited by the firm in that period divided by the total number of migrants recruited by the firm in that period. The share is set to zero if the firm did not recruit any migrants from the country in that period. Column (3) represents the logarithm of migrant flow from the district to the employer in the subsequent 3 quarters. Column (4) indicates whether the employer recruited any worker from the district in the subsequent three quarters. Column (5) represents the logarithm of contractual wages among the worker recruited by the employer from the district in the subsequent three quarters. Wages are replaced as zero if there have been no migrant flow during the period. The sample is limited to employers who have recruited from Nepal for at least 3 years in the 2007–2015 period. The second panel further limits the sample to employers who recruited from Nepal for at least 5 years in the same period. The third and fourth panels limit the sample to the top 20 and 10 percent of the employers. Each column in each panel is a separate regression. For all specifications, standard errors are reported in parenthesis and are clustered at the district ∗∗∗ ∗∗ ∗ level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1.

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Journal of Development Economics 141 (2019) 102368 Table B.3 Effects of deaths on total recruitment by major employers. Log(migration) 2 quarters after death (1)

3-Q later 3 quarters after death (2)

Top 50 percent of employers (95 percent of recruitment) −0.0041∗∗∗ −0.0043∗∗∗ Total deaths (0.0004) (0.0003) Obs 209,840 209,840 Adj R2 0.737 0.813 # Employers 10,492 10,492 Top 10 percent of employers (64 percent of recruitment) Total deaths −0.0087∗∗∗ −0.0073∗∗∗ (0.0017) (0.0015) Obs 21,040 21,040 Adj R2 0.855 0.902 # Employers 1052 1052 Top 5 percent of employers (49 percent of recruitment) −0.0087∗∗∗ −0.0073∗∗∗ Total deaths (0.0017) (0.0015) Obs 21,040 21,040 Adj R2 0.855 0.902 # Employers 1052 1052

Has migration (3)

Log wages (4)

−0.0012∗∗∗

−0.0113∗∗∗

(0.0001) 209,840 0.808 10,492

(0.0014) 209,840 0.810 10,492

−0.0015∗∗∗

−0.0138∗∗∗

(0.0003) 42,260 0.887 2113

(0.0028) 42,260 0.888 2113

−0.0005

−0.0046

(0.0004) 21,040 0.912 1052

(0.0038) 21,040 0.913 1052

Top 1 percent of employers (22 percent of recruitment) Total deaths −0.0124∗∗∗ −0.0100∗∗∗ (0.0042) (0.0037) Obs 4200 4200 Adj R2 0.907 0.939 # Employers 210 210

−0.0003

−0.0026

(0.0008) 4200 0.942 210

(0.0078) 4200 0.943 210

Top 0.5 percent of employers (15 percent of recruitment) −0.0121∗ −0.0104∗ Total deaths (0.0064) (0.0054) Obs 2100 2100 Adj R2 0.924 0.952 # Employers 105 105

0.0005 (0.0010) 2100 0.950 105

0.0053 (0.0100) 2100 0.950 105

−0.0000

0.0011 (0.0138) 1080 0.962 54

Top 0.25 percent of employers (10 percent Total deaths −0.0104 (0.0092) Obs 1080 Adj R2 0.939 # Employers 54

of recruitment) −0.0087 (0.0078) 1080 0.962 54

(0.0014) 1080 0.962 54

Note: This table presents the results of estimating the following equation. yeq,x = 𝛽 Ddq + 𝜉eq + 𝜉1 t + 𝜉eq · t + 𝜉eq · t 2 + 𝜉eq · t 3 +𝜖eq where y is the outcome for the employer - quarter (e-q) cell, measured x quarters after the migrant death, and migrant death is the number of migrant deaths in the quarter in the destination country. The column headings indicate the outcomes used in the estimation. Columns (1) and (2) represent the logarithm of the number of migrant workers recruited by the employer in the subsequent two and three quarters. Column (3) indicates whether there was any migrant workers recruited by the employer in the subsequent three quarters. Column (4) represents the logarithm of contractual wages among migrants recruited by the employer in the subsequent three quarters. Wages are replaced as zero if the employer has no migrants. The sample is limited to employers who have recruited from Nepal for at least 3 years in the 2007–2015 period. The second panel further limits the sample to employers who recruited from Nepal for at least 5 years in the same period. The third and fourth panels limit the sample to the top 20 and 10 percent of the employers. Each column in each panel is a separate regression. For all specifications, standard errors ∗∗∗ ∗∗ ∗ are reported in parenthesis and are clustered at the employer level. ∶ p < 0.01; ∶ p < 0.05; ∶ p < 0.1.

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Journal of Development Economics 141 (2019) 102368 Table B.4 Effects of deaths on drastic reductions in migrant flows. migration flow in next 4 months, relative to last 4 months from any to 0 migrants (1)

from ≥ p25 to 0 (2)

from ≥ p25 to ≤ p10 (3)

from ≥ p50 to ≤ p10 (4)

from ≥ p75 to ≤ p50 (5)

Deaths in month

−0.002

−0.001

−0.001

−0.000

Average

(0.001) 0.069

(0.001) 0.018

(0.001) 0.018

(0.000) 0.002

0.003∗∗ (0.001) 0.009

Note: This table shows the effect of a death of a migrant in a district-destination cell on drastic reductions on migrant flows, estimated using Equation (1). The column headings indicate how the migrant flow changes in the 4 months after the death, relative to 4 months prior to the death. In the column headings pX refers to the Xth percentile of the destination specific migrant flows in the preceding 4 months. The last row presents the mean of the dependent variable in the sample. Each column is a separate regression. Standard errors are reported in parenthesis and are clustered at the district level. ∗∗∗ ∶ p < 0.01;∗∗ ∶ p < 0.05; ∗ ∶ p < 0.1.

Table B.5 Effects of different types of deaths on migrant outflows. To same destination

By cause of death Deaths by natural causes Deaths by other causes p-value: natural = other Cause × Destination Natural x Malaysia x Qatar x Saudi Arabia x UAE x Kuwait x Bahrain Other cause x Malaysia x Qatar x Saudi Arabia x UAE x Kuwait x Bahrain

To other destinations

6 months after death (1)

9 months after death (2)

12 months after death (3)

6 months after death (4)

9 months after death (5)

12 months after death (6)

−0.001

−0.000

−0.001

−0.001

(0.005) −0.021∗∗∗ (0.006) 0.017

0.002 (0.004) −0.022∗∗∗ (0.006) 0.003

−0.001

(0.005) −0.020∗∗∗ (0.007) 0.036

(0.002) 0.005∗∗ (0.002) 0.063

(0.002) 0.005∗∗ (0.002) 0.059

(0.002) 0.006∗∗ (0.002) 0.034

0.031∗∗ (0.014) −0.021∗∗ (0.009) −0.014 (0.010) 0.014 (0.014) 0.134∗∗ (0.059) −0.098∗ (0.058) −0.023∗∗ (0.011) −0.009 (0.013) −0.025∗∗ (0.013) −0.023 (0.015) 0.003 (0.038) 0.013 (0.047)

0.029∗∗ (0.014) −0.020 ∗∗ (0.008) −0.012 (0.008) 0.016 (0.012) 0.132∗∗ (0.052) −0.072 (0.052) −0.027∗∗∗ (0.010) −0.004 (0.011) −0.020 ∗ (0.011) −0.023 (0.014) −0.002 (0.032) −0.032 (0.044)

0.027∗∗ (0.013) −0.016∗∗ (0.007) −0.011 (0.009) 0.020∗ (0.012) 0 .108∗∗ (0.048) −0.009 (0.045) −0.030∗∗∗ (0.010) −0.003 (0.009) −0.019∗ (0.010) −0.018 (0.013) −0.012 (0.028) −0.049 (0.038)

−0.014

−0.012

−0.013∗

(0.009) 0.000 (0.002) 0.005∗ (0.003) −0.001 (0.002) −0.006 (0.004) 0.003 (0.002) 0.009∗ (0.005) −0.011∗ (0.006) 0.009∗∗ (0.004) 0.001 (0.003) 0.000 (0.002) −0.002 (0.002)

(0.008) 0.001 (0.002) 0.005∗ (0.002) −0.002 (0.002) −0.006 (0.004) 0.001 (0.002) 0.011∗∗ (0.005) −0.011∗ (0.006) 0.008∗∗ (0.003) 0.001 (0.003) −0.000 (0.002) −0.003∗∗ (0.002)

(0.007) 0.001 (0.002) 0.005∗∗ (0.002) −0.003 (0.002) −0.006 (0.003) 0.000 (0.002) 0.013∗∗ (0.005) −0.012∗ (0.006) 0.007∗∗ (0.003) 0.002 (0.002) −0.001 (0.002) −0.003∗ (0.002)

Note: This table shows the effect of different types of deaths of a migrant in a district-destination cell on logarithm of migrant flows, estimated using Equation (1). The first 3 columns show the effect on subsequent flows to the same destination in the next 6, 9, and 12 months respectively. The last 3 columns show the effect on migration flows to other destinations over the next 6, 9, and 12 months. The top panel shows the results by the gender of the deceased migrant. The last row in this panel presents the p-value from the test of equality of the two coefficients. The second panel shows the results by cause of death. The last row in this panel presents the p-value from the test of equality of the two coefficients. The last panel shows the results by cause of death disaggregated for each of the destination countries. Each column in each panel is a separate regression. For all specifications, standard errors are reported in parenthesis and are clustered at the district level. ∗∗∗ ∶ p < 0.01;∗∗ ∶ p < 0.05; ∗ ∶ p < 0.1.

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Journal of Development Economics 141 (2019) 102368

Table B.6 Heterogeneous effect by district and destination characteristics. A. log(migration to same destination in next 12 months) (1) Death rate in destination Deaths in month

× characteristics

Deaths in month

× characteristics

−0.009∗∗ (0.004) −0.004 (0.005) (6) Average years of education −0.009∗∗ (0.004) −0.008∗ (0.004)

(2) share of construction migrants −0.010∗∗∗ (0.004) −0.005 (0.003)

(3) share of labor migrants

(7) Log(population density)

(8) share of upper caste population −0.010∗∗ (0.004) 0.005 (0.006)

−0.008∗ (0.004) −0.011∗ (0.005)

B. log(migration to other destination in next 12 months) (1) (2) Death rate in destination share of construction migrants Deaths in month 0.002 0.003∗∗ (0.001) (0.001) × characteristics 0.002 −0.001 (0.001) (0.001)

Deaths in month

× characteristics

(6) Average years of education 0.002 (0.001) 0.002 (0.001)

(7) Log(population density) 0.001 (0.001) 0.005∗∗ (0.002)

−0.011∗∗∗ (0.004) 0.003 (0.004)

(3) share of labor migrants 0.003∗∗ (0.001) 0.001 (0.001) (8) share of upper caste population 0.002 (0.001) −0.002 (0.002)

(4) 2001 migration rates to destination −0.011∗∗∗ (0.004) −0.002 (0.003)

(5) TV prevalence

(9) poverty rate

(10) Mid- & Far-Western region −0.012∗∗∗ (0.004) 0.003 (0.013)

−0.009∗∗ (0.004) 0.006 (0.005)

−0.010∗∗∗ (0.003) −0.006 (0.004)

(4) 2001 migration rates to destination 0.003 (0.002) 0.000 (0.001)

(5) TV prevalence

(9) poverty rate

(10) Mid- & Far-Western region 0.003∗∗ (0.001) 0.002 (0.005)

0.003 (0.002) 0.000 (0.002)

0.002∗ (0.001) 0.003∗∗ (0.001)

Note: This table shows how the impact of migrant death on migration outflows differs by various district and destination characteristics. The estimates reported are 𝛽 and 𝛼 from Equation (2) and the heterogeneity measure X is indicated in the column headings. To ease interpretation, characteristic X is standardized by subtracting the sample mean and dividing by its standard deviation. Panel A examines the impact on flow in the same origin-destination cell in the subsequent 12 months. Panel B examines the impact on the flow to other destinations in the subsequent 12 months. Each column in each panel is a separate regression. For all specifications, standard errors are reported in parenthesis and are clustered at the district level. ∗∗∗ ∶ p < 0.01;∗∗ ∶ p < 0.05; ∗ ∶ p < 0.1.

C. A brief summary of the experimental setup in Shrestha (2019) In Shrestha (2019), I conduct a randomized controlled trial that provides information and observes changes in expectations and subsequent migration decisions. The sample was 3319 potential migrants who came to Kathmandu to apply for a passport in January 2015. These individuals were selected for their desire to migrate to Malaysia and the Persian Gulf countries for work. Out of them, 1411 were inexperienced migrants (had never migrated abroad for work) whereas the rest had migrated at least once for work. Since Nepal was undergoing a transition from older paper-passports to machine readable passports, potential migrants from all over the country would have to come to Kathmandu to apply for a new passport before they were able to leave the country. In this sample, I provide three types of information. Basic information, which simply read “Every month, XXXX people from Nepal leave for work in DEST”, was provided to everyone.37 Wage information, which read “In YYYY, migrants to DEST earned NRs. EEEE in a month” was randomized.38 A third of the sample did not receive any wage information. Another third received the high variant of the information from year 2013, and the rest received the low variant of information from 2010. Death information, which read “Last year, NN individuals from DIST, one of Nepal’s 75 districts, died in DEST”, was cross-randomized with the wage information.39 A third of the sample did not receive any death information. Another third received the high variant of the information where the reference district was picked from the top 25th percentile of the migrant death distribution in 2013. The rest received the low variant of the information with the reference district selected from the bottom 25th percentile of the distribution. After the information was provided to the sample, I elicited their beliefs about expected earnings and expected mortality rates abroad. And three months later, in April 2015, I followed-up with the sample to determine their migration status. Column 2 of Table 8 presents the 2-SLS estimate of 𝛾 estimated from a regression of migration status on the expected mortality rate with the treatment assignments as exogenous instruments. This estimation is limited to the inexperienced migrants in the sample. Columns 3 and 4 are mean migration rates and average expected mortality for the control group which does not receive any information.

37 38 39

DEST referred to the destination they were interested in, and XXXX was the number of migrants. YYYY refers to the year, and EEEE refers to the monthly contractual wage reported in the DoFE database. NN referred to the number of deaths, and DIST referred to a reference district. 25

M. Shrestha

Journal of Development Economics 141 (2019) 102368

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