metadata: authors: 0: author: Adhikari,Samik
1: author: Changom,Narcisse
2: author: Kaila,Heidi Kristiina
3: author: Shrestha,Maheshwor
count: World
docna: 0: docna: Do Bilateral Labor Agreements Increase
            Migration ? Global Evidence from 1960 to 2020
docty: Policy Research Working Paper
abstracts: cdata!: This paper estimates the impact of
            bilateral labor arrangements on migration between two
            countries. It uses comprehensive data on bilateral migration
            and bilateral labor agreements across all country pairs for
            each decade from 1960 to 2020, and employs an empirical
            specification with a rich set of fixed effects. In the
            preferred and most stringent specification, the findings
            show that signing a bilateral labor agreement increases
            migration from an origin country to a destination country by
            76 percent (0.57 log points) in the decade of signing. The
            effect persists for up to three decades. The impacts are
            higher for corridors without a pre-existing regular flow and
            for destinations in the Gulf Cooperation Council. In
            contrast, the effect is virtually absent for origin
            countries in Africa, driven by countries with weak
            government effectiveness. The estimates imply that bilateral
            labor agreements can lead to substantial welfare gains: low-
            and lower-middle-income countries can earn an additional
            US$120 million annually from a bilateral labor agreement. If
            countries in Sub-Saharan Africa were to experience similar
            impacts, the welfare gain from a single BLA could be as high
            as US$51 million per year for these origin countries.
display_title: Do Bilateral Labor Agreements
            Increase Migration ? Global Evidence from 1960 to 2020
listing_relative_url: /research/2024/12/34435931/bilateral-labor-agreements-increase-migration-global-evidence-1960-2020
topicv3: Labor Markets m1326488 855,Evidence-Based Policy m1326939 706,Markets and Institutions for Poverty
            Reduction and Shared Prosperity m1503847 3781,Migration Policies and Jobs m1504161 2836,Aging and Jobs m1504213 2886
publishtoextweb_dt: 2024-12-13T00:00:00Z
dois: 10.1596/1813-9450-11000
no_of_pages: 49
author_keys: 0: author_key: 1311263
1: author_key: 2144048
2: author_key: 1594212
3: author_key: 1039316
model_extractions: Policy Research Working Paper 11000

# Do Bilateral Labor Agreements Increase Migration?

Global Evidence from 1960 to 2020 _Samik Adhikari Narcisse Cha’ngom Heidi Kaila Maheshwor Shrestha_ Social Protection Department & Development Economics December 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 11000

# **Abstract**

This paper estimates the impact of bilateral labor arrangements on migration between two countries. It uses comprehensive data on bilateral migration and bilateral labor agreements across all country pairs for each decade from 1960 to 2020, and employs an empirical specification with a rich set of fixed effects. In the preferred and most stringent specification, the findings show that signing a bilateral labor agreement increases migration from an origin country to a destination country by 76 percent (0.57 log points) in the decade of signing. The effect persists for up to three decades. The impacts are higher for corridors without a pre-existing regular flow and for destinations in the Gulf Cooperation Council. In contrast, the effect is virtually absent for origin countries in Africa, driven by countries with weak government effectiveness. The estimates imply that bilateral labor agreements can lead to substantial welfare gains: low- and lower-middle-income countries can earn an additional US$120 million annually from a bilateral labor agreement. If countries in Sub-Saharan Africa were to experience similar impacts, the welfare gain from a single BLA could be as high as US$51 million per year for these origin countries.

This paper is a product of the Social Protection Department and Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mshrestha1@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Do Bilateral Labor Agreements Increase Migration? Global Evidence from 1960 to 2020<sup>∗</sup> Samik Adhikari, Narcisse Cha’ngom, Heidi Kaila, and Maheshwor Shrestha _Keywords_ : Bilateral Labor Agreements, BLAs, migration, development _JEL_ : F22, J08, O15

> ∗This paper serves as a background paper for the Africa Regional Report on Migration (Abdel Jelil et al., 2024). The authors would like to thank Andrew Dabalen for guidance and Quy-Toan Do, David McKenzie, Mauro Testaverde, and Soonhwa Yi for helpful discussions and feedback.
# **1 Introduction**

International migration has the potential to generate trillions of dollars in global economic gains, yet numerous natural and policy barriers prevent people from fully capturing these benefits (Clemens, Montenegro and Pritchett, 2019). Consistent with the welfare loss due to limited migration, studies such as Gibson et al., 2018, McKenzie, Stillman and Gibson, 2010, Mobarak, Sharif and Shrestha, 2023 show that the earnings of migrants can be 3-5 times higher than their counterfactual incomes had they not migrated. Factors such as lack of information, misinformation, liquidity constraints, the financial costs of migration, psychological hurdles, and restrictive policies all serve to limit mobility (see McKenzie, 2023, for a recent review). Understanding these barriers is crucial to identifying strategies that can unlock the full economic potential of migration. This paper contributes to this understanding by examining how the relaxation of legal barriers – through bilateral labor agreements (BLAs) between two countries – affects migration flows.

BLAs are international agreements to regulate the flow of workers between destination and origin countries (Chilton and Posner, 2018, Saez, 2013). Broadly, BLAs set the terms and conditions under which migrant workers from origin countries enter and exit destination countries, outline the responsibilities of the destination countries in receiving and hosting migrant workers for the duration of their stay, and allow the two sets of countries to store and share information and resolve disputes (Chilton and Woda, 2022). The early BLAs date as far back as 1893 when South Africa and Portuguese officials governing Mozambique signed an agreement for migration of mine workers (Peters, 2019 _b_ ). BLAs to manage the flow of migrant workers accelerated in the mid-20th century to fill the post-World War II demand for labor in Europe and North America in sectors like agriculture and construction (Chilton and Posner, 2018, Peters, 2019 _b_ , Saez, 2013). Outside destinations in North America and Europe, the use of BLAs only started getting momentum in late twentieth century, particularly among destination countries in the Gulf Cooperation Council (GCC) and origin countries in South and East Asia (Chilton and Woda, 2022).

Countries usually enter BLAs to regulate seasonal and temporary labor migration for lowskilled workers, though BLAs can also facilitate permanent migration (Chilton and Woda, 2022, United Nations, 2022). Many BLAs specify skill requirements for destination countries, 2
enabling targeted recruitment of workers with the requisite skills, and include provisions for worker rights, such as wages, working conditions, and access to services (Biehler, Kipp and Koch, 2024, Chilton and Woda, 2022). While BLAs relax some regulatory barriers, they do not address all informational or financial barriers that limit broader migration.

Though conceived as a tool to facilitate migration, it is not obvious that BLAs necessarily increase migration for various reasons. First, significant numbers of migrants move between countries outside the framework of BLAs and BLAs could be designed to restrict or control the flow of migrant workers (Livnat and Shamir, 2022). Second, instead of increasing net migration flows, BLAs could be a policy tool to regularize the irregular migration already taking place (Bither and Ziebarth, 2018, Clemens, 2024). Third, even if BLAs may be intended to increase migration, the efficacy of such policies – like with many policies – is not obvious ex-ante. Such concerns may be particularly strong in settings with high costs of migration, high extent of misinformation, and low actual or perceived returns to migration as well as in settings where low capacity and monitoring limits the efficacy of BLA implementation.<sup>1</sup> Finally, even if BLAs were effective in facilitating migration, it is not clear _how_ effective they can be as a policy tool. Determining the magnitude of migration impacts and its persistence is important for migration policies.

To understand the impact of BLAs on migration, we combine two comprehensive data sources on migration and BLAs across all country pairs. The global bilateral migration data captures the migration (measured by people living in a country that is different from the country of birth) between all country pairs for each decade from 1960 to 2020, compiled by the World Bank and the United National Department of Economic and Social Affairs (UNDESA, 2020, World Bank, 2022). A comprehensive dataset on all BLAs for the same period comes from Chilton and Posner (2018), Peters (2019 _a_ ) and Chilton and Woda (2022). We merge the two sets of databases by converting the BLA database into country-pair _×_ decade format for analysis. For each of the BLAs, we designate the country with a lower income per-capita as the _origin_ country and the other as the _destination_ country.

> 1The enforcement of BLAs is often measured through reports from international agencies such as the International Labor Organization, labor inspection data from national governments, monitoring of complaints from migrant workers and worker welfare organizations, or academic research studying the impact of BLAs in specific corridors (United Nations (2022)). However, factors such as lack of data, limited monitoring capacity of national governments, under-reporting of issues by migrant workers, and difficulties in cross-border mechanisms of enforcement can make monitoring effective implementation of BLAs challenging (Chilton and Woda, 2022, United Nations, 2022).

3
We tease out the impact of the BLAs on migration after imposing a series of fixed effects to control for the various confounds. Our most stringent, and preferred, specification controls for corridor (origin country _×_ destination country) fixed effects, origin _×_ decade, and destination _×_ decade fixed effects. That is, if country _A_ signs a BLA with country _B_ , the identifying variation comes from comparing migration within the _AB_ corridor before and after the signage (corridor fixed effects); from comparing migration from country _B_ to country _A_ relative to all potential destinations for migrants in country _B_ (origin-time fixed effects); and from comparing migration to country _A_ from country _B_ relative to all potential origin countries for migrants to country _A_ (destination-time fixed effects).

These extensive sets of fixed effects ensure that some corridor-specific factors (e.g., historic bilateral relationships between two countries), or country-time-specific factors that can affect migrant labor demand or supply (such as economic cycles or the labor market situation in origin or destination countries) do not drive the relationship and that the impact can be attributed to the BLA itself. One of the key assumptions for the identification of the impact of the BLAs is that of parallel trends. We employ an event-study design to check for any pre-trends on migration prior to the signing of the BLAs (in addition to using this to explore persistence of effects).<sup>2</sup> However, as migration data is measured every ten years, the pre-trend test cannot test for anticipatory BLA signing in response to, say, temporary shocks within the decade of signing.

We find that signing a BLA increases migration from the origin country to the destination country by 76 percent (0.57 log points) in the decade of signing the BLA. The migration effect lasts up to three decades after signing a BLA. The impact of BLAs is muted at 25 percent (0.24 log points) for ‘regular’ corridors - corridors with at least 10 migrants in all periods.<sup>3</sup> The estimates are of similar magnitudes with alternative sets of fixed effects, and slightly larger with an instrumental variable specification where we use ‘leave-out’ mean as the instrument. These estimates can be considered as a lower-bound of the true impact of a BLA on facilitating migration as the data on migration only captures the stock of migration measured every ten years and misses temporary migration that might have happened between the periods. We also

> 2We also test the assumption of no pre-trends using a heterogeneity-robust estimator applied to gravity settings following the recent advancements in the difference-in-difference methodologies (as developed in, Nagengast and Yotov, Forthcoming, Nagengast, Rios-Avila and Yotov, 2024) and fail to reject the null of no pre-trends.

> 3The results are robust to alternative thresholds for defining which corridors count as ‘regular’.

4
find that the impact on migration persists for up to three decades after the signing of the BLA. Reassuringly for the identifying assumption, we do not find any impacts (or trends) prior to the signing period.

We also uncover interesting and relevant heterogeneities in the migration impacts of the BLAs. The migration impacts are significantly higher at 834 percent (2.24 log points) if the BLA involves a destination country in the Gulf-Cooperation Council (GCC). Likewise, the migration impacts are significantly higher for low- and lower-middle income origin countries (311 percent, 1.4 log points). However, the impacts are virtually absent, at an insignificant 15 percent (0.14 log points), for African origin countries. We show that the lack of impact among African origin countries is driven by countries with weaker institutions as proxied by measures of government effectiveness. This underscores the importance of strong institutions and systems for translating these agreements into actual migration.

Our estimates imply large welfare gains from signing a BLA to the migrants and their families. The empirical literature finds that a low-skilled worker can experience significant earnings gain, ranging from a doubling to a quadrupling of their incomes, through migration. Using the migration impact from our study, the average GDP per-capita as a proxy of their incomes at home, and the returns to migration from the literature, we find large earnings gain from such agreements, particularly for low- and middle-income (LLMIC) countries. The welfare gain - measured by increased migrant earnings - from an agreement can be US$120 million annually for LLMICs. Gains are even higher, at US$2.1 billion annually for agreements with the GCC destinations. The persistence of the migration impacts for multiple decades suggest that the gains accrue over time to US$1.6 billion for agreements with an average destination country and US$28 billion for agreements with GCC destinations. Gains are much lower for countries in Sub-Saharan Africa due to low volume of migration as well as lower impacts of BLAs, with US$2.4 million annually (US$32 million over time). If countries in Sub-Saharan Africa were to have similar migration impacts as the average impact for LLMICs – say, through strengthened capacity to implement such agreements – gains would increase to US$51 million annually (US$676 million over time) to an average destination and US$413 million annually (US$5.5 billion over time) to a GCC destination country.

This paper contributes to the literature on the study of BLAs. Most literature examining the impact of labor agreements has focused on specific corridors or regions (Arpaia et al., 5
2018, Clemens, Lewis and Postel, 2018, Mobarak, Sharif and Shrestha, 2023). Information on country or region-specific bilateral or multilateral agreements is often more readily available whereas aggregate global analysis on the impact of labor agreements has been limited due to the lack of global datasets documenting these bilateral and multilateral treaties. This has changed recently with the emergence of large datasets on BLAs (Chilton and Woda, 2022, Chilton and Posner, 2018, Peters, 2019 _b_ ). Using these datasets, new and emerging research has examined the relationship between BLAs and trade flows between countries (Maximova and Paraschiv, 2022), the relationship between cross-border property rights and innovation (Bian, Meier and Xu, 2023) and emigration and democratization (Peters and Miller, 2022). However, to our knowledge, no paper has so far documented the impact of BLAs on migration flows between countries. Both Peters (2019 _b_ ) and Chilton and Woda (2022) find positive association but do not document the impact rigorously.

This paper relates to literature on how explicit policies related to migration affect migration. A few papers examine the role of policies such as border walls, minimum wage, passport and other legal hurdles in facilitating or hindering migration (Allen, de Castro Dobbin and Morten, 2018, Feigenberg, 2020, McKenzie, 2007, McKenzie, Theoharides and Yang, 2014). Yet, another set of papers study the impact of specific migration restriction programs (Clemens, Lewis and Postel, 2018, Di Iasio and Wahba, 2023, Mayda et al., 2018). In a similar vein, Ortega and Peri, 2013 examine the relative effects of income and a broader set of immigration policies on immigration to the OECD countries and find that restrictive policies can rapidly reduce migration. While the literature has largely examined the role of restrictive immigration policies, this paper examines the impact of a migration facilitation policy.

The paper also adds to the large literature on relieving constraints to migration more broadly. Studies have reconciled the high returns to migration with the low observed rates of migration as stemming from high (utility) cost of migration (Bryan and Morten, 2019, Imbert and Papp, 2020). Other studies have examined the role of reducing various dimensions of costs, such as informational frictions, insurance failure, social networks, risk aversion, difficulty to learn about risks, liquidity, and financial costs of migration (see Angelucci, 2015, Bah et al., 2023, Baseler, 2023, Bazzi, 2017, Bazzi et al., 2021, Beam, McKenzie and Yang, 2016, Bryan, Chowdhury and Mobarak, 2014, Cai, 2020, Dao et al., 2018, Gazeaud, Mvukiyehe and Sterck, 2023, Munshi, 2020, Munshi and Rosenzweig, 2016, Shrestha, 2019, 2020, for key studies on these). This 6
paper complements the literature by providing a better understanding of the role of policies in reducing migration barriers.

The remainder of the paper is organized as follows: section 2 describes the data sources, section 3 describes the empirical specification, section 4 describes the results, section 5 presents the potential welfare impacts of the BLAs, and section 6 concludes.

# **2 Data**

## **2.1 Data on migration**

This paper uses the most comprehensive data set on bilateral migration stocks spanning from 1960 to 2020, in ten-year intervals. Migration stocks for years 1960, 1970, 1980, 1990, and 2000 are taken from World Bank (2022) (see Ozden<sup> ̈</sup> et al., 2011, for methodological details). The data for 2010 and 2020 come from UNDESA (2020) which employs similar methodology. These data sets use information collated from censuses and surveys at destination and origin countries to construct a matrix of bilateral migration stock data from each of the origin countries to each potential destination country. This combination of datasets is used widely by many research studies and reports (including in Clemens, 2020, Shrestha, 2023, World Bank, 2023).

A migrant, in these data sets, is typically a person who is born in a country that is different from the country in which they are currently living. To that extent, it includes, in principle, people who have migrated for various reasons and channels, including those who migrated through irregular channels (Ozden<sup> ̈</sup> et al., 2011). However, it is possible that these data sources may misreport irregular migrants for various reasons. To the extent such misreporting exists, measurement errors can mute the estimated relationship.

In addition, this dataset only captures the _stock_ of migrants and does not capture the _flow_ of people between these countries. Migrants who move temporarily, particularly between the census years, will not be fully captured by this dataset. If BLAs increase temporary migration, then the full extent of the impact on migration will not be captured by this dataset. Hence, we interpret our estimates as a lower bound of the migration impacts.

7
# **2.2 Data on BLAs**

The data we use for the BLAs is from Chilton and Woda (2022). This dataset contains a comprehensive set of 1,222 BLAs between two countries since World War II. The dataset contains information on the year the BLA was signed and the two countries between which the treaty was signed. The dataset is the most comprehensive dataset on BLAs to date and builds on the data compiled by Chilton and Posner (2018), Peters (2019 _a_ ) and Peters (2019 _b_ ). A richer description of the dataset is presented in Appendix A.

We transform the dataset to match the format of the migration dataset. That is, for each BLA in this dataset, we identify the country of origin, the country of destination, and assign it to the subsequent year in migration data.<sup>4</sup> That is, a BLA signed between 1970 to 1979 is assigned to _t_ = 1980. To simplify categorization, we label the country with a lower GDP per-capita as the origin country and the other as the destination country. The origin-destination-year cells without a BLA is assumed to have no BLA.

# **2.3 Descriptive statistics**

The combined dataset used in this paper has information on BLAs and migration from 198 countries to 197 potential destinations (i.e., 39,006 origin-destination corridors) for 7 periods from 1960 to 2020 at ten-year intervals. A common feature of the bilateral migration dataset, as seen in table 1, is that most (61 percent) of the corridors do not have any migration. About 12 percent of the corridors have at least 10 observed migrants across all time periods; we term these as _regular_ corridors. The shares of non-empty corridors and regular corridors are similar across the key subsets used in the analysis.

Likewise, BLAs are also relatively rare in the data. About 1.6 percent of corridors have a BLA signed between them, with the share lower for African origin countries (0.80 percent) and higher for GCC destinations (3.4 percent). BLAs are more common in regular corridors with close to 9 percent of the regular corridors having a BLA. Similar pattern holds across the various sub-samples used in the analysis.

Of the 261 million migrants in our combined dataset in 2020, 82 million (31 percent) are in corridors with a BLA. The proportion of migration accounted by corridors with a BLA is

> 4To address the issues of countries and names that changed over time, we use the same principles and coding applied in the migration data (Ozden<sup> ̈</sup> et al., 2011).

8
slightly lower for origin countries in Africa and in low- and lower-middle income countries, and higher for GCC destination countries. The proportions are similar across time as well (table B.1).

Table 1: Structure of migration and BLA datasets

||Full sample<br>(1)|African origins<br>(2)|LLMIC<br>(3)|UMHIC<br>GC<br>(4)|C destinations<br>(5)|
|---|---|---|---|---|---|
|||Bilater|al migratio|n data||
|Share of non empty corridors|0.386|0.352|0.370|0.397|0.353|
|Share of regular corridors|0.122|0.091|0.103|0.135|0.088|
|||Bilateral la|bor agree|ment data||
|Share of corridors with BLA|0.016|0.008|0.011|0.019|0.034|
|Share of regular corridors with BLA|0.089|0.055|0.063|0.103|0.192|
|||Migrant sto|ck (in milli|on) in 2020||
|All corridors|261.1|36.8|119.8|141.3|29.8|
|Regular corridors|242.0|33.4|108.7|133.3|27.3|
|Corridors with ever a BLA|81.8|8.1|34.9|46.9|16.5|

_Notes_ : The sample includes 39,006 origin-destination corridors for 7 periods from 1960 to 2020 at ten-year intervals. Regular corridors are corridors with at least 10 migrants in the corridor for each of the periods in the data.

# **3 Empirical specification**

## **3.1 Baseline analysis**

To investigate the impact of signing Bilateral Labor Agreement (BLAs) on migration flows, we rely on a structural gravity model for migration derived from the Random Utility Framework (Beine, Bertoli and Fern ́andez-Huertas Moraga, 2016). This framework posits that migration flows between two locations **i** (origin) and **j** (destination) depend on the relative attractiveness of **j** over **i** net of the migration cost between **i** and **j** . Consequently, the implied baseline empirical equation to be tested is written as: where _Mijt_ is the migration stock from country _i_ to country _j_ measured at time _t ∈{_ 1960, 1970, 1980, 1990, 2000, 2010, 2020 _}_ . _BLAijt_ indicates whether _i_ and _j_ have signed a BLA at any time in the decade preceding _t_ . That is, _BLAij_ 2000 indicates whether country pair _ij_ signed a BLA between 1991 and 2000. The _αit, αjt,_ and _αij_ represent the origin-time, destination9
time, and origin _×_ destination corridor fixed effects respectively. Origin and destination-time fixed effects capture origin and destination specific and time varying factors likely to affect migration flows (push and pull factors) including multilateral resistance to migration (Bertoli and Moraga, 2013). Likewise, the corridor fixed effects capture the time-invariant historical relationship between the two countries that affect migration flow. The _εijt_ is the error term. The formulation in Equation (1) lends directly to our key empirical specification: where _εijt_ = ln _εijt_ is the idiosyncratic error term correlated within each corridor. This rich set of fixed effects makes sure that the only source of variation in our estimation is made of _ijt_ factors. We conduct robustness checks that control for _Xijt_ which measures the cumulative number of BLAs signed by the country pair prior to the decade.

The specific nature of the migration data presents some issues for estimation. In particular, a large fraction of the corridors in our data have zero migration. About 61 percent of the 273,042 _ijt_ cells have zero migration.<sup>5</sup> These corridors are essential for estimation as BLAs may be particularly effective (or ineffective) in influencing migration in those corridors. There are two simple transformations to get around this issue, adding 1 to _M_ prior to the log transformation or using the inverse hyperbolic sine transformation which has similar interpretation as the logarithmic transformation (Burbidge, Magee and Robb, 1988, MacKinnon and Magee, 1990, Pence, 2006). Each method has some limitations (Bellemare and Wichman, 2020), but we confirm that results are qualitatively and quantitatively similar with both methods. For simplicity, we present the results with inverse hyperbolic sine transformation.

Alternative way to get around the large number of zeros is to estimate Equation (1) with Poisson Pseudo Maximum Likelihood (PPML) methods. However, PPML estimated in levels, tends to over-weight large corridors (Orefice, 2015) which is an issue in our data. For instance, the top 10 corridors (of 39,006 corridors each year) represent somewhere between 14 and 34 percent of all migration in each year; the top 20 corridors represent 22 to 45 percent of all migration. That is, large corridors such as Mexico to the United States, Pakistan to India, India to Pakistan, Poland to Germany, and India to the United Arab Emirates will get disproportionate

> 5The number of cells represents data on 198 origin countries, 197 potential destinations, and 7 time periods.

10
weights in PPML estimation.

To address these concerns, we present OLS estimations of Equation (2) for the full sample as well as for a sub-sample of about 33,000 regular corridors which we define as corridors with at least 10 migrants in all periods. As a robustness exercise, we also present results of PPML estimations in both samples. We also conduct robustness checks with PPML estimates excluding the top 1% migration flows.

Even though the rich set of fixed effects control for a wide array of confounds, there may be outstanding concerns of endogeneity. Specifically, omitted variables such as corridor-specific change in broader policies that directly affect both the BLA and migration, historical BLAs affecting additional BLAs and migration flows. Inclusion of _Xijt_ , the cumulative BLAs signed in prior decades address some, particularly the latter, but not all of these concerns. To address this, we employ an instrumental variable approach. Our instrumental variable approach builds on the “ _leave-one-out_ ” approach taken from Bryan and Morten (2019). That is, we use the total number of BLAs signed by the destination country _j_ with all countries other than _i_ ⌝ (denoted by _i_ ) to serve as an instrument for the BLAs between country-pair _ij_ . That is, the total number of BLAs, _{BLA_ ⌝ _ij,t}_ , serves as an instrument for _BLAij,t_ . We also use a complementary instrumental variable where the instrumental variable is the number of BLAs signed by the origin country with all other destination ( _{BLAi_ ⌝ _j,t}_ ) in that period.

So far, we have considered a setting in which the mean estimated effect is identical across different groups. However, these groups are likely heterogeneous and we should expect heterogeneous migration responses as well. To dig into that, we further perform heterogeneity analysis as follows: where X are dimensions of heterogeneity. Key dimensions of heterogeneity include the income level of the origin country, whether the origin country is in Africa or not, and whether the destination country is in the Gulf Cooperation Council (GCC) or not. Heterogeneity along a few other dimensions is presented in the appendix.

11
## **3.2 Event study design**

Finally, we explore the persistence of the impact of signing a BLA using an event-study like design. We transform the data around each event - signing of a BLA between the country pair - so that time is coded relative to the decade in which the BLA was signed.<sup>6</sup> The estimating equation is: where **D** _ijt−e_ indicates that a BLA was signed by the country-pair _ij_ exactly _e_ periods prior to time _t_ . That is, the coefficients _γe_ denotes the effect of a BLA _e_ periods after the signing. Positive values of _e_ , that is, _{γ_ 0 _, · · · , γ_ 5 _}_ capture the persistence of the impacts of a single BLA. Negative values, that is _{γ−_ 5 _, · · · , γ−_ 2 _}_ , indicate pre-trends in outcome in prior period. Significant pre-trends suggest potential mis-specification (Lovenheim and Will ́en, 2019, Miller, 2023). The coefficients are normalized to period _−_ 1, the decade prior to the signing of the BLA.

# **4 Results**

## **4.1 Benchmark estimates**

In this section, we report the estimate of the overall average effect of BLAs on migration flows. In the next section, we show that this benchmark estimate hides important and interesting heterogeneity along several dimensions including the destination type, the region of origin and the development level of origin countries. Then after, we investigate the dynamic of the estimated average effect in an event study design.

Table 2 reports our benchmark estimate of the migration response to the signing of a bilateral labor agreement (BLA). We consider three sets of specifications: a first set that includes only origin-destination pair and decade fixed effects, a second set that adds the origin-decade fixed effect to the previous one, and the last set that includes origin-destination pair, origin-decade, and destination-decade fixed effects. Given the existence of empty corridors, we apply the inverse

> 6For corridors with multiple BLAs, we duplicate the time series for the corridors for each of the BLAs and code the time period relative to a single BLA. We then weight our regression by the inverse of the number of duplications (i.e., the number of BLAs signed by a corridor).

12
hyperbolic sine transformation to the migration numbers so that we can approximately interpret the estimated coefficients as percentage changes. Although our preferred specification is the one with three-way fixed effects, we find an overall positive and significant effect of signing a BLA on migration flows regardless of the specification considered. As reported in column (3) of table 2, signing an additional BLA increases migration by about 77 percent (0.569 log point) from the origin country to the destination country within a decade of signing the BLA. However, one might be concerned that corridors with small numbers of migrants might be driving the results, as a marginal change in the number of migrants in these corridors over time will translate into a higher percentage change, while overall being meaningless for the overall migration pattern. To address this concern, we restrict our analysis to what we call “regular corridors,” which we capture as corridors with at least 10 migrants over the period of interest (1960-2020). The results are reported in columns (3) to (6) with the same sets of fixed effects as described above. Our preferred specification (column 6) confirms the positive effect of BLA on migration flows. Indeed, entering into a new BLA increases migration in regular corridors from the origin country to the destination country by 27.5 percent (0.243 log point) within the decade of signing the BLA.

Table 2: Migration response to BLAs (Benchmark)

|||Full sample||Regular|corrdiors (|_Mij ≥_10)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Bilateral labor agreement (BLA)|0.593<sup>_∗∗∗_</sup>|0.570<sup>_∗∗∗_</sup>|0.569<sup>_∗∗∗_</sup>|0.398<sup>_∗∗∗_</sup>|0.306<sup>_∗∗∗_</sup>|0.243<sup>_∗∗∗_</sup>|
||(0.106)|(0.103)|(0.090)|(0.059)|(0.055)|(0.051)|
|Observations|273,042|273,042|273,042|33,306|33,257|33,222|
|Origin country x decade FE|No|Yes|Yes|No|Yes|Yes|
|Destination country x decade FE|No|No|Yes|No|No|Yes|
|Origin x destination countries FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Decade FE|Yes|||Yes|||
|Migrants per decade||154,063,957|||144,293,52|7|
|Number of BLAs per decade||146|||111||

_Notes_ : The table presents the estimate of _γ_ from OLS estimate of equation (2). It shows the impact of a BLA on migration. The outcome variable is the inverse hyperbolic sine of migration. Columns (4) to (6) restrict the analysis to ‘regular’ corridors which have at least 10 migrants across all time period. Standard errors in parentheses are clustered at the origin-destination level.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

These estimates are likely lower bound of the total impact on migration due to our data limitations. Since our data captures the stock of migration between two countries at ten-year intervals, it likely misses or underestimates short-term migration, particularly those that happen between the periods of measurement. This may be particularly salient for BLAs that facilitate 13
temporary migration for a relatively short period of time. For instance, most of the migration to the Gulf Cooperation Council (GCC) countries are temporary in nature with workers migrating with an employment contract of two to three years.

# **Robustness**

To address the concerns around the estimation methods, we conduct a battery of robustness checks. Table B.2 reports the results using the PPML methods; table B.3 reports the result of excluding the top 1 percent of the corridors. We also conduct robustness checks using logarithms as well as levels of migration. The results are qualitatively and quantitatively similar across these specifications.

# **Instrumental variable estimates**

Table 3 reports the IV regression. Columns (1) to (3) of panels A and B report the first stage correlation between our instrument and the main variable of interest (BLA), for the full sample and for regular corridors respectively. Regardless of the instrument considered, there is a negative and significant relationship between our instrument and the BLA. Moreover, the first stage F-statistic ranges between 177.0 and 290.7, indicating the relevance of our instrument. The negative first stage coefficients indicate that the probability of _j_ signing a BLA with _i_ decreases with the total number of BLAs _j_ has already signed with countries other than _i_ . In other words, to the extent that BLAs are signed for the purpose of importing workers for a given duration, the need to import workers decreases with the number of BLAs already signed. Columns (4) to (6) in panels A and B confirm the positive impact of signing a BLA on migration flows. The IV estimate shows that signing an additional BLA increases migration from the origin to the destination country by 49.9 to 85.2 percent in the full sample and by about 20.7 to 31.9 percent in the regular corridors. We explain the smaller effect in the regular corridors by the fact that the magnitude of the change is smaller in corridors with larger initial migration flows than in corridors with smaller initial migration flows. The main channel through which this effect occurs is essentially through the reduction in migration costs that BLAs offer, ranging from easy access to visas to housing facilities at the destination.

14
Table 3: Migration response to BLAs (Instrumental variable)

||Leave one <br>��<br>_j _<sup>_B_</sup>|origin out<br><sup>_LAi¬jt_</sup><br>�|Leave one d<br>(<sup>�</sup><br>_i _<sup>_B_</sup>|estination out<br><sup>_LA¬ijt_)</sup>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||**Panel A**- **Full s**|**ample**|||
||First stage|IV|First stage|IV|
|Bilateral labor agreement (BLA)||0_._405<sup>_∗∗∗_</sup><br>(0_._100)||0_._616<sup>_∗∗∗_</sup><br>(0_._106)|
|Leave one out|_−_1_._210<sup>_∗∗∗_</sup><br>(0_._055)||_−_1_._497<sup>_∗∗∗_</sup><br>(0_._087)||
|Observations<br>K Paap Fstat. (First stage)|273_,_042<br>476_._42|273_,_042|273_,_042<br>295_._72|273_,_042|
||**Panel B**-**Regul**|**ar corridors**|||
||First stage|IV|First stage|IV|
|Bilateral labor agreement (BLA)||0_._188<sup>_∗∗∗_</sup><br>(0_._058)||0_._277<sup>_∗∗∗_</sup><br>(0_._071)|
|Leave one out|_−_1_._147<sup>_∗∗∗_</sup><br>(0_._067)||_−_1_._399<sup>_∗∗∗_</sup><br>(0_._105)||
|Observations|33_,_222|33_,_222|33_,_222|33_,_222|
|K Paap Fstat. (First stage)|290_._70||177_._00||
|Origin country x decade FE|_Y es_|_Y es_|_Y es_|_Y es_|
|Destination country x decade FE|_Y es_|_Y es_|_Y es_|_Y es_|
|Origin x destination countries FE|_Y es_|_Y es_|_Y es_|_Y es_|

Notes: The table presents the first stage regression and the estimate of _γ_ from IV estimate of equation (2). It shows the suggestive causal impact of a BLA on migration. The outcome variable in columns (1) and (3) is the dummy variable indicating whether the corridor of interest during the decade of interest has signed a BLA or not. In columns (2) and (4), the outcome variable is the inverse hyperbolic sine of migration. Panel A focuses on the full sample while panel B restricts the analysis to ‘regular’ corridors which have at least 10 migrants across all time period. The set of fixed effects in the bottom rows of the table are common to the two panels. Standard errors in parentheses are clustered at the origin-destination level.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

15
# **4.2 Heterogeneity analysis**

So far, it has been implicitly assumed that migration from countries around the world responds homogeneously to Bilateral Labor Agreements (BLAs). However, not only initial conditions at origin matter, but also, both the number and timing of BLA signings and the main destination countries have changed significantly over the years. As shown in Figure A.2, during the period of Western economic prosperity immediately following World War II (1945-1973), the majority of destination countries involved in BLAs were Western countries seeking to import workers to rebuild their war-damaged infrastructure and industries. This period was followed by economic stagnation, which was also reflected in a lower growth in the number of BLAs signed between 1973 and 1990. In the post-1990s, most of the growth in BLAs can be explained by the intensive use of these instruments by GCC countries. However, regions such as Sub-Saharan Africa have made very limited use of BLAs over the years. Overall, these different phases in the time dynamics of BLAs suggest that the migration response to BLAs may differ along different dimensions. To account for this heterogeneity, we further examine whether the estimated average effect is heterogeneous along three dimensions: (i) the level of development of the country of origin (ii) whether the destination country is a GCC country or not, and (iii) whether the country of origin is African or not.

Table 4 reports the heterogeneous migration response to BLAs along these three dimensions. Columns 5 and 6 report the average migration response to entering a BLA across the development level of the origin country. While entering a BLA increases migration between the origin country and the destination country involved in that BLA by about 310 percent within a decade when the origin country is a LLMIC, this effect falls to 24 percent when the origin country belongs to the upper-middle and high-income group. This result suggests that the poorer the country of origin, the larger the effect of BLAs on migration flows. This suggests that BLAs expand the economic opportunities especially when there is a huge place premium.

We further investigate whether the extensive use of BLAs by GCC countries during the new golden age of BLAs has played a role. Although entering a BLA increases migration between origin country and destination country worldwide, the effect is ten times larger for migration to the GCC destinations as compared to non-GCC destinations (columns 1 and 2). This is likely explained by the fact that BLAs account for a large share of low- and middle-skilled 16
Table 4: Heterogeneous migration response to BLAs

||GCC de|stination|Africa|n origin|Developm|ent group|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Bilateral labor agreement (BLA)|0.468<sup>_∗∗∗_</sup>|0.552<sup>_∗∗∗_</sup>|0.650<sup>_∗∗∗_</sup>|0.640<sup>_∗∗∗_</sup>|1.522<sup>_∗∗∗_</sup>|1.414<sup>_∗∗∗_</sup>|
||(0.106)|(0.099)|(0.115)|(0.102)|(0.228)|(0.199)|
|BLA X GCC destination|1.558<sup>_∗∗∗_</sup><br>(0.413)|1.688<sup>_∗∗∗_</sup><br>(0.430)|||||
|BLA X African origin|||-0.568<sup>_∗∗_</sup><br>(0.240)|-0.500<sup>_∗∗∗_</sup><br>(0.170)|||
|BLA X UM-HIC|||||-1.351<sup>_∗∗∗_</sup><br>(0.251)|-1.200<sup>_∗∗∗_</sup><br>(0.221)|
|BLA + Interaction|2.026<sup>_∗∗∗_</sup>|2.239<sup>_∗∗∗_</sup>|0.081|0.139|0.170|0.214<sup>_∗∗_</sup>|
||(0.398)|(0.417)|(0.211)|(0.136)|(0.105)|(0.093)|
|Observations|273,042|273,042|273,042|273,042|273,042|273,042|
|R-sq|0.820|0.839|0.820|0.875|0.820|0.875|
|Origin country x decade FE|Y|Y|Y|Y|Y|Y|
|Destination country x decade FE||Y||Y||Y|
|Origin x destination countries FE|Y|Y|Y|Y|Y|Y|

_Notes_ : Standard errors in parentheses are clustered at the origin-destination level. BLA (dummy) in decade N takes the value 1 if a BLA was signed in the last 10 years and zero otherwise. The dependent variable is the number of dyadic migrations in protocols. Columns (1) and (2) report heterogeneity with respect to whether the country of destination is in the GCC or not. Columns (3) and (4) report the heterogeneous response of migration to BLA depending on whether country of origin is Africa or not. The last two columns examine the heterogeneity with respect to the level of development of the country of origin (low and lower middle income, upper middle income, and high income). For each of the highlighted categories, we consider two specifications: one that includes origin-destination and origin-time fixed effects, and another that complements this set of fixed effects with destination-time fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

migration to the GCC countries. Consequently, whenever a GCC country enters a BLA with another country, nearly all variations in migration flows in that corridor are explained by the BLA. Table 4 reports the heterogeneous migration response to BLAs along the three dimensions indicated.

Furthermore, we investigate whether BLAs have so far affected migration flows from Africa. However, BLAs do not seem to have a significant effect on migration flows from Africa (columns 3 and 4). This finding is intriguing, given that African countries make up a substantial portion of low- and lower-middle-income countries, which generally experience the largest migration increases in response to signing a new BLA (columns 5 and 6). This heterogeneous effect illustrates what we refer to as the “ _African Puzzle_ ”. On one hand, BLAs increase migration flows, especially from LLMIC; on the other hand, there is no significant migration response to BLAs in Africa. This is somewhat puzzling given that of the 79 countries classified as low- and lower-middle-income in our sample, 45, or more than half, are in Africa.

To better understand this “puzzle”, we further investigate whether the quality of institutions 17
plays a role. We created institutional tiles based on the average government effectiveness index by country from the World Governance Indicator database. Government effectiveness measures the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government’s commitment to improving or maintaining these aspects (Kaufmann, Kraay and Mastruzzi, 2010). Our general intuition is that developing countries with low government effectiveness are not only less able to engage in BLAs but also less able to enforce the BLAs they do sign with partner countries. This likely translates into less effective BLAs and, consequently, lower migration flows.

To test this hypothesis, we created two, three, and four tiles corresponding to halves, terciles, and quartiles. Countries in the top tiles are characterized by more effective government. Results reported in table 5 strongly suggest that, although the migration response to BLAs in Africa is null, African countries belonging to the upper tiers of the government indicators do experience a positive migration response to BLAs. Conversely, African countries with poor governance indicators show no significant migration response to BLAs.

# **4.3 Persistence of the impacts**

We now turn our attention to the timing of the effect of BLAs on migration flows using an event study design. It is worth noting that one of the key advantages of the event study design is the possibility to check if the estimated effect is long lasting or if it quickly vanishes after a certain period of time. Figure 1 depicts the benchmark estimate timing of the effect of BLAs on migration flows. First, there is no pre-trend prior to the treatment. Second, it appears that the positive effect of BLAs on migration flows lasts up to 3 decades after BLAs are signed before vanishing.

Consistently, heterogeneity results show that this positive effect is driven by non-African origins (panel 2a of figure 2). An interesting result of this event study design emerges when comparing the effects on GCC and non-GCC destinations. Essentially, non-GCC destinations replicate the average timing of the effects of BLAs on migration, while GCC destinations show a persistent effect of BLAs on migration flows. A simple explanation is that most BLAs signed in the aftermath of 1990 involve GCC countries. This is also why there is a smaller number of decades in the post treatment period for GCC destinations. Moreover, BLAs are almost the only channel through which migrant workers enter the GCC.

18
Table 5: Heterogeneous migration response to BLAs by governance category

|||Governance catego|ries|
|---|---|---|---|
||Top half|Top two terciles|Top three quartiles|
||(1)|(2)|(3)|
|Bilateral labor agreement (BLA)|1_._281<sup>_∗∗∗_</sup>|2_._004<sup>_∗∗∗_</sup>|1_._979<sup>_∗∗∗_</sup>|
||(0_._263)|(0_._457)|(0_._577)|
|BLA _×_ Africa|_−_1_._194<sup>_∗∗∗_</sup>|_−_1_._985<sup>_∗∗∗_</sup>|_−_2_._283<sup>_∗∗∗_</sup>|
||(0_._325)|(0_._528)|(0_._668)|
|BLA _×_ Top half|_−_0_._821<sup>_∗∗∗_</sup><br>(0_._286)|||
|BLA _×_ Middle tercile||_−_0_._825<sup>_∗_</sup><br>(0_._497)||
|BLA _×_ Top tercile||_−_1_._924<sup>_∗∗∗_</sup><br>(0_._469)||
|BLA _×_ Third quartile|||_−_1_._009<br>(0_._639)|
|BLA _×_ Second quartile|||_−_0_._869<br>(0_._609)|
|BLA _×_ First quartile|||_−_1_._947<sup>_∗∗∗_</sup><br>(0_._590)|
|BLA _×_ Africa _×_ Top half|0_._983<sup>_∗∗∗_</sup><br>(0_._377)|||
|BLA _×_ Africa _×_ Middle tercile||1_._066<sup>_∗_</sup><br>(0_._579)||
|BLA _×_ Africa _×_ Top tercile||1_._624<sup>_∗∗_</sup><br>(0_._657)||
|BLA _×_ Africa _×_Third quartile|||1_._684<sup>_∗∗_</sup><br>(0_._748)|
|BLA _×_ Africa _×_ Second quartile|||1_._462<sup>_∗∗_</sup><br>(0_._714)|
|BLA _×_ Africa _×_ First quartile|||1_._869<sup>_∗∗_</sup><br>(0_._873)|
|Observations|268_,_905|268_,_905|268_,_905|
|R-sq|0_._875|0_._876|0_._875|
|Origin country x decade FE|_Y es_|_Y es_|_Y es_|
|Destination country x decade FE|_Y es_|_Y es_|_Y es_|
|Origin x destination countries FE|_Y es_|_Y es_|_Y es_|

Notes: Standard errors in parentheses are clustered at the origin-destination level. BLA (dummy) in decade N takes the value 1 if a BLA was signed in the last 10 years and zero otherwise. The dependent variable is the inverse hyperbolic ssine of the number of dyadic migrations in protocols. For each of the highlighted categories, we consider consider the specification with the most stringent set of fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

In summary, our results show that Bilateral Labor Agreements (BLAs) significantly increase migration flows worldwide. This positive effect is particularly pronounced for destinations within the Gulf Cooperation Council (GCC) countries. While LLMIC origin countries experience the most significant positive impact of BLAs on migration flows, African countries do not seem to be significantly affected, leading to what we refer to as the “ _African puzzle_ ”. Further investigation 19
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these agreements is also crucial. Thus, it is not only the act of entering into a BLA that matters, but also the effectiveness of the government in maintaining and monitoring these agreements. This underscores the importance of a strong institutional setup in improving the effectiveness of BLAs in promoting migration flows.

# **4.4 Heterogeneity-robust treatment effects**

The recent econometrics literature has increasingly scrutinized the biases of the canonical twoway (group and time) fixed effects estimates in a difference-in-difference setup when treatment effects are heterogeneous by group or over time (see de Chaisemartin and d’Haultfœuille, 2022, for a survey). The bias could be due to, for instance, “forbidden comparisons” that use already-treated groups as controls for later-treated groups (Borusyak, Jaravel and Spiess, 2024, de Chaisemartin and d’Haultfœuille, 2020). Likewise, in dynamic specifications, where outcomes are affected by past treatments, it is likely that the treatment effect across groups first treated at different times biases the identification of the long-run effect and especially the persistence of the policy effect. To account for this heterogeneity, the literature has developed heterogeneity-robust estimators that improve upon the canonical estimator (Borusyak, Jaravel and Spiess, 2024, Callaway and Sant’Anna, 2021, de Chaisemartin and d’Haultfœuille, 2024, Sun and Abraham, 2021, Wooldridge, 2023).

The empirical application of these approaches that is closest to our setting is the evaluation of the impact of regional trade agreements on bilateral trade (Nagengast and Yotov, Forthcoming), and the impact of membership in the European Union single market on international trade (Nagengast, Rios-Avila and Yotov, 2024). Like our setting, both papers have a panel gravity model with a dyad (an origin-destination pair) as the treatment unit but also allows for complex set of fixed effects, including origin _×_ time and destination _×_ time fixed effects.<sup>7</sup> With the new approach, they find that, the canonical estimates significantly underestimate the effect of trade policy on international trade by roughly 50%.

Hence, we follow the approach developed in Nagengast and Yotov (Forthcoming), Nagengast, Rios-Avila and Yotov (2024) to estimate heterogeneity-robust impact of BLAs on migration. We

> 7However, our setting presents an additional complication - the treatment in our case is non-staggered (it switches on and off over time) and that previous treatment may still affect later outcomes. To the best of our knowledge, an estimator for panel gravity settings with non-staggered outcomes is not yet available.

21
find that, as in these studies from the trade literature, our canonical estimates are qualitatively similar, but smaller in magnitudes than the heterogeneity robust estimators (see Table C.1). This also applies to the results for the period 0 estimates in the event study framework (see Figures C.2 and C.3).<sup>8</sup> We also use our event-study framework to test for pre-trends using the heterogeneity-robust estimator and fail to reject the null of no pre-trends (figure C.1) with a p-value of 0.66.

# **5 Welfare impacts**

Large migration impact of a BLAs suggests that a BLA can have significant welfare implications. Here, we calculate the welfare impacts, in terms of increased incomes for migrants, of the migration impacts of signing a BLA.

In 2020, an average corridor with LLMIC origin country had a migrant stock of about 8,000 individuals. With the estimates in column 6 of table 4), this implies an increase of 25,000 migrants within a decade in response to a BLA. Corridors from LLMIC to GCC countries had a larger stock of 56,000 migrants in 2020. A BLA, based on the impacts in column 2 of table 4, would increase the migration stock by 472,000 migrants within a decade. The average Sub-Saharan African country had a lower migration stock of 3,500 and 11,500 to the average and GCC destinations, respectively. A BLA would increase this only by about 500 migrants given the low impacts of BLAs observed in the African origin countries (column 4, table 4). If migration impacts were the same as the average LLMIC country, then the number would be much higher at 11,000 to an average destination and 96,000 to a GCC destination.

To convert the migration impacts to earnings, we use the average nominal GDP per-capita to proxy for earnings at origin and the returns to migration estimated in the literature focusing on low-skilled migration from LLMICs. Rigorous empirical research on the returns to migration establishes that income increases several-folds even for low-skilled workers migrating to highand upper-middle-income destinations. As summarized in World Bank (2023), estimates range from 118 to 298 percent across these studies (Clemens, 2019, Clemens and Tiongson, 2017, Gaikwad, Hanson and T ́oth, 2023, McKenzie, Stillman and Gibson, 2010, Mobarak, Sharif and

> 8However, the results on persistence differ from the canonical estimates presented in figure 1. This could be driven by the fact that treatments are non-staggered in our settings and the estimators are not well adapted to this difference.

22
Shrestha, 2023). We use a simple average of these studies, a return of 227 percent, to estimate earnings increase from migration to an average destination and of 208 percent to estimate earnings increase from migration to the GCC countries.

Our estimates imply a BLA results in an annual earnings gain of US$120 million for an LLMIC origin country. A BLA with a GCC destination would result in an annual earnings gain of US$2.1 billion. For countries in Sub-Saharan Africa, the annual gains are a modest US$2.4 million, due to a lower migration stock and the limited impact of BLAs. If Sub-Saharan African countries were to have the same impact of BLAs as the average LLMIC, the annual earnings gain from a BLA would be US$51 million. This suggests an annual loss of about US$48.6 million due, largely, to the weaker capacity in African origin countries to implement such BLAs. Similarly, the annual gains for a Sub-Saharan African country from signing a BLA with a GCC destination country (and improving capacity so that migration impacts are similar to that of the average LLMIC countries) could be as high as US$413 million.

We find that the migration impact persists beyond the decade of signing the BLA for up to three decades (figure 1). This means that the earnings gain can accrue over multiple decades. Based on our estimates, we assume that the migration impacts persists for 25 years and that the earnings are discounted by 3 percent each year. We further assume that it takes the first five years for the BLAs to have the estimated migration impact over which period no gains are realized.<sup>9</sup> With these assumptions, we calculate that the total earnings gain from a BLA for LLMIC origin country can be US$1.6 billion, and the gains from a BLA with a GCC destinations can be US$27.6 billion. For countries in Sub-Saharan Africa, the gains are modest at US$32 million. However, assuming the same impact of a BLA as the average LLMIC origin countries, the gains for a Sub-Saharan African origin country can be US$676 million from a BLA with an average destination and US$5.5 billion from a BLA with a GCC destination country.

> 9 We are underpowered to estimate the annual trajectory of migration impact within the first decade. But preliminary estimates suggest that BLAs start to have migration impacts within the first five years of signing. In that regard, the assumption made here is very conservative and would underestimate the long-term earnings gains from BLAs.

23
# **6 Conclusion**

The demographic divergence between the aging high- and upper-middle-income countries – with shrinking workforces – and the LLMICs – with still expanding youth population – provides a huge opportunity for many countries to leverage migration for their own benefits (Abdel Jelil et al., 2024, World Bank, 2023). As seen in this study, BLAs can be a credible policy instrument to facilitate such flows between countries. For LLMICs, the welfare gain from an agreement can be US$120 million annually, and US$2.1 billion for agreements with destinations in the GCC countries where the migration impacts of the BLAs are large. However, this study points to an important role of government capacity – particularly for origin countries in Africa where most of the youthful population is and will be concentrated – to implement such agreements and realize the gains from migration.

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