metadata: authors: 0: author: Artuc,Erhan
1: author: Ozden,Caglar
count: United States
docna: 0: docna: Transit migration : all roads lead to America
docty: Policy Research Working Paper
subtopic: Indigenous Peoples Law,Indigenous Communities,Indigenous Peoples,Labor Markets,Rural Labor Markets,Migration and Development,International Trade and Trade Rules,Trade and Services
teratopic: Culture and Development,Law and Development,Communities and Human Settlements,Social Protections and Labor,Rural Development,International Economics and Trade,Poverty Reduction
abstracts: cdata!: The paths of many migrants include
            multiple destinations and transit routes, yet this pattern
            is almost never reflected in empirical analyses. For
            example, 9 percent of recent immigrants to the United States
            arrived from a transit country as opposed to the country
            where they were born. Among those arriving from many
            high-income countries, the transit migration ratio exceeds
            30 percent. To explain these patterns, this paper constructs
            a dynamic model of global migration that allows transit
            migration opportunities to impact the attractiveness of
            locations. After estimating the structural parameters of the
            model, the paper simulates various counterfactual scenarios
            to highlight the spillovers of transit migration paths.
geo_reg_key: 119228,517191
repnme: repnme: Transit migration : all roads lead to America
display_title: Transit migration : all roads lead
            to America
listing_relative_url: /research/2016/11/26917248/transit-migration-all-roads-lead-america
topicv3: Labor Markets m1326488 855,Trade m1326924 698,Trade in Services m1326928 780,Indigenous Peoples m1326947 749,Emigration m1504169 2844
publishtoextweb_dt: 2016-11-07T00:00:00Z
historic_topic: Culture and Development,Law and Development,Communities and Human Settlements,Social Protections and Labor,Rural Development,International Economics and Trade,Poverty Reduction
no_of_pages: 48
author_keys: 0: author_key: 1140434
1: author_key: 658703
model_extractions: WPS7880 Policy Research Working Paper 7880 Transit Migration All Roads Lead to America _Erhan Artuc Caglar Ozden_ Development Research Group Trade and International Integration Team November 2016
Policy Research Working Paper 7880

# **Abstract**

The paths of many migrants include multiple destinations and transit routes, yet this pattern is almost never reflected in empirical analyses. For example, 9 percent of recent immigrants to the United States arrived from a transit country as opposed to the country where they were born. Among those arriving from many highincome countries, the transit migration ratio exceeds 30 percent. To explain these patterns, this paper constructs a dynamic model of global migration that allows transit migration opportunities to impact the attractiveness of locations. After estimating the structural parameters of the model, the paper simulates various counterfactual scenarios to highlight the spillovers of transit migration paths.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at eartuc@worldbank.org and cozden@worldbank.org.

_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
# **Transit Migration: All Roads Lead to America**<sup>_∗_</sup>

Erhan Artuc<sup>_†_</sup> and Caglar Ozden<sup>_‡_</sup> JEL Codes: J61, F22, F16, J68 Keywords: International Migration, Transit Migration, Migration Policy

> _∗_ We thank two anonymous referees, Frederic Vermeulen, Michel Beine, Simone Bertoli, Michael Clemens, Hein de Haas, David McKenzie, Jesus Fernandez-Huertas Moraga, Chris Parsons, Hillel Rapoport, Moritz Ritter, Mathis Wagner and participants at the International Migration and Development Conference at Oxford University for comments and feedback. We are grateful to Zovanga Kone for his excellent assistance in constructing the data files and catching many of our errors. We also thank Varanya Chaubey and Maggie Liu for their editorial help. Research for this paper has been supported in part by the Knowledge for Change Program, by the Multidonor Trust Fund for Trade and Development and by the Strategic Research Program of the World Bank. All errors and omissions are the authors’ responsibility.

> _†_ World Bank, Development Research Group

> _‡_ World Bank, Development Research Group
The migration decisions and paths of many international migrants include multiple destinations and transit routes. Many people leave their birth countries and live in different locations before settling permanently in a foreign country or returning home. For example, around 9% of the people who migrated to the United States during 2001-2012 were living in a country other than their birthplace prior to their arrival. This pattern is even more common for migrants with tertiary education: nearly 14% of such migrants did not come directly from their birth countries.<sup>1</sup> High-income OECD countries are particularly important transit stops for immigrants. Among the people who were living in Australia, Canada, or the United Kingdom just before their arrival in the United States, over 30% were born in a different country and would already be classified as international migrants.

Despite their prevalence and potential economic significance, transitory migration patterns and the dynamic decision processes behind them are not explored in depth in the international migration literature. The determinants of migration flows or stocks are generally estimated as functions of the bilateral mobility barriers between geographic locations and the differences between the utility (or income) levels at alternative destinations and origins.<sup>2</sup> The standard model that validates this approach assumes that migrants make a permanent decision at a single point in time to move to a foreign country or to stay at home (see Hanson, 2010, for a discussion).

Actual migration decisions are, however, more complex and dynamic in nature. Current and potential migrants continuously update their information sets and review their decisions. They might make (or delay) certain decisions or move to a specific location at different points in time. These decisions are based on current location-specific utility levels, migration costs, as well as (expected) future opportunities that will become available only at a new location . For example, the decision to move from Morocco to Spain might not be based _only_ on the relatively

> 1We should note that there are a handful of birth or last residence countries where the transit migration ratio is lower among the tertiary educated.

> 2These empirical approaches are usually based on the random utility maximization model of McFadden (1978).

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higher income levels or amenities. Spain’s proximity to more attractive labor markets in other European countries, which are possibly less accessible via direct migration, is likely to enter into the decision processes and utility calculations of Moroccan migrants.

The objective of this paper is to explore dynamic migration decisions and transit paths using analytical modeling and empirical analysis. We first construct a sequential multi-period model of migration by drawing on recent papers on dynamic decision making processes in labor markets (such as Kennan and Walker, 2011 and Artuc, Chaudhuri and McLaren, 2010). Agents are identified by their current country of residence in addition to their birth countries. In each period, they can move to a third country or remain in their current location. Their mobility decisions depend on location-specific utility flows as well as bilateral moving costs between each pair of countries and a stochastic individual component for each choice. The decision to move or to stay put is reconsidered in every period. The dynamic structure of the decision process results in a continuation value for each potential destination. The main insight of the model is that the continuation value includes an option value for each location in addition to an instantaneous utility. This option value of a destination is based on the access it creates to other higher value destinations.

Proper accounting of dynamic decision processes, such as reversing migration decisions or moving to third countries in later periods, leads to better identification of spillovers between alternative destinations. Otherwise, we would end up with biased estimates when we try to identify the determinants of migration patterns between pairs of birth and destination countries or to assess the effectiveness of migration policies. Bertoli, Brucker and Fernandez-Huertas Moraga’s (2014) analysis, in the context of the European crisis, shows how biases can occur in estimating the determinants of migration when sequential decision processes and resulting spillovers are ignored.

Incorporating the option value of a location and viewing migration processes from this dynamic lens can generate many additional insights. In standard static models, decisions to migrate to 3
a location, say France, cannot be analyzed in isolation from alternative destinations available, like Germany. As a consequence, changes in the relative attractiveness of Germany will also affect migration incentives and, therefore, flows to France.<sup>3</sup> Our dynamic model, however, exposes another channel: the option value of available destinations for future mobility decisions. The ease of movement between a pair of countries affects migration from third countries. For example, when Poland joins the European Union and gains visa-free access to the internal labor market in the United Kingdom, more Belarusian migrants might decide to move to Poland as they hope to eventually end up in the United Kingdom. Similarly, closing down bilateral corridors might not fully eliminate migration from certain origin countries. As long as large income gaps exist, people will try to find a path to a specific destination via transit countries, no matter how circuitous that route might be. In other words, the availability of transit routes might significantly reduce the effectiveness of restrictive migration policies.

Using the estimated parameters of our dynamic migration model, we perform several simulation exercises where we ‘close’ certain bilateral corridors to highlight the spillovers between alternative locations as discussed above. We chose the migration corridors based on their importance for immigration to the United States, where detailed transit migration data are available. The migration paths we consider are direct migration (i) from Canada to the United States, (ii) from developing countries to Canada, and (iii) from developing countries to the United States. Our fourth and final simulation blocks all transit migration to the United States and only allows direct migration from the migrants’ birth countries. In each of these scenarios, we analyze how these policy experiments would affect migration from different countries to Canada, to the United States, and to other transit countries, like the United Kingdom. As expected, there are significant spillovers. For example, when people cannot move to the United States directly (Simulation 3), both permanent and transit migration to Canada increases. On the other hand, when transit migration to the United States is banned (Simulation 4), then direct migration to

> 3Such links between alternative destinations in static models need to be empirically addressed in gravity models in international trade models (Anderson, 2011) and migration models (Bertoli and Fernandez-Huertas Moraga, 2013).

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Canada also drops since Canada’s option value diminishes significantly.

While issues related to international transit migration receive relatively little attention in economics, they have a more prominent place in other fields, such as demography and sociology, with varying definitions and approaches. More specifically, the phrase ‘transit migration’ is used to describe the migration patterns emerging in Europe following restrictions on legal migration and border controls that were introduced in the 1990s. As a result, many migrants from Eastern Mediterranean and African countries moved to countries at the periphery of the European Union, such as those in North Africa and Eastern Europe, while waiting for a chance to move to the European Union countries (Collyer and de Haas, 2012; Collyer et al, 2012). The paths of most recent refugees from Syria, Iraq and other source countries follow similar transit routes, such as through Turkey, as they try to reach Western Europe. The phrase is also used extensively to refer to low-skilled and mostly undocumented migrants from Central America who travel through and temporarily live in Mexico before reaching the United States. In other words, transit migration has a strong affiliation with irregular and undocumented migrants and refugees as well as a strong sense of temporariness (see Duvell, 2012). Even among the few papers in the economics literature (such as Djajic, 2013), this is the general perception.

We use the expression ‘transit migration’ to describe any migrant who lives in a foreign country before moving to a second foreign country. Our data from the American Community Survey indicate that transit migrants, defined as those living in a country other than their birth country ‘one year’ prior to their move to the United States, are somewhat different than those the demography and sociology literatures focus on. First, transit migrants to the United States are more likely to have tertiary education as opposed to primary or secondary education. Second, most transit migrants are born in high-income European (and some African and East Asian) countries as opposed to most Latin American migrants who come directly from their countries of birth.

In terms of international migration, our paper is related to the large literature on the deter5
minants of migration patterns such as Mayda (2010), Grogger and Hanson (2011), Beine et al (2011) and Belot and Ederveen (2012). Several recent papers (such as Bertoli and FernandezHuertas Moraga, 2013; Bertoli et al, 2013), were influenced by new developments in international trade literature on gravity models (discussed in Anderson, 2011). They aim to relax certain assumptions used in the earlier models. In this regard, the paper closest to ours in terms of insights and approach is Bertoli et al (2014) which analyzes the impact of the financial crisis on intra-European migration patterns. Using a model that incorporates expectations about future economic conditions and the attractiveness of alternative destinations, they show how the determinants of migration patterns change when dynamic considerations are incorporated. While our empirical approach and questions are different, our analytical models share many insights.

Our paper is related to several other strands in the labor economics literature. First, there is an extensive literature in labor economics on dynamic choice models, which explore various dimensions of mobility such as occupational mobility (Keane and Wolpin, 1997), internal mobility (Kennan and Walker, 2011), and job search (McCall and McCall 1987). These papers usually rely on rich individual panel data, and focus on endogeneity of the human capital accumulation process . A more closely related paper is Artuc, Chaudhuri and McLaren (2010) on the sectoral mobility of workers in domestic labor markets in the presence of international trade shocks. They estimate moving cost frictions using a dynamic discrete choice model, highlighting the importance of the option values of available choices.

The use of option values of locations as a concept in the migration literature is more sparse. The first paper, as far as we are aware, is Burda (1995) who models the timing of a single migration decision. There is an option value to wait due to underlying volatility in the economic environment. Locher (2001) explores the same concept in a two-period framework, using data on ethnic German migration from CIS countries. The next important contribution is Bayer and Juessen (2012) who model internal migration decisions in the United States and estimate the structural parameters of a dynamic model. The option value again arises from underlying 6
economic volatility.

Dynamic considerations are extensively analyzed in a series of recent papers on temporary and return migration (see Dustmann and G ̈orlach (2016) for a recent survey). As several papers illustrate, return migration levels have always been quite high. Bandiera, Rasul and Viarengo (2013) show out-migration rates from the United States were over 60% during the age of mass migration at the turn of the 20th Century. Bijwaard, Schluterare and Wahba (2014) use administrative data from the Netherlands and Bratsberg, Raaum and Sorlie (2007) use register data from Norway and Sweden to explore more recent patterns. Several papers on return and circular migration have used more formal and explicit dynamic models. Among the first examples, Kirdar (2012) develops a dynamic stochastic model to jointly explore return migration and savings decisions of migrants and estimates it using panel data from Germany. Among more recent and prominent examples are Thom (2015) and Lessem (2015) who use data from the Mexican Migration Project on detailed migration histories of individuals. Among their findings is the importance of border enforcement (similar to our simulations of blocked corridors) to circular and return migration decisions. Using multiple data sources from Mexico, G ̈orlach (2016) develops a comprehensive dynamic life cycle model to analyze the role of financial constraints on emigration, return migration and re-emigration decisions. In an another lifecycle model, Adda, Dustmann and G ̈orlach (2015) explore human capital accumulation and migration duration decisions using data on Turkish migrants in Germany. All of these papers rely on detailed panel data from a single origin or destination country (or a single corridor) as the data requirements of such dynamic models are quite demanding. One of our main contributions is to analyze a model of global migration patterns and find empirical solutions to address some of the data constraints.

The next section presents the dynamic structural model. Then, we discuss the data used in the paper, followed by the estimation algorithm. We next present the estimation results and the simulation exercises. We conclude with a discussion of future research paths and policy analysis.

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# **1 Model**

We present a dynamic model that allows repeated migration between alternative destinations in each period. Thus, the attractiveness of a destination from a given origin is not solely based on the income gap between them, but it is also a function of future migration opportunities that become available at the new location. Unlike static models where migration decisions are made at a single point in time without further migration possibilities, our model captures economically important and ubiquitous sequential and transit migration patterns.

The agents in the model differ according to their current location, birth country and skill category. An agent’s country of birth is indexed by _i_ , country of residence is indexed by _j_ and skill level is indexed by _s_ . There are _n_ countries in the model and _i, j ∈_ 1 _,_ 2 _, .., n_ . An agent chooses a destination country based on his expectations. If _i_ = _j_ , the person continues to live in his birth country and is not considered to be a migrant. If _i_ = _j,_ the agent is considered to be a migrant. In every period, an agent living in _j_ , can choose to migrate to any other country _k_ = _j_ , regardless of whether he is already a migrant or not. Alternatively, he can choose to stay in _j_ .

An agent with skill level _s_ living in _j_ receives instantaneous (flow) utility _φ_<sup>_s,j_</sup> _t_ at time _t_ . This utility depends on the current location and skill level, but it is independent of the agent’s birth country. The next components of the utility function are composed of moving costs which influence mobility decisions in almost every migration model. Moving costs are incurred if an agent born in country _i_ decides to move from country _j_ to another country _k_ = _j_ . We assume that the moving cost is equivalent to a one time reduction in the utility and is given by _C_<sup>_s,i,j,k_</sup> + _ε_<sup>_a,k_</sup> _t_<sup>.Thefirstcomponentofthismovingcost,</sup><sup>_Cs,i,j,k_,isfixed(non-random)and</sup> is a function of the agent’s birth country, current country, destination country and the skilllevel. This component does not vary among agents with the same characteristics. The second component, _ε_<sup>_a,k_</sup> _t_<sup>,israndomandvariesbyagent(indexedby</sup><sup>_a_),bytimeandbydestination</sup> country _k_ . More specifically, _ε_<sup>_a,k_</sup> _t_ is assumed to be i.i.d. and drawn from a mean zero Gumbel 8
distribution with scale parameter 1. Furthermore, we assume that the fixed cost component is zero for stayers, i.e. _C_<sup>_s,i,j,j_</sup> = 0, _∀j ∈_ 1 _,_ 2 _, .., n_ . However, the random cost _ε_<sup>_a,k_</sup> _t_ is incurred by both movers and stayers.

Under the assumptions outlined above, the present discounted choice-specific utility of risk neutral and rational agent _a_ , from origin country _i_ , living in country _j_ is equal to where _ε_<sup>_a_</sup> _t_<sup>isavectorofrandomshocks,</sup><sup>_k_representstheavailablechoicesofmigrationdestina-</sup> tions and _β_ refers to the discount factor between _t_ and _t_ +1. We take expectations of the above expression with respect to agent specific shocks to get the Bellman equation which is expressed as where _Vt_<sup>_s,i,j_</sup> = _EεUt_<sup>_s,i,j_</sup> ( _ε_<sup>_a_</sup> _t_<sup>) is the expected value of location</sup><sup>_j_for agent</sup><sup>_a_from country</sup><sup>_i_at time</sup> _t_ , conditional on idiosyncratic shock vector _ε_<sup>_a_</sup> _t_<sup>.</sup> In this setting, as agents decide whether to stay in their current residence country _j_ or to migrate to another country _k_ , they have to take the following into consideration as seen in expression (1) above: (i) the instantaneous utility _φ_<sup>_s,j_</sup> _t_ of staying in _j_ , (ii) expected future continuation payoffs of every potential destination _k_ , represented by _Vt_<sup>_s,i,k_</sup> +1<sup>and (iii) the moving cost from</sup><sup>_j_to</sup> _k_ expressed as _C_<sup>_s,i,j,k_</sup> + _ε_<sup>_a,k_</sup> _t_<sup>.Ex-ante identical agents may still make different mobility decisions</sup> because of the random component of their moving costs. These are equivalent to agent-specific utility shocks that affect attractiveness of different destinations. Although agents are more likely to migrate from countries with small _φ_<sup>_s,j_</sup> _t_ to those with large _φ_<sup>_s,j_</sup> _t_<sup>,thereverseisstill</sup> possible if the differences between random shocks are large enough. This is the mechanism through which the model would generate simultaneous migration flows from _j_ to _k_ and _k_ to 9
_j_ . However, the future utility at a given destination _k_ is not fully known, thus agents form expectations on these values and optimize accordingly. The instantaneous utility at a location, _φ_<sup>_s,j_</sup> _t_<sup>,isnotafunctionoftheagent’sbirthcountry,buttheexpectedvalue,</sup><sup>_V_</sup> _t_<sup>_s,i,j_</sup> is a function of both birth country _i_ and current residence country _j_ . This is due to the assumption that the fixed component of the moving cost _C_<sup>_s,i,j,k_</sup> , depends on the birth, current and destination countries. This assumption is supported by the data which show the immigrants in a given country _j_ are more likely to move to another country _k_ when compared to the natives of _j_ .

The distributional assumption of the random shock, _ε_ , allows us to solve the moving probabilities analytically. An agent born in _i_ , currently living in _j_ has the following probability of moving to _k_ : The probability of moving from _j_ to _k_ is a function of location specific values and moving costs. This means we need to solve the Bellman equations to pin down the moving cost parameters _C_<sup>_s,i,j,k_</sup> and location specific instantaneous utility parameters, _φ_<sup>_s,j_</sup> _t_<sup>.Thisprocesswillallowus</sup> to calculate the direct and transit migration probabilities implied by the model as expressed in (2). The Bellman equations need to be solved recursively for any given parameter set and we will discuss the solution algorithm in detail in the next section.

The next step is to simplify the Bellman equation. The expected incremental utility of being able to move from a given country _j_ at time _t_ can be written as: which is the expected maximum future value minus the discounted value of the current choice. This is the _option value_ of location _j_ and it is a key feature of our model and empirical analysis.

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Similar to the moving probabilities, the “ _E_ max”expression above can be solved analytically, thanks to the distributional assumptions on the random shock.<sup>4</sup> This leads to the following expression: The Bellman equation can now be simply expressed using the option value term:<sup>5</sup> This expression shows that the value of location _j_ has three components: (i) the current instantaneous utility _φ_<sup>_s,j_</sup> _t_<sup>,(ii)thediscountedexpectednextperiodutilityofstayingin</sup><sup>_j_,given</sup> by _βEtVt_<sup>_s,i,j_</sup> +1<sup>,and(iii)theoptionvalueof</sup><sup>_j_(duetoitsaccesstootherdestinationcountries)as</sup> denoted by Ω<sup>_s,i,j_</sup> _t_ .

The option value term in (3), as mentioned earlier, is the feature that separates our model from the standard approaches where migration is a single static decision. In standard models, migrants choose among the set of potential destinations and move from their birth country. Migrants live in that destination for the remainder of time, essentially a single period, and receive their payoff. In our setting, the agents face the migration decision repeatedly, regardless of whether they are living in their birth countries or have already migrated. They make their decisions based on the instantaneous payoff, location specific moving costs and expected continuation payoffs in those destination choices, as expressed in (4).

The option value associated with each potential destination in our model is similar to the “option value”concept in the finance literature. Agents value their ability to move and this

> 4Artuc, Chaudhuri and McLaren (2010) show the derivation of the option value equation in their online appendix.

> 5The expression for the option value is related to the inversion equation of Hotz and Miller (1993). Unlike them, we do not invert empirical probabilities to estimate values. However, we simplify the Bellman equation by taking advantage of the distributional assumption similar to Artuc Chaudhuri and McLaren (2010).

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valuation is location specific. The option value of country _j_ is going to be larger if it provides easier access to other and possibly higher income countries. We argue and show that the option value of such locations (such as Canada, the United Kingdom and Australia) is one of the main motivations behind the observed transit and sequential migration patterns. Finally, we should emphasize that the option value of a location Ω<sup>_s,i,j_</sup> _t_ = _−_ log( _p_<sup>_s,i,j,j_</sup> _t_ ) decreases as the probability of moving decreases. If the agents were completely immobile and were stuck in _j_ forever, say due to complete border closures between _j_ and other destinations, then the option value of _j_ would decline to zero.

# **1.1 Functional Form of the Moving Costs**

The moving costs are one of the key determinants of the mobility patterns generated in the model. We assume that the fixed moving cost from country _j_ to country _k_ has two distinct components. The first component, denoted by _C_ 1<sup>_s,j,k_</sup> , is the cost of moving from _j_ to _k_ . This is assumed to be common for all agents whether they were born in _j_ (locals) or were born in another country _i_ and had migrated in an earlier period to _j_ (immigrants). The second component, denoted by _C_ 2<sup>_s,i,k_</sup> , is the additional moving cost for immigrants born in _i_ and are currently living in _j_ . We refer to this component as the cost of transit migration and do not have a priori expectation on its value. The data, however, indicate immigrants are more likely to move on to another country _k_ from _j_ when compared to locals, which implies this second component is likely to be negative. We assume that the transit cost is a function of birth country _i_ and destination country _k_ .<sup>6</sup> The common cost component is given by

> 6Unfortunately we cannot use destination specific parameters in these expressions, since we use only the transit migration data to the US. However, we can use bilateral and origin specific parameters.

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and the additional transit component is equal to where _dist_ ( _j, k_ ) is the distance between countries _j_ and _k_ , _lang_ ( _j, k_ ) is an indicator variable that is equal to 1 if countries _j_ and _k_ share a common language and _gdp_ ( _j_ ) is the GDP per capita in country _j_ . Total fixed moving cost of moving from _j_ to _k_ for an agent born in _i_ is, thus, given by where _C_ 1<sup>_s,j,j_</sup> = 0, _C_ 2<sup>_s,i,i_</sup> = 0, and the indicator function **1** _i_ = _j_ = 1, if _i_ = _j_ and 0 otherwise. In other words, the fixed moving cost is equal to zero for stayers independent of their birth country; it is equal to _C_ 1<sup>_s,j,k_</sup> for people moving from their birth country to country _k_ and it is equal to _C_ 1<sup>_s,j,k_</sup> + _C_ 2<sup>_s,i,k_</sup> for transit migrants - people born in country _i_ , living in _j_ and moving to _k_ .<sup>7</sup>

# **2 Data**

Among the main constraints faced in estimating dynamic migration models is the availability of detailed and comprehensive data. Most global bilateral migration data sets are based on censuses and population registers of the destination countries where the migrants currently reside. These data sources record only the country of birth or citizenship of migrants. Other important variables, such as the year of arrival or migration status, are not included in most surveys. Detailed migration histories tend to be available only in small and specialized surveys

> 7In reality, both direct and transit cost components might be functions of the time a migrant spends at the current location. Furthermore, the random component might show persistence over time. We abstract away from these issues in our model and estimation since it is not feasible to identify duration of stay of migrants at a given location.

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which are not nationally representative.<sup>8</sup> Without comprehensive global data that cover all possible destinations, it is difficult for empirical and analytical papers to explore beyond static models.

The data in this paper come from two different sources. The global bilateral skilled migration database, as described in great detail in Artuc et al (2015), provides migrant stocks by gender and education level (tertiary and non-tertiary) for 1990 and 2000 for each pair of countries in the world. Part of the data comes from original statistical sources and comprises around 80% of the migrant stock in the world. The rest are imputed using gravity based models. Artuc et al (2015) describe the empirical methods behind the imputation based on a gravity model. Since the imputed data might bias our results, we use only the raw data for 2000. The parallel data for 2010 for OECD destinations come from the DIOC database which is described in Arslan et al (2014). The DIOC 2010 database also has information on the time of arrival of migrants by skill level and country of origin for all of the OECD destinations. This new disaggregation, unavailable for earlier decades, allows us to construct the number of new arrivals by skill and origin country for 2000-2010. We discuss in the next section how we use this new data in our empirical analysis. Finally, we have collected additional bilateral migrant stock data by skill level for around 50 non-OECD destinations in 2010. We use this data to complement the DIOC data.

The second data source is the annual American Community Survey (ACS) of the United States for 2001-2011. ACS provides detailed information on a large sample of the population, including migrants who are defined as people born outside the United States, regardless of their citizenship or residency status. The survey asks the country of birth, the year of migration to the US and where people were living a year earlier. Using these questions, we take the people who declared that they had moved to the US within the previous year, record their country of birth, and identify their country of residence prior to their move. This procedure allows us to identify those who came directly from their birth countries and those who came from another country.

> 8See Carletto et al, 2015, and Dustmann and G ̈orlach, 2016, for a detailed discussion of data shortcomings.

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If a recent migrant was residing in a country other than their birth country, we consider them to be transit migrants. No other data source, to our knowledge, has this level of detailed and extensive information about immigrants and their migration paths. Despite this unique information, we do not actually know when transit migrants moved to their country of last residence. This information is not needed for our modeling purposes but would be quite useful for future analysis.

We construct our database in the following manner. We only include people between the ages of 18 and 65 at the time of the survey. Dropping the elderly (65+) reduces the sample by around 4% while removing the children and young adults shrinks it by around 24%. The main reason for removing young migrants is that they are likely to be moving with their parents and are unlikely to be making the individual mobility decisions that form the basis of our model. We include everybody in our sample who states that they arrived during the year of the survey or during the previous year and were abroad in the year before they arrived.<sup>9</sup> This leads to slightly over 9 million people in the sample.

The ACS surveys have, on average, 175 different countries and territories as possible answers to the place of birth question. On the other hand, ‘country of residence 1 year ago’ question has only 67 categories listed. Many smaller countries in a given region are aggregated into regional categories, such as ‘Other South America’ or ‘Other West Asia’. To complicate the

> 9There are two questions we use for this purpose. The first question asks when the migrant moved to the United States and the second asks the following: ‘Where did this person live 1 year ago?’ We should note these two questions seem to create a certain level of confusion which possibly arises from when the survey is conducted within that year. Suppose the survey is conducted in September 2006 and the migrant arrived in the United States in March 2005. The answer to the first question will be that the migrant moved to the United States during 2005. In other words, this question is likely to capture more than 12 months of arrivals. That is why, the total number of recent arrivals (current or previous year) obtained by summing up each individual survey year is actually more than the number of people who arrived during 2001-11 based on the 2012 ACS data. For the second question, some people will interpret ‘1 year ago’ as November 2005 and state where they were living in the United States. Other respondents will interpret one year as the previous calendar year and report the foreign country they were in before coming to the United States. As a result, around 40% of the people who stated they migrated during that year or the previous year also reported a location in the United States to this second question. We drop these observations from our sample. This adjustment brings our total number of observations to 9.03 million, which is now 10% less than the number of people who arrived during 2001-11 according to the 2012 ACS data.

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issue even further, many countries, such as Chile, Peru and Turkey, are individually identified in some of the survey years but aggregated into the regional categories in others, making the analysis close to impossible. To address these complications, we construct the smallest set of countries and regional aggregates that are consistent over time, ending up with 40 geographic units, including the United States. Among these, 25 are separate countries and 15 are regional aggregates of the 150 other countries (or regions) that are listed in birthplace category. The list of our 40 groups and the countries they include are presented in Table 1. This type of aggregation of the world into 40 countries or groups potentially lowers our estimates and the extent of transit migration in the data. For example, if a migrant who was born in Peru goes to Argentina before moving to the United States, we will not be able to identify him as a transit migrant since Peru and Argentina are part of the same category (Other South America) in our data and estimation. For compatibility, we group the countries in the global bilateral migration database and DIOC/DIOC-E along the same lines. Finally, in addition to their country of birth and last residence, we split all migrants as tertiary or non-tertiary educated.

Figures 1 and 2 as well as Tables 2 and 3 provide information on the extent of transit migration to the United States. Table 2 shows what percentage of migrants born in a given country was residing in a different country before migrating to the US. For example, 7% of Canadian born migrants came to the US from a different country. The same ratio is only 1% for Mexican migrants, but over 20% for migrants born in many European countries like the United Kingdom, Italy and the Russian Federation. The same information is presented visually in Figure 1 to highlight regional differences and similarities. We see that transit migration is higher among migrants born in Africa and the Middle East and lower in Latin America, as migrants from the latter region have more direct access to the United States due to geographic proximity and diaspora linkages.

Table 3 presents the percentage of migrants coming from a given location who were born somewhere else and Figure 2 displays the same information visually. The data indicate that transit migration is quite high among the migrants who were living in higher income OECD countries.

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For example, 30% of migrants from Canada to the US were born in another country; for the United Kingdom, that ratio is 37%. Despite the public perception, the data do not indicate that migrants coming from Central American or Caribbean countries are generally transit migrants. Of course, many Central American migrants might simply be traveling through Mexico without living there for an extended period of time.

Our final observation is that transit migration is more common among high-skilled migrants as seen when we compare the second and third columns of Tables 2 and 3. In the aggregate, close to 14% of high-skilled but only 6% of low-skilled migrants are transit migrants. These differences indicate that higher skilled migrants are more mobile, not only in terms of leaving their home countries for higher income countries (Artuc et al, 2015) but also in terms of moving between multiple destinations.

# **3 Solution and Estimation Algorithm**

We solve the model at steady state to calculate implied migration probabilities to be used in the estimation algorithm (i.e we assume that _V_ 0<sup>_s,i,j_</sup> = _Vt_<sup>_s,i,j_</sup> and _φ_<sup>_s,j_</sup> 0 = _φ_<sup>_s,j_</sup> _t_ for every _t_ ). At steady state, we can express the present discounted expected utility as where the moving probability from _j_ to _k_ of migrants from _i_ is given by The model has the following parameters that need to be estimated (or calibrated) based on the equations (5), (6), (8) and (9): (i) the moving cost parameters, _α_ , (ii) the location specific instantaneous utility parameters, _φ_ , and (iii) the discount factor, _β_ . We define the parameter 17
vector _θ_ , which consists of all parameters of the model for skill group _s_ , i.e. _α_ ’s, _φ_ ’s, and _β_ . Since there are eight moving cost parameters, _n_ location utility parameters and _β_ , our _θ_ vector has _n_ + 8 + 1 elements.

Solving the equilibrium values involves finding a fixed point. We first consider the matrix _V_ 0<sup>_s_</sup> consisting of all _V_ 0<sup>_s,i,j_</sup> s and define the function _V_ 0<sup>_s_=</sup><sup>_F_(</sup><sup>_V_</sup> 0<sup>_s_;</sup><sup>_θ_),usingequations(5),(6),(8)</sup> and (9). Finding the fixed point of function _F_ , given the parameter vector _θ_ , leads us to the equilibrium values of _V_ 0<sup>_s,i,j_</sup> . We should note that _V_ 0<sup>_s_is an</sup><sup>_n×n_dimension matrix which means</sup> we have a large system of _n_<sup>2</sup> equations with _n_<sup>2</sup> unknowns.<sup>10</sup> After solving for _V_ 0<sup>_s,i,j_</sup> s, we can insert them in (9) to calculate the moving probabilities, _p_<sup>_s,i,j,k_</sup> 0 . This procedure allows us to write these moving probabilities as a function of the parameter vector, denoted as _p_<sup>_s,i,j,k_</sup> ( _θ_ ). Then it becomes possible to calculate the log-likelihood contribution of an agent from origin _i_ , living in _j_ , and moving to _k_ as log _p_<sup>_s,i,j,k_</sup> ( _θ_ ) for any given _θ_ .

# **3.1 Likelihood Function:**

In the American Community Survey (ACS) data, as discussed in the data section above, we have detailed mobility and birthplace information for people who migrated to the United States. Transit migrants are defined as the people born in _i_ and living in _j_ before moving to the US. We denote their number as _M_<sup>_s,i,j,U_</sup> .<sup>11</sup> Next, using the stocks of migrants born in _i_ and living _j_ in 2000 from the global bilateral migration database and DIOC (see Artuc et al, 2015, and Arslan et al, 2014, for details) and _M_<sup>_s,i,j,U_</sup> matrix, we calculate the approximate number of people who are staying in their current country or moving to destinations other than the US. We denote this matrix as _M_<sup>_s,i,j,S_</sup> . In other words, we divide the migrants born in _i_ living in _j_ ( _i_ = _j_ ) into two groups: those who move to the US, given by _M_<sup>_s,i,j,U_</sup> , and those who moved elsewhere or stayed in _j_ , given by _M_<sup>_s,i,j,S_</sup> .

Using these data matrices, vectors and implied migration probabilities, the log-likelihood con-

> 10Fortunately, the equations can be solved by iteration since _F_ is a contraction mapping.

> 11Note that the United States is the “only” destination for which we have transit migration data.

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tribution of transit migrants can now be expressed as where _k_ = _U_ means the destination _k_ is any country other than USA.

Our next dataset, the 2010 DIOC provides data on the number of people who migrated within the last 10 years to OECD destination countries. This allows us to calculate the number of natives who moved from _j_ to _k_ within the last 10 years if _k_ is an OECD country. We denote this matrix _M_<sup>_s,j,j,k_</sup> . Next, we define _M_<sup>_s,j,j,N_</sup> as the number of people who move to non-OECD destinations (labelled by _N_ collectively) and _M_<sup>_s,j,j,j_</sup> as the number of people who stayed in their birth country _j_ and did not migrate to any country. Both of these are 40 _×_ 1 vectors for each skill group _s_ . In summary, natives (who were born in _j_ and living in _j_ ) are divided into the following groups: Staying at home, _M_<sup>_s,j,j,j_</sup> , moving to a specific OECD destination _k_ , _M_<sup>_s,j,j,k_</sup> and moving to any non-OECD destination, _M_<sup>_s,j,j,N_</sup> .

The log-likelihood contribution of direct migrants is equal to where _k ∈/ OECD, k_ = _j_ means _k_ is different from birthplace and it is not an OECD country. _k ∈ OECD, k_ = _j_ means destination _k_ is different from birthplace _j_ and is an OECD country. We merge all non-OECD destinations when we calculate the likelihood contribution since we do not have individual flow data for those. Finally the log-likelihood contribution of stayers is equal to Based on log-likelihood contributions from equations (10), (11) and (12), the parameter vector 19
_θ_ is given by

- (13) _θ_ = arg max [log _L_ 1( _θ_ ) + log _L_ 2( _θ_ ) + log _L_ 3( _θ_ )] .

We assume that _β_ = 0 _._ 95, normalize the fixed instantaneous utility of USA to _φ_<sup>_s,U_</sup> = 8 _._ 0, and estimate the remaining _n −_ 1 + 8 parameters. In other words, we fix the values of 2 elements of _θ_ and estimate the remaining 47 parameters.

The algorithm is quite demanding computationally, since it involves calculation of 40 _×_ 40 _×_ 40 probabilities. It is necessary to solve for all of them simultaneously since they are functions of each other. Another computational challenge is to search over 47 parameters that maximizes the log-likelihood function. The steps of the estimation algorithm are the following:

1. We guess a parameter vector _θ_

2. Solve the values, _V_ 0<sup>_s,i,j_</sup> , and migration probabilities, _p_<sup>_s,i,j,k_</sup> 0 using the current _θ_ via the following iteration:

- a. Start with an initial matrix _V_ 0<sup>_s,i,j_</sup>

- b. Calculate _p_<sup>_s,i,j,k_</sup> 0 by substituting _V_ 0<sup>_s,i,j_</sup> in equation (9)

- c. Solve for the new _V_ 0<sup>_s,i,j_</sup> using the current probabilities _p_<sup>_s,i,j,k_</sup> 0 with equation (8)

- d. Compare _V_ 0<sup>_s,i,j_</sup> with its value from the previous iteration: If the sum of square differences is less than _ε_ = 10<sup>_−_8</sup> , the algorithm stops and moves on to step #3. Otherwise it updates _V_ 0<sup>_s,i,j_</sup> , goes back to step (b) and continues to iterate.

3. The algorithm evaluates log-likelihood function, log _L_ 1 + log _L_ 2 + log _L_ 3 using _p_<sup>_s,i,j,k_</sup> 0 , and data matrices via equations (10), (11), (12) and (13).

4. Then, the optimization algorithm checks to see if there is room to improve the log-likelihood function. If this is the case, it goes back to step #2. If the algorithm has converged, it stops.

The function in the inner loop is a contraction mapping, and generally requires less than 50 iterations to converge. The outer loop, however, usually requires more than 10,000 iterations. We repeat this procedure for both skill groups and take the numerical derivatives around _θ_ to 20
calculate the standard errors.

# **4 Empirical Results**

We estimate parameters of the model separately for high-skilled and low-skilled individuals. As discussed above, the vector _θ_ for each skill group _s_ has 49 elements - 40 value parameter for each country/region, 8 moving cost parameters and _β_ . As discussed above, we set _φ_<sup>_s,U_</sup> = 8 _._ 0 for the US, _β_ = 0 _._ 95 and estimate the remaining 47 parameters.

Table 4 presents the 8 moving cost parameters for high and low-skilled workers with the standard errors reported in parentheses. We find that the intercepts of basic moving cost for direct migration for both low- and high-skilled migrants, denoted as _αs,_<sup>1</sup> 0<sup>inequation(5),arepositive</sup> and significant at the 1% level. The additional cost intercepts, denoted as _αs,_<sup>2</sup> 0<sup>inequation(6),</sup> for transit migrants are negative for both skill levels. This is consistent with the fact that immigrants from country _i_ living in country _j_ are much more likely to migrate to another country _k_ when compared to the natives of _j_ . The magnitude of these transit migration coefficients are smaller than those of the basic cost coefficients, indicating that the combined intercept for transit migration is still positive.

The most important migration cost variable is likely to be distance. The additional distance coefficient for transit migrants is positive and significant for low-skilled migrants, and insignificant for high-skilled migrants. As expected, distance increases migration frictions for direct migrants and the cost imposed by distance is significantly higher for the lower skilled migrants. This pattern has been identified repeatedly in the literature as the higher skilled can more easily overcome physical and financial costs that are proxied by distance (Hanson, 2010 and Beine, Docquier and Ozden, 2011). This difference is also one of the reasons why migrants tend to be positively selected in terms of education levels. On the other hand, the distance coefficient for the transit cost component is only significant for the low-skilled migrants. This result indicates 21
that the distance between birth country _i_ and final destination _k_ seems to matter only for the low-skilled once migrants are already in the transit country _j_ . High-skilled migrants, in contrast, are not significantly affected by this distance.

GDP per capita is another determinant of moving costs and the effect operates through several channels, such as financial barriers. The coefficient is negative and significant for basic migration for both types of migrants, with a higher (more negative) value for the low-skilled. The implication is that direct migration from a poorer country is more costly, especially for low-skilled migrants. The GDP per capita coefficients in the transit cost component are both positive, but significant only for low-skilled migrants. This is similar to the pattern we observed for the distance variable. Low-skilled people from poorer countries are more likely to be transit migrants when compared to those from high income countries. Another possible explanation is that a transit migration experience abroad might increase labor market returns in final destination countries. This effect will be stronger for those coming from lower income countries and will be captured by a positive coefficient of the GDP per capita component of transit migration cost.<sup>12</sup> Linguistic overlap decreases migration frictions and increases mobility as demonstrated in numerous studies using gravity models (Beine, Bertoli and Fernandez-Huertas Moraga, 2016). Common language decreases basic migration costs for both skill levels, as demonstrated by the statistically significant negative coefficients, with a larger effect on the high-skilled. The interesting result is that linguistic similarity continues to be important for transit migration costs, and still more so for high-skilled migrants. For example, an Australian migrant in France faces a lower cost of migrating to the United States than a German migrant in France, and the cost is even lower if he is high-skilled.

Our final set of results presents the instantaneous utility parameters, _φ_<sup>_s,j_</sup> 0 for each country/region _j_ and skill level _s_ in our model. Note that we set _φ_<sup>_s,U_</sup> = 8 _._ 0 for the United States

> 12We thank an anonymous referee for this interpretation.

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and estimate the parameters for the other 39 locations. Figure 3 presents the results for both skills levels, with results for the high-skilled on the left panel and the parameter values ranked in declining order in each panel.<sup>13</sup> We should emphasize that these parameters are estimated separately for each skill level and the right comparisons are within, not across, skill levels. First, we observe, as expected, a high degree of correlation with the income levels in these locations. High-income OECD countries, such as Canada, Australia and European countries, have higher values while poorer African, South East Asian, Caribbean and Central American countries are placed towards the bottom of the rankings. Second, the values for the high-skilled (left panel) show more variation, especially towards the bottom. This pattern indicates high-skilled migrants enjoy much larger instantaneous utility gains when they move, especially from low income to high income countries. Third, several developing countries, such as India and China, have large utility values for the low-skilled. This pattern is consistent with the low emigration rates of these countries and might result from the non-pecuniary benefits these locations provide to natives or from country-specific mobility costs that the utility parameters capture.

# **5 Simulations**

This section presents the results of a range of policy simulations that use the estimated coefficients of moving costs and location specific values. Our main goal is to identify the main spillovers between direct and transit migration options and how various simulated policies, such as complete blockage of certain migration corridors, impact bilateral and global migration patterns. In all of these simulations, we consider all origin countries/regions and two representative transit countries: Canada and the United Kingdom.

Each simulation involves solving the equilibrium outcomes for all 40 countries/regions, but we selected only a sample of representative countries for presentation purposes. The simulation algorithm is similar to the one used in the estimation. This time, however, _Vt_<sup>_s,i,j_</sup> = _Vt_<sup>_s,i,j_</sup> +1<sup>,so</sup>

> 13All of these instantaneous utility parameter estimates are significant at the 99% level with t-statistics between 14 and 70.

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we use equations (2), (3) and (4), rather than (8) and (9). The simulations use the original population and migrant distributions from the data as the initial state variable. The population distribution includes agents’ birthplace, current location, and skill level, making it a 40 _×_ 40 matrix for each skill level.

The number of type _s_ individuals who were born in country _i_ living in country _j_ at time _t_ is denoted as _L_<sup>_s,i,j_</sup> _t_ . For the simulation, we change the parameter vector of the model to _θ_<sup>_′_</sup> . Then, we solve the optimization problem using the new parameters, _θ_<sup>_′_</sup> , and calculate the new migration probabilities, _p_<sup>_s,i,k,j_</sup> _t_ ( _θ_<sup>_′_</sup> ), for each agent type. Next, we find the new migrant stocks, _L_<sup>_s,i,j_</sup> _t_ +1<sup>usingthenewprobabilitiesandflowequationwhichisgivenbythefollowing:</sup> We repeat this exercise for each simulation scenario as described in the following sections.<sup>14</sup> Figure 4 helps to visualize the simulation scenarios where the United States is the final desti— nation. For simplicity, we present three countries in the figure Canada, the UK and other — countries even though the simulations are solved for all countries separately. People can migrate directly to the United States or via other countries. As we mentioned earlier, Canada and the United Kingdom are presented as the two possible transit countries in the figure and the discussion, but the model is solved for all potential transit routes.

# **5.1 Simulation 1: Moving cost from Canada to the US increases**

In our first set of simulations, we artificially increase the moving cost from Canada to the United States to a level such that migration levels decrease by more than 99%. Technically,

> 14We should note that the simulation exercises are of partial equilibirum nature, as we assume that countryspecific wages and other measures of attractiveness are fixed. However, it is possible that changes in migration flows due to policy restrictions will impact wages, further affecting migration flows across the world. Unfortunately, it would be impossible to fully endogenize these country parameters, without estimating production functions of all countries and regions.

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100% prohibitive costs do not exist since the i.i.d. shocks have an infinite support. This is presented as the removal of the arrow between Canada and the United States in Figure 4 and implies that both current migrants and native citizens in Canada are no longer able to migrate to the United States. This change would force them to reconsider all of their migration decisions.

The results of the simulation are presented in Table 5. The main finding of this simulation is that migration from other countries to Canada significantly decreases as seen in columns 1 and 3, even though there are no changes to migration costs or benefits regarding Canada. Recall that migration to Canada is attractive for two reasons. First, the instantaneous utility parameter for Canada is high compared to many other countries (the _φ_ term in the value function expression (4)). Second, moving from Canada to the United States is relatively easy which generates a high option value associated with being in Canada (the Ωterm in the value function). Therefore when it becomes almost impossible to move from Canada to the US, the option value of migration to Canada decreases for almost all migrants. The final effect is a significant decline in the overall migration levels from other countries to Canada. Note that migration does not drop to zero, because it is still possible to move from Canada to other countries and Canada still has a relatively high per-period payoff.

In terms of specific outcomes, the number of immigrants moving to Canada decreases between 3% to 18% for people from other countries. The impact is especially high for Latin American and Caribbean migrants. If we were to compare the impact on low- and high-skilled migrants, we see that it is marginally stronger for the low-skilled regardless of the country of birth. The gap is also high for Latin American migrants, where the impact on the low-skilled is almost twice as much.

Another potential effect is the impact on other critical transit countries such as the United Kingdom. We see in columns 2 and 4 that there is almost no impact on migration from most countries to the United Kingdom when the Canada-USA border is closed. However, there is 25
some increase in high-skilled migration from several countries, such as Jamaica (1.8%) and Other Caribbean countries (1.6%) as the United Kingdom becomes more attractive. Finally, we observe increased migration from Canada to the United Kingdom (1.9% for the low-skilled and 2.8% for the high-skilled) since the Canadians themselves are also unable to move to the United States.

# **5.2 Simulation 2: Moving cost to Canada increases**

Our second simulation involves increasing the moving costs to Canada from all developing (only non-OECD) countries to prohibitively high levels. This is presented as the removal of arrow #2 in Figure 4. In other words, people are no longer able to move from India or China to Canada whether they would like to settle there permanently or use it as a transit stop on their way to other countries such as the United States. This policy change is similar to the spillover effects of bilateral visa policies implemented by destination countries on other potential destinations, as explored by Bertoli and Fernandez-Huertas Moraga (2015).

Table 6 presents results only for high-skilled migrants since the impact on low-skilled migrants are negligible, with the exception of the obvious decline of 100% in direct migration to Canada. This is probably due to the fact that low-skilled transit migration via Canada is already relatively small and has minimal impact on other transit paths. For high-skilled migrants, the number of transit migrants moving from Canada to the United States decreases significantly, as seen in column 2, but not by the full 100%. This is due to the fact there are some migrants, say from Mexico, who were already in Canada before the border closed and they continue to move to the United States over time. We also observe that there is a decline in transit migration from OECD countries (such as France) to the United States via Canada, even though migration from France to Canada is not blocked. This arises from the fact that transit migration of French-born migrants from developing countries to Canada is also blocked, resulting in a small decline in the overall migration of the French-born.

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There is a small, and in some cases considerable impact on all other paths of migration, especially direct migration to the United States and transit migration via the United Kingdom. For example, direct migration to the United States (first column) increases for Asian and Caribbean countries, up to 3% for Jamaica, 6% for other Caribbean Countries, and slightly over 1% for Republic of Korea, the Philippines, Western and Eastern Africa. Similarly, there is some increase in transit migration from the United Kingdom to the United States (third column) indicating some degree of replacement. For example, there is an increase of around 2% in transit migration from Jamaica and 4% from other Caribbean countries as well as around 1% from African regions. These are again due to global spillovers from changes in bilateral corridors.

# **5.3 Simulation 3: Moving Cost to the United States Increases**

Our next simulation increases the direct moving cost to the United States to a prohibitive level for direct migrants from developing (non-OECD) origin countries. This is equivalent to the removal of arrow #3 in Figure 4 and the results are presented in Table 7. We again only present the results for high-skilled migrants since the patterns are qualitatively the same but quantitatively smaller in the case of low-skilled migrants. Since transit migration forces are stronger for high-skilled individuals, they are more responsive to changes in moving costs and the United States is more attractive for the high-skilled migrants from most developing countries.

Interestingly enough, closing the borders for migrants from developing countries is among the main proposals put forward by some of the leading candidates in the 2016 US election. Our results indicate that the impact of this policy simulation is actually quite large. First, we should highlight that the United States is already the largest destination for high-skilled migrants from most origin countries. For example, more than 70% of high-skilled workers who left the Philippines already live in the United States.

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When direct migration to the United States becomes impossible, Canada and the United Kingdom become very attractive destinations. Both of these countries are high-income economies with similar income opportunities for migrants so there is a direct spillover in this scenario. These effects are presented in the second and fourth columns for Canada and the United Kingdom, respectively, as many high-skilled migrants move to these close substitutes. For example, as reported in columns 2 and 4, migration to Canada and the United Kingdom increases by between 25 and 45% for many Central American and Caribbean countries and by a staggering 165% for the Other Caribbean region that includes Haiti and other smaller island countries. The increase is between 5 and 15% for the African regions and between 3 and 15% for many non-OECD Asian countries. Although we do not report it, we observe positive but smaller changes in migration flows to other high-income OECD countries.

Canada and the United Kingdom also provide a pathway to the United States. As a result, transit (or the option value of) migration to these countries increases when direct migration to the United States is no longer possible and migrants try to find alternative routes. This motivation is clearly seen in column 1 where we observe a significant increase in transit migration to the US via Canada for a range of birth countries. For example, there is a 15-30% increase in transit migration from Latin American and Caribbean countries (and again a staggering 95% from Other Caribbean countries), between 3 and 10% from many Asian countries and around 5-10% from the African regions.

# **5.4 Simulation 4: Block all transit migration to the United States**

The final simulation is a slightly unconventional scenario to highlight the importance of transit migration. Suppose, the United States government declares that you can only move to the United States from your birth country and all other transit migration routes are banned. In other words, if you are an Egyptian living in France, you would either have to stay in France or go back home to be able to move to the United States (even if you have French citizenship).

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In terms of our model, we simply remove the option value of moving to France from Egypt.<sup>15</sup> The implications of eliminating transit migration for total migration to these countries are quite striking as presented in Table 8. We observe that the level of migration to transit countries declines significantly even though these are still as attractive for permanent settlement. For example, as reported in column 1, the migration of high-skilled workers to key transit countries like Canada, the United Kingdom and Australia from high-income countries (such as Germany, Italy and France) declines by 4.6%, 1.8% and 3.2% receptively. Similarly, migration from low-income countries to these three destinations declines by larger amounts: 7.0%, 2.4% and 4.5% respectively. There are similar significant declines in migration to other OECD transit countries. This result is one of the clearest and direct evidence of the significance of transit migration and importance of many OECD destinations as transit countries, especially those with lower moving costs to the US.

The decline in low-skilled migration is slightly smaller but still relatively high. For example, low-skilled migration from high-income countries (column 3) to Canada is down by 3.1%. The decline in low-skilled migration from low-income countries (column 4) is also larger, 11.6% for Canada and 2.0% for the United Kingdom.

Finally, we observe bigger declines in percentage terms in migration levels to many of the smaller developing countries, especially for the high-skilled migrants from other lower-income countries. This is simply an artifact of the data. Since the original migration levels to these countries are already so small, any decline translates to a relatively large percentage change.

> 15This is why we cannot represent this simulation in Figure 4. While the previous simulations correspond to closure of single corridors, this simulation blocks certain types of people while keeping all corridors open for direct migration.

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# **6 Conclusion**

Most empirical analyses of migration decisions and patterns are based on static models. A potential migrant is assumed to make a single decision between alternative destinations and his current place of residence based on location-specific utility differences and bilateral mobility costs. Once migration takes place, the payoff and costs are realized and, most importantly, there are no additional movements. The data indicate that this simplistic view is quite off the mark. Many migrants’ paths involve multiple countries and are likely to be the outcomes of complicated dynamic decision processes. Historical evidence and current data provide ample evidence that many migrants live in a series of countries for different lengths of time, experience repeated migration episodes, or have circular migration paths.

There is a relatively new and exciting strand of the literature that is based on innovative dynamic models. They rely on detailed survey data on individual migration histories from single origin or destination countries to estimate the determinants of return or circular migration behavior. Unfortunately, the absence of detailed individual data at the global level prevents us from shedding light on some of the other processes. Our contribution is to construct a novel dynamic model of global migration with the goal of incorporating and explaining transit migration patterns, using the available data. In our model, agents decide to stay in their current location or move to another one every period, taking instantaneous utility payoffs and bilateral mobility barriers into account. This dynamic structure leads to an option value associated with each location, making it attractive as it provides easier access to other locations in future periods. This attractiveness is exactly the basis of the transit migration behavior we observe.

Our empirical analysis relies on transit migration data from the American Community Survey, which asks migrants their place of birth and where they were living the year before they came to the United States. This data indicate that transit migration—people coming to the United States from places other than their birth countries—is actually high, especially among highskilled migrants coming from other high-income OECD countries. We combine the American 30
data with global bilateral migration data and adopt estimation methods to address the data challenges. We finally simulate certain scenarios that highlight the dynamic interactions and spillovers between different migration paths. For example, blocking certain paths might decrease or increase migration flows in other corridors as the option values of the impacted locations change. In other words, governments need to incorporate the externalities created by their policy actions, especially on their neighbors, via direct and transit migration channels.

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