metadata: authors: 0: author: Devictor,Xavier
1: author: Do,Quy-Toan
2: author: Levchenko,Andrei A.
count: World
docna: 0: docna: The Globalization of Refugee Flows
docty: Policy Research Working Paper
subtopic: Social Cohesion,Ports & Waterways,Social Conflict and Violence
teratopic: Transport,Social Development
abstracts: cdata!: This paper analyzes the spatial
            distribution of refugees over 1987-2017 and establishes
            several stylized facts about refugees today compared with
            past decades. Refugees still predominantly reside in
            developing countries neighboring their country of origin.
            However, compared to past decades, refugees today (i)
            travel longer distances, (ii)  are less likely to seek
            protection in a neighboring country, (iii) are less
            geographically concentrated, and (iv) are more likely to
            reside in a high-income OECD country. The findings bring new
            evidence to the debate on refugee responsibility-sharing.
repnme: repnme: The Globalization of Refugee Flows
display_title: The Globalization of Refugee Flows
listing_relative_url: /research/2020/04/31936259/globalization-refugee-flows
topicv3: Forced Displacement m1326032 740,Fragility, Conflict and Violence m1326586 2432,Ports m1327399 167,Conflict and Violence m1327549 2433,Host Country m1939429 4820
publishtoextweb_dt: 2020-04-07T00:00:00Z
historic_topic: Transport,Social Development
origu: DECRG: Poverty & Inequality (DECPI)
no_of_pages: 28
author_keys: 0: author_key: 125656
1: author_key: 433894
2: author_key: 1132316
model_extractions: Policy Research Working Paper 9206 The Globalization of Refugee Flows _Xavier Devictor Quy-Toan Do Andrei A. Levchenko_ Fragility, Conflict and Violence Global Theme & Development Economics Development Research Group April 2020
Policy Research Working Paper 9206

# **Abstract**

This paper analyzes the spatial distribution of refugees over 1987–2017 and establishes several stylized facts about refugees today compared with past decades. Refugees still predominantly reside in developing countries neighboring their country of origin. However, compared to past decades, refugees today (i) travel longer distances, (ii) are less likely to seek protection in a neighboring country, (iii) are less geographically concentrated, and (iv) are more likely to reside in a high-income OECD country. The findings bring new evidence to the debate on refugee responsibility-sharing.

This paper is a joint product of the Fragility, Conflict and Violence Global Theme and the Development Research Group, 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 abonfield@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
# The Globalization of Refugee Flows<sup>_∗_</sup>

Xavier Devictor<sup>_†_</sup> Quy-Toan Do<sup>_‡_</sup> Andrei A. Levchenko<sup>_§_</sup> _Updated January 2021_ _JEL codes_ : J15, F22, F55 _Keywords_ : refugees, forced displacement, responsibility-sharing, UNHCR

> _∗_ We are grateful to the editor (Andrew Foster), two anonymous referees, C ̧a ̆glar  ̈Ozden, Philip Verwimp, and workshop participants at the Research Conference on Forced Displacement in Copenhagen for helpful suggestions, and to Chau Hai Le for superb research assistance. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.

> _†_ World Bank: xdevictor@worldbank.org

> _‡_ World Bank: qdo@worldbank.org

> _§_ University of Michigan, CEPR, and NBER: alev@umich.edu
# **1 Introduction**

By the end of 2017, the world had witnessed its highest recorded number of forcibly displaced people worldwide to date at 68.5 million persons, including 3.1 million asylumseekers and 25.4 million refugees.<sup>1</sup> While that number includes 4.0 million refugees from older conflicts in Afghanistan and Somalia, 10.1 million of these are from the more recent crises in Syria, Myanmar, and South Sudan.

The 1951 Convention Relating to the Status of Refugees (complemented by the 1967 Protocol) determines that refugee status shall be granted to any person who finds her or himself displaced “owing to well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion” (Art. 1.A.2.). Signatory states commit to provide treatment “no less favorable than nationals of foreign countries in the same circumstances” with respect to employment (Art. 17), housing (Art. 21), education, and public relief (Art. 22 and 23).

Most importantly, the Convention underlines the need for solidarity among countries in sharing the responsibility for hosting refugees. Yet, the non-refoulement clause (Art. 33) implies that first countries of contact with asylum seekers are often those who have to provide protection. Other signatories, on the other hand, can voluntarily decide on their involvement in responsibility-sharing, potentially leading to free-riding (Suhrke 1998, Bubb, Kremer and Levine 2011). This creates a fundamental imbalance across UN Member States, with political and fiscal constraints of host countries having been associated with a lack of adequate assistance (Hathaway and Neve 1997, Crisp 2003) and an additional reason for setting up refugee camps (Smith 2004).

On December 17, 2018, the United Nations General Assembly affirmed the Global Compact on Refugees (UN General Assembly 2018), after two years of extensive consultations led by UNHCR with UN Member States, international organizations, refugees, civil society, the private sector, and experts. The Global Compact on Refugees opens by stressing that “there is an urgent need for more equitable sharing of the burden of and responsibility for hosting and supporting the world’s refugees, while taking ac-

> 1Asylum seekers have crossed a national border and have applied for international protection. When asylum is granted, they become refugees. These figures only include refugees and asylum-seekers under the mandate of the United Nations High Commissioner for Refugees (UNHCR). Palestine refugees account for another 5.3 million persons and are under the mandate of the United Nations Relief and Works Agency for Palestine refugees in the Near East (UNRWA).

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count of existing contributions and the differing capacities and resources among States.” It formally “intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other relevant stakeholders as appropriate.” Underpinning the global debate on responsibilitysharing is the assumption that “the grant of asylum may place unduly heavy burdens on certain countries” (UN General Assembly 2018), typically countries neighboring a conflict area. In this perspective, the number of refugees a country is to host is simply a function of its geography.

This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017.

Our main findings can be summarized as follows. While refugees still remain overwhelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diffusion. We begin by showing that the global population of refugees has been increasingly dispersed across host countries, as captured by a falling Herfindahl index of host-country shares.

We then document several features of the data underlying this greater diffusion, using four outcome measures of refugee spatial distribution. First, we compute the average distance refugees have traveled between their country of origin and their country of destination. The average distance traveled by refugees has increased substantially over time. Next, we look at the probability that source and host countries are contiguous, and show that the share of refugees going to an adjacent country has fallen. Third, we construct a measure of refugee spatial dispersion by computing the Herfindahl index of refugee shares by source country. The Herfindahl index of refugee shares fell substantially over time, indicating that refugees from a given conflict are now more dispersed across host countries.

These results paint a picture of a more globalized and far-reaching refugee network and imply a more equal distribution of the responsibility for hosting refugees, albeit with the backdrop of highly unequal distribution to start with. To get some indication 3
about where refugees go, we compute the share of refugees seeking protection in highincome OECD countries. We find that high-income OECD countries host an increasing share of the global refugee population. As of 1990, under 5 percent of refugees resided in a high-income OECD country. This share grew to nearly 25 percent by the mid-2000s, before falling somewhat to 15 percent, still triple the 1990 value.

To alleviate compositional issues, we project the matrix of distances traveled on source country fixed effects and time effects. Source fixed effects control for timeinvariant country characteristics, and thus allows us to rule out that changes over time in average distance traveled by refugees are driven by differences over time in which countries experience conflict. Moreover, analyzing each refugee situation as an eventstudy, we observe that the increased spatial dispersion of refugees is a phenomenon that occurs at the onset of a situation rather than the outcome of geographical diffusion over time.

The theoretical literature on refugee hosting has advocated for an international system of quotas (Hathaway and Neve 1997), which could even be tradeable (Schuck 1997). However, there are few empirical analyses of refugee data that can inform policy. A notable exception is Dreher, Fuchs and Langlotz (2019), which looks at bilateral aid flows and argues that donor countries use aid as a way to reduce the flow of refugees entering their territory. As such, they establish the existence of some form of bargaining with transferable utility between potential host countries. To further the debate on refugee hosting, Bubb et al. (2011) discuss a system of financial transfers from richer countries to poorer ones for hosting refugees and at the same time distinguishing the international protection of asylum seekers from economic migration. One could see such mechanisms at work in recent cooperation agreements between the EU and third countries such as Jordan or Turkey (Temprano Arroyo 2019).

The rest of the paper is organized as follows. Section 2 describes the data used in the analysis. Section 3 presents the results. Section 4 concludes.

4
# **2 Data**

Our analysis is primarily based on data on refugee stocks compiled by the UNHCR. UNHCR annually publishes the data on refugee stocks by source and destination country pair. The term “refugee” includes both refugees and asylum seekers. Under the 1951 Convention Relating to the Status of Refugees and the 1967 Protocol, a refugee is defined as “a person who has been forced to flee his or her country because of persecution for reasons of race, religion, nationality, political opinion or membership in a particular social group” (Art 1.A.2.).

The UNHCR Population Statistics Reference database contains data for the period 1951 – 2017 (released on June 19, 2019). The data set compiles annual stocks of refugees and asylum seekers at the source-destination level for 197 destination and 223 source countries. The ultimate source of the data is the authorities of each receiving country. While in principle there are observations going back to 1951, coverage prior to the late 1980s is too sparse to be usable. Thus, our analysis covers the period 1987-2017. Overall, we have 112,522 non-zero observations for bilateral stocks over the period 1987-2017.

Since the data are not recorded at the individual level, we cannot reliably calculate refugee flows. Thus, the main variable used in the analysis is the refugee stocks. By definition, the stock of refugees in any particular year mixes individuals that arrived at different times. Since our main object of interest is changes in refugee behavior over time, analyzing stocks will if anything attenuate temporal differences.

To better approximate flows, we restrict the sample to large refugee events. A refugee event begins in the year in which the global stock of refugees from a particular source country first exceeds 25,000. An event ends when the stock falls below 25,000, or 10 years after initiation if the destination is an OECD country, whichever comes first.<sup>2</sup> Capping the termination date of the event also puts earlier and later years in the sample on a more equal footing, as stocks in later years contain earlier vintages of refugees. An added benefit of restricting the sample to large refugee events is that this procedure also removes source countries with small numbers of refugees, who fled not

> 2For OECD countries, UNHCR does not include most individuals who have been in the country for more than 10 years in their published refugee statistics.

5
due to armed conflict but for more idiosyncratic reasons. We check robustness of this approach in two ways: (i) using all of the refugee stock observations available in the data set, and (ii) computing refugee flows as the positive time differences in refugee stocks from year to year (setting negative time differences to zero). The results are robust to these two alternatives.

<!-- Start of picture text --> 1987 1992 1997 2002 2007 2012 2017<br>Year<br>20<br>15<br>10<br>Number of refugees (million)<br>5<br><!-- End of picture text --> Figure 1: Global refugee population, 1987-2017 Note: This figure plots the global stock of refugees.

The data on bilateral distance and contiguity come from CEPII. The distance variable refers to the great circle distance between the most populated cities of each country in the pair. The contiguity indicator is equal to one if the two countries share a land border.

Figure 1 charts the global refugee population over time. The sharp increase in the number of refugees over the past decade is evident. Such refugee movements can have significant impacts on the destination countries. Table 1 reports the top 10 destination and top 10 source countries in the most recent available year and illustrates that the 6
overwhelming majority of refugees are hosted in the developing world, something to which we will return.

Table 1: Top 10 destination and top 10 source countries, 2017

|Destination|No. persons|Source|No. persons|
|---|---|---|---|
|Turkey|3,789,119|Syrian Arab Rep.|6,455,328|
|Germany|1,399,554|Afghanistan|2,958,208|
|Pakistan|1,396,619|South Sudan|2,446,277|
|Uganda|1,395,115|Myanmar|1,197,794|
|Lebanon|1,014,165|Somalia|1,044,646|
|Iran (Islamic Rep. of)|979,519|Dem. Rep. of the Congo|756,899|
|Bangladesh|932,319|Sudan|746,663|
|United States of America|929,762|Iraq|635,101|
|Sudan|924,789|Eritrea|564,447|
|Ethiopia|891,990|Central African Rep.|558,487|

**Notes:** This table reports the top 10 source and top 10 destination countries, measured by stocks of refugees recorded in 2017.

# **3 The Globalization of Refugee Movements**

How is the responsibility for hosting refugees shared? To get a better sense of the spatial distribution of refugees across countries, we first construct a measure of refugee dispersion. Namely, we compute a host country Herfindahl index, which measures how concentrated or dispersed refugees are across all destination countries worldwide. Concretely, the (weighted) destination Herfindahl index is defined as where _Rdt_ is the total number of refugees at destination _d_ at time _t_ , so the denominator is simply the global stock of refugees. Refugee numbers _Rdt_ are weighted by _ωdt_ . When _ωdt_ = 1, the result is the textbook Herfindahl index, which takes a value of 1 if one country ends up hosting all the refugees in the world and converges to 0 if every country hosts the same number of refugees. Alternatively, a GDP-weighted version of the destination Herfindahl index is calculated by setting _ωdt_ = 1 _− GDPGDPworldtdt_<sup>andaccounts</sup> 7
<!-- Start of picture text --> TM<br>%<br>Ww s<br>aol %<br>; 5<br>iD 5 ‘<br>ae) %<br>£ ‘<br>i.<br>=;‘%<br>*<br>M<br>SESS _<br>Wwras a Sa ~<br>_ co _ “Oo _ rH<br>a ao o o —_ =<br>fa] fa) o oa ao a<br>7 ty “ ° “<br>ro cM rH ao re ra<br>oo a a oa 2 =<br>a fa) a a o a<br>—_ —_ —_ TM | Lm|<br>period<br>——— simple hhi ————- GDPweighted hhi<br><!-- End of picture text -->
<!-- Start of picture text --> period period<br>(a) Average distance (b) Share contiguous<br>period period<br>(c) Average Herfindahl (d) Share wealthy OECD<br>2200 100<br>2000<br>90<br>1800<br>distance (km) 1600 share (%) 80<br>1400<br>70<br>1200<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br>25<br>.6<br>20<br>.5<br>index 15<br>share (%)<br>.4 10<br>5<br>.3<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br><!-- End of picture text --> Figure 3: Trends in refugee reach, 1987-2017 Note: This figure plots the average distance traveled by a refugee, the share of refugees finding themselves in a contiguous country, the average Herfindahl index of refugee shares by source country, and the share of refugees finding themselves in a wealthy OECD country.

a pronounced upward trend: the average distance traveled rises from around 1300km at the beginning of the sample to 2200km in the mid-2000s, before settling at around 1800km in the last decade. Panel (b) plots the share of refugees that find themselves in a country that shares a land border with their country of origin. At the beginning of the sample 95 percent of refugees were in a country contiguous with their home country. That share fell to 77 percent in the the period 2012-2017.

Another manifestation of the increasing geographical reach of refugees is the greater number of destination countries to which they go. Similar to the global destination Herfindahl index above, we construct a source-specific Herfindahl index that captures whether refugees from a given source country diversify their destinations over time. That is, for a specific source country _s_ and year _t_ , the source Herfindahl index is 9
as where _Rsdt_ is the number of refugees from _s_ in _d_ at time _t_ . The source Herfindahl index takes a maximum value of 1 when all the refugees from _s_ go to a single _d_ , so that _d_ ’s share is 1. The lower is the Herfindahl index, the more spread out is the pattern of refugee flows across destinations.

We then compute the simple mean of _Hst_ for each year, and plot the 5-year averages of this mean. Figure 3, panel (c) reports the results. There is a substantial decrease in the Herfindahl of destination shares over time, from an average of 0.62 at the beginning to 0.36 at the end. The fall in the Herfindahl indicates greater diversification of refugee across locations over time.<sup>3</sup> Finally, to isolate the increasing impact of refugee inflows on high-income countries, panel (d) plots the share of the global refugee stock in high-income OECD country destinations.<sup>4</sup> The increasing importance of wealthy countries as hosts of refugees is evident.

**Frictionless benchmark** While we are not making any normative statements on the optimal spatial distribution of refugees, we compare the actual spatial distribution with the one obtained in alternative benchmark scenarios. In particular, we recompute these four measures in the counterfactual world where the number of refugees leaving each source country is the same as in the data, but is distributed uniformly across all the countries in the world in proportion to the destinations’ total GDP. The counterfactual stock of refugees in _d_ from source _s_ is _Rsdt_<sup>_c_thuscomputedas</sup>

> 3Because the Herfindahl is a simple average across source countries at a point in time, it may be dominated by smaller source countries, which do not account for much of refugee flows. To check whether this is driving the results, we also examined the evolution of the average Herfindahls for only the top 10 and top 5 source countries in each year (which countries are in the top 10 or 5 changes from year to year, as different countries undergo conflicts). The pattern of increased source diversification is quite similar for the top source country samples. The results are available upon request.

> 4High-income OECD countries in our sample are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. We thus exclude the newer members of the OECD, such as South Korea, Mexico, or Turkey.

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Perhaps it is unrealistic to expect refugees to diffuse frictionlessly literally across the entire globe. We thus compute a variant in which refugees diffuse uniformly to only countries within 5000km of the source: Table 2 summarizes the 4 main diffusion measures in this counterfactual world, and compares them to the data for each 5-year period. Two clear patterns emerge. First, a uniform distribution of refugees either worldwide or within a 5000km radius would imply much larger distances traveled, much lower contiguous share, much lower Herfindahl, and a much higher share going to the wealthy OECD group. The differences provide a stark illustration of the starting point of the paper: the current sharing of the responsibility for hosting refugees is far from proportional. In the data, refugees are much more concentrated in nearby and poorer countries than would be the case if there were no geographic or policy frictions. Second, there is little or no trend in these counterfactual patterns over time. The observed distribution of refugees today is closer to this counterfactual distribution than it was at the beginning of our sample. This suggests that the patterns of greater diffusion documented above are not driven by underlying trends in the “frictionless” counterfactual benchmark.

# **Understanding the trends**

**Changes in composition** We first assess whether the time trends documented above are driven by the changing composition of refugee source countries over time. For instance, if conflicts that give rise to refugee flows occurred in more remote countries in the more recent periods, then the distance traveled would increase. This would not be because it is now easier for refugees to travel farther, but rather because of the changing geography of conflict. To rule out pure compositional changes, we estimate the following regression at the source-time period level:

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Table 2: Distribution of Refugees across Destinations: Frictionless Benchmark

|||**Actual**|**Uniform**<br>**global**|**Uniform**<br>_<_ **5000km**|
|---|---|---|---|---|
||Distance (km)|1281|7612|3659|
|1987-1991|Share contiguous (%)|94.74|11.17|14.05|
||Herfndahl|0.62|0.14|0.23|
||Share OECD (%)|2.89|67.39|38.56|
||Distance (km)|1609|7631|3400|
|1992-1996|Share contiguous (%)|86.4|6.49|10.65|
||Herfndahl|0.59|0.12|0.17|
||Share OECD (%)|9.54|67.88|43.90|
||Distance (km)|1777|7542|3422|
|19972001|Share contiguous (%)|79.95|6.35|11.83|
|-|Herfndahl|0.54|0.12|0.16|
||Share OECD (%)|14.08|67.38|44.85|
||Distance (km)|2202|7643|3410|
|20022006|Share contiguous (%)|67.67|5.45|13.01|
|-|Herfndahl|0.45|0.11|0.18|
||Share OECD (%)|23.15|65.75|42.48|
||Distance (km)|1712|7709|3458|
|20072011|Share contiguous (%)|76.77|5.88|18.02|
|-|Herfndahl|0.39|0.10|0.15|
||Share OECD (%)|14.85|61.23|38.13|
||Distance (km)|1796|7669|3423|
|20122017|Share contiguous (%)|76.5|5.33|16.36|
|-|Herfndahl|0.36|0.10|0.18|
||Share OECD (%)|14.97|57.18|39.00|

_Notes:_ This table reports the average distance traveled by a refugee, share of refugees going to contiguous countries, the average Herfindahl of refugee shares across destinations by source country, and the share going to wealthy OECD countries, in the data (“Actual”), and in the counterfactual scenarios in which refugees are distributed across destination countries according to destination total GDP, globally (“Uniform global”), and within a 5000km radius of the source country (“Uniform _<_ 5000km”).

12
<!-- Start of picture text --> period period<br>upper/lower coef upper/lower coef<br>(a) Average distance (b) Share contiguous<br>period period<br>upper/lower coef upper/lower coef<br>(c) Average Herfindahl (d) Share wealthy OECD<br>.8 0<br>.6<br>−.1<br>.4<br>−.2<br>.2<br>0 −.3<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br>0<br>.2<br>−.1<br>.15<br>−.2 .1<br>−.3 .05<br>−.4 0<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br><!-- End of picture text --> Figure 4: Trends in refugee reach, time effects controlling for source effects, 1987-2017 Note: This figure plots the time effects on the average distance traveled by a refugee, the share of refugees finding themselves in a contiguous country, the average Herfindahl index of refugee shares by source country, and the share of refugees finding themselves in a wealthy OECD country. Throughout, source country effects are netted out.

– where _Outcomest_ is one of the four outcomes reported above log average distance traveled by a refugee, share of refugees going to a contiguous country, the Herfindahl – index of destinations, or share in a high-income OECD country from country _s_ in time period _t_ , and _δt_ and _δs_ are time and source country effects.

Source country effects imply that we are exploiting time variation within a source country over time in how far refugees travel. The coefficients of interest are the time effects _δt_ . The regression is weighted by total refugee stock, to obtain estimates of how outcome variables changed at a refugee, rather than country level. Standard errors are clustered at the source country level.

Panel (a) of Figure 4 plots the time effects for the average distance traveled along with 95 percent confidence intervals. Since the distance traveled is in logs, the coeffi13
cients are interpretable as the percentage increase in the average distance traveled by a refugee in period _t_ relative to the omitted period, which in our case is the first 5 years of data. The upward trend is evident, and the differences with respect to the initial period are statistically significant. In the final 5-year period, the average distance traveled is about 40 percent larger than in the first period. This proportional difference is quite similar in magnitude to the unconditional increase reported in Figure 3. Panel (b) reports the time effects on the share of refugees found in a contiguous country. Since the left-hand side is a share, the time effect point estimates correspond to the difference in that share relative to the initial period. The share of refugees in a contiguous country falls by 16 percentage points after controlling for source effects. Once again, this difference is not far from the unconditional difference. Panels (c) and (d) plot the time effects on the Herfindahl and the share going to high-income OECD countries, respectively. The trends evident in unconditional data are equally strong when controlling for source country effects and total refugee outflow. For all four outcome variables, the differences between the initial and later periods are highly statistically significant.

**Impact vs. diffusion over time** We next address the question of whether the trends documented in Figures 3-4 are due to the initial decision of refugees of where to flee from their homeland, or subsequent movements to third countries. Note that we cannot answer this question definitively without individual-level panel data. In our data, we do not observe the country from which a refugee entered their current host country, and thus cannot tell whether a given refugee in a given host country came from their homeland, or from yet another host country.

Nonetheless, we perform the following exercise. We are working with a set of refugee events defined in Section 2. An event is combination of a source country, a year of onset, and an end year. Thus, we can compute the evolution of all of our outcome variables – – distance, contiguous share, Herfindahl, and share in high-income OECD for each specific event and each year following its onset. We then plot these outcome variables in event time, with year 0 indicating the initial year of the event, up to year 10 of the event.

Figure 5 plots the four outcome variables in event time, for events starting in 4 different sub-periods. The main conclusion from this figure is that the differences across 14
<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>year since crisis onset year since crisis onset<br>crisis started in 1992−1996 crisis started in 1997−2001 crisis started in 1992−1996 crisis started in 1997−2001<br>crisis started in 2008−2012 crisis started in 2013−2017 crisis started in 2008−2012 crisis started in 2013−2017<br>(a) Average distance (b) Share contiguous<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>year since crisis onset year since crisis onset<br>crisis started in 1992−1996 crisis started in 1997−2001 crisis started in 1992−1996 crisis started in 1997−2001<br>crisis started in 2008−2012 crisis started in 2013−2017 crisis started in 2008−2012 crisis started in 2013−2017<br>(c) Average Herfindahl (d) Share wealthy OECD<br>8000 80<br>6000 60<br>4000 40<br>share (%)<br>distance (km)<br>2000 20<br>0 0<br>.8 80<br>.6 60<br>.4 40<br>index<br>share (%)<br>.2 20<br>0 0<br><!-- End of picture text --> Figure 5: Trends in refugee reach in event time Note: This figure plots the four outcome variables in event time, for 4 sub-periods separately.

time in refugee reach are already apparent at the initiation of a refugee crisis. That is, distance traveled by a refugee rises monotonically from earlier to later refugee crises already in years 1 to 3 of a refugee crisis. While the pictures are somewhat noisy, it is not the case that the globalization of refugees trends documented above are due purely, or even primarily, to stronger diffusion of refugees over time.

# **Robustness**

Finally, we assess robustness of the results in a number of dimensions. First, since the beginning of our data, a number of country boundaries have changed. Prominent examples are the unification of Germany, the breakup of the Soviet Union and Yugoslavia, and the independence of South Sudan. To check whether the results are driven by changing country boundaries, Appendix Figure A1 reports the time effects conditional on source effects in a sample of entities where country definitions and boundaries are 15
fixed throughout the sample period. There is virtually no difference in the results.

Second, we control for the total (log) stock of refugees from country _s_ in time period _t_ when estimating equation (1). Controlling for the overall stock of refugees allows for non-linear effects of total outflows on spatial diffusion. For example, refugees might end up more geographically dispersed during relatively larger refugee events if greater refugee numbers create stronger political opposition in receiving countries, limiting the number of individuals in any single country. On the other hand, large flows of refugees might differ from smaller ones in their composition: if the marginal refugee is less mobile, then larger total stocks will coincide with less geographical dispersion. Appendix Table A1 shows the results of estimating (1) controlling for total stocks from _s_ . The coefficients on refugee stocks are consistently negative and significant for three of the four outcomes of interest. However, the magnitude of these coefficients is small, suggesting that the scale of a forced displacement crisis has little bearing on the subsequent geographic dispersion of refugees. Appendix Figure A2 reports the time effects controlling for total stock. There is virtually no detectable difference relative to the baseline.

The third set of robustness checks probes our definition of refugee stocks. Appendix Figure A3 replicates the analysis using all refugee stocks available in the data, without constraining the sample to refugee events. The results are very similar to the baseline. Taking another approach, Appendix Figure A4 instead uses refugee flows. As argued above, without individual-level data, flows cannot be computed precisely. We build flows by taking annual time differences in stocks by source-destination pair. In some instances, stocks fall over time. Since we do not have confidence that a reduction in – stocks represents a return to the home country as opposed to transition to another – host country we set flows to zero whenever the difference in stocks is negative. As evidenced in the figure, the point estimates of the time effects and their statistical significance are quite similar for flows to the baseline.

Fourth, it may be that the destination-specific conditions (such as the global financial crisis) also affect the distance traveled by refugees, or the probability of not going to a contiguous country. To account for this possibility, we net out the time variation in the destination country conditions as follows. In step 1, we project the refugee stocks at the 16
source-destination-year level on source-time, destination-time, and source-destination fixed effects in a gravity-like specification: We estimate this equation by Poisson Pseudo-Maximum Likelihood (Eaton, Kortum and Sotelo 2012), pooling countries and years (and thus including observations with zero bilateral stocks). We then construct a destination-adjusted refugee stock by subtracting the destination-time from the actual stock: Then, we compute the average distance traveled, share of refugees going to a contiguous country, the Herfindahl index of destinations, and share in wealthy OECD countries using this adjusted refugee data set instead of the actual data. Appendix Figure A5 reports the results. Netting out destination-time effects prior to carrying out the analysis leaves the main results virtually unchanged.

# **4 Conclusion**

Our analysis suggests that the assumption underpinning the debate on responsibilitysharing may need to be partly revisited. Countries neighboring a conflict do host a majority of refugees and are hence bearing a disproportionate portion of the responsibility for providing asylum to those who are fleeing from violence and oppression. Yet, the share of refugees who move to further-away destinations, including OECD countries, has been growing over time. In other words, responsibilities are increasingly shared across countries.

As it explores the notion of responsibility-sharing, the challenge for the international community is hence to determine how such trends can be sustained, at a pace which is optimal from a protection perspective, but also taking into account economic and political considerations across all potential refugee-hosting countries.

In parallel, it is important to recognize that the current distribution remains deeply 17
uneven. This is especially problematic as most refugee-producing crises are protracted, implying that the composition of the “main host countries” remains somewhat stable over large periods of time. Sharing the responsibility for hosting refugees also implies, therefore, that increased support is warranted to maintain the current system and the international protection that it provides for those who are subject to persecution and violence.

# **References**

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- **Crisp, Jeff** , “No Solution in Sight : the Problem of Protracted Refugee Situations in Africa,” 2003.

- **Dreher, Axel, Andreas Fuchs, and Sarah Langlotz** , “The effects of foreign aid on refugee flows,” _European Economic Review_ , 2019, _112_ , 127 – 147.

- **Eaton, Jonathan, Samuel S. Kortum, and Sebastian Sotelo** , “International Trade: Linking Micro and Macro,” February 2012. NBER Working Paper 17864.

- **Hathaway, James C. and Alexander Neve** , “Making International Refugee Law Relevant Again: A Proposal for Collectivized and Solution-Oriented Protection,” _Harvard Human Rights Journal_ , 1997, _10_ , 115–211.

- **Schuck, Peter H.** , “Refugee Burden-Sharing: A Modest Proposal,” _Faculty Scholarship Series_ , 1997.

- **Smith, Merrill** , “Warehousing Refugees,” _World Refugee Survey_ , 2004, _38_ , 38–56.

- **Suhrke, Astri** , “Burden-Sharing during Refugee Emergencies: The Logic of Collective versus National Action,” _Journal of Refugee Studies_ , 1998, _11_ (4), 396–415.

- **Temprano Arroyo, Heliodoro** , _Using EU aid to address the root causes of migration and refugee flows_ , Florence: European University Institute, 2019.

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- **UN General Assembly** , “Report of the United Nations High Commissioner for Refugees - Part II: Global Compact on Refugees,” Technical Report, United Nations 2018.

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