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# Immigration, integration, and the informal economy in OECD countries

Oussama Ben Atta, Isabelle Chort, Jean-Noël Senne

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Oussama Ben Atta, Isabelle Chort, Jean-Noël Senne. Immigration, integration, and the informal economy in OECD countries. 2022. hal-03822494v2

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# Immigration, integration, and the informal economy in OECD countries

Oussama Ben Atta, Isabelle Chort, Jean-Noël Senne

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## To cite this version:

Oussama Ben Atta, Isabelle Chort, Jean-Noël Senne. Immigration, integration, and the informal economy in OECD countries. 2025. hal-03822494v3

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https://univ-pau.hal.science/hal-03822494v3

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entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

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destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
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Distributed under a Creative Commons Attribution 4.0 International License
TREE WP No. 11
First version : October 2022
Revised : April 2025

IMMIGRATION, INTEGRATION,
AND THE INFORMAL
ECONOMY IN OECD
COUNTRIES

Oussama BEN ATTA
Isabelle CHORT
Jean-Noël SENNE
Immigration, integration, and the informal economy
in OECD countries*

Oussama Ben Atta†

Isabelle Chort‡

Jean Noël Senne§

October 25, 2022

Abstract

This article assesses the impact of immigrant and asylum seeker inflows on the size of the informal sector in host countries from a macroeconomic perspective. We use two indicators of informality provided by Medina and Schneider (2019) and Elgin and Oztunali (2012) combined with migration data from the OECD International Migration Database and data on asylum seeker flows from the UNHCR for the period 1997-2017. We estimate a first-difference model, instrumenting immigrant and asylum seeker flows by their predicted values derived from the estimation of a pseudo-gravity model. Results suggest that both immigrant and asylum seeker inflows increase the size of the informal sector at destination, but the size of the effect is very small: a one percentage point increase in the stock of immigrants as a share of population leads to an increase of the informal sector as a share of GDP of 0.05-0.06 percentage points. Unsurprisingly, the effect is about four times larger for asylum seeker flows, but remains economically insignificant. We investigate several potential channels, and find that integration policies do matter. We find no impact of imported norms or institutions, but rather that the effect is larger in destination countries with a large informal sector. Finally, we estimate a VAR model and find that the impact of inflows on informality is long-lasting.

*Keywords*: migration, informal economy, asylum seekers, integration policies, shadow economy

JEL classification : F22 ; E26 ; J46 ; K37

<footnote>*We are grateful to Friedrich Schneider and Ceyhun Elgin for providing us with the updated versions of their shadow economy datasets.</footnote>

<footnote>†(1) Université Paris-Saclay, Univ Evry, EPEE, Evry-Courcironnes ; (2) Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne</footnote>

<footnote>‡(1) Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne ; (2) Institut Universitaire de France (IUF), Paris, France ; (3) IZA, Bonn, Germany ; (4) GLO. Email: isabelle.chort@univ-pau.fr</footnote>

<footnote>§Université Paris Saclay, RITM</footnote>
Immigration, integration, and the informal economy in OECD
countries*

Oussama Ben Atta†

Isabelle Chort‡

Jean Noël Senne§

April 10, 2025

Abstract

This article investigates the impact of immigrants and asylum seekers on the size of the informal sector in OECD host countries. We use two indicators of informality combined with immigration data from the OECD International Immigration Database and data on asylum seekers from the UNHCR over 1997-2020. We use a gravity-based instrument to address endogeneity issue. We find that immigrants and asylum seekers contribute to the growth of the informal sector, but the impact of asylum seekers is about six times larger. For both, the impact is mediated by integration policies and access to the formal labour market.

*Keywords* : immigration, informal economy, asylum seekers, integration policies, shadow economy

JEL classification : F22 ; E26 ; K37 ; J46

<footnote>*We thank Olivier Bargain, Simone Bertoli, Ekrame Boubtane, Matthieu Crozet, Jérémie Gignoux, Elise Huillery, Marion Mercier, Eva Moreno-Galbis, Ahmed Tritah, and participants to workshops and conferences at Bordeaux, Bayonne, Saint-Étienne, Paris-Saclay, the Paris School of Economics (ICDE 2023) and Aix-Marseille (LAGV 2023). We are grateful to Friedrich Schneider and Ceyhun Elgin for sharing with us the updated versions of their shadow economy datasets.</footnote>

<footnote>†Université Jean Monnet Saint-Étienne, CNRS, Université Lyon 2, GATE Lyon Saint-Étienne UMR 5824, F-42023, Saint-Étienne, France</footnote>

<footnote>‡(1) Université de Pau et des Pays de l'Adour, E2S UPPA, CNRS, TREE, Bayonne ; (2) Institut Universitaire de France (IUF), Paris, France ; (3) GLO ; (4) IZA, Bonn, Germany. Email: isabelle.chort@univ-pau.fr</footnote>

<footnote>§(1) RITM, Université Paris-Saclay, Sceaux, France ; (2) IRD, UMR LEDa-DIAL, PSL, Université Paris-Dauphine, Paris, France</footnote>
# 1 Introduction

Periodically, inflows of immigrants or asylum seekers receive high media coverage in developed countries. Massive inflows of refugees and immigrants are traditionally fed by conflicts, as recently experienced in Europe in the aftermath of wars in Syria and more recently in Ukraine, but new push-factors are also emerging, as a consequence of climate change. In many host countries, public opinion is sensitive to politically-oriented xenophobic ideologies that exploit fears and lack of information of natives. In anticipation of an expected surge of immigrants or refugees the question of the impact of incoming flows of population on host economies remains highly relevant. Researchers have long shown that far from being a burden for destination countries, immigrants contribute positively to various aspects of host economies. However, a major dimension largely remains in the shadows: very little is known regarding the impact of immigration on the shadow economy. The term here refers to all economic activities that remain unregistered, rather than criminal activity. The lack of studies on the impact of migrant or refugee inflows on the informal sector may be partly explained by the difficulty to measure the informal economy. This is yet a key issue, as it relates to both labor market outcomes and the integration of newcomers, and their contribution to public finance through the issue of fiscal evasion.

We provide in this paper a macroeconomic analysis of the impact of migrant and asylum seeker inflows on informality in OECD countries over a 20 year period (1997-2017). We use two alternative measures of the informal sector, developed by Medina and Schneider (2019) and Elgin and Oztunali (2012), that we combine with migration data from the International Migration Database (IMD) provided by the OECD and asylum seeker flow data gathered by the UNHCR.

We estimate the impact of both immigrants and asylum seekers on the size of the informal sector in destination countries using migrant and asylum seeker flows as a proxy for variations in their stocks and estimate a first-difference model to net out the effect of time-invariant destination-specific factors. Our main empirical model is similar to the approach chosen by Bell et al. (2013) to analyze the impact of immigrants on crime in the U.K.. We address the issue of the potential endogeneity of immigrant and asylum seeker flows by using an instrumental variable approach. We instrument actual flows by predicted flows obtained from the estimation of a gravity based equation using the full dyadic structure of the IMD database. As an alternative strategy, we estimate a panel fixed-effects model on flow data. Last, we combine this analysis with a VAR model approach similar to that of d'Albis et al. (2019) to investigate the dynamics of the response of informality to immigration shocks.
# 1 Introduction

Periodically, inflows of immigrants or asylum seekers receive high media coverage in developed countries. Massive inflows of refugees and immigrants are traditionally fed by conflicts, as recently experienced in Europe in the aftermath of wars in Syria and more recently in Ukraine, but new push-factors are also emerging, as a consequence of climate change. In many host countries, xenophobic ideologies tend to exploit public fears about immigration for electoral purposes, usually based on partial or incorrect information about the impact of these flows on the economy. In anticipation of an expected surge of immigrants or refugees, the question of the impact of incoming flows of population on host economies remains highly relevant. Researchers have long shown that far from being a burden for destination countries, immigrants contribute positively to various aspects of host economies.<fnref n="1" /> However, a major dimension has long remained in the shadows: the impact of immigration on the shadow economy. This is yet a key issue, as it relates to both labour market outcomes and the integration of newcomers, and their contribution to public finance through the issue of fiscal evasion.

We contribute to the literature by providing causal evidence of the impact of both immigrant and asylum seeker flows on the size of the informal economy in host countries. We conduct a cross-country analysis of both the impact of immigrant and asylum seeker inflows on informality, while dealing with the endogenous response of incoming flows to the host country's adjustment of the informal sector. Indeed, although several papers empirically explore the immigration-informality nexus, theoretically modelled by Camacho et al. (2017), they either focus on a single country (Bohn and Owens, 2012; Bosch and Farré, 2010; Ceritoglu et al., 2017; Altındağ et al., 2020; Bracco and Onnis, 2022), which questions the external validity of their estimates, or focus on the subpopulation of refugees, leaving aside endogeneity issues (Berdiev, 2023).<fnref n="2" /> We provide in this paper a cross country-level analysis of the impact of both immigrant and asylum seeker inflows on informality, on a panel of 35 OECD countries over a 24 year period (1997-2020), using the OECD International Migration Database (IMD) and UNHCR data. We resort first-difference panel data estimations and a gravity-based instrument in order to address the endogeneous destination choice of asylum seekers and immigrants. We instrument actual flows by predicted flows obtained from the estimation of a gravity based equation using the full dyadic structure of the IMD and UNHCR data.

Our identification strategy relies on the use of dyadic data which provide information for each country of destination on the nationality of incoming immigrants and asylum seekers. However, such dyadic data are available on a yearly basis for OECD countries only. Going

<footnote n="1">¹Using data on OECD countries, Docquier et al. (2014) find that immigration has a positive effect on the wages of low-educated natives. Aubry et al. (2016) find substantial welfare gains of immigration in OECD countries that are mainly due to market size effects, i.e. an increase in the number of varieties of goods and services available to consumers. Other channels for the positive effects of immigration on destination countries are knowledge diffusion, as documented by Bahar and Rapoport (2018). Using panel VAR methods, d'Albis et al. (2019) find a positive effect of immigration on fiscal balance. Focusing on The Netherlands, United Kingdom and Norway, Zorlu and Hartog (2005) find very small effects of immigration on the wages of native populations.</footnote>

<footnote n="2">²In addition, Reyneri (2003) and Samers (2004) provide insightful discussions of potential channels, but they lack econometric estimations.</footnote>
We find that migrant and asylum seeker inflows both increase the size of the informal sector in the host country GDP, but the magnitude of estimated coefficient is very small. We explore different channels for this impact: the role of integration policies at destination, the quality of institutions in both destination and origin countries, and the characteristics of migrant waves. We find no evidence that the impact of inflows on informality is due to migrants importing institutions and norms. By contrast, our findings suggest that integration policies and institutions at destination have a major role. Our results are driven by destination countries with a larger informal sector, and targeted integration policies reduce the estimated effects. Panel fixed-effects estimations on flow data confirm the small and positive impact of immigration and asylum seeker flows on informality. Impulse-response functions obtained from the VAR model suggest in addition that the impact is long lasting as it remains significant for 10 to 15 periods.

Our results emphasize the role of institutions and integration policies in host countries: the impact of immigration flows is intrinsically linked to the question of the integration of immigrants and asylum seekers and their capacity to enter the formal labor market. Immigrants are more likely to contribute to the growth of the informal sector in countries that pass restrictive integration laws or where the informal sector is already large.

This paper relates to three different strands of the literature. First, it contributes to the literature that investigates the impacts of immigrant flows on destination countries. Recent macroeconomic studies suggest that the impact of immigration is largely positive on different dimensions. Using data on OECD countries, Docquier et al. (2014) find that immigration has a positive effect on the wages of low-educated natives. Aubry et al. (2016) find substantial welfare gains of immigration in OECD countries that are mainly due to market size effects, ie an increase in the number of varieties available to consumers. Other channels for the positive effects of immigration on destination countries are knowledge diffusion, as documented by Bahar and Rapoport (2018). Using panel VAR methods, d'Albis et al. (2019) find a positive effect of immigration on fiscal balance. Our contribution in this article is to explore the impact of immigration on the size of the informal sector with a macroeconomic approach, which has not been studied yet. Even micro-economic studies on this issue are scarce: in the U.S. context, Bohn and Owens (2012) find that immigration is positively associated with informal labor in specific sectors (construction and landscaping). Bosch and Farré (2010) show that immigration in Spain in the 2000s is highly correlated with unregistered unemployment. Altındağ et al. (2020) exploit the massive and sudden wave of refugees from Syria taking refuge in Turkey and find that this inflow generated an increase in the activity of firms in the informal sector.

This paper thus connects to the literature that analyses the determinants and correlates of informality. Most recent macroeconomic studies on this issue use the measures of
beyond correlations to consistently identify the causal effects of immigrants and asylum seekers on the informal sector therefore comes at the cost of focusing on a subset of middle to high income countries. However, although informality is more pervasive in developing countries, it is far from anecdotal in high income countries. According to Elgin et al. (2021) the informal sector amounted to an average of 19 percent of GDP in advanced economies over 1990-2018. We however observe considerable spatial variability in our sample: while the informal sector is below 10% of GDP in Japan, Austria or the U.S., it represents more than 25% of GDP in Italy, Turkey, or Latvia, among others.

Measuring the informal economy is a complex issue, as it cannot be directly observed. Indeed, the notion of informal or shadow economy covers economic activities that are not officially registered (Elgin and Oztunali, 2012).<fnref n="3" /> As recalled by Medina and Schneider (2019), the informal economy should not be confused with illegal activities such as drug trafficking or corruption: the informal economy mainly refers to legal activities that are unrecorded for monetary, regulatory or institutional reasons.<fnref n="4" /> To circumvent methodological issues linked to the choice of one single indicator of the shadow economy (Schneider, 2021), we rely on two widely used measures of the size of the informal sector as a share of GDP, developed by Medina and Schneider (2019) and Elgin and Oztunali (2012).

Our main empirical model is similar to the approach chosen by Bell et al. (2013) to analyze the impact of immigrants on crime in the U.K. We estimate the impact of both immigrants and asylum seekers on the size of the informal sector in destination countries with a first-difference model to net out the effect of time-invariant destination-specific factors and address the non-stationarity of our dependent variables.

We find that migrant and asylum seeker inflows both increase the size of the informal sector in the host country GDP. The effect is 6 to 7 times larger for asylum seeker flows, suggesting that restrictions of access to the formal labour market, faced by asylum seekers in most OECD countries, may at least partly explain the contribution of this subpopulation to the shadow economy. Our heterogeneity analysis emphasizes the major role of integration policies and the quality of institutions at destination in shaping the impact of different types of migration on host countries’ economies. Indeed, our results are driven by host countries with a larger informal sector, and targeted integration policies are found to reduce the estimated effects, in particular for asylum seekers. By contrast, we find no evidence that the impact of inflows on informality is due to migrants importing institutions and norms. The impact of immigration flows therefore appears intrinsically linked to the question of the integration of immigrants and asylum seekers and their capacity to enter the formal labour market.

Our paper relates to the literature that studies the impact of immigrant or refugee inflows on

<footnote n="3">³More precisely, Elgin and Oztunali (2012) choose to use a definition of the informal economy as “economic activities that take place outside the framework of bureaucratic public and private sector establishments.”</footnote>

<footnote n="4">⁴In a connected though distinct field of research, several studies have shown that contrary to popular opinion, inflows of refugees or immigrants do not increase crime in host countries Bell et al. (2013); Nunziata (2015); Leiva et al. (2020); Kayaoğlu (2022); Kırdar et al. (2022); Maghularia and Uebelmesser (2023). But whereas crime is directly observable, informality, by definition, is not. The relatively low number of studies on the impact of migrant or refugee inflows on the informal sector may be partly explained by the difficulty to measure the informal economy.</footnote>
the informal sector developed by Medina and Schneider (2019), and Elgin and Oztunali (2012) who provide consistent yearly series for a large number of countries. Most papers in this area focus on the role of institutions (Dreher et al., 2009; Dreher and Schneider, 2010) or political environment (Elbahnasawy et al., 2016). Teobaldelli and Schneider (2013) question the influence of direct democracy, while Berdiev et al. (2018) link the question of informality to that of economic freedom. With a more direct connection to the topic of this article, Berdiev and Saunoris (2018) show that globalisation, approximated by an index that synthesizes three forms of globalisation - economic, political, and social (Dreher, 2006) - decreases the size of the informal sector, with a more prominent effect of political globalisation. We show in this paper that immigration flows rather tend to increase the size of the informal sector, but this effect is dependent on the level of informality and integration policies in destination countries.

As a third contribution our paper points to the role of immigration policies and migrant integration in that they alter the impact of immigration on destination countries' economic outcomes. Our findings are consistent with previous works on the detrimental effect of administrative delays in the treatment of asylum demands on the integration of refugees (Ukrayinchuk and Havrylchyk, 2020). On a slightly different note, using survey data on 22 European countries Helbling et al. (2020) find little support to the popular assumption that restrictive immigration policies would foster the integration of immigrants. They indeed find that restrictive immigration policies are associated with a better economic integration for specific subgroups of migrants only, whereas restrictive immigration policies are correlated with a lower level of political integration for all groups of migrants. We add to this literature by showing that more restrictive policies tend to reduce the integration of immigrants and asylum seekers into the formal sector.

The remainder of the paper is organized as follows: Section 2 details the empirical strategy, discusses the identification assumptions, and presents the different econometric models that are estimated. Section 3 presents the data. Estimation results are reported and discussed in Section 4. Finally, Section 5 concludes.

# 2 Empirical strategy

## 2.1 Main econometric model

We first intend to estimate the impact of an increase in the stock of migrants or asylum seekers on the size of the informal sector. Our baseline empirical model is very similar to the one used by Bell et al. (2013) who study the impact of immigration on crime in the U.K. We write variations in the size of the informal sector as a linear function of
the informal sector in specific contexts. In the U.S. context, Bohn and Owens (2012) find that immigration is positively associated with informal labour in specific sectors (construction and landscaping). Bosch and Farré (2010) show that immigration in Spain in the 2000s is highly correlated with unregistered unemployment. Ceritoglu et al. (2017) exploit the massive and sudden wave of refugees from Syria taking refuge in Turkey and find evidence of an increased competition for informal jobs. Consistent with this result, in the same context, Altındağ et al. (2020) show that this inflow of refugees generated an increase in the activity of firms in the informal sector. Our paper adds to Berdiev (2023) by expanding the analysis to immigrant and asylum seeker inflows, instead of refugees, and by addressing endogeneity issues through panel data estimations over a long period and an instrumental variable approach.

Second, our paper obviously connects to the literature that analyses the determinants and correlates of informality. Most recent cross-country studies on this issue use the two measures of the informal sector developed by Medina and Schneider (2019), and Elgin and Oztunali (2012) who provide consistent yearly series for a large number of countries. Research in this area focuses on the role of institutions (Dreher et al., 2009; Dreher and Schneider, 2010), trust (D'Hernoncourt and Méon, 2012) or political environment (Elbahnasawy et al., 2016). Teobaldelli and Schneider (2013) question the influence of direct democracy, while Berdiev et al. (2018) link the issue of informality to that of economic freedom. With a more direct connection to the topic of this article, Berdiev and Saunoris (2018) show that globalisation, approximated by an index that synthesizes three forms of globalisation - economic, political, and social (Dreher, 2006) - decreases the size of the informal sector, with a more prominent effect of political globalisation. Complementary analyses by Berdiev et al. (2023) suggest that this effect is driven by OECD countries. We show in this paper that in OECD countries human capital inflows rather tend to increase the size of the informal sector, but this effect is highly dependent on the initial level of informality and integration policies in host countries.

As a third contribution, our paper points to the role of immigration policies and migrant integration in that they shape the impact of immigration on destination countries' economic outcomes. Our findings are consistent with previous works on the detrimental effect of administrative delays in the treatment of asylum applications on the integration of refugees (Ukrayinchuk and Havrylchyk, 2020). On a slightly different note, using survey data on 22 European countries, Helbling et al. (2020) find little support to the popular assumption that restrictive immigration policies would foster the integration of immigrants. They indeed find that restrictive immigration policies are associated with a better economic integration for specific subgroups of migrants only, while being correlated with a lower level of political integration for all groups of migrants. The need for a better integration of asylum seekers in particular, including a facilitated access to the labour market, has long been discussed (Zimmermann, 2017). We add to this literature by providing suggestive evidence that more restrictive policies tend to reduce the integration of immigrants and asylum seekers into the formal labour market and increase their contribution to the shadow economy.

This article is organized as follows: Section 2 discusses the theoretical mechanisms. Section 3
variations in the stock of either immigrants or asylum seekers and a set of controls:

$$ \Delta Informal_{j,t} = \beta \Delta M_{j,t} + \sum_{k} \delta_k \Delta X_{j,t-1}^k + D_t + \epsilon_{j,t} \quad (1) $$

$\Delta Informal_{j,t}$ is the change in the size of the informal sector as a share of the GDP for host country j from year $t-1$ to year $t$. We use two different measures of the informal sector, derived from Medina and Schneider (2019) and Elgin and Oztunali (2012), as detailed in Section 3.2 below. $\Delta M_{j,t}$ is the change in the stock of immigrants or asylum seekers between period $t-1$ and $t$. Following Bell et al. (2013), we proxy changes in stocks from $t-1$ to $t$ by new inflows in $t$.<fnref n="1" /> We additionally control for first-differenced time-varying characteristics of destinations that are determinants of the size of the informal sector (Dreher and Schneider, 2010; Torgler et al., 2010; Dreher et al., 2014; Elbahnasawy et al., 2016; Elbahnasawy, 2021). For all the variables included in our set of controls $X$, we control for the difference between $t-2$ and $t-1$ to limit reverse causality concerns. The vector $X$ includes government expenditures (as a share of GDP) to control for government size. As noted by Berdiev and Saunoris (2018), the effect of government size on the informal economy is ambiguous: larger public expenditures may signal a greater human and financial capacity to limit the size of the informal sector, but a larger government may also mean more administrative complexity that could increase informal activities. Government expenditures may also be correlated with the quality of institutions, which is a key determinant of the size of the informal sector (Dreher et al., 2009; Teobaldelli and Schneider, 2013; Goel and Saunoris, 2014). Second, we account for the impact of international trade by considering the degree of trade openness. We define trade openness as the sum of exports and imports of goods and services as a share of GDP. The association between trade and informality is unclear from both theoretical empirical perspectives (Goldberg and Pavcnik, 2003), but macro evidence suggests that trade liberalization is associated with a growth of the informal sector (Fugazza and Fiess, 2010). Next, we control for investment through physical capital accumulation using gross capital formation as a share of GDP. The informal sector being less capital-intensive than the formal sector, an increase in investment may reduce the relative size of the informal sector. Labor market conditions are captured by the unemployment rate. Finally, we control for inflation, measured by the consumer price index. High inflation rates are expected to increase the relative size of the informal sector, in particular through tax evasion (Bittencourt et al., 2014).

<footnote n="1">¹Note that this approach tends to overestimate the actual change in the stock of migrants or asylum seekers as it does not account for return migration, or change in status after obtaining nationality or refugee protection.</footnote>
presents the data and the methodological challenges raised by the measurement of immigration
flows and the size of the shadow economy. Section 4 details the empirical strategy, discusses
our identification assumptions, and presents our econometric model. Estimation results are
reported and discussed in Section 5. Finally, Section 6 concludes.

## 2 Conceptual framework

Immigrants and asylum seekers may contribute to the informal economy through different channels. For the sake of simplicity, throughout this section the term “immigrants”, when not specified otherwise, will encompass both migrant and asylum seekers.

The existence and what is more the persistence of a sizeable informal sector beside the
formal sector in developed countries has long been overlooked. The term “informal” itself was
coined no earlier than in the 1970s and first applied to urban labour markets in developing
countries (Hart, 1973; Mazumdar, 1976). The informal sector in developed countries was then
considered a remnant of pre-industrialized economies that was bound to disappear thanks to
economic development. The application of dual economy or two-sector models to developed
countries has been a way to account for the survival of the informal sector, although such a
representation underestimate the entanglement of the formal and informal sectors (Moser, 1978;
Nadin and Williams, 2012). Immigration from less developed countries was then perceived as
a natural candidate for explaining the existence, and even the growth, of informal activity in
developed economies. However, as argued by Portes and Sassen-Koob (1987), the relationship
between immigration and informality is more complex than it first appears: while the authors
relate the growth of the informal sector in the U.S. in the 1970s and the early 1980s to an inflow
of immigrant labour, their analysis of Northern Italy evidences that the underground economy
can develop even in the absence of large-scale immigration by relying on domestic labour. Their
comparative analysis thus suggests that immigration increases the size of the informal sector if
the following two conditions are met : (i) in the presence of a demand for cheap labour that
is not met by domestic labour supply, and (ii) with “the acquiescence, if not the support, of
national and local authorities” (Portes and Sassen-Koob, 1987).

Condition (i) is more likely to hold if the income differential is high between origin and
destination countries. All else equal, and in particular once accounting for different levels of
tolerance to the informal sector, we thus expect the impact of immigrants on the informal sector
to be paradoxically greater in developed host countries than in developing ones. Condition (ii)
implies that the contribution of immigrants to the informal economy will vary across countries
and over time, depending on the degree of tolerance of governments for informal activities.

Besides these two conditions, participation of immigrants to the informal labour market
depends on their more or less constrained access to the formal sector. In that respect, different
categories of migrants face dissimilar barriers (Fasani et al., 2021, 2022; Brell et al., 2020). In
many developed countries, asylum seekers are imposed temporary employment bans (see Fasani
et al. (2021)) and are thus denied access to the formal labour market during their application for
refugee status is processed, leaving them with the only employment opportunities offered by the
Although GDP is commonly included as a determinant of the size of the informal sector in the empirical literature, we choose not to include GDP growth in our main specification because many countries at least partially correct official GDP figures to take into account the size of the informal sector (Dreher et al., 2014). As a result, official GDP figures and measures of the informal sector are likely to be spuriously correlated. However this choice to exclude GDP growth from the set of controls does not impact our findings and our results are robust to adding GDP growth from $t-2$ to $t-1$ to our set of controls (see Appendix Table 11). $D_t$ are year fixed-effects and $\epsilon_{j,t}$ is the error term clustered at the country level.<fnref n="2" />

Our choice of a first-difference model is first justified by the stationarity properties or our dependent variables. Ignoring the non-stationarity of time series may lead to spurious regression results. Results from a panel unit root test based on individual Augmented Dickey-Fuller (ADF) regressions (Im et al., 2003) reported in Appendix Table 9 show that our two informal economy measures are non stationary. The same test however concludes that the unit-root hypothesis can be rejected for the first-difference of the two variables. By estimating a first-difference model, we are thus able to address this non stationarity issue.<fnref n="3" />

Second, as argued by Aleksynska and Tritah (2015), based on Wooldridge (2010), the choice of a first-difference model may be justified by the time persistence of migration due to network effects (Pedersen et al., 2008).

In order to assess the causal impact of migrant or asylum seeker inflows on the size of the informal sector, we need to address several identification issues. First, reverse causality issues may emerge if countries with a large and dynamic informal sector are attractive to migrants and asylum seekers. In addition, our results may be biased by the omission of unobserved characteristics of host countries determining both the size of the informal economy and migrant or asylum seeker inflows. Since model 1 is estimated in first-differences, time-invariant factors that are specific to each destination country and that affect the size informal economy and migrant or asylum seeker inflows are differed out. However, the first-difference model does not capture time-varying unobserved confounding factors. We present in the next section the instrumental variable strategy that we use to address potential endogeneity issues.

<footnote n="2">²Given the relatively small size of our sample of countries (34), there may be a concern that our estimated standard errors maybe too small. We thus additionally provide the estimates p-value for our coefficients with a small-sample correction using the Stata `boottest` command</footnote>

<footnote n="3">³While first introduced in studies based on time series, the detection of non-stationarity has become a matter of great importance in panel data analysis since the seminal works of Levin and Lin (1992) and Quah (1994). The advantage of panel data in the detection of unit roots is linked to the increase in the number of observations owing to the double dimension: individual and time. Non-stationarity may lead to spurious results, as it may indicate relationships between variables when they do not exist. By definition, a data series is defined as non-stationary if its statistical properties are time dependent.</footnote>
informal sector. Although employment bans are peculiar to persons applying for refugee status, more restrictive immigration policies tend to similarly increase the contribution of immigrants to the informal sector. States that pass laws restricting legal access to their territory do not necessarily have the capacity or will to enforce them (Massey, 1999). Their primary consequence in many contexts is thus to mechanically increase undocumented migration. Even an increased enforcement effort may result in an unintended surge in the stock of undocumented immigrants, as evidenced by by Massey et al. (2016) in the case of the U.S. By lowering the prospects of a possible re-entry in the U.S., enhanced border enforcement reduced return migration and shifted traditional circular undocumented migration between Mexico and the U.S. into permanent stay in the U.S. Similar mechanisms, that also emphasize the permeability between legal and illegal migration, are theorized by Djajić and Mesnard (2015) with an application to guest workers programs. While their model predict that more repressive enforcement measures targeting illegal aliens expectedly reduce the probability of overstaying, by contrast, stricter regulation in the form of a shorter visa duration encourages the contribution of migration to the informal economy by providing them incentives to overstay their visa.

As Berdiev (2023) summarized, in line with the above discussion, immigrants may theoretically contribute to the growth of the shadow economy either by increasing the supply of labour in the informal sector, or by increasing the demand for specific goods and services produced by the informal sector. An additional explanation could be the adjustment of firms’ production technology due to the presence of migrant workers, implying more complex dynamics such as a change in labour market equilibrium, and possibly an increase in informal labour of native workers themselves (Lewis, 2013; Edo, 2019; Ottaviano et al., 2018). All these mechanisms likely coexist. Unfortunately, the main data that we use provides global estimates over a long period of the size of the informal economy as a share of GDP (see Section 3 for a description) and do not allow us to robustly identify the relative contribution of each channel separately.

In this paper, we first empirically investigate the global impact of immigrants and asylum seekers on the size of the informal sector in OECD destination countries. To explore the underlying mechanisms at play, we complement this analysis by looking more specifically at the impact of immigrants and asylums on the growth of informal employment, using additional data from the ILO on informal employment in OECD countries. While this complementary analysis does not entirely rule out the fact that informal employment might react to a change in the demand for informal goods and services and to the readjustment of the economy to inflows of immigrants (with potential externalities on native workers), it nevertheless provides further evidence on the predominance of labour market effects.

Moreover, the above discussion suggests that the main factors that are likely to explain the contribution of immigration to the informal economy lie in host countries’ institutions and (migration) policies rather than in the characteristics of immigrants. Therefore, to further uncover the channels of impact, we conduct an additional heterogeneity analysis that focuses on the role of the different factors that either amplify or reduce the contribution of immigrants to the growth of the informal sector. In this perspective, we identify four propositions that we
## 2.2 Identification strategy: zero-stage gravity equation

We follow Alesina et al. (2016) and Docquier et al. (2020) and estimate a gravity model to predict the bilateral flows of immigrants and asylum-seekers. This strategy, initially used in the trade literature, has been widely applied to the analysis of migration flows (Felbermayr et al., 2010; Aleksynska and Tritah, 2015; Docquier et al., 2016; Bove and Elia, 2017; Bahar and Rapoport, 2018). We estimate the following zero-stage gravity equation: .

$$M_{i,j,t} = \gamma Z_{i,j,t} + D_i + D_j + D_t + \varepsilon_{i,j,t} \quad (2)$$

$M_{i,j,t}$ alternately represents migrant and asylum-seeker inflows from origin country $i$ to OECD destination country $j$ in year $t$. The vector $Z_{i,j,t}$ includes time-invariant dyadic variables that are traditional determinants of migration: the log of capital-to-capital distance between $i$ and $j$, and binary variables taking value one if $i$ and $j$ are in a colonial relationship or share a common language (if at least 9% of the populations of $i$ and $j$ share a common language).<fnref n="4" /> In addition, we include in the vector $Z$ a time-varying binary variable for conflicts at origin, as well as its interaction with all three dyadic variables. The conflict variable is constructed using the index of civil war combat deaths provided by the Uppsala Conflict Data Program (Davies et al., 2022): our binary variable is equal to one for origin $i$ and year $t$ if more than 25 civil war deaths were registered in country $i$ and civil year $t$. Our identification strategy relies on the reasonable assumption that civil conflicts at origin are independent from economic conditions at destination. The rationale behind the interaction between the conflict variable and the geographic and cultural controls is twofold. Those interaction terms generate additional time-varying regressors that allow us to obtain predictions of yearly flows that do not solely rely on the time-varying conflict variable.<fnref n="5" /> Second, this specification is more flexible in that it allows conflicts at origin to alter the impact of distance, colonial links or common language on bilateral migration flows. $D_j$, $D_i$ and $D_t$ are destination, origin and year fixed effects, and $\varepsilon_{i,j,t}$ is the error term. Standard errors are clustered at the country dyad $i, j$ level. We use the same set of variables to predict both migrant flows and asylum seeker flows. One limitation of this approach for asylum seeker flows is the globalisation of refugee flows, highlighted by Devictor et al. (2021). The interaction terms between conflicts and dyadic variables allow us to partly capture the weaker influence of geographic controls at the end of the period.

<footnote n="4">⁴Geographical and cultural bilateral data are taken from the CEPII database (Head et al., 2010).</footnote>

<footnote n="5">⁵Bahar and Rapoport (2018) generate time-variation in their model by simply interacting time-invariant dyadic determinants of migration with time period dummies but they exploit only two time periods while we use yearly data over 30 years.</footnote>
will test throughout our empirical analysis.<fnref n="5" />

*Proposition 1:* We expect asylum seekers flows to have a greater impact on the size of the informal sector than (legal) migrant flows, as they face more legal constraints to participate to the formal labour market.

*Proposition 2:* We expect stricter internal regulation and a lower degree of integration to increase the contribution of immigrants and asylum seekers to the informal economy.

*Proposition 3:* We expect the contribution of immigrants and asylum seekers to the informal economy to be larger in countries where the degree of tolerance for the informal sector and the resulting demand for informal labour are high.

*Proposition 4:* We expect the contribution of immigrants and asylum seekers to the informal economy to increase with the income differential between origin and host countries.

Finally, an alternative explanation of the impact of immigrants on the informal economy was provided by Borjas (2015), who formulated the hypothesis that migrants may alter institutions at destination by importing the institutions and norms that are prevailing in their country of origin. Although such a hypothesis is not supported by empirical evidence (Bologna Pavlik et al., 2019; Forrester et al., 2019), it would imply, if valid in our setting, that the composition of migration flows in terms of institution strength and tolerance for informality at origin would influence their contribution to the informal sector at destination. We additionally test (in Section 5.3.3) this last hypothesis that emphasizes the influence of immigrants’ origin: the contribution of immigrants to the informal sector would be attributable to the import of norms or preferences regarding participation in the informal sector.

# 3 Data

## 3.1 Migration and asylum seekers

The measurement of immigration flows and the production of data that are comparable across countries raises several methodological challenges. We use in this study the OECD International Migration Database (IMD) that provides annual data on international migrants and asylum seekers flows in OECD countries. The IMD has been extensively used in the migration literature (Ortega and Peri, 2009; Coniglio and Pesce, 2015; Adsera and Pytlikova, 2015; Backhaus et al., 2015). The database contains annual data for international migration and asylum seekers flows to OECD destinations from about 200 origin countries from 1995 onwards.<fnref n="6" /> The

<footnote n="5">⁵More specifically, Propositions 1 to 3 are empirically explored in Sections 5.1, 5.3.1, and 5.3.2. Note that Proposition 4 requires data on non-OECD countries to be examined, which are only partially available and to which we cannot apply our main identification strategy. As a consequence, it is explored in the Appendix (Section A.3).</footnote>

<footnote n="6">⁶We drop years 1995 and 1996 from our sample due to numerous missing values</footnote>
Because of the high proportion of zeroes in our dependent variables, we follow Silva and Tenreyro (2006) and estimate Equation 2 with a Pseudo-Poisson Maximum Likelihood (PPML) estimator. Estimation of equation 2 provides us with the predicted values of yearly bilateral inflows of migrants (resp. asylum seeker) $\hat{M}_{i,j,t}$

We then aggregate predicted bilateral flows across origin countries and construct the yearly predicted value of total inflows of immigrants (or asylum seekers) $\hat{M}_{j,t} = \sum_{i \neq j} \hat{M}_{i,j,t}$ in each OECD country $j$ included in our sample.

We divide this predicted amount of migrants $\hat{M}_{j,t}$ by destination country population to obtain the predicted share $\hat{m}_{j,t}$ of migrant (respectively asylum seekers) inflows in host country's population. $\hat{m}_{j,t}$ is then used as an instrument for the share of migrant (resp. asylum seekers) inflows $m_{j,t}$ in our main model.

Estimation results of Equation 2 for immigration and asylum seeker flows are reported in Appendix Table 10. Appendix Figure 2 illustrates the strong positive correlation between observed migrant (resp. asylum seeker flows) and their prediction based on the pseudo-gravity model. The Kleibergen-Paap F (KPF) statistic for weak identification that is reported in each second-step result table is well above the critical values provided by Stock and Yogo (2002), suggesting that our instrument is not weak.

The identification of a causal effect of migrant or asylum seeker inflows on the size of the informal economy relies on the assumption that our instrument is orthogonal to $\epsilon_{j,t}$. In other words, relative geography variables that are used to predict migrant and asylum seeker flows should have no direct impact on the size of the informal economy. They may affect the informal economy only through their impact on incoming flows, once including controls for potential confounding factors. One such likely confounding factor is international trade, however we control for trade openness in our main model. Moreover all time-invariant destination specific characteristics that could jointly affect countries' attractiveness to immigrants and the size of their informal sector are controlled for by first-differencing all variables in our main model. Finally the effects of shocks common to all destination countries such as the 2008 Global Financial Crisis are captured by year dummies.

## 2.3 Fixed-effects model

To assess the robustness of our results to different specifications, we estimate an alternative version of our baseline model, in which we investigate the impact of a change in either migrant or asylum seeker *inflows*, instead of a change in their *stock*, on the size of
IMD is far from perfect and suffers mainly weaknesses that are intrinsically linked to the chal-
lenge of collecting and compiling national data in order to produce macroeconomic indicators.
International migrants are defined as foreign-born individuals but primary data sources used to
construct the IMD are not similar in all countries: they range from national statistical offices,
the ministry of Justice, population registers, etc. Consequently, the definition of immigrants
is not homogeneous across countries. As noted by Ortega and Peri (2013), this is a particu-
lar concern for cross-country comparisons. However, our empirical strategy fully exploits the
panel dimension of the data, so that the lack of comparability across countries is less of an
issue. We estimate a first-difference model using immigrant flows as proxies of variations in
their stock, which implies that time invariant country characteristics are differenced out, and
that we compare evolutions over time within countries (see Section 4). Second, in most OECD
countries, only migrants holding a residence or work permit are recorded, which implies that
illegal migrants are not well represented in the IMD. This is an obvious limitation, that we
will keep in mind in interpreting our results. We indeed expect illegal migrants to contribute
more than legal migrants to the informal sector, for lack of legal documents that would gave
them access to formal jobs. This suggests that if the two types of flows, legal and illegal, are
positively correlated, our estimate might be a lower bound of the actual effect of immigration
on the informal sector at destination. In order to try to circumvent this limitation, we also use
data on asylum seeker flows by country of origin and destination made available by the OECD
and gathered from records provided by the United Nations High Commissioner for Refugees
(UNHCR). Asylum seekers are defined as applicants for asylum whose request is awaiting de-
cision. In most countries, they have no or restricted right to work until a decision is taken.
Although the IMD also contains data on the stocks of migrants by country of origin, stock
series are incomplete for many countries, which is why we use data on migrant inflows instead.

In spite of those obvious limitations, the main advantage of the IMD database is the avail-
ability of bilateral data on migration flows and asylum seekers for a large set of destination
countries and all possible origins on an annual basis that allows us to study the dynamics of
migration flows at the country level. Our final sample consists of 35 OECD countries for which
data on migrant and asylum seekers flows are available. In particular, our sample includes 10
non-European countries : Australia, Canada, Chile, Israel, Japan, Mexico, New Zealand, South
Korea, Turkey and the United States ; and 25 European countries : Austria, Belgium, Czech
Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Iceland,
Italy, Latvia, Luxembourg, Netherlands, Norway, Spain, Slovakia, Slovenia, Sweden, Switzer-
land, Poland, Portugal and the United Kingdom. Our sample includes major destinations of
international migrants. According to the United Nations (2017) estimates for 2015, 45% of all
international migrants lived in one of the OECD countries that is included in our sample.

Using population data from the World Development Indicators database, we compute for
each destination the share of immigrants and asylum seekers inflows in the total population.
Figure 1 depicts the evolution of migrant and asylum seeker inflows to OECD countries that
are included in our sample, over the period 1997-2020. What is clearly shown is the fairly
the informal sector. We estimate the following panel fixed-effects model:

$$ Informal_{j,t} = \gamma m_{j,t} + \sum_{k} \kappa_k X_{j,t-1}^k + D_j + D_t + \nu_{j,t} \quad (3) $$

The dependent variable is one of the two above described measures of the size of the informal sector as a share of GDP in country j and year t. $m_{j,t}$ are migrant (resp. asylum seekers) inflows in year t as a share of the population in destination country j. $X_{j,t-1}^k$ is the same set of lagged control as in Equation 1, $D_j$ and $D_t$ are destination country and year fixed-effects, and $\nu_{j,t}$ is the error term.

To overcome endogeneity issues, we use the same instrumental variable strategy as described in Section 2.2 above. $m_{j,t}$ is instrumented by its predicted value obtained from the estimation of the pseudo-gravity model (Equation 2).

Country fixed-effects included in Equation 3 allow us to net out the effect of time-invariant destination countries' characteristics, which is achieved through first-differencing in our baseline model. The $\gamma$ coefficient here captures the impact of a change in migrant (resp. asylum seeker) inflows. Estimation results are reported and discussed in Section 4.3.

## 2.4 VAR model

We complement our empirical analysis with a study of the dynamic relations between migrants or asylum seeker inflows and the informal economy. We estimate a panel vector autoregression (VAR) model that is specified as follows:

$$ \begin{aligned} Y_{jt} &= A(L)Y_{jt} + D_j + D_t + \lambda_j t + \eta_{jt} \\ &= \sum_{s=1}^{p} A_s Y_{jt-s} + D_j + D_t + \lambda_{jt} + \eta_{jt} \end{aligned} \quad (4) $$

where $Y_{jt}$ is a vector of K endogenous variables. More specifically in our setting K is equal to four and $Y_{jt}$ is the following system:

$$ Y_{jt} = [\log(1 + m_{jt}), \log(g_{jt}), \log(y_{jt}), \log(i_{jt})] \quad (5) $$

where $m_{jt}$ are migrant (resp. asylum seekers) inflows as a share of population in destination country j, $g_{jt}$ is the per capita government expenditures, $y_{jt}$ is the per capita real GDP and $i_{jt}$ is the per capita informal economy. All variables are in log. L is the lag operator, and $A_s$ with $1 \le s \le p$ are the $(K \times K)$ related matrices of coefficients. $D_j$ are
large increase in migration flows over the past two decades, if we except the sudden drop in 2020 due to the Covid crisis. This upward trend is partly due to a number of events such as military conflicts in Afghanistan and Iraq, the Balkans' ethnic conflicts, the Arab spring in Middle Eastern and North African countries as well as political turmoil in sub-Saharan African and central Asian countries. The share of asylum seekers (on the right scale) peaks in 2015 as a consequence of the Syrian conflict and reaches a maximum of 0.25% of host population. In our sample, the average migration flow as a share of host population is 0.66%, with a lowest value of 0.02% for Mexico and a highest one of about 3% for Luxembourg. For asylum-seekers inflows, the average ratio is 0.09% over the entire period.

<figure><img src="image_3.png" /><figcaption>Figure 1: Immigrants and asylum seekers inflows to OECD countries (as a share of the total population)</figcaption></figure>

Data source: OECD-IMD

## 3.2 Measures of the informal economy

The measurement of the informal economy is particularly challenging, since, by definition, the informal economy encompasses all economic activity that is not officially recorded.

We use in this paper the data series provided by Elgin and Oztunali (2012) and Medina and Schneider (2019) that provide yearly estimates of the size of the informal economy for a large set of developing and developed countries over the last decades. Although they agree on a similar definition of the informal economy, Elgin and Oztunali (2012) and Medina and Schneider (2019) adopt different approaches to approximate its size. Medina and Schneider (2019) builds on Schneider et al. (2010) and use a structural equation model (MIMIC) to estimate the size of the informal sector. The informal sector is modelled as a latent unobservable
destination country fixed-effects, $D_t$ are year fixed effects, and $\lambda_{jt}$ are country-specific time trends. Lastly, $\eta_{jt} = (\eta_{jt}^1, ..., \eta_{jt}^K)$ is the $(K \times 1)$ vector of residuals.

To account for the bias induced by the finite time dimension (Nickell, 1981), we use the bias-corrected fixed effects estimator developed by Hahn and Kuersteiner (2002).<fnref n="6" /> In order to be able to estimate a panel VAR model, we restrict our sample to the subset of countries for which data are available over the entire period. We end up with a sample of $N = 22$ countries and $T = 21$ periods when we focus on migration flows and $N = 26$ and $T = 21$ when we analyze asylum seeker flows. We therefore estimate Equation 4 using the implementable version of Kiviet (1995) biais-corrected fixed effects estimator provided by Hahn and Kuersteiner (2002). Based on the Akaike information criterion (AIC) and Bayesian information criterian (BIC), our panel VAR includes only first-order lags. This leads us to re-write the model presented in Equation 4 as follows:

$$Y_{jt} = AY_{jt-1} + D_j + D_t + \lambda_j t + \epsilon_{jt} \quad (6)$$

Our identification strategy is based on the Cholesky decomposition. In other words, we need to impose a recursive ordering in the VAR structure to identify orthogonal shocks. The Cholesky decomposition relies on the assumption that variables ordered first in the VAR affect other variables contemporaneously, whereas variables that follow can affect those ordered before only with a lag. Consistent with the literature that applies VAR models to immigration issues, we order migration variables first in our recursive identification (d'Albis et al., 2019, 2021). This ordering implies that migration may influence contemporaneous economic outcomes in destination countries but only react to them with a one-year delay. Such a choice may be justified by the fact that migration is a lengthy process, as the decision to migrate is commonly driven by the prevailing economic situation in the destination country during the previous years (Mayda, 2010). An additional rationale supporting our identification scheme is linked to the administrative delays imposed to migration plan. Migration to OECD countries most often requires the acquisition of a visa or a residence permit, which usually entails a lengthy process. Although current economic conditions at destination may affect without delay the decision to migrate, migration plans are likely to be achieved with delay due to administrative and logistic constraints (Czaika and de Haas, 2017). Regarding the remaining variables in the recursive scheme, government expenditures come second, followed by GDP per

<footnote n="6">⁶Other studies that estimate macro panel VAR models rely on the generalized method of moments technique in order to deal with the Nickell bias (see for instance Rousseau and Wachtel (2000) Love and Zicchino (2006), or Acheampong (2018)). However, the GMM estimators were designed for datasets with a large cross-sectional dimension relative to the time dimension. In particular, Hahn and Kuersteiner (2002) conduct Monte Carlo experiments and show that the efficiency of the bias-corrected estimator measured by the root mean squared error (RMSE) often dominates that of the GMM estimator.</footnote>
variable, correlated with a number of observable indicators and caused by a set of factors that include trade openness, unemployment, government size, fiscal freedom, rule of law, control of corruption, and government stability. The so called indicator variables selected by the authors are the share of currency in broad money, labour force participation, and the size of the economy proxied by nighttime light intensity. Medina and Schneider (2019) provide estimates of the size of the informal economy as a share of GDP (scaled from 0 to 100) for 157 countries, that include all destination countries contained in our sample, from 1991 to 2017. The second dataset that we use comes from Elgin and Oztunali (2012) who estimate a micro-founded two-sector (official and informal) dynamic general equilibrium model. They produce estimates of the size of the informal economy in 161 countries over 1950-2017, but estimates covering the recent years (up to 2020) are available in Elgin et al. (2021). Although early contributions in empirical economics related to the informal sector used alternative measures of the informal sector, using in particular electricity consumption (Chong and Gradstein, 2007), recent articles addressing the issue of informality from a macroeconomic perspective use either one or both of the two indicators presented above (Biswas et al., 2012; Gutiérrez-Romero, 2021; Goel and Saunoris, 2014; Elbahnasawy et al., 2016; Berdiev et al., 2020; Elbahnasawy, 2021).

One reassuring point is that although they are constructed based on different methodological grounds these two measures of the informal economy are strongly correlated: the raw correlation coefficient is 0.93 in our sample over 1997-2017. The average size of the informal economy in our sample is around 16% of GDP based on the index by Medina and Schneider (2019) and 18.3% according to data from Elgin and Oztunali (2012), with a large heterogeneity across countries. The estimated size of the informal sector ranges from a minimum of 6% in Switzerland up to 30% in Turkey.

Last, we use data on the share of illegal employment as a share of total employment (from 0 to 100) provided by the International Labour Organisation. Although this dataset is an unbalanced panel covering only 28 countries from our initial sample over the sub-period 2000-2020, we use it in complementary analyses in order to estimate the impact of immigrants and asylum seekers on the informal labour market.

## 3.3 Immigration regulation, integration policies and additional data sources

To the extent that inflows of migrants and asylum seekers impact the size of the informal sector, their relation is likely to depend on the type of integration policies in destination countries. In what follows, we describe the two indices that we use to measure immigration policies, both in terms of regulation and integration, in OECD countries.

Comparing migrant integration policies across time and space is a major empirical challenge (Rayp et al., 2017). In this paper, we rely on data produced by two different projets. We first use the DEMIG POLICY database compiled as part of the Determinants of International Migration (DEMIG) project (De Haas et al., 2015). It provides the description of the direction and magnitude of 6,500 changes in immigration policies in 45 countries up to 2014, forming the
capita, and the informal economy per capita. Note that since our migration (respectively asylum seeker) variable is ordered first in the identification scheme, the sequence of the remaining variables does not matter for the analysis of the response of informality to migration shocks.

# 3 Data

## 3.1 Migration and asylum seekers

We employ data on international migrants and asylum seekers flows from the OECD International Migration Database (IMD). The IMD has been used extensively in the migration literature (Coniglio and Pesce, 2015; Adsera and Pytlikova, 2015). It contains annual data for international migration and asylum seekers flow to OECD destinations from about 200 origin countries from 1995 onwards. International migrants are defined as foreign-born individuals, and flow data are gathered from national population registers or residence and/or work permits. In addition, the OECD IMD contains information on asylum seekers for each country of origin and destination that are gathered from records provided by the United Nations High Commissioner for Refugees (UNHCR). These are commonly first instance applications made at or within asylum country borders, as notified by governments to the UNHCR. The main advantage of the IMD database is the availability of data on migration flows and asylum seekers on an annual basis that allows us to study the dynamics of the migration process.

Our final sample consists of 34 OECD countries for which data on migrant and asylum seekers flows are available. In particular, our sample includes 9 OECD non-European countries: Australia, Canada, Chile, Israel, Japan, Mexico, New Zealand, Turkey and the United States; and 25 European countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Iceland, Italy, Latvia, Luxembourg, Netherlands, Norway, Spain, Slovakia, Slovenia, Sweden, Switzerland, Poland, Portugal and the United Kingdom. Our sample includes major destinations of international migrants. According to the United Nations (2017) estimates for 2015, 45% of all international migrants lived in one of the OECD countries that is included in our sample.

Using population data from the World Development Indicators database, we compute for each destination the share of immigrants/asylum seekers inflows in the total population. Figure 1 depicts the evolution of migrant and asylum seeker inflows to selected OECD countries over the period 1997-2017. What is clearly shown is the fairly large increase in migration flows over the past two decades. This is due to a number of events
largest change-tracking database completed to date<fnref n="7" />. The DEMIG data provide information on changes in the restrictiveness of the legal framework regarding migration policies, categorized in different areas and policy tools. DEMIG further categorizes policy changes depending on their magnitude using a four level scale. We construct two different measures of integration policy changes adapted to the two types of flows that we study: migrants, and asylum seekers. As concerns migrants, we focus on policy changes related to employer liabilities and work visa/permit. For asylum seekers, we focus on policy changes that specifically target "refugees, asylum seekers, and other vulnerable people". For both migrants and asylum seekers we construct two binary variables that take the value one for more and less restrictive policies respectively (no change being the reference category), and limit our analysis to major policy changes that refer to the top level of the above mentioned four-level scale.

We additionally use the IMPIC (Immigration Policies in Comparison) database, which provides information on the migration policies of 33 OECD countries over the period 1980-2018. The IMPIC database is a project of the German Research Foundation set up to provide a measure of immigration regulations. All indices provided in the IMPIC dataset range from 0 (open) to 1 (restrictive). A description of the database is provided by Helbling et al. (2017) and Berger et al. (2024). We follow Fasani et al. (2022) by considering *internal* and *external* regulation policies. Indeed, IMPIC data provide information on the degree of regulation for various policy areas (family reunification, labor migration, asylum and refugees, and co-ethnics) at both internal and external levels. External policies encompass regulation of entry, including conditions (e.g. financial or accommodation requirements) or eligibility (e.g. labour quotas, age limits), while internal policies concern individuals already at destination, and include associated rights (e.g. flexibility of permit) or status security (e.g. work permit validity). We focus in our study on internal and external policies affecting labour migrants and asylum seekers.

As for measures of integration, we use information provided by the Migrant Integration Policy Index (MIPEX).<fnref n="8" /> MIPEX data contain an assessment of migrant integration in eight areas <fnref n="9" /> for 52 countries, including all OECD countries, over the 2007-2019 period.<fnref n="10" /> The index ranges from 0 to 100 with higher values indicating better integration. In our case, the main shortcoming of this index is its limited time span. However, for any given country there is small variation in the MIPEX index over the period. For instance, for France, the index was equal to 58.26 in 2007 and 57.93 in 2019. In Germany, over the same time period, the index increased from 57.07 in 2007 to 58.21 in 2019. We therefore take for each country the average value of the MIPEX over the entire period and create a binary variable equal to one for countries with an average value of the index that is above the sample median.

All the variables used as controls in our main regressions come from the World Bank's World

<footnote n="7">⁷Nevertheless, the DEMIG database does not contain information for Estonia and Latvia that are included in our initial sample. As a result, in the estimates aiming to investigate the heterogeneous effects of migrant inflows depending on policy changes, both the number of countries and periods are reduced.</footnote>

<footnote n="8">⁸See https://www.mipex.eu/</footnote>

<footnote n="9">⁹These areas are labour market mobility, family reunification, education, health, permanent residence, political participation, access to nationality and anti-discrimination.</footnote>

<footnote n="10">¹⁰Data for 2020 are available for four (non-OECD) countries only.</footnote>
such as military conflicts in Afghanistan and Iraq, the Balkans' ethnic conflicts, the Arab spring in Middle Eastern and North African countries as well as political turmoil in sub-Saharan African and central Asian countries. In our sample, the average migration flow as a share of host population is 0.66%, with a lowest value of 0.02% for Mexico and a highest one of about 3% for Luxembourg. For asylum-seekers inflows, the average ratio is 0.09%.

<figure><img src="image_2.png" /><figcaption>Figure 1: Immigrants and asylum seekers inflows to OECD countries</figcaption></figure>

Data source: OECD-IMD

## 3.2 Measures of the informal economy

Measuring the informal economy is a complex issue, as it cannot be directly observed. Indeed, the notion of informal or shadow economy covers economic activities that are not officially registered (Elgin and Oztunali, 2012).<fnref n="7" /> As recalled by Medina and Schneider (2019), the informal economy should not be confused with illegal activities such as drug trafficking or corruption: the informal economy mainly refers to legal activities that are unrecorded for monetary, regulatory or institutional reasons. We use in this paper the data series provided by Elgin and Oztunali (2012) and Medina and Schneider (2019) that provide yearly estimates of the informal economy for a large set of countries over

<footnote n="7">⁷More precisely, Elgin and Oztunali (2012) choose to use a definition of the informal economy as “economic activities that take place outside the framework of bureaucratic public and private sector establishments.”</footnote>
Development Indicators (WDI) database. Additional controls on credit and labour market regulation included in robustness tests come from the Fraser Institute<fnref n="11" />, and the democracy index was obtained from the Polity V database.<fnref n="12" />

# 4 Empirical strategy

## 4.1 Main econometric model

Our goal is to estimate the impact of an increase in the stock of migrants or asylum seekers on the size of the informal sector. Our empirical model is very similar to the one used by Bell et al. (2013) who study the impact of immigration on crime in the U.K. We write variations in the size of the informal sector as a linear function of variations in the stock of either immigrants or asylum seekers and a set of controls:

$$ \Delta Informal_{j,t} = \beta \Delta M_{j,t} + \sum_k \delta_k \Delta X_{j,t-1}^k + D_t + \epsilon_{j,t} \quad (1) $$

$\Delta Informal_{j,t}$ is the change in the size of the informal sector as a share of the GDP for host country j from year $t-1$ to year $t$. We use two different measures of the informal sector, derived from Medina and Schneider (2019) and Elgin and Oztunali (2012), as detailed in Section 3.2 above. $\Delta M_{j,t}$ is the change in the stock of immigrants or asylum seekers between period $t-1$ and $t$. As explained above, although data on bilateral stocks of migrants are also provided by the OECD, the series are less complete and include many missing values. For this reason we use flows, for both immigrants and asylum seekers, as a proxy of changes in stocks from $t-1$ to $t$, following Bell et al. (2013). Table A1, in Appendix, shows a positive correlation between flows and changes in stocks. The use of migration flows as a proxy for stocks may however lead to overestimate the actual change in the stock of migrants or asylum seekers as it does not account for return migration, or change in status after obtaining nationality or refugee protection.<fnref n="13" /> We thus test the robustness of our results to directly using (less complete) stock data on immigrants and reassuringly find similar results (see Section 5.2 below). Since we use inflows as a proxy of changes in stocks, our model is not technically a first-difference model. However it shares with first-difference models a number of desirable characteristics, and in particular is better adapted to the non-stationarity of the dependent variable. Indeed, as discussed in Appendix section A.2, our two measures of the informal economy are non-stationary. We however test the robustness of our results to estimating a fixed-effects model (see Section 5.2 below).

In our main empirical model represented by Equation 1, we estimate the contemporaneous

<footnote n="11">¹¹The data can be downloaded here: https://www.fraserinstitute.org/economic-freedom/dataset</footnote>

<footnote n="12">¹²https://www.systemicpeace.org/polityproject.html</footnote>

<footnote n="13">¹³Note that inflows and changes in migration stocks are positively correlated in case of an increase in the immigrant population, but the correlation is expected to be lower the immigrant population is decreasing. However, such decreases are very rare over the period, except in some specific countries, like Spain, after the 2008 financial crisis.</footnote>
the last decades. Although they agree on a similar definition of the informal economy, Elgin and Oztunali (2012) and Medina and Schneider (2019) adopt different approaches to approximate its size. Medina and Schneider (2019) builds on Schneider et al. (2010) and use a structural equation model (MIMIC) to estimate the size of the informal sector. The informal sector is modelled as a latent unobservable variable, correlated with a number of observable indicators and caused by a set of factors that include trade openness, unemployment, government size, fiscal freedom, rule of law, control of corruption, and government stability. The so called indicator variables selected by the authors are the share of currency in broad money, labor force participation, and the size of the economy proxied by nighttime light intensity. Medina and Schneider (2019) provide estimates of the size of the informal economy as a share of GDP for 158 countries, that include all destination countries contained in our sample, from 1991 to 2015. The second dataset that we use comes from Elgin and Oztunali (2012) who estimate a micro-founded two-sector (official and informal) dynamic general equilibrium model. They produce estimates of the size of the informal economy in 161 countries over 1950-2017.<fnref n="8" /> Although early contributions in empirical economics related to the informal sector used alternative measures of the informal sector, using in particular electricity consumption (Chong and Gradstein, 2007), recent articles addressing the issue of informality from a macroeconomic perspective use either one or both of the two indicators presented above (Biswas et al., 2012; Gutiérrez-Romero, 2021; Goel and Saunoris, 2014; Elbahnasawy et al., 2016; Berdiev et al., 2020; Elbahnasawy, 2021).

One reassuring point is that in spite of their divergent methodological grounds the two measures of the informal economy that we use are strongly correlated: the raw correlation coefficient is 0.93 in our sample. The average size of the informal economy in our sample is 15.6 percent of GDP, with a large heterogeneity across countries. The estimated size of the informal sector ranges from a minimum of 5.1% in Switzerland up to 30% in Turkey.

## 3.3 Integration policies and additional data sources

To the extent that inflows of migrants and asylum seekers impact the size of the informal sector, their relation is likely to depend on the type of integration policies in destination countries. In what follows, we describe the two indices that we use to measure integration policies in OECD countries.

Comparing migrant integration policies across time and space is a major empirical challenge (Rayp et al., 2017). In this paper, we rely on data produced by two different projects. We first use the DEMIG POLICY database compiled as part of the Determinants

<footnote n="8">⁸Elgin and Oztunali (2012) provides data series for 1950-2009 only, but the authors shared with us estimates of the informal economy for 2010-2017.</footnote>
effect of a change in the stock of migrants or asylum seekers on the informal sector. We
indeed expect new-comers to be more likely to contribute to the informal sector upon arrival,
rather than one or two years later.<fnref n="14" /> We additionally control for first-differenced time-varying
characteristics of destination countries that are standard determinants of the size of the informal
sector in the literature (Dreher and Schneider, 2010; Torgler et al., 2010; Dreher et al., 2014;
Elbahnasawy et al., 2016; Elbahnasawy, 2021). For all the variables included in our set of
controls X, we control for the difference between t − 2 and t − 1 to limit reverse causality
concerns. The vector X includes government expenditures (as a share of GDP) to control for
government size. As noted by Berdiev and Saunoris (2018), the effect of government size on
the informal economy is ambiguous: larger public expenditures may signal a greater human
and financial capacity to limit the size of the informal sector, but a larger government may
also mean more administrative complexity that could increase informal activities. Government
expenditures may also be correlated with the quality of institutions, which is a key determinant
of the size of the informal sector (Dreher et al., 2009; Teobaldelli and Schneider, 2013; Goel and
Saunoris, 2014). Second, we account for the impact of international trade by considering the
degree of trade openness. We define trade openness as the sum of exports and imports of goods
and services as a share of GDP. The association between trade and informality is unclear from
both theoretical and empirical perspectives (Goldberg and Pavcnik, 2003), but macro evidence
suggests that trade liberalization is associated with a growth of the informal sector (Fugazza and
Fiess, 2010). We also control for foreign direct investment outflows as a share of GDP, as they
may affect informality and correlate with migrant and asylum seeker inflows. Next, we control
for investment through physical capital accumulation using gross capital formation as a share
of GDP. The informal sector being less capital-intensive than the formal sector, an increase in
investment may reduce the relative size of the informal sector. Labour market conditions are
captured by the unemployment rate.

Although GDP is commonly included as a determinant of the size of the informal sector in
the empirical literature, we choose not to include GDP growth in our main specification because
many countries at least partially correct official GDP figures to take into account the size of
the informal sector (Dreher et al., 2014). As a result, official GDP figures and measures of the
informal sector are likely to be spuriously correlated. We however test the robustness of our
results to the inclusion of a different set of controls that includes GDP per capita, as well as
three indices for credit and labour markets regulation, and democracy, following Dreher and
Schneider (2010) (see Section 5.2). In this alternative specification, the credit index measures
the regulation of the credit market according to three dimensions (i) Bank ownership (ii) Private
sector credit (iii) Interest rate controls. The labour market regulation index accounts for 5 sub-
components : (i) Hiring regulation and minimum wage (ii) Hiring and firing regulation (iii)
Centralized collective bargaining (iv) Hours regulation (v) Mandated cost of worker dismissals.
Both indices range from 0 to 10, higher values indicating lower regulation. The Democracy

<footnote n="14">¹⁴We however estimated the same equation with a one year lag, by replacing inflows in *t* by inflows in *t* − 1.
Results are very similar to those presented in the paper, suggesting that the effects are rather long-lasting - at
least more than one period (estimation tables are available upon request).</footnote>
of International Migration (DEMIG) project (De Haas et al., 2015). It provides the description of the direction and magnitude of 6,500 changes in immigration policies in 45 countries, forming the largest change-tracking database completed to date<fnref n="9" />. The DEMIG data provide information on changes in the restrictiveness of the legal framework regarding migration policies, categorized in different areas and policy tools. DEMIG further categorizes policy changes depending on their magnitude using a four level scale. We construct two different measures of integration policy changes adapted to the two types of flows that we study: migrants, and asylum seekers. As concerns migrants, we focus on policy changes related to employer liabilities and work visa/permit. For asylum seekers, we focus on policy changes that specifically target "refugees, asylum seekers, and other vulnerable people". For both migrants and asylum seekers we construct two binary variables that take the value one for more and less restrictive policies respectively, and limit our analysis to major policy changes that refer to the top level of the above mentioned four-level scale.

Second, we use information provided by the Migrant Integration Policy Index (MIPEX).<fnref n="10" /> MIPEX data contains an assessment of migrant integration in eight areas<fnref n="11" /> for 52 countries, including all OECD countries, over the 2007-2019 period. The index ranges from 0 to 100 with higher values indicating better integration. In our case, the main shortcoming of this index is its limited time span. However, for any given country there is small variation in the MIPEX index over the period. For instance, for France, the index value of 51 is identical for the years 2007 to 2019. In Germany, over the same time period, the index increased from 57 in 2007 to 58 in 2019. We take for each country the average value of the MIPEX over the entire period and create a binary variable equal to one for countries with an average value of the index that is above the sample median.

All the variables used as controls in our regressions come from the World Bank's World Development Indicators (WDI) database.

<footnote n="9">⁹Nevertheless, the DEMIG database does not contain information for Estonia and Latvia that are included in our initial sample. As a result, in the estimates aiming to investigate the heterogeneous effects of migrant inflows depending on policy changes, both the number of countries and periods are reduced.</footnote>

<footnote n="10">¹⁰See https://www.mipex.eu/</footnote>

<footnote n="11">¹¹These areas are labor market mobility, family reunion, education, health, permanent residence, political participation, access to nationality and anti-discrimination.</footnote>
index is an additive eleven-point scale (0-10) with higher values meaning stronger institutions.

$D_t$ are year fixed-effects and $\epsilon_{j,t}$ is a country-time specific error term clustered at the country level.<fnref n="15" />

In order to assess the causal impact of migrant or asylum seeker inflows on the size of the informal sector, we need to address several identification issues. First, reverse causality may emerge if countries with a large and dynamic informal sector are attractive to migrants and asylum seekers. In addition, our results may be biased by the omission of unobserved characteristics of host countries determining both the size of the informal economy and migrant or asylum seeker inflows. Since model 1 is estimated in first-differences, time-invariant factors that are specific to each destination country and that simultaneously affect the size of the informal economy and migrant or asylum seeker inflows are differenced out. However, the first-difference model does not capture time-varying unobserved confounding factors. We present in the next section the instrumental variable strategy that we use to address potential endogeneity issues.

## 4.2 Identification strategy: zero-stage gravity equation

We follow Alesina et al. (2016) and Docquier et al. (2016) and estimate a gravity model to predict the bilateral flows of immigrants and asylum-seekers, in order to use predicted flows as instrumental variables for observed flows. This identification strategy, initially used in the trade literature to instrument flows of goods and services, has been widely applied to the analysis of migration flows (Felbermayr et al., 2010; Aleksynska and Tritah, 2015; Bove and Elia, 2017; Bahar and Rapoport, 2018; Docquier et al., 2020). We estimate the following zero-stage gravity equation:

$$M_{i,j,t} = \gamma Z_{i,j,t} + D_i + D_j + D_t + \epsilon_{i,j,t} \quad (2)$$

$M_{i,j,t}$ alternately represents migrant and asylum-seeker flows from origin country $i$ to OECD destination country $j$ in year $t$. The vector $Z_{i,j,t}$ includes two subsets of variables. First, dyadic variables that are traditional determinants of migration: the log of capital-to-capital distance between countries $i$ and $j$, and binary variables taking the value one if $i$ and $j$ were in a colonial relationship or share a common language.<fnref n="16" /> Those dyadic variable are time-invariant, but we need our independent variables to show time variation within dyads so that we can explain changes in dyadic migration flows from $i$ to $j$. To this end, we estimate three different versions of our model, and show that our second-stage results are similar whatever the source of time variation used in our zero-stage equation. In our preferred specification, we add to the vector $Z$ a binary variable for conflicts at origin, that is time-varying, as well as its interaction with all three dyadic variables. The conflict variable is constructed using the index of civil

<footnote n="15">¹⁵Given the relatively small size of our sample of countries (35), our estimated standard errors may be too small. In our main estimations we thus additionally provide the estimated p-value with a small-sample correction using the Stata *boottest* command.</footnote>

<footnote n="16">¹⁶Geographical and cultural bilateral data are taken from the CEPII database (Head et al., 2010).</footnote>
# 4 Results

## 4.1 Immigration, asylum seekers, and the informal economy

We present estimation results of equation 1 in Table 1. The dependent variable is alternatively the first-difference of the size of the informal sector as a share of GDP provided by Medina and Schneider (2019) (in columns (1) and (3)), and Elgin and Oztunali (2012) (in columns (2) and (4)). We explore separately the impact of immigration and asylum seekers inflows considered as a proxy for first-differenced stocks of migrants and asylum seekers, respectively. Our equation includes year fixed-effects, and a set of first-differenced country level controls measured as a share of country GDP, namely government expenditures, trade openness, gross capital accumulation, as well as inflation and unemployment rates.

The main explanatory variable is immigration flows as a share of total population at destination in columns (1) and (2) and asylum seekers flows as a share of population in columns (3) and (4). Immigration and asylum seeker flows are instrumented by predicted flows resulting from the estimation of a gravity model, as detailed in Section 2.2.

We find positive and significant coefficients on immigrant and asylum seeker inflows, whatever informality measure we use as dependent variable. These findings suggest that an increase in the stock of either migrants or asylum seekers increases the share of the informal sector in destination country's GDP. Coefficients on asylum seeker flows are about four times larger than those on immigration flows. This result is not surprising given labor market restrictions imposed on asylum seekers in host countries (Fasani et al., 2021, 2022; Brell et al., 2020). However, although the coefficients are all significant, estimated effects are very small: a one percentage point increase in the stock of immigrants as a share of population leads to an increase of the informal sector as a share of GDP of 0.05 to 0.06 percentage points, depending on the index that is used. As a reference point, in 2016 the stock of foreign-borns amounted to 11.9% of the population in France, 12.7% in Spain and 13.7% in the U.K.<fnref n="12" /> A one percentage point increase in immigrant flow as a share of the population would represent a 8.4% increase in the stock of foreigners in France, a 7.8% increase in Spain, and a 7.3 increase in the U.K. The informal sector, as estimated by Medina and Schneider (2019) represents 12.5% of GDP for France over 1997-2017, 21.4% for Spain, and 10.1% in the U.K. Therefore in all three cases, a large increase in the stock of migrants would result in an economically negligible expansion of the informal sector.

Larger estimated coefficients on the asylum seeker flow variable suggest that asylum

<footnote n="12">¹²The same figures for foreigners are 7.0%, 9.5% and 9.0% respectively.</footnote>
war combat deaths provided by the Uppsala Conflict Data Program (Davies et al., 2022): our binary variable is equal to one for origin i and year t if more than 25 civil war deaths were registered in country i and civil year t. Our identification strategy relies on the assumption that civil conflicts at origin are independent from economic conditions at destination. The rationale behind the interaction between the conflict variable and the geographic and cultural controls is twofold. Those interaction terms generate additional time-varying regressors that allow us to obtain predictions of yearly bilateral flows, and it allows conflicts at origin to alter the impact of distance, colonial links or common language on bilateral migration flows, and vice versa. Second, instead of conflict at origin, we interact dyadic regressors with natural disasters at origin. Again, the identifying assumption is that natural disasters at origin only affect economic conditions at destination through bilateral migration. Our indicator of natural disaster is a binary variable equal to one when origin country is affected by any natural disaster, according to the EM-DAT (Emergency and Disaster database), from the Centre for Research on the Epidemiology of Disasters. Last, we follow the approach chosen by Bahar and Rapoport (2018), and generate time-variation by simply interacting time-invariant dyadic determinants of migration with year dummies. All three alternatives provide very similar second-stage results, as discussed in Section 5.2.

Variables $D_j$, $D_i$ and $D_t$ are destination, origin and year fixed effects, and $\varepsilon_{i,j,t}$ is the dyad-time specific error term. Standard errors are clustered at the country dyad i, j level. Including a full set of fixed-effects in the zero-stage equation prevents weak instrument issues as they are necessary to generate precise estimates of the set of $\gamma$ coefficients (Bove and Elia, 2017). However, we do not account for destination, origin, and year fixed-effects in the prediction of bilateral flows to avoid inflating the correlation between predicted and actual flows and reintroducing endogeneity in the generated instrument (Bahar and Rapoport, 2018). Note that we use the same set of variables to predict both migrant flows and asylum seeker flows. One limitation of this approach for asylum seeker flows is the phenomenon of globalization of refugee flows, highlighted by Devictor et al. (2021). However, the interaction terms between conflicts and dyadic variables allow us to partly capture the weaker influence of geographic and cultural factors at the end of the period.

Because of the high proportion of zeroes in our dependent variables, we follow Silva and Tenreyro (2006) and estimate Equation 2 with a Pseudo-Poisson Maximum Likelihood (PPML) estimator. Estimation of equation 2 provides us with the predicted values of yearly bilateral flows of migrants (resp. asylum seeker) $\hat{M}_{i,j,t}$. Zero-stage results are reported in Appendix Table A2. As expected, we find a negative impact of bilateral distance, a positive impact of common language and colonial relationship on bilateral immigrant flows, while war in origin country tends to limit the positive effect of common language. As for asylum seekers flows, only the coefficient on colonial relationship is not significant, though positive. By contrast, and unsurprisingly, war in origin countries increase asylum seekers flows, and the occurrence of war magnifies the effect of distance whereas it reduces the positive effect of common language.

We then aggregate predicted bilateral flows across origin countries and construct the yearly
seekers have a greater impact on the informal sector than migrants, but this impact is still very limited. As an example, a one percentage point increase in the stock of asylum seekers as a share of the population would be equivalent to four times the mean yearly value of asylum seeker flows (0.25%) observed for France over 1997-2017.

<table><caption>Table 1: Immigrant/Asylum seekers flows and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.064***<br>(0.018)</td><td>0.048***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.262**<br>(0.120)</td><td>0.242***<br>(0.067)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr><tr><td>F statistic KPW</td><td>9,851.95</td><td>9,851.95</td><td>404.82</td><td>404.82</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

## 4.2 Channels and interpretations

### 4.2.1 Integration policies

Table 2 analyzes the role played by immigration-related policies at destination. We interact migrant and asylum seeker flow variables with two binary variables that capture positive and negative changes in the degree of restrictiveness of immigration policies. We construct these two variables using the classification and coding of host countries' policies provided by the DEMIG project (see Section 3.3 above). As regards migrant flows (col. (1) and (2)), we focus on policy changes defined as major in the DEMIG database, in areas that are expected to have a direct impact on migrants' employment and labor market insertion and that correspond to the employer liabilities and work visa/permit categories in the DEMIG classification. For asylum seeker flows, we construct different binary variables based on major policy changes that apply specifically to asylum seekers and refugees. The coefficient on the non interacted immigration or asylum seekers flow share represents the impact of inflows in the absence of change in relevant policies at destination.
predicted value of total inflows of immigrants (or asylum seekers) $\hat{M}_{j,t} = \sum_{i \neq j} \hat{M}_{i,j,t}$ for each year $t$ and OECD destination country $j$ included in our sample. We divide predicted migrant inflows $\hat{M}_{j,t}$ by the destination country population to obtain the predicted share $\hat{m}_{j,t}$ of migrant (respectively asylum seekers) inflows in host country $j$'s population. $\hat{m}_{j,t}$ is then used as an instrument for the observed migrant (resp. asylum seekers) inflows as a share of the population $m_{j,t}$ in our main model.

Appendix Figure A1 illustrates the strong positive correlation between observed migrant (resp. asylum seeker) flows and their predictions based on our pseudo-gravity model. The Kleibergen-Paap-Wald (KPW) F-statistics for weak identification that are reported in each second-step result table are well above the critical values provided by Stock and Yogo (2002), suggesting that our different sets of instruments are not weak.<fnref n="17" />

The identification of a causal effect of migrant or asylum seeker inflows on the size of the informal economy relies on the assumption that our instrument is orthogonal to $\epsilon_{j,t}$. In other words, relative geography variables that are used to predict migrant and asylum seeker flows should have no direct impact on the size of the informal economy once potential confounding factors are controlled for. It may be argued that conflict or disasters at origin could have a direct impact on informality at destination, in particular if origin and host countries are neighbours. However, our set of destinations only includes OECD countries, which makes this direct channel less probable. One likely confounding factor is international trade, however we control for trade openness in our second-stage regressions. Another is international investment: conflicts at origin may affect investments at destination, with a potential impact on informality. We thus include FDI outflows in our second-stage model. Moreover all time-invariant destination specific characteristics that could jointly affect countries' attractiveness to immigrants and the size of their informal sector are controlled for by first-differencing all variables in our main model. Finally the effects of shocks common to all destination countries such as the 2008 Global Financial Crisis are captured by year dummies.

# 5 Results

## 5.1 Immigration, asylum seekers, and the informal economy

We present estimation results of equation 1 in Table 1. The dependent variable is alternatively the variation in the size of the informal sector as a share of GDP provided by Medina and Schneider (2019) (in columns (1) and (3)), and Elgin and Oztunali (2012) (in columns (2) and (4)). We explore separately the impact of immigration and asylum seekers inflows used as proxies for first-differenced stocks of migrants and asylum seekers, respectively. Our equation includes year fixed-effects, and a set of lagged first-differenced destination country level controls measured as a share of country GDP, namely government expenditures, trade openness, gross

<footnote n="17">¹⁷Note that the especially high-values obtained for the F-statistics in our main regressions results are due to the fact that we are estimating a first-difference model. The first-stage F-statistics values in the fixed-effects version of our model, estimated in the robustness check section, vary from 24 to 74 (see Appendix Table A5).</footnote>
<table><caption>Table 2: Immigrants/Asylum seekers flows, immigration policies and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&O index</th><th>Δ M&S index</th><th>Δ E&O index</th></tr></thead><tbody><tr><td>Immigration flow share t-1</td><td>0.069**<br>(0.031)</td><td>0.063***<br>(0.024)</td><td></td><td></td></tr><tr><td>Immigration flow share in t-1 x more restrictive policy in t-1</td><td>0.034<br>(0.062)</td><td>0.088***<br>(0.033)</td><td></td><td></td></tr><tr><td>Immigration flow share in t-1 x less restrictive policy in t-1</td><td>-0.024*<br>(0.013)</td><td>-0.005<br>(0.008)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t-1</td><td></td><td></td><td>0.149<br>(0.190)</td><td>0.465***<br>(0.113)</td></tr><tr><td>Asylum seekers flow share in t-1 x more restrictive policy in t-1</td><td></td><td></td><td>0.642*<br>(0.330)</td><td>0.390<br>(0.424)</td></tr><tr><td>Asylum seekers flow share in t-1 x less restrictive policy in t-1</td><td></td><td></td><td>0.112<br>(0.178)</td><td>-0.187*<br>(0.110)</td></tr><tr><td>More restrictive policy in t-1</td><td>-0.041<br>(0.043)</td><td>-0.014<br>(0.027)</td><td>-0.049<br>(0.051)</td><td>-0.030<br>(0.070)</td></tr><tr><td>Less restrictive policy in t-1</td><td>0.031<br>(0.022)</td><td>-0.010<br>(0.014)</td><td>-0.008<br>(0.038)</td><td>0.025<br>(0.026)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>466</td><td>497</td><td>497</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

Results from Table 2 suggest that policy changes in host countries influence the integration of asylum seekers and migrants and their impact on the informal sector. Although results slightly differ depending on the measure of the informal sector, estimation results consistently suggest that the impact of inflows on the size of the informal sector is either magnified by more restrictive policies (in col. (2) and (3)), or reduced by less restrictive policies (col. (1) and (4)).

We explore further the effect of integration policies in Table 3 by using the migrant integration policy index (MIPEX). We interact our flow variables with a binary indicator equal to one for countries with an average value of the MIPEX over 2007-2017 above the sample median. As discussed above, the MIPEX varies little over time. Index mean values over 2007-2017 may thus be extrapolated over the entire period. For this reason, and in order to avoid halving our sample size, we estimate our baseline model augmented with this interaction term over the whole period (1997-2017). The MIPEX is coded on a 0-100 scale, and the mean value of the index is 45 for countries below the median, and 68 for countries above the median.<fnref n="13" /> Results unambiguously show that the positive impact of migrant and asylum seekers inflows on the size of the informal sector is driven by countries that perform poorly in terms of migrant integration. In this group of countries, that include countries as diverse as Greece, Japan, Poland or Denmark, a one percentage point increase in migrant and asylum seeker inflows increases the size of the informal sector as a share of the GDP by 0.13-0.15 and 0.33-0.41 percentage points respectively. In

<footnote n="13">¹³MIPEX values for countries in our sample are reported in Table 12.</footnote>
capital accumulation, FDI, and unemployment rates. The full set of estimated coefficients, including control variables, is reported in Appendix Table A3. The main explanatory variables are immigration inflows as a share of total population at destination in columns (1) and (2) and asylum seekers flows as a share of population in columns (3) and (4). Immigration and asylum seeker inflows are instrumented by predicted flows resulting from the estimation of a gravity model, as detailed in Section 2.2.

We find positive and significant coefficients on immigrant and asylum seeker inflows, what-
ever the informality measure we use as a dependent variable. These findings suggest that an
increase in the stock of either migrants or asylum seekers increases on average the share of the
informal sector in destination countries’ GDP. The coefficients on asylum seeker flows are about
six to seven times larger than those on immigration flows. As discussed in Section 2 and sum-
marized in Proposition 1, this result is not surprising given labour market restrictions imposed
on asylum seekers in host countries (Fasani et al., 2021, 2022; Brell et al., 2020). However,
although the coefficients are all significant, the magnitude of estimated effects is small: a one
percentage point increase in the stock of immigrants as a share of population leads to an increase
of the informal sector as a share of GDP of 0.05 to 0.06 percentage points, depending on the
index that is used. As a reference point, in 2016 the stock of foreign-borns amounted to 11.9%
of the population in France, 12.7% in Spain and 13.7% in the U.K.<fnref n="18" /> A one percentage point
increase in immigrant flow as a share of the population would therefore represent a large (and
implausible) 8.4% increase in the stock of foreigners in France, a 7.8% increase in Spain, and a
7.3% increase in the U.K. The informal sector, as estimated by Medina and Schneider (2019),
represents on average 12.5% of GDP for France over the period 1997-2017, 21.4% for Spain, and
10.1% for the U.K. Therefore in all three cases, a large increase in the stock of migrants would
result in an economically negligible expansion of the informal sector.

Larger estimated coefficients on the asylum seeker inflow variable suggest that asylum seekers have a greater impact on the informal sector than migrants, but this impact is still limited in terms of economic significance. As an example, a one percentage point increase in the stock of asylum seekers as a share of the population would cause a 0.32 to 0.46 percentage points increase in the informal sector as a share of GDP. However, a one percentage point increase in the stock of asylum seekers is incredibly high, as it represents approximately an increase four times as high as the mean yearly value of asylum seeker inflows (0.25%) observed for a country like France over the period 1997-2017.

<footnote n="18"><sup>18</sup>The same figures for foreigners are 7.0%, 9.5% and 9.0% respectively.</footnote>
the group of countries with values of the integration index above the sample median, the impact of migrant inflows is 5 to 6 times smaller (between 0.02 and 0.03 percentage point depending on the index) and less significant, as indicated by the p-value reported at the bottom of the table. For asylum seeker, a one percentage point increase in the stock of asylum seekers, as a share of host population is associated with a 0.33 to 0.41 percentage point increase in the informal sector in countries characterized by an integration index below the median, whereas it has no significant effect on informality in the group of countries with an integration index above the median.

<table><caption>Table 3: Immigrant/Asylum seekers flows, host countries integration index, and the informal sector first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th></th><th></th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th></th><th></th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>0.145***<br>(0.037)</td><td>0.135***<br>(0.024)</td><td></td><td></td></tr><tr><td>Immigration flow share <i>t</i> × Integration above median</td><td>-0.117***<br>(0.041)</td><td>-0.116***<br>(0.027)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i></td><td></td><td></td><td>0.329**<br>(0.162)</td><td>0.408***<br>(0.089)</td></tr><tr><td>Asylum seekers flow share <i>t</i> × Integration above median</td><td></td><td></td><td>-0.155<br>(0.213)</td><td>-0.321***<br>(0.115)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr><tr><td>Sum coeff.</td><td>0.028</td><td>0.019</td><td>0.174</td><td>0.087</td></tr><tr><td>P-value</td><td>0.071</td><td>0.042</td><td>0.255</td><td>0.154</td></tr><tr><td>F statistic KPW</td><td>3,214.49</td><td>3,214.49</td><td>41.64</td><td>41.64</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

### 4.2.2 Quality of institutions

**Informality at origin** The transfers of norms by immigrants from destination back to their origin country is well documented in the literature (Docquier et al., 2020; Spilim-bergo, 2009; Valette, 2018). The reverse may also be true, although studies that investigate the potential import of corruption (Bologna Pavlik et al., 2019) or terrorism (Forrester et al., 2019) by immigrants at destination find no empirical evidence of such transfers. In Table 4 we thus explore another possible channel for the impact of migrant and asylum seekers inflows on informality related to such transfers. Immigrants from countries with a high level of informality would import this informality at destination. To test this channel, we include in our model an additional variable reflecting the change in a composite informality index that reflects informality at origin for each country of origin weighted by the share of migrants from this country. Formally, our index writes:

$$ \Delta \text{M&S informality index at origin}_{j,t} = \sum_{i} I_{i,t}^{\text{M&S}} \frac{Y_{ij,t}}{Y_{j,t}} - \sum_{i} I_{i,t-1}^{\text{M&S}} \frac{Y_{ij,t-1}}{Y_{j,t-1}} $$
<table><caption>Table 1: Immigrant/Asylum seekers flows and informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.066***<br>(0.019)</td><td>0.049***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.456***<br>(0.171)</td><td>0.319***<br>(0.112)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>8,664.00</td><td>8,331.61</td><td>449.99</td><td>667.26</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

## 5.2 Robustness tests

We test the robustness of our results to changes in the specification of our empirical model and to various sets of control variables. First, as mentioned in Section 4.1, our results are robust to using stock variables that are directly available for immigrants only. As discussed above, we choose to use flows as proxies for changes in stocks for both immigrants and asylum seeker flows in our main specification, because immigrant stock data contain numerous missing values, and stock data are not available for asylum seekers. Results from the first-difference model when using first-differenced immigrant stocks as our variable of interest are reported in Appendix Table A4 and are consistent with our main results. We find for both dependent variables a positive and significant impact of a change in stocks on the size of the informal sector.

Second, the rationale for using a first-difference rather than a fixed-effect model is rooted in the non-stationarity of our dependent variables (see also Appendix Section A.2 for a more detailed discussion of this issue). We however test the sensitivity of our findings to estimating a fixed-effect model, using immigrant stocks as our main variable of interest. Results are reported in Appendix Table A5. Consistent with our main results, we find a positive and significant impact of immigrant stock change on the shadow economy at destination, although estimated coefficients are larger in this specification. The difference in the size of the estimated coefficients can be explained in part by the fact that the fixed-effects model does not address non-stationarity issues.

Third, as mentioned in Section 4.1, we show that our second-stage results are robust to different specifications of our zero-stage equation. Besides our preferred specification, in which we interact the dyadic determinants of bilateral flows with a dummy for conflicts, we estimate alternative versions of the zero-stage equation in which we interact dyadic variables with a dummy for natural disasters, or with both conflicts and natural disasters, and last, by interacting dyadic variables with year dummies (following Bahar and Rapoport (2018)). The second-stage estimation results for these four different versions of our zero-stage equations are reported in Table A6, in Appendix. Whatever the specification of the zero-stage equation, second-stage estimation results are very similar.

In a fourth set of robustness tests, we address the issue of potentially endogenous control
with $I_{i,t}^{M&S}$ the index of informality from Medina and Schneider (2019) origin country $i$ for year $t$, $Y_{ij,t}$ migrant or asylum seeker flows from country $i$ to destination $j$ in $t$, and $Y_{j,t}$ total migrant or asylum seeker inflows to destination $j$ for year $t$.

After controlling for the change in the size of the immigrant or asylum seeker stock proxied by new inflows, we find that a change in the potentially imported informality has no impact on informality at destination, the coefficient on the informality index at origin being non significantly different from zero in all four regressions. This finding suggests that institutions or norms at origin are unlikely to explain the impact of migrants and asylum seekers on informality at destination.

<table><caption>Table 4: Asylum seekers flows, informality at origin, and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&O index</th><th>Δ M&S index</th><th>Δ E&O index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.059***<br>(0.017)</td><td>0.046***<br>(0.015)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.254**<br>(0.111)</td><td>0.206***<br>(0.064)</td></tr><tr><td>Δ M&S informality index at origin</td><td>0.030<br>(0.027)</td><td>-0.002<br>(0.004)</td><td>0.031<br>(0.029)</td><td>0.002<br>(0.004)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>569</td><td>569</td><td>548</td><td>548</td></tr><tr><td>F statistic KPW</td><td>9,275.27</td><td>9,275.27</td><td>378.84</td><td>378.84</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

**Informality at destination** We explore in this section the role played by institutions at destination. The quality of institutions is a major determinant of the size of the informal sector (Friedman et al., 2000; Dreher and Schneider, 2010; Dreher et al., 2009; Teobaldelli, 2011; Goel and Saunoris, 2014). As such, it is likely to affect newcomers' integration at destination and their participation to the informal sector. We test the heterogeneous impact of migrant and asylum seeker inflows on informality depending on the quality of host country's institutions by interacting our flow variables with a binary variable that equals one for countries with an average level of informality over the 1997-2017 period that is above the sample median. Results are reported in Table 5, and suggest that the impact of migrant and asylum seeker flows on informality is driven by destinations where the informal sector is the largest. In the case of migrant flows, results differ depending on the informality indicator that is chosen as a dependent variable: while the coefficient on the interaction term is not significantly different from zero in column (1) for the informality indicator from Medina and Schneider (2019), it is positive and significant in column (2). As for asylum seeker flows, the coefficient on the interaction is positive and very close in size in the two specifications, but it is significant at conventional
variables. Indeed, some of the controls that we include in our main specification, although they
are commonly used in the literature as determinants of the informal sector, may be considered
endogenous because they are used by Medina and Schneider (2019) in the construction of
their informality measure. We thus estimate an alternative model with a more parsimonious
and arguably more exogenous set of controls, that however includes GDP, following Dreher
and Schneider (2010). Results are reported in Appendix Table A7). Consistent with our main
results, estimated coefficients on immigrant and asylum seekers flows are positive and significant
whatever the informality measure that we use, although their magnitude is smaller than in our
main specification, in particular when the index of Elgin and Oztunali (2012) is involved.

Last, we report non-instrumented results in Appendix Table A8. Estimated coefficients on both flow variables are positive and significant in all regressions although they are smaller in size, especially for asylum seeker flows.

**5.3 Heterogeneity analysis and underlying mechanisms**

**5.3.1 Immigration and integration policies**

Table 2 analyzes the role played by immigration-related policies at destination. We interact migrant and asylum seeker flow variables with two binary variables that capture positive and negative changes in the degree of restrictiveness of immigration policies. We construct these two variables using the classification and coding of host countries’ policies provided by the DEMIG project (see Section 3.3 above). Since DEMIG data are available only up to 2014, the period of analysis is restricted to 1997-2014. As regards migrant flows (col. (1) and (2)), we focus on policy changes defined as major changes in the DEMIG database, in areas that are expected to have a direct impact on migrants’ employment and labour market insertion and that correspond to the employer liabilities and work visa/permit categories in the DEMIG classification. For asylum seeker flows, we focus on major policy changes that apply specifically to asylum seekers and refugees. The coefficient on the non interacted immigration or asylum seekers flow share represents the impact of inflows in the absence of change (the references category) in relevant policies at destination.
levels only in column (4).

<table><caption>Table 5: Immigrant/Asylum seekers flows, informality at destination, and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&O index</th><th>Δ M&S index</th><th>Δ E&O index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.037***<br>(0.010)</td><td>0.016<br>(0.011)</td><td></td><td></td></tr><tr><td>Immigr. t x Inf above median</td><td>-0.014<br>(0.044)</td><td>0.070**<br>(0.032)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>-0.023<br>(0.125)</td><td>0.024<br>(0.053)</td></tr><tr><td>Asyl. t x Inf above median</td><td></td><td></td><td>0.384<br>(0.288)</td><td>0.369***<br>(0.123)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr><tr><td>Sum coeff.</td><td>0.023</td><td>0.086</td><td>0.361</td><td>0.392</td></tr><tr><td>P-value</td><td>0.583</td><td>0.003</td><td>0.168</td><td>0.000</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

### 4.2.3 Characteristics of migrant waves

Table 6 investigates the effects of the characteristics of migrant waves, and in particular their concentration in terms of origins. More concentration, or less diversity, may give rise to ethnic networks (Dagnelie et al., 2019) and enclaves that could contribute to the growth of the informal sector (Awaworyi Churchill and Danquah, 2022; Lassen, 2007). We first test this assumption by exploring the heterogeneity in the impact of migrants and asylum seeker flows on informality in countries depending on the average origin composition of flows that they receive. We compute the Herfindahl fractionalization index of flows in terms of origin for each destination and year, referred to as diversity in Table 6, and define a binary variable equal to one for countries with an average value of the fractionalization index over the observation period that is above the sample median. Results are reported in Table 6: in all columns, the coefficient on the interaction between the flow variable and our dummy for flows above median in terms of fractionalization is negative, significant, and the same size as the coefficient on the flow variable. These results suggest that inflows of immigrants and asylum seekers increase the size of the informal sector only in countries hosting migrants and asylum seekers flows that are less diverse in terms of origins. However this model does not allow us to separate the composition effect (more or less diverse in terms of origin) from the size effect of flows.

To overcome this issue we estimate an alternative model in which we include the first-differenced Herfindahl fractionalization index of flows in terms of origin. Results reported in Table 7 suggest that once controlling for variations in the stock or immigrants or asylum seekers, an increase in the diversity index may be associated with a larger informal sector.
<table><caption>Table 2: Immigrants/Asylum seekers flows, host countries' immigration policies and the informal sector, first-difference model (IV) (1997-2014)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.106**<br>(0.042)</td><td>0.067**<br>(0.026)</td><td></td><td></td></tr><tr><td>Immigration flow share in t × more restrictive policy in t</td><td>0.037<br>(0.092)</td><td>0.051<br>(0.040)</td><td></td><td></td></tr><tr><td>Immigration flow share in t × less restrictive policy in t</td><td>-0.027<br>(0.017)</td><td>-0.010<br>(0.010)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.674**<br>(0.295)</td><td>0.592***<br>(0.225)</td></tr><tr><td>Asylum seekers flow share in t × more restrictive policy in t</td><td></td><td></td><td>0.805<br>(0.545)</td><td>0.459<br>(0.644)</td></tr><tr><td>Asylum seekers flow share in t × less restrictive policy in t</td><td></td><td></td><td>-0.428*<br>(0.253)</td><td>-0.332**<br>(0.169)</td></tr><tr><td>More restrictive policy in t</td><td>0.018<br>(0.048)</td><td>0.014<br>(0.029)</td><td>-0.086<br>(0.086)</td><td>-0.063<br>(0.106)</td></tr><tr><td>Less restrictive policy in t</td><td>0.018<br>(0.021)</td><td>0.002<br>(0.013)</td><td>0.074*<br>(0.039)</td><td>0.053<br>(0.033)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>478</td><td>478</td><td>509</td><td>509</td></tr><tr><td>Immig. + Immig. × More restrictive</td><td>0.143</td><td>0.118</td><td>1.479</td><td>1.051</td></tr><tr><td>Immig. + Immig. × More restrictive (p-value)</td><td>0.164</td><td>0.008</td><td>0.009</td><td>0.128</td></tr><tr><td>Immig. + Immig. × Less restrictive</td><td>0.079</td><td>0.057</td><td>0.246</td><td>0.260</td></tr><tr><td>Immig. + Immig. × Less restrictive (p-value)</td><td>0.008</td><td>0.002</td><td>0.179</td><td>0.023</td></tr><tr><td>F statistic KPW</td><td>61.75</td><td>61.75</td><td>37.19</td><td>37.19</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

Results from Table 2 consistently suggest that policy changes in host countries condition the integration of asylum seekers and their impact on the informal sector. All coefficients on the interactions between flows and the more restrictive policy dummy are positive, whereas the coefficients on interactions between flows and the less restrictive policy variables are negative. More precisely, whereas immigration policies in host countries are not found to significantly alter the contribution of immigrants to the informal sector, our results suggest that the impact of asylum seeker inflows on the size of the informal sector is strongly reduced by less restrictive policies (col. (3) and (4)).

We further explore the role of the legal context by investigating the role of internal and external regulation, using IMPIC data. In Table 3, we focus on asylum seeker flows, and interact incoming flows with a binary variable equal to one for countries with an average value of the IMPIC index that is above the sample median over the observation period, indicating higher restrictiveness. In columns (1) and (2), we consider an index computed on dimensions that correspond to external regulation, that is, relating to new entries, while in columns (3) and (4), we focus on internal regulation that concerns asylum seekers already in host countries. Unsurprisingly, we find that the impact of asylum seeker flows on the size of the informal sector is larger in countries with more restrictive internal regulations. Conversely, the effect of external regulation is not significant. The same analysis replicated for immigrants shows no impact of external nor internal regulation restrictiveness, as measured by the IMPIC index (see Appendix Table A9).
<table><caption>Table 6: Immigrant/Asylum seekers flows, diversity in terms of origin, and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>0.072***<br>(0.018)</td><td>0.055***<br>(0.018)</td><td></td><td></td></tr><tr><td>Immigr. <i>t</i> x Diversity above median</td><td>-0.058**<br>(0.026)</td><td>-0.049**<br>(0.025)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i></td><td></td><td></td><td>0.393**<br>(0.193)</td><td>0.370***<br>(0.091)</td></tr><tr><td>Asyl. <i>t</i> x Diversity above median</td><td></td><td></td><td>-0.322<br>(0.233)</td><td>-0.308***<br>(0.119)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

However the coefficient is not significant in specifications that use as a dependent variable the index developed by Elgin and Oztunali (2012).

<table><caption>Table 7: Immigrant/Asylum seekers flows, change in diversity, and the informal sector, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>0.057***<br>(0.016)</td><td>0.048***<br>(0.015)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i></td><td></td><td></td><td>0.242**<br>(0.118)</td><td>0.243***<br>(0.067)</td></tr><tr><td>Δ Diversity index</td><td>1.155**<br>(0.565)</td><td>0.079<br>(0.092)</td><td>1.435***<br>(0.515)</td><td>-0.101<br>(0.070)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

## 4.3 Alternative model: Fixed-effects estimates

As an alternative approach to our baseline first-difference model, we estimate a fixed-effect model using the share of immigrant (respectively asylum seeker) flow in total host population. Results are reported in Table 8. We find in all specifications a positive and significant coefficient on the immigrant or asylum seeker flow variable, which suggests that an increase in the flow of immigrants or asylum seekers with respect to the population in the country of destination increases the size of the informal sector in the country. Results are consistent whatever the measure of informality that is chosen. Time invariant destination characteristics are accounted for by country fixed-effects. Although estimated coefficients in this fixed effect model cannot be directly compared to those of our main (first-difference) model, the magnitude of the effect of migrant and asylum seeker flows
<table><caption>Table 3: Asylum seekers flows, asylum regulation restrictiveness and the informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&O index</th><th>Δ M&S index</th><th>Δ E&O index</th></tr></thead><tbody><tr><td>Asylum seekers flow share t</td><td>0.383*<br>(0.230)</td><td>0.224*<br>(0.125)</td><td>1.053***<br>(0.323)</td><td>0.077<br>(0.050)</td></tr><tr><td>Asy. seekers flow share t ×</td><td>0.123</td><td>0.212</td><td></td><td></td></tr><tr><td>External regulation index above median</td><td>(0.333)</td><td>(0.191)</td><td></td><td></td></tr><tr><td>Asy. seekers flow share t ×</td><td></td><td></td><td>0.804*<br>(0.426)</td><td>0.595***<br>(0.141)</td></tr><tr><td>Internal regulation index above median</td><td></td><td></td><td></td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>602</td><td>634</td><td>602</td><td>634</td></tr><tr><td>Sum coeff.</td><td>0.506</td><td>0.436</td><td>1.856</td><td>0.672</td></tr><tr><td>P-value</td><td>0.039</td><td>0.009</td><td>0.000</td><td>0.000</td></tr><tr><td>F statistic KPW</td><td>40.35</td><td>70.40</td><td>64.88</td><td>67.66</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

We further explore the effect of integration policies in Table 4 by using the migrant integration policy index (MIPEX). We interact our flow variables with a binary indicator equal to one for countries with an average value of the MIPEX above the sample median over the period 2007-2019. As discussed above, the MIPEX varies little over time. Index mean values over 2007-2019 may thus be extrapolated over the entire period. In order to avoid halving our sample size, we therefore estimate our baseline model augmented with this interaction term over the whole period (1997-2019). The MIPEX is coded on a 0-100 scale, and the mean value of the index is 40 for countries below the median, and 65 for more integrative countries with index values above the median. Results unambiguously show that the positive impact of migrant and asylum seekers inflows on the size of the informal sector is driven by countries that perform poorly in terms of migrant integration. In this group, that includes countries as diverse as Greece, Japan, Poland or Denmark, a one percentage point increase in immigrant and asylum seeker inflows increases the size of the informal sector as a share of the GDP by 0.13-0.16 and 0.47-0.53 percentage points respectively. In the group of countries with values of the integration index above the sample median, the impact of migrant and asylum seeker inflows on the informal sector is smaller (0.03 for immigrant flows, 0.19-0.39 for asylum seekers) and significant only at the 10% level for asylum seeker flows, as indicated by the p-values reported at the bottom of the table.
is very limited. For example, a one percentage point increase in the flow of immigrants as a share of the total population entails a 1.6 percentage point increase of the size of the informal sector as a share of the population. To put this figure into perspective, in France the share of immigrants in the population strongly increased from 2010 to 2015, jumping from 0.22 to 0.36% of the population, which represents a 160% increase. According to our estimates, in order to generate a 1.6 percentage point increase in the size of the informal sector, France should have experienced a 550% increase in immigrant inflows as a share of its population (from 0.22% of the population to 1.22% of the population).

<table><caption>Table 8: Immigration/asylum seekers flows and informal sector, fixed-effect model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Medina and Schneider (2019) index</th><th>Elgin and Öztunali (2012) index</th><th></th><th></th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>1.637***<br>(0.503)</td><td>1.147***<br>(0.281)</td><td></td><td></td></tr><tr><td>Immigration flow share <i>t</i>-1</td><td></td><td>1.580***<br>(0.468)</td><td>1.102***<br>(0.256)</td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>610</td><td>601</td><td>610</td><td>601</td></tr><tr><td>F statistic KPW</td><td>20.28</td><td>24.19</td><td>20.28</td><td>24.19</td></tr></tbody></table><table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Medina and Schneider (2019) index</th><th>Elgin and Öztunali (2012) index</th><th></th><th></th></tr></thead><tbody><tr><td>Asylum seekers flow share <i>t</i></td><td>1.716***<br>(0.659)</td><td>1.790**<br>(0.709)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i>-1</td><td></td><td>1.309**<br>(0.544)</td><td>1.159**<br>(0.508)</td><td></td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>644</td><td>636</td><td>644</td><td>636</td></tr><tr><td>F statistic KPW</td><td>22.84</td><td>23.43</td><td>22.84</td><td>23.43</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

## 4.4 Dynamic effects: VAR approach

In this section, we present the estimates obtained from the panel VAR model described in Section 2.4 through a discussion of the impulse response functions. Our objective is to estimate the dynamic effects of a migration shock on the informal economy in OECD destination countries. As explained above, we estimate a panel VAR model using the bias-corrected fixed effects estimator provided by Hahn and Kuersteiner (2002).

The impulse response functions are represented in Appendix Figures 4 to 5. Since the variables are in log, the responses are expressed in percentages. We first observe that immigration or asylum seeker flows (expressed as a share of total population at destination) monotonically respond to their own shock. This is shown in all models
<table><caption>Table 4: Immigrant/Asylum seekers flows, host countries immigrant integration index and the informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.157***<br>(0.043)</td><td>0.134***<br>(0.036)</td><td></td><td></td></tr><tr><td>Immigration flow share t × Integration above median</td><td>-0.123***<br>(0.048)</td><td>-0.102***<br>(0.038)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.528**<br>(0.242)</td><td>0.473***<br>(0.170)</td></tr><tr><td>Asylum seekers flow share t × Integration above median</td><td></td><td></td><td>-0.133<br>(0.335)</td><td>-0.286<br>(0.192)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>Sum coeff.</td><td>0.034</td><td>0.032</td><td>0.395</td><td>0.188</td></tr><tr><td>P-value</td><td>0.075</td><td>0.003</td><td>0.098</td><td>0.100</td></tr><tr><td>F statistic KPW</td><td>1,412.31</td><td>1,292.15</td><td>49.21</td><td>127.29</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

Overall, our results thus suggest that, in line with Proposition 2, the contribution of immigrants and asylum seekers depends on host countries' policies. For both populations, the contribution to the informal sector is lower in countries characterized by more integrative policies. Moreover, the contribution of asylum seekers to the informal sector is more sensitive to restrictive policies, and in particular to internal regulation than that of immigrants, which echoes the average stronger impact of asylum seekers on informality, in line with proposition 1, and reflects the more constrained access of asylum seekers to formal employment.

### 5.3.2 Host countries' tolerance for informality

We explore in this section the role played by institutions at destination. The quality of institutions is a major determinant of the size of the informal sector (Friedman et al., 2000; Dreher and Schneider, 2010; Dreher et al., 2009; Teobaldelli, 2011; Goel and Saunoris, 2014). As such, it is likely to affect newcomers' integration at destination and their participation to the informal sector, as suggested by Proposition 3, in Section 2. We therefore explore the potentially heterogeneous impact of migrant and asylum seekers inflows on informality depending on the quality of host country's institutions by interacting our immigration and asylum seeker flow variables with a binary variable that equals one for countries with an average level of informality over the 1997-2020 period that is above the sample median. Results are reported in Table 5, and suggest that the impact of migrant and asylum seekers flows on informality is driven by host countries where the informal sector is the largest. In the case of migrant flows, although results marginally differ depending on the informality measure that is chosen as a dependent variable, they consistently suggest that the impact of immigrant flows on the informal sector is larger in host countries with a high level of informality. While the sum of the coefficients on the interaction term and the flow variable is not statistically significantly in column (1) it is positive and significant in column (2), and very close in magnitude in those two columns. As for
provided in figures 4-5. The increase remains significant for approximately ten years. Regarding the economic consequences, we observe that a migration or asylum seeker inflow shock results in a significant increase in government expenditures that peaks four years after the shock for immigrants and five years for asylum seekers. Similar findings of a positive reaction of government expenditures to a migration shock were obtained by d'Albis et al. (2019). Consistent with previous contributions (Ortega and Peri, 2014), our results also show that following a migration shock, the response of GDP per capita is positive and significant.

Focusing on our own contribution, we find that a positive shock to immigrant inflows leads to a significant increase in the size of the informal sector. The effect is instantaneous, is maximum two to four years after the shock, and remains significantly different from zero for about ten to fifteen years depending on the measure of informality that is used. As for asylum seeker flows, impulse response functions shown in Figures 6 and 5 display a similar pattern but the effect is not significant the very year of the shock. The difference between the response of the informal sector to the arrival of migrants and asylum seekers may be explained by the fact that asylum seeker enter the informal sector later, after a period of adaptation or language acquisition to the country of destination, or after emergency assistance expires.

# 5 Conclusion

We empirically investigate in this paper the impact of immigrant and asylum seeker flows on the size of the informal sector in destination countries, with a macroeconomic approach. We focus in our main analysis on OECD countries over 1997-2017. We find that an increase in the stock of immigrants or asylum seekers significantly increases the size of the informal sector at destination. However, the magnitude of the effect is small: a one percentage point increase in the flow of immigrants (respectively asylum seekers) as a share of host population leads to an increase of the informal sector as a share of GDP of 0.05-0.06 (respectively 0.24-0.26) percentage points. Note that the median value for the flow of immigrant is 0.66% of the host population in our sample, the median value for the flow of asylum seeker is 0.09% of the population, and the median size of the informal sector is 15.6% of GDP. We then explore the mechanisms that explain this positive impact of incoming flows on the informal sector. We find evidence of an impact of immigration and integration policies in destination countries: The effect tends to be larger when more restrictive policies regarding employer liabilities or work or visa permits are implemented, and is reduced by less restrictive policies. In addition, the effect of immigration is two to three times larger in countries characterized by a low value of the MIPEX migrant
asylum seeker flows, the sum of the coefficients on the flow variable and the interaction term is positive and significant in both columns. Taken together, these results suggest that the impact of immigrant flows is small or zero in countries with a low level of informality contrary to those with a high level of informality, and asylum seeker inflows increase the size of the informal sector only in the latter group of countries.

<table><caption>Table 5: Immigrant/Asylum seekers flows, informality at destination and the informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index<br>1997-2017</th><th>Δ E&O index<br>1997-2020</th><th>Δ M&S index<br>1997-2017</th><th>Δ E&O index<br>1997-2020</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.028*<br>(0.016)</td><td>0.017<br>(0.011)</td><td></td><td></td></tr><tr><td>Immigr. t x Inf above median</td><td>0.103<br>(0.092)</td><td>0.119**<br>(0.047)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>-0.083<br>(0.182)</td><td>0.017<br>(0.080)</td></tr><tr><td>Asyl. t x Inf above median</td><td></td><td></td><td>0.783**<br>(0.323)</td><td>0.398***<br>(0.135)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>Sum coeff.</td><td>0.131</td><td>0.136</td><td>0.700</td><td>0.415</td></tr><tr><td>P-value</td><td>0.145</td><td>0.003</td><td>0.010</td><td>0.001</td></tr><tr><td>F statistic KPW</td><td>426.67</td><td>368.66</td><td>82.98</td><td>163.71</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01. The sum of the coefficients refers to the coefficient on the immigration (respectively asylum-seekers) flow share and the coefficient on its interaction with the dummy variable for informality above the median.

### 5.3.3 Informality at origin

In Table 6, we explore another possible channel for the impact of migrant and asylum seekers inflows on informality, according to which immigrants from countries with a high level of informality would import informality norms, or preferences for informality, at destination. In order to explore this channel, we add to our list of controls a variable that captures changes in the composition of the stock of immigrants and asylum seekers in terms of informality at origin. In line with our main specification, we use flows to proxy for stock changes. More specifically, we include in our model an additional variable reflecting the change in a composite informality index that accounts for the level of informality in each origin country weighted by the share of migrants from this country at destination. Formally, our index writes:

$$ \text{M&S informality index at origin}_{j,t} = \sum_{i} I_{i,t}^{\text{M&S}} \frac{Y_{ij,t}}{Y_{j,t}} $$

where $I_{i,t}^{\text{M&S}}$ is the size of the informal sector as a share of GDP from Medina and Schneider (2019) for origin country i in year t, $Y_{ij,t}$ are migrant or asylum seeker flows from country i to destination j in year t, and $Y_{j,t}$ are total migrant or asylum seeker inflows to destination j in year t.

After controlling for the change in the size of the immigrant or asylum seeker stock proxied
integration index than in countries with an integration index above the median. In the latter group of countries, the impact of asylum seeker flows becomes insignificant. Second, we question the role of norm transfers by immigrants, and investigate the impact of informality in countries of origin of migrants and asylum seekers on informality at destination. We find however no evidence of an impact of the level of informality at origin, suggesting that informality is not imported by immigrants. Conversely, we find that institutions at destination do matter, in that the impact of immigrant and asylum seeker inflows of informality is driven by countries where the size of the informal sector is above the median. We analyse in addition the impact of the diversity of migrant or asylum seeker waves, and find rather ambiguous effects: inflows affect informality only in countries that host the less diverse flows, but controlling for flow size, we find that an increase in the fractionalization index of inflows tends to reduce the impact of inflows on the shadow economy. Finally, we explore the dynamics of the relationship between immigration and asylum seeker inflows on the informal sector with a VAR model. The analysis of the impulse-response functions suggest that a shock in immigrant or asylum seeker inflows generates a positive response of the informal sector that is maximum two to four years after their arrival but remains significant up to 10 years after the initial shock. This last result, together with our finding that immigration policies at destination alter the impact of migrant and asylum seeker inflows in informality, suggests that adequate policies that facilitate the integration of immigrants in host countries and their access to the formal sector should be perceived as high-paying long-term investments.
by new inflows, we find that a change in imported informality has no significant impact on informality at destination. This result suggests that imported institutions or norms at origin are unlikely to explain the impact of migrant and asylum seeker inflows on informality at destination.

<table><caption>Table 6: Immigrant/Asylum seekers flows, informality at origin and the informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.064***<br>(0.019)</td><td>0.042**<br>(0.017)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.487***<br>(0.189)</td><td>0.299**<br>(0.121)</td></tr><tr><td>M&S informality index at origin<sub>t</sub></td><td>-0.001<br>(0.003)</td><td>-0.002<br>(0.004)</td><td>0.003<br>(0.003)</td><td>0.003<br>(0.004)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>596</td><td>629</td><td>629</td></tr><tr><td>F statistic KPW</td><td>6,111.41</td><td>6,111.41</td><td>498.55</td><td>498.55</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

## 5.4 Impact of immigrants and asylum seekers on informal employment

In this section, we complement our analysis by focusing on the impact of immigrant and asylum seeker inflows on a different measure of the size of the informal sector. More specifically, we focus here on informal employment, using data compiled by the ILO. The advantage of this measure is that, unlike informality indices, it does not rely on modelling assumptions or calibration choices. Moreover, it allows us to directly explore the impact of migrant and asylum seeker flows through their integration in the informal labour market. However, the data are available only for a subset of 28 countries and over a limited time period. Keeping in mind these limitations, we report estimation results in Table 7. We find that a one percentage point increase in the stock of immigrants (proxied by immigrant inflows) results in an increase of informal employment as a share of total employment by 0.11 percentage points. The impact of asylum seekers is three to four time higher, as a one percentage point increase in their stock (proxied by flows) is associated with a 0.99 percentage point increase in the share of informal employment in total employment. In the OECD, employment rates fluctuate around 65% over the period under study. A gross calculation therefore suggests that a 1 percentage point increase in the stock of asylum seekers as a share of the population increases informal employment by 0.65 percentage point. These additional findings are consistent with our main results showing that immigrant integration policies on the labour market do matter and that asylum seeker inflows have a larger impact on the informal economy. They point to the role of restrictions imposed by host countries on asylum seekers' rights that limit their access the formal labour market and push them into informality.
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We empirically investigate in this paper the impact of immigrant and asylum seeker flows on the size of the informal sector in destination countries, with an aggregate cross-country approach. We focus on OECD countries over the period 1997-2020 and address endogeneity issues by estimating a first-difference model on panel data, and instrumenting immigrant and asylum seeker flows by their values predicted by a gravity model based on dyadic flow data. We find that an increase in the stock of immigrants or asylum seekers significantly increases the size of the informal sector at destination. However, the magnitude of the effect is small for immigrants: a one percentage point increase in the flow of immigrants as a share of the host country population leads to an average increase of the informal sector as a share of GDP by 0.05-0.06 percentage points. The impact of an increase in the stock of asylum seekers is six to seven times larger but remains relatively limited in terms of economic significance. Note that the median value for the flows of immigrants is 0.47% of the host country population in our sample, the median value for the flows of asylum seekers is 0.04% of the population, and the median size of the informal sector is 13.68% of GDP. Therefore, a large increase in the flows of immigrants or asylum seekers would lead to a small increase in the size of the informal sector in destination countries. We then explore the mechanisms that explain this positive impact of incoming flows on the informal sector. We find evidence of an impact of immigration and integration policies in destination countries, especially for asylum seekers: their impact on the informal sector tends to be lower when less restrictive policies regarding employer liabilities or work or visa permits are implemented. In addition, the contribution of immigration and asylum seeker flows to the informal sector is larger in countries characterized by a low value of the MIPEX migrant integration index than in countries with an integration index above the median. Second, we explore the role of institutions at destination, and in particular tolerance for informality. We find that the impact of immigrant and asylum seeker inflows on informality is driven by countries where the size of the informal sector is larger. By contrast, we find no evidence of an impact of the level of informality at origin, suggesting that our results are not driven by imported informality by immigrants or asylum seekers. We complement our analysis with a direct estimation of the impact of immigrant and asylum seeker inflows on informal
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Our findings suggest that policies facilitating the labour market integration of immigrants in host countries should be perceived as a high-paying long-term investment that would contribute to strengthening institutions and limiting the growth of the informal sector.
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Elgin, C. and Oztunali, O. (2012). Shadow economies around the world: model based estimates. *Bogazici University Department of Economics Working Papers*, 5(2012):1–48.

Fasani, F., Frattini, T., and Minale, L. (2021). Lift the ban? initial employment restrictions and refugee labour market outcomes. *Journal of the European Economic Association*, 19(5):2803–2854.

Fasani, F., Frattini, T., and Minale, L. (2022). (the struggle for) refugee integration into the labour market: evidence from europe. *Journal of Economic Geography*, 22(2):351–393.
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# Appendix

<table><caption>Table 9: Panel unit-root Im et al. (2003) test</caption><thead><tr><th>Variables</th><th></th><th>Without trend</th><th>With trend</th></tr></thead><tbody><tr><td rowspan="2">Informal economy - M&S</td><td>Levels</td><td>0.796</td><td>-0.668</td></tr><tr><td>Differences</td><td>-15.589***</td><td>-12.659***</td></tr><tr><td rowspan="2">Informal economy - E&O</td><td>Levels</td><td>-0.717</td><td>3.494</td></tr><tr><td>Differences</td><td>-3.738***</td><td>-3.569***</td></tr></tbody></table>

<table><caption>Table 10: Zero-stage regressions (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>Bilateral immigrants flows</th><th>Bilateral asylum-seekers flows</th></tr></thead><tbody><tr><td>Log of distance</td><td>-1.016***<br>(0.054)</td><td>-1.106***<br>(0.104)</td></tr><tr><td>Common language</td><td>1.331***<br>(0.105)</td><td>1.035***<br>(0.212)</td></tr><tr><td>Colonial relationship</td><td>0.699***<br>(0.142)</td><td>0.040<br>(0.242)</td></tr><tr><td>War in origin country</td><td>-1.493<br>(1.215)</td><td>5.768***<br>(0.908)</td></tr><tr><td>Log of distance × War in origin country</td><td>0.213<br>(0.139)</td><td>-0.617***<br>(0.105)</td></tr><tr><td>Common language × War in origin country</td><td>-0.392*<br>(0.200)</td><td>-0.463***<br>(0.180)</td></tr><tr><td>Colonial relationship × War in origin country</td><td>0.114<br>(0.247)</td><td>-0.031<br>(0.314)</td></tr><tr><td>Origin-country fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination-country fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>144795</td><td>141855</td></tr></tbody></table>

Standard errors reported in parentheses are clustered at the destination-origin country pair level. * p<0.10, ** p<0.05, *** p<0.01.

The table reports results using the PPML estimator. Data sources: OECD, CEPII & Upssala Conflict Data.

<table><caption>Table 11: Immigrant/Asylum seekers flows and informal sector controlling for lagged GDP, first-difference model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.064***<br>(0.016)</td><td>0.043***<br>(0.012)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.251**<br>(0.123)</td><td>0.220***<br>(0.054)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>582</td><td>582</td><td>615</td><td>615</td></tr><tr><td>F statistic KPW</td><td>7,800.59</td><td>7,800.59</td><td>398.72</td><td>398.72</td></tr></tbody></table>

Robust standard errors clustered by country in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

<footnote>*** p<0.01</footnote>
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<figure><img src="image_3.png" /><figcaption>Figure 2: First Stage</figcaption></figure>
# A Appendix

## A.1 Summary statistics and additional results

<table><caption>Table A1: Observed stock of immigrants and immigrant flows</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th colspan="2">Changes in stock of immigrants (% pop.)</th></tr></thead><tbody><tr><td>Immigrant flows (% pop.)<sub>t</sub></td><td>0.460***<br>(0.020)</td><td>0.459***<br>(0.020)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>563</td><td>563</td></tr><tr><td>R-squared</td><td>0.483</td><td>0.506</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

<table><caption>Table A2: Zero-stage regressions (1997-2020) - PPML estimates</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>Bilateral immigrants flows</th><th>Bilateral asylum-seekers flows</th></tr></thead><tbody><tr><td>Log of distance</td><td>-0.988***<br>(0.053)</td><td>-1.088***<br>(0.115)</td></tr><tr><td>Common language</td><td>1.298***<br>(0.108)</td><td>1.288***<br>(0.234)</td></tr><tr><td>Colonial relationship</td><td>0.661***<br>(0.135)</td><td>0.228<br>(0.277)</td></tr><tr><td>War in origin country</td><td>-0.862<br>(1.105)</td><td>4.104***<br>(1.295)</td></tr><tr><td>Log of distance x War in origin country</td><td>0.132<br>(0.128)</td><td>-0.429***<br>(0.148)</td></tr><tr><td>Common language x War in origin country</td><td>-0.361*<br>(0.189)</td><td>-0.591***<br>(0.189)</td></tr><tr><td>Colonial relationship x War in origin country</td><td>0.283<br>(0.236)</td><td>-0.135<br>(0.379)</td></tr><tr><td>Origin-country fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination-country fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>161280</td><td>157920</td></tr></tbody></table>

Standard errors clustered at the destination-origin country pair level. * p<0.10, ** p<0.05, *** p<0.01.
<figure><img src="image_4.png" /><figcaption>Figure 3: Responses to migration shock</figcaption></figure>

Notes. The solid lines provide the impulse responses to migration shock. Dashed lines provide the 90% confidence intervals generated by Monte Carlo with 5000 replications. Informal economy measure is provided by Elgin and Oztunali (2012).
<table><caption>Table A3: Immigrant/Asylum seekers flows and informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>0.066***<br>(0.019)</td><td>0.049***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i></td><td></td><td></td><td>0.456***<br>(0.171)</td><td>0.319***<br>(0.112)</td></tr><tr><td>Trade openness (% GDP) <i>t</i>-1</td><td>-0.011**<br>(0.004)</td><td>0.002<br>(0.001)</td><td>-0.012**<br>(0.005)</td><td>0.002**<br>(0.001)</td></tr><tr><td>Government expenditures (% GDP) <i>t</i>-1</td><td>0.137***<br>(0.037)</td><td>-0.028***<br>(0.010)</td><td>0.081<br>(0.065)</td><td>-0.028**<br>(0.011)</td></tr><tr><td>Gross capital formation (% GDP) <i>t</i>-1</td><td>-0.012<br>(0.017)</td><td>-0.001<br>(0.002)</td><td>-0.010<br>(0.017)</td><td>-0.003<br>(0.003)</td></tr><tr><td>Unemployment rate <i>t</i>-1</td><td>0.038<br>(0.027)</td><td>-0.015**<br>(0.006)</td><td>0.036<br>(0.028)</td><td>-0.013*<br>(0.007)</td></tr><tr><td>Foreign direct investment (% GDP) <i>t</i>-1</td><td>0.001<br>(0.001)</td><td>-0.000<br>(0.000)</td><td>0.001<br>(0.001)</td><td>0.000<br>(0.000)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>8,664.00</td><td>8,331.61</td><td>449.99</td><td>667.26</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

<table><caption>Table A4: Immigrant stock and informal sector, first-difference model (IV)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>Δ M&S index<br>1997-2017</th><th>Δ E&Ö index<br>1997-2020</th></tr></thead><tbody><tr><td>Immigrants stock (% pop.) <i>t</i></td><td>0.128***<br>(0.040)</td><td>0.093**<br>(0.037)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>489</td><td>575</td></tr><tr><td>F statistic KPW</td><td>75.62</td><td>101.60</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
<figure><img src="image_5.png" /><figcaption>Figure 4: Responses to migration shock</figcaption></figure>

Notes. The solid lines provide the impulse responses to migration shock. Dashed lines provide the 90% confidence intervals generated by Monte Carlo with 5000 replications. Informal economy measure is provided by Medina and Schneider (2019).
<table><caption>Table A5: Immigration/asylum seekers flows and informal sector, fixed-effect model (IV) (1997-2017)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Medina and Schneider (2019) index</th><th></th><th>Elgin and Öztunali (2012) index</th><th></th></tr></thead><tbody><tr><td>Immigration flow share <i>t</i></td><td>1.607***<br>(0.591)</td><td></td><td>1.366***<br>(0.515)</td><td></td></tr><tr><td>Immigration flow share <i>t</i>-1</td><td></td><td>1.653***<br>(0.520)</td><td></td><td>1.797***<br>(0.688)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>624</td><td>613</td><td>725</td><td>715</td></tr><tr><td>F statistic KPW</td><td>28.23</td><td>74.42</td><td>24.78</td><td>34.58</td></tr></tbody><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Medina and Schneider (2019) index</th><th></th><th>Elgin and Öztunali (2012) index</th><th></th></tr></thead><tbody><tr><td>Asylum seekers flow share <i>t</i></td><td>3.590***<br>(1.290)</td><td></td><td>4.596***<br>(1.758)</td><td></td></tr><tr><td>Asylum seekers flow share <i>t</i>-1</td><td></td><td>3.057***<br>(1.150)</td><td></td><td>4.458***<br>(1.715)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Destination country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>659</td><td>651</td><td>763</td><td>756</td></tr><tr><td>F statistic KPW</td><td>45.97</td><td>47.57</td><td>31.30</td><td>33.36</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
<figure><img src="image_6.png" /><figcaption>Figure 5: Responses to asylum-seeker shock</figcaption></figure>

Notes. The solid lines provide the impulse responses to asylum-seeker shock. Dashed lines provide the 90% confidence intervals generated by Monte Carlo with 5000 replications. Informal economy measure is provided by Elgin and Oztunali (2012).
<table><caption>Table A6: Immigrant/Asylum seekers flows and informal sector, first-difference model (IV)</caption><thead><tr><th></th><th colspan="4">Zero-stage exog. variation : War</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr></thead><tbody><tr><td>Immigration flow share t</td><td>0.066***<br>(0.019)</td><td>0.049***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.456***<br>(0.171)</td><td>0.319***<br>(0.112)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>8,664.00</td><td>8,331.60</td><td>449.99</td><td>667.26</td></tr><tr><th></th><th colspan="4">Zero-stage exog. variation : Natural disasaters</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr><tr><td>Immigration flow share t</td><td>0.066***<br>(0.019)</td><td>0.049***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.463***<br>(0.173)</td><td>0.319***<br>(0.113)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>9,721.14</td><td>9,158.25</td><td>454.92</td><td>658.22</td></tr><tr><th></th><th colspan="4">Zero-stage exog. variation : War and natural disasaters</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr><tr><td>Immigration flow share t</td><td>0.066***<br>(0.019)</td><td>0.049***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.470***<br>(0.176)</td><td>0.319***<br>(0.112)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>9,502.29</td><td>8,890.82</td><td>461.07</td><td>670.75</td></tr><tr><th></th><th colspan="4">Zero-stage exog. variation : Interactions with year fixed effects</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Δ M&S index</th><th>Δ E&Ö index</th><th>Δ M&S index</th><th>Δ E&Ö index</th></tr><tr><td>Immigration flow share t</td><td>0.061***<br>(0.018)</td><td>0.048***<br>(0.016)</td><td></td><td></td></tr><tr><td>Asylum seekers flow share t</td><td></td><td></td><td>0.440**<br>(0.177)</td><td>0.292***<br>(0.107)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Time dummies</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>596</td><td>697</td><td>629</td><td>733</td></tr><tr><td>F statistic KPW</td><td>9,266.54</td><td>5,517.71</td><td>326.52</td><td>606.54</td></tr></tbody></table>

Standard errors clustered at the country level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
<figure><img src="image_7.png" /><figcaption>Figure 6: Responses to asylum-seeker shock</figcaption></figure>

Notes. The solid lines provide the impulse responses to asylum-seeker shock. Dashed lines provide the 90% confidence intervals generated by Monte Carlo with 5000 replications. Informal economy measure is provided by Medina and Schneider (2019).