metadata: authors: 0: author: Bettin,Giulia
1: author: Presbitero,Andrea F.
2: author: Spatafora,Nikola L.
count: World
docna: 0: docna: Remittances and vulnerability in developing countries
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,International Trade and Trade Rules,Economic Growth,Economic Stabilization,Employment and Unemployment,Financial Structures,Remittances
teratopic: Health, Nutrition and Population,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: This paper examines how international
            remittances are affected by structural characteristics,
            macroeconomic conditions, and adverse shocks in both source
            and recipient economies. The paper exploits a novel, rich
            panel data set, covering bilateral remittances from 103
            Italian provinces to 87 developing countries over the period
            2005-2011. Remittances are negatively correlated with the
            business cycle in recipient countries and increase
            especially strongly in response to adverse exogenous shocks,
            such as natural disasters or large terms-of-trade declines.
            Financial development in the source economy, which eases
            access to financial services for migrants and reduces
            transaction costs, is positively associated with
            remittances. Conversely, recipient-country financial
            development is negatively associated with remittances,
            suggesting that remittances help alleviate credit constraints.
repnme: repnme: Remittances and vulnerability in developing countries
display_title: Remittances and vulnerability in
            developing countries
listing_relative_url: /research/2014/03/19272414/remittances-vulnerability-developing-countries
topicv3: Economic Growth m1326241 471,Macroeconomic Stability and Growth
            m1326923 1088,Trade m1326924 698,International Remittances m1326998 355,Migrant Workers m1504032 2729
publishtoextweb_dt: 2014-03-01T00:00:00Z
historic_topic: Health, Nutrition and Population,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 30
author_keys: 0: author_key: 1431598
1: author_key: 792084
2: author_key: 877681
model_extractions: WPS6812 Policy Research Working Paper 6812 Remittances and Vulnerability in Developing Countries _Giulia Bettin Andrea F. Presbitero Nikola Spatafora_ The World Bank East Asia and the Pacific Region Office of the Chief Economist March 2014
Policy Research Working Paper 6812

# **Abstract**

This paper examines how international remittances are affected by structural characteristics, macroeconomic conditions, and adverse shocks in both source and recipient economies. The paper exploits a novel, rich panel data set, covering bilateral remittances from 103 Italian provinces to 87 developing countries over the period 2005–2011. Remittances are negatively correlated with the business cycle in recipient countries and increase especially strongly in response to adverse exogenous shocks, such as natural disasters or large terms-of-trade declines. Financial development in the source economy, which eases access to financial services for migrants and reduces transaction costs, is positively associated with remittances. Conversely, recipient-country financial development is negatively associated with remittances, suggesting that remittances help alleviate credit constraints.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nspatafora@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Remittances and Vulnerability

# in Developing Countries<sup>_∗_</sup>

Giulia Bettin<sup>_†_</sup> Andrea F. Presbitero<sup>_‡_</sup> Nikola Spatafora<sup>_§_</sup> March 20, 2014

**JEL Codes:** F33, F34, F35, O11

**Keywords:** Remittances, Shocks, Business Cycle, Vulnerability, Gravity Model

> _∗_ We thank Giacomo Oddo, Roberto Tedeschi and Simonetta Zappa at the Bank of Italy for clarifications on the remittances data set. We also thank Caglar Ozden, Dilip Ratha, and participants at the 4th International Conference “Economics of Global Interactions: New Perspectives on Trade, Factor Mobility and Development” and at seminars at the International Monetary Fund and KNOMAD (The World Bank) for useful comments. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent the views of the International Monetary Fund, the World Bank, or the policies of these institutions.

> _†_ Giulia Bettin, Università Politecnica delle Marche (Italy) and MoFiR. E-mail: g.bettin@univpm.it.

> _‡_ Andrea F. Presbitero, International Monetary Fund and MoFiR. E-mail: apresbitero@imf.org.

> _§_ Nikola Spatafora, The World Bank. E-mail: nspatafora@worldbank.org.

1
# **1 Introduction**

Developing countries remain extremely vulnerable to adverse exogenous shocks. The global financial crisis and the world food price crisis of 2008 delivered a reminder of their macroeconomic, external, and fiscal vulnerabilities (Figure 1, left panel). In addition, over the past decades developing countries have been increasingly subject to natural disasters, again with severe consequences in terms of output, trade, and fiscal balances (Raddatz, 2007; Noy, 2009). As a result, the policy debate is again focusing on developing countries’ vulnerability (Schindler _et al._ , 2011).

This paper examines how remittances are affected by structural characteristics, macroeconomic conditions, and adverse shocks in both source and recipient economies. It therefore sheds light on whether remittances should be viewed as a countercyclical shock absorber, helping smooth consumption during a downturn, in contrast to the typically pro-cyclical private capital flows. This issue is particularly salient for two reasons. First, spurred by increasing migration, remittances to developing countries have grown steadily relative to capital flows (Figure 1, right panel). Remittances to developing countries are projected to reach USD 414 billion in 2013, more than three times the size of official development assistance, and USD 540 billion by 2016 (The World Bank, 2013). Second, remittances have proved very resilient since the onset of the global financial crisis.

The existing empirical evidence on the role of remittances as a shock absorber is inconclusive. Some studies suggest that remittances are countercyclical with respect to output in the recipient country, because they are driven by altruism (Agarwal and Horowitz, 2002; Osili, 2007), or because household members migrate as part of a risk-diversification strategy aiming to insure against income shocks (Yang and Choi, 2007). Other studies emphasize that remittances can be pro-cyclical, because migrants’ decision to remit is also driven by factors such as investment in physical and human capital (Yang, 2008; Adams Jr. and Cuecuecha, 2010; Cooray and Mallick, 2013).

This paper re-examines the question using a novel, rich panel dataset, covering bilateral remittances from 103 Italian provinces to 87 developing countries over the period 2005-2011, to estimate a model for bilateral remittances. In this dataset, remittances display significant variability, both over time and across source provinces and recipient countries. Italy can be considered a representative case study given that remittance outflows increased substantially in the last years, notwithstanding the global slowdown, and they are destined to all world regions and not limited to neighboring regions such as Eastern Europe, Central Asia or Northern Africa.

More specifically, the paper makes three main contributions to the literature. First, the availability of bilateral panel data for a large sample of recipients makes it possible to analyze systematically the correlation between remittances and the business cycle in both source and recipient economies, washing out the effect of time invariant factors at the level of country-province pairs. We consider separately the cyclical and trend components in GDP per capita. In addition, we control for specific factors of vulnerability in recipient countries, including in particular natural disasters, large declines in the terms of trade, and armed conflicts. In contrast, the existing literature focuses mainly either on bilateral remittances for a limited sample of receiving countries (Lueth and Ruiz-Arranz, 2008; Frankel, 2011; Docquier _et al._ , 2012), or on country pairs, such as the US-Mexico or the GermanyTurkey corridors (Sayan, 2004; Vargas-Silva, 2008). As a result, existing works fail to settle the 2
Figure 1: Vulnerability and capital flows in developing countries <!-- Start of picture text --> .6 600<br>.5<br>400<br>.4<br>.3<br>200<br>.2<br>1990 1993 1996 1999 2002 2005 2008 2011 0<br>1990 1993 1996 1999 2002 2005 2008 2011<br>External sector Fiscal sector<br>Overall economy and institutions Overall index FDI ODA Remittances Private debt & portfolio eq.<br>(a) Vulnerability Index (b) Capital flows<br>current USD bln<br>Vulnerability index (Dabla Norris & Bal Gunduz 2012)<br><!-- End of picture text --> Source: World Development Indicators and International Debt Statistics (developing countries are defined as low and middle income countries), The World Bank, for the left panel. Data for the right panel are elaboration on the Vulnerability Index data set. See Dabla-Norris and Bal-Gunduz (2012) for details on how the index and its sub-components are constructed.

empirical debate on the correlation between remittances to developing countries and the business cycle in the source and recipient economies.

Related to this, our data on remittances cover the periods before and after the 2007-08 financial crisis, allowing an analysis of the response of remittances to economic conditions in source and recipient economies during the global financial crisis. This is particularly relevant because the global financial crisis affected jointly the migrants’ home and host countries, with an _a priori_ ambiguous aggregate effect on remittances. On the one hand, the downturn in the home country might induce a positive change in remittances driven by altruism or insurance. On the other hand, the recession in the host country would reduce the income of migrants, including in particular temporary workers employed in the construction sector.<sup>1</sup> Second, we deal with the possible endogeneity of the business cycle in the recipient country more satisfactorily than previous studies. Reverse causality from remittances to output may significantly bias estimates, since remittances often represent a large share of developing countries’ GDP, and they have been found to affect both output growth and financial development (Gupta _et al._ , 2009; Giuliano and Ruiz-Arranz, 2009; Bettin and Zazzaro, 2012). In some cases, most of the transfers come from a limited set of origin countries, so that even bilateral remittances represent a significant share of GDP, potentially leading to reverse-causality issues in cross-country bilateral data. According to the data in Lueth and Ruiz-Arranz (2008), during 2002-2004 remittances from Russia to Tajikistan equaled on average almost 12 percent of Tajikistan’s GDP, while remittances from the USA to the Philippines represented almost 5 percent of the Philippines’ GDP.<sup>2</sup> To the

> 1According to the Italian National Institute of Statistics, the unemployment rate for foreign-born workers increased from 10.2 percent in 2005 to 12.1 percent in 2011. The figures for native workers were respectively 7.6 percent in 2005 and 8.0 percent in 2011.

> 2We thank Marta Ruiz Arranz for sharing the data on bilateral remittances.

3
best of our knowledge, the existing studies either disregard this issue, or deal with it relying on lagged values or other internal instruments. However, such a strategy is not likely to fully solve the problem. Finding good instruments in a cross-country dataset is challenging, since one needs a variable which is related to economic conditions in the recipient country, but not to remittances (Arezki and Brückner, 2012). The structure of our dataset makes it possible to circumvent this problem: considering only remittances from Italian provinces, rather than aggregate remittance inflows, significantly attenuates the endogeneity of the recipient country’s business cycle.

Third, we investigate the relationship between remittances and financial development in the remittance _source_ economy. The literature has generally focused on financial development in the recipient country, finding that remittances promote financial development (Gupta _et al._ , 2009) and that financial development enhances the impact of remittances on growth (Giuliano and RuizArranz, 2009; Bettin and Zazzaro, 2012). In contrast, we exploit the cross-sectional dimension of the bilateral dataset to test whether the degree of development and the _functional_ proximity of source-province credit markets to local economies play a role in fostering remittances.

We find that remittances from Italian provinces are negatively correlated with the business cycle in recipient countries, and increase especially strongly in response to adverse exogenous shocks, such as natural disasters or large declines in the terms of trade. In addition, remittances are positively correlated with potential GDP in recipient countries. These results are consistent with remittances being driven by both altruism and investment motives.

Remittances are also positively correlated with economic conditions in the source province. Nevertheless, in the presence of similar negative shocks to both source and recipient economies, remittances remain countercyclical with respect to the recipient country.

Financial development in the source province is positively associated with remittances, likely because it reduces transaction costs and eases access to financial services for migrants. Conversely, financial development in the recipient country is negatively associated with remittances, suggesting that remittances help alleviate credit constraints.

The paper is structured as follows. Section 2 offers a detailed review of the existing literature on the macroeconomic determinants of remittances. Section 3 describes the data and the estimated model. Section 4 presents selected statistics about remittances outflows from Italian provinces to developing countries. Section 5 discusses the empirical results.

# **2 The determinants of migrants’ remittances**

## **2.1 The cyclicality of remittances**

There exists a large literature on the determinants of migrants’ remittances.<sup>3</sup> The empirical evidence so far, however, remains inconclusive as to how remittances react to business cycles in the migrants’ home country, and whether they help mitigate economic hardship. At the microeconomic level, some studies find that remittances increase to compensate relatives for negative shocks to

> 3Rapoport and Docquier (2006) provide an exhaustive review of modern theoretical and empirical literature on remittances.

4
their income—the _altruism_ motive (Agarwal and Horowitz, 2002). Others find a positive correlation between remittances and the economic conditions of families back home, suggesting that remittances are driven by self-interest motives such as _investment_ or inheritance.<sup>4</sup> In any case, positive shocks to migrants’ income in host countries are likely to translate into larger remittances (Bettin _et al._ , 2012).<sup>5</sup> Likewise, some macroeconomic studies find that remittances are negatively correlated with income levels in the recipient country (El-Sakka and McNabb, 1999; Frankel, 2011; Singh _et al._ , 2011), mitigate the adverse effect of food-price shocks on the level and instability of household consumption in vulnerable countries (Combes _et al._ , 2014), reduce output growth volatility in developing economies (Bugamelli and Paternò, 2011; Chami _et al._ , 2012), and react positively to natural disasters (Yang, 2008; Mohapatra _et al._ , 2012; Ebeke and Combes, 2013). In contrast, other studies find that remittances are procyclical with respect to the recipient countries, consistent with an investment motive (Sayan, 2004, 2006; Lueth and Ruiz-Arranz, 2008; Giuliano and Ruiz-Arranz, 2009; Durdu and Sayan, 2010; Cooray and Mallick, 2013). Finally, some works do not find any significant correlation either with the business cycle in migrants’ home countries (Akkoyunlu and Kholodilin, 2008) or with specific shocks such as armed conflicts (Naudé and Bezuidenhout, 2012).

Ruiz and Vargas-Silva (2014) argue that results on the cyclicality of remittances with respect to the receiving economy are country- or corridor- specific and unlikely to be stable over time. In addition, how remittances’ react to negative economic shocks in recipient countries may depend on other country-level characteristics. Arezki and Brückner (2012), for example, show that the impact of rainfall-driven income shocks on remittance inflows decreases with the level of financial development in the country.<sup>6</sup>

# **2.2 Limitations of existing studies**

Cross-country analyses of the macroeconomic determinants of remittances typically use data on remittance inflows in developing countries, and disregard heterogeneity across source economies. To overcome this limitation, some studies use bilateral data on remittances to control for the host countries’ characteristics, such as output fluctuations. In most cases, however, the geographical coverage is limited to a single remittance corridor.<sup>7</sup>

> 4Lucas and Stark (1985) and Osili (2007) both show that remittances are positively correlated with the income of recipient households. Analogously, de la Briere _et al._ (2002) and Hoddinott (1994) show that remittances are positively correlated with household wealth.

> 5Macroeconomic studies have considered a wide range of potential determinants, including exchange rates (Faini, 1994), interest rate differentials (El-Sakka and McNabb, 1999), the size of the diaspora abroad and transaction costs (Freund and Spatafora, 2008), the skill and gender composition of migrant stocks (Faini, 2007; Adams Jr., 2009; Niimi _et al._ , 2010), and the interaction with immigration policies (Docquier _et al._ , 2012). Recent studies have also investigated whether remittances may represent an important channel in propagating global shocks (Chami _et al._ , 2010; Barajas _et al._ , 2012).

> 6Sayan (2006) considers a sample of 12 countries, and highlights the acyclical behavior of remittances in some countries. The differences between the results obtained for the whole group and for single countries translate into a warning that cross-country results might conceal possibly significant differences across individual countries.

> 7Sayan (2004) and Akkoyunlu and Kholodilin (2008) focus on the Germany-Turkey remittance corridor, while Vargas-Silva (2008) and Ruiz and Vargas-Silva (2014) look at U.S.-Mexico remittances. Durdu and Sayan (2010) consider both corridors.

5
A few studies use bilateral data but adopt a wider geographical perspective. Lueth and RuizArranz (2008) use a panel dataset on bilateral remittances for 11 European and Asian recipient countries during the period 1980-2004. Frankel (2011) merges their data with other bilateral data on remittances from the Inter-American Development Bank and the European Commission (JimenezMartin _et al._ , 2007). Docquier _et al._ (2012) merges the sources used by Frankel (2011) with a database from the European Central Bank and a Romanian database; the resulting dataset include 89 sending countries but is still limited to 46 receiving countries, both developing and developed.

As remittances represent a nonnegligible share of GDP in many recipient countries, the results of existing studies could be severely biased by reverse causality from remittances to GDP, with the exception of the few studies that focus on exogenous income shocks (see, for instance Yang, 2008). In cross-country aggregate level analyses this issue is seldom addressed by means of instrumental variables (Singh _et al._ , 2011) or GMM techniques (Cooray and Mallick, 2013). In the context of bilateral data, Frankel (2011) addresses endogeneity issues concerning the size of migrant stocks, but disregards the possible bias related to the receiving country’s GDP. Lueth and Ruiz-Arranz (2008) acknowledge the endogeneity problem and maintain that GMM estimates that use lagged values of growth in the recipient countries confirm their results about the procyclical pattern of remittances. However, it remains unclear whether GMM estimates that use lagged values of growth address the issue satisfactorily, as concerns about the capacity of GMM to address causality are mounting, for instance because of weak instruments and the over-fitting of endogenous variables (Roodman, 2009; Bazzi and Clemens, 2013).<sup>8</sup>

# **3 Empirical Strategy and Data**

## **3.1 The empirical model**

To identify the effect of business cycle fluctuations and financial development on remittances we estimate a model in which bilateral remittances are a function of a set of independent variables constructed by exploiting information on both migrants’ origin countries and their host Italian provinces. In the baseline specification, total bilateral remittances between the source province _i_ and the recipient country _j_ at time _t_ ( _REMi,j,t_ ) are a function of the logarithm of actual GDP per capita over potential GDP per capita in the source province ( _CY CLEi,t_ ) and in the recipient country ( _CY CLEj,t_ ), the log of trend GDP per capita ( _TRENDi,t_ , _TRENDj,t_ ), the log of 1 + the bilateral stocks of migrants ( _MIGRANTSi,j,t_ ), and the log of population levels ( _POPi,t_ , _POPj,t_ ): 8 Results on specific remittance corridors might also suffer from reverse causality. Sayan (2004) and Durdu and Sayan (2010) investigate the cross-correlations between remittances from Germany to Turkey and cyclical fluctuations in Turkish and German GDP without discussing the direction of causality. Akkoyunlu and Kholodilin (2008) estimate a VAR model and find no evidence in favor of Granger causality from remittances from Germany to Turkish GDP.

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where _εi,j,t_ is the standard error term. To control for any time invariant bilateral unobservables, we include country-province pair fixed effects ( _μi,j_ ) in equation 1.

The key coefficients of interest are the correlation between remittances and the business cycle in, respectively, the source province, _α_ 1, and the recipient country, _α_ 2. Remittances are countercyclical with respect to output fluctuations in the recipient country if _α_ 2 _<_ 0; this case suggests an altruistic motivations behind transfers. A positive correlation between remittances and the long-run output trend in the recipient country, _α_ 4 _>_ 0, instead offers evidence in favor of an investment motive for remittances: investment-driven remittances may be particularly sensitive to long-term prospects in migrants’ home country.

The model in equation 1 can be augmented to include additional source-province and recipientcountry controls: where _Xi,t_ and _Zj,t_ refer respectively to province- and country-level characteristics.

We deepen our analysis of the role of remittances as shock absorbers in recipient countries by including among the country-level characteristics, _Zj,t_ , three specific factors of vulnerability for developing countries: an indicator equal to 1 if country _j_ experienced natural disasters in year _t_ ( _DISASTERj,t_ ); an indicator equal to 1 if armed conflicts occurred in country _j_ at time _t_ ( _WARj,t_ ); and an indicator equal to 1 if country _j_ experienced a major negative shock to the terms of trade ( _TT SHOCKj,t_ ), defined as an observation falling in the lowest 5 percent of the distribution of the annual variation in the terms-of-trade index. Adverse shocks in these exogenous variables, controlling for output per capita, may be particularly likely to evoke a sympathetic (or, alternatively, insurance-type) response among migrants.

We also examine the impact of financial development on remittances. First, we consider differences in financial development across recipient countries, as proxied by the logarithm of the share of credit to the private sector over GDP ( _FINDEVj,t_ ). Their effect is a priori ambiguous. On the one hand, countries with more developed credit markets should attract greater remittances, as a result of either lower transaction costs (Freund and Spatafora, 2008), or the capacity of an efficient banking system to channel profit-driven remittances towards growth-enhancing projects (Bettin and Zazzaro, 2012). On the other hand, remittances and financial development may be substitutes: migrants whose relatives have limited access to financial resources at home may transfer resources to relax liquidity constraints and fund either consumption or investments in physical and human capital (Giuliano and Ruiz-Arranz, 2009).

Second, we consider differences in financial development across Italian provinces, the source of remittances. We expect more developed provincial financial markets to be correlated with greater remittance outflows, for two reasons. Greater provincial banking-sector penetration, as proxied by the number of local bank branches per inhabitant ( _BANKj,t_ ),<sup>9</sup> will reduce the trans-

> 9This is a widely used measure of local financial development; for an application to Italy, see Bonaccorsi di Patti and Gobbi (2001).

7
action costs associated with remittance transfers, and encourage greater remittances (particularly through the formal sector) (Freund and Spatafora, 2008).<sup>10</sup> In addition, the propensity of migrants to remit (again, particularly through formal channels) may depend on the institutional, cultural, and informational gaps between migrants and the host province’s financial system (Albareto and Mistrulli, 2011). We proxy these gaps using a measure of the functional distance between banks and local economies, based on whether banks are headquartered in the relevant province ( _FUNCTIONAL DISTANCEj,t_ ). Intuitively, when banks are headquartered in an area, they are better able to collect local information, and as a result are more likely to serve the economic needs of the area (Alessandrini _et al._ , 2009), including the needs of resident migrant workers.

Since the dependent variable _REMi,j,t_ has a significant share of non-randomly distributed zeros (that is, many empty country-province cells), equation 1 and 2 are estimated using the Fixed Effects Poisson estimator. Despite deriving originally from the analysis of count data, the Poisson estimator can also be applied to non-negative continuous variables (Wooldridge, 2010). Poisson regression estimates are consistent in the presence of heteroskedasticity and reasonably efficient, especially when considering large samples. Thanks to its multiplicative form, the Poisson specification also provides a natural way to deal with zero observations in the dependent variable instead of either transforming or excluding them from the sample (Silva and Tenreyro, 2006; Burger _et al._ , 2009). In the baseline estimates, we include country-province pair and time fixed effects and additionally control for the potential correlation of errors at the bilateral level by clustering standard errors by country-province pairs.<sup>11</sup>

# **3.2 Data sources**

The variables used in equations 1 and 2 are constructed using data collected from many sources. Here we provide an overview; a precise definition of each variable and of its sources is in Table 1.

The main data source is a detailed panel dataset on bilateral outward remittances from 103 Italian provinces to 87 developing countries, providing annual data at constant prices for the period 2005-11, compiled by the Bank of Italy (see Table 2 for a list of recipient countries included in the sample).<sup>12</sup> The dataset covers remittances sent through formal channels, and predominantly reflects transfers carried out through money-transfer operators and the postal system. The banking system has been included in the survey only since 2010, and accounts for 5 to 10 percent of total remittances. All formal transactions are reported, regardless of the amount. As a _caveat_ , the dataset does not include remittances sent through informal channels.

> 10 Ideally, we should rely on a more precise measure of transaction costs, such as the service fees charged by banks and money transfer operators for international transfers, as done by Freund and Spatafora (2008). However, those data are not available at the provincial level and for the time span of our analysis. We employ bank branches penetration as a proxy for transaction costs and for the level of financial development at the provincial level.

> 11In the robustness section, we control for unobservables by separately including country and province fixed effects instead of country-province pair fixed effects, so that we can also identify the effect of specific time invariant variables at bilateral level.

> 12Data on remittance flows to 204 destination countries are collected as part of a monthly survey carried out by the Bank of Italy on a provincial basis since 2005. The dataset is publicly available at: www.bancaditalia.it/statistiche/rapp_estero. Our sample is limited to the 87 developing countries where data on the control variables for the extended model specification are available.

8
Bilateral data on migrant stocks for the period 2005-11, collected by the Italian National Institute of Statistics (ISTAT) from the population registers of Italian municipalities, represent the stock of the foreign population resident in each Italian province, by citizenship, at the beginning of each year. Data on the age structure of the foreign resident population in each province are unavailable. Instead, in the robustness section we use the total growth rate of the number of migrants over 2005-11 in each province as a measure of how recently established a migrant community is. The data refer to the number of official foreign residents, and do not account for undocumented migrants residing in Italy.

For each recipient country, GDP at constant prices for the period 1950-2012 is drawn from the IMF _World Economic Outlook_ database. The cyclical and trend components are extracted using the Hodrick-Prescott filter. Data on total population for the period 2005-11, as well as the level of financial development, proxied by domestic credit to private sector as a share of GDP, are drawn from the _World Development Indicators_ database.

The annual frequency of natural disasters is drawn from the _International Emergency Disasters_ database (EM-DAT) built by the _Centre for Research on the Epidemiology of Disasters_ .<sup>13</sup> Data on armed conflicts are drawn from the UCDP/PRIO Armed Conflict Dataset (Themnér and Wallensteen, 2013).<sup>14</sup> The terms of trade are drawn from the IMF _World Economic Outlook_ database.

For each province, real GDP for the period 1995-2010 is drawn from ISTAT and the _Istituto Guglielmo Tagliacarne_ .<sup>15</sup> The cyclical and trend components are again extracted using the HodrickPrescott filter. Data on total provincial population for the period 2005-2011 are provided by ISTAT. The number of bank branches is provided by the Bank of Italy.<sup>16</sup>

# **4 Descriptive analysis**

## **4.1 Remittances from Italy to Developing Countries**

Total remittances from Italy to developing countries doubled between 2005 and 2011, reaching almost e 7 billion, in line with the growth in the stock of foreign residents in Italy (Figure 2). After 2007, however, the growth rate of remittances slowed down significantly, reflecting the impact of the global financial crisis and of the euro area crisis on Italian output and unemployment. Indeed, remittances declined in 2010, although 2011 saw a rapid recovery, consistent with the global pattern of international remittances (Figure 1, right panel).

The geographic distribution of remittances from Italy largely mimics the global distribution,

> 13 The data are accessible at www.cred.be/emdat/. A disaster is defined as a “situation or event, which overwhelms local capacity, necessitating a request to national or international level for external assistance”. Formally, an event is classified as a disaster whenever it fulfills at least one out of four selection criteria: ten or more people killed; 100 or more people affected, injured or homeless following the disaster; declaration of a state of emergency; or calls for international assistance. See www.emdat.be/criteria-and-definition.

> 14The most recent version (4-2013) is available at www.pcr.uu.se/research/ucdp/datasets/ucdp_prio_armed _conflict_dataset/.

> 15Data from ISTAT cover the period 1995-2007 while those from _Istituto Guglielmo Tagliacarne_ go from 2007 to 2010. The two series hence overlap in two years, highlighting minor differences.

> 16Data on the provincial presence of money-transfer operators, which could represent a better measure of the access of migrants to remittance-transfer services, are not publicly available for the period of the analysis.

9
Figure 2: Remittances outflows to developing countries and foreign residents in Italy <!-- Start of picture text --> 7000 40<br>6000<br>30<br>5000<br>20<br>4000<br>10<br>3000<br>0<br>2000<br>Year<br>Remittance outflows Foreign residents<br>% change in remittances<br>Source: Bank of Italy and ISTAT.<br>Figure 3: Remittances by region of destination<br>100%  100%<br>90%  90%<br>80%  80%<br>70%  70%<br>60%  60%<br>50%  50%<br>40%  40%<br>30%  30%<br>20%  20%<br>10%  10%<br>0%  0%<br>2005  2006  2007  2008  2009  2010  2011  2005  2006  2007  2008  2009  2010  2011<br>East  Asia  and  Pacific  Europe  and  Central  Asia  La@n  America  and  Caribbean  East  Asia  and  Pacific  Europe  and  Central  Asia  La@n  America  and  Caribbean<br>Middle  East  and  Northern  Africa  South  Asia  Sub-­‐Saharan  Africa  Middle  East  and  Northern  Africa  South  Asia  Sub-­‐Saharan  Africa<br>(a) Italy (b) World<br>2005 2006 2007 2008 2009 2010 2011<br>Remittance outflows (millions, Euro)<br>Stock of foreign residents (thousands)<br>Annual change in remittance outflows (%)<br><!-- End of picture text --> Source: Bank of Italy and World Bank Migration & Remittances Factbook 2011.

suggesting that Italy represents a relevant and representative case study (Figure 3). The East Asia and Pacific region is the main recipient of both Italian and global remittances to developing countries. The region’s share of remittances from Italy increased by 10 percentage points between 2005 and 2011. Europe and Central Asia’s share of remittances from Italy is twice as high as its share of global remittances, reflecting the relatively large number of migrants from Eastern Europe in Italy. South Asia accounts for a rising share of remittances from both Italy and the world. In contrast, Sub-Saharan Africa accounts for a limited share of remittances.

10
Focusing on individual countries, China, Romania, and the Philippines were the major recipients of remittances from Italy in both 2005 and 2011.<sup>17</sup> Transfers to Bangladesh, Sri Lanka and Georgia increased dramatically between 2005 and 2011. Colombia is the only country listed that registered a decrease in remittances from Italy over this period. The stock of resident migrants by country of origin is positively correlated with remittances to the relevant recipient country in 2011.<sup>18</sup>

# **4.2 Provincial heterogeneity**

Italy is highly heterogeneous in terms of economic and financial development. Figure 4 (Panels a and b) shows the geographic dispersion, over the sample period, of GDP per capita and financial development, as proxied by the functional distance of local credit markets. There is a strong divide between provinces in the North and in the South of the country. In addition, even within the North and the South, there are significant differences across provinces. The geographical dispersion of the degree of functional distance is greater than that of GDP, and follows a somewhat different pattern, so that financial and economic development cannot be considered two sides of the same coin (Alessandrini _et al._ , 2010).

Since we are interested in the impact of business cycle fluctuations on remittance outflows, it is important to consider not just the level but also the growth rate of provincial GDP. Figure 4 (Panel c) shows that the economic slowdown during 2008-10 affected different Italian provinces differently, without any clear North/South divide. Real GDP contracted by 6.8% (its largest drop) in the southern province of Matera and by 5.4% in the northern province of Turin, while it increased by 3.7% in the southern province of Catanzaro and by 3.8% in the northern province of Pavia.

Migrants’ remittances also show a great degree of variability across Italian provinces (Figure 4, panel d). Between 2005 and 2010, in the median province remittance outflows on average accounted for almost 0.2% of GDP, ranging from less than 0.1% of GDP in ten Italian provinces to 0.3% of GDP in Genoa, 0.4% in Milan, 0.6% in Florence, 0.8% in Rome and 3.9%, the maximum, in Prato. Larger remittance outflows reflect primarily the presence of larger communities of migrants, and do not mirror the North/South economic divide.

In sum, the richness of the dataset in terms of destination countries and the heterogeneity across source provinces make it possible to identify how remittances react to economic shocks in not just destination countries but also source economies. In addition, we can assess how a contemporaneous negative shock in source and destination economies affects overall remittance flows. Finally, the provincial heterogeneity in credit-market structure enables us to assess the effect of source-economy financial development on remittances.

> 17The Italy-China remittance corridor was the single most important at the EU level in 2010. The Italy-Romania and Italy-Philippines corridors were among the ten biggest corridors from Europe. See http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Migrant_remittance_and_crossborder_or_seasonal_compensation_transfer_statistics.

> 18There are some outliers, notably China, whose share of total remittances significantly exceeds its share of total migrants. This may reflect an incorrect classification of some trade payments to China as remittances. When estimating our baseline model, we therefore exclude China. However, when discussing the robustness of the results, we also present estimates including China.

11
Figure 4: Remittances, economic and financial development across Italian provinces <!-- Start of picture text --> Quintiles Quintiles<br>[12718,15712] [.841,2.52]<br>(15712,20732] (2.52,3.07]<br>(20732,24357] (3.07,3.6]<br>(24357,26732] (3.6,4.46]<br>(26732,35061] (4.46,5.92]<br>(a) Per capita GDP (aver. 2005-10) (b) Functional distance (aver. 2005-10)<br>Quintiles<br>[-.0678,-.0215] Quintiles<br>(-.0215,-.0102] [.000742,.00127]<br>(-.0102,-.0000343] (.00127,.00165]<br>(-.0000343,.0116] (.00165,.00203]<br>(.0116,.0833]No data (.00203,.00242](.00242,.0394]<br>(c) GDP growth 2008-2010 (d) Remittances as a share of GDP (aver.<br>2005-10)<br><!-- End of picture text --> Source: Bank of Italy, ISTAT and Istituto Tagliacarne. See Table 1 for definitions.

# **5 Results**

## **5.1 Remittances as a countercyclical financial flow**

Remittances increase in response to cyclical output declines in the recipient country, _CY CLEj_ . The response is statistically significant across all specifications in Table 3. The elasticities range between 1.8 and 2.5. This suggests that remittances can indeed play a significant role in stabilizing output during downturns, smoothing consumption, and mitigating the effects of macroeconomic fluctuations in developing countries.

Even after controlling for output per capita, remittances increase significantly in response to different factors of vulnerability in recipient countries. Remittances are approximately 3 percent larger when recipient countries experienced natural disasters (Yang, 2008; Mohapatra _et al._ , 2012; Ebeke and Combes, 2013), and 7.6 percent larger in the case of a significant terms-of-trade deterioration (see Table 3, column 6 and 7, respectively).<sup>19</sup> The outbreak of armed conflicts is not associated with

> 19 Such effects are computed by means of the following formula: (exp( _βi_ ) _−_ 1) _∗_ 100, where _βi_ is the estimated

12
a significant impact on remittances, in line with Naudé and Bezuidenhout (2012).<sup>20</sup> These results are consistent with a particularly altruistic response to major and/or clearly exogenous shocks.

Remittances are also positively and significantly correlated with trend GDP per capita in recipient countries, _TRENDj_ , across all specifications. This supports the hypothesis that remittances are at least partly driven by investment motives.

Moreover, remittances seem to be influenced by economic conditions in the migrants’ host province, consistent with Barajas _et al._ (2012), although the significance of the coefficient on _CY CLEi_ varies across specifications. As discussed, recessions may have a significant impact on relatively low-skilled migrant workers reducing their capacity to remit. A one percentage point reduction in provincial GDP relative to its long-term trend, _CY CLEi_ , on average translates into a 1.6 percentage point reduction in transfers from that province. Nevertheless, a twin shock to both source province and recipient country (equal to one standard deviation of, respectively, _CY CLEi_ and _CY CLEj_ ) boosts overall remittances, although the positive effect is small (0.03 percentage points; calculations based on estimates in Table 3, column 4).

# **5.2 Remittances and financial development**

Remittances are negatively correlated with financial development in recipient countries (Table 3, column 9). On average, a 1 percent reduction in the ratio of domestic credit to the private sector over GDP translates into a 0.7 percent increase in migrants’ transfers. This suggests that remittances may help overcome the financing constraints of households living in countries with less efficient financial institutions, in line with Giuliano and Ruiz-Arranz (2009).

By contrast, remittances are positively correlated with financial development in the _source_ province. Specifically, remittances decrease with the functional distance of the provincial banking system from the host province (column 11). The impact of local financial development on remittance outflows is economically significant: a reduction of the functional distance of the banking system from the highest level observed (in Messina) to the lowest level (Bolzano) is associated with a 0.9 percent increase in remittances.<sup>21</sup> The coefficient on the number of bank branches per inhabitant, although positive, is not statistically significant (column 10).

These results continue to hold, and the magnitude of the coefficients remains relatively unchanged, when controlling jointly for all three measures of source-province and recipient-country financial development (column 12), as well as all sources of vulnerability in recipient countries (column 13).

# **5.3 Other results**

Bilateral remittances are, not surprisingly, strongly correlated with the size of the relevant migrant community in the relevant province. The elasticity is generally around 0.5, and does not vary significantly across alternative specifications.

> 20These results continue to hold when disasters are expressed in terms of their annual frequency, and when the terms of trade are entered as the total terms-of-trade index. In addition, when armed conflicts are expressed in terms of their annual frequency, they have a significant, positive impact on remittances.

> 21Calculations based on data for 2010 and on the estimation results in Table 3, column 12.

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The populations of both the host Italian province, _POPi_ , and the home country, _POPj_ , are positively correlated with remittances, even after controlling for migrant stocks, indicating the presence of scale effects. Larger host-province populations may reflect better employment opportunities for migrants; larger home-country populations may reflect better investment opportunities for remitters.

# **5.4 Robustness checks**

This section tests the robustness of the findings. We first investigate the impact of changes in the sample composition (Table 4). We then allow for additional covariates (Table 5). Finally, we employ a different estimation method (Table 6).

**Different samples** The sample underlying our earlier results excludes remittances to China: these appear to be an outlier, possibly reflecting poor data quality (see footnote 18). However, we also estimate the baseline model including China, since this is the largest recipient of remittances from Italy. The earlier results are largely confirmed (Table 4, column 1).

We also restrict our sample to low-income countries, to analyze whether the countercyclical behavior of remittances depends on the recipient country’s income level. However, the coefficient on _CY CLEj_ remains negative, significant and similar in size to the baseline model in Table 3, column 4 despite the big drop in the number of observations.

Finally, we drop observations which may add noise and lead to small-sample bias. In particular, we are concerned about province-country pairs that are characterized by a limited number of resident migrants. Here, remittances may be driven by idiosyncratic factors, which could be largely unrelated to macroeconomic conditions in the recipient country as a whole. To avoid this possibility, we exclude all observations where the migrant community numbers less than 100 migrants ( _MIGRijt <_ 100). Although this threshold reduces the original sample by almost three-quarters, the results from our baseline model remain valid (column 4).<sup>22</sup> A related concern is that, in large recipient countries, macroeconomic conditions could be highly heterogeneous within the country. Further, migrant remittances may be largely driven by conditions within some region of the country, rather than in the recipient country as a whole. Hence, we drop from the sample the recipient countries with the largest population (Bangladesh, Brazil, China, India, Indonesia, Nigeria and Pakistan)<sup>23</sup> . Again, our general findings are confirmed.

**Additional covariates** We next augment our baseline model with a set of additional regressors (Table 5). In the first column we control for foreign aid (measured as aid per capita); aid and remittances are substitutes, in line with Amuedo-Dorantes _et al._ (2007). Columns 2 to 4 add, one by one, the different measures of macroeconomic stability. Remittances are larger in countries with better macroeconomic and institutional conditions. The coefficients on the fiscal balance (as a ratio of GDP) and on the ratio of external debt over GDP are, respectively, positive and negative, although the latter is not statistically significant. The elasticity of remittances to _CY CLEj_ , however, is

> 22 Results are robust to alternative specifications of the threshold up to _MIGRijt <_ 500.

> 23We drop countries with a total population above the 95th percentile of the sample distribution.

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almost unaffected. The positive coefficient on the variable measuring constraints on the executive indicates that remittances are larger for countries with stronger institutions (Singh _et al._ , 2011).

Finally, the negative effect on remittances of the cyclical component of output in recipient countries holds even when controlling jointly for these covariates (column 5).

**Different estimator** The last robustness exercise relates to the estimation method. Here, we drop country-province pair fixed effects, enabling us to identify the effect of variables which vary only across province-country pairs and not over time, such as the log of distance between province _i_ and country _j_ ( _DISTANCEi,j_ )<sup>24</sup> and the percentage growth in the bilateral stocks of migrants (∆ _MIGRANTSi,j_ ).

We use the Poisson Pseudo-Maximum Likelihood estimator (Silva and Tenreyro, 2006), which provides unbiased estimates in presence of heteroskedasticity and a high proportion of zeros in the dependent variable. The specification separately includes country and province fixed effects, instead of province-country pair fixed effects; however, we control for the potential correlation of errors at the bilateral level by clustering standard errors by country-province pairs. Our main results from the baseline and the augmented specification, and in particular the significant negative coefficient on _CY CLEj_ , are largely confirmed even when including separate province and country fixed effects (Tables 6). The magnitude of the elasticities is also quite similar, lending support to the validity of our main findings. In addition, remittances are also positively correlated with the fraction of recent migrants (as proxied by ∆ _MIGRANTSi,j_ , the growth rate of the migrant stock over the period 2003-11), with an elasticity of around 0.8. This may reflect either altruism or investment motives. Recent migrants are more likely than older migrants to have strong emotional ties to their home country, including to relatives and friends left behind. They may also need to repay family loans used to defray migration costs. At the same time, recent migrants are more likely to be aware of solid investment opportunities in their home country. They may also be more likely to return, increasing their incentive to invest, for instance in real estate.

The distance to migrants’ home country, _DISTANCEi,j_ , is positively correlated with remittances. A priori, we would instead expect distance to be positively correlated with remittance transfer costs, and therefore negatively correlated with remittances. The result may arise because remittance data only takes into account official transactions. Migrants from nearby regions, such as Eastern Europe or the Mediterranean, may send remittances informally, for instance bringing them in person when they travel back home. In contrast, migrants from distant countries are relatively more likely to use formal, if expensive, remittance channels.

# **6 Conclusions**

This paper examines the role of remittances as a source of external finance that may help mitigate the macroeconomic and external vulnerabilities of developing countries. The global financial crisis and the volatility of commodity prices have hit developing countries severely; increasing their

> 24Bilateral distances (in kilometers) between Italian provinces and recipient countries are calculated using the geographical coordinates of the administrative capitals of provinces and nations.

15
resilience to external shocks is a key objective of international financial institutions and policymakers. Concessional lending and foreign aid are traditional ways to address vulnerabilities, but their effectiveness is highly disputed. The use of contingent financing instruments has so far been quite limited (International Monetary Fund and World Bank, 2011). Many countries are increasingly relying on international reserves as a stabilization tool, but this imposes high social and economic costs (Rodrik, 2006). Removing barriers to remittances may be a useful complement to such measures.

We analyze how remittances are affected by structural characteristics, macroeconomic conditions, and adverse shocks in both source and recipient economies, using a novel, rich panel dataset on bilateral remittances from 103 Italian provinces to 87 developing countries over the period 20052011. Remittances are negatively correlated with the business cycle in recipient countries, and increase especially strongly in response to adverse exogenous shocks, such as natural disasters or large declines in the terms of trade. In addition, remittances are positively correlated with potential GDP in recipient countries. These results are consistent with remittances being driven by both altruism and investment motives.

Remittances are also positively correlated with economic conditions in the source province. Nevertheless, in the presence of similar negative shocks to both source and recipient economies, remittances remain countercyclical with respect to the recipient country. All these results are robust to potential reverse causality from remittances to macroeconomic conditions in the recipient country.

Financial development in the source province is positively associated with remittances, likely because it reduces transaction costs and eases access to financial services for migrants. Conversely, financial development in the recipient country is negatively associated with remittances, suggesting that remittances help alleviate credit constraints. All these results hold even controlling for unobserved country-province pairs fixed effects, which capture time-invariant institutional and geographical factors which may drive remittance flows.

We conclude that remittances may indeed contribute significantly to macroeconomic stability in recipient countries. This effect should be considered together with their positive impact on poverty alleviation and growth, emphasized in the existing literature. From a policy perspective, our results suggest that one way to increase developing countries’ resilience to exogenous shocks is to increase their reliance on remittances, rather than on more volatile capital inflows. One – direct – way to achieve this goal is to reduce barrier to international migration (Clemens, 2011). A second way is to promote migrants’ access to financial services in host countries. Overall, these findings corroborate the efforts being carried out by international financial institutions and the private sector to reduce the costs of migrant remittances and to foster freer remittance flows.

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# **Tables**

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|St. Dev.|1.14E+07<br>0.029<br>0.031<br>0.251<br>2.493<br>2.096<br>0.376<br>0.437<br>0.214<br>0.734<br>1.592<br>0.798<br>0.370<br>0.341|
|---|---|
|Mean|652969.4<br>0.000<br>0.001<br>9.994<br>10.837<br>3.220<br>0.170<br>0.742<br>0.048<br>13.072<br>16.554<br>3.257<br>1.744<br>1.176|
|Source|Bank of Italy<br>ISTAT and Istituto<br>Tagliacarne<br>WEO<br>ISTAT and Istituto<br>Tagliacarne<br>WEO<br>ISTAT<br>UCDP/PRIO Armed<br>Confict Dataset<br>EM-DAT, CRED<br>WEO<br>ISTAT<br>WDI<br>WDI<br>Bank of Italy and IS-<br>TAT<br>Bank of Italy|
|Defnition|Total ofcial remittances at constant prices from province _i_ to country<br>_j_ in year _t_<br>Logarithm of actual GDP over potential GDP in province _i_ in year<br>_t_; potential GDP is calculated by applying the H-P flter to the GDP<br>series at constant prices<br>Logarithm of actual GDP over potential GDP in country _j_ in year _t_;<br>potential GDP is calculated by applying the H-P flter to the GDP<br>series at constant prices<br>Logarithm of potential GDP in province _i_ in year _t_, calculated by ap-<br>plying the H-P flter to the GDP series at constant prices<br>Logarithm of potential GDP in country _j_ in year _t_, calculated by ap-<br>plying the H-P flter to the GDP series at constant prices<br>Logarithm of 1 + the stock of migrants living in province _i_ and coming<br>from country _j_ in year _t_<br>Indicator = 1 if country _j_ experienced armed conficts in year _t_; both<br>interstate and intrastate conficts are considered, in which the govern-<br>ment of country _j_ represents one of the warring parties<br>Indicator = 1 if country _j_ experienced natural disasters in year _t_<br>Indicator = 1 if country _j_ experienced a large negative shock to the<br>terms of trade, defned as an observation falling in the lowest 5 percent<br>of the distribution of the annual variation in the terms-of-trade index<br>Logarithm of population in province _i_ in year _t_<br>Logarithm of population in country _j_ in year _t_<br>Logarithm of the ratio of domestic credit to the private sector over<br>GDP in country _j_ in year _t_<br>Logarithm of the number of bank branches per 10,000 inhabitant in<br>province _i_ in year _t_<br>Logarithm of the ratio of the number of branches in province_i_weighted<br>by the logarithm of 1 plus the kilometric distance between the province<br>of the branch and the province where the parent bank is headquartered,<br>over total branches in the province _i_ in year _t_.|
|Variable|_REMi,j,t_<br>_CY CLEi,t_<br>_CY CLEj,t_<br>_TRENDi,t_<br>_TRENDj,t_<br>_MIGRANTSi,j,t_<br>_WARi,t_<br>_DISASTERi,t_<br>_TT SHOCKi, t_<br>_POPi,t_<br>_POPj,t_<br>_FINDEVi,t_<br>_BANKi,t_<br>_FUNCTIONAL_<br>_DISTANCEi,t_|

22
|St. Dev.|1.386<br>0.043<br>0.591<br>1.904<br>0.107|0.764|
|---|---|---|
|Mean|3.296<br>-0.015<br>0.465<br>4.733<br>0.037|8.415|
||- Center for<br>Peace|TATA rou-|
|Source|WDI<br>WDI<br>WDI<br>Polity IV<br>Systemic <br>ISTAT|Built-in S<br>tine|
||ta received in country _j_ in year _t_<br>it) as a share of GDP in country<br>f GDP in country _j_ in year _t_<br>dex in country _j_ in year _t_ (1 =<br>ve parity or subordination)<br> _MIGij_ over 2003-2011|ce between province_i_and country<br>O: World Economic Outlook (IMF).|
||i<br>fc<br> o<br>n<br>ti<br>k|an<br>E|
||cap<br> de<br>re <br>s’ i<br>cu<br>toc|ist<br> W|
||r <br> - <br>ha<br>e<br>xe<br> s|d<br>);|
|tion|ithm of ofcial aid pe<br> balance (+ surplus/ <br>ear _t_<br>nal debt stocks as a s<br>raint on the executiv<br>ited authority; 7 = E<br>th rate of the migrant|ithm of the kilometric<br>icators (The World Bank|
|ni|ar<br>al<br> y<br>r<br>st<br>m<br>w|ar<br>nd|
|Def|Log<br>Fisc<br>_j_ in <br>Exte<br>Con<br>unli<br>Gro|Log<br>_j_<br>pment I|
||_CEi,t_<br>_NAL_<br>_ONSTi,t_<br>_ANTSi,j_|_CEi,j_<br> World Develo|
|ble|_i,t_<br>_CAL_<br>_AN_<br>_ER_<br>_Ti,t_<br>_C C_<br>_GR_|_TAN_<br>WDI:|
|ia|_D_<br>_S_<br>_L_<br>_T_<br>_B_<br>_E_<br>_I_|_S_<br>|
|Var|_AI_<br>_FI_<br>_BA_<br>_EX_<br>_DE_<br>_EX_<br>∆_M_|_DI_<br>_Notes_:|

23
Table 2: List of countries

|Albania|Gabon|Morocco|
|---|---|---|
|Algeria|Gambia|Mozambique|
|Argentina|Georgia|Nicaragua|
|Armenia|Ghana|Niger|
|Azerbaijan|Guatemala|Nigeria|
|Bangladesh|Guinea|Pakistan|
|Belarus|Guinea-Bissau|Panama|
|Benin|Haiti|Paraguay|
|Bolivia|Honduras|Peru|
|Bosnia and Herzegovina|India|Philippines|
|Brazil|Indonesia|Russia|
|Bulgaria|Iran|Senegal|
|Burkina Faso|Jamaica|Seychelles|
|Burundi|Jordan|Sierra Leone|
|Cambodia|Kazakhstan|South Africa|
|Cameroon|Kenya|Sri Lanka|
|Cape Verde|Kyrgyzstan|Sudan|
|Chad|Laos|Tanzania|
|Chile|Lebanon|Thailand|
|Colombia|Libya|Togo|
|Congo|Lithuania|Tunisia|
|Costa Rica|Macedonia|Turkey|
|Cote d’Ivoire|Madagascar|Uganda|
|Dominica|Malaysia|Ukraine|
|Dominican Rep.|Mali|Uruguay|
|Ecuador|Mauritania|Venezuela|
|Egypt|Mauritius|Vietnam|
|El Salvador|Mexico|Yemen|
|Ethiopia|Moldova|Zambia|

24
|(13)<br>0.475***<br>[0.113]<br>1.543**<br>[0.766]<br>1.502*<br>[0.808]<br>1.147*<br>[0.634]<br>-1.857***<br>[0.702]<br>0.712<br>[0.475]<br>1.978***<br>[0.497]<br>0.003<br>[0.052]<br>0.011<br>[0.018]<br>0.177***<br>[0.039]<br>-0.702***<br>[0.138]<br>0.430<br>[0.449]<br>-0.467***<br>[0.177]<br>34,494<br>5,881<br>%; **<br>fcial<br>uded.|
|---|
|(12)<br>0.475***<br>[0.113]<br>1.531**<br>[0.768]<br>1.637**<br>[0.827]<br>1.131*<br>[0.633]<br>-1.798***<br>[0.690]<br>0.702<br>[0.482]<br>1.966***<br>[0.511]<br>-0.681***<br>[0.136]<br>0.431<br>[0.447]<br>-0.466***<br>[0.177]<br>34,494<br>5,881<br>cant at 10<br>e of total o<br>ies are incl|
|(11)<br>0.498***<br>[0.122]<br>0.856<br>[0.570]<br>4.380***<br>[1.137]<br>0.824<br>[0.701]<br>-2.485***<br>[0.693]<br>0.412<br>[0.526]<br>1.954***<br>[0.516]<br>-0.454**<br>[0.210]<br>34,494<br>5,881<br>rs. * signif<br> is the valu<br>r (_t_) dumm|
|(10)<br>0.501***<br>[0.125]<br>1.859*<br>[1.016]<br>4.328***<br>[1.187]<br>1.726*<br>[0.953]<br>-2.472***<br>[0.695]<br>0.549<br>[0.548]<br>1.904***<br>[0.519]<br>0.400<br>[0.463]<br>34,494<br>5,881<br>rovince pai<br>nt variable <br>_, j_) and yea|
|(9)<br>0.473***<br>[0.111]<br>1.760**<br>[0.690]<br>1.649*<br>[0.847]<br>1.941**<br>[0.807]<br>-1.780**<br>[0.691]<br>0.915*<br>[0.498]<br>1.917***<br>[0.510]<br>-0.680***<br>[0.148]<br>34,494<br>5,881<br>y country-p<br>he depende<br>ntry pairs (_i_|
|(8)<br>0.497***<br>[0.122]<br>1.455*<br>[0.743]<br>4.256***<br>[1.233]<br>1.658*<br>[0.930]<br>-2.534***<br>[0.697]<br>0.554<br>[0.545]<br>1.977***<br>[0.524]<br>-0.078<br>[0.050]<br>0.038**<br>[0.017]<br>0.101***<br>[0.034]<br>34,494<br>5,881<br>lustered b<br>timator. T<br>ovince-cou|
|(7)<br>0.498***<br>[0.123]<br>1.486**<br>[0.735]<br>4.349***<br>[1.218]<br>1.675*<br>[0.920]<br>-2.492***<br>[0.694]<br>0.590<br>[0.543]<br>1.909***<br>[0.518]<br>0.073**<br>[0.032]<br>34,494<br>5,881<br>rd errors c<br>d Efects es<br>a set of pr|
|(6)<br>0.503***<br>[0.123]<br>1.478**<br>[0.737]<br>4.290***<br>[1.226]<br>1.681*<br>[0.922]<br>-2.571***<br>[0.691]<br>0.567<br>[0.542]<br>1.921***<br>[0.518]<br>0.031*<br>[0.018]<br>34,494<br>5,881<br>bust standa<br>oisson Fixe<br>onstant and|
|(5)<br>0.493***<br>[0.122]<br>1.462**<br>[0.741]<br>4.354***<br>[1.213]<br>1.650*<br>[0.920]<br>-2.395***<br>[0.702]<br>0.575<br>[0.543]<br>1.946***<br>[0.524]<br>-0.067<br>[0.050]<br>34,494<br>5,881<br>sociated ro<br>using the P<br>_Mi,j,t_). A c|
|(4)<br>0.499***<br>[0.123]<br>1.483**<br>[0.736]<br>4.360***<br>[1.213]<br>1.674*<br>[0.917]<br>-2.470***<br>[0.694]<br>0.587<br>[0.542]<br>1.906***<br>[0.519]<br>34,494<br>5,881<br>ets) the as<br>ed out by <br>ear_t_(_RE_|
|(3)<br>0.509***<br>[0.125]<br>1.106*<br>[0.652]<br>3.152**<br>[1.245]<br>1.431*<br>[0.858]<br>-1.787**<br>[0.731]<br>34,494<br>5,881<br> (in brack<br>s are carri<br>ntry_j_ in y|
|(2)<br>0.509***<br>[0.125]<br>1.106*<br>[0.652]<br>3.152**<br>[1.245]<br>1.431*<br>[0.858]<br>-1.787**<br>[0.731]<br>34,494<br>5,881<br>fcients and<br> Estimation<br>ince_i_to cou|
|(1)<br>MIGRANTS_i,j,t_<br>0.537***<br>[0.138]<br>POP_i,t_<br>0.381<br>[0.300]<br>POP_j,t_<br>2.913**<br>[1.275]<br>CYCLE_i,t_<br>CYCLE_j,t_<br>TREND_i,t_<br>TREND_j,t_<br>WAR_j,t_<br>DISASTER_j,t_<br>TT SHOCK_j,t_<br>FINDEV_j,t_<br>BANK_i,t_<br>FUNCTIONAL DISTANCE_i,t_<br>Observations<br>34,494<br>Number of pair<br>5,881<br>_Notes_: The table reports regression coe<br>signifcant at 5%; *** signifcant at 1%. <br>remittances at constant prices from prov|

25
Table 4: Robustness: sample definition

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Including<br>China|Low Income<br>Countries|Large migrant<br>communities|No large<br>recipients|
|CYCLE_i,t_|1.526|0.290|1.712*|1.642|
||[1.005]|[1.215]|[0.971]|[1.016]|
|CYCLE_j,t_|-2.505***|-2.033*|-2.914***|-2.469***|
||[0.924]|[1.146]|[0.711]|[0.745]|
|TREND_i,t_|0.234|3.361**|0.724|0.643|
||[0.671]|[1.696]|[0.570]|[0.571]|
|TREND_j,t_|1.732**|-1.345*|1.931***|2.563***|
||[0.766]|[0.698]|[0.607]|[0.612]|
|MIGRANTS_i,j,t_|0.120**|0.445***|0.729***|0.523***|
||[0.055]|[0.095]|[0.193]|[0.140]|
|POP_i,t_|0.329|1.816**|1.554**|1.524*|
||[0.871]|[0.854]|[0.766]|[0.805]|
|POP_j,t_|4.484***|-17.825***|4.888***|4.583***|
||[1.474]|[1.652]|[1.405]|[1.446]|
|Observations|38,851|6,686|10,127|31,939|
|Number of pairs|6,563|1,175|1,845|5,501|

_Notes_ : The table reports regression coefficients and (in brackets) the associated robust standard errors clustered by country-province pairs. * significant at 10% level; ** significant at 5% level; *** significant at 1% level. Estimations are carried out by using the Poisson Fixed Effects estimator. The dependent variable is the value of total official remittances at constant prices from province _i_ to country _j_ in year _t_ ( _REMi,j,t_ ). A constant and a set of provincecountry pairs ( _i, j_ ) and year ( _t_ ) indicators are included.

26
Table 5: Robustness: additional covariates

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|CYCLE_i,t_|1.266*|1.562|1.699*|1.813*|1.878|
||[0.705]|[1.051]|[0.916]|[1.088]|[1.471]|
|CYCLE_j,t_|-2.609***|-3.668***|-2.883***|-2.922***|-3.371***|
||[0.399]|[0.922]|[0.761]|[0.483]|[1.243]|
|TREND_i,t_|0.588|0.508|0.663|1.364**|1.318|
||[0.564]|[0.647]|[0.527]|[0.682]|[0.855]|
|TREND_j,t_|1.379***|1.563*|2.038***|0.417|1.820***|
||[0.469]|[0.864]|[0.570]|[0.549]|[0.598]|
|MIGRANTS_i,j,t_|0.661***|0.574***|0.519***|0.337***|0.362***|
||[0.108]|[0.153]|[0.131]|[0.071]|[0.078]|
|POP_i,t_|1.285**|1.528*|1.533**|1.306|1.729|
||[0.616]|[0.917]|[0.757]|[0.941]|[1.262]|
|POP_j,t_|2.638***|5.061***|3.774***|1.277|-6.691**|
||[0.947]|[1.832]|[0.971]|[0.846]|[2.663]|
|AID_j,t_|-0.108***||||-0.218***|
||[0.013]||||[0.037]|
|FISCAL BALANCE_j,t_||2.521***|||1.344**|
|||[0.753]|||[0.641]|
|EXTERNAL DEBT_j,t_|||-0.212||-0.664|
||||[0.313]||[0.525]|
|EXEC CONST_jt_||||0.117***|0.282***|
|_,_||||[0.029]|[0.044]|
|Observations|32,055|24,152|34,127|15,996|10,706|
|Number of pairs|5,496|4,402|5,800|2,701|1,903|

_Notes_ : The table reports regression coefficients and (in brackets) the associated robust standard errors clustered by country-province pairs. * significant at 10% level; ** significant at 5% level; *** significant at 1% level. Estimations are carried out by using the Poisson Fixed Effects estimator. The dependent variable is the value of total official remittances at constant prices from province _i_ to country _j_ in year _t_ ( _REMi,j,t_ ). A constant and a set of provincecountry pairs ( _i, j_ ) and year ( _t_ ) indicators are included.

27
|(13)<br>0.845<br>[0.658]<br>-1.640**<br>[0.694]<br>0.609<br>[0.498]<br>1.807***<br>[0.526]<br>0.911***<br>[0.036]<br>0.861***<br>[0.134]<br>1.297<br>[0.796]<br>2.413***<br>[0.838]<br>0.315**<br>[0.152]<br>0.018<br>[0.053]<br>0.018<br>[0.019]<br>0.183***<br>[0.040]<br>-0.728***<br>[0.141]<br>0.512<br>[0.459]<br>-0.501***<br>[0.178]<br>34,494<br>5,881<br>%; **<br>006).<br>vince|
|---|
|(12)<br>0.825<br>[0.658]<br>-1.544**<br>[0.683]<br>0.584<br>[0.502]<br>1.799***<br>[0.541]<br>0.911***<br>[0.036]<br>0.860***<br>[0.134]<br>1.281<br>[0.798]<br>2.582***<br>[0.858]<br>0.315**<br>[0.152]<br>-0.703***<br>[0.137]<br>0.518<br>[0.457]<br>-0.499***<br>[0.178]<br>34,494<br>5,881<br>cant at 10<br>Tenreyro, 2<br> set of pro|
|(11)<br>0.495<br>[0.720]<br>-2.264***<br>[0.693]<br>0.344<br>[0.551]<br>1.796***<br>[0.544]<br>0.911***<br>[0.037]<br>0.853***<br>[0.133]<br>0.511<br>[0.574]<br>5.355***<br>[1.131]<br>0.314**<br>[0.152]<br>-0.467**<br>[0.206]<br>34,494<br>5,881<br>rs. * signif<br> (Silva and <br>nstant and a|
|(10)<br>1.426<br>[0.974]<br>-2.240***<br>[0.692]<br>0.482<br>[0.582]<br>1.779***<br>[0.543]<br>0.911***<br>[0.037]<br>0.852***<br>[0.133]<br>1.570<br>[1.019]<br>5.208***<br>[1.130]<br>0.316**<br>[0.153]<br>0.442<br>[0.467]<br>34,494<br>5,881<br>ovince pai<br> estimator <br>_,j,t_). A co|
|(9)<br>1.675**<br>[0.832]<br>-1.539**<br>[0.685]<br>0.816<br>[0.526]<br>1.787***<br>[0.535]<br>0.911***<br>[0.036]<br>0.859***<br>[0.133]<br>1.466**<br>[0.703]<br>2.558***<br>[0.863]<br>0.319**<br>[0.153]<br>-0.693***<br>[0.146]<br>34,494<br>5,881<br>y country-pr<br>od (PPML) <br>ar _t_ (_REMi_|
|(8)<br>1.356<br>[0.946]<br>-2.349***<br>[0.693]<br>0.501<br>[0.583]<br>1.850***<br>[0.547]<br>0.911***<br>[0.037]<br>0.854***<br>[0.133]<br>1.133<br>[0.742]<br>5.133***<br>[1.187]<br>0.316**<br>[0.153]<br>-0.067<br>[0.051]<br>0.045**<br>[0.018]<br>0.104***<br>[0.035]<br>34,494<br>5,881<br>lustered b<br>m Likeliho<br>try _j_ in ye|
|(7)<br>1.363<br>[0.937]<br>-2.269***<br>[0.693]<br>0.532<br>[0.577]<br>1.785***<br>[0.541]<br>0.911***<br>[0.037]<br>0.852***<br>[0.133]<br>1.155<br>[0.736]<br>5.240***<br>[1.165]<br>0.316**<br>[0.153]<br>0.078**<br>[0.032]<br>34,494<br>5,881<br>rd errors c<br>do Maximu<br>e _i_ to coun|
|(6)<br>1.378<br>[0.940]<br>-2.373***<br>[0.688]<br>0.510<br>[0.577]<br>1.802***<br>[0.541]<br>0.911***<br>[0.037]<br>0.854***<br>[0.133]<br>1.152<br>[0.737]<br>5.153***<br>[1.179]<br>0.316**<br>[0.153]<br>0.039**<br>[0.019]<br>34,494<br>5,881<br>bust standa<br>oisson Pseu<br>rom provinc|
|(5)<br>1.345<br>[0.936]<br>-2.184***<br>[0.701]<br>0.511<br>[0.578]<br>1.814***<br>[0.546]<br>0.911***<br>[0.037]<br>0.852***<br>[0.133]<br>1.134<br>[0.740]<br>5.259***<br>[1.161]<br>0.316**<br>[0.153]<br>-0.055<br>[0.051]<br>34,494<br>5,881<br>sociated ro<br>sing the P<br>ant prices f|
|(4)<br>1.363<br>[0.934]<br>-2.245***<br>[0.694]<br>0.525<br>[0.575]<br>1.781***<br>[0.541]<br>0.911***<br>[0.037]<br>0.852***<br>[0.133]<br>1.152<br>[0.737]<br>5.251***<br>[1.160]<br>0.316**<br>[0.153]<br>34,494<br>5,881<br>ets) the as<br>d out by u<br>s at const|
|(3)<br>1.196<br>[0.889]<br>-1.601**<br>[0.737]<br>0.911***<br>[0.037]<br>0.850***<br>[0.132]<br>0.869<br>[0.674]<br>4.118***<br>[1.188]<br>0.319**<br>[0.153]<br>34,494<br>5,881<br> (in brack<br>s are carrie<br> remittance|
|(2)<br>1.191<br>[0.885]<br>-1.666**<br>[0.738]<br>0.919***<br>[0.037]<br>0.877<br>[0.671]<br>3.905***<br>[1.160]<br>0.276*<br>[0.157]<br>34,494<br>5,881<br>fcients and<br> Estimation<br>otal ofcial <br>e included.|
|(1)<br>0.920***<br>[0.038]<br>0.280<br>[0.311]<br>3.570***<br>[1.108]<br>0.276*<br>[0.157]<br>34,494<br>5,881<br>ession coe<br>ant at 1%. <br> value of t<br>ummies ar|
|CYCLE_i,t_<br>CYCLE_j,t_<br>TREND_i,t_<br>TREND_j,t_<br>MIGRANTS_i,j,t_<br>∆MIGRANTS_i,j_<br>POP_i,t_<br>POP_j,t_<br>DISTANCE_i,j_<br>WAR_j,t_<br>DISASTER_j,t_<br>TT SHOCK_j,t_<br>FINDEV_j,t_<br>BANK_i,t_<br>FUNCTIONAL DISTANCE_i,t_<br>Observations<br>Number of pair<br>_Notes_: The table reports regr<br>signifcant at 5%; *** signifc<br>The dependent variable is the<br>(_i_), country (_j_) and year (_t_) d|

28