Migrant Remittances Provide ResilienceAgainst Disasters in Africa

Wim A. Naudé & Henri Bezuidenhout

# International Atlantic Economic Society 2014

Abstract How responsive are migrant remittances to various disasters, both naturaland human-made? Would remittances be affected by systemic financial crises, such asthe 2008–09 financial crisis, or more recent crises affecting the Eurozone? Using paneldata on 23 sub-Saharan African (SSA) countries from 1980 to 2007, we find thatremittances are slow to respond to natural disasters, unresponsive to outbreaks ofconflict, and will slowly decline following a systemic financial crisis. This suggeststhat, given its stability, remittances are sources of resilience in SSA.

Keywords Remittances .Migration . Disasters . Global financial crisis . Africa

JEL F24 . F22 . O55

Introduction

Disasters affect millions of sub-Saharan Africans every year. In 2012 no less than30 % of global victims of disasters were in sub-Saharan Africa (SSA) (Guha-Sapiret al. 2013). SSA suffers from droughts more than any other region in the world(Centre for Research on the Epidemiology of Disasters [CRED] 2004). Drought isan important cause of slower economic growth. It has been estimated that droughtcan explain 36 % of the difference in average per capita GDP between SSA andother developing countries (Barrios et al. 2003). The region is also afflicted bydisasters such as wars (Nillesen and Verwimp 2010). More than a third of allglobal armed conflicts between 1989 and 2012 were fought in Africa (Themner

Atl Econ JDOI 10.1007/s11293-014-9403-9

W. A. NaudéMaastricht School of Management, United Nations University (UNU-MERIT), Maastricht,The Netherlandse-mail: naude@msm.nl

H. Bezuidenhout (*)North-West University, Potchefstroom, South Africae-mail: henri.bezuidenhout@nwu.ac.za

and Wallensteen 2013). Wars, or violent conflict, reduce consumption and produc-tion, and destroy infrastructure akin to natural disasters. Moreover, violent conflictvastly worsens the impact of droughts and floods (Brück et al. 2013). Consistingmostly of small, open economies, SSA countries are vulnerable to external shocks,as many regional and global financial crises have illustrated (Naudé 2010a; Naudéand Bezuidenhout 2012:338).

Migrant remittances can potentially be a source of resilience in disaster-proneSSA. Remittances enable households to have diversified incomes, to build better andmore robust dwellings, to keep their children in school, to have access to food andwater, and even to start up new entrepreneurial firms (see Naudé and Bezuidenhout2012:338; Mohapatra et al. 2012). The question is, how responsive, if at all, areremittances to various disasters? How are remittances affected by global or systemicfinancial crises such as the 2008 global financial crisis, or the more recent Eurozonecrises?

In this paper, we investigate these questions using data on 23 Sub-SaharanAfrican (SSA) countries over the period 1980 to 2007. Apart from being disasterprone, SSA has experienced increasing rates of emigration (Naudé 2010b). Concernis growing about the dangers that many of these migrants sustain in their attempts tomigrate to the west, particularly Europe.1 Moreover, SSA has been neglected inscholarly research on both migration (Naudé 2010b) and remittances (Barajas et al.2010). In this paper, we build and extend upon the earlier work of Naudé andBezuidenhout (2012) that focused on whether remittances are a source of resiliencein the face of a globally synchronized financial shock. We extend the analysis tonatural disasters and conflict.

The paper is structured as follows. In second section, we provide a review of therelevant literature on remittances and disasters, and we draw out some implications andhypotheses. Third section describes our methodology. Fourth section contains ourregression results. Fifth section concludes.

Literature Survey: Remittances and Disasters

In this section we discuss the relationship between remittances and each of the threetypes of disasters identified in the introduction: natural disasters, conflict and globalfinancial crises. However, we first discuss the measurement and determinants ofremittances.

Measurement and Determinants

‘Remittances’ refer to financial transfers by migrants that go abroad (Chami et al.2008). Most developing countries do not report on these components separately butrather on total remittances. Inadequate financial records, underdeveloped financial

1 In October 2013 more than 300 African migrants died when the boat they used to cross the Mediterraneansunk. The Maltese Premier described the Mediterranean as a ‘cemetery’ (see http://www.bbc.co.uk/news/world-europe-24502279).

W.A. Naudé, H. Bezuidenhout

sectors, and “In kind” remittances have lead many to conclude that remittance data maybe under-reported (Page and Plaza 2006).

The typical determinants of the size and growth of remittance flows includefactors since as the characteristics of individual migrants and their sending house-holds, and the features of host- and home-countries (see e.g. Freund and Spatafora2008; Page and Plaza 2006; El-Sakka and McNabb 1999; Beck and Peria 2009;Singh et al. 2009; Farrant et al. 2006; Chami et al. 2008; Bollard et al. 2010;Lucas and Stark 1985).

Remittances and Natural Disasters

On January 12, 2010, Haiti was struck by an earthquake wherein more than 200,000people lost their lives. Could remittances have contributed towards helping Haitiansrecover? In principle, according to Ratha (2010), they could, seeing that more than amillion Haitians are migrants. He estimated that if the average remittance permigrant increased by only 20 %, that would provide, over 3 years, an additionalU.S. $1 billion for the reconstruction of the economy. The question is whetherremittances are indeed so responsive. Mohapatra et al. (2009) provided a number ofexamples of countries where remittance flows have increased after a natural disaster,such as Bangladesh (1998), China ( 1999), El Salvador (1986), Guyana (2004),Honduras (1998), India (1992) and Jamaica (1989 and 2004). In these cases, thenatural disasters in question were sudden-onset, such as earthquakes, hurricanes,floods and landslides. They point out that in slower-onset disasters, such as drought,we might not always observe a sudden spike in remittances, as in these countries“remittances are factored into the inter-temporal consumption decisions” (Mohapatraet al. 2009:4). In analysing the patterns of remittances in the face of natural disastersfor SSA, where droughts are the predominate natural disaster, and very oftenmigration is a deliberate livelihood strategy to mitigate the impact of drought, thisneeds to be kept in mind. For instance, it may mean that in the case of drought SSAmay not experience an increase in remittances. Finally, it may even happen that thevolume of remittances to a country may fall after a natural disaster has struck acountry. This may be the case if the impact of the natural disaster is such that itdestroys or damages banking and other infrastructures used to send remittances(David 2010).

Remittances and Armed Conflict

Like the impact of a natural hazard, the outbreak of armed conflict could adverselyaffect a household’s welfare, by destroying or reducing their income sources and assets.In such cases, one could expect remittance flows to increase to conflict countries. Thereis increasing recognition in the literature that remittances can be important for humansecurity in conflict and in post-conflict situations (e.g. Harris and Terry 2013; Fagenand Bump 2006).

In a situation of prolonged violent conflict, a country would have a larger share of itspopulation abroad as a diaspora. In such countries remittances could show an upwardspike following the outbreak of conflict as the number of refugees rises, although thismay take a while to show up in national accounts. Many countries with high ratios of

Migrant Remittances Provide Resilience Against Disasters in Africa

remittances to GDP are in fact countries in conflict or countries that had been in conflict(Haiti is a good example).

However, while the above suggests an increasing trend for remittances in response toconflict or post-conflict reconstruction, it may be difficult to remit funds to householdsin conflict countries. Banks are often damaged, or regulated, and migrants or refugeesoften do not trust these institutions. Governments in conflict countries may be antag-onistic towards their diasporas and could interpret remittances as undermining orfueling conflict. And “remittances themselves become a highly prized resource attrac-tive to thieves and even warring parties” (Young 2006:28). Hence, in conflict situations,officially measured remittances may fall or not show significant growth, whereas moreinformally remitted funds may increase.

Remittances and Global Economic Shocks

A global financial crisis2 is likely to reduce remittance flows because it reduces themigration to developed countries and is likely to reduce migrants’ incomes (Calì andDell’Erba 2009). According to Barajas et al. (2010), African migration is largelyconfined to the continent itself. As a result they predicted that “the impact of the globalfall in remittances on African countries’ GDP growth is expected to be fairly mild”(Ibid, p.10). According to Ratha et al. (2008) remittances are more resilient thanportfolios, FDI, or foreign aid flows. Migrants often maintain their remittances evenif their host countries’ economic conditions deteriorate. Therefore, we can concludethat whereas SSA’s possible lack of extensive and diversified emigration has left itrelatively less exposed to a global economic shock, it may be more exposed to naturalhazards and conflict.

Methodology

Hypotheses

Based on the disaster profile of SSA and the literature surveyed, the followinghypotheses can now be forwarded:

H1: Remittances to SSA countries are not significantly responsive to natural disastersor the outbreak of conflict, and will not show an immediate increase thereafter.

H2: Remittances to SSA countries will decline during a globally synchronisedfinancial crisis.

Hypothesis H1 is derived from the fundamental determinants of remittances such asa country’s migrant stock and the sophistication of its financial system, aspects that arerelatively stable over the short term. Furthermore, in SSA, as we mentioned, mostdisasters are relatively slow-onset disasters to which migration is an age-old response orcoping strategy. Hypothesis H2, taken from Naudé and Bezuidenhout (2012:337–347)

2 For a more in-depth discussion on the role of remittances and migration during a global financial crisis, seeNaudé and Bezuidenhout (2012:337–347).

W.A. Naudé, H. Bezuidenhout

results from the standard determinant that a host country’s income is an importantdeterminant of remittances. Because a significant number of African migrants findthemselves in the EU, a global financial crisis could affect their ability to remit.3

We have three reasons, however, to suspect that H2 may be rejected. One is thatmany migrant workers in the EU may be in jobs that are recession-proof. Asecond is that there are more SSA migrants in other SSA countries than EUcountries (see above). The third reason is that a global financial crisis may causeSSA countries’ exchange rates to depreciate, which in turn may stimulate remit-tances from abroad.

Estimation Strategy

Our estimation strategy (see Naudé and Bezuidenhout 2012, who also quotes Freundand Spatafora 2008 and Lueth and Ruiz-Arranz 2006) is to estimate

Rit ¼ τ t þ xitβ þ ci þ uit ð1ÞFor i=1,….N and t=2,….T and where Rit=remittances to country i in period t; xit=a

1×K vector of explanatory variables. Some of these vary over t; ci = unobservedcountry characteristics that are constant (fixed) over the time period and influence Rit;τ = year-specific fixed effects, and uit = a random error term with the usual properties(Naudé and Bezuidenhout 2012: 346).

xt is a vector containing the explanatory variables. These include the variables ofinterest, namely:

& The number of disasters to be experienced by country i in year t. If our hypothesisH1 holds, we will find the coefficient on this variable to be positive and significant.

& Whether a country is in conflict or not in year t (a dummy variable). If ourhypothesis H1 holds, we will find the coefficient on this variable to be positiveand significant.

& Whether there has been a globally synchronised recession in year t, or not (a time-dummy variable). If our hypothesis H2 holds, we will find the coefficient on thisvariable to be negative and significant.

If our hypothesis H2 holds, then the absolute value of the coefficient on the time-dummy for globally synchronised recessions will be larger in size than in the case ofdisasters and conflicts. Furthermore, the control variables are also contained in xit. Theyare, as we also explain in Naudé and Bezuidenhout (2012:346–347):

& Gross national income (GNI) per capita of the home country. If the coefficient is 1>0, then remittances are pro-cyclical, and if <0, then remittances are counter-cyclical.

& GNI per capita of the host country. It is expected that if the coefficient is > 0, and ifthe coefficient is moreover > 1, then it would indicate that increases in host countryincomes are passed on more in proportion to their home countries (see Freund andSpatafora 2008).

3 We dealt in more detail with this issue in Naudé and Bezuidenhout (2012) and in this regard rely much onthis earlier paper.

Migrant Remittances Provide Resilience Against Disasters in Africa

& Credit extended to the private sector in country i. This is a proxy for the financialdevelopment of country i. The higher the extended credit level is, the easier it willbe to remit, and therefore we expect a positive coefficient.

& The level of the domestic (home country) exchange rate. As remittances are sent asforeign currency, changes in the exchange rate will change the local currency value.If the coefficient on this variable is found to be larger than zero, the coefficientwould indicate that in the case of depreciation in the domestic (home country)exchange rate, a large dollar amount of remittances will be sent in order to make useof the higher, more favourable rate.

& Official Development Assistance (ODA) to country i. Literature indicates that ODAis often claimed to affect remittances. For instance, some consider that an increasein ODA to a country will offset remittances to the country. If this is the case, thecoefficient on ODA to a country will be negative.4

& The population of a country. More populous countries will have larger migrantpopulations in absolute terms, and therefore a higher absolute level of remittances.We include a country’s population in the regression analysis to control for the effectof population size.

Dummy variables have been included, where necessary, to deal with time trends andto reflect country fixed effects.

Estimation

We first estimate (1) with a pooled-data OLS. Robust standard errors are calculated totake into consideration the likelihood of heteroscedasticity in the error terms given thatcross-sectional data is used. The shortcoming of using OLS is that it is likely to result inbiased coefficients in this case because of omitted variables, endogeneity and dynamiceffects. For instance, remittances may very well influence exchange rates, or thevolume of aid that countries receive, or influence GNI per capita in poor countries.Remittances are also highly likely to depend on the number of migrants, and thus topersist. As such it is better to use an estimating method that allows one to includelagged dependent variables. Following Naudé and Bezuidenhout (2012) we also usethe ‘difference’ (Arellano-Bond) GMM estimator.

Data and Variables

Our data come from 23 SSA countries5 spanning the years of 1980 to 2007. We useddata starting no earlier than 1980 due to availability considerations, because earlyestimates of remittances were subject to considerable measurement error (Calì andDell’Erba 2009). We stop at 2007 due to unavailability of more recent consistentestimates of conflict data (battle deaths)—the UCDP/PRIO dataset at the time of our

4 For the effects and interplay of ODA and remittances, see OECD (2005).5 The countries are Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Comoros, Cote d’Ivoire,Ethiopia, The Gambia, Ghana, Kenya, Lesotho, Madagascar, Mali, Mauritania, Mozambique, Niger, Nigeria,Rwanda, Sudan, Swaziland and Togo.

W.A. Naudé, H. Bezuidenhout

study only covers 1946 to 2008. The variables and the sources of data are summarizedin Table 1.

Table 1 Summary of variables and data sources

Measures Description Sources of data

Dependent variable: remittances

Remittances Inflows measured in U.S. dollars WBWorld Development Indicators online(WDI) (see World Bank 2013)

Variables: disasters

Natural disastersa The total number of natural disasters in acountry in a particular year. Timevarying.

CRED online (see Guha-Sapir et al. 2013)

Conflictb A dummy variable = 1 if there was conflictin the country in that year. Conflict isdefined as at least 25 battle deathsoccurring.

UCDP PRIO (2013) Armed Conflictdataset: Available at: www.prio.no/CSCW/Datasets/Armed-Conflict/UCDP-PRIO/

Globallysynchronisedrecession

A dummy variable = 1 if there was aglobally synchronised recession in theparticular year. According to the IMF,these were in 1987, 1997 and 2002.

IMF - Laeven and Valencia (2008).

Variables: controls

GNI per capita inhome country

The gross national product per capita in thehome (African) country in period t.

WDI

GNI per capita inhost country

The gross national product per capita in thehost country in period t. Here we takethe host country income to be that of theSSA average—the main destinations ofAfrican migrants are other Africancountries.c

WDI

Credit to privatesector

The credit extended to the private sector inan African home country in period i, asa proportion of GDP.

WDI

Exchange rate level The local currency value of a U.S. dollar.d WDI

Aid received The amount, in U.S. dollars, of aid fromthe European Union received.

WDI

Population The total population in a country in aparticular year.

WDI

Time dummies To control for time varying shocks andtrends

Authors’ compilation, see also Naudé and Bezuidenhout 2012a Natural disaster occurrences are used as the number of people affected in Africa is not readily available (seeGuha-Sapir et al. 2013)b The violent conflict variable has been obtained from the UCDP PRIO (2013) Armed Conflict dataset:Available at: www.prio.no/CSCW/Datasets/Armed-Conflict/UCDP-PRIO/ (see Gleditsch et al. 2002)c It might be construed that most recorded remittances do not flow from the SSA countries. However, there isno unified single data source that will enable a more definitive breakdown of remittances such as percentagefrom other source countriesd Local currency in U.S. dollars is used, as this represents an incentive to remit funds

Migrant Remittances Provide Resilience Against Disasters in Africa

Empirical Results

Regression Results

Tables 2 and 3 contain the pooled OLS regression results. Table 2 contains the resultswhen no controls are included. The table shows that natural disasters have a positiveand significant impact on remittance flows to SSA, but that the impact of conflict isinsignificant. Financial crises significantly reduce remittances (see also Naudé andBezuidenhout 2012: 349).

Table 3 contains the results from four regressions: (1) a basic regression without anyof the variables of interest, as a base case, and three further models, introducing (2)natural disasters, (3) conflict and (4) global financial crises (also reported in Naudé andBezuidenhout 2012: 349). Time-dummies and dummies for country fixed-effects areincluded.

Table 3 shows that neither conflict nor global financial crises have statisticallysignificant impacts on remittances in the presence of controls. Conflict has little impacton remittances and financial crises impact negatively through their impact on creditextended to the private sector (the banking sector) and on income of the host country.

Natural disasters have a significant and positive effect on remittances, even in thepresence of controls. This implies that remittances respond positively to naturaldisasters in SSA, even if these disasters are slow-onset. We can thus accept H1—disasters will lead to a positive response in remittances, but not conflict.

Regarding the control variables, the estimates are robust across the different models.The single largest determinant of the level of remittances is a country’s population. Thisis indicative of the fact that larger countries can have, in absolute terms, larger migrantpopulations. To the extent that a country’s population is a proxy for its migrant stock

Table 2 Pooled OLS regression results (dependent variable = log of remittances in U.S. dollars): no controlvariables

Variable (1) (2) (3)

Natural disasters Conflict Global financial crises

Constant 18.37 (68.5)* 18.49 (71.3)* 18.49 (71.6)*

No. of disasters 0.08 (2.77)**

Incidence of conflict −0.00 (−0.03)Global financial crises −1.25 (−4.37)*Diagnostics

R-square 0.77 0.77 0.77

Time dummies Yes Yes Yes

Country fixed effects Yes Yes Yes

N= 644 644 644

F= 66.18* 66.21 67.16

Authors’ estimations. Robust t-ratios in brackets. An * indicates significance at the 1% level and a ** at the5 % level

W.A. Naudé, H. Bezuidenhout

the finding is consistent with the proxy supposition. In a dynamic model, this wouldmean that lagged remittances ought to have a substantial and significant effect.

Table 3 also indicates that a country’s home GNI per capita is an importantdeterminant of remittances—poorer countries receive more remittances than richercountries. The negative coefficient suggests that remittances in SSA are counter-cyclical—a resilience factor. However in many other studies (e.g. Freund and Spatafora2008), home country income is pro-cyclical.

We find that the host country’s income per capita, in this case of SSA in general, issignificant. A decline in SSA income per capita, such as during a global economiccrisis, may thus have a strong negative impact on remittances to SSA.

Credit extended to the private sector and the level of the local currency against theU.S. dollar has positive signs. Therefore, a better-developed financial system is impor-tant to raise remittances. In the case of SSA it seems that remittances may increase afternominal exchange rate depreciation. There is no evidence of any significant relation-ship between remittances and aid (ODA) from the EU. In other words, foreign aid doesnot seem to displace remittances to SSA.

Table 4 contains the ‘difference’ GMM dynamic panel estimation results. Thedependent variable is the changes in remittances. All the explanatory variables, except

Table 3 Pooled OLS regression results (dependent variable = log of remittances in U.S. dollars): controlsincluded

Variable (1) (2) (3) (4)

Basic model With naturaldisasters

With conflict With globalfinancial crises

Constant −8.72 (−0.59) −8.18 (−0.56) −8.76 (−0.59) −8.72 (−0.59)GNI home −1.92 (−5.86)* −1.88 (−5.79)* −1.92 (−5.80)* −1.92 (−5.86)*Income host (SSA GDP pc) 0.44 (2.10)*** 0.42 (2.03)*** 0.44 (2.08)*** 0.44 (2.10)***

Credit private sector 0.61 (5.23)* 0.61 (5.16)* 0.61 (5.23)* 0.61 (5.23)*

Exchange rate 0.33 (4.52)* 0.31 (4.44)* 0.33 (4.52)* 0.33 (4.52)*

Aid from EU 0.02 (0.17) −0.00 (−0.01) 0.02 (0.17) 0.02 (0.17)

Population 2.30 (2.56)** 2.26 (2.54)*** 2.29 (2.55)*** 2.29 (2.56)***

No. of disasters 0.05 (1.79)***

Incidence of conflict 0.01 (0.08)

Global financial crises −0.90 (−1.32)Diagnostics

R-square 0.85 0.86 0.86 0.86

Time dummies Yes Yes Yes Yes

Country fixed effects Yes Yes Yes Yes

N= 300a 300 300 300

F= 75.29* 70.12* 73.19* 75.29*

Authors’ estimations. Robust t-ratios in brackets. An * indicates significance at the 1 % level, a ** at the 5 %level and a *** at the 10 % levela Added variables have missing values and therefore the number of observations is substantially lower thanTable 2

Migrant Remittances Provide Resilience Against Disasters in Africa

for the dummy variables for crises and conflict, are in first differences. These resultstherefore focus on the short-term impacts of disasters as opposed to the results inTable 3, which we show are the longer-term impacts (Naudé and Bezuidenhout2012).

Table 4 shows that the dynamic panel estimations are overall significant with gooddiagnostics. Lagged values of remittances are the single most significant and importantdeterminant of current remittances. The results may seem bland, but they actually saymuch. To be precise, the results indicate that there is persistence in remittances.Because of the estimation method (using differences) the results in Table 4 reflectshort-term changes in remittances (Naudé and Bezuidenhout 2012). Therefore, theconclusion is that remittance flow over the short-term depends only on the extent ofmigration. Households remit what they can over the short term, with significantchanges only taking place slowly, and in response to institutional changes. This findingis consistent with some recent micro-evidence in the literature, for instance Buckleyand Hofman (2012) who, using micro-level data from Tajikistan over an 8 year period,could not find a significant developmental role for remittances.

Table 4 Difference-GMM dynamic panel estimation results (dependent variable: first difference ofremittances)

Variable n (2) (3) (4)

Basic model With naturaldisasters

With conflict With global financialcrises

Constant 0.06 (0.55) 0.06 (0.58) 0.06 (0.58) 1.31 (1.31)

ΔRemittances lagged 0.43 (6.73)* 0.43 (6.72)* 0.41 (6.62)* 0.71 (18.8)*

ΔGNI home 0.35 (0.72) 0.24 (0.45) 0.23 (0.46) −0.30 (−1.14)ΔGNI hosti 0.06 (0.67) 0.05 (0.62) 0.07 (0.82) −25.00 (−1.40)ΔCredit private sector −0.16 (−1.36) −0.16 (−1.36) −0.21 (−1.73) 0.10 (1.58)

ΔExchange rate 0.13 (0.85) 0.11 (0.71) 0.14 (0.92) 0.14 (3.50)*

ΔAid from EU −0.02 (−0.44) −0.02 (−0.53) −0.02 (−0.53) 0.02 (0.45)

ΔPopulation 2.13 (1.32) 1.87 (1.11) 2.50 (1.54) −0.25 (−0.44)No. of disasters – 0.00 (0.55) –

Incidence of conflict – – 0.06 (1.52) –

Global financial crises – – −0.50 (−1.02)Diagnostics

Wald χ2 93.90 93.36* 95.77 666.82

Time dummies Yes Yes Yes Yes

Number ofobservations

213a 213 213 560

Number of groups 23 23 23 23

Sargan test 211.78 209.85 215.90 340.31

Authors’ estimations and Naudé and Bezuidenhout 2012. z-ratios in brackets. An * indicates significance atthe 1 % level, a ** at the 5 % level and a *** at the 10 % level; In the case of specification (4), we used GNIper capita of EU, as that of SSA was collinear with the dummy for the financial crisesaMissing observations and the use of only first differences reduce the number of observations we haveavailable for the regression

W.A. Naudé, H. Bezuidenhout

Concluding Remarks

In Naudé and Bezuidenhout (2012) we asked whether remittances can act as a bulwarkagainst global financial crises. In this paper we extended the econometric analysis toalso include natural disasters and conflict. This is because Africa is even moresusceptible to natural disasters and conflict.

We established that remittances to SSA are relatively stable over the short term anddo not seem to react to disasters. Long term remittances to SSA are mainly determinedby institutional determinants, such as the development of the financial system and theeconomic development of host and home countries (see also Naudé and Bezuidenhout2012:341). We found that, in the long term, only natural disasters seem to havesignificant impacts on remittances (remittances will increase after a natural disaster)even in the presence of controls. This suggests that remittances respond to naturaldisasters independently of incomes, exchange rates, or the banking sector. While thismeans that there are not likely to be huge increases in remittances immediately after theoutbreak of conflict, or a natural disaster in an African country, it nevertheless meansthat the stability of remittances can be counted on to support household incomes andconsumption, even though it may perhaps not be a driver of development as such.

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Migrant Remittances Provide Resilience Against Disasters in Africa

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