Migration-Letters

Migration Letters, Volume: 7, No: 2, pp. 231 – 240
ISSN: 1741-8984 & eISSN: 1741-8992
October 2010
www.migrationletters.com
CASE STUDY
From shock absorber to shock
transmitter: Determinants of
remittances in Sub-Saharan Africa RAJU JAN SINGH *
Abstract
Workers’ remittances to developing countries have substantially increased over the
past decade, both globally and in sub-Saharan Africa. They have been argued to be
shock absorbers, increasing when home economies face economic difficulties and
have been shown to alleviate poverty. During economic downturns, however, migrant
workers are often the most vulnerable. As migrants lose their incomes or even their
jobs, the global scope of the current crisis may turn remittances into a shock trans-
mitter. Faced by this perspective, what can home countries do to shelter themselves?
This paper investigates the determinants of remittances in sub-Saharan Africa and
suggests some possible policy responses.
Keywords: remittances, migration, global crisis, Africa
Remittances in Sub-Saharan Africa
Reported remittances have substantially increased throughout the developing world
(Figure 1), rising from about US$20 billion in 1980 to an estimated US$336 billion in
2008. In sub-Saharan Africa (SSA), an estimated US$20 billion in remittances in
2007 corresponded to about 2½ percent of regional GDP, an amount similar to the
official development assistance the region received. However, on a global scale
remittance flows to SSA are quite small; they account for only 5 percent of total
remittances to developing countries, and in terms of GDP are dwarfed by the
amounts received in the Middle East and South Asia.
The general picture hides striking variations by country (Figure 2). Of the 25
largest recipients of remittances in 2008 in terms of GDP, four were in Africa
(Lesotho, Togo, Cape Verde, and Senegal). As a source of foreign exchange, in
Benin, Cape Verde, Gambia, Lesotho, Senegal, Sierra Leone and Uganda,
remittances in 2008 represented more than 25 percent of each country’s export
earnings. Furthermore, while for the region as a whole the amounts of aid and
recorded remittances are similar, in numerous countries remittances were a
multiple of official assistance.
With about 80 percent of their remittances coming from advanced economies,
SSA countries are particularly vulnerable to an economic slowdown in these
countries. The expected increase in unemployment would be concentrated in
countries and sectors where migrant workers are heavily represented (e.g.
advanced economies, and the construction and transport sectors). This would imply
reduced job opportunities for migrants and lower remittance flows. According to
Ratha et al. (2009), remittances are expected to have declined by about 7-10
percent in 2009, putting poverty reduction and employment in home countries at
risk.
*
Raju Jan Singh is affiliated with the World Bank. Email: rsingh9@worldbank.org.

REMITTANCES IN SUBSAHARAN AFRICA
232
Figure 1. Remittances by major region
Sources: IMF, World Bank, and authors’ calculations.
Going forward, there are concerns about a possible rise in discrimination and
xenophobia, migrant workers being perceived as taking jobs away from local
workers or competing for welfare benefits. A number of host countries have
stopped or imposed restrictions on new admissions of migrants for employment.
Home countries are already experiencing inflows of returning migrants, which may
result in economic and social instability in poorer countries. 1
Understanding what
1
Many governments have already adopted more restrictive policies (e.g. Australia, Korea, Russia, U.S.)
and some have even introduced financial incentives to encourage migrant workers to return home (e.g.
Japan, Spain, U.K.).

SINGH
© migration letters
233
drives remittances is therefore crucial. Yet, little research has been done on the
determinants of remittances to Africa.
Figure 2. Main recipients of remittances
Sources: IMF, World Bank, and authors’ calculations.

234
Empirical Analysis
Empirical Approach
We estimate the following equation describing the determinants of remittances
and including explanatory and control variables that have been shown significant in
previous studies 2
:
Equation (1):
,itεitDual
8
βitID
7
βitlnREX
6
βitlnIns
5
β
it)ln(Mig/Pop
4
β*
itlny
3
βitlnFinDev
2
βitlny
1
βtγiαit)ln(REM/GDP
+++++
+++++=
where REM/GDP denotes the ratio of remittances to GDP, y is home income,
FinDev stands for an index for the financial development, y* is host income, Mig/Pop
is the ratio of expatriates to population, Ins denotes institutional quality, REX is the
real exchange rate, ID is the interest rate differential, Dual is the dual exchange rate
dummy variable, and iα and tγ are country- and time-specific dummies. Panel
fixed effect (FE) and fixed-effect two-stage least square (FE 2SLS) estimation
methods were used.3
The sample comprises 36 countries in SSA for 1990 through 2005. Data on
remittances are drawn from the IMF’s Balance of Payments Statistics Yearbook
(BOPSY). To estimate the annual stock of expatriates, we started with the data
compiled by Parsons et al. (2007) on international bilateral migration. This database
provides the number of migrants from each of 226 origin countries to each of
226 destination countries in 2000. From this we inferred data on the stock of
expatriates for our 36 SSA countries during 1990–2005 using World Development
Indicators (see Appendix B for a more detailed discussion). Measures of the
differentials in interest rates and income between the home and host countries
were constructed as an average of bilateral differentials, weighted by the shares of
migrants (from Parsons et al., 2007).
Results
Table 1 reports the estimation results. Remittances to SSA do seem to play a
shock-absorbing role. The coefficient of real per capita GDP in the home country is
negative regardless of the choice of estimation methods. This suggests that when
adverse economic shocks decrease incomes in their home country, migrants would
remit more to protect their family from those shocks.
The coefficients of host country income and stock of expatriates are, however,
positive and robust. Countries with a large diaspora attract more remittances and
the location of expatriate communities matters: the wealthier the country where
expatriates are located, the higher the remittances they send back home. This
2
See Rapoport and Docquier (2006) for a survey of various theories and empirical evidence on motiva-
tions to remit.
3
The dependent variable used here is the ratio of remittances to GDP. We also tried different meas-
ures, such as remittances to population or just the volume of remittances, but the results were robust to
the choice of measure for remittances.

SINGH
235
result would suggest that, as the global crisis erodes the incomes and the number of
migrants, remittances should be expected to decline, spreading the crisis to home
countries rather than sheltering them.
Table 1. Determinants of remittances
Variables (all in logs)
M2/GDP DC/GDP [1] [2]
Home income -3.236***
(-6.08)
-2.952***
(-4.48)
-3.158***
(-5.14)
-3.258***
(-3.02)
M2/GDP 0.698***
(3.37)
1.232***
(3.06)
Domestic credit/GDP 0.160
(1.15)
0.890***
(3.86)
Host income 4.255***
(3.64)
4.555***
(3.60)
2.567***
(2.09)
3.690***
(2.66)
Expatriates/Population 0.024***
(3.59)
0.021***
(2.85)
0.027***
(3.29)
0.016
(1.59)
Institutions 0.400***
(2.72)
0.378***
(2.43)
0.491***
(3.21)
0.274
(1.60)
Real exchange rate -0.765***
(-3.06)
-0.581**
(-2.14)
-0.760**
(-2.39)
-0.699**
(-1.99)
Interest rate differential -0.039***
(-3.56)
-0.039***
(-4.30)
-0.030***
(-3.52)
-0.025**
(-2.64)
Dual exchange rate -0.131
(-0.83)
-0.029
(-2.16)
-0.126
(-0.83)
0.113
(0.61)
Observations 352 334 318 296
R squared 0.8171 0.8122 0.8251 0.8129
For weak instruments N.A. N.A. 31.289 52.756
p-value for overidentification
test of all instruments
N.A. N.A. 0.3162 0.2796
Note: 1) Standard errors are robust to autocorrelation in errors.
2) t-values are in parentheses.
3) ***, **, and * indicate 1%, 5% and 10% significance.
4) Time-specific dummies are included but estimates are not reported here.
[1] Financial depth: M2/GDP
Instrumented: Home income, M2/GDP
Instruments: 1st lag of real GDP per capita and institutions; 1st and 2nd lags of M2/GDP
[2] Financial depth: DC/GDP
Instrumented: Home income, DC/GDP
Instruments: 1st lag of real GDP per capita and institutions; 1st and 2nd lags of DC/GDP
Remittances also reflect a portfolio choice about investment opportunities in
the home country. The coefficient on institutional quality is significantly positive and
robust. This result suggests that countries with better institutions or a more stable
political system would receive more remittances relative to GDP. Institutional
quality can be viewed as reflecting the business environment, which in turn should
influence the amount of remittances driven by the investment motive.
Once migrants have decided how much to remit, they must then decide how to
send it. Remittances are estimated to be positively correlated with financial
deepening. Countries with more developed financial markets would attract more

236
remittances relative to GDP. Financial development should ease the process of
money transfers and may reduce the fee associated with sending remittances
through competition, so that it can raise the amount or share of remittances
transferred through official channels, which our data on remittances captures.
Conclusions: What can be done?
The findings suggest that remittances vary countercyclically with variations in
GDP per capita in the home country, consistent with the hypothesis that
remittances can help mitigate economic shocks. However, the size, the location,
and the income of the diaspora are also important determinants of remittances.
These results would suggest that this time around remittances should not be
expected to shelter their home economies from adverse economic shocks, but on
the contrary could contribute propagating them. The global scope of the current
crisis could turn remittances into shock transmitters.
Against this backdrop, what could home countries do? The results presented in
this paper would suggest several policy options:
• Just as protectionism in trade needs to be avoided, rising protectionism in
human mobility in host countries should be resisted, keeping the number of mi-
grants in host countries.
• Efforts should be stepped up in home countries to improve the quality of
their institutional environment, particularly their business climate, to encourage
migrants to send more remittances.
• In particular, measures should be taken to deepen financial intermediation
and facilitate remittance flows through formal channels by lowering transaction
costs associated with sending remittances.

SINGH
237
Appendix A. List of variables and countries used for the analysis
Variables Description Source
Remittances
Sum of workers’ remittances, compensation of
employees, and migrants’ transfers (expressed in
US$)
BOPSY (IMF), WDI
(World Bank), and
African Department at
the IMF
Real GDP per capita Real GDP per capita in 2000 constant US$ WDI
Nominal GDP Nominal GDP in US$
World Economic
Outlook (WEO; IMF)
Population Population WDI
Nominal exchange
Rate
Nominal exchange rate measured as the amount
of USD for one unit of local currency unit
(US$/local currency unit)
WEO
CPI
Consumer Price Index
(100 in 2000)
WEO
Inflation CPI inflation Authors’ computation
Investment Gross investment in US$ WEO
Dual exchange rate
regime
Dual exchange dummy, 1 for dual or multiple
exchange rate regime
Annual Report on
Exchange Arrange-
ments and Exchange
Restrictions (AREAER;
IMF)
M2 Money and quasi-money (M2) in US$ WDI
Terms of trade
Export price index/ Import price index
(100 in 2000)
WEO
Trade openness (Imports + Exports)/GDP WEO
Stock of expatriates
Number of expatriates by origin (see Appendix
B for details.)
WDI and Parsons et
al. (2007)
Private investment Private investment in US$ WEO
Public investment Public investment in US$ WEO
Institutional quality
ICRG political risk index (0: highest risk, 100:
lowest risk)
International Country
Risk Guide (ICRG;
Political Risk Service
Group)
Deposit rate Deposit rate IFS
Real exchange rate Real exchange rate against US$ (
US
CPI
iCPI
iLCU
USD ) Authors’ computation
Government expendi-
ture
General government total expenditure and net
lending in US$
WEO
Host income
Weighted average of real per capita GDP in top
4 expatriates-receiving countries (in 2000 con-
stant US$)
WDI and Parsons et
al. (2007)
Nominal interest rate
differential
Deposit rate of home country – Deposit rate of
country with largest migrants share from that
country
IFS and Parsons et al.
(2007)
Domestic credit Domestic credit provided by banks (% of GDP) WDI
* Countries in our Sample (in alphabetical order)
Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Comoros, Republic of Congo, Côte d'Ivoire,
Eritrea, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Madagascar, Ma-
lawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé & Príncipe,
Senegal, Seychelles, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, and Togo. (36 countries)

238
Appendix B. Construction of the stock of expatriates data
This appendix describes in detail how we construct data on the stock of expatriates from available
sources of migration data. The data we use to compute the stock of expatriates include net migration
into each country and the stock of migrants within each country (both from the WDI but recorded only
every five years as well as the international bilateral migration database compiled by Parsons et al.
(2007).
Suppose there is a country, which we call home. We call the rest of the world foreign. Assume for
simplicity that place of birth determines citizenship. Assume further that all available stock data are
measured at the end of a given period.
Let us define the following variables (see the diagram below):
1. Stocks
tH : number of people born in home and living there
*
tH : number of people born in home but living in foreign
tF : number of people born in foreign but living in home
*
tF : number of people born in foreign and living there
tP : population of home ( = tt FH + )
2. Flows
tEH : number of home-born people who migrate from home to foreign
tIH : number of home-born people who migrate back to home from foreign
tEF : number of foreign-born people who migrate from home to foreign
tIF : number of foreign-born people who migrate from foreign to home
tE : number of out-migration from home ( = tt EFEH + )
tI : number of in-migration to home ( = tt IFIH + )
tM : net migration ( = tt EI − )
*
tDH : number of home-born people who die in foreign
tDF : number of foreign-born people who die in home
What we know is: tP , tF (migration stock from the WDI), hence tH , and tM (net migration
from the WDI). But what we want to know is: *
tH (stock of expatriates). The flow of migration is
characterized by the following equations:
ttttt IHEHDHHH −+−= −
**
1
* (B1)
ttttt EFIFDFFF −+−= −1
(B2)
Note that births to migrants are counted as increases in the natives for the country where they live
on the assumption we made earlier. Turning to net migration we know by definition,
)()( ttttttt EFIFEHIHEIM −+−=−= ,
which implies
ttttt MEFIFIHEH −−=− )()( . (B3)
Combining (1), (2), and (3), we have
ttttttt MDFFFDHHH −+−+−= −− 1
**
1
*
. (B4)
To construct the stock of expatriates from home, we need a value of *
tH for some period t as
well as the number of deaths of migrants, i.e., *
tDH and tDF . We address these issues as follows:
First, to obtain the stock of expatriates from home at some period, we make use of the international
bilateral migration database of Parsons et al. (2007). Then, to estimate the number of deaths of migrants,
we first assume the death rate depends only on place of birth.

SINGH
239
On this assumption, we can compute the death of migrants as follows:
*
1
*
−= ttt HdDH ,
ttt FdDF *
= , (B5)
where td is the death rate of home-born people and *
td the death rate of foreign-born people.
We use the crude death rate of home, available from the WDI, to measure td and a simple average of
crude death rates for our sample countries to measure *
td . Combining (B4) and (B5) yields the
equation for computing the stock of expatriates:
ttttttt MdFFdHH −−−+−= −− )1()1( *
1
*
1
*
. (B6)
One remaining issue in constructing the data as described so far is that data on migration stock
within a country, tF in our term, are available only every five years. Thus we interpolate between two
recorded observations linearly to obtain annual data on the stock of expatriates.
Acknowledgement
The paper draws on a wider research project including Markus Haacker (London School of
Hygiene and Tropical Medicine), Kyung-woo Lee (Columbia University), and Maëlan Le Goff
(CERDI-University of Auvergne). This work was carried out when Markus Haacker, Kyung-
woo Lee, and the author were at the African Department of the International Monetary
Fund. The views expressed in this paper are those of the authors and do not necessarily
represent those of the IMF or IMF policy.
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