WP/07/38

Impact of Remittances on Poverty and

Financial Development in Sub-Saharan
Africa
Sanjeev Gupta, Catherine Pattillo, and
Smita Wagh
 © 2007 International Monetary Fund WP/07/38

IMF Working Paper

Impact of Remittances on Poverty and Financial Development in Sub-Saharan Africa

Prepared by Sanjeev Gupta, Catherine Pattillo, and Smita Wagh 1

February 2007

Abstract

This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent
those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are
published to elicit comments and to further debate.

This paper assesses the impact of the steadily growing remittance flows to sub-Saharan Africa (SSA).
Though the region receives only a small portion of the total recorded remittances to developing
countries, and the volume of aid flows to SSA swamps remittances, this paper finds that remittances,
which are a stable, private transfer, have a direct poverty mitigating effect, and promote financial
development. These findings hold even after factoring in the reverse causality between remittances,
poverty and financial development. The paper posits that formalizing such flows can serve as an
effective access point for “unbanked” individuals and households, and that the effective use of such
flows can mitigate the costs of skilled out-migration in SSA.

JEL Classification Numbers:

Keywords:

Author’s E-Mail Address: sgupta@imf.org, cpattillo@imf.org, swagh@imf.org

1
The authors thank, without implicating, Benedicte Vibe Christensen, Anne-Marie Gulde, Theirry Tressel,
Dilip Ratha, and Sanket Mohapatra for their helpful comments.
 2

Contents

Page

I. Introduction ............................................................................................................................3

II. Remittances to Sub-Saharan Africa ....................................................................................4

A. Recent Trends ...........................................................................................................4
B. Characteristics of Remittances to SSA......................................................................7
C. Remittances and Brain Drain ....................................................................................8

III. Impact of Remittances .......................................................................................................11

A. Direct Income and Consumption Effect of Remittances ........................................11
B. Impact on Financial Development ..........................................................................16

IV. Improving the Effectiveness of Remittance Flows...........................................................21

A. Channeling Remittance Flows to Formal Providers ...............................................21
B. Using Remittances Effectively................................................................................24

Tables
1. Expatriation Rates: Top Ten Countries................................................................................10
2. Three-Stage Least Squares Estimation ...............................................................................16
3. Baseline Panel Estimation....................................................................................................19
4. Panel Instrumental Variables Estimation.............................................................................20
5. Fees for Remittances Sent Through Money Transfer Operators in the U.K. ......................21
6. Fees for Remittances from South Africa .............................................................................22

Figures
1. Top Ten Recipients of Remittances in Sub-Saharan Africa ..................................................5
2. Inflows to SSA Countries ......................................................................................................6
3. Volatility and Cyclicality of Flows to Sub-Saharan Africa ...................................................7
4. Regional Expatriation to OECD Countries..........................................................................10

Appendix..................................................................................................................................26

References................................................................................................................................38
 3

I. INTRODUCTION

The flow of remittances into developing countries is attracting increasing attention because
of their rising volume and their impact on the receiving countries. In 2005, they totaled
US$188 billion—twice the amount of official assistance developing countries received.2
Moreover, there is evidence that such flows are underreported. Remittances through informal
channels could add at least 50 percent to the globally recorded flows (World Bank, 2006).3
Since 2000, remittances to developing countries have increased on average by 15 percent in
annual terms. Though at least some part of the growth is attributable to better reporting by
recipient countries, it appears that over the last decade remittances have outpaced private
capital flows and official development assistance (World Bank, 2006).

Remittances are perceived as being more stable than other external flows. To the extent that
migrants are motivated by altruism and send more money home in times of economic
distress, remittances may actually be countercyclical. The stability of these inflows also
opens up an opportunity for developing countries to lower borrowing costs in international
capital markets by securitizing future flows of remittances.4 Because remittance receipts are
widely dispersed, they may not cause the real exchange rate to appreciate; they may also
obviate the deleterious effect on home country institutions observed in short-lived natural
resource booms.

There are marked regional differences in remittance flows.5 Since the 1980s, remittances to
countries in Latin America, the Caribbean, and the East Asia and Pacific regions have grown
more rapidly than the average for developing countries generally. In 2005, the top three
recipients—China, India and Mexico—accounted for more than one-third of the remittances
to developing countries. Among the top 25 recipients of remittances, only one (Nigeria) is in

2
As in other studies on the topic, the remittance data referred to here are aggregate worker remittances,
compensation to employees, and migrant transfers series from the IMF Balance of Payments database,
supplemented by the data from World Bank (2006). All 2005 remittance data are estimates provided by Dilip
Ratha of the World Bank. Appendix Table 1 has details on remittance flows to SSA countries over the last ten
years.
3
Even where migrants use formal channels, the reporting of “small” remittances is not mandatory in most
countries.
4
However, since remittances are private transfers foreign borrowing against such flows would only be possible
with additional stipulations like surrender requirements, prohibition of foreign currency accounts and/or taxes
on remittances.
5
See Appendix Figure 1.
 4

sub-Saharan Africa (SSA) but three of the eight countries in South Asia (Bangladesh, India,
and Pakistan) appear on the list.6

Studies using household-level data from individual countries in SSA have yielded some
insights into how remittances are used at the micro level. In studying the impact of
remittances at the aggregate level most analysts have concentrated on Latin America or
South Asia, where the volumes swamp those going to SSA. But at their core remittances are
private intrafamily/intracommunity income transfers that directly address the single most
relevant challenge for SSA—poverty. Further, the long-term development potential of such
transfers is determined by the use of the portion of remittances left over after basic
consumption needs are met. The purpose of this paper is to study both these issues in a part
of the world where the role of remittances has received comparatively little attention.

This paper analyzes the size and significance of remittance flows to SSA. Section II
documents the volume and characteristics of remittances to the region, and discusses the
dimensions and the related cost of brain drain from SSA countries. Section III estimates their
impact; first the immediate consumption effect of remittances on poverty is investigated,
using a cross-section dataset comprised in significant proportion of SSA countries. This is
followed by the analysis of the indirect consequence of remittances. Because migrant
transfers entail cross-border flows of relatively modest sums of money to low-income
households, they present an opportunity for these households to access formal financial
services. The paper therefore investigates how remittances affect financial development in
SSA countries. Section IV concludes with a discussion of the market for money transfers in
SSA and suggests how to enhance the effectiveness of remittances in the region.

II. REMITTANCES TO SUB-SAHARAN AFRICA

A. Recent Trends

Sub-Saharan Africa has been part of the increasing global trend; remittances to SSA have
increased by over 55 percent in U.S. dollar terms since 2000, while they increased for
developing countries as a group by 81 percent.7 However, the recorded remittances are only a
small fraction of total remittances to SSA. Freund and Spatafora (2005) estimate that
informal remittances to SSA are relatively high at 45–65 percent of formal flows, compared
to only about 5–20 percent in Latin America.

6
Sub-Saharan Africa refers to the 44 countries listed in the Data Appendix.
7
This growth at least in some part reflects better reporting. Also, since the underlying data are in U.S. dollars,
changes in the value of the dollar are captured in measuring the growth of nondollar remittances.
 5

In 2005, remittances to the 34 SSA countries reporting are estimated to have been about
US$6.5 billion. Remittance flows to SSA are relatively small, 4 percent of total remittances
to developing countries and just 33 percent of those to India, which receives the most. In
contrast, countries in Latin America and the Caribbean received 25 percent of all remittances,
as did the countries of the East Asia and Pacific region.8

Relative to GDP, too, the volume of remittances to SSA is generally smaller than in other
developing countries. On average remittances in the region are about 2.5 percent of GDP,
compared to almost 5 percent for other developing countries. However, there are striking
exceptions in SSA. In particular, remittances were almost 28 percent of GDP in Lesotho, and
more than 5 percent in Cape Verde, Guinea-Bissau, and Senegal. In absolute terms, however,
Kenya, Nigeria, and Senegal are the largest recipients of remittances in the region.

For some countries, remittances are also an important source of foreign exchange. For
Lesotho, Cape Verde, Uganda, and Comoros, for instance, remittances have since 2000
amounted on average to more than 25 percent of export earnings (Figure 1).
Figure 1. Top Ten Recipients of Remittances in Sub-Saharan Africa
Fig.1a. Total Flows (millions of US dollars) Fig. 1b. Ratio to GDP (percent)

Nigeria Lesotho

Kenya Cape Verde

Senegal Guinea-Bissau
South Africa Senegal
Uganda Togo
Lesotho Uganda
Mauritius Comoros
Côte d'Ivoire Swaziland
Mali Mauritius
Togo Kenya

0 500 1000 1500 2000 2500 0 5 10 15 20 25 30

Fig. 1c. Ratio to Export Earnings (percent)

Lesotho
Cape Verde
Uganda
Comoros
Senegal
Guinea-Bissau
Benin
Togo
Burkina Faso
Kenya
0 10 20 30 40 50 60 70

Source: IMF, Balance of Payments Yearbook , 2006; World Economic Outlook , 2006; World Bank staff estimates.
Note: Rankings are based on average remittance inflows for 2000–05.

8
See Appendix Figure 2.
 6

In SSA, aid flows are considerably higher than remittance receipts (Figure 2). Since 2000 aid
flows to the region increased on average by about 13 percent a year and reported remittances
by almost 10 percent. However, during the 1990s, when aid flows to the region were more or
less stagnant, remittances grew annually at more than 13 percent. And in 2005 when aid
flows to the region (excluding Nigeria) fell, remittances were stable (OECD/DAC, 2006).
While it is true that the region as a whole receives more aid than recorded remittances, for
countries like Lesotho, Mauritius, Nigeria, Swaziland, and Togo, remittances are consistently
greater than official assistance.

Figure 2. Inflows to SSA countries, 1975-2004

(Millions of U.S. dollars)

25,000
Remittances
Direct investment
Aid
20,000

15,000

10,000

5,000

0
1975 1980 1985 1990 1995 2000 2005

Source: IMF Balance of Payments Yearbook , 2006; IMF African Department database, 2006;
-5,000 OECD/DAC database 2006.

The balance of payments data used above probably underreports remittance flows between
developing countries. Despite the paucity of records there is reason to believe that
intraregional migration is common in SSA. Botswana and South Africa tend to attract
migrants from neighboring countries (largely unskilled) in search of employment. The strong
sociocultural ties in West Africa also encourage labor mobility. In East Africa, political
turmoil seems to be the driving force in migration.
 7

B. Characteristics of Remittances to SSA

One reason remittances have attracted attention is that they are seen as more stable than other
foreign currency flows to developing countries. This is especially relevant to SSA, where
official aid flows have fluctuated considerably from year to year. Remittances to SSA are not
just consistently less volatile than official aid, they are also less volatile than FDI, which is
usually seen as the most stable private flow (Figure 3a). In the 1990-2004 period however
export earnings are more stable than remittances.

Remittances might also be expected to be countercyclical to the extent that they are
motivated by the altruism of migrant workers and increase in times of economic distress in
their home countries. Remittances to SSA are counter-cyclical only in the 1980s (Figure 3b).
Since 1990 remittances have been procyclical, though less so than either official aid or export
earnings. The low (though positive) correlation coefficient demonstrates the stability of
remittances over time rather than any strong relationship to growth cycles.9 The
countercyclicality of FDI flows in the latter time period must be viewed in the context of the
very high volatility of such flows.

Figure 3. Volatility and Cyclicality of External Flows to Sub-Saharan Africa

3a. Volatility 3b. Cyclicality

1980s 1990-2004 1980s 1990-2004

120 0.7
100 0.6
0.5
80
0.4
60 0.3 Remittances
40 0.2 Official aid
20 0.1 FDI
0 Exports
0
-0.1
-0.2

Source: IMF Balance of Payments Yearbook, 2006; IMF African Department database, 2006;World Bank staff
estimates; OECD/DAC, 2006.

9
Lueth and Ruiz-Arranz (2006) also find that remittances do not increase after a natural disaster, and are in fact
aligned with business cycles for 11 recipient countries in Asia and Central Europe.
 8

Remittances can also contribute to stability by lowering the probability of current account
reversals. Because they are a cheap and stable source of foreign currencies, remittances are
likely to stem investor panic when international reserves are falling or external debt is rising.
These beneficial effects are particularly strong for countries where remittances are above
3 percent of GDP (Bugamelli and Paterno, 2006). While the average SSA ratio is just below
that threshold and currrent account reversals driven by investor panic are rare, for some
countries this effect might be an additional benefit from remittances.

The impact of remittances on the real exchange rate and export competitiveness, their Dutch
disease effect, is a matter of debate. As in the case of any other transfer (for instance, official
aid) the effect depends on the proportion of such flows spent on domestic goods, in
particular non-tradables (Gupta, Powell, and Yang, 2006). Since remittances are private
transfers dispersed over a large number of poor households it has been argued that their
impact on domestic demand differs from that of donor-funded infrastructure projects (World
Bank, 2006). Remittances may in fact be self-correcting as an overvalued currency deters
remittances, and hence Dutch disease effects are not sustained (Rajan and Subramanian,
2005). However, studies in Latin America (Amuedo-Dorantes and Pozo, 2004) and Cape
Verde (Bourdet and Falck, 2006) have found evidence that remittances do have Dutch
disease effects on the competitiveness of the tradable sector. In countries where remittances
inflows are large compared to the size of the economy, where supply constraints are a
significant hindrance to the expansion of the nontradables sector, and where a significant
portion of remittances are spent on domestic goods policymakers will need to be alert to the
possibility of a Dutch disease phenomenon.

C. Remittances and Brain Drain

Remittances are only one dimension of the phenomenon of migration from low-income
countries. In particular, skilled migration has always been associated with concerns about
brain drain, which might be especially costly for some SSA countries (Kapur and McHale,
2005; Carrington and Detragiache, 1998). Pond and McPake (2006) detail the human
resource crisis in the health sector in SSA countries that arises as skilled health care
professionals increasingly find employment in the high-demand OECD countries. They
calculate that almost a quarter of the new overseas-trained physicians that registered with the
U.K.’s National Health Service between 2002 and 2003 came from SSA. Similarly,
Bach (2006) documents the high job vacancy rates in the public health systems of countries
like Ghana due to large-scale migration. He estimates that in Zambia and Zimbabwe the
annual rate of attrition in public health employment can range from 15 to 40 percent.

On average 20 percent of the SSA tertiary-educated population older than 15 work in OECD
countries. Less than 10 percent of the comparator group from South Asia is found there. For
some countries, such as Angola, Guinea-Bissau, and Mozambique, expatriation rates are in
excess of 50 percent of the educated population.
 9

We look into the issue of brain drain by calculating the difference between the expatriation
rates of the educated over-15 population from country i and the rate at which the general
over-15 population migrate to an OECD country.10 Because the emigrant population tends to
be better educated, it is to be expected that in general the difference between the educated
and the general expatriation rates will be positive. With a few exceptions, such as Mexico,
Turkey, Bulgaria, and several OECD countries, this holds true. Moreover, the larger the
difference between the educated and general expatriation rates, the higher the propensity of
skilled workers to emigrate compared to the general propensity to emigrate.11

There are interesting regional differences in the extent to which the educated exceeds the
general expatriation rate (Figure 4). Within the OECD countries there is almost no
difference. Among developing countries the largest difference is observed for SSA countries,
reflecting the strain on domestic economies from skilled emigration.12

10
“Educated” refers to the segment of the population that has received a tertiary education. Expatriation rates
are calculated as the ratio of emigrant population to the total population (emigrant plus resident) within a group.

11
This makes it possible to distinguish countries like Barbados where both general and educated migration rates
are high from countries like Burundi where educated migration rates are far greater than the general propensity
to migrate. The two phenomena are likely to impact the local labor markets quite differently, but the latter is
closer to what we understand as brain drain.

12
Since the data refer to migration to OECD countries, they may overstate the difference between general and
skilled migration from SSA. General expatriation rates for SSA are likely to be underestimated given the high
volumes of intraregional, undocumented migration by low-skilled workers.Low-skilled workers in SSA do not
have the same geographic proximity to OECD countries as those in North Africa or East and Central Asia or
Latin America, so in SSA intraregional migration is a more likely option for low-skilled workers. At the same
time, the high expatriation rates of skilled workers reflect at least to some extent the small base of such workers
in SSA populations.
 10

Figure 4. Regional Expatriation to OECD Countries

(Percent)
25

20
General expatriation rate

Educated expatriation rate

15

10

0
East Asia and East and Central Latin America and Middle East and South Asia Sub-Saharan
Pacific Europe Carribean North Africa Africa

Source: OECD, Trends in International Migration database, 2006.

Note: The data are from census and labor force surveys carried out in OECD countries in or about 2000.

For some countries in SSA the shortage of skilled personnel can be quite severe; more than a
third of their educated workforce migrates (Table 1). Among the top 10 countries listed, six
are from SSA. Among the top 20 countries, 75 percent are in SSA.

Table 1. Expatriation Rates: Top Ten Countries

Educated General
Expatriation Rate Expatriation Rate Difference
Guinea-Bissau 70.4 3.6 66.8
Haiti 68.0 8.8 59.2
Mozambique 52.3 0.8 51.5
Angola 53.8 2.9 51.0
Trinidad and Tobago 66.1 22.1 43.9
Jamaica 72.6 30.6 42.0
Mauritius 50.3 9.3 41.0
Guyana 76.9 36.5 40.4
Gambia 42.4 2.6 39.8
Burundi 35.0 0.3 34.7

Source: OECD, Trends in International Migration database, 2006.

Note: Countries are ranked by the difference between the educated and the
general expatriation rates.
 11

III. IMPACT OF REMITTANCES

A. Direct Income and Consumption Effect of Remittances

In SSA, remittances are part of a private welfare system that transfers purchasing power from
relatively richer to relatively poorer members of a family or community. They reduce
poverty, smooth consumption, affect labor supply, provide working capital, and have
multiplier effects through increased household spending. Anecdotal evidence suggests that
most often women head the recipient households.

For the most part, remittances seem to be used to finance consumption or investment in
human capital, such as education, health, and better nutrition.13 In Zimbabwe, for instance,
households with migrants have less cultivated land but tend to be slightly better educated (de
Haan, 2000). Quartey and Blankson (2004) find that migrant remittances to Ghana are in fact
countercyclical and are effective in helping smooth household consumption and welfare over
time, especially for food crop farmers, who are typically the most disadvantaged
socioeconomic group. Similarly, using data from a large household survey Adams (2006)
finds that international remittances significantly relieved poverty among the “poorest of poor
households.” Ratha (2003) suggests that remittances that raise the consumption levels of
rural households might have substantial multiplier effects because they are more likely to be
spent on domestically produced goods. Some studies (Hanson and Woodruff, 2003; Cox
Edwards and Ureta, 2003) have found evidence for “forward” linkages between remittances
and human capital formation in Latin America.

The evidence on the direct impact of remittances on poverty and inequality seems to vary
according to the sample (Adams, 1991; Barham and Boucher, 1998). Earlier studies posited
that migration was likely to increase rural inequality because only relatively better-off
households were able to finance a member’s search for better employment in urban areas or
abroad (Stahl, 1982; Lipton, 1980). More recently, it has been found that migration patterns
in East European and former Soviet Union countries are such that richer households receive
greater remittances than do poorer households (World Bank, 2007). However, Koechlin and
Leon (2006) find that as migrant communities form close networks in a foreign country, the
cost of migration falls and remittances no longer reinforce inequalities in the recipient
country. Other localized studies have concluded that remittances tend to improve the welfare

13
Altruism may not completely explain the intrafamily transfer of resources. Often migrant workers remit
money to maintain their stake in family property, perhaps with a view to returning in the future. Lucas and Stark
(1985) found that in Botswana not only do remittances rise with the size of the migrant’s income but there is
also a positive relationship between the level of remittances and the receiving household’s preremittance
income. The insurance motive for remittances was supported by a study using survey data from Western Mali
(Gubert, 2002).
 12

of poorer rural households (Stark and Taylor, 1989; Adams, 1991). Studies covering a larger
sample of countries have found evidence that remittances tend to lower poverty (Adams and
Page, 2005; Spatafora, 2005).

In the rest of this section, we investigate the direct poverty-reducing impact of remittances
using a sample that gives greater representation to SSA countries than other studies.14

Empirical model

We use a methodology similar to that of Adams and Page (2005), to examine the impact of
incoming remittances on poverty. We build on their model by adopting the three-stage least
squares estimation technique that allows for the simultaneous determination of poverty and
remittances. Based on Ravallion (1997) and Ravallion and Chen (1997) we model poverty as
a function of mean income, some measure of income distribution, and the variable of interest,
remittances. The baseline specification is

Log (Pit) = αi +β1 log (µit) + β2 log (git)+ β3 log (xit )+ εit , (1)

(i = 1.....N, t = 1....Ti ),

where P is poverty in country i at time t; αi captures fixed effects; µ is per capita income,
which functions as a measure of average consumption; g is income inequality as measured by
the Gini index; and x is remittances. The model assumes that poverty is reduced as mean
income rises; hence, β1 is expected to be negative. Based on previous studies we expect
higher poverty to be associated with greater income inequality; hence, β2 is expected to be
positive. Controlling for these two variables the model estimates the sign and magnitude of
β3, which indicates the direct impact of remittances on poverty.

Data

Making use of poverty surveys beginning in 1980, the dataset consists of 76 countries and
233 observations.15 SSA countries are substantially represented: 23 percent of the

14
Using poverty surveys restricts the number of data points so that estimation results from any single regional
group are not significant.

15
Appendix Table 2 lists the countries and survey years of the dataset.
 13

observations come from the 24 SSA countries in the sample. To our knowledge giving this
weight to SSA countries is atypical for cross-country studies on remittances.16

The poverty and inequality measures used here are from the World Bank’s PovcalNet
database,17 which incorporates various measures of poverty: headcount poverty measures the
percentage of the population living on less than one PPP dollar a day. The poverty gap, the
mean distance below the poverty line as a proportion of the poverty line, tells us how poor
the poor are—how far below the poverty line the average poor person’s income is. The
squared poverty gap, which is the mean of the squared distance below the poverty line as a
proportion of the poverty line, is more sensitive to the distribution of the poor below the
poverty line. The income distribution measure, the Gini coefficient, is available from the
same survey data.

Remittances are expressed as a ratio of the GDP of recipient countries. The income variable
is per capita GDP in constant 2000 U.S. dollars. Other variables used in the three-stage
estimation are educational attainment, proxied by average years of schooling for the over-25
population, and openness, measured by the ratio of imports plus exports to GDP. These
variables are all measured as five-year averages corresponding to the survey year in the
PovcalNet database. (Appendix Tables 3 and 4 provide detailed descriptions of the raw
dataset.)

Results

The following estimation techniques were applied to equation 1. The log transformation of
all the variables allows us to interpret the coefficients as elasticities. Regional dummies have
been introduced to control for fixed effects.

Ordinary least squares (OLS) estimates from our sample conform to the predictions of the
model (Appendix Table 5). Regardless of the measure of poverty used as the dependent
variable, per capita income has a negative and significant coefficient. A positive and
significant coefficient for the Gini index indicates that greater inequality is associated with
higher poverty. We estimate a negative elasticity between poverty and incoming remittances;
this result is quite consistent. Except in the case where the left side variable is the squared
poverty gap , this result is always significant. Prima facie our findings indicate that a
10 percent rise in the inflow of remittances is associated with about a 1 percent fall in

16
Almost 32 percent of the observations in the dataset come from Latin American and Caribbean countries,
11 percent from the East Asia and Pacific region, almost 18 percent from East Europe and Central Asia, 10
percent from the Middle East and North Africa, and 6 percent from South Asia.

17
For details on this and other data sources see the Data Appendix.
 14

headcount poverty and the poverty gap. 18 In keeping with the regional focus of the paper, we
also introduce an interaction term between remittances and a dummy for SSA
(Appendix Table 6). While the overall poverty reducing effect of remittances remains, the
coefficient on the interaction term comes in with a positive sign. Although this effect is not
always significant it raises the possibility that in SSA the severity of poverty might be
motivating greater out-migration, so that poverty is positively associated with remittances.19
The issue of reverse causality is taken up next.

Ordinary least squares estimates are likely to be biased when any right side variable is
endogenous. Moreover, we can argue that the relationship between poverty and remittances
is unlikely to be unidirectional. To tackle this issue a system estimation technique that
allows for both poverty and remittances to be determined simultaneously is adopted. Three-
stage least squares is often described as the system equivalent of a two-stage least squares.20
The advantage is that estimating a system of equations where both poverty and remittances
are endogenously determined allows us to observe not just the effect of remittances on
poverty, but also the reverse effect of poverty of remittances. The price for this is that a
misspecification error in one of the system equations is transmitted through the system.

The specification for the poverty equation is the same as in equation 1. We also estimate
remittances (Rem) as a function of poverty (Pov), trade openness (Trade), schooling (Sch),
distance (Dist) from the main remittance source country, a dummy for dual exchange markets
(Dual), and lagged remittances (Remt-1).
Log (Remit) = αi +β1 log (Povit) + β2 log (Tradeit) + β3 log (Schit) + β4 log (Dist) + β5 Dual
+ β6 log (Remit-1) + εit , (2)
(i = 1.....N, t = 1....Ti),

18
Appendix Table 7 reports the OLS results when the sample is restricted to countries where remittances
amount to more than 1 percent of GDP. This is a macro replication of the micro idea that the poverty-reducing
effect of remittances is likely to be enhanced when the sample includes only households that have migrant
workers—those that actually receive income transfers. The higher elasticities with this restricted sample support
the idea that a more general sample dilutes the poverty-reducing impact of remittances.

19
A postestimation test of the OLS coefficients suggests that the sum of the average effect of remittances for all
countries and the coefficient on the interaction term is not different from zero. While this does suggest that the
relationship between poverty and remittances in SSA might be different there are not enough observations from
the region to pursue this issue. Instead we explore the issue of reverse causality using a three-stage least squares
estimation for the full sample.

20
The three-stage least squares technique involves simultaneously generating two-stage least squares estimates
of all the equations in the system. The technique allows for nonzero contemporaneous correlations between the
disturbances in different equations. If the disturbances are uncorrelated, the three-stage least squares technique
reduces to a two-stage least squares.
 15

Migration is the best determinant of remittances, but migration data are likely to suffer from
the same problems as data on remittances. Thus we use other variables suggested by the
literature on the motivation to migrate and remit. To the extent that remittances represent a
private welfare transfer, we can expect them to be higher where there is widespread poverty;
hence, we expect a positive sign for β1. If labor mobility and commodity trade are
complementary in more open economies, we can also expect a positive sign on the openness
variable. If, on the other hand, goods mobility substitutes for labor mobility, β2 would be less
than zero.

The sign of β3 is subject to two countervailing influences. Because the general tendency is for
the migrant population to be better educated than the general population, we can expect more
schooling to be associated with greater migration and remittances. At the same time,
educational attainment also serves as a proxy for development in the recipient country and
hence more years of schooling may indicate less need to seek employment abroad. The
distance variable here is the geographic distance between the recipient country and the
OECD country with the largest migrant population from the recipient.21 The expectations for
the sign of its coefficient are ambiguous. On the one hand, because distance captures the
difficulty of migration, one can expect β4 to be less than zero. On the other hand, because of
the implication that it takes a more educated migrant to overcome the higher cost of
migration, one can expect higher remittances from the source country. Restrictions in the
foreign exchange market can be deterrent to remittances (or at least the flows going through
formal channels) and hence β5 is expected to have a negative sign. And finally, given the
stability of out-migration and remittance flows, we can expect the previous period’s
remittances to be a significant predictor of this period’s remittances, and hence β6 is expected
to be greater than zero.

Table 1 reports the results from the three-stage least squares estimation. The hypothesis of
reverse causality between poverty and remittances finds support in the positive coefficient on
poverty as a right hand side variable when remittances are endogenously modeled. Trade
openness is also a consistently positive and significant determinant of remittances in this two
equation system. As expected lagged remittances are significant, positive predictor of current
remittances. For our sample of countries, none of the other control variables are significant
determinants of remittances.

21
Since only OECD members keep detailed records of their immigrant population we are restricted to using
only these countries as the source countries for remittances. This assumption may be questioned in SSA where
intraregional migration is common, and where, for instance, South Africa might be a more significant source
country.
 16

Table 2. Three-Stage Least Squares Estimation

Headcount Poverty Poverty Gap Squared Poverty Gap
Poverty Remittances Poverty Remittances Poverty Remittances
Per capita GDP (constant 2000 dollars) -1.14*** -1.33*** -1.38***
(-10.06) (-10.47) (-9.43)
Gini coefficient 1.97*** 1.96*** 2.44***
(4.16) (3.66) (4.04)
Inflow of remittances (ratio to GDP) -0.15*** -0.11** -0.08
(-2.86) (-1.89) (-1.23)
Poverty 0.21* 0.19** 0.21**
(1.86) (1.98) (2.08)
Schooling -0.05 -0.05 0.09
(0.20) (0.19) (0.34)
Trade openness 0.65*** 0.68*** 0.65***
(2.71) (2.87) (2.65)
Distance 0.01 0.08 0.10
(0.08) (0.58) (0.72)
Dual exchange market (dummy) -0.01 -0.02 -0.02
(-0.05) (-0.07) (-0.09)
Lagged remittances 0.70*** 0.69*** 0.69***
(11.52) (11.52) (11.36)
Europe and Central Asia -1.94*** -1.05** -0.38
(-4.69) (-2.28) (-0.72)
East Asia and Pacific -0.40 -0.50 -0.20
(-1.15) (-1.27) (-0.44)
Latin America and Caribbean -0.16 0.60 0.71
(-0.44) (1.34) (1.50)
Middle East and North Africa -1.86*** -1.72*** -1.57***
(-4.87) (-4.03) (-3.24)
Sub-Saharan Africa -0.70* -0.28 -0.18
(-1.97) (-0.71) (-0.40)
Constant 12.32*** -3.13* 11.92*** -3.54** 11.61*** -3.45**
(12.47) (-1.96) (10.96) (-2.24) (9.21) (-2.10)
Observations 156 156 155 155 152 152
2
Adj R .72 0.53 0.70 0.54 0.64 0.55
F-Statistic 51.45 30.71 45.93 30.95 34.61 31.02
Note: ***,**,and *, indicate significance at the 1, 5 and 10 percent.

The effect of per capita income and income inequality is consistent with the OLS results.
When endogenously determined in this manner, the poverty-reducing effect of remittances
still remains, and the magnitude of this effect is very similar to the OLS estimates. However,
the average remittance-inducing elasticity of poverty is consistently greater than the average
poverty-reducing elasticity of remittances. This suggests that for SSA countries in the sample
the impact of poverty on out-migration and remittances might be greater than the impact of
remittances on poverty.

B. Impact on Financial Development

The immediate welfare-enhancing role of remittances is critical at both the household and the
country level. However, it does not fully explain the usefulness of remittances as a source of
 17

development finance. To understand how remittances affect long-term growth potential we

next turn our attention to an indirect consequence of cross-border money transfers: their
impact on financial development. Because migrant transfers entail cross-border flows of
relatively modest sums of money, they present an opportunity for low-income households to
access formal financial services. This most likely begins with savings products but the
growing interest that microfinance institutions have shown in this segment of the market
raises the possibility of access to small business start-up capital for individuals previously
excluded from the formal sector.

The impact of remittances on growth depends on how recipient households use them. Once
again empirical studies yield an array of possibilities. One view is that remittances would
mostly be used for consumption, sometimes even conspicuous consumption, and that the
same community characteristics that led to migration also dampen the productive use of
incoming remittances. Caceres and Saca (2006) find that in El Salvador remittances were
accompanied by a sharp decline in savings, so that economic activity actually contracted. Yet
Woodruff and Zenteno (2001) estimate that remittances accounted for about 20 percent of the
capital invested in microenterprises in urban Mexico and that the impact is stronger for
female-owned businesses. Lucas (1987) found that any effects on rural output of the loss of
labor due to migration to South African mines from Botswana, Lesotho, and Malawi are
offset in the long run by investments in farm technology. However, Rozelle, Taylor, and
deBrauw (1999) estimate that farm investments only partially offset the decline in rural
output due to migration.22

Given the decentralized decision-making process that characterizes the use of remittances, it
is difficult to gauge their aggregate effect. The impact of remittances on growth in cross-
country studies is inconclusive. Studies that focus on the labor supply response of recipient
households find that remittances lower growth (Chami, Fullenkamp, and Jahjah, 2003; Azam
and Gubert, 2005). However studies that link remittances to investment, where remittances
either substitute for or improve financial access, tend to conclude that remittances stimulate
growth (Giuliano and Ruiz-Arranz, 2005; Toxopeus and Lensink, 2006). While the evidence
on the contemporaneous impact of remittances on growth may be mixed, it is likely that
remittances can affect long-term growth by fostering financial deepening.

The positive impact of financial development on growth has been extensively documented
(Levine, 1997, 2004; Rajan and Zingales, 1998; Beck, Demirguc-Kunt, and Levine, 2004).

22
Asymmetric information does raise the possibility of moral hazard on the recipient’s side. Since migrant
workers are typically unable to monitor the use of their transfers, there is an incentive for household members to
curtail their own labor effort, using the supplemental income from remittances to maintain their standard of
living. Azam and Gubert (2005) found that in the Kayes region of western Mali widespread migration lowered
recipient productivity.
 18

For SSA countries in particular, lack of access to formal financial services is a significant
impediment to financial deepening (Gulde and others, 2006). Migrant transfers can create an
avenue for unbanked households to avail themselves of some of the products offered by
formal financial providers.

Data and model

We investigate the impact of remittances on financial development in SSA countries using an

unbalanced panel of 44 countries and six time periods, composed of five-year averages from
1975 through 2004. Our baseline specification closely follows Aggarwal, Demirguc-Kunt,
and Peria (2006), but we restrict our sample to observations from SSA only. Financial
development is alternatively proxied by the ratio of bank deposits to GDP and the ratio of M2
to GDP. Remittances are measured in relation to recipient country GDP, as defined
elsewhere in the paper. The regressions also include the following control variables:

• The size of the economy is captured by the log of GDP in constant U.S. dollars.

• Per capita GDP is a proxy for the degree of institutional development.

• Inflation is measured as the annual change in the CPI.

• A dummy variable signifies a dual exchange rate regime as a measure of capital

account openness.

• The ratio of import and exports to GDP proxies current account openness.

• The sum of FDI and development assistance to GDP serves as an alternative measure
of openness.23

The core model can therefore be written as

FDit = β1 Remit + β2 Xit + αi + uit (3)

where i identifies the cross-section and t the time period, Rem is the variable of interest, X is
the vector of control variables, αi captures the country-specific effect, and uit is the error
term.

23
The dataset is described in detail in Appendix Tables 8 and 9.
 19

Results

Table 3 reports the results from both the random and the fixed effects panel regressions. In
all instances remittances are significant as a positive determinant of financial development.
For SSA countries the size of the economy seems unrelated to financial development.24
Similarly, while per capita GDP seems to significantly affect financial development, the
magnitude of the effect is surprisingly small. Capital and current account openness are both
associated with greater financial development. 25

Table 3. Baseline Panel Estimation

Random Effects Fixed Effects
Deposits M2 Deposits M2
Remittances to GDP 0.65*** 0.44*** 0.56* 0.47***
(2.66) (3.51) (1.87) (3.21)
Log(GDP) 3.06*** 1.68 2.32 0.06
(2.47) (1.21) (1.15) (0.02)
Per capita GDP 0.01*** 0.01*** 0.01*** 0.01***
(7.39) (5.48) (5.92) (4.57)
Inflation -0.003 0.002 -0.001 0.004
(-0.52) (0.43) (-0.11) (0.71)
Dual -3.46** -3.04 -3.92** -3.21*
(-1.98) (-1.61) (-2.18) (-1.66)
Trade openness 0.05 0.09*** 0.08** 0.15***
(1.62) (2.51) (2.04) (3.18)
Other capital flows to GDP 0.2 -0.02 0.21* -0.06
(1.87) (-0.18) (1.70) (0.46)
Constant -62.75** -23.32 -50.69 6.09
(-2.30) (-0.76) (-1.18) (0.12)
Observations 150 162 150 162
2
Adj R 0.46 0.35 0.36 0.29

Note:***,**, and * signify 1, 5, and 10 percent significance levels.

24
This result holds even when South Africa is excluded from the sample.

25
Recent studies have emphasized the role of non-economic factors in financial development among low-
income countries (Detragiache, Gupta, and Tressel, 2005). In Appendix Table 10 we include corruption,
internal conflict and political risks as additional control variables, though the limited time series availability of
these variables restricts our observations to less than 60 percent of those reported in Table 2. The results
indicate that even when the signifcant effect of internal conflict and political risk on financial development in
SSA is taken into account, remittances are still positively and signicantly associated with financial development.
 20

Once again these estimates can be biased by endogeneity between financial development and
remittances. It can be argued that better-developed financial institutions have a positive effect
on remittances flowing through formal channels. To address this we adopt three instrumental
variables from Aggarwal, Demirguc-Kunt, and Peria (2006) based on macroeconomic
conditions in source countries. Unemployment, GDP growth, and per capita GDP in the
source country, while related to remittances, are independent of financial development and
other conditions in the recipient country. The results are reported in Table 4.

Table 4. Fixed Effects Panel Instrumental Variables Estimation

Deposits M2
(1) (2) (1) (2)
Instrumented Variable
Remittances to GDP 3.47** 2.67*** 0.39 4.75***
(2.61) (2.37) (0.44) (2.99)
Exogenous Variables
Log(GDP) 2.36 -3.80 -2.07 -10.75
(0.68) (-0.61) (-0.43) (-1.25)
Per capita GDP 0.02*** 0.014*** 0.02*** 0.01**
(6.40) (2.75) (5.64) (1.30)
Inflation 0.01 -0.001 0.004 0.003
(0.18) (-0.29) (0.68) (0.50)
Dual -3.85 -4.10* -3.25 -2.45
(-1.52) (-1.78) (-1.49) (0.71)
Trade openness 0.05 -0.09 0.18*** -0.05
(0.88) (-1.20) (3.48) (-0.42)
Other capital flows to GDP 0.19 0.48*** -0.03 0.14
(1.09) (2.70) (-0.15) (0.65)
Corruption -1.67 -2.77
(-1.20) (-1.39
Internal conflict -1.97** -2.80**
(2.46) (-2.61)
Political risk 0.69** 0.99***
(2.66) (2.82)
Constant -64.15 68.61 44.86 223.66
(-0.86) (0.52) (0.44) (1.25)

Observations 134 89 145 93

Cragg Donald F-statistic for weak instruments 2.05 3.47 2.13 3.18
2
Adj R 0.08 0.12 0.40 0.41
Note:***,**, and * signify 1, 5, and 10 percent significance levels.

The instrumented remittances variable comes in with a positive coefficient of a magnitude

greater than previously estimated. While the impact of per capita GDP on financial
development is consistent with the panel estimation, in this specification current and capital
account openness are less significant. Source country variables do not perform very strongly
as instruments for remittances in our sample, although the Cragg Donald statistic is above the
 21

critical value. 26 In general, however, the estimated effect of remittances on financial

development in SSA compares well with the effect estimated by Aggarwal and others (2006)
using a larger sample.

IV. IMPROVING THE EFFECTIVENESS OF REMITTANCE FLOWS

A. Channeling Remittance Flows to Formal Providers

While remittances can facilitate the entry of households into formal financial markets, only a
fraction of the sums remitted by migrant workers from SSA finds its way into the formal
system. The high fees formal providers charge is a deterrent for poor migrants who want to
send small sums of money home, and even if a migrant has access to banks the recipient may
not. So migrants rely more on import-export operators, retail shops, and currency
dealerships—but there are no records of the transactions these conduct (Sander and Maimbo,
2005). Informal money transfer systems modeled closely on the hawala system in the Middle
East dominate the remittance market in several African countries (El Qorchi, Maimbo,and
Wilson, 2003). Informal providers offer numerous client-friendly features, such as
anonymity, minimal paperwork, and speed.

The cost of transferring funds, especially small sums, is indeed high. A survey of money
transfer operators (MTO) in the U.K. found that the fee on money transfers was lower in
high-volume corridors like U.K.-India and higher for UK-Africa (Table 5).27

Table 5. Fees for Remittances Sent Through Money Transfer Operators in the U.K.
(Percent of amount)
Ghana Kenya Nigeria India Speed of transfer
Transfer amount £100 £500 £100 £500 £100 £500 £100 £500

Chequepoint 3 3 6 4.2 5 5 n.a. n.a. Up to 24 hours

First Remit 5 4.2 n.a. n.a. 5 4.2 5 4.2 Up to 24 hours
Money Gram 12 7.2 12 7.2 7 5 12 4 10 minutes/instant
Travelex Money Transfer 7.5 4.8 n.a. n.a. 7.5 4.8 n.a. 4.8 10 minutes/instant
Western Union 12 6.4 14 7.4 12 6.4 4 0.8 10 minutes/instant
Source: DFID, 2006.
Note: Since the fees can change due to exchange rate changes, the number should be interpreted as indicative rather than precise.

26
We also weight the source country variables by the general expatriation rate to improve the fit of the
instruments. The results are reported in Appendix Table 11 and are not materially different from those reported
above.

27
DfID, 2006. This pattern also holds for the high-volume U.S.-Mexico corridor, where since 2000 the cost of
remitting money has almost halved (Serrano, 2006).
 22

The market in money transfers between developing countries in SSA is underserved by

formal institutions, and the prohibitive fees they charge severely depress their use. A study in
South Africa (Genesis Analytics, 2003) found that the comparative cost of an R250
international transfer was the lowest when a “friend” or taxi driver was used to effect the
transfer and highest when banks were used. Though cross-border Post Office transfers are
competitively priced, they are not as fast or as secure. Table 6 compares the cost of remitting
R300 from South Africa by provider and method of transfer.

The absence in South Africa of a major MTO like Western Union further limits competition
among the players in the formal market and increases the likelihood that migrant workers
will use informal channels to send money home. The terrorist attacks of September 11, 2001,
have increased the scrutiny of international money transfers and many banks are imposing
more identification requirements on both individuals and small MTOs (Sander and Maimbo,
2003). In South Africa only authorized dealers, who must have a banking license and have
invested in an expensive exchange control reporting system, can remit funds. By further
increasing the effective cost the rules discourage remittances through formal channels
(Genesis Analytics 2005).

Table 6. Fees for Remittances from South Africa

(Percent of 300 Rand Transfer)

Amount Received in Local Currency
Method of Transfer
Transfer Provider Fee Botswana Lesotho Malawi Mozambique
Bank draft FNB 52.6 120.8 142.1 2688.5 142.1
Nedbank 68.2 76.4 95.5 2005.5 95.5
Standard Bank 35.0 195.0 195.0 195.0 195.0
Electronic ABSA 33.3 178.3 200.0 4370.0 815660.0
FNB 52.6 120.8 142.1 2688.5 142.1
Nedbank 62.5 90.0 112.5 2362.5 112.5
Postbank 19.2 242.5 254.0
Standard Bank 61.7 115.0 115.0 115.0 115.0
Mail transfer Postbank 8.2 275.5 278.5 275.5

Moneygram Standard Bank 25.3 224.0 n.a.

Online iKobo 6.2 247.7 281.5 1239469.9
Source: DFID and FinMark Trust, 2006.

Note: Shaded cells indicate the rand value of the money transfer since the transfer is converted to local currency by
the receiving organization.
 23

Banks are not always interested in the small remittances market. Most analysts see significant
opportunities for banks to reduce the transaction costs on remittances, especially small
remittances sent by poor migrants. Freund and Spatafora (2005) find that concentration in the
banking sector, financial risk, and exchange rate variability typically increase transaction
costs. Financial sector reforms that address any or all of these structural problems in the
receiving and sending countries are also likely to lower the cost of remittances.28 Cross-
border uniformity in the regulations related to remittances and regulatory interventions where
fees are prohibitive have been proposed as other cost-reducing measures (Ratha and
Riedberg, 2005; Sanders and Maimbo, 2005).

Among formal providers many smaller banks and microfinance institutions have already
gauged the untapped potential of this market. Where there is a long history of migration some
small banks have adapted to the needs of the migrant community. For instance, Theba Bank,
a miners’ bank, offers low-cost transfers from South Africa to families that have bank
accounts in Mozambique and Swaziland (Orozco, 2003). International Remittance Network
(IRnet) consists of about 200 credit unions that offer low-cost services in 40 countries in
Asia, Africa, Europe, and Latin America (Samuels, 2003). The network does not require that
the receiving family have an account with a credit union.

Lately, in well-developed financial markets like the United States the growing demand for
remittance services has caught the attention of major commercial banks like Citizen’s Bank
and Wells Fargo. These banks see remittance services as an effective way to draw the
attention of a significant unbanked population to their more mainstream financial products.
In an arrangement with two banks in Cape Verde Citizen’s Bank offers Cape Verdean
migrants a remittance facility that is low cost compared to Western Union. In three years of
operation this program has made over 1,000 formerly unbanked migrants Citizen’s
customers.29

There are already signs that the window of opportunity for financial institutions to tap into
this highly profitable and rapidly innovating market might be narrow. Recent strides in cell
phone encryption technology have facilitated fast, low-cost money transfers between OECD
countries and recipient countries as diverse as the Philippines and Zambia, allowing

28
For instance, eliminating the discrepancies between the official and parallel market exchange rates in either
the sending or the receiving country can make formal channels more attractive. In Uganda measures permitting
residents to open foreign currency accounts led to a dramatic surge in private transfers in the early 1990s
(Kasekende, 2000, cited in Ratha, 2003).

29
However, since most such programs require that the migrant open a checking or savings account, they are
unlikely to appeal to undocumented workers.
 24

customers to avoid the higher fees and longer waiting periods associated with MTOs and
banks (Jordan, 2006).

B. Using Remittances Effectively

Bringing recipient households into the formal financial sector is only the first step in using
remittances more effectively. Country-specific surveys indicate that while typically a large
proportion of remittances are spent the propensity to save from remittances among some
households can be as high as 40 percent (UNDP, 2005). For policy-makers the challenge is to
channel these savings into productive uses.

Most studies indicate that remittances not used to pay for the immediate consumption needs
of the recipient household are used for human capital development or conspicuous
consumption. While the long-term benefits of the former are apparent, not all conspicuous
consumption is wasteful. The construction of very large houses for migrant workers in West
Africa has spurred local economic activity through multiplier effects. In Mexico, the
Sociedad Hipotecaria Federal, a government financial institution established to build primary
and secondary mortgage markets, provides long-term financing and partial mortgage
insurance to Mexican sofols (mortgage providers) that extend loans to immigrants for
housing construction (Serrano, 2006). The loans are denominated in Mexican pesos. Migrant
workers are given some flexibility about the method of income verification and there is no
credit history requirement. Mortgage payments are made in the workers place of residence.
Inadequate financial infrastructure makes launching of similar schemes in Africa more
challenging, but they can spur a sustained housing boom with positive spillovers on both real
and financial sectors of the economy.

By bundling financial services like savings products and entrepreneurial loans for remittance-
receiving households, financial institutions, especially banks, can activate the investment
channels through which remittances can promote growth.30 Given the paucity of assets that
can serve as collateral in SSA a steady future flow of migrant remittances could be used to
secure small business loans—though small retail businesses started entirely with remittance
savings face expansion limits unless they can access additional long-term funding. 31

The surge in remittances to India over the last few years is attributed in some part to
incentive schemes launched by the government such as the Resurgent India Bond to

30
At present the market is dominated by specialized MTOs like Western Union that are less likely to offer
ancillary financial products to their clients.

31
This micro-level replication of recipient countries gaining favorable access to capital markets by securitizing
future remittance flows is likely to be perceived as less risky by local financial institutions if accompanied by
entrepreneurial training for receiving households.
 25

encourage the inflow of diaspora savings. While such flows are more likely to be subject to
speculative reversals than intrafamily transfers they can significantly supplement domestic
investible resources (World Bank, 2006).32

Remittances are not a panacea for all that ails low-income countries. They cannot be a
substitute for a sustained, domestically engineered development effort. Moreover, large-scale
migration can have a deleterious effect on domestic labor markets in specific sectors,
particularly where those leaving are largely skilled workers. Nevertheless, migrant transfers
can help ease the immediate budget constraints of recipient households. For developing
countries as a whole they are a larger transfer of resources than all development assistance
and have a more direct impact on poverty. And the vast untapped market in money transfers
is an opportunity for small savers to gain a foothold in the formal financial sector.

32
Funds invested directly at attractive rates in deposit schemes or bonds are not strictly speaking remittances;
because they are not intrahousehold transfers and there is a monetary quid pro quo. However, since such funds
are typically converted to local currency and stay in the recipient country they can be an important source of
savings.
 26

Appendix

Variable Source
Remittances (sum of receipts of worker
remittances, employee compensation, migrant Balance of Payments (supplemented by World
transfers) Bank staff estimates for 2005)
Poverty Regressions
PovcalNet database (available at
http://iresearch.worldbank.org/PovcalNet/jsp/in
Poverty indicators dex.jsp.)
PovcalNet database (available at
http://iresearch.worldbank.org/PovcalNet/jsp/in
Gini index dex.jsp.)
Per capita GDP (constant 2000 US dollar) World Development Indicators
Schooling (average schooling years among
over 25 population) Barro-Lee database
Trade openness ((imports + exports)/GDP) World Development Indicators
Annual Report on Exchange Arrangements and
Dual exchange market dummy Exchange Restrictions, IMF
Financial Development Regressions
M2/GDP International Financial Statistics
Bank deposits/GDP International Financial Statistics
GDP (constant 2000 U.S.$) World Development Indicators
Per capita GDP (constant 2000 U.S $) World Development Indicators
Inflation (annual percentage change in CPI) World Development Indicators
Trade openness ((imports + exports)/GDP) World Development Indicators
Foreign direct investment World Economic Outlook
Official development assistance OECD/DAC database
Annual Report on Exchange Arrangements and
Dual exchange market dummy Exchange Restrictions, IMF
OECD Trends in International Migration
General and Educated Expatriation Rate database
Corruption ICRG database
Internal conflict ICRG database
Political Risk ICRG database

List of Countries
Angola Cote d'Ivoire Madagascar Sierra Leone
Benin Equatorial Guinea Malawi South Africa
Botswana Eritrea Mali Swaziland
Burkina Faso Ethiopia Mauritius Tanzania
Burundi Gabon Mozambique Togo
Cameroon Gambia, The Namibia Uganda
Cape Verde Ghana Niger Zambia
Central African Republic Guinea Nigeria Zimbabwe
Chad Guinea-Bissau Rwanda
Comoros Kenya São Tomé & Príncipe
Congo, Rep. of Lesotho Senegal
Congo, Dem. Rep. of Liberia Seychelles
 27

Appendix Table 1. Workers' remittances, compensation of employees, and migrant transfers

(Millions of U.S. Dollars )
2005 2006
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 estimate estimate
Angola .. 5 .. .. .. .. .. .. .. .. .. ..
Benin 100 86 71 90 77 87 84 76 55 55 55 55
Botswana 59 50 48 43 34 26 26 27 39 39 39 39
Burkina Faso 80 80 80 80 80 67 50 50 50 50 50 50
Burundi .. .. .. .. .. .. .. .. .. 0 0 0
Cameroon 11 11 11 11 11 11 11 11 11 11 11 11
Cape Verde 106 100 76 74 79 87 81 85 92 92 92 92
Central African Republic .. .. .. .. .. .. .. .. .. .. .. ..
Chad .. .. .. .. .. .. .. .. .. .. .. ..
Comoros 12 12 12 12 12 12 12 12 12 12 12 12
Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. ..
Congo, Rep. 4 8 5 2 12 10 1 1 13 15 11 11
Cote d'Ivoire 151 147 136 143 138 119 116 120 142 148 148 148
Equatorial Guinea 0 0 .. .. .. .. .. .. .. .. .. ..
Eritrea .. .. .. 3 4 3 .. .. .. .. .. ..
Ethiopia 27 16 9 27 34 53 18 33 47 134 134 134
Gabon 4 6 6 6 4 6 5 3 6 6 6 6
Gambia, The 19 20 6 6 7 14 7 7 8 8 8 8
Ghana 17 28 26 30 31 32 46 44 65 82 99 120
Guinea 1 1 1 5 6 1 9 15 111 42 42 42
Guinea-Bissau .. 2 2 2 2 2 10 18 23 23 23 23
Kenya 298 288 352 348 432 538 517 395 494 494 494 494
Lesotho 411 388 379 295 276 252 209 194 288 355 355 355
Liberia .. .. .. .. .. .. .. .. .. .. .. ..
Madagascar 14 11 12 11 12 11 11 17 16 16 16 16
Malawi 1 1 1 1 1 1 1 1 1 1 1 1
Mali 112 111 92 84 86 73 88 137 154 155 155 155
Mauritius 132 160 168 180 178 177 215 215 215 215 215 215
Mozambique 59 61 64 46 38 37 42 53 70 58 57 57
Namibia 16 14 13 11 10 9 9 7 12 16 16 16
Niger 8 4 5 19 27 14 22 19 26 26 26 26
Nigeria 804 947 1,920 1,544 1,301 1,392 1,167 1,209 1,063 2,273 2,273 2,273
Rwanda 21 5 5 5 5 7 8 7 9 10 9 9
Sao Tome and Principe .. .. .. 1 1 0 1 1 1 1 1 1
Senegal 146 150 150 147 186 233 305 344 511 511 511 511
Seychelles 1 1 0 0 0 3 2 2 5 7 11 11
Sierra Leone 24 25 6 20 22 7 7 22 26 25 2 2
South Africa 105 102 206 283 327 344 297 288 435 523 658 658
Swaziland 83 76 84 78 70 74 74 62 88 89 89 89
Tanzania 1 19 2 12 7 8 16 12 9 11 16 16
Togo 15 29 26 19 23 34 69 104 148 148 148 148
Uganda .. .. .. .. 233 238 338 416 285 347 642 642
Zambia .. .. .. .. .. .. .. .. .. .. .. ..
Zimbabwe .. .. .. .. .. .. .. .. .. .. .. ..
Source: World Bank (2006)
 28

Appendix Table 2: Poverty Dataset Details

Country Survey Year Country Survey Year Country Survey Year
Albania 1996 Czech Rep. 1988 Iran 1998
Albania 2002 Czech Rep. 1993 Jamaica 1988
Algeria 1988 Czech Rep. 1996 Jamaica 1992
Algeria 1995 Dominican Rep. 1986 Jamaica 1996
Benin 2003 Dominican Rep. 1992 Jamaica 2000
Bolivia 1990 Dominican Rep. 1996 Jordan 1986
Bolivia 1997 Dominican Rep. 2000 Jordan 1992
Bolivia 2002 Ecuador 1987 Jordan 1997
Botswana 1985 Ecuador 1994 Jordan 2002
Botswana 1993 Ecuador 1998 Kenya 1992
Brazil 1981 Egypt 1990 Kenya 1997
Brazil 1987 Egypt 1995 Kyrgyz Rep. 1988
Brazil 1992 El Salvador 1989 Kyrgyz Rep. 1993
Brazil 1997 El Salvador 1997 Kyrgyz Rep. 1997
Brazil 2002 El Salvador 2002 Kyrgyz Rep. 2002
Burkina Faso 1994 Estonia 1988 Laos 1992
Burkina Faso 1998 Estonia 1993 Laos 1997
Burkina Faso 2003 Estonia 1998 Laos 2002
Cambodia 1997 Estonia 2002 Lesotho 1986
Cameroon 1996 Ethiopia 1981 Lesotho 1993
Cameroon 2001 Ethiopia 1995 Lesotho 1995
Central African Rep. 1993 Ethiopia 2000 Lithuania 1988
Chile 1987 Gambia, The 1992 Lithuania 1993
Chile 1992 Gambia, The 1998 Lithuania 1998
Chile 1998 Ghana 1988 Lithuania 2002
Chile 2000 Ghana 1991 Madagascar 1980
China 1984 Ghana 1998 Madagascar 1993
China 1987 Guatemala 1987 Madagascar 1997
China 1992 Guatemala 1998 Madagascar 2001
China 1997 Guatemala 2002 Malawi 1997
China 2001 Guyana 1992 Malawi 2004
Colombia 1980 Guyana 1998 Malaysia 1984
Colombia 1988 Haiti 2001 Malaysia 1987
Colombia 1991 Honduras 1986 Malaysia 1992
Colombia 1996 Honduras 1992 Malaysia 1997
Colombia 2003 Honduras 1998 Mali 1989
Costa Rica 1981 Honduras 2003 Mali 1994
Costa Rica 1986 India 1977 Mali 2001
Costa Rica 1993 India 1983 Mauritania 1987
Costa Rica 1997 India 1987 Mauritania 1993
Costa Rica 2001 India 1992 Mauritania 1995
Côte d'Ivoire 1987 India 1997 Mauritania 2000
Côte d'Ivoire 1993 Indonesia 1987 Mexico 1984
Côte d'Ivoire 1998 Indonesia 1993 Mexico 1989
Côte d'Ivoire 2002 Indonesia 1998 Mexico 1992
Croatia 1988 Indonesia 2002 Mexico 1996
Croatia 1998 Iran 1986 Mexico 2002
Croatia 2001 Iran 1994 Morocco 1984
 29

Appendix Table 2: Poverty Dataset Details (Continued)

Country Survey Year Country Survey Year
Morocco 1990 Rwanda 1999
Morocco 1998 Senegal 1991
Mozambique 1996 Senegal 2001
Namibia 1993 Sierra Leone 1989
Nepal 1995 Slovak Rep. 1988
Nepal 2003 Slovak Rep. 1992
Nicaragua 1993 Slovak Rep. 1996
Nicaragua 1998 Slovenia 1987
Nicaragua 2001 Slovenia 1993
Niger 1992 Slovenia 1998
Niger 1995 South Africa 1993
Nigeria 1985 South Africa 1995
Nigeria 1992 South Africa 2000
Nigeria 1996 Sri Lanka 1985
Nigeria 2003 Sri Lanka 1990
Pakistan 1987 Sri Lanka 1995
Pakistan 1992 Sri Lanka 2002
Pakistan 1996 St Lucia 1995
Panama 1979 Swaziland 1994
Panama 1989 Thailand 1981
Panama 1991 Thailand 1988
Panama 1997 Thailand 1992
Panama 2002 Thailand 1996
Paraguay 1990 Thailand 2002
Paraguay 1997 Trinidad & Tobago 1988
Paraguay 2002 Trinidad & Tobago 1992
Peru 1985 Tunisia 1985
Peru 1990 Tunisia 1990
Peru 1996 Tunisia 1995
Peru 2002 Tunisia 2000
Philippines 1988 Turkey 1987
Philippines 1994 Turkey 1994
Philippines 1997 Turkey 2002
Philippines 2000 Venezuela 1981
Poland 1987 Venezuela 1987
Poland 1992 Venezuela 1993
Poland 1998 Venezuela 1997
Poland 2002 Venezuela 2000
Romania 1989 Yemen 1992
Romania 1992 Yemen 1998
Romania 1998 Zimbabwe 1990
Romania 2002 Zimbabwe 1995
Russia 1988
Russia 1993
Russia 1998
Russia 2002
Rwanda 1984
 30

Appendix Table 3. Descriptive Statistics of Regression Variables

Standard
Observations Mean Median deviation Range

Headcount poverty 233 17.7 9.4 19.4 79.3

Poverty gap 233 6.4 2.7 8.8 51.4
Squared poverty gap 233 3.3 0.9 5.6 37.9
Gini index 233 0.4 0.4 0.1 0.5
Per capita income 228 1,770.3 1,352.7 1,581.1 8,361.2
Remittances to GDP 216 3.5 1.1 7.5 72.9
Trade openess 224 70.0 60.8 37.1 213.3
Schooling 187 4.9 4.7 2.3 10.0
Note: These are raw data series, before the log transformation.

Appendix Table 4. Bivariate Correlations of Regression Variables

Squared
Headcount Poverty Poverty Per capita Remittances Trade
Poverty Gap Gap Gini Index income to GDP Openness Schooling
Headcount poverty 1.00
Poverty gap 0.94* 1.00
Squared poverty gap 0.84* 0.97* 1.00
Gini index 0.20* 0.31* 0.35* 1.00
Per capita income -0.58* -0.49* -0.41* 0.02 1.00
Remittances to GDP 0.01 0.06 0.07 0.12* -0.14* 1.00
Trade openess -0.25* -0.16* -0.11* 0.05 0.21* 0.26* 1.00
Schooling -0.61* -0.55* -0.48* -0.22* 0.56* -0.07 0.30* 1.00
Note: * indicates significant at 10 percent.
 31

Appendix Table 5. Ordinary Least Squares Estimation (With and Without Regional Dummies)
Headcount Poverty Poverty Gap Squared Poverty Gap
(1) (2) (1) (2) (1) (2)
Per capita GDP (constant 2000 dollars)
-1.21*** -1.07*** -1.26*** -1.20*** -1.22*** -1.19***
(-10.56) (-6.62) (-10.58) (-5.93) (-10.29) (-5.07)
Gini coefficient 3.30*** 1.95*** 3.66*** 2.03*** 3.80*** 2.36***
(6.76) (3.74) (7.56) (3.39) (7.00) (3.60)
Inflow of remittances (ratio to GDP) -0.14*** -0.11** -0.13** -0.08 -0.10 -0.05
(-2.53) (-2.38) (-2.07) (-1.48) (-1.55) (-0.81)
Europe and Central Asia -2.01*** -1.46** -0.10
(-3.84) (-2.01) (-1.10)
East Asia and Pacific -0.48 -0.65 -0.45
(-1.00) (-0.98) (-0.60)
Latin America and Caribbean -0.26 0.27 0.39
(-0.51) (0.40) (0.49)
Middle East and North Africa -1.88*** -1.78*** -1.64**
(-3.21) (-2.43) (-1.94)
Sub-Saharan Africa -0.62 -0.28 -0.11
(-1.46) (-0.49) (-0.16)
Constant 13.22*** 11.86*** 12.59*** 11.22*** 11.59*** 10.45***
(16.06) (9.53) (16.59) (7.64) (14.24) (5.89)
Observations 212 212 211 211 208 208
2
Adj R 0.60 0.72 0.58 0.68 0.53 0.61
F-Statistic 58.93 44.10 87.21 53.30 71.35 33.58
Note: ***,**,and * indicate significant at 1, 5,and 10 percent. T-Statistics are reported in parentheses. Standard errors are
clustered by country to eliminate any downward bias.

`
 32

Appendix Table 6. Ordinary Least Squares Estimation (With Interaction Term)

Headcount Poverty Poverty Gap Squared Poverty Gap

(1) (2) (1) (2) (1) (2)
Per capita GDP (constant 2000 dollars) -1.24*** -1.08*** -1.29*** -1.21*** -1.25*** -1.21***
(-10.69) (-6.70) (-10.61) (-6.04) (-10.26) (-5.17)
Gini coefficient 3.29*** 1.93*** 3.66*** 2.01*** 3.80*** 2.35***
(6.86) (3.77) (7.76) (3.39) (7.16) (3.59)
Inflow of remittances (ratio to GDP) -0.18*** -0.16*** -0.17** -0.12 -0.15* -0.09
(-2.65) (-2.45) (-2.15) (-1.61) (-1.68) (-1.01)
Remittances*Sub-Saharan Africa (interaction term) 0.16* 0.14* 0.16* 0.14 0.16 0.12
(1.70) (1.91) (1.71) (1.54) (1.42) (1.11)
Europe and Central Asia -2.06*** -1.50** -1.03
(-3.99) (-2.10) (-1.16)
East Asia and Pacific -0.53 -0.69 -0.49
(-1.11) (-1.06) (-0.67)
Latin America and Caribbean -0.28 0.25 0.38
(-0.56) (0.37) (0.48)
Middle East and North Africa -1.86*** -1.76*** -1.62**
(-3.28) (-2.47) (-1.95)
Sub-Saharan Africa -0.62 -0.28 -0.11
(-1.51) (-0.51) (-0.17)
Constant 13.38*** 11.96*** 12.76*** 11.32*** 11.76*** 10.55***
(16.40) (9.70) (16.83) (7.70) (14.44) (5.92)
Observations 212 212 211 211 208 208
2
Adj R 0.61 0.72 0.58 0.68 0.53 0.61
F-Statistic 46.52 43.91 71.29 55.29 58.86 33.97
Note: ***,**,and * indicate significant at 1, 5,and 10 percent. T-Statistics are reported in parentheses. Standard errors are
clustered by country to eliminate any downward bias.
 33

Appendix Table 7. Ordinary Least Squares Estimation for Rem>1 Sample

Squared
Headcount Poverty Poverty
Poverty Gap Gap
Per capita GDP (constant 2000 dollars)
-1.28*** -1.29*** -1.14***
(-6.52) (-5.55) (-4.57)
Gini coefficient 3.03*** 3.28*** 3.74***
(-4.20) (4.42) (4.817
Inflow of remittances (ratio to GDP) -0.26** -0.19* -0.22
(-2.06) (-1.35) (-1.40)
Europe and Central Asia -1.80*** -1.50* -1.36
(-2.94) (-1.84) (-1.42)
East Asia and Pacific -0.74 -1.08 -0.87
(-1.26) (-1.44) (-1.18)
Latin America and Caribbean -0.27 0.13 0.08
(-0.45) (0.17) (0.09)
Middle East and North Africa -1.86*** -1.96*** -1.96**
(-3.30) (-2.81) (-2.40)
Sub-Saharan Africa -0.75* -0.37 -0.16
(-1.45) (-0.59) (-0.21)
Constant
14.44*** 13.20*** 11.69***
(9.31) (7.72) (6.34)
Observations 112 111 109
2
Adj R 0.75 0.74 0.71
F-Statistic 36.39 41.54 33.10
Note: ***,**,and * indicate significant at 1, 5,and 10 percent. T-Statistics are
reported in parentheses. Standard errors are clustered by country to eliminate
any downward bias
 34

Appendix Table 8. Descriptive Statistics for Regression Variables

Standard
Observations Mean Deviation Minimum Maximum
Bank deposits to GDP 188 18.06 14.27 1.16 93.21
M2 to GDP 233 26.81 18.04 0.81 165.25
Remittances to GDP 198 3.62 9.94 0 75.33
Log(GDP) 245 21.38 1.42 17.42 25.68
Per capita GDP 245 807.16 1,210.59 84.76 7,164.45
Inflation 207 59.85 459.36 -5.61 6424.99
Trade openness 244 71.53 37.07 12.88 224.21
Other capital flows to GDP 248 14.91 14.66 -2.09 104.61

Appendix Table 9. Bivariate Correlations of Regression Variables

Other
Dual Capital
Bank Deposits M2 to Remittances Per Capita Exchange Trade Flows to
to GDP GDP to GDP Log(GDP) GDP Inflation Rate Openness GDP
Bank deposits to GDP 1.00
M2 to GDP 0.97 1.00
Remittances to GDP 0.22 0.15 1.00
Log(GDP) 0.14 0.01 -0.25 1.00
Per capita GDP 0.62 0.38 -0.08 0.14 1.00
Inflation -0.10 -0.06 -0.04 0.08 -0.05 1.00
Dual exchange rate 0.07 0.05 0.08 0.23 0.03 -0.01 1.00
Trade openness 0.43 0.34 0.33 -0.24 0.45 -0.03 0.04 1.00
Other capital flows to GDP -0.24 0.01 0.10 -0.60 0.23 0.04 0.02 0.15 1.00
 35

Appendix Table 10. Baseline Panel Estimation

Random Effects Fixed Effects
Deposits M2 Deposits M2
Remittances to GDP 0.74** 1.66*** 0.76* 1.72***
(2.10) (3.30) (1.90) (2.90)
Log(GDP) 3.69*** 2.79** 0.97 -5.54
(2.99) (1.94) (0.21) (-0.81)
Per capita GDP 0.00*** 0.01 0.01*** 0.01
(2.63) (0.76) (2.75) (1.01)
Inflation -0.02 0.002 -0.01 0.003
(-0.59) (0.52) (-0.29) (0.56)
Dual -2.16 -0.98 -3.57** -0.82
(-1.29) (-0.42) (-1.87) (-0.30)
Trade openness -0.29 0.01 -0.01 0.08
(-0.80) (0.22) (-0.14) (0.98)
Other capital flows to GDP 0.24** 0.03 0.32** -0.04
(2.31) (0.25) (2.46) (-0.27)
Corruption -0.28 0.21 0.43 -1.20
(-0.30) (0.20) (-0.42) (-0.78)
Internal conflict -1.14*** -1.50** -1.06** -1.69**
(-2.49) (-2.26) (-1.99) (-2.17)
Political risk 0.41*** 0.48*** 0.39** 0.61***
(3.37) (2.77) (2.29) (2.43)
Constant -62.75** -57.28* -29.74 117.82
(-2.30) (-1.81) (-0.30) (0.83)
Observations 89 93 89 93
2
Adj R 0.45 0.36 0.38 0.26
Note:***,**, and * signify 1, 5, and 10 percent significance levels.

The ICRG database measures political risk on a scale of 1 to 100 with higher
values implying less risk. So a positive coefficient on political risk indicates that
lower political risk is associated with greater financial development.
 36

Appendix Table 11. Fixed Effects Panel Instrumental Variables Estimation

Deposits M2
(1) (2) (1) (2)
Instrumented Variable
Remittances to GDP 4.04*** 1.81* 3.71** 7.99***
(2.39) (1.67) (1.91) (2.74)
Exogenous Variables
Log(GDP) 2.7 -1.64 12.34 -16.30
(0.70) (-0.29) (1.12) (-1.29)
Per capita GDP 0.02*** 0.01*** 0.01** .014
(5.84) (2.79) (2.04) (1.25)
Inflation 0.002 -0.001 0.01 0.004
(0.20) (0.30) (0.66) (0.38)
Dual -3.65 -3.86* -6.07 -4.17
(-1.30) (-1.89) (-1.17) (-0.83)
Trade openness 0.04 -0.05 0.13 -0.18
(0.66) (-0.76) (1.08) (1.02)
Other capital flows to GDP 0.18 0.40*** -0.64 0.35
(0.90) (2.53) (-1.36) (1.03)
Corruption -1.11 -4.45
(-0.88) (-1.49)
Internal conflict -1.56** -3.98
(-2.11) (-2.38)
Political risk 0.56** 1.39
(2.32) (2.53)
Constant -72.40 24.16 -266.39 336.65
(-0.88) (0.20) (-1.13) (1.28)
Cragg Donald F-statistic for weak instruments 1.70 3.15 2.19 2.93
Observations 134 89 145 93
2
Adj R 0.50 0.30 0.29 0.02
Note:***,**, and * signify 1, 5, and 10 percent significance levels.
Instruments weighted by expatriation rate
 37

Appendix Figure 1. Remittances to Developing Countries by Region, 1975-2005

(Millions of US dollars)

50,000 180,000

45,000 Sub-Saharan Africa South Asia 160,000

Middle East and North Africa East Asia and Pacific
Latin America and Caribbean East and Central Asia
40,000 Developing countries
140,000
Right Axis
35,000
120,000

30,000
100,000
25,000
80,000
20,000

60,000
15,000

40,000
10,000

5,000 20,000

0 0
1975 1980 1985 1990 1995 2000 2005

Source: IMF Balance of Payments Yearbook , 2006; World Bank staff estimates

Appendix Figure 2. Regional Shares of Remittances to Developing Countries, 2000-05

(Millions of U.S. dollars)

Sub-Saharan Africa
4.1
East and Central Asia
12.5

South Asia
20.3

Latin America and Caribbean

24.9

Middle East and North Africa

14.4

East Asia and Pacific

23.9

Source: IMF, Balance of Payments Yearbook , 2006; World Bank staff estimates.
 38

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