




































Economy 
ISSN: 2313-8181 
Vol. 2, No. 2, 44-48, 2015 
www.asianonlinejournals.com/index.php/Economy 

 

 

 

 

 

44 

 

An Empirical Investigation of the Impact of Foreign 

Remittances on Poverty in Developing Countries 
 

Mohammad Imran Hossain
1
 

 
1
 Graduate School of Asia Pacific Studies, Ritsumeikan Asia Pacific University, Japan 

 

Abstract 
 

 

 

 

 

 

 

 

 

 

 

 

 

 
This work is licensed under a Creative Commons Attribution 3.0 License 

Asian Online Journal Publishing Group 

 

Contents 
1. Introduction ............................................................................................................................................................................... 45 

2. Methodology .............................................................................................................................................................................. 46 

3. Results and Discussion .............................................................................................................................................................. 46 

References ...................................................................................................................................................................................... 48 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Remittances sent to home countries by migrant workers became significant in amount. Such funds can 

have profound implications for economic development, human welfare and poverty reduction in a 

developing country context. This paper examines the impact of foreign remittances on poverty in 

selected developing countries. A set of time series data has been utilized to empirically check the 

relationship between remittances and poverty for a list of 44 developing countries worldwide. For the 

purpose of the study, the ‗Three Stage Least Squares‘ (TSLS) regression technique has been applied. A 

separate analysis for a group of countries among the list which recorded a remittances to GDP ratio of 

2% or more has been performed. The study finds that remittances have a significant negative impact on 

poverty in a developing country. 
 

        Keywords: Foreign remittances, Poverty, Developing countries, TSLS. 

http://creativecommons.org/licenses/by/3.0/


Economy, 2015, 2(2): 44-48 

 

 

 

 

45 

 

1. Introduction 
Workers‘ remittances represent a significant portion of financial flows to a developing country (Okodua and 

Olayiwola, 2013). Billions of such funds are transferred through official as well as unofficial channels. In 2012, total 

official flows of remittances to developing countries were estimated at $401 billion, an increase of 5.3% over the 

previous year. The figure becomes $529 billion if remittance flows to high-income countries are also included. 

Remittances sent home by migrants to developing countries are equivalent to more than three times the size of 

official development assistance (ODA) and can have very important implications for economic development and 

human welfare (World Bank, 2012).  

For many developing countries the amount of remittances flow has reached to such a large level that it becomes 

significant in terms of the share in GDP (International Monetary Fund, 2009). In many developing countries, more 

than 20% of GDP is contributed by remittances. Examples include Tonga (38%), Nepal (22%) and the Kyrgyz 

Republic (28%). For some of the developing countries, remittances form almost 40-50% of their GDP (World Bank, 

2013). For instance, in 2012 the remittances to GDP ratio in Tajikistan was as high as 47% (World Bank, 2012). 

Hence, today in many developing countries worldwide, remittances play an important role in the national economy 

and because of the large magnitude of remittances relative to their total income flows, those countries rely on 

remitted funds in many ways (MPI, 2011).  

Certainly, remittances do not go unnoticed in most of the countries that receive them (IMF, 2009) and recent data 

indicates that remittances are increasingly becoming an important source of external financing for many developing 

countries (Martín et al., 2007). On the other hand, in developing countries large numbers of people live under 

poverty and remittances help lift a huge number of people out of poverty. The phenomenon has been explained by 

the World Bank (2013) as such: 

―Remittances can contribute to lowering poverty and building human and financial capital for the poor. 

Remittances generally reduce the level and severity of poverty, typically leading to: higher human 

capital accumulation; greater health and education expenditures; better access to information and 

communication technologies; improved access to formal financial sector services; enhanced small 

business investment; more entrepreneurship; better preparedness for adverse shocks such as droughts, 

earthquakes, and cyclones; and reduced child labor.‖ 

 

 
Figure-1. Remittances as a share of GDP (in 2011) for selected developing countries (%)  

               Source: World Bank (2013) 

 

Typically, migrant people leave their family members behind in their country of origin and support their 

livelihood with a steady flow of remittances. This implies that remittances transferred by migrant workers directly 

affect many more people because remitted funds are generally spent on consumption necessities which include food, 

clothing, shelter, medicine, and education etc. Thus, remittances support poor families with a higher level of 

consumption than would otherwise be possible. In this way, at least on the surface, remittances help reduce poverty. 

Although the empirical literature on the effects of remittance flows on growth appears to be inconclusive, there is 

a lot of evidence to support the hypothesis that foreign remittances significantly affect poverty and development. 

IMF (2005) found no statistically significant effect of remittances on economic growth. But Giuliano and Ruiz-

Arranz (2005) and Ramirez and Sharma (2009) found that remittances appear to have positive effects on growth only 

in countries that possess small financial sectors. Chami et al. (2003) and Chami et al. (2005) found that remittances 

and economic growth are negatively correlated.  

Nevertheles, remittances can impact family welfare, poverty and economic development in direct as well as 

indirect ways. For an example, Pant (2008) concludes that remittances may stimulate demand for other goods and 

services in an economy and consequently impact the economy positively. Faini (2003) also suggests a positive 

relationship between migrant remittances and economic growth. According to Hildebrandt and McKenzie (2005), 

remittances cause knowledge transfer and change in attitudes of the members of the recipient family. Yang (2003) 



Economy, 2015, 2(2): 44-48 

 

 

 

 

46 

 

and Woodruff and Zenteno (2001) concluded that remittances can spur entrepreneurial activity at the household 

level. Lopez Cordova (2005) and Adams and Page (2005) indicated a strong and statistically significant negative 

impact of remittances on poverty (i.e. remittances help reduce poverty). Taylor (1992) and Gustafsson and 

Makonnen (1993) discovered that international remittances help reduce both poverty and inequality. Adams (2004) 

also found a similar result and concluded that foreign remittances help reduce the squared poverty gap.  

 

2. Methodology 
This study makes an effort to estimate the impact of remittances on poverty in selected developing countries. We 

use cross-country panel data to gather enough observations to analyze the remittance-poverty nexus. The ultimate 

objective of this research is to measure the impact of remittances on ‗Poverty Headcount‘ ratios, ‗Poverty Gap‘ at $ 

1.25 a day (PPP) and ‗Poverty Gap‘ at $2 a day (PPP) using a set of panel data starting from 1990 to 2012 for 44 

developing countries. In order to test whether the impact of remittances share in GDP is stronger beyond a certain 

threshold level, a separate analysis is undertaken for 27 countries within the list which have recorded a remittance to 

GDP ratio of 2% or higher. This paper provides the associated systematic theoretical analysis and robust empirical 

estimation, using the most accurate and comprehensive remittances data available in the World Bank Database.  

We derive a simple set of two simultaneous equations incorporating the remittance variable, and then we perform 

several estimations for the defined model by implementing the study‘s empirical research on remittances and 

poverty.  

One important objective of this study is to trace a relationship between remittances and poverty in the context of 

selected developing countries. In the econometric model two simultaneous equations are introduced following United 

Nations (2011). In the first equation ‗poverty‘ (Poverty Headcount ratios, Poverty Gap at $ 1.25 a day, and Poverty 

Gap at $2 a day) is considered as the dependent variable while ‗Per capita GDP‘, ‗Inequality‘ (Gini coefficient), and 

‗Remittances as a share of GDP‘ are modeled as independent variables (Ravallion, 1997). 

In developing countries poverty trends seem to maintain a robust relationship with the choice of poverty estimates. 

The ‗Poverty Headcount Ratio (PCR)‘ only measures the percentage value of poverty incidence, but does not say 

anything about the distance of the poor households from the estimated poverty lines. To overcome this problem and to 

measure the depth of poverty accurately we need to employ one of the two important tools: the Poverty Gap (PG) 

index and the Squared Poverty Gap (SPG) index. This pair of distributional sensitive techniques can appropriately 

estimate the depth of poverty in the population by closely tracking the movement in the headcount ratios. Considering 

the fact that differences in income or consumption between the poor and the poorest group have an important 

implication for the poverty reality in any developing country, the present study uses both poverty headcount ratio and 

poverty gap indices. 

On the other hand, following IMF (2007), the second equation captures the determinants of remittances by 

indicating that ‗remittance‘ (as a share of GDP) is explained by ‗poverty‘, ‗trade‘ (as a share of GDP), ‗education‘ 

(secondary school enrolment ratio) and lagged values of remittance. Hence, the empirical model for this study is 

obtained as follows: 

                                                           …………..………………...…(1) 

                                                                                …(2) 

where in equation (1), POV stands for poverty, PCY represents per capita GDP, the variable INEQ represents 

inequality in terms of Gini coefficient, and REMIT indicates remittances as a share of GDP. In the second equation, 

TRADE stands for the trade openness indicator in the form of the total volume of export and import as a share of GDP, 

EDU represents adult literacy rate which is a proxy for secondary school enrolment ratio and            is lagged 

remittances.     in both equations is for error terms where              . Here    is an individual (country) effect, 

   is a time effect, and     is independently and identically distributed among countries and years. For this study all the 

variables are in real terms and the errors are assumed to be independently and identically distributed. In order to take 

account of the endogeneity problem (that there is a possibility of having a bi-directional relationship within two 

variables such as poverty and remittance) we estimate through the ‗Three Stage Least Squares‘ (TSLS) method using 

two equations, as indicated above (IMF, 2007). 

We assume    to be negative because poverty is expected to decline as per capita income rises. On the other hand, 

literature suggests that greater inequality leads to higher poverty rate; thus    is expected to be positive. The proposed 

model estimates the sign and magnitude of    which defines the impact of remittances on poverty. For the above 

model,    is expected to be positive because it is likely that a higher rate of poverty will cause more people to migrate 

and thus more remittances will be flowing to a certain country. The coefficient    is also expected to be positive 

because the more open an economy is the more amount of remittances it will receive. The reason is that more openness 

will allow remittances to flow in the country easily; also compared to a less open economy labor mobility also may 

take place more easily. Next, the coefficient of the variable ‗education‘,    may take a positive (negative) sign if the 

situation of the country is in a way that more (less) educated people migrate. Finally, the last variable ‗lagged 

remittance‘ captures the dynamic impact. In the model a log transformation has been adopted for all the variables, 

which allows us to interpret the coefficients as elasticities. 

The scope of this paper covers a panel data set from 1990 to 2012 in estimating the specified model. The World 

Bank Data Bank and World Development Indicators act as the main source. Another source that was also utilized is 

the International Financial Statistics (IFS) database.  

 

3. Results and Discussion 
The empirical findings of the study are presented below. Table 1 shows the statistical estimations for a Three 

Stage Least Squares Estimations where the dependent variables, ‗poverty‘ (Poverty Head Count Ratio, Poverty Gap 

at $ 1.25 a day, and Poverty Gap at $ 2 a day) and ‗remittances‘ were estimated against six explanatory variables 

namely ‗per capita GDP‘ (Current US $), ‗Gini Coefficient‘, ‗remittances as a share of GDP‘, ‗lagged remittances‘, 



Economy, 2015, 2(2): 44-48 

 

 

 

 

47 

 

‗trade as a share of GDP‘ and ‗education‘ (secondary School enrolment rate, %) for the whole sample.  

Table 2 depicts the empirical results for 27 countries that have reported remittances to GDP ratio of 2% or more. 

The ‗three stages least square‘ estimation results show that remittances have a significant negative impact on Poverty 

Gap at $ 1.25 a day but the impact on other measures of poverty is not statistically significant (Table 2). In both cases 

GDP per capita significantly affects poverty negatively at 1% significance level as it was expected. Hence, the 

explanatory variable GDP per capita has a viable effect in poverty reduction. Similarly, Gini Coefficient turns out 

significantly positive meaning that with increasing ‗inequality‘, ‗poverty‘ also increases. Other variables like ‗trade‘ 

and ‗education‘ are found to have the right signs and to be statistically significant. 

The impact of poverty on remittances appeared to be statistically insignificant but lagged remittances is found to 

have a significant impact on remittances. It can be inferred here that countries with higher remittances in the initial 

year, possibly indicating a higher migrant stock, have higher remittances. However, the study shows better results 

when the analysis is undertaken for countries with ‗remittances as a percentage of GDP‘ of 2% or more (Table 3). 

Remittances are found to have a significant impact on two of the three measures of poverty namely Poverty Head 

Count and Poverty Gap at $1.25 a day. This is indicative of the fact that in developing countries remittances have a 

stronger impact on poverty reduction if they are above the threshold of 2% of GDP. 

That remittances can reduce poverty in some developing countries has been reflected in the empirical findings of 

the study; however, the process of how remittances work for poverty cannot be identified. In the short run, 

remittances lift poor people out of poverty through helping them finance their necessities. But even if the poor 

receive the remittances, proper use of them is more important for the sustainable reduction of poverty in the context 

of a developing country. For this purpose, remittances are required to serve as a source of investments. Therefore 

with proper policies, remittances may be channeled to achieve both of these ends.  

 
Table-1. Three Stage Least Squares Estimations: Dependent Variables—Poverty and Remittances (44 developing countries; 1995-2010) 

Variables Dependent Variable-Poverty 

HCR at $1.25 a day (PPP)(% of 

population 

Dependent variable-Poverty 

Gap at $1.25 a day (PPP)(%) 

Dependent variable-Poverty 

Gap at $2 a day (PPP)(%) 

 PHCR Remittances Pov. G1 Remittances Pov. G2 Remittances 

Per capita 

GDP(Current US 

$) 

-0.0013*** 

(0.0002) 

 -0.0004***  

(0.00007) 

 -0.001*** 

(0.0001) 

 

Gini Coefficient 0.2471*** 

(0.058) 

 0.174*** 

(0.0223) 

 0.189*** 

(0.0437) 

 

Remittances as a 

ratio to GDP 

0.1733 

(0.1173) 

 0.0201 

(0.044) 

 0.119 

(0.088) 

 

Poverty  0.065 

(0.0796) 

 0.118 

(0.1797) 

 0.0792 

(0.0997) 

Lagged 

Remittances 

 6.459*** 

(0.6268) 

 6.592*** 

(0.5754) 

 6.544*** 

(0.6434) 

Trade (% of 

GDP) 

 0.072*** 

(0.0122) 

 0.073*** 

(0.0136) 

 0.070*** 

(0.0126) 

Education  0.124*** 

(0.024) 

 0.1231*** 

(0.0267) 

 0.125*** 

(0.0242) 

Constant 0.600*** 

(3.31) 

13.27*** 

(2.67) 

-3.501*** 

(1.294) 

-13.177*** 

(3.055) 

-3.18*** 

(0.76) 

-13.29*** 

(2.714) 

R square 0.3549 0.6409 0.4439 0.6516 0.3960 0.6438 

Chi2 102.22 299.68 137.35 315.81 118.04 310.28 

 
Table-2. Three Stage Least Squares Estimations: Dependent Variables—Poverty and Remittances (27 Countries with Remittances as a Ratio 

of GDP as 2% or more) 

 

Independent 

Variables 

Dependent Variable-Poverty 

HCR at $1.25 a day (PPP) 

Dependent variable-Poverty 

Gap at $1.25 a day (PPP) 

Dependent variable-Poverty 

Gap at $2 a day (PPP) 

 PHCR Remittances Pov. G1 Remittances Pov. G2 Remittances 

Per capita 

GDP(Current US $) 

-1.1*** 

(0.09) 

 -1.11***  

(0.098) 

 -0.91*** 

(0.08) 

 

Gini Coefficient 1.67*** 

(0.41) 

 2.52*** 

(0.43) 

 0.87*** 

(0.34) 

 

Remittances as a 

ratio to GDP 

-0.53*** 

(0.096) 

 -0.76*** 

(0.101) 

 0.45*** 

(0.08) 

 

Poverty  0.064** 

(0.026) 

 0.054** 

(0.026) 

 0.081** 

(0.031) 

Lagged 

Remittances 

 0.97** 

(0.024) 

 0.99*** 

(0.027) 

 0.97*** 

(0.024) 

Trade (% of GDP)  0.10** 

(0.05) 

 0.103* 

(0.05) 

 0.116** 

(0.54) 

Education  -0.013 

(0.12) 

 -0.02 

(0.11) 

 -0.044 

(0.114) 

Constant 2.10*** 

(0.74) 

-0.46 

(0.27) 

-0.42 

(0.78) 

-0.17* 

(0.26) 

-3.18*** 

(0.76) 

-0.167 

(0.26) 

R square 0.6295 0.9592 0.69 0.95 0.61 0.96 

Chi2 156.47 2146.47 209.93 2132.64 147.14 2204.60 
  Note: In both tables ***   **   and *   indicate a significant level of 1%, 5%, and 10% respectively. 



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Views and opinions expressed in this article are the views and opinions of the authors, Economy shall not be responsible or answerable for any loss, damage 

or liability etc. caused in relation to/arising out of the use of the content. 

 

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