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ANALYSIS OF IMPACT OF REMITTANCE ON POVERTY AND INEQUALITY IN NIGERIA
A RESEARCH DRAFT FINAL REPORT SUBMITTED TO POVERTY AND
ECONOMIC POLICY PMMA NETWORK
BY
NNAEMEKA CHUKWUONE Centre for Entrepreneurship and Development Research and Department of Agricultural Economics, University of Nigeria, Nsukka, Enugu State, Nigeria
E-mail: [email protected], [email protected]
EBELE AMAECHINA Department of Agricultural Economics, University of Nigeria, Nsukka, Enugu State,
Nigeria E-mail: [email protected]
EVELYN IYOKO Department of Economics, Novena University Ogume, PMB 2 Kwale, Delta State,
Nigeria. E-mail: [email protected]
SUNDAY EMEKA ENEBELI-UZOR
Research and Economic Intelligence Group, Zenith Bank PLC, 7th Floor, Zenith Heights, Plot 87, Ajose Adeogun Street, Victoria Island, Lagos
E-mail: [email protected]
AND BENJAMIN OKPUKPARA
Centre for Entrepreneurship and Development Research and Department of Agricultural Economics, University of Nigeria, Nsukka, Enugu State, Nigeria
E-mail: [email protected]
November 10, 2008
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Abstract This study analyzes the impact of remittances on poverty and inequality in Nigeria.
The data from Nigerian National Living Standard Survey, 2004, was used.
Instrumental variable (IV) technique and propensity score matching technique were
used to measure the impact of remittance on poverty. Impact estimation using
instrumental variable technique shows that amount of remittance received by
households had significant positive impact on poverty. Estimation from propensity
score matching shows that impact of remittance on poverty was significant.
Households that receive remittances are less likely to be poor. Inequality analysis
suggests that inequality is more with remittance receiving households than
remittance non-receiving households. Thus, a remittance increases inequality. The
findings provide enough evidence that remittances can carefully be used as a tool to
fight poverty and inequality. Poverty alleviation measures could be targeted at
remittance non-receiving households while mechanism for remittances transfers
should be deregulated, for example, using post offices beside banks.
Keywords: Remittances, Poverty, Inequality, Instrumental Variable Technique,
Propensity Score Matching, Nigeria
JEL Classification: D63 I31 I32 O15 P46
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1 Introduction 1.1 Remittances in Nigeria
Migration, which involves a relocation of residence of various duration and nature,
assumed a phenomenal dimension in Nigeria in the past decades. This was
accentuated by two decades of economic stagnation and macroeconomic instability,
corruption and poor resource management. Most Nigerians, especially young
people, consider migration as a panacea to economic problems. In recent years,
there has been unprecedented rate of rural-urban migration and emigration into
other countries of Africa, Europe and America. For example, due to migration and
subsequent urban growth, Lagos, a city in Nigeria, which did not appear in the list of
fifteen largest cities in 1950, occupied the fifteenth position in 1995 and is expected
to jump to number three position in 2015 with over 24 million inhabitants (Todaro,
1997). As regards movement outside Nigeria, there has been a remarkable increase
in emigration to Europe, North America, the Middle East and South Africa from
1980’s following economic downturn, introduction of liberalization measures and
emergence of repressive military dictatorship (Adedokun, 2003). Thousands of
professionals, especially scientists, academics, and those in the medical fields have
emigrated, mainly to Western Europe, the United States and Persian Gulf States. At
the same time, unskilled Nigerians with little education have gone abroad to work as
street cleaners, security guards, taxi drivers, and factory hands. In southern Nigeria,
for example, between 50 and 80 percent of households have at least one migrant
member (Bah et al, 2003). Migration is considered essential to achieving success
and young men who do not migrate or commute to town or abroad are often labeled
as idle and may become object of ridicule.
These migrants often remit or send a sizeable portion of their increased earnings to
families and acquaintances back home. In fact workers remittances have become a
major source of external development finance. It is estimated that migrant remittance
flows to developing countries now surpass official development aid receipts in many
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developing countries (Ratha, 2005). Migrants’ remittances are currently ranked as
the second largest source of external inflows to developing countries after foreign
direct investment. For example, in 2001, official development finance transfers to
developing countries were about US$57 billion (OECD, 2003); this compares with
recorded global remittances of US$72.3 billion the same year (World Bank, 2003).
Over the last decade, Nigeria is the single largest recipient of remittance in Sub-
Saharan Africa (Maimbo and Ratha, 2005). Nigeria receives between 30 percent
and 60 percent of remittance to the region (Orozco, 2003). Remittance from
Nigerians in various parts of the world was USD 2.8 billion in 2004 (IMF, 2004),
ranking second only to oil exports as a source of foreign exchange earnings. Nigeria
was among the top 20 developing countries recipients of remittance in 2003 (Ratha,
2005). Commercial bank executives report that in 2006 the recorded flows were
estimated at US$4.2 billion dollars, representing 700,000 transactions and a 30
percent increase from 2005 (Orozco and Millis, 2007). The overwhelming majority of
remittances in Nigeria are person-to-person flows mainly from the United States, the
United Kingdom, Italy, and other Western European countries. Most transfers are
through Money Transfer Organizations (MTO’s). Currently 21 out of 25 banks
operating in Nigeria have agreements with MTOs. Fifteen banks work with Western
Union, five with Money Gram, and one with Coinstar and Vigo Corporation (Vigo is
owned by Western Union). Estimate of internal remittance is not known. Some
economists believe that inflows from abroad have been a key factor to the stability of
Nigerian naira against other international currencies in the past two years.
However, despite the ever increasing size of remittances, both internal and
international, there has been little effort to analyze its effect on economic
development especially on poverty in Nigeria. Adams, (2005) observes that little
attention has been paid to examining the economic impact of these transfers on
households in developing countries despite the ever increasing size of official
international remittances. In fact, notwithstanding that remittance has been
implicated as a vital source of income with crucial income smoothening effect and
contribution to improved standard of living, its effect in Nigeria is not known. Thus
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because of the poor understanding of the impact of remittance in Nigeria’s economic
and national development, remittances are poorly managed. Nightingale (2003)
observes that in spite of the recognized advantages of a well articulated remittance
management regime to aid growth and development by providing much needed
foreign exchange, and as a source of liquidity and a palliative for its balance of
payment deficit, Nigeria does not put remittance of migrant workers to their best use.
Besides, despite the amount of remittances coming from Europe and America into
Nigeria, no specialized remittance products exists for this corridor outside the
generic favored cash-to-cash money transfer operators (MTO) transfers and the
expensive and lengthy account-to-account bank transfers.
1.2 Questions and Objective of the Study
The key policy question is: how do remittances affect poverty and inequality in
Nigeria? Specifically, what is the difference in poverty level as measured by depth
and severity of poverty between households that receive remittance (internal and
international) and those who do not? Do the poor benefit from remittance more than
the non poor? What is the frequency of reception of remittance? What is the relative
importance of in-country, inter-regional and intra-regional remittances? Is there a
level of inequality between remittance receiving and non-receiving households?
What is the proportion of remittance to rural as opposed to urban households? Is
remittance progressive? What is the difference in poverty between households that
do not receive remittance and those who receive cash and non cash remittances?
Hence, the main objective of this study is to ascertain the impact of international
(inter and intra-regional) and internal remittances on poverty and inequality in
Nigeria. Specifically, the study seeks to; ascertain the socioeconomic differences,
including gender perspectives, between households that receive internal and
international remittances; analyze the impact of remittance on poverty using
expenditure per capita as outcome indicator; ascertain the level of inequality
between remittance receiving and non-receiving households; study the progressivity
of remittances; decompose total inequality into income components when one the
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components is remittance; and decompose poverty indices into income components
when one of these components is remittance.
This study is justified by the fact that the financial infrastructure for remittance in
Nigeria is still limited. Thus, a lot of remittances go through informal channels and
discourage their use for savings and investment. Nigeria has a leissez-fare
emigration policy and as such, no structures or measures hinder or facilitate the
movement of citizens outside the country beyond normal immigration requirements
and therefore no initiatives for pre-departure training and management of remittance.
Therefore, a study on the impact of remittance will help justify the need for policy
initiatives to develop financial structures and infrastructure for remittance and for a
credible immigration policy.
Moreover, the findings of this study will facilitate the initiation and implementation of
antipoverty policies. It will enhance designing of policies that will facilitate providing
of less costly and hassle-free means of transmission of remittance thus encouraging
remittance and mainstreaming remittance into national development. The
government will further provide better strategies and environment for private sector
participation in investment in Nigeria to encourage remitters to invest their money
especially in small/medium scale but high productivity activities. This will then
enhance employment generation and growth, reduce the dual nature of the economy
and thus enhance Nigeria’s competitiveness. Generally, a positive effect of
remittance will equally encourage policy makers to initiate policies to improve the
business environment (trade facilitation, business registration and licensing and
contract enforcement) in Nigeria so as to encourage Nigerians outside Nigeria to do
business in Nigeria and possibly recover some of the losses due to brain drain. Poor
access to finance is among the constraints to Nigeria’s economic growth and
competitiveness and hence poverty reduction. Policies to enhance remittance will
help facilitate access to long term finance made available by remitters especially
through their investment in the capital market. These initiatives can be incorporated
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in the National Economic Empowerment and Development Strategy II (NEEDS II)
document which the process of development has just started.
Finally, this study will facilitate the designing of antipoverty programme, for example,
policy measures can be designed to enhance the transfer of resources to non-
remittance receiving households, for example, by taxing remittance income. The
findings as regards internal remittance will guide government on decisions to support
or discourage internal migration. This could be by focusing on rural area
development.
The remaining part of the paper is organized as follows: section 2 is on literature
review which includes conceptual overview of remittances, empirical studies on
remittances and poverty, and Nigeria poverty profile and causes of poverty; section
3 discusses data and household survey; section 4 is on analytical framework and
findings which includes remittances and household wellbeing and remittances,
poverty and inequality decompositions; section 5 is application of results while
section 6 is conclusion.
2 Literature Review 2.1 Conceptual Overview of Remittance Remittances are financial resource flows arising form the cross border movement of
nationals of a country (Kapur, 2004). Remittances come in form of money, assets or
informal or non-monetary forms. Non-monetary forms may include clothing,
medicine, gifts, dowries, tools and equipment. In recent years, remittance flows rank
behind foreign direct investment (FDI) as source of external funding for developing
countries. Global flows of migrant worker remittances were estimated at US$182
billion in 2004, up 5.7 percent from their level in 2003 and 34.5 per cent compared to
2001 (World Bank, 2004). Remittances to Developing countries from overseas
resident and nonresident workers exceeded US$126 billion or 1.8 per cent of GDP in
2004 (Ratha, 2005). Although remittance to Sub-Saharan Africa is low, 5 per cent of
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global estimates in 2003, Nigeria remains the single largest recipient in Sub-Saharan
Africa. International remittances enter Nigeria through formal and informal sources.
The Western Union Money Transfer mechanism is one of the major ways through
which remittances enter Nigeria. Nigerians are also allowed to operate foreign
currency dominated domiciliary account in Nigeria and remittances are received in
Nigeria through this means. Informal sources include relatives and town unions and
individuals entering Nigeria from their domicile foreign countries among others.
Motivation to remit, as reflected by some schools of thought, includes risk sharing,
altruistic or livelihood and risk sharing with altruism. The risk-sharing school
maintains that remittances are installments for individual risk management (Stark,
1991; Stark and Lucas, 1988). The altruism or livelihood school considers remittance
to be an obligation to the household and remittances are sent out of affection and
responsibility towards the family (Chimhowu et al, 2003). The evidence from U.S.-
Nigeria migration study (Osili, 2006) suggests that transfers to origin family are
motivated by altruistic considerations, with poorer origin-family members in Nigeria
receiving larger transfers. The migrant is simply part of a spatially extended
household that is reducing the risk of impoverishment by diversifying across a
number of activities (de Haan, 1998; Agrawal and Horowitz, 2002). The third school
sees both altruism and self-interest as playing a role in the motivation to migrate and
remit (Ballard, 2001; Clarke and Drinkwater, 2001).
On the impact of remittance, two dominant perspectives are emerging in literature.
The neo-liberal-functionalist persuasion suggests that remittances are beneficial at
all levels particularly the individual, household, community and national level
(Orozco, 2002; Skeldon, 2002; Ratha, 2003). Remittances are seen to play a crucial
role in developing local level capital markets and productive infrastructure as well as
increasing the effective demand for local goods and services. On the other hand,
those looking at remittances from historical-structuralist perspective consider
remittances to be responsible for creating dependant relations between the sending
and the receiving countries (Portes and Borocz, 1989). Remittances are seen to
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cause inequality in households and macro-economic distortion especially in
countries with low GDP. Generally, it remains controversial whether remittances
have an overall positive or negative impact on a receiving country’s economy and its
migrant-producing communities (Page and Plaza, 2005). However, whilst the overall
poverty effect as seen from literature remains ambiguous, the overwhelming results
from empirical studies show that, apart from possible increase in social inequality
and social differentiation, remittances make a powerful contribution to reducing
poverty or vulnerability in the majority of households and communities (Chimhowu et
al, 2003). However, this remains to be investigated in Nigeria.
2.2 Empirical studies on Remittance and Poverty Stark (1991) and Adams (1991) pioneered the effort to assemble household data
that could shed light on the impact of remittances on welfare. However, their findings
were limited by small sample size. Some recent studies have been carried out to
estimate the impact of remittances on welfare. Paulson and Miler (2000) found that
households who more insured by remittances shift their portfolios towards riskier
investments. Adams and Page (2003) in a study of poverty, migration and
remittances for 74 low and middle-income developing countries found that both
international migration (the share of a country’s population living abroad) and
international remittances (the share of remittances in country GDP) have a strong,
statistical impact on reducing poverty in the developing world. On average, a 10
percent increase in the share of international migrants in a country’s population will
lead to a 1.6 percent decline in the poverty headcount. Adams and Page (2004)
used the result of household surveys in 71 developing countries to analyze the
impact of international migration and remittances on poverty. They found out that a
10 per cent increase in per capita official international remittances in a developing
country will lead to a 3.5 per cent decline in share of people living on less than
$1/person/day in that country. Adams (2004) also found that remittances reduce the
severity of poverty in Guatemala. He also found out that Guatemalan families who
report remittance tend to spend a lower share of total income on food and other non-
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durable goods, and more on durable goods, housing education and health. Taylor,
Mora and Adams (2005), in a study in rural Mexico, found that international
remittances account for a sizeable proportion of total per capita household income in
rural Mexico and that international remittances reduce both the level and depth of
poverty. Yang and Martinez (2005) in a study in Philippines found that remittance
lead to reduction in poverty migrants’ origin households. Fajnzylber and Humberto
Lopez (2007) in their study on impact of remittance in Latin America found out that
nine out of eleven countries in Latin America and Caribbean exhibit higher Gini
coefficients for non-remittance income, suggesting that if remittances were
exogenously eliminated, inequality would increase. Also, their comparison of poverty
headcounts before and after excluding remittance from the total income of recipients
do suggest large reductions in poverty levels, especially in those countries where
migrants tend to come from the lower quintiles of income distribution. In addition,
they found out that the failure to correct for the reduction in income associated with
the absence of migrants from their households may lead to grossly over estimating
the poverty-reducing effect of remittances. They observed that the effect may be
responsive to the use of different econometric methodologies. Moreover, they found
out that children from families receiving remittance are more likely to remain in
school.
2.3 Nigeria Poverty Profile and Causes of Poverty
Nigeria has been characterized as a country of poverty amid plenty. Thus, despite
the country oil wealth, poverty is widespread and Nigerian basic social indication her
among the 20 poorest countries in the world. Although poverty has existed in Nigeria
especially after the civil was in 1970, poverty however became prominent in 1980
with the collapse of world oil prices and a sharp decline in petroleum output. Data
from the Federal office of statistics shows that the incidence of poverty increased
sharply between 1980 and 1985 and between 1992 and 1996, however there was a
decrease in poverty between 1985 and 1992. United Nation, (2003) reports show
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that poverty is still deepening with over 70.2% of the people earning less than US1$
a day.
Inadequate economic growth is the main cause of poverty in Nigeria (National
Planning Commission (NPC), 2004). Nigeria economy has a very narrow and weak
base, depending mostly or exportation of petroleum crude oil as a major source of
income; the agricultural base of the economy had been frustrated and marginalized
(Oyeduntan, 2003). High and growing unemployment has also exacerbated the level
of poverty in Nigeria. Other factors that have contributed to the level and evolution of
poverty in Nigeria include problems in the productive sector, widening income
inequality, weak governance, social conflict and gender, intersectoral and
environmental issues (NPC, 2004). Poverty in especially in the urban area has been
made severe by low labour absorption capacity of the nonagricultural sector,
especially manufacturing, which is as a result of limited growth of investment and
technological innovation. Weak governance which is manifested in corruption, rent
seeking, inappropriate planning and neglect of the private sector have contributed
immensely to corruption in Nigeria. Furthermore, empirical evidence shows that
poverty and environmental degradation are inextricably linked in Nigeria, because 75
percent of rural people depend on natural resources for their livelihood, hence
environmental degradation reduces opportunities for poor people to earn sustainable
income (NPC, 2004). Globalization equally worsens the situation of poverty as the
basis of challenge and competitions are lacking thus this has manifested in several
ways. For instance, the debt burden increased from $14.28 billion in 1980 to about
$32 billion in 2000 (Oyeduntan, 2003).
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3 Data and Household Survey The data obtained from the Nigerian National Living Standard Survey (NNLSS)
conducted in 2004 was used for the study. In the NNLSS data was collected on
some indicators which include demography, education, health, employment and time
use, migration, housing, social capital and community participation, agriculture,
household expenditure, non-farm enterprise, credit, assets and saving, income
transfer and household income schedule. In the NNLSS a two stage stratified
sample design which include a cluster of housing units called Enumeration Area
(EA) and then the housing unit was used for data collection. One hundred and
twenty (120EAs) were selected for each of the 36 states while 60 EAs were selected
for the Federal Capital Territory. Ten EAs with five housing units were studied per
month. This implied that fifty (50) housing units in a state were canvassed for in a
month. The study lasted for twelve months.
Some questions on remittance were asked. These include, has this household
received or collected money or goods from absent member? During the last 12
months, has this household received or collected money or goods from any other
individual? List each persons name from whom household received money or
goods? Id code if person is an absent member of the household? If not a household
member, relationship to the household head and sex? Where these remittances
received on a regular basis? Will you have to repay these? What was the total
amount of cash this household received from this individual during the past 12
months? What was the total value of food received from this individual during the
last 12 months? What was the value of other goods (non-food items) received from
this individual during the last 12 months? Where does this individual live Lagos, etc.,
Abroad (Africa or other)? Source of remittance in terms of agency, formal or informal
and use of remittance income was not covered.
The files containing the remittance variables were merged with the files containing
the household roster variables and other socioeconomic variables used for the
analysis. Five files altogether were merged. After the merging, a total of 8096
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households whose were used for the analysis. Out of 8096 households used for the
study, 16.77% (1358) received remittances while 83.23% (6738) did not receive
remittances. The population weight was used as the weighing variable while the
household size was used as the size variable.
The Variables used and their definitions are presented in Table 1
4 Analytical Framework and Results 4.1 Remittances and Household Wellbeing 4.1.1 Instrumental Variable estimation
In order to ascertain the impact migrant remittances have on household welfare or
poverty profile, a poverty function is specified. The poverty profile function to be
estimated is specified as:
iijji XuLog εβα ++= ∑)(
Where iε is the error term which is assumed to be independent and normally
distributed, is real per capita expenditure and Xs are a vector of explanatory
variables including migrant remittances and economic shocks (as measured by the
food and non-food prices).
iu
Estimating the impact of remittance with this function implicitly make the unrealistic
assumption that remittances can be treated as exogenous transfers by migrants.
The problem is that in many cases migration also entails potential losses of income
associated with the migrants’ absence from their families and communities. In other
words, remittances are not exogenous transfers but rather they substitute for the
home earnings that migrants would have had if they had not decided to leave their
countries to work abroad. To consider these effects one needs to estimate the value
that household income would have had if migrants had stayed in their households.
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Hence to capture the impact of remittance on poverty, one of the preferred
methodologies is to first determine a counterfactual situation without remittance. See
Adams (2004) and Fajnzylber and Lopez (2007). The income of households
excluding remittance situation if all migrants would have stayed and worked at home
would be, firstly, determined. This would be done by estimating the per capita
income of all households excluding remittance. The predicted income equation
would then be used as a basis for evaluating the impact of remittance on poverty
when internal, intra-regional and inter-regional remittances are included in per capita
household incomes and also for households that receive cash and non-cash
remittances. The impact of remittance on poverty as measured by its effect on depth
and severity of poverty will then be determined.
However, the use of predicted income or expenditure equation is been criticized
because of the following reasons:
• The predicted error at the level of the household can exceed the level of the
remittances. This casts doubt on predicted expenditure without remittances.
• The implicit hypothesis of the approach, which is estimating the expenditures of
the counterfactual group, is in similarity between the group that receives
remittances and the other that does not. For example, suppose that a high
proportion of those households that do not receive remittances are poor. In that
case, the bias is evident and expenditures for the counterfactual group are
underestimated.
Thus the use of instrumental variables is recommended. Instrumental variables (IV)
are variables that matter to remittance but not to the poverty given remittance.
Instrumental variable are correlated with the probability of treatment but uncorrelated
with unobserved determinant of outcomes. IVs estimates are predicated entirely on
the validity of the instrument and unobserved determinant of treatment effects can
result in serious biases (Ravallion, 2001). The instrumental variables are first used to
predict remittance, then one sees how the poverty varies with the predicted value
conditional on other characteristics. Therefore, there will be two stage equations, the
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first predicting remittance and the second predicting poverty given remittance. The
residual in the first equation predicting remittance will be included in the second
equation thus treating remittance as exogenous.
The major problem with the use of instrumental variable is that it is difficult to obtain
a good instrument from a cross sectional data set. Normally, the validity of the
exclusion restriction required by IVE is questionable with only a single cross-
sectional data set; while one can imagine many variables that are correlated with
remittance, such as household characteristics, community characteristics,
geographic characteristics of an area etc. , it is questionable on a priori grounds that
those variables are uncorrelated with poverty given remittance. Hence the major
issue is to have a good instrument that can be used for estimation. Instrumental
variable estimation was used in this study given the availability of instruments.
In carrying out the instrumental variable technique, two decisions were instrumented:
how much money to remit and whether to migrate or not. It is expected that how
much money to remit and whether to migrate or not would directly influence
remittance but would not directly influence poverty given remittance. Since the data
on migrants are not available, variables covering the two decisions obtained from the
household data were used as proxy. Based on this, the amount to remit was covered
by the source of remittance whether internal, international (Africa) and international
(elsewhere, namely Europe and America). It was expected that source of remittance
would determine the amount of remittance since the economic situation in the
source region/country differs. Higher amount of remittance would come from areas
with good economic condition e.g. Europe and America than with areas with poor
economic condition e.g. Africa. However, the source of remittance would not have
effect on poverty given remittance. The second decision that was instrumented,
namely, whether to migrate or not was done using a variable, household with a
migrant and that without a migrant. Some households that receive remittance do not
have a member that migrated. It was expected that having a household migrant
15
member would influence the amount of remittance but would not influence poverty
given remittance.
Thus two stage equations were used. The first estimated remittance and the second
estimated poverty given remittance. In the second equation, the residual from the
first equation was included to serve as the remittance variable. The equations are
presented thus:
equationuXXRLog ii −−−−+++= 2211)( ββα 1
equationXULog iijji −−−−−++= ∑ εβα)( 2
The R in equation (1) represents the amount remitted; and are instruments.
In the first estimation the two decision were instrumented namely, how much money
to remit was captured by location where remittance is coming from whiles whether to
migrate or not was captured by whether a household has a migrant member or is
without a migrant; is the residual. Three independent variables were used as
instruments namely, source of remittance Africa, Source of Remittance
Europe/America and having a migrant (household member lives away). The
dependent variable was log of total amount of remittances received. In equation (2)
1X 2X
iu
iε was the error term which is assumed to be independent and normally distributed,
is real per capita expenditure and Xs are a vector of explanatory variables
including the residual from the first equation representing migrant remittances. The
dependent variable was poverty which was captured by log of household
expenditure per capita. Independent variables include log of age of household head,
log of household size, sex, sector (urban and rural) among others. In the actual
estimation, two-stage least squares was used for the estimation using SATA
software. It is important to emphasize that in this study, per capita consumption
expenditure rather than income data was used as outcome indicator because of the
following facts: firstly, poverty economists prefer to use expenditure rather than
income data to identify poverty since expenditure provide a more accurate measure
of an individual’s welfare over time; secondly, income data is prone to measurement
iU
16
error especially underreporting of income which is prevalent in Nigeria; and thirdly,
the fact that the poverty line to be used in the study is based on expenditure data.
Remittance, generally, was measured as amount remitted by a migrant member of
household less the amount to be repaid.
Results The results are presented in Table 2. The result shows that the total remittance had
a positive and high significant effect on poverty (expenditure per capita) at 0.05 level
of probability. The higher the total remittances received, the higher the expenditure
per capita. This suggests that amount of remittance received by households had
significant positive impact on poverty. Households that receive remittances are less
likely to be poor. Other variables that positively and significantly influenced
expenditure per capita are log of household size, sex of household head, log of age
of household head, South East location as against North West, south south location,
south west location, northeast location, north central location, polygamous marriage
as against never married, monogamous marriage as against never married,
widowed as against never married, and definitely access to credit. Male headed
households had more expenditure than female headed households. Also, urban
households have more expenditure per capita and less poverty than rural
households, households in South East have more expenditure per capita and less
poverty than households in the North Nest. These findings are in line with NBS
(2004) that showed that poverty was higher in rural areas (63.3) than urban areas
(43.2). South East had the least poverty of 26.7 as against 71.2 of North West.
4.1.2 The Propensity Score Matching Approach
Another method that can be used to overcome these criticisms of predicted income
or expenditure equation and capture the impact of remittance on poverty is
propensity score matching technique. Propensity score matching in its simplest form
involves predicting the probability of treatment on the basis of observed covariates
for both the treatment and the control group samples (Rawlings and Schardy, 2002).
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The propensity score matching method summarizes the pre-treatment
characteristics of each subject into a single index variable, the propensity score,
which is then used to match similar individuals (Esquivel and Huerta-Pineda, 2007).
In propensity score matching, one picks an ideal comparison group from a larger
survey and then matches the comparison group to the treatment group on the basis
of set of observed characteristics on the predicted probability of participation given
observed characteristics (“propensity score”) (Ravallion, 2001). The observed
characteristics are those used in selecting individuals but not affected by programme
participation. For example, for estimating the impact of remittance on poverty, two
groups are identified, those with remittance (denoted as Ri =1 for household i and
those without (Ri = 0). Those with remittance (treated) are matched to those without
(control group) on the basis of the propensity score: (probability of receiving
remittance given observed characteristics)
)ii ))|1()( i xRprobxp == 1(0( << ixp
where xi is a vector of pre-remittance control variables. If the Ri’s are independent
over all i, and the outcomes are independent of remittance transfers given xi then
outcomes are also independent of remittances given p(xi), just as they would be if
remittances were transferred randomly.
Propensity score matching is a better method of dealing with differences in
observables. However, a few tests that have been dome suggest that with good
data, propensity score matching can greatly reduce the overall bias and outperforms
regression-based methods (Raviallion, 2001).
Rosenbaum and Rubin (1983) established the following conditions in order to be
able to estimate Average Treatment on the Treated (ATT) effect based on the
propensity score:
18
Condition 1: The Balancing Hypothesis
)(| XpXR ⊥
This means that for observations with the same propensity score, the distribution of
pre-treatment characteristics must be the same across control and treated groups.
That is, conditional on the propensity score, each individual has the same probability
of assignment to treatment, as in a randomized experiment.
Condition 2: Unconfoundedness Given the Propensity Score:
)(|,,|,, 0101 XpRYYXRYY ⊥⇒⊥
If assignment to treatment is unconfounded conditional on the variables pre-
treatment, then assignment to treatment is unconfounded given the propensity
score.
The estimation of the average treatment effect on the treated (ATT) based on the
propensity score matching for with and without remittance was carried out based on
the following procedures:
Pooling of two groups of individuals, that is the treatment and comparison
group of those who receive remittance and those who do not receive
remittance. After the pooling, a logit model of remittance receiving and non-
remittance receiving as a function of some socio-economic variables will be
estimated. The variables to be selected are those that were not affected by
receiving remittance. Some of the socio-economic variables included are
age, household size, number of years of schooling, gender. The equation is
put thus:
GenderbschoolbHousesizebAgebaOP
PP iiii 4321
1 log1
log ++++==−
=
From the logit regression, a predicted value of the probability of remittance
receiving was created. These were the propensity scores. Each individual had
a propensity score.
19
For each remittance receiving household, a non-remittance receiving
household that has the closest propensity score, as measured by the
absolute difference in scores, referred to as nearest neighbor was obtained.
For more precise estimate, the nearest five neighbors was be used. Thus the
nearest neighbour matching was used.
The mean values of the outcome indicator (per capita expenditures) for the
nearest neighbors were calculated. The difference between the mean and
actual value for the remittance receiving households (beneficiaries) is the
estimate of the gain due to remittance
The mean of individual gains is calculated to obtain the average overall gain –
the average treatment effect on the treated (ATT).
Results The result of the logit estimation for the derivation of the p-scores is presented in
Table 3. The dependent variable for the logit was receiving or not receiving
remittance coded as 1 and 0. The result shows that age of the respondents, sex and
total income significantly influenced remittance receiving. The result showing the
average overall gain in expenditure per capita due to remittance (step 5 as
described in the analytical framework) after the nearest neighbor matches of
propensity scores obtained after the logit analysis is presented in Table 4. The result
shows that the average treatment effect on the treated (ATT) that is average gain in
expenditure per capita by remittance receiving households, using nearest neighbor
matches is 8350.48. This shows that the remittance receiving households gained
by 8350.48 after the matches. The t value (3.98) for test of difference between the
expenditure per capita based on the gain between remittance receiving and non-
receiving households, as shown in Table 4, was greater than the tabular value of
1.96 at a probability value of 0.05. This shows that the impact of remittance on per
capita expenditure between remittance receiving and non-receiving households was
different. Thus, using propensity score matching, the findings suggest that
20
remittance had significant effect on poverty as the gain in expenditure per capita due
to remittance was positive and significant.
To show the expected level of remittances according to income level, a non-
parametric regression was carried out. The curve (Figure 1) shows that the expected
per capita remittances differs with income level. Initially, the expected remittances
increased with income level up to a point after which it either decreased or increased
with income. Generally, those with more income are more likely to receive more
remittances than those with less income.
4.2 Remittances, Poverty and Inequality 4.2.1 Decomposing of Total Poverty by Income Components The decomposition of total poverty by income components was done using the
Shapley approach. The income components were per capita remittances and per
capita income without remittances. The result shows that remittance reduces poverty
more than income without remittances. In other words, households with remittances
experience less poverty than those without remittances. The absolute contribution of
remittances to poverty was 0.0046 while that of income without remittances was
0.202. Also, the relative contribution of remittance income to poverty was 0.022
while that of income without remittance was 0.978. Thus households with
remittances are less likely to be poor than those without remittances.
4.2.2 Decomposition of Total Inequality by Income Sources The decomposition of total inequality by income sources was done using Rao’s
approach. The result shows that the estimate of S-Gini was 0.61. The coefficient of
concentration for per capita remittances was more than that of income without
remittances, 0.64 as against 0.61. Thus inequality is more with remittance receiving
households than without remittance receiving households. In order to show how
21
remittances contribute to inequality, the concentration curves was drawn (Figure 2).
The curves show that remittances increase inequality.
5 Application of Results The findings suggest that remittances can carefully be used as a tool to fight poverty
and inequality. Remittances could be used as a means of fighting poverty in Nigeria
considering the fact that remittances had effect on poverty. However considering the
fact that remittances could lead to inequality, policy measures against poverty using
remittances must be cautious. Poverty alleviation intervention projects especially
carried out presently in Nigeria by National Poverty Eradication Programme
(NAPEP) could be focused on households that do not receive remittances. On the
other hand, policies to encourage remittances could be encouraged, for example,
providing hassle free means of bringing in and transferring remittance and removing
taxes on remittance. By focusing poverty alleviation on non-remittance receiving
households, the inequality that could be created by remittances would be reduced.
Also, by improving the ability of remittance receiving households to receive
remittances, for example, by making remitting and receiving measures hassle free
and by reducing taxes on remittance, poverty can be reduced among remittance
receiving households. Also, more people could then be encouraged to remit thus
reducing poverty generally.
In addition, to support remittances in Nigeria, a policy on remittances should be
instituted in addition to reforming the regulatory environment. In this regard, with the
recent consolidation of banks in Nigeria, remittances should be made to be part of
Nigeria banking system and Nigerian banks with branches abroad could help
facilitate remittances. This would to help reduce the cost of remittances and to
discourage the transfer of remittances through informal sources. Currently most
Nigeria banks are only agents of Money Transfer Organizations (MTO’s). Also to
facilitate easy and hassle free remittance transfers, regulatory policy could include
using post offices to act as agents for money transfer beside banks. Participants in a
22
roundtable workshop organized as part of this study observed that remittance
transfers from Nigerians abroad are controlled by agencies the government do not
have complete control on and that a lot of people have lost their money because
they remitted through informal means thus there was need to stabilize formal
transfers through an enabling remittance policy and improved regulatory
environment.
6 Conclusion
This study, analyzed the impact of remittances on poverty and inequality in Nigeria
using the Nigerian National Living Standard Survey, 2004 data. The findings provide
enough evidence to show that remittances have significant effect on poverty and
inequality. Remittances receiving households are less poor than non-remittance
receiving households and poverty decreases with amount of remittances received. It
was equally found out that remittances could deepen inequality. Thus incorporating
the findings through some policy measures as indicated would help reduce poverty
and inequality in Nigeria.
23
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Tables and Figures Table: 1: Variables used in estimations
Variable Definitions Age Age of the household head Agefirst Age of first marriage of the household head Hhsize Household size Pcexpdr Per capita household expenditure Corepr Core poor (Based on 1/3 of the weighted mean household per capita expenditure)Modpr Moderately Poor based on 2/3 of the weighted mean household per capita expendSector1 Sector (urban and rural) Southeas South East Location Southsou South south location Southwest South West location Northeas North east location Northcen North central location Northwes North west location Sex Sex of household head Christia Christian Muslim Muslim Tradobel Tradional belief Monamarr Monogarmous marriage Pmarriag Poly gamous marriage Informal Informal Divorced Divorced Separated Separated Widowed Widowed Nevermar Never married Particic Partipating in community programme Houaffco Household affected by conflict Recived Received from migrant and non migrant Received Received from migrants only Recieve0 Received from non migrants only Recieve1 Received no remittance Absent_memhouse Absent member of household Source_n Nigeria s source of remittance Source_a Africa as source of Remittance Source_e Europe and America as sources of remittance Totalremittance Total amount received Recieve2 Remittance receiving and non receiving Defacc_cre Definitely access to credit Proacc_cre Probably access to credit Unacc_cre Unsure access to credit Pronotacc_e Probably no access to credit Defnotacc_e Definitely not access to credit Father_noedu Father with no education Mother_noedu Mother with no education
28
Table 2: Parameter estimates of impact of remittances Variable Regression
Coefficients Logtotalremittance 0.256***
(0.083) Loghouseholdsize -0.465***
(0.035) Logage -0.276***
(0.097) Sex 0.225***
(0.056) Father_noedu -0.058
(0.046) Sector1 0.085
(0.080) Southeast 0.486***
(0.077) Southsou 0.238***
(0.082) Southwes 0.323***
(0.081) Northeas 0.240***
(0.090) Northcen 0.428***
(0.085) Christia -0.027
(0.057) Monmarr 0.211**
(0.102) Pmarriag 0.309**
(0.119) Informal -0.163
(0.471) Divorced 0.086
(0.169) Separate 0.223
(0.149) Widowed 0.275**
(0.125) Houaffco 0.013
(0.091) Defacc_c 0.131*
(0.076) Proacc_c 0.016
(0.078) Unacc_cr -0.064
(0.089) Pronotac 0.069
(0.103) Constant 9.03***
(0.557) Source: Calculations from NLSS merged data file Note: Variables in parenthesis are standard errors. Unstandardized residual represents Remittances, Number of observations = 1358, wald chi2(23) = 667.71*** *** indicate significant at 0.01 probability level, ** indicate significant at 0.05
29
Table 3: Parameter estimates of remittance receiving and non-receiving for the propensity score matching
Variable Logit Equation Results Age 0.023***
(0.003) Sex -0.643***
(0.122) Father_noedu -0.213
(0.082) Sector1 0.137
(0.101) Southeast -0.433
(0.146) Southsou -0.046
(0.147) Southwes 0.16
(0.138) Northeast -0.860
(0.138) Northcen -0.186
(0.142) Christia -0.141
(0.158) Muslim -0.227
(0.179) Monmarr -0.479*
(0.265) Pmarriage -0.586
(0.280)** Informalmarriage -0.215
(0.854) Separate -0.114
(0.303) Widowed 0.250
(0.271) Agefirst -0.023
(0.006) Popwt -0.00001
(7.11x10-6) Constant 0.864
(0.361) Source: Calculations from NLSS merged data file Variables in parenthesis are standard errors
Log likelihood = -2971.94 *** indicate significant at 0.01 probability level, ** indicate significant at 0.05, * indicate significant at 0.10 probability level
30
Table 4: Result of the propensity score matching showing the overall gain (ATT) in expenditure per capita of remittance receiving households
Method No of treatment No of controls ATT Standard error
t-value
Analytic standard error
1358 1482 8350.48 2097.84 3.982
Bootstrapped standard error
1358 1482 8350.48 2341.09 3.56
Source: Calculations from NLSS merged data file Note: The numbers of treated and controls refer to actual nearest neighbor matches. ATT refers average treatment effect on the treated using nearest neighbor matches
Figure 1 : Non parametric regression of per capita income on per capita remittances
31
Figure 2: Lorenz and concentration cures for remittances
32
Appendices Appendix 1: Descriptive statistics of the variables used in remittance estimation The result showing the descriptive statistics for the parameters used in the estimations are shown in Table 1 of appendices. The result shows that the mean age of the respondents was 47.23 years. The mean age of the respondents at first marriage was 25.70 years. The average household size was 4.91 while the average per capita expenditure was N34,516.14. At a poverty line of N23,733, ( Table 2 of appendices) the result shows that the head count index (P0) that is the proportion of the population with expenditure below the poverty line for remittance receiving households was 0.64 while that of remittance non-receiving households was 0.76. On the other hand, the P1 measure, that is, the percentage of expenditure/income required to bring each individual below the poverty line up to the poverty line, for remittance receiving households was less (0.39) than that of non-remittance receiving households which was 0.53. Thus individuals in remittance receiving households need lesser amount to bring them up to the poverty line than the amount needed by remittance non-receiving households. Table: 1 of appendices: Descriptive statistics of the variables used in estimations
Variable Observation Mean Standard deviation
Minimum Maximum
Age 8096 47.23 14.55 13 99 Agefirst 7403 25.70 601 13 77 Hhsize 8096 4.91 2.98 1 24 Pcexpdr 8096 34516.14 40268.11 1853.65 1717056 Corepr 8096 1.73 0.45 1 2 Modpr 8096 1.55 0.50 1 2 Sector1 8096 0.23 0.42 0 1 Southeas 8096 0.16 0.36 0 1 Southsou 8096 0.15 0.36 0 1 Southwest 8096 0.16 0.36 0 1 Northeas 8096 0.19 0.40 0 1 Northcen 8096 0.12 0.33 0 1 Northwes 8096 0.21 0.41 0 1 Sex 8096 0.85 0.35 0 1 Christia 8096 0.50 0.50 0 1 Muslim 8096 0.46 0.50 0 1 Tradobel 8096 0.03 0.18 0 1 Otherreligion 8096 0.002 0.04 0 1 Monamarr 8096 0.63 0.48 0 1 Pmarriag 8096 0.15 0.36 0 1 Informal 8096 0.001 0.04 0 1 Divorced 8096 0.01 0.11 0 1 Separated 8096 0.03 0.17 0 1
33
34
Widowed 8096 0.12 0.32 0 1 Nevermar 8096 0.06 0.24 0 1 Particic 8096 0.56 0.50 0 1 Houaffco 8096 0.04 0.20 0 1 Recived 8096 0.03 0.18 0 1 Received 8096 0.03 0.18 0 1 Recieve0 8096 0.04 0.20 0 1 Recieve1 8096 0.89 0.31 0 1 Absent_memberhousehold 8096 0.037 0.19 0 1 Source_n 8096 0.10 0.31 0 1 Source_a 8096 0.001 0.04 0 1 Source_e 8096 0.002 0.05 0 1 Totalremittance 8096 3561.52 21154.76 0 845000 Recieveremittnce 8096 0.17 0.37 0 1 Defacc_cre 8096 0.43 0.50 0 1 Proacc_cre 8096 0.35 0.48 0 1 Unacc_cre 8096 0.10 0.30 0 1 Pronotacc_e 8096 0.06 0.24 0 1 Defnotacc_e 8096 0.06 0.23 0 1 Father_noedu 8096 0.69 0.46 0 1 Mother_noedu 8096 0.74 0.44 0 1 Father_primaryedu 8096 0.11 0.31 0 1 Mother_primaryedu 8096 0.05 0.22 0 1 Householdtotalincome 8096 3561.52 21154.76 0 1 Totalincome_lessremittance 8096 85863.44 131420.10 -60000 1920000
Source: Calculations from NLSS merged data file Table 2 of appendices: FGT poverty indicators of remittance receiving and non receiving households
Group Parameter alpha
Estimate Standard Deviation
Poverty line based on 2004 survey
Remittance Receiving 0 0.64 0.02 23733 Remittance Non-receiving 0 0.76 0.02 23733 Remittance Receiving 1 0.39 0.02 23733 Remittance Non-receiving 1 0.53 0.01 23733 Remittance Receiving 2 0.29 0.01 23733 Remittance Non-receiving 2 0.43 0.02 23733 Remittance Receiving 0 0.43 0.02 11867 Remittance Non-receiving 0 0.58 0.01 11867 Remittance Receiving 1 0.24 0.04 11867 Remittance Non-receiving 1 0.38 0.01 11867 Remittance Receiving 2 0.17 0.02 11867 Remittance Non-receiving 2 0.30 0.01 11867