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May 2019
Volume: 4, No: 1, pp. 67 – 86
ISSN: 2059-6588
e-ISSN: 2059-6596
www.tplondon.com/rem
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
Article history: Received 16 May 2018; accepted 12 M ay 2019
DOI: https://doi.org/10.33182/rr.v4i1.665
The Impact of International
Remittances on Poverty Alleviation
in Bangladesh
Bezon Kumar
Abstract
This article mainly explores to what extent international remittances alleviate household
poverty in Bangladesh. This study uses primary data collected from 216 households and
employs multi-methods. Firstly, I measure the level of household poverty through Foster-
Greer-Thorbecke index. The article secondly focuses on the impact of remittances on
household poverty using a binary logistic regression model. I found that the level of
poverty among remittance recipient households is notably lower than households that
are not receiving remittances. Similarly, the probability of a household being poor is
alleviated by 28.07 per cent if the household receives remittance. It can be suggested
that nursing international remittances can be useful for poverty alleviation in
Bangladesh.
Keywords: remittances; poverty; FGT index; logit model; Bangladesh.
JEL Classification: F14, F24
Introduction
International migration and remittances are an important and stable
source of income in the developing world. Among the developing
countries, Bangladesh is one of the major remittance recipients in the
world where one in every three households is involved in migration
and receives remittance, and most of the households are rural and
poor (Bayes et al. 2015). In the rural areas of Bangladesh, the rate of
poverty and unemployment is notably higher, which stress people to
migrate. Currently, about more than eight million people of
Bangladesh are now living and working abroad as migrant and
transferring income to their families in the home country (Kundu,
2016). According to the Bureau of Manpower Employment and
Training (2018), Bangladesh has received $13.53 billion remittances in
2017, which is 4.35 per cent of the country’s GDP. This remittance is
contributing to alleviate poverty and unemployment (Ghelli, 2018).
Since little attention, despite its economic importance, has been paid
on this issue, migration and remittance literature is till now widely
Bezon Kumar, Lecturer, Department of Economics, Rabindra University, Bangladesh. E-mail:
bezon.kumar3@gmail.com.
68 Impact of International Remittances on Poverty Alleviation in Bangladesh
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underrepresented in Bangladesh. Although a few studies have been
conducted on remittances and poverty in Bangladesh, most of them
rely on secondary data. Besides this, studies on other aspects of
remittance have also been covered contextualising Bangladesh.
Such as, Kumar et al. (2018) nicely summarise the significant factors
and utilisation of international remittances in Bangladesh. On the
other hand, Ahmed et al. (2018) provided the welfare impact of
remittances while Hasan and Shakur (2017) provided the non-linear
effects of remittances on the GDP growth of Bangladesh. This scenario
reveals that remittances-poverty relationship is analysed either
theoretically (Bayes et al., 2015) or drawing on secondary data such
as Hatemi-J and Uddin (2014) and Raihan et al. (2009). In this article, I
focused on the impact of remittances on household poverty in
Bangladesh using primary data.
This study, unlike earlier studies in Bangladesh, which bases the
analysis on the impact of international remittances on household
poverty through a binary logistic regression model, mainly aims at
measuring the level of household poverty in both remittance recipient
and non-recipient households. In addition, it will examine the extent
of the impact that international remittances have on household
poverty. The paper is structured as follows: after presenting the
relationship between remittances and poverty, a brief survey of the
literature is reviewed in Section 2, while data and methodology are
introduced in Section 3. Section 4 provides a descriptive comparison
of remittance recipient and non-recipient households. The main
analytical results are presented in Section 5. Section 6 concludes and
recommends policies.
Relationship between remittances and poverty
Reviewing previous literature, both positive and negative relationships
between remittance and poverty are found by the researchers. For
example, Acosta et al. (2008) explored that remittances increase
economic growth and reduce poverty in Latin American and
Caribbean countries, and remittances have negative and poverty
plummeting effects. On the other hand, Siddique et al. (2016) stated
that remittances had reduced poverty in Pakistan significantly. Like
other countries, remittances reduce poverty in Bangladesh as well
(Bayes et al., 2015; Hatemi-J and Uddin, 2014 and Raihan et al., 2009).
Furthermore, a negative relationship is also found between
international remittances and poverty in Bangladesh from the analysis
of data. Figure 1 shows that both the inflows of remittances and the
rate of poverty have declined over time. This does not provide a clear
direction about the relationship between remittances and poverty in
Bangladesh.
Kumar 69
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
Figure 1: Remittance and Poverty Scenario of Bangladesh
Source: World Development Indicator
For clarification of this critical situation, it is mandatory to describe the
mechanism of how remittances alleviate poverty. Figure 2 illustrates
how, possibly, remittances may have such an effect on poverty.
Figure 2: Framework of remittance and poverty alleviation
Source: Adapted from Guinigundo (2007).
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
1985 1990 1995 2000 2005 2010 2015 2020
Poverty rate Remittance as % of GDP
Remittances
Increase
income
Increase
consumption
Enhance family’s
social relationship
Decrease
vulnerability
Increase
savings
Improve quality of
life
Reduce poverty
70 Impact of International Remittances on Poverty Alleviation in Bangladesh
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Like Acosta et al. (2008), Guinigundo (2007) also reveals a two-way
relationship between remittances and poverty, which has been
stated in Figure 2. The figure implies that remittances cause poverty
reduction and poverty reduction also causes remittances receipt. The
figure 2 shows that remittances directly increase the level of income
of the remittance recipient households, increase consumption and
savings, decrease vulnerability, and enhance family’s social
relationship which consequently improves the quality of life and
alleviates poverty. On the contrary, reduction of poverty improves the
quality of life, which increases income, consumption, savings and
decreases vulnerability and enhances the family’s social relations.
Review of Literature
This section introduces the remittances and poverty-related facts of
many other countries besides Bangladesh, which, like the other
developing countries, has also been facing some socio-economic
challenges over a long time. The country was historically one of the
poorest countries in the world, and it is still struggling with a higher level
of poverty, unemployment and low per capita income although a
little progress is noticed recently. Several important factors, of which
the contribution of international remittance is notable, have
contributed to this little improvement of recent time. It is observed that
one in every three households receives international remittance in the
country (Bayes et al. 2015). Indeed, the potential of remittances to
reduce poverty significantly is widely discussed in the earlier literature.
A good number of researchers studied remittances in Bangladesh
(Kumar et al., 2018; Ahmed et al., 2018; Hasan and Shakur, 2017;
Wadood and Hossain, 2016; Regmi and Paudel, 2016; Haider et al.,
2016; Bayes et al., 2015; Hatemi-J and Uddin, 2014; Chowdhury, 2014;
Masuduzzaman, 2014; Al-Mukit et al., 2013; Khan and Islam, 2013;
Raihan et al., 2009). However, most of these studies are either
descriptive or not focusing on the link between remittances and
poverty. Although Bayes et al. (2015), Hatemi-J and Uddin (2014) and
Raihan et al. (2009) have studied recently on the relationship between
remittances and poverty, to the best of knowledge, these studies are
not based on primary data. This study is, therefore, a pioneering effort
to shed light on remittances’ possible role in poverty alleviation in
Bangladesh.
To examine the impact of international remittances on household
poverty, different methods have been used and hence, results varied.
I have reviewed and classified some of the empirical studies by
variables and methods used, as summarised in Table 1.
Kumar 71
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
Table 1: Empirical effects of variables on poverty
Authors
Method
Age
Sex
Edu
HS
Ocu
IL
TL
PCE
Rem
Cuecuecha
and Adams
(2016)
Probit
model
+
+
Wurku and
Marangu
(2015)
Logit
model
-
+
-
+
-
Abbas et al.
(2014)
Logit
model
+
-
-
+
+
-
-
Raihan et
al. (2009)
Logit
model
+
+
-
+
-
-
-
-
Note: Edu = education, HS = household size, Ocu = occupation, IL = income from
livestock, PCE = per capita expenditure and Rem = remittance
Source: Author’s classification based on existing literature
Sometimes scholars have found both the unidirectional and bi-
directional relationship between remittances and poverty. For
example, Hatemi-J and Uddin (2014) have found a bi-directional
impact that remittances cause poverty reduction is greater than
poverty causes remittances receipt. Contrarily, Gaaliche and Zayati
(2014) found the reverse direction. In addition, some studies have
argued that international remittances significantly reduce poverty in
Bangladesh (Bayes et al., 2015 and Raihan et al., 2009). Rural poverty
of the country has declined at an accelerated pace over the
decade of the 2000s, which is consistent with the observed rapid
growth of the economy as a whole combined with a stable
distribution of consumption expenditure (Osmani and Latif, 2013).
Remittances have not only statistically significant impacts on poverty
but also on financial development (Masuduzzaman, 2014) and
attributing food and aggregate consumption expenditure in addition
to savings (Haider et al., 2016). Khan and Islam (2013) showed that a
one per cent increase in remittance inflows increases inflation rate by
2.48 per cent in the long run but no effects in the short run. Besides
these impacts, remittances have significant impacts on household
welfare in Bangladesh as well (Ahmed et al., 2018) and Wadood and
Hossain, 2016).
Several previous studies focused on poverty and remittances
relationship in country contexts other than Bangladesh. Pekovic
(2017a) showed that remittances have a larger impact on poverty
reduction of rural households in East Serbia while in another study
(Pekovic, 2017b), it was argued that a 10 per cent increase in
remittances per capita would lead to a decline, on average a 4.7 per
cent in poverty headcount, and also 5.2 per cent in poverty depth
72 Impact of International Remittances on Poverty Alleviation in Bangladesh
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and 5.8 per cent in poverty severity. By analysing 25 developing
countries for three years, Pradhan and Mahesh (2016) found that
remittances reduce poverty significantly in the developing countries
while Islam and Rayhan (2016) found the same findings by analysing
15 developing countries. Masron and Wari (2018) interpreted that
remittances reduce poverty by increasing household income of the
poor and utilising remittances in more productive activities. Similar
findings have been found by Bam et al. (2016) that there is an inverse
relationship between remittance and poverty headcount ratio and
poverty gap. Remittances from abroad are found to have a
statistically significant and positive impact on poverty alleviation only
for upper middle-income countries (Azam et al. (2016) while Simiyu et
al. (2018) found that there were significant welfare and poverty level
differences between remittance recipient and non-recipient
households. Besides, Huary and Bani (2017) found that a 1 per cent
increase in remittances decreases the poverty headcount by 0.41 per
cent. Waheed et al (2013) found that a 10 per cent increase in
domestic remittances decreased poverty incidence, poverty gap
and squared poverty gap by 1.80 per cent, 1.60 per cent and 1.60 per
cent while 10 per cent rise in foreign remittances reduced poverty
incidence, poverty gap and squared poverty gap by 0.86 per cent,
0.62 per cent and 0.62 per cent respectively in rural Nigeria. Naghar
and Arshad (2017) found that an increase in remittances led to a
reduction in poverty by 2.56 per cent. Cuecuecha and Adams (2016)
found that remittance recipient households are less likely to be poor
compared to remittances non-recipient households.
Although the role of remittances in poverty alleviation is widely
discussed, there are also other effects and variations. For instance, the
South Asian region draws nearly one-fourth of global remittance
volume that contributes on average to over 10 per cent of GDP of
South Asian countries (Rahman et al., 2014a). Although male migrants
earn and keep their earnings, in most cases, female migrants’
earnings go to their male counterparts (Ullah, 2014). In this regard,
Ullah (2013) argued that females remit a higher proportion of their
income than men, but they enjoy less ‘exposure to remittance’ than
men. Remittance also reduces infant mortality rate and increases
children school attendance rate of remittance recipient households
(Cordova, 2004). Rahman et al. (2014b) thoroughly investigate the
diverse mechanisms through which migrant communities remit,
investigating how recipients engage in the development process in
South Asia. South Asian countries did not fully benefit from migrant
remittances, although there is huge potential to contribute to the
development (Ullah, 2017). The number of family members working
abroad declined by about 7 per cent, and a corresponding 6.4 per
Kumar 73
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
cent drop was seen in the number of households receiving
remittances and a 19 per cent decline in remittance income (Sirkeci
et al. 2012, p 178-179). Besides it was also suggested that remittances
were related to better economic conditions for Bangladeshi and
Pakistani migrants in the United States during the crisis period (Sirkeci
et al. 2012, p 168). Adhikari (2016) found that the remittance has
contributed 35.6 per cent in total expenses of remittance receiving
household, whereas there was only 2.3 per cent contribution made
by non-remittance. By analysing household survey data of Fiji and
Tonga, Brown and Jimenez (2007) found that the positive effects of
migration and remittances on poverty alleviation and income
distribution are found to be stronger when the more rigorous,
counterfactual income estimates are used.
Earlier literature showed that international remittances not only
reduce poverty but also lessen inequality, unemployment, inflation
and enhance welfare, economic growth, the balance of payments,
and so on. It is also found that besides the positive impacts,
international remittances have negative impacts on the economy as
well. For example, remittances increase the dependency behaviour
among the members of remittance recipient households and make
them idle (Abbas et al., 2014). Also, migration creates moral and
social problems such as parentless children, broken family incidents
and failure of women in taking a strong decision in the absence of
family’s male member (Chami et al., 2003). In addition, remittance
also causes brain drain, which has strong negative effects on a
country’s long-run economic growth (Faini, 2007). The literature show
both positive and negative impacts of international remittances at
household, community, and national levels, as presented in Table 2.
Besides the studies in Table 2, Ewubare and Okpoi (2018) found an
interesting result for Nigeria. They found that in the long run, while
domestic remittances intensified poverty, foreign remittances
reduced poverty incidence. On the contrary, in the short run,
domestic remittance has diverse effects on poverty reduction while
foreign remittances have no effects. Tsaurai (2018) has used two
different approaches and found contradictory results. Such as the
fixed effects approach results that remittances led poverty reduction
hypothesis, whereas the pooled ordinary least squares framework
reveals that remittances inflow into the selected emerging markets
led to an increase in poverty levels.
On the other hand, Mollers and Meyer (2014) found that remittances
have no impact on the extremely poor, but lift around 40 per cent of
migrant households above the vulnerability threshold. International
remittances have positively and significantly contributed to absolute
74 Impact of International Remittances on Poverty Alleviation in Bangladesh
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poverty reduction, while negatively and insignificantly contributed to
relative poverty augmentation in Sri Lanka (Karunaratne and
Dassanayake, 2018). Majeed (2015) showed that remittances have
no direct effect on poverty; rather, the effect of remittances on
poverty depends on the level of financial development of a
remittance receiving economy. Migration and remittance may not
be able to eradicate all types of poverty, and may even exacerbate
some, but the alternative of attempting to limit or restrict migration is
likely to be much less productive (Skeldon, 2006).
Table 2: Summary of literature about the impacts of international remittances
Recipient
Positive impacts
Negative impacts
Household
Increase household
income and savings
Smooth consumption
Improve education and
health condition
Reduce child labour
Enhance access to
information
Remittances are used
mostly on consumption
instead of productive
purposes
Create recipient family
idol and dependent
Increase inequality by
age and sex within
the family
Community
Create local
employment
opportunities
Expand local capital
markets
Improve local physical
infrastructure
Enhance the
community’s welfare
Increase inequality
between the recipient
and non-recipient
families
Hinder simultaneous
development of
community
Degrade social and
cultural customs and
practices
National
Improve the balance of
payments and foreign
exchange reserve
Boost economic growth
Decrease
unemployment,
inequality and poverty
Improve human capital
Deteriorate exchange
rate
Increase inflation
Cause brain drain and
Dutch disease
Distort property
markets
Source: Author’s classification from the literature
In summary, the literature does not provide clear directions for the
impacts of international remittances on household poverty. Therefore,
the study bases analysis of Bangladesh on the widely accepted
hypothesis that remittances have negative impacts on poverty.
Data and Methods
This paper focuses on primary data collected from both rural
remittance recipient and non-recipient households. For this study,
Noakhali district of Bangladesh is selected as the study area because
Kumar 75
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
this is one of the districts from which most of the households have
migrated abroad and sends remittances to their families. Also, almost
two decades ago, most of the people of the district lived in a rural
area and was dependent on agriculture. Also, there was scant non-
farm employment opportunities and low standard of living in the
district. Thus, most people migrated abroad to improve their socio-
economic conditions.
In selecting the sample, the study employed a multi-stage random
sampling technique. From Noakhali district Begumganj Upazila from
nine Upazilas was selected randomly. Then, three unions from the
Upazila were selected randomly, such as Gopalpur, Chaoyang and
Rajgnuj. In the next step, three villages were selected randomly from
each union. In this stage, the total number of remittance recipient
and non-recipient households of the selected villages were collected
from each union council office. Finally, fifteen per cent from both
types of household heads from each village were selected randomly
for an interview following Abbas et al. (2014) and Wurku and Marangu
(2015). By this way, a total of 216 household heads were face to face
interviewed from March to June 2018 with a well-structured
questionnaire. The sample distribution is presented in Table 3.
Table 3: The distribution of sample by village
Union
Village
Remittance recipient
households
Remittance non-
recipient households
Total
Sample
Total
Sample
Modhupur
80
12
100
15
Gopalpur
Kalikapur
107
16
73
11
Basantabag
53
8
67
10
Ramswarpur
93
14
107
16
Choyani
Vabani Jibonpur
87
13
87
13
Gangabar
60
9
47
7
Aladinagar
113
17
93
14
Rajgunj
Dililpur
73
11
60
9
Alampur
53
8
87
13
Total number of sample
108
108
Empirical methods
Foster-Greer-Thorbecke index
James Foster, Joel Greer and Erik Thorbecke first stated the Foster-
Greer-Thorbecke (FGT) index in 1984, which measures incidence,
depth and severity of poverty. The index is calculated with the
following formula:
)(
1
1
H
i
i
z
yz
N
FGT
(1)
76 Impact of International Remittances on Poverty Alleviation in Bangladesh
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where, H is the total number of poor households whose income lie
below the poverty line, yi is the income of ith individual household, N is
the total number of households and z is the poverty line. In this study,
US$1.25 as daily per capita income (monthly Tk.2925) is used as
poverty line in measuring household poverty following the World
Bank’s declaration in 2008 for developing countries. α is a parameter.
With the variation in the value of the parameter, the index gives
different measures of poverty. When α equals 0, the formula gives
headcount index and the formula also gives poverty gap and poverty
severity with α equals to 1 and 2, respectively.
The index is used in this study to measure the level of household
poverty of remittance recipient and non-recipient households
following Foster et al. (1984), Wurku and Marangu (2014) and Pekovic
(2017a).
The logistic regression model
Being informed of the level of household poverty, the study examines
the extent of the impact of remittances on household poverty. In this
study, household poverty is considered as a dichotomous variable
having two categories such as poor and non-poor. That is why, the
study applies a binary logistic regression model to find out a cause
and effect relationship between household poverty and a set of
explanatory variables following Raihan et al. (2009), Wurku and
Marangu (2015) and Abbas et al. (2014). Equation (2) states the
relationship as follows:
)( ii XfP
(2)
where Pi is household poverty and Xi is the set of explanatory variables
that affect household poverty. Household poverty is measured
through the poverty line i.e. Tk.2925 per month. A household with
income below the poverty line is assigned as poor, otherwise non-
poor.
Let us suppose that the probability of a household being poor is:
ii XXYE 21i )/1(P
(3)
where Pi is the probability of being poor, Xi is a set of explanatory
variables, and Y = 1 means that the household is poor. Equation (3)
can also be written as:
)()( 1
1
1
1
)/1(
21 ii ZX
iii
ee
XYE
(4)
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Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
Where Zi = β1 + β2Xi is known as (cumulative) logistic distribution
function. In this case, Zi ranges from –∞ to +∞ and Pi ranges between
0 and 1 and it is non-linearly related to Zi (i.e. Xi). This satisfies the
conditions of the probability model but violates one of the
assumptions of the classical linear regression model as Pi is not only
non-linearly related to Xi but also to βi. In this circumstance, the OLS
method is not applicable. However, 1-Pi, the probability of a
household not being poor, is:
)(
1
1
1i
Z
ie
P
(5)
Thus, the ratio of a household being poor to a household not being
poor is:
(6)
where,
i
i
P
P
1
is the odds ratio in favour of a household being poor.
Taking natural log in both sides of equation (6), an appropriate
function is found as:
iiii XPPL 21
1/ln
(7)
Here, Li is the log odds ratio or logit which is not only linearly related to
Xi but also to βi. Therefore, the specified model is:
i
iii
uXXXXX
XXXX
PPL
9988776655
443322110
1/ln
(8)
where, Li represents the log odds ratio in favour of a household being
poor; β0….…β9 are parameters to be estimated; X1, X2….…X9 are the
explanatory variables and ui is the stochastic disturbance term. The
explanatory variables of the model are described in Table 4.
These below variables have been considered in the regression model
and their expected sign has been assumed following earlier literature.
The list and relationship of these variables with the dependent
variable have been presented in Table 2.
i
i
iz
Z
Z
i
ie
e
e
P
P
)(
)(
1
1
1
1
1
78 Impact of International Remittances on Poverty Alleviation in Bangladesh
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Table 4: Description of explanatory variables used in the logistic regression model
Variables
Type
Measurement
Expected
sign
Age (X1)
Continuous
Age of household head (Years)
-
Sex (X2)
Dummy
Household head’s sex (1 for male,
otherwise 0)
-
Education (X3)
Continuous
Household head’s education
(years of schooling)
-
Household size (X4)
Continuous
Total number of family members
+
Occupation (X5)
Dummy
Household head’s occupation (1
for non-agriculture, otherwise 0)
-
Income from
livestock (X6)
Continuous
Household’s total income from
livestock (Tk./year)
-
Land (X7)
Continuous
Household’s total amount of land
(Bigha)
-
Per capita
expenditure (X8)
Continuous
Household’s per capita
expenditure (Tk./year)
-
Remittance (X9)
Dummy
Remittance (1 for recipient
household, otherwise 0)
-
Socio-economic and demographic features of households
This section presents the socio-economic and demographic features
including age, sex, education, household size, occupation, income
from livestock, total land, per capita expenditure and poverty of
remittance recipient and non-recipient households. These features
are analysed with a one way ANOVA test, which implies the
statistically significant variation in different features by different
categories of households. This analysis is measured through SPSS 23.00
and presented in Table 5.
Table 5: Features of remittance recipient and non-recipient households
Variables
(Mean Value)
Remittance
recipient
households
Remittance
non-recipient
households
F
Sig.
Age
45.74
43.74
1.24
0.266
Sex (male)*
0.68
0.81
5.58
0.019
Education**
5.72
4.41
4.43
0.036
Household size**
4.95
4.50
3.89
0.050
Occupation (non-
agriculture)
0.31
0.32
0.09
0.771
Income from livestock*
2320.96
4473.98
14.51
0.000
Total land**
4.54
3.24
5.15
0.024
Per capita expenditure*
14431.13
1972.77
45.62
0.000
Poverty (poor)*
0.13
0.58
61.89
0.000
Note: * and ** means 1 and 5 per cent level of significance
Source: Field survey, 2018
Table 5 shows that the mean age of remittance recipient households’
head is 45.74 years while it is 43.74 years for non-recipient households,
and the variation is statistically insignificant. On the other hand, it is
found that household head is male is higher for remittance recipient
households than compared to non-recipient households, and the
Kumar 79
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
variation is significant at 1 per cent level of significance. This interprets
that male member of remittance recipient households have migrated
abroad and the female member is maintaining the family. Like sex,
education, household size, income from livestock, total land, per
capita expenditure and poverty are statistically significant, and
occupation is statistically insignificant. The table also shows that the
number of poor households is higher for remittance non-recipient
households than recipient households and the variation is significant
at 1 per cent level of significance. This variation explains that
remittances have a significant influence on poverty reduction.
Results of FGT index
The result of the Foster-Greer-Thorbecke index is calculated through
MS-Excel (2010) and presented in graphical form. Figure 3 presents the
level of household poverty, including remittance recipient, non-
recipient households and total households.
Figure 3: Level of household poverty (%)
Source: Field survey, 2018
From Figure 3, it is found that the headcount index for all households
is 34.31%. This means that 34.31% of the households live below the
poverty line out of the total households. The poverty gap index is
found to be 16.15%. This reveals that on average, the income needed
Rmittance recipient
households
Rmittance non-
recipient
households
Total households
8.25
39.53
34.31
0.92
32.29
16.15
0.18
22.03
11.04
Headcount poverty Poverty gap
80 Impact of International Remittances on Poverty Alleviation in Bangladesh
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to eliminate poverty in the country should be increased by 16.15%.
The poverty severity of the households is 11.04%.
The breakdown of the poverty indices with the response to remittance
illustrates that remittance non-recipient households have the highest
percentage of poor people compared to remittance recipient
households. The FGT analysis shows that 8.25% of remittance-receiving
households is under the poverty line, while 39.53% of remittance non-
recipient households live below the poverty line. Similarly, the poverty
gap is higher among the remittance non-recipient households
compared to remittance recipient households. For remittance
recipient households, the cost of eliminating poverty is 0.92% of the
poverty line while it is 32.29% for non-recipient households. The poverty
severity index is widely used to compare poverty rankings between
two groups. The higher the value of severity index, the greater the
inequality of the distribution among the poor and the severity of
poverty. Figure 3 shows that the amount of poverty severity for
remittance recipient households is 0.18% while it is 22.03% for non-
recipient households.
From the FGT analysis, it is found that the rate of poverty in all forms of
remittance recipient households is lower than that of remittance non-
recipient households. It can be interpreted by the fact that
remittance recipient households are being able to meet up the
regular demand by the remittance, but non-recipient households are
not being able there is few scopes of other non-farm employment
opportunities in the rural areas of Bangladesh. Therefore, it can be
concluded that international remittance plays a significant influence
on household poverty reduction.
Results of the logistic regression model
The binary logistic regression model for measuring the extent of the
impact of international remittances on household poverty is analysed
with STATA-13, and the result is presented in Table 6.
From the above table, it is found that the log likelihood statistic (Log
likelihood = - 79.03) indicated by Chi2 statistic is highly significant
(Prob.>chi2=0.00000) suggests that the model has strong explanatory
power. The Pseudo R2 = 0.4383 indicates that variables included in the
model maximised the likelihood of data in poverty reduction by 44 per
cent. The study finds that household size, income from livestock, total
land and remittance have a significant influence on the alleviation of
household poverty. On the other hand, age, sex, education, per
capita expenditure and occupation have no significant influence on
the alleviation of household poverty, although all variables exhibited
expected signs.
Kumar 81
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
Table 6: Results of the binary logistic regression
Variable
Coefficient
Odds
ratio
Std. Err.
Z
dy/dx
Sig.
Age
-0.02279
0.978
0.0193
-1.18
-0.0027
0.24
Sex
-0.00389
0.996
0.3890
0.01
0.0005
0.99
Education
-0.04163
0.959
0.0587
-0.71
-0.0051
0.48
Household size*
0.24742
1.28
0.1237
2.00
0.0294
0.05
Occupation
-0.39945
0.671
0.4812
-0.83
-0.0475
0.41
Income from
livestock*
-0.000167
0.999
0.00008
-2.03
-0.00002
0.04
Total land**
-0.333492
0.716
0.1344
-2.48
-0.0397
0.01
Per capita
expenditure
-0.000153
0.999
0.0002
-0.74
-0.000018
0.46
Remittance**
-2.3603
0.094
0.549
-4.30
-0.2807
0.00
Log likelihood = -79.03; LR chi2 (9) = 47.35; Prob.> chi2 =0.000; Pseudo R2 = 0.4383; Total
number of observations = 216.Note: ** and * equals 1 percent and 5 percent level of
significance.
Source: Field survey, 2018
Table 6 also shows that the odds ratio of household size reveals that
an increase in household size by one unit reduces the log odds of
households being poor by 1.28 and it is negatively significant at 5 per
cent level of significance. The result is interpreted by the fact that if
the household size is large, more members can engage them in
income earning activities which ultimately reduces poverty. The
similar view is also reported by Raihan et al. (2009).
The odds ratio of the income from livestock reveals that for one unit
increase in household’s income from livestock, the log odds of
household poverty decreases by 0.999 which is negatively significant
at 5 per cent level of significance. The result is interpreted by the fact
that the paper is studied in rural areas where livestock is an important
source of household income. This reveals that households with higher
income from livestock have a higher probability of being non-poor.
Abbas et al. (2014) have also found a similar result.
Similarly, the log odds of household poverty are decreased by 0.716
with one unit increase in the total land which is negatively significant
at 1 per cent level of significance. The rational explanation is that total
land generates household income directly which ultimately reduces
household poverty. The result is in line with Raihan et al. (2009).
The study finds that if a household receives international remittances,
the log odds of a household being poor reduce by 0.094 which is
negatively significant at 1 per cent level of significance. A logical
interpretation is that international remittances directly reduce budget
constraint of the recipient households by increasing the level of
income which conspicuously reduces the hardship of poverty (Abbas
et al., 2014; Raihan et al., 2009 and Wurku and Marangu, 2015).
82 Impact of International Remittances on Poverty Alleviation in Bangladesh
www.tplondon.com/rem
As the estimated coefficient of the logistic model has no direct
economic interpretation, the most preferred way in this regard is to
find out the marginal effects (Gujarati and Poter, 2009). The marginal
effect of a particular variable expresses the probability estimation of
household poverty that means the probability of a household being
poor keeping other variables constant. Table 6 also presents the
marginal effects of the logit model.
The result of the marginal effect indicates that the probability of a
household being poor may be reduced by 2.94 per cent if the
household size is increased by one unit. On the other hand, if a
household’s income from livestock is increased by one unit, the
probability of a household being poor may be reduced by 0.002 per
cent. The marginal effect of total land shows that an increase in one
unit of total land reduces the probability of the household being poor
by 3.97 per cent. The marginal effect result also reports that the
probability of a household being poor may be reduced by 28.07 per
cent if the household receives international remittances.
Conclusion
This article addresses two separate questions. First, what is the level of
poverty among not only remittance recipient households but also
remittance non-recipient households? Second, how does
international remittances impact on household poverty in
Bangladesh? I use household-level survey data from both remittance
recipient households but also remittance non-recipient households to
estimate a remittance non-recipient counterfactual household
poverty scenario, against which to compare actual, with remittance
recipient household.
My results are interesting in a number of respects. Firstly, this study, in
line with the findings of the literature and similar to various cases
around the world, provided evidence for the argument that the
propensity of people in poverty among remittance recipient
households is lower than households that are not receiving
remittances in Bangladesh. I find that the impact of remittance on
poverty alleviation is stronger in remittance recipient households,
enjoys a considerably lower incidence and depth of poverty than the
counterfactual remittance non-recipient households. Based on the
empirical findings, secondly, it is shown that if a household receives
international remittances, the probability of that household being
poor is decreased by 28.07 per cent.
On the basis of these findings, the study recommends nursing
international remittances as an important poverty reduction tool in
Bangladesh. In order to get optimal benefit from remittances, the
Kumar 83
Copyright @ 2019 REMITTANCES REVIEW © Transnational Press London
country should pay attention to two things. First, proper technical and
vocational education, training, loan facilities, easing visa processing
and other migration-related facilities should be provided to the
migrants prior to their migration to entice their interest to send more
remittance to home country. Second, migrant families should be
made aware of opportunities and offered advice in utilising
remittances in productive purposes like setting up business, investing
in education and improving commercialised farming. As this article is
carried out taking very small sampling, short time and limited self-
funding, the core representative findings has not been brought to light
by this study. Therefore, I like to suggest researchers to carry out further
research on this issue.
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