Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Educational Impact of Migrant Labours on Remittance: An
Empirical Study on Bangladesh
Md. Abdullah Shihab*, Md. Rabiul Islam Rabi** and S. M. Saleh Reza***
Remittance is one of the major sources of earning foreign exchange for
Bangladesh. Flow of remittance can be determined by many factors, but
quality of the worker is the most important among them. Quality of migrants
can be easily observed by the educational background. Here our main
purpose of estimation is finding the relationship between educational
background and the flow of remittance. Literature describes this
relationship as debatable, but this study has found a negative but
insignificant relationship between them. This negative relationship does not
justify that, low level of education is better than high education for
remittance, rather it describes that, low educated migrants send more
remittance than the highly educated migrants.
Keywords: Immigrant Labour, Employment Creation, Employment Data, Human Capital
JEL Codes: J2, J610, J600
1. Introduction
Remittances are household income received from abroad, remitting mainly from the
international migration of workers. Bangladesh now has remittance-based economy. It
sends out a significant number of migrant workers annually and the remittances sent by
them become a significant source of funds for the economic development of the country.
Government considers worker migration as helping to curtail unemployment, for reduction
of poverty and earning foreign exchange through remittances and worker migration as one
of the key economic policy priorities. Historically, South Asian migrant workers are
categorized into two types and the categories are obviously applicable for Bangladesh as
well. The first category includes the immigrants whose are permanent residents to
industrialized countries in the 1950s and 1960s, mostly professionally qualified individuals
such as medical doctors, academicians and engineers, who have migrated to the more
developed countries, especially western countries. The second type of group includes
semi-skilled or unskilled migrant workers. The number of the second group of workers has
increased especially in the 1970s when the surge in oil prices led to the oil-producing
Middle East countries heavily investing in infrastructure development. The second group
of migrant workers is found in the Middle East especially in Saudi Arabia, the United Arab
Emirates (UAE), and Kuwait etc.
Now the common belief about positive relation between skilled migration and remittance
may provoke skilled migration oriented policies, which may not increase remittances due
to a fact that the more educated remit less.
This belief is accepted as fact by many; for example, the OECD (2007, P11) writes “low
skilled migrants tend to send more money home”. These facts can be justified from two
papers (Faini, 2007 and Nimmi, et al. 2008) which use cross-country macroeconomic
approaches to claim that the highly skilled (defined as those with tertiary education) remit
less. But there are many reasons not to believe these cross-country estimates or to
*Md. Abdullah Shihab, Southeast University, Dhaka, Bangladesh. Email: shihab@econdu.ac.bd
** Md. Rabiul Islam Rabi, Lead to Live Foundation, Dhaka, Bangladesh. Email: eco.rabi@gmail.com
***S M Saleh Reza, Daffodil International University, Dhaka, Bangladesh. Email: saleh1403@gmail.com
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
consider them useful for policy. Both studies show the relation between the amount of
remittances received to the share of migrants with tertiary or high education, telling us
whether countries with larger share of highly skilled migrants receive less or more
remittances than countries with relatively fewer skilled migrants. But this does not answer
the factual question of “do more educated individuals remit more or less?”
This paper revisits the relationship between remittances and the educational level of
migrants using micro data from the HIES (Household Investment and Expenditure
Survey), allowing us to compute the association between an individual’s education level
and remitting behaviour. With micro data we can ask whether or not more educated
individuals are more or less likely to remit and whether they send more or less remittances
if they do remit.
2. Literature Review
A number of empirical studies examine the economic determinants of international
remittances. In general, while these studies find that the possibility to remit is highest
when migrants are married and there is in middle, the effect of other factors on the
remittance is debatable. One of the more important determinants of remittances is
education, in particular, how the educational level of international migrants (educated or
uneducated) affects the amount of remittances. Two recent studies where cross-country
data is used from different developing countries (Adams, 2008 and Faini, 2007) find that
skilled (educated) migrants remit less than unskilled migrants. According to them, as
skilled migrants are more likely to bring their families and to spend more time working
abroad, they tend to remit less than unskilled labours. Using data from 76 developing
countries, Adams, Jr. Richard (2008) has analysed that how variables such as the skill
composition of migrant labours, poverty, and interest and exchange rates affect the level
of international remittances received by different developing countries. It finds that
countries which export a larger share of high-skilled (educated) migrants receive less per
capita remittances. Again, Faini, Riccardo (2007) examines how the skill level of migrants
(skilled or unskilled) affects the level of remittances sent home by international migrants.
Using different kinds of approaches, including instrumental variables, it concludes that
skilled migrants remit less than unskilled migrants. Since skilled migrants are more
familiar to bring their families and to spend more time in abroad, their possibility to remit is
less than that of unskilled migrants. The author concludes that the skilled labour migration
may not increase the amount of remittances to developing countries.
However, these findings have been contested by Bollard et al (2009). This paper uses
micro data from immigrant surveys for the 11 OECD countries to examine the relationship
between education and remitting behaviour. It finds that education is directly and
positively related to the amount remitted. By controlling various factors, migrants with a
university or college degree remit $300 more per year than migrants without such a
degree. Better educated migrants earned higher income, rather than family background,
explains much of the higher amount of remittance by educated migrants.
Durand et al (1996) has used a large, non-representative household survey from Mexico
(1982-92) to analyse the individual, household and in community-level components of
international remittances. The probit-OLS model is used by the authors’ o test for the
propensity of sending remittances and find that the propensity to remit is highest for the
married migrants and middle aged (40s) migrants. With respect to the amount remitted,
the authors find that the amount, which is sent home, is positively related to education and
income. For example, with an additional year of schooling, the amount of remittance
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
increases by 4 percent and with each extra $1000 in monthly income, the amount of
remittance increases by 17 percent.
3. Objective
The main objective of this research is to find the impact of education on remittance. It has
tried to expose the relationship between education level and the remittance. So research
question is:
“What is the effect of education on remittance (both goods and money send to the
household)?”
4. Motivation:
At present, Bangladesh is one of the highest remittance-receiving countries of the world.
In 1976, Bangladesh has earned only 23.71 million USD whereas in 1990 781.54 million
USD, and in 2013 Bangladesh has earned 11,560.48 million USD. Graph below describes
the flow of remittance from year 1976 to 2013(up to November).
Figure 1: Flow of remittance from year 1976 to 2013(up to
November).
Source: Bureau of Manpower Employment and Training (BMET)
Here, we see an upward trend in the flow of remittance. So it can be easily said that,
remittance is one of the most important components of Bangladesh’s economy.
With the flow of remittance, employment level in migrants is also rising gradually. In 1976,
total employment was only 6087, where in 2013 employment is 371,647 estimated by
Bureau of Manpower Employment and Training (BMET). Figure below describes the
employment level from 1976 to 2013-
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Figure 2: Employment Level of Migrants from 1976 to 2013
Source: Bureau of Manpower Employment and Training (BMET)
The UN estimates that from 2005 to 2050, nearly 100 million migrants will leave
developing countries for rich nations. Therefore, in the near future, Bangladesh may solve
the serious problem of unemployment by creating such kind of workers, which is
demanded by the world job market. For this, we should be concerned about the skill level
or educational level of the workers.
In order to find whether our stock of labour supply is skilled or not, we should determine
their educational background. And, to find the relationship between educational
background and remittance, the research will determine the present status of our labour
supply stock and it also helps to find the way of how we can increase our flow of
remittance.
5. Theoretical framework1:
Here, the study wants to make a theoretical basis about the relationship between
education and remittance. For this a theoretical model is constructed where;
Rijt =The aggregate amount of bilateral remittances (remittance send by both skilled and
unskilled) sent by migrants born in country i (the recipient country) and living in country j
(the transferring country) at time t
MK ijt =Stands for the bilateral migration stock of type-k migrants (k equals s for highskilled, u for low-skilled, and s + u for total).
TK ijt =Presents the amount remitted per migrant
Then bilateral remittance can be expressed as:
Rijt = MSijtTSijt+MUijtTUijt
To emphasize the effect of the skill composition of migration on remittances, the above
equation can be rewritten as:
Rijt = MS+U ijt TU ijt [1+ MS ijt /MS+U ijt * (TS ijt – TU ijt)/ TU ijt ]
Where,
1
This theoretical framework is obtained from Frederic Docquier, Hillel Rapoport, Sara Salomone (2011),
“Remittances, Migrants’ Education and Immigration Policy: Theory and Evidence from Bilateral Data”.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
MS ijt /MS+U ijt Ξ σijt is proportion of highly skilled in the bilateral migration stock from i
to j,
(TS ijt – TU ijt)/ TU ijt = Δijt is excess amount remitted by a highly skilled individual
relatively to a low skilled one (which can be positive or negative)
Taking logs, and considering a linear approximation given that σijt* Δijt (the product of two
proportions) is not too large, we have:
lnRijt = lnMS+U ijt + ln TU ijt + σijt*Δijt
If highly skilled migrants remit more than low-skilled migrants (Δijt > 0), then the aggregate
amount of remittances should increase with migrants’ education level. If they remit less
(Δijt < 0), then the aggregate amount of remittances decreases with migrants’ education
level.
6. Data and Methodology:
6.1. Dependent variable:
As our focus is the amount of money received from abroad, so our dependent variable is
remittance. But in creation of this dependent variable, two components are used to
capture the effect of remittance. That are- amount of money received and the value of the
goods that are received from the last twelve months for a particular household. It is
because there may be some individuals who are not sending any money but are sending
different kinds of valuable goods. In that case, second component helps us to identify the
actual amount of what a particular household received in monetary terms.
In the construction of dependent variable, there are some facts that should be taken in to
consideration. However, for the unavailability of data the study cannot include all those
facts in the dependent variable. First, considering the value of goods, it should be
determined about the classification of goods, that is whether the goods are normal or
luxurious in nature. Another thing is that whether goods are sent regularly over months or
years. If an individual send some goods that are not usually sent then dependent variable
may be overestimated to consider the value of that goods. As the research wants to find
out what an individual usually send to his household, unusual sending of goods may
overestimate the dependent variables.
In contrast, in some cases dependent variable may also be underestimated for some
individuals who lost job or his work in the period we considered. By taking these kinds of
observations, amount of money sending may be too little or zero.
6.2. Explanatory variable:
As our main concern is the impact of education on remittance, so our explanatory variable
is the ‘education’. Here ‘education’ variable takes the continuous value for each level of
education, that is, value will be zero for no education after that value will rise from 1 to 17
respectively with each higher level of education. From the level of no education to HSC, it
takes value 0 to 12. And for graduate and post graduate level it takes 16 and 17 because
after HSC it generally takes 4 years to complete graduate and an additional year is
needed for post graduate. Here minimum and maximum value of ‘education’ is zero and
17, that is, for any higher level than postgraduate and other categories of education such
as medical, engineering, vocational, technical education- our explanatory variable takes
the value 17.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
6.3. Control variable:
Monthly income of household:
Monthly income of household is considered as one of the control variable in our
estimation. The amount of money send by an individual may be depends on his respective
household’s total monthly income. It is most likely that households with less income may
get higher amount of remittance than a household with high income given that those two
individuals earn the same amount of salary. So money received by the household is
influenced by the monthly income of that household. Therefore, this variable should be
controlled.
Household size:
Household with poor income sometimes suffered with a problem of large household size.
So an individual who belongs to that household, tend to send more money than an
individual who has fewer family members. Therefore, household size may affect the
amount of remittance.
Age:
Generally individual’s working efficiency and earnings depends on his age. Common belief
is that, age and earnings have positive relations in certain range of age. It is because, an
older implies higher level of experience and it leads to larger gains than a novice low
younger individual. So age has some positive effects in determining the amount of
remittance. But, it is not obvious that older people earn more; variability in education,
training, skills may change this positive relationship. In order to find the effect of education
on remittance, this study should keep this ‘age’ variable constant.
Duration:
The variable ‘duration’ captures of how long an individual has been living in abroad.
Generally for any individual, it takes time to cope up with the new circumstances and
hence the money sent may be too little in one’s early work period. Moreover, one may not
send any money because of unavailability of work in abroad.
It may be thought that, after a long time has passed, one may send more remittance to his
relatives and family. However, in some studies a negative correlation has been found
between the time the migrants spend away from home and the amount of remittances
they send home, particularly if migrants bring their families or when they form new families
in the host country. According to a recent US study for Latino migrants (Bendixen and
Associates 2004), citizens are less likely than non-naturalized immigrants to send money
to the home country.
So, the duration of time spent is one of the determinants of remittance, and therefore it is
included as a control variable. Here, duration is measured by the number of month spent
by the migrants.
Value of non-operated land:
Amount of money received by the households from abroad may vary with the household’s
economic status. Economic status can be quantified by considering the household’s
income and the initial level of wealth. So that, to identify the household’s initial wealth
level, amount of non-operated land (the land from which no income is generated) can be
used. Here present price of non-operated land is used instead of total price of operated
and non-operated land because as we include household’s monthly income, there may
arise multicollinearity problem between operated land and monthly income and it is most
likely that they have positive correlations. So price of non-operated land is one of our
control variable.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Skill level:
One of the important determinants of remittance is types of occupation that are performed
by migrants. Occupation type and job opportunities are mainly associated with the skill
level of an individual. At present, many developed countries classified the migrants as
skilled or unskilled. To find out the effect of education on remittance, we should consider
the migrant’s occupation and skill level. But no common yardstick has not been formed to
define the different levels of skills or to distinguish one kind of skill from another.
In general, skill can be defined with the help of education and occupational level. The
international classification which is based on education is known as International Standard
Classification of Education (ISCED) and the other, based on occupational level, is
International Standard Classification of Occupation (ISCO). The education approach is
concerned with the supply side of human resources and occupational approach is
concerned with demand side of skilled labour.
The Bureau of Manpower Employment and Training (BMET) categorized migrants into
four categories, they are professional, skilled, semi-skilled, and low skilled. According to
BMET, during 1990 and 2009, about 2.7% of the migrants were professional, 31.32%
skilled and 16.27% semi-skilled and the remaining nearly half – 49.65% ---are unskilled.
This classification, on which these data is based on, is not consistent with ISCO-08.In
educational approach, some problems may arise such as, a migrant may be skilled in his
own country in terms of education but may perform an unskilled job in the abroad or
migrant may have low educational background in his country but may perform a skilled job
in abroad. Moreover, as our explanatory variable is education so we should consider skill
level in terms of occupational approach that is skill level is classified by the category of
occupation according to the International Standard Classification of Occupation (ISCO).
According to ISCO, skill is defined as the ability to carry out the tasks and duties of a given
job. Here two dimensions of skill is used which are skill level and skill specialization. There
are four hierarchic skill levels and those can describe all the ten major occupation groups
categorized by the ISCO. Under the major groups, there are sub-major groups, minor
groups and unit groups. And, all these groups can explained by the four skill level that are
Skill Level 1, Skill Level 2, Skill Level 3 and Skill Level 4.
Occupation at Skill Level 1 is associated with the performance of simple and routine
physical work such as office cleaners, garden labourers, kitchen assistants etc.
Occupation at Skill Level 2 is associated with performance of tasks such as operating
machinery and electronic equipment; driving vehicles, repair of electrical and mechanical
equipment; ordering and storage of information.
Occupation at Skill Level 3 is involved with the performance of complex and technical and
practical tasks that require extensive body of technical and procedural knowledge such as
shop managers, medical technicians, commercial sales representatives, legal secretaries
etc.
Occupation at Skill Level 4 is typically involve the performance of tasks that require
complex problem solving, decision making and creativity based on an extensive body of
theoretical and factual knowledge in a specialized field such as civil engineers, secondary
school teachers, musicians etc.
Table 1 below show the 10 major groups and associated skill level:
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Table 1: Major Groups and Associated Skill Level
Major Groups
Skill Level
1) Managers
4 (Except hotel and shop managers)
2) Professionals
4
3) Technicians and Associate Professionals 3
4) Clerical support workers
2
5) Service and sales workers
2
6) Skilled agricultural, forestry and fishery 2
workers
7) Craft and related trades workers
2
8) Plant and machine operators
2
9) Elementary occupations
1
0) Armed forces occupations
1,2 and 4
Source: Table is created from ISCO-08
According to above classification, types of occupation for migrants’ labour can be
expressed in four categories, so the research uses three dummy variables to capture this.
Assigning the skill level over occupation, it needs different types of information regarding
the task performed but as data is collected from the HIES-10, the type of occupation is the
only available information. So that, there some problems arise in determining skill level
such as for hotel owners, it is difficult to find particular skill level. In this case, the study
assign skill level 3 assuming for starting and operating hotel there may need some sort of
special knowledge. Using all those information, three dummy variables is constructed
where base category is skill level 1.
6.4. Data Ranges and Sources:
It is cross section data analysis using 1109 observations for the year 2010. All the data
are collected from Household Income-Expenditure Survey 2010. This survey is conducted
by Bangladesh Bureau of Statistics.
6.5. Methodology:
For the estimation, survey data or non-experimental data is used. From the data set of
HIES-10, all the migrants are considered irrespective of any country that is, the research
does not distinguish observations by country or by some region. Here the study considers
observations in individual level, that is, 1109 individuals have been considered for
estimation. Though the study includes some of the data, which are in household level,
those are household size, household’s monthly income and household’s non-operated
land. For precise estimation, the study takes log form for the variables remittance, monthly
household income and for value of unused land. In addition, for categorize the skill level
we follow the ISCO-08 rules.
Here all the explanatory variables are in linear form and as type of dependent variable is
continuous, so we can use Ordinary Least Square (OLS) method for estimation. Using
1109 observations for the year 2010, we use cross section data analysis.
As it is a cross section data analysis, the model is free from autocorrelation problem, but
multicollinearity and heteroscedasticity problem may arise. To check multicollinearity
problem, the study uses Variance Inflating Factor (VIF) and for heteroscedasticity problem
we use Breusch-Pagan-Godfrey (BPG) test (Annex-1 and Annex 2). The test concludes
that the model has no multicollinearity problem but it has problem of heteroscedasticity. In
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
spite of being heteroscedasticity, the research finds unbiased as well as consistent
estimates, but efficient estimators cannot be found. So that, we use ‘robust’ estimate.
7. Empirical estimation:
ln(Remittance) = α0 + α1 Xeducation + α2 Xage + α3 Xduration + α 4 XHH size+
α5 ln (XHH monthly income)+ α6 ln(Xvalue non operated land) + α 7 D skill 1+ α8D
skill 2+ α 9Dskill 3 + μ
Where,
ln(Remittance) = total money and value of goods send in last 12 months (tk)
Xeducation= Education level
Xage = Age of migrants (year)
Xduration = Time spent in abroad(months)
XHH size = Household size
ln (XHH monthly income)= ln of Monthly income of households (taka)
ln(Xvalue non operated land) = ln of value of non operated land (taka)
D skill 1 = Dummy variable for skill level (1 for skill level=2)
D skill 2 = Dummy variable for skill level (1 for skill level=3)
D skill 3 = Dummy variable for skill level (1 for skill level=4)
8. Empirical Results:
8.1. Descriptive Statistics:
Here descriptive statistics of all the variables are given below:
Table 2: Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Remittance(taka
in
12 140188
220868
100
months)
Education
7.27748
3.87619
0
Age (year)
33.5622
9.65357
0
Duration (month)
62.1443
67.465
1
Household size
4.77117
2.24991
1
Monthly income (taka)
20069.7
24059.7
500.32999
Value of non-operated land 28667.7
(taka)
282425
0
Max
5000000
17
71
1155
17
420666.6
6
5000000
For the dependent variable, mean value of remittance is 140188 and standard deviation is
220868. For education, average level of education is up to class 7 as mean of education is
7.02252. Here number of observations with no education is 114, which is 10.28% of total
sample and the number of observations with highest level of education that is graduate,
post graduate and others is 50 (4.51%).
For the age variable, there are two observations for those no age is reported, so we take
zero for them. For the skill dummy variable, the study has 324 (29.215%) observations
with skill level 1, 661(59.6%) with skill level 2, 67(6%) observations with skill level 3 and 57
(5.14%) observations with skill level 4.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
8.2. Results:
Number of obs =
1109
F( 9, 1099) = 47.83
Prob > F
= 0.0000
R-squared = 0.2814
Adj R-squared = 0.2755
Root MSE
= .82174
Dependent variable= ln (remittance)
Variables
Coefficient
‘t’ value
‘t’ value (robust)
Education
-.0066622
-1.01
-1.01
Age
.0119011
4.24***
3.44***
Duration
.0003513
0.88
0.40
***
Household size
-.0655158
-5.60
-5.08***
***
ln (monthly income)
.6621814
19.53
13.43***
ln(value of non
-.0134536
-1.38
-1.04
operated land)
Skill dummy 1
-.0268938
-0.48
-0.50
Skill dummy 2
-.2426436
-2.18**
-1.87*
Skill dummy 3
.0281724
0.24
0.25
***
Constant
5.068612
15.60
10.91***
*** Significant at 1%, ** significant at 5%, * significant at 10%
In our estimation, education has a negative impact on remittance that is, beta coefficient
of this variable is negative. Moreover, education has no significant impact on remittance,
which is not consistent with our priori.
Though, effect of age is very little but age has positive as well as significant impact on the
amount of remittance. Here if age increases by one year, remittance is increased by
1.19% holding other things constant.
Time spent in abroad or duration has very little and insignificant impact on remittance.
In case of household size, it has significant negative impact, which is not consistent with
our priori. Here, increase in household size by one person, remittance decreased by 6.5%
keeping other things unchanged.
Monthly income of household has very significant and positive relationship with
remittance. Here as monthly income increases by 1%, remittance will also increase by
.66% holding other things constant.
Effect of non-operated land over remittance is negative as well as insignificant.
There are three skill dummy variables and only of them is significant. Here skill dummy 2
is significant. That indicates- migrants with skill level 3 send on average 24.26% less
remittance than the base category of skill level 1. There are several studies, which have
found similar results; including one by Nimo, Yoko (2008) sponsored by Asian
Development Bank (ADB) which conclude that remittance level is inversely related to skill
level; the higher the skill the lower the level of remittances.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
9. Analysis of the Result:
Negative sign is taken by the explanatory variable education; that indicates as education
rises, amount of remittance will decrease. The main reason behind this result is that, in
our country, people who are less skilled or have low level of education are more interested
to go to abroad for searching for jobs. Moreover there are common belief among the less
skilled unemployed people that they may find work and earn more in abroad than in our
country. In that case, they may work in less skilled job in abroad and they are more likely
to send remittance as much as they can. An individual migrant belong to poor or semi
poor family; that migrant is highly concerned to send remittance to his family as main
source of income of that family is the money that migrant sent.
Sometimes students leave their home countries for acquiring knowledge in the best
centers of the world. In the most of these cases, student’s families have strong financial
support. Again, some countries encourage emigration of students by offering financial
support for foreign students to pursue Masters Degrees, PhD or Post-Doctoral studies
abroad. In those cases, they do not send any money or send very little amount money to
the home country. So for any developing country, it is most likely that there should be
negative relationship between remittance and the level of education.
10. Problems with Measurement:
There are some other determinants, which may affect the flow of remittance such as
income differential between host country and migrated country. But it is difficult to deal
with because we would have to consider each observations and corresponding individual’s
country.
Remittances also increase as the exchange rate improves or cost of money-transfer
declines, but it is also difficult to observe. It would better, if we can take sample from
particular country or region, but in that case sample size would too small to estimate.
For determining remittance, we should consider wage rate for different countries. As
because there are different occupation in different countries, so wage rate is rarely
observable. Some difficulties also arise for determining skill level for the unavailability of
information according to ISCO-08.
Another drawback of this estimation is that we cannot include whether an individual has
some other trainings for job because of the data unavailability.
If we could have considered the problem above mentioned, then we would have better
estimate of remittance.
11. Policy Implication
In light of the observation stated above, there is one important policy implication in this
regard. The research in this paper shows that level of education has no significant impact
on remittance. Considering the plausible amount of unreported remittance and limitations
of the study, this paper suggests that the migrant workers should be trained appropriately
which must match the employers required skills.
A low skilled migrant, when equipped with necessary skill set, can facilitate the employer’s
needs which will eventually raise the income earning opportunities of the migrant labour.
For example, Japan will import manpower to expedite the country’s construction works.
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Now, if the employment sector is confirmed beforehand, then the government can impart
training to the low skilled workers. As education level is insignificant, then imparting
necessary training will help the migrant labours in the labour market.
12. Conclusion
At present, Bangladesh’s migrant low educated workers are contributing to the economy
of Bangladesh by sending huge amount of remittance. But it may gradually decrease for
the shortage of proper qualification and skill. Now, most of the developed countries
demand highly skilled workers in different sectors. But in supplying skilled workers,
Bangladesh is in weak position compared to other developing countries. So, in order to
reap the benefits of the overseas labour market, this paper emphasizes on imparting
training on which must be tailored to the skills required by the overseas employers.
References
Adams, Jr. Richard (2008), “The Demographic, Economic and Financial Determinants of
International Remittances in Developing Countries”. World Bank Policy Research Working
Paper 4583, Washington, DC: World Bank.
Albert Bollard, David McKenzie, Melanie Morten and Hillel Rapoport. (2009), “Remittances
and the Brain Drain Revisited: The Micro data Show that More Educated Migrants Remit
More”. World Bank Policy Research Working Paper 5113. Washington, DC: World Bank.
Bangladesh Bureau of Statistics (BBS): Household Income-Expenditure Survey-2010
Bureau of Manpower Employment and Training (BMET); www.bmet.org.bd
Durand, Jorge, William Kandel, Emilio Parrado and Douglas Massey. (1996).“International
Migration and Development in Mexican Communities”.
Faini, Riccardo. (2007). Remittances and the Brain Drain: Do more skilled migrants remit
more?” World Bank Economic Review 21(2)
Frederic Docquier, Hillel Rapoport, Sara Salomone (2011), “Remittances, Migrants’
Education and Immigration Policy: Theory and Evidence from Bilateral Data”.
International Labour Organization, ILO report April 2010): Study on the International
Demand for Semi-skilled and Skilled Bangladeshi Workers under Technical and
Vocational Education and Training (TVET) Systems Reform Program; Maxwell Stamp
Limited,(
International Standard Classification of occupations; ISCO-08/ International Labour office
– Geneva: ILO, 2012 (volume 1)
Mayumi Ozaki (2012), “Worker Migration and Remittances in South Asia”.
Nimmi, Yoko, Caglar Ozden, and Maurice Schiff (2008) “Remittances and the Brain Drain:
Skilled Migrants do remit less”, IZA Working Paper no. 3393
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-93-1
Siddiqui T. and C. Abrar. (2003). “Migrant Worker Remittances and Micro Finance in
Bangladesh”. Social Finance Program Working Paper.
World Bank (2008) Migration and Remittances Fact book 2008. World Bank, Washington
D.C.
Appendix
Annex-1(Test of Multicollinearity):
12.2. Annex-2 (Test of Heteroscedasticity; BPG test):