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Investment Expenditure Behavior of Remittance Receiving Households: An Analysis Using Reserve Bank of India Data

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Although it is the world's largest recipient of remittances, India lacks information about the investment behavior of its remittance receiving households. Using data from Reserve Bank of India and the Tobit analysis, this paper examines how remittances, different household and migrant characteristics have affected both the propensity to invest and the amount of investment by the remittance receiving households. The findings have significant implications for policy purposes. For example, government programs can create incentives for older migrants to have more remittance transfers. Remittance money used for children's education could be matched to create robust flow of educational investments.
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July 2018
Volume: 15, No: 3, pp. 303 320
ISSN: 1741-8984
e-ISSN: 1741-8992
www.migrationletters.com
Copyright @ 2018 MIGRATION LETTERS | Transnational Press London
Article History: Received: 26 April 2018 Accepted: 3 June 2018.
Investment Expenditure Behavior of
Remittance Receiving Households: An
Analysis Using Reserve Bank of India Data
Bharati Basu
Irudaya Rajan ¥
Abstract
Although it is the world’s largest recipient of remittances, India lacks information about the
investment behavior of its remittance receiving households. Using data from Reserve Bank of
India and the Tobit analysis, this paper examines how remittances, different household and
migrant characteristics have affected both the propensity to invest and the amount of investment
by the remittance receiving households. The findings have significant implications for policy
purposes. For example, government programs can create incentives for older migrants to have
more remittance transfers. Remittance money used for children’s education could be matched to
create robust flow of educational investments.
Keywords: Remittances, household investment expenditure; India.
Introduction
It has long been recognized that remittances affect the economic development
of remittance receiving countries (for an overview see Taylor and Martin, 2001,
Bhagwati, 2003; Birdsall et al., 2005) by working as substitutes for well-
functioning credit and capital market. They can promote investment, generate
financial or physical or human capital and thus, can affect both the total
household expenditure and the budget share of each item (used by the
household) in that expenditure.
There is evidence that remittances have increased student retention rate in El
Salvador ( Edwards and Ureta, 2003), investment in entrepreneurship in Mexico
(Woodruff and Zenteno, 2007), expenditure on agricultural investment in
China (Taylor, Rozelle and Brauw, 2003), expenditure on housing in Nigeria
(Osili, 2004), schooling and entrepreneurial activities in Philippines (Yang, 2006
& 2008), landholding in Pakistan, expenditure on housing and education in
Guatemala and expenditure on health, housing and education expenditure in
Ghana (Adams 1998, 2010, and 2013). Zachariah & Rajan, (2007a, 2007b) find
that remittance-based investment has taken over from remittance based
consumption, as the new driver for economic growth in India, Bangladesh and
Pakistan (see Combes and Ebeke, 2011; Quisumbing and Mcnien, 2010;
Bharati Basu, Central Michigan University, United States. E-mail: basu1b@cmich.edu.
¥ Irudaya Rajan, Centre for Development Studies, Kerala, India. E-mail: rajan@cds.ac.in.
304 Investment Expenditure Behavior of Remittance Receiving Households
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Mesnard, 2004; Chami et al., 2003; McCormick et al., 2001, Rosenzweig and
Stark, 1989).
However, it is surprising that there is no analysis about the expenditure pattern
of the remittance receiving households in the top remittance receiving countries
of the world like India ($70b in 2013) and China ($60b). This gap in the
literature begs lots of question when the government of the labor sending
countries like India engages in boosting the flow and use of the remittances to
facilitate the goal of an ambitious rate of growth for the economy.
1
The
objective of this study is to take advantage of a data set collected by Reserve
Bank of India (RBI) on “Private Remittances to India” to fill up this gap in the
literature. More specifically, we examine the factors that may have affected the
investment expenditure of remittance receiving households.
2
The survey we use
covers areas with high (more than 90%) concentration of inward remittances
and thus presents a representative snapshot of remittance transaction in India.
Both the importance of the topic and the timing of the analysis can’t be
overemphasized because after years of corruption, failed policy reforms,
bureaucratic red tapes, growth inhibiting infrastructure, government regulation
for setting up new industries, India’s present government is trying to increase
the pace of investment anyway possible (WSJ, May 26, 2015). Efforts are
underway to encourage higher inflow of remittances to the country and to boost
the investment expenditure of remittance receiving households.
Our data set collects information from those migrant households in India,
whose migrant member has opened up a Non-Resident Rupee Account
(NRRA).
3
It should be noted that the purpose of NRRA is to encourage capital
inflow in India. Since not all remittance receiving households may have engaged
in investment expenditure, investment expenditure would generate ‘zeros’ for
some households and continuous positive values for others. From the
alternatives available in dealing with this mixture of continuous and discrete
distributions, we have selected the Tobit analysis for estimation.
1
Wall Street Journal (September 25, 2015) reports “ Trooping across the globe, from Japan to China and
the U.S., the Indian prime minister has connected with the India-origin community, motivating its
members to reconnect with their motherland and contribute with investments, technology and 21st century
solutions to India’s stickiest problems”.
2
Remittances in this data set are the total values in Rupees of everything sent by the migrants to their
families back at home.
3
The RBI has used NRRA to contact the migrant household, since migrant member has an option to use
the household address as migrant’s address in India. Historically, these accounts have increased capital
inflow in India. The accounts are opened by migrants themselves and fees are waived for encouraging
deposits. Migrant owners of the accounts have full control of the accounts. The account holders don’t
necessarily remit and remitting migrants with NRRA have also sent remittances through some other means.
It is important to remember that not all migrant households of our sample have received remittances
through NRRA. see http://www.smartpaisa.in/2013/02/non-resident-indian-nri-different-type-bank-
accounts-nro-nre-fcnr.html
Basu, Rajan 305
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We find that in our sample, households have engaged in investment
expenditure when remittances are mostly from male migrants and when the
migrants are, on average, older. Larger households have higher budget share of
investment expenditure. Household asset holding has statistically significant
effect on investment expenditure. Ownership of bank account affects
investment expenditure negatively and migration duration doesn’t have any
effect on the investment expenditure in our sample. The findings for ownership
of bank account and migration duration contrast the findings of the existing
literature. For this sample, migrant’s income has a negative effect (hinting at
insurance motive for sending remittances) on investment expenditure.
Entrepreneurial jobs are the most preferred jobs for the migrants of this
sample. However, households with self-employed migrants and households
whose migrants have worked as Seamen have invested more in our sample
compared to the households whose migrants are entrepreneurs.
The rest of the paper is organized as follows. After presenting the conceptual
background and the estimation strategy in section 2, we provide the description
of data in section 3. Section 4 and 5 report the results and their robustness. We
conclude in section 6.
4
Conceptual Background and Estimation Strategy
We allow each household to maximize its utility from two goods subject to the
budget constraint that includes remittances along with other types of income.
5
From this utility maximization, we derive for each good a demand function
which depends on remittances along with other things.
6
We then re-write the
4
As regards the history of remittances in India, in 2010, India was the largest recipient (66%) of remittances
to the South Asian region which brought in $55 billion (IMF). Although there were a few ups and downs
(for example, a 12% fall in 1992, and another 8% fall during the Asian Financial Crisis of 1997-1998),
remittances to India grew at a fast pace during 1990 to 2010, reaching an average yearly growth rate of
about 18%. This growth included a 60% growth from 1995-1997 and another surge of almost 50% from
2001-2003 while India experienced a recession. Remittance receipts doubled from 2005 to 2008 preceding a
slight decline during the global recession of 2008. In 2010, India’s per capita remittances were about $47
surpassing only the per capita remittances received by Maldives (about $11) among all its South Asian
neighbors. In 2010, the share of remittances to GDP in India was 3.6%. Currently, this share has increased
to about 4% with about 70b remittances. India receives its international remittances primarily from
countries in the Middle East, and also from countries with which it shared a colonial history, such as the
United Arab Emirates, Kuwait, the United States, UK, Canada and Australia.
5
As explained in the previous section we are looking into all migrant households which means that each
household in our sample has at least one migrant. The reason for migration is to take advantage of
economic opportunity in the destination country or the earning gaps between the origin and destination.
This fits well into each household’s objective of utility maximization. See Lucas and Stark, 1985; and Stark,
1991.
6
Consider a household that maximizes its utility that depends on a vector of goods and leisure used
by the household. Then the household maximizes   (1) subject to the full income constraint
     (2) Here U denotes the utility; denotes amounts used of g goods; denotes the
prices of those goods; I     is the earned income of all the individuals i in the migrant
household that doesn’t count migrant income and R is the remittance receipt. Assume that the utility
function is separable in consumption and leisure and wage rates, time endowment and prices are
306 Investment Expenditure Behavior of Remittance Receiving Households
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demand functions in the budget share form (i.e., the expenditure share of each
good in the total household expenditure) to have our primary estimating
equation as         where is the share of
household investment expenditure in the total household expenditure, is
the effect of R (remittances) on and denotes a set of household and
migrant characteristics that affect the share of the investment expenditure in
the total household expenditure.
This enables us to see how different factors together with remittances have
affected the probability of investment and the amount of investment by the
remittance receiving households. We expect that remittances would have
positive impact on both of them. The Z vector includes some of the household
characteristics like family size, presence of young children, presence of
unemployed person (s) in the household, the age, gender and education of the
head of the household, household income and asset holdings and ownership of
bank accounts by the households. We expect the size of the household, the
presence of unemployment and young children to generate negative effects and
the age and education of the head of the household, ownership of bank
account, household income and asset holding to have positive impacts. We also
investigate how migrant characteristics like migrant education or income,
migration duration, migrant’s age and marital status and migrant occupation
affect the household investment behavior. Migrant’s education and income are
expected to have positive effects and migrant’s age and marital status may
generate negative effects.
Issue with Censored Observation
Since not all remittance receiving households in our sample have engaged in
investment undertakings, we observe zero investment for some households and
positive amount of investment for others which generate a mixture of discrete
and continuous distribution. The literature provides different techniques for
estimation with this type of data set. We have chosen the Tobit model as a
preferable way to estimate our equation.
7
exogenously given. Equation (1) and (2) can be used to obtain demand functions for each good as
follows: 
    (3).
7
One of the techniques suggests to first estimate the decision to invest with a Probit model and
subsequently estimate how much to invest using Ordinary Least Square (OLS) with a term (the inverse
Mills ratio of the probability calculated in the first stage) to correct the sample selection bias. However, this
approach requires us to use different sets of variables for two estimations (Probit and OLS). In other
words, there must be some identifying covariates to separate the Probit model from the OLS model.
Hoddinott (1992, 1994), Frankenhouser (1995) Cox et al. (1998) along with quite a few other scholars have
used this technique. However, it may be difficult to come up with identifying variables that would affect the
probability of investment without affecting the amount of investment. Furthermore, the results become
sensitive to the choice of identification exclusions. The alternative used by quite a few other researches
(Brown 1997, Ravallion and Dearden, 1988, and Basu and Bang, 2011 & 2014) is the Tobit model. The
Basu, Rajan 307
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Issue with Endogeneity of Remittances and Investment
A remittance receiving household’s decision to investment may be influenced
by the amount of remittances it receives or remitters may be tempted to remit
more to reward the investment propensity of the households. To deal with this
endogeneity issue, we need to have an instrument for remittances. The literature
suggests to choose a variable that would affect migrants’ decision about sending
remittances without having any effect on the decisions of remittance receiving
households.
Our data set has information for a variable (i.e., direct deposits to NRRA by
the migrant) that is expected to reduce the cost of sending remittances and thus
would affect remittance payments positively.
8
We have chosen this variable
instead of choosing wage or employment as our instrument.
9
The lower cost
for sending remittances boosts remittances like favorable wage or favorable
employment opportunity in the destination country. However, like wages and
employment in the destination country, this is not expected to affect the
investment behavior of the remittance receiving household
10
The first stage
results show that it has explained remittances well.
11
The test for endogeneity
is shown in section 5.
The dependent variable for the analysis of investment is the share of investment
expenditure in the total expenditure of the household. To start with, we
Tobit model allows to estimate the quantity invested together with the likelihood of investing using the
same covariates. The disadvantage is that the likelihood of investing and the amount invested would be
affected in a similar way (Wooldridge, 2013). However, it is difficult to find suitable identifiers that affect
the decision to invest without affecting the amount invested and the results are sensitive to the choice of
identifiers. So Tobit analysis seems preferable. In writing the Tobit model we use Y for and R and Z are
included in the X vector. The Tobit model we estimate is presented by
 
   with
 
and    .
is the latent investment,
represents the vector of exogenous variables presenting remittances,
household characteristics and a few migrant characteristics.
8
Irrespective of their remitting habits, all migrants (i.e., both remitting and non-remitting migrants) are
eligible for opening this account with a small cost or in most cases without any cost. Migrants who want to
remit through this account can then write a check (in Rs.) on this account and send that to their family.
Thus, migrants either have transferred money without any cost or transferred it with a significant reduction
in cost.
9
Usually the wages or earnings or employment rates in the host country or anything else that facilitates
remittance payments can be used as an instrument. However, the wage and employment data in our sample
are not suitable for instruments. For quite a few migrants this data set doesn’t mention the host country,
rather it shows which continent the migrant has moved to. For example, for some migrants we only know
whether a migrant has gone to Africa or Western Europe, etc., but we don’t know which country in Africa
or Western Europe he has moved into. Relevant table could be made available if requested. The destination
wage or employment of a migrant can’t thus serve as an appropriate instrument for our data set.
10
To make sure that NRRA held by the migrant has not impacted the investment behavior of the
remittance receiving household, we have looked into modes of remittance transfer; our sample shows that
remitting migrants even with the ownership of a NRRA have chosen different modes of remittance transfer
and sometimes these modes have excluded NRRA.
11
See A1 in the Appendix for the first stage regression and robustness checks for identification issue.
308 Investment Expenditure Behavior of Remittance Receiving Households
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measure investment expenditure as the sum of expenditures on health,
education, buying land, stocks, bonds, and other financial assets, and
investment in business. Since expenditure on health may be due to the health
shocks, we have checked results when health expenditure is excluded from total
investment expenditure and also when both health expenditure and education
expenditure are excluded.
Description of Data
The data we use come from a survey by Reserve Bank of India in 2009-2010
on private remittances to India for 2007-2008 and 2008-2009. Three thousands
households were randomly chosen from nine districts of India.
The objective of the survey is to identify the economic profile of the remittance
receiving households, the source, mode and usage of remittances. The survey
questionnaire has asked detailed questions about household composition,
demographic and socioeconomic information of each member of the
household and existing household assets and liabilities.
12
Table 1 gives the
descriptive statistics of these variables.
As we can see from Table 1, the households have received over 430,000 Rs.
over the year as remittances over half of which comes from the direct deposits
to NRRA.
13
The households on average have accumulated a decent amount of
net asset. More than two bank accounts are held by the households on average.
The average size of the households is not very big in our sample; the head of
the household is about 50 years of age and the migrants mostly are very close
to 40 years of age with roughly seven years of education on average. They are
also mostly male leaving the woman of the family to head the households. Not
many households have children of school age. The duration of migration is
about 10 years on average. Software and entrepreneurship are the two preferred
occupations for the migrants. The gender variable is a dummy with 1 for male
and 2 for female. Thus a value above 1 means that there are female heads of
the household and some of the migrants are female members. Occupations are
used as dummy variable and their average values show which occupations were
preferred.
12
Another useful category of information is the breakdown of total household expenditure, e.g., how much
is spent on food, or health or education or buying a piece of land or maintaining or starting a business. A
similar breakdown is available for usage of remittances although we could not use it because of a large
number of missing values.
13
We use remittances amount of 2008
Basu, Rajan 309
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Table 1. Descriptive Statistics for Variables Used
Variable
Mean
Std. Dev.
Amt. remitted
Rs.430246.300
439452.000
Direct deposits to NRRA
Rs.258376.2
322293.900
No. of bank account
2.426
1.715
No. of school-aged children
0.368
0.696
Net asset
Rs.5133633
6522450.000
Household size
3.304
1.379
Household Head's
education
5.290
1.484
Household Head's age
49.258
14.192
No. of unemployed
0.821
0.383
Migrant's age
37.217
9.205
Migrant's education
6.676
1.633
Migration duration
9.654
6.747
Immobile asset
1.272
0.470
Mobile asset
Rs.1530.009
76189.580
Migrant's marital status
0.868
0.338
Migrant's income
Rs.707520
779720.700
Occup. Software
0.040
0.195
Occup. Seaman
0.012
0.107
Occup. Entrep
0.161
0.367
Household Head's gender
1.462
0.499
Migrant's gender
1.080
0.271
Urban
0.553
0.497
Results
As mentioned in our estimation strategy, the objective of this research is to
examine how remittance receipts, household characteristics and migrant
characteristics have affected the household investment behavior. In examining
those effects we would also compare the overall role of household
characteristics with that of migrant characteristics.
We first present estimates from Ordinary Least Square (OLS) and then from
Two-stage Least Square (2SLS) technique to mitigate the problem of
endogeneity and finally show results from IVTobit to take care of both the
endogeneity issue and the problem associated with the mixed distribution of
the data. For each of these techniques we have used three specifications. First
we estimate with household characteristics only (HH); next we use
characteristics of individual migrants only (IND), and lastly we use both the
household and individual characteristics (HH and IND). To save space and
keep our focus, we present results with the last specification (i.e., with both
household and migrant characteristics) in Table 2.
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Column 2 of Table 2 presents results of OLS, column 3 and 4 present results
of 2SLS and IVTobit. The qualitative (sign) and quantitative (size) components
of the estimates are very similar for all three techniques and for most of the
variables, although the statistical significance vary in a few cases. However, it is
worth mentioning that for both OLS and 2SLS, there were anomalies for some
of the household characteristics and migrant characteristics when we have
switched to the third specification that uses all characteristics. These anomalies
disappear for the IVTobit estimation. Our main outcome variable namely the
effect of remittances show a strong positive impact on household investment
expenditure although it shows a small effect (less than a percentage point).
However, considering the amount of remittances received, it has important
implications.
It is reasonable to think that the net asset holdings of a household would affect
its investment behavior. Ownership of assets means that households are already
engaged in investment activities and most probably are aware of the importance
of investment. It should be noted that when we pay attention to both the
endogeneity issue and the selection problem, the effect of Net assets confirms
our expectation irrespective of which specification we use. The age and
education of the head of the household do not have any impact on the
household investment expenditure. Bigger size households and households
with more school children invest more (one and two percentage point increase
respectively) probably because they want to build a better future for their
children.
This favorable effect of household size holds when we interact household size
with net assets. Bigger households are expected to have more children. The
results for household size and the number of school aged children thus look
very consistent. Usually, bigger size households are expected to have high
consumption expenditure dampening the investment expenditure. However,
that is not the case for our sample. The effect of Household income is positive
and statistically significant.
14
Only two household characteristics, namely the presence of unemployed
member in the households and the number of bank accounts held by the
households have negative effects. The effect of the presence of unemployed
member is expected since that reduces household’s income and the ability to
invest. However, the result for the ownership of bank account contradicts some
findings in the literature.
14
We have estimated the equations with and without a quadratic term for the household income. The
square of Household income has a negative impact in Table 2. To save space we report the results without
the quadratic term from Table 3. Results using the quadratic term can be made available if requested.
Basu, Rajan 311
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This data set not only reports assets and liabilities of the households but it also
gives detailed breakdown of the movable and immovable assets. While movable
assets like auto-rickshaw, two or four wheeler give greater flexibility to take
advantage of new investment opportunities in a place different from the current
residence area, immovable assets like land or any other form of real estate may
be more valuable to build up investment capabilities (enough financial
resources that help to invest). In our analysis we see that movable assets are
effective in boosting investments (increases investment by almost seven
percentage point); however, that is not the case with the immovable assets. Net
assets defined as the assets net of liabilities show statistically significant positive
effect (only .5% of our households had negative net asset).
Focusing on migrant’s characteristics only, we see that all the variables have
statistically significant effect except migrant’s education level, migrant duration
and two types of occupation. Usually, migrant’s income is expected to be higher
if migrant has a higher level of education. Our data suggest that about 70% of
our migrants have at least a high school degree. However, migrant’s income has
negative effects in our analysis. Both higher level of education and higher
income increase migrant’s ability to support himself and migrant as a result
become less dependent on his family back at home and does not feel like
sending remittance as a premium for insurance from the family in case of a
future turmoil. This is a plausible explanation of the negative effect of income
and it hints to the insurance motive for sending remittances and not the
altruistic motive (Gubert, 2003).
Migrants are expected to send less remittance when they have their spouses and
children with them because they may not have close ties with their family back
at home in this situation and sometimes they can’t afford to send remittances
after providing for their spouses and children. This supports the negative effect
of marital status in Table 2. However it is significant only at 10% level. The
literature on remittances has explained both positive and negative effect of
migration duration (Dustmann et al., 2002). Usually, longer the migrant
duration, one may expect more household investment expenditure. It suggests
that more acclimation to the host country may imply more secured feeling by
the migrant to help families back at home and that may boost household
investment; however it is not statistically significant when we try both
household and individual characteristics.
Information about the occupation of the migrants has helped us to show
whether the occupation of migrant has shaped the investment sentiment of the
migrant household. Two occupations that migrants like most in this sample are
entrepreneurship and jobs related to software. Following the data, we have
grouped the occupations into five categories (each presented by a dummy) and
used entrepreneurial jobs as the base occupation. It should be noted that the
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Table 2. Comparison of Estimates of Household Investment Expenditure
Variable
OLS
2SLS
Tobit
Amount remitted
0.001***
0.001***
0.001 ***
(0.000)
(0.000)
(0.000)
No. of bank account
-0.019***
-0.025***
-0.025 ***
(0.002)
(0.002)
(0.002)
No. of school-aged children
0.019***
0.017***
0.017 ***
(0.004)
(0.004)
(0.004)
Net asset
0.001 ***
0.001 **
0.001 ***
(0.000)
(0.000)
(0.000)
Household size
0.007***
0.007***
0.007***
(0.002)
(0.002)
(0.002)
Household Head's education
0.001
-0.002
-0.002
(0.002)
(0.002)
(0.002)
Household Head's age
0.001 **
0.002
0.002
(0.000)
(0.000)
(0.000)
No. of unemployed
-0.019***
-0.021***
-0.021 ***
(0.006)
(0.007)
(0.007)
Net asset * Household size
0.001 *
0.001**
0.001 **
(0.000)
(0.000)
(0.000)
Immobile asset
0.041**
0.033
0.032
(0.020)
(0.020)
(0.02)
Mobile asset
0.054 **
0.065***
0.065***
(0.023)
(0.023)
(0.023)
Household Head's gender
0.007
0.010
0.01
(0.006)
(0.007)
(0.007)
Urban
0.002
0.002*
0.002
(0.005)
(0.005)
(0.005)
Household Income
0.001***
0.001***
0.001***
(0.000)
(0.000)
(0.000)
Square of Household Income
-0.001***
-0.001***
-0.001***
(0.000)
(0.000)
(0.000)
Migrant's age
0.002 ***
0.002***
0.002 ***
(0.000)
(0.000)
(0.000)
Migrant's education
0.003
0.001
0.001
(0.002)
(0.002)
(0.002)
Migration duration
0.001
0.001
0.002
(0.000)
(0.001)
(0.001)
Migrant's marital status
-0.011
-0.013*
-0.013*
(0.008)
(0.008)
(0.008)
Migrant's income
-0.001 ***
-0.001 ***
-0.001 ***
(0.000)
(0.000)
(0.000)
Migrant's Occu. Software
-0.002
0.010
0.008
(0.014)
(0.014)
(0.015)
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Table 2. Continued.
Migrant Occu. Farmer
-0.063
-0.046
-0.046
(0.163)
(0.165)
(0.166)
Migrant Occu. Seaman
0.101***
0.106***
0.106***
(0.024)
(0.025)
(0.025)
Migrant Occu. Selfemp
0.056***
0.059***
0.059***
(0.10)
(0.01)
(0.01)
Migrant's Occu. other
-0.030 ***
-0.027***
-0.027***
(0.007)
(0.008)
(0.008)
Migrant's gender
0.024 ***
0.030***
0.030***
(0.009)
(0.009)
(0.009)
R-squared
0.1629
0.1362
Wald chi2
693.02
687.36
Prob>chi2
0
0.000
Log likelihood
-65849.3
Number of observations
4795
4792
4792
Notes: Dependent variable is the share of the household’s investment expenditure in
total expenditure. All monetary variables are in Rupees. Estimating equation is given by
(1). Robust standard errors are in parentheses. Significance: *** p<0.01; ** p<0.05; *
p<0.1.
majority of these migrants has moved to the oil rich countries of the gulf area.
Although employment in software industry or as entrepreneurs are two major
occupational categories for this data set (in the sense that the majority of the
migrants are employed in these occupations), the US has attracted a majority of
entrepreneurs. Thus, for our sample, we see that self-employed migrants and
migrants who worked as Seamen have consistently affected the investment
behavior more positively than any other occupation. Occupations related to
software industry don’t have much effect on investment behavior when
compared with entrepreneurship.
The coefficients of column 4 in Table 2 presenting the results from IVTobit
estimation measure the partial effects of changes in our independent variables
on the expected value of the latent variable
. However, the variable we would
like to understand better is the observed investment (
). In addition, our
objective is to investigate how remittances, other household characteristics and
migrant characteristics affect the likelihood of investment and the amount of
investment. Therefore, Table 3 presents the effect of our independent variables
on the probability of investing (column 3) or
  ) as well as
the information on the sensitivity of volume of investment to changes in the
explanatory variables i.e.,

  (in column 4). For
convenience of the readers, we also present column 4 of table 2 (effect on the
latent variable) as our column 2 in Table 3. We see that all the results from the
last column of Table 2 (where we combined both household and individual
314 Investment Expenditure Behavior of Remittance Receiving Households
www.migrationletters.com
characteristics) hold good except that immobile assets like the mobile assets
affect both the probability of investment and the amount of investment
positively.
15
Robustness of Results
Although our first stage regression shows that our instrument explains the
endogenous variable well, in order to see whether there could be additional
issues involving endogeneity, we have tested for endogeneity with the null
hypothesis that variables are exogenous. The Durbin (score) with (1) is
17.67 (pvalue = 0) and the Wu-Hausman F(1, 4854) = 17.68 (pvalue =0). The
F (1, 4854) statistics for the first stage regression (1798.24) exceeds the critical
value of 16.38 (for nominal 5% Wald test) for the null hypothesis that
instrument is weak. Sargan statistics shows that the equation is exactly
identified. In addition, while using Sargan statistics we also have checked
whether the instrument “directnr” or “migrant’s direct deposits to NRRA” is
endogenous to itself. The results shows a (1) with p-value as zero.
It is expected that the remittance behavior of those who have used NRRA as a
mode of remittance transaction could be different from the remittance behavior
of those who haven’t used NRRA as a mode of remittance transaction. To look
into it, we have divided the sample into these two groups and used OLS to
check the behavior of the variable “Amount remitted” for each group
separately.
16
The effect of remittance receipts remain the same irrespective of
these characteristics.
Investment expenditure in our analysis includes expenditure on health,
education, buying land, stocks, bonds, and other financial assets, and
investment in business. The data set does not say anything about whether the
health expenditure includes expenses for health shocks. Since expenses
covering health shocks may have affected the result, we have checked Tobit
estimates after excluding health expenditure from investment expenditure. We
have also checked estimates when expenditure on both health and education
are excluded. All results hold good.
Discussion and Conclusion
Economic reason for migration is the income difference between the labor
sending and the labor receiving countries. The labor sending countries usually
15
The household expenditure is influenced not just by the amount of household income but also by the
position or location of that income in the entire income distribution chart. We therefore have estimated the
effects by income quartiles and remittance quartiles. Results show that effects vary considerably.
16
We could not use IVTobit or 2SLS for this comparison because the variable, directnr, is not available for
those who didn’t use NRRA for remittance transfer. Note that all migrants whether they remit or not, can
open NRRA. Even when they have access to NRRA they don’t have to remit through NRRA.
Basu, Rajan 315
Copyright @ 2018 MIGRATION LETTERS | Transnational Press London
try to close the gap by promoting higher rate of growth through higher level of
investment. In the absence of properly functioning capital and credit market,
remittances can be a suitable vehicle to increase investment. To encourage
investment performance of the remittance receiving households, the policy
makers need to know which factors can impact the investment behavior of
remittance receiving households.
Table 3. Tobit Estimates of Household Investment Expenditure with Partial
Effects
Variable
Coefficient
Partial effect
on the prob.
of inv.
Partial effect on
the expected
amount of inv.
Amount remitted
0.001***
0.001
0.001
(0.000)
(0.000)
(0.000)
No. of bank account
-0.024***
-0.009***
-0.016***
(0.002)
(0.001)
(0.002)
No. of school-aged children
0.014***
0.008 ***
0.015 ***
(0.004)
(0.002)
(0.003)
Net asset
0.001**
0.001 ***
0.001 ***
(0.000)
(0.001)
(0.000)
Household size
0.009 ***
0.004 ***
0.007 ***
(0.002)
(0.001)
(0.002)
Household Head's education
-0.002
-0.001
-0.002
(0.002)
(0.001)
(0.002)
Household Head's age
0.002
0.001
0.002
(0.000)
(0.000)
(0.000)
No. of unemployed
-0.025***
-0.010***
-0.019 ***
(0.007)
(0.003)
(0.006)
Net asset * Household size
0.001 ***
0.001 ***
0.001 ***
(0.000)
(0.000)
(0.000)
Immobile asset
0.037*
0.020**
0.037 **
(0.020)
(0.009)
(0.017)
Mobile asset
0.063***
0.025**
0.045 * *
(0.023)
(0.011)
(0.019)
Household Head's gender
-0.002
-0.001
-0.002
(0.007)
(0.003)
(0.005)
Urban
0.002
0.001
0.001
(0.005)
(0.002)
(0.004)
Household Income
0.001***
0.001***
0.001***
(0.000)
(0.000)
(0.000)
Migrant's age
0.002 ***
0.002 ***
0.002 ***
(0.000)
(0.000)
(0.000)
Migrant's education
0.002
0.002*
0.003*
(0.002)
(0.001)
(0.002)
316 Investment Expenditure Behavior of Remittance Receiving Households
www.migrationletters.com
Table 3. Continued.
Variable
Coefficient
Partial effect
on the prob.
of inv.
Partial effect on
the expected
amount of inv.
Migration duration
0.002
0.002
0.002
(0.000)
(0.000)
(0.000)
Migrant's marital status
-0.013
-0.005
-0.009
(0.008)
(0.004)
(0.006)
Migrant's income
-0.001 ***
-0.001
-0.001 ***
(0.000)
(0.000)
(0.000)
Migrant's Occu. Software
0.010
-0.002
-0.002
(0.015)
(0.007)
(0.012)
Mirgrant's Occu. Farmer
-0.041
-0.027
-0.049
(0.168)
(0.076)
(0.14)
Migrant Occu. Seaman
0.088***
0.042 ***
0.076***
(0.025)
(0.011)
(0.021)
Migrant Occu. Selfemp
0.053***
0.024***
0.044 ***
(0.010)
(0.005)
(0.008)
Migrant Occu. other
-0.034***
-0.016 ***
-0.030 ***
(0.008)
(0.004)
(0.006)
Migrant's gender
0.035***
0.013 ***
0.024 ***
(0.009)
(0.004)
(0.008)
Wald chi2
626.03
Prob>chi2
0.000
Log likelihood
-66006.5
Number of observations
4792
4792
4763
Notes: Dependent variable is the share of the household’s investment expenditure in
total expenditure. All monetary variables are in Rupees. Estimating equation is given by
(1). Robust standard errors are in parentheses. Significance: *** p<0.01; ** p<0.05; *
p<0.1.
This paper provides some insights for that purpose. It is especially useful for a
country like India which is one of the largest recipient of international
remittances and also for which no study has been done so far either for the
investment use of remittances or for the investment expenditure behavior of
remittance receiving households. This paper shows that the size of the
remittance receiving households, number of young children in those
households, net asset holding of the household and asset types, education, age
and marital status of the migrants sending the remittances are the factors which
can impact investments.
For example, government programs can create incentives for older migrants to
have more remittance transfers. Remittance money used for children’s
education could be matched to create robust flow of educational investments.
Since asset holding encourages more asset building, the government can come
Basu, Rajan 317
Copyright @ 2018 MIGRATION LETTERS | Transnational Press London
forward with onetime interest rate subsidy for borrowing money or onetime
lump sum subsidy for down payment used to build up asset. Furthermore, a
single policy may not be effective for all households since effects vary by
income quartiles and remittance quartiles. In implementing policies for
boosting remittance flow and investment expenditure of remittance receiving
households, policy makers should take that into consideration. The findings not
only provide directions for the policy makers but it gives suggestions for the
researchers as well to decide how to move forward to provide more insights
into the analysis of how remittances can affect the investment expenditure of
remittance receiving households.
More insights we have about our migrants and migrant households, the better
off the country would be in providing training for them and in planning for
appropriate assimilation of the migrants when they return. The government can
also engage directly in building up migration corridors to have favorable job
contract for migrants, to reduce cost of migration and to boost the flow of
foreign funds in the form or remittances. In addition, the limitation of this study
is the relatively small size of the data set. Future research should take care of all
the relevant issues with a bigger dataset.
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320 Investment Expenditure Behavior of Remittance Receiving Households
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Appendix
Table A1. First Stage Estimates for Investment Expenditure of Remittance Receiving
Households
Variable
HH + Ind
Direct deposits to NRRA
0.726***
(0.037)
No. of bank account
56128.79***
(3759.249)
No. of school-aged children
35377.49**
(14230.7)
Net asset
0.01***
(0.003)
Household size
-8001.616*
(4099.816)
Household Head's education
6028.909
(4099.731)
Household Head's age
223.296
(461.768)
No. of unemployed
30051.430***
(10391.16)
Net asset * Household size
-0.002***
(0.000)
Immobile asset
72713.230***
(13552.28)
Mobile asset
-95068.990**
(46356.95)
Household Head's gender
777.261
(22962.88)
Urban
943.067
(9587.605)
Migrant's age
767.547
(1049.713)
Migrant's education
21174.230***
(4811.709)
Migration duration
-909.0192
(916.039)
Migrant's marital status
19192.890**
(8870.245)
Migrant's income
0.075***
(0.021)
Migrant's Occu. Software
-110690***
(21688.85)
Migrant's Occu. Seaman
-9219.91
(61610.56)
Migrant's Occu. Selfemp
-24903.74*
(12873.61)
Migrant's Occu. Other
-13144.42
(12313.5)
Migrant's gender
-65340.48***
(13322.32)
R-Squared
0.35
Number of Observations
4791
... Then, in Turkey, there are some factors that influence saving behavior carried out by the family including age, sex, educational level, and the numbers of the family. Likewise, Basu and Rajan [19] shows that the investment spending, households, and individual characteristics influence investment behavior, where investments made mostly by former migrant workers are educational investments. However, Kristanti et al. [20] conducted a research that is contrary to this research. ...
... From the estimation results, it is known that the marital status variable has a significance value of 0.011 which means that it influences the investment decision of the ex-migrant workers. Basu and Rajan [19] say that marital status also influences the decision to invest. From the interpretation of the direction of the coefficient, it is obtained negative results, which means that ex-migrant workers with marital status have a tendency not to invest, while ex-migrant workers with unmarried status tend to invest. ...
... Dustmann and Kirchkamp [2] assert that education influences ex-migrants to engage in entrepreneurial activities. Basu and Rajan [19] also mention that the education influences the investment of migrants' families. Meanwhile, the coefficient shows a negative sign. ...
... The main issue of remittances regarding development is that the majority of remittances are spent on consumption, and minimal share is invested (Basu & Rajan, 2018). It should be noted that recently the spending on education and health of remittance-receiving households is considered as investment in human capital as in the long run they contribute to the capacity development of labor force in the country. ...
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