Determinants of Remittances for South Asian Countries: Gravity Model Approach

Abstract:

Impact of macroeconomic variables on remittances is estimated using a gravel model approach on a

panel of select South Asian countries (India, Pakistan, Bangladesh and Sri Lanka). Bilateral remittances

data from 27 host countries is used to estimate the macroeconomic determinants of remittances from

2010-2016. The study employs the micro foundation to macro variables and finds that apart from core

gravity variables, demographic and risk variables both in home and host country have significant impact

on remittances. Larger dependent population and exposure to natural disasters in the home country

attracts larger remittances whereas political stability reduces remittances to the home country. On the

contrary higher political risk in the host countries is associated with increase in remittances suggesting

that migrants tend to remit more to their home countries with rising risks in the host countries.

Keywords: Gravity model, remittances, South Asia.

JEL: C23; J61; O11

1. Introduction

Studies on remittances have garnered importance due to the sheer volume of funds transferred from

source to destination countries. Much of this flows from developed and industrialized nations to

developing and emerging ones. The total remittance inflows during 2015 were estimated at US$ 601

billion, of which US$ 440 billion (73 per cent of total remittance inflows) was directed towards

developing countries. Remittance inflows were three times more than official aid, higher than foreign

direct investment inflows (not considering China) and almost on par with other debt and equity

investments (foreign portfolio investments) (World Bank, 2016). As of 2017, remittances to developing

countries reached a staggering US$ 466 billion from US$ 426 billion in 2016. The highest remittance

receiving country was India (US$ 69 billion), followed by China (US$ 64 billion), Philippines (US$ 33

billion) and Mexico (U$ 31 billion), (World Bank, 2018). The top three regions include East Asia and

Pacific which received highest remittance inflows (US$ 130 billion), followed by South Asia (US$ 117

billion) and Latin America and Caribbean (US$ 80 billion) for 2017. It is interesting to note that while

both East Asia and Pacific and Latin America and Caribbean have 24 developing countries each, South

Asia has 8 countries characterized by high density of population experiencing a transition in its age

structure.

Other reasons for its growing importance include the nature of remittances, as they are observed to be a

stable source of external funds. Unlike other external flows which are influenced by interest rates, growth

prospects and financial stability in the recipient country, remittances have increased steadily resilient to

global economic disturbances. Given this nature, remittances positively contribute to output (IMF, 2005;

World Bank, 2006 and Chami et al., 2008), reduce poverty (Dilip Ratha, 2012, Yoshino, et al. 2017),

improve financial sector and reduce credit constraints on domestic investments (Aggarwal et al., 2006 and

Guiliano and Ruiz-Arranz, 2009). Apart from contributing to domestic sector, remittances influence

external sector through impact on exchange rates by appreciation of real exchange rate and the subsequent

impact on cost competitiveness detrimental to the trade balance of developing countries (Dutch-disease)

(Amuedo-Dorantes and Pozo, 2004, Acosta et al., 2007, Ratha, 2013 and Guha, 2013). Remittances also

contribute positively towards current account under the balance of payments by reducing the probability

of current account reversals (Buch and Kuckulenz, 2010) and by ensuring long run sustainability of

current account (Hassan and Holmes, 2016).

Given the macroeconomic impact of remittances, understanding the drivers of remittances may provide

key insights to design appropriate policies and strategies to better mobilize and utilize these unrequited

flows into the economy. This paper delves into estimating the macroeconomic determinants of

remittances for select South Asian countries (India, Bangladesh, Pakistan and Sri Lanka) using the micro

foundations for empirical analysis. The paper contributes to the existing literature on remittances in South

Asian region and uses the micro foundations to identify and estimate the macroeconomic determinants by

adopting a panel data analysis using bilateral remittances data and employing gravity model approach.

Secondly, it includes demographic factors such as skill and age structure variables to provide an

understanding on how changes in these variables may impact remittances. The policy changes in the

developed world with respect to increasing anti-immigration sentiments leading to tightening of

immigration policies by US, and European countries is seen as a major challenge for migrants. Also

labour market adjustment and preference for local labour in Gulf Cooperation Council (GCC) countries is

seen as new threat for aspiring South Asian emigrants to these countries. Given the fact that nearly 50 per

cent of the migrants from South Asia migrate towards GCC countries and 25 per cent towards North

America and Europe, the rising risk in these countries may pose significant impact on remittances. The

analysis in the paper is an extension of the work by McCracken et al. (2016) in the context of Latin

American and Caribbean countries by the inclusion of political risk in the host/source country and cost to

remit variable. Apart from capturing the risk in the home country, the paper attempts to analyse the

impact of growing political risk in the host countries as well. The paper is structured as follows. Section

2 presents some basic data followed by review of related literature in Section 3. Section 4 describes the

data and variables. Methodology is discussed in Section 5. Empirical results are analysed in section 6 and

Section 7 concludes the paper.

2. Basic Trends and Stylized Data

This section explores the magnitude and growth of remittance flows across the developing world, Table 1

presents the total remittance flows grouped by region and income from 2010 to 2017. Among the

developing countries, East Asia and Pacific (EAP) had the highest share of remittance flows. Nearly 28

per cent of the total flows to developing countries were routed to EAP with China, Philippines and

Vietnam receiving the highest remittances among EAP countries. The South Asian region (SAR)

accounted for 25 per cent of total remittances to the developing world with India receiving the highest

remittances, Pakistan and Bangladesh also being among the top ten recipient countries in the world.

India, Pakistan, Bangladesh and Sri Lanka together received 94 per cent of the remittances directed

towards South Asia (or US$ 109 billion) in 2017. Most of the regions showed a decline during 2015-2016

period due to sluggish economic activity in the developed world but the predicted valued for 2017 suggest

an upswing in remittances for all regions across the board, main reason being economic recovery and

higher investments in North America and Europe. Also firming up of oil prices which increases demand

for labour and subsequently wages in Gulf nations is considered as another factor for reversing the decline

of -0.9 per cent in 2015 and -2.5 per cent in 2016 to a considerable growth of 8.6 per cent for the

developing countries.

Table 1. Remittances Flows to Developing Countries, 2010-2017.

Regions 2010 2011 2012 2013 2014 2015 2016 2017#

(US$ billion)

Developing countries 333 373 392 404 444 440 429 466

East Asia and Pacific 95 107 107 112 121 126 123 130

Europe and Central Asia 32 38 39 43 52 41 40 48

Latin America and Caribbean 56 59 60 61 65 68 74 80

Middle East and North Africa 40 42 47 46 54 51 49 53

Sub-Saharan Africa 29 31 31 32 37 36 34 38

South Asia 82 96 108 111 116 118 110 117

Growth rate

Developing countries 10.3 12.0 5.1 3.1 9.9 -0.9 -2.5 8.6

East Asia and Pacific 20.2 12.6 0.0 4.7 8.0 4.1 -2.4 5.7

Europe and Central Asia -0.8 18.8 2.6 10.3 20.9 -21.2 -2.4 20.0

Latin America and Caribbean 1.1 5.4 1.7 1.7 6.6 4.6 8.8 8.1

Middle East and North Africa 18 5.0 11.9 -2.1 17.4 -5.6 -3.9 8.2

Sub-Saharan Africa 7 6.9 0.0 3.2 15.6 -2.7 -5.6 11.8

South Asia 9.4 17.1 12.5 2.8 4.5 1.7 -6.8 6.4

Source: Remittances and Migration Factbook, World Bank (Various Issues).

# Data for 2017 is predicted value.

Focusing on South Asian Region, SAR (Figure 1), there is a stark difference between the remittances

received by India and the other select South Asian countries (Bangladesh, Pakistan and Sri Lanka). The

second highest recipient was Pakistan which overtook Bangladesh in 2014 and as of 2017 it received

nearly US$ 20 billion. The remittance flows to Bangladesh were US$ 15 billion during 2014 and 2015

and have reduced to US$ 13 billion for 2017, whereas Sri Lanka has maintained stable remittances of

US$ 6 to 7 billion since 2014. Comparing the remittances as a share of GDP results an interesting picture,

among the four select SAR countries, Sri Lanka has the highest share of remittances to GDP which is

around 9 per cent since 2014, followed by Pakistan with 7 per cent. The share of remittances against GDP

for Bangladesh fell from a peak of 10 per cent in 2012 to 5.4 per cent in 2017. India also witnessed a

decline after 2013, from 3.8 per cent it declined to 2.8 per cent in 2017. Though, India received the

highest amount of remittances in absolute sense since 2010, but when compared as a share of GDP it

stands as the last among the select South Asian countries.

Figure 1. Remittances to Select South Asian countries, 2010-2017.

Source: World Bank, 2018.

Apart from having the second highest share in remittance flows, SAR also has India, Pakistan and

Bangladesh among the top ten migrant origin countries. As of 2017, it is estimated that India has nearly

16.4 million migrants, followed by Bangladesh with 7.8 million and Pakistan with 6.1 million. Thus, the

study of determinants of remittances for SAR countries will shed light on the specific factors that could

affect the flow of remittances to these select countries which have the highest inflow of remittances and

highest outflow of migrants in the world.

3. Review of Related Literature

The literature on determinants of remittances can be divided into theoretical and empirical. Theory

identifies three key aspects that determine timing and volume of remittances. Using remitters’ utility

function, two basic motivations that increase the utility of remitter by remitting to households in home

country are identified as altruism exchange (Johnson and Whitelaw, 1974 and Lucas and Stark, 1985) and

self-interest (Poirine, 1997; Ilahi and Jafarey, 1999), this is the first aspect and can be termed as

motivations to remit. The second aspect is with regard to intended use of remittances which could be for

risk-sharing (insurance) or smoothening inter-temporal path of consumption, saving and investment

(Hoddinott, 1994) or to pay for overhead costs (payment in lieu of services offered by family in home

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0

10

20

30

40

50

60

70

80

2010 2011 2012 2013 2014 2015 2016 2017

Pe

rce

nta

ge o

f G

DP

US$

bill

ion

Bangaldesh India Pakistan Sri Lanka

Bangaldesh India Pakistan Sri Lanka

country on behalf of remitter). Stark (1991), Aggarwal and Horowitz (2002), Gubert (2002) and Yang and

Choi (2007) find that family reduce their risk from income shocks by depending on remittances from

migrant family members (risk-sharing). The third aspect is end use of remittances which could be for

final consumption of goods and services, purchase of financial assets or real assets (including expenditure

on human capital, e.g. education, health care etc.), see Brown (1994), Adams (1998), Cox-Edwards and

Ureta (2003), Taylor et al. (2003).

Chami et al (2008) analysed that from micro-level survey studies aforementioned it was difficult to

identify and separate among the two motivations to remit (altruism and self-interest) by looking at the

intended use and end uses of remittances. Their analysis suggested that either of the two motivations or

both could be determining remittances for one or more of its intended uses (inter-temporal smoothening

of economic activities, insurance etc.) by utilizing it for multiple end uses by recipient family members.

Chami et al (2008) distinguish the motivations to remit by its purpose or economic outcome into two,

compensatory or opportunistic. If the purpose to remit is to compensate migrant’s family members then, it

will be used to insulate the family from adverse economic situations and emanating from altruistic

intentions. Whereas, if opportunistic tendencies dominate remittances then, it will be dependent on the

benefits remitter can receive from family members (self-interest exchange motive).

The empirical literature on remittances distinguishes between macroeconomic determinants by

categorizing them as home country and host country effects. Home country effects include economic

situation, institutional infrastructure (facilities to transfer), political stability, prevalence of disasters

(wars, droughts, floods etc.). Host country factors include economic growth, wage rates, employment

conditions, also differences in real returns, interest rates, exchange rates between home and host country

are other variables included in empirical macroeconomic studies. The earliest work on macroeconomic

variables analyzing their impact on remittances by Swamy (1981) included variables such as economic

activity of host/labour importing country (measured by nominal GDP), difference in interest rate on

deposits in home and host countries, differences in black market rate and official rate on foreign exchange

in home country, difference in real rate of return on assets in home country and deposit rate in host

country and number of females in migrant population in host country. The study found that GDP of host

country had significant positive impact on remittance inflows into home/labour exporting country. It

further analysed other macroeconomic determinants of outflow of remittances from Germany into

Yugoslavia, Greece and Turkey which included the rest of the macroeconomic variables to find that host

country factors (wages and number of migrant workers) had strongest positive impact on remittances

whereas, differences in interest rates, foreign currency exchange rates and real rate of return were

insignificant. Greater difference in interest rates and real rate of return between countries will impact the

remittances only if they are for investment purpose (opportunistic motive). Thus, the study suggested that

remittances primarily exhibit compensatory behaviour rather opportunistic behaviour. Some of the other

studies which state that remittances are affected largely by host country factors ascribing importance to

compensatory behaviour than opportunistic tendencies include Chami, Fullenkamp, and Jahjah (2003),

Chami et al. (2008), IMF (2005a), El-Sakka and Mcnabb (1999) and Vargas-Silva and Huang (2006).

Though most of the literature on analyzing determinants of remittances has been either microeconomic

approach with analysis based on survey data and second being macroeconomic approach using balance of

payments data. Studies like Docquier and Rapoport (2005) weave both the theoretical determinants of

remittances with macroeconomic effects, also Schiopu and Siegfried (2006) study which builds a

macroeconomic empirical model to capture the determinants of remittances using bilateral data for 21

host countries and 7 home countries using micro-foundations. Study by McCracken et al.(2016) merge

microeconomic foundation (motives to remit) with macroeconomic variables for 18 host countries and 27

Latin American Countries (home countries) and employ gravity model approach to analyse the

macroeconomic determinants of remittances.

The present paper builds on the same line of using micro-foundations to study macroeconomic variables

and uses the theoretical model and empirical approach adopted by McCracken et al. (2016) for analyzing

macroeconomic determinants for select South Asian countries (India, Pakistan, Bangladesh and Sri

Lanka) by estimating a gravity model using bilateral remittances data from 27 host/origin countries which

is the first key contribution to existing literature. The second addition is the inclusion of political risk

variable in the host country, given the adverse sentiments towards migrants in the developed world, the

paper incorporates political risk in the home and host country to analyse its impact on remittances. The

third is the inclusion of the cost to remit variable. The study by Singh (2009) in the Indian context lays

emphasis on transaction fee stating higher costs reduce remittances in the short run and this variable is

included in empirical model.

4. Methodology

4.1. Theoretical Model

The theoretical structure built by McCracken et al. (2016) is adopted to understand the explicit

relationship between micro foundations and macroeconomic variables. The theoretical framework of

McCracken et al (2016) is developed from the works of Schiopu and Siegfried (2006) and Docquier and

Rapoport (2006). The theoretical model explained in this section helps understand the behaviour of

remittances to changes in the microeconomic variables by linking the reasons to remit with

macroeconomic factors. The study by Chami et al. (2008) distinguished macroeconomic variables as

either opportunistic or compensatory depending upon the motivations to remit (altruistic or self-

interested) similarly this paper grounds the empirical analysis in theoretical model of McCracken et al

(2016) and extending the empirical analysis by incorporating new macroeconomic variables and using

them to study the determinants of remittances for select South Asian countries.

The theoretical framework is briefly explained as follows:

The migrant has migrated from home country j to host country i and there are two time periods, with per

period utility of the migrant described as ( ) ( ). The total utility of the migrant is equal to utility

from first period consumption ( ) plus second period consumption(

) of the migrant in the host

country and family’s consumption in the home country( ).

( ) (

) ( ) (1)

Where ∈ (0,1] is the discount factor, ( ) is the expected utility from the second period

consumption of the migrant in the host country and ∈ (0,1] is the degree of altruism. Thus, the income

of the migrant earned in host country i is which is spent on first period consumption of the migrant in

host country ( ), savings (S) and remittances to home country ( ).

(2)

Where is the cost to remit to home country and in order to send amount of remittances,

amount is spent by the migrant. The migrant’s family in the home country spends all the income which

includes earnings in home country ( ) and remittances ( ) for its first period consumption.

(3)

The next step understands the choice of asset allocation by the migrant between home and host country

which captures the self-interest motive. In the theoretical model developed by Schiopu and Siegfried

(2006) the returns of assets are assumed to be exogenously given and the migrant allocates the savings

between home country assets ( ) and ( ) in order to maximize the portfolio returns which is the

summation of returns from both the assets [( ) ( )

The extension to this was made by McCracken et al (2016) by distinguishing between home and host

country returns as risky and non-risky. Though the study by Schiopu and Siegfried (2006) adds costs to

investing in home country in form of monetary costs (fees and charges for making investments in home

country) and other risks but McCracken et al (2016) introduce probability associated with risky returns in

home country. They assume that savings are divided between home and host country assets where host

country has rate of return and is the amount of investment made. Home country assets are

categorized as risky with rate of return with a probability p and with probability (1-p) and

amount of investment is made. Only is the return from home country assets ( ) is greater than

host country assets will the migrant invest in both the assets otherwise the entire portfolio will be

allocated to safe host country assets.

After solving the migrant’s portfolio sub-allocation problem to arrive at the proportion of savings allotted

to and and the maximization of the utility function eq. (1), the comparative statics of McCracken et

al. (2016) can be summarized as:

REMij = ( ) + ( ) (4)

Remittances to home country are summation of which captures the altruistic nature or the

compensatory motive and which captures the self-interest motive or opportunistic behaviour of the

migrant.

REMij = f ( ) (+) (+/ -) (-) (+) (+) ( -/+)

The comparative static effects unambiguously determine the sign of migrant’s income in the host country,

rate of return in home and rate of return in host country. Higher income in the host results in higher

remittances to home country, the impact of higher rate of return in host country leads to reduced

remittances as the migrant will optimize the portfolio allocation by investing more in host country assets

leaving fewer saving for remittances. Whereas, higher rate of return in home country leads to greater

allocation in home country given that the probability of positive retuns is not zero hence, p has a positive

impact on remittances. Family’s income in the home country and cost to remit have ambiguous signs but

according to Schiopu and Siegfried (2006) they find and to have negative impact on remittances.

Higher incomes in the home country may reduce the desire of the migrant to remit, curbing the motive to

compensate the family as it already enjoys greater earnings and higher costs associated with transfer of

funds will deter the migrant from making remittances to home country.

4.2. Microeconomic Foundation to Macroeconomic Variables

The review of literature discussed in section 3 pertaining to macroeconomic variables is linked to the

theoretical motives of altruism and self-interested exchange in this section. The migrant income in the

host country and family income in the home country are captured by the GDP (El-Sakka and McNabb,

1999; Swamy, 1981). Chami et al. (2008) and IMF (2005) use the difference between the home and host

country GDP and per capita to capture the compensatory nature of remittances. Higher difference

between incomes of host and home country implies higher transfer of remittances. The return on assets in

home and host country are analysed through the interest rate differential (interest rates on deposits or

loans). Higher interest rates in home country leads to higher investments in home country assets (higher

transfers), study by Gupta (2006) included growth rates of stock market indicies for India and US (BSE

and NASDAQ respectively) to capture impact of asset returns. In order to incorporate cost of remitting

studies have used distance between home and host country as a proxy in case of bilateral studies (Lueth

and Ruiz-Arranz, 2008 and McCracken et al 2016). Higher distance which is a proxy to higher cost must

reduce remittances.

Apart from the core varaibles explained in the theoretical framework, macroeconomic studies have used

numerous other variables to capture the compensatory and opportunistic motive of the migrant. Though

these variables have been mentioned in section 3, their explicit relationship to the microeconomic motives

are explained in the following. Chami et al. (2008) find that exchange rate deprciation in the home

country leads to decline in remittances to GDP ratio highlighting the compensatory/altruistic behaviour of

trasfers as lesser remittances are required to maintain the same level of consumption of the family,

whereas if home country currency depreciation led to higher remittances would indicate towards self-

interest motive. Increased penetration of financial sector and financial deepening must reduce remittaces

if they are primarily for altruistic reasons, as increase in the availability of financial services (loans, short

term credit) must reduce the dependence on remittances to meet financial short-comings of the family, an

increase in remittances on account of financial deepening would indicate self-interest/ opportunitic

behaviour as it displays the desire of the migrant to benefit from the financial sector participation.

Other macroeconomic variables incorporated in the sudy by McCracken et al. (2016) with theoretical

underpinings include the difference between the skill levels at home and host country and size of family.

They argue that highly skilled migrants may not want to return to home country hence they may choose to

not remit. This captures the inheritance motive (subset of self-interest), the migrant does not wish to gain

any inheritance from the family and hence does not contribute to the creation of assets of the family.

Therefore, a greater difference between host and home country skill level may lead to lower remittances if

the migrant is motivated by the self-interest. The inheritance motive can also be captured through the

dependency ratios between home and host countries. A higher dependency ratio in the home country

implies that there is lower probability of claiming assets of the family in the home country and hence

lower transfer of remittances, however, if remittances have a positive sign when there is large difference

between the dependency ratios of home and host country it indicates altruistic behaviour, as the migrant

choooses to financially support the dependent members of the family. The risk sharing motive or

insurance motive is captured by the political risk, economic distress and other climatic disasters. A steady

flow of remittances during difficult times ensures that remittances are guided by altruism whereas if they

decline or recede it implies that they are motivated by opportunistic or self-interested tendencies.

4.3. Empirical Model

This section develops the empirical model to estimate the macroeconomic determinants of remittances

discussed in the previous section. The empirical model is specified as follows:

REMtij = β0 + β1 GDP

ti + β2GDP

tj + β3DPCGDP

tij + β4DISTij + β5DINT

tji + β6D LANGij + β7EXR

ti

+ β8EXRtj + β9CREDIT

ti + β10CREDIT

tj + β11DSKILL

tij + β12DDEP

tji + β13(DSKILL

tij* DDEP

tji)

+ β14COSTtj + β15DIASTER

tj + β16RISK

ti + β17RISK

tj + φ

t + εij (5)

Where REMtij is the bilateral remittances from host country i to home country j, the intercept term is

denoted by β0, φt captures the time effects and εij is the error term. The gravity model is apllied to

estimating the macroeconomic determinants of bilateral remittances for select South Asian countries

(India, Pakistan, Bangladesh and Sri Lanka). Among the eight nations that constitute the South Asian

region, these four countries make up more than 95 per cent of the remittances inflows into the region1.

Also, these countries have similar distribution of remittances across host countries i.e. Gulf countries have

the highest share in remittances towards these countries followed by North America and Europe.

The model incorporates the economic size of the home and host country and other gravity variables such

as distance and lanugage. McCracken et al. (2016) and Schiopu and Siegfried (2006) state that coefficient

for GDPti (proxy for ) must have a positive sign, DINT

tji must have a positive coefficient as well (as

reduces remittances to home country). DISTij and COSTtj which are distance variable, cost to

remit to home country must have negative sign.

Table 2 gives a summary of the microeconomic motive captured by the variable according to sign of its

coeffiecient.

Table 2. Expected Sign of Macroeconomic Variables.

1Other nations in the South Asian Region include Bhutan, Nepal, Maldives and Afghanistan. These nations are not considered

due to paucity of data on required macroeconomic variables.

Variable Measurement Expected sign

Host country GDP GDPti US$ constant 2010 (+)

Home country GDP GDPtj US$ constant 2010 (+)

Difference per capita

income

DPCGDPtij US$ constant 2010 Altruism (+)

Self-interest (-)

Distance DISTij Kilometers between the economic

centres of the two countries

(-)

Language LANGij Dummy, English speaking (+)

Difference interest rate DINTtji Real deposit interest rate Self-interest (+)

Host country exchange

rate

EXRti Real exchange rate ( per US$) Atruism (+)

Self-interest (-)

Home country exchange

rate

EXRtj Real exchange rate ( per US$) Atruism (-)

Self-interest (+)

Host country private

sector credit

CREDITti Domestic credit to private sector

(% of GDP)

Atruism (+)

Self-interest (-)

Home country private

sector credit

CREDITtj Domestic credit to private sector

(% of GDP)

Atruism (-)

Self-interest (+)

Difference skill DSKILLtij Gross enrolment in teriary

education

Atruism (+)

Self-interest (-)

Difference dependency

ratio

DDEPtji number of dependents (aged

under 15 and above 65) as a ratio

of working age population (aged

between 15 and 64)

Altruism (+)

Self-interest (-)

Interaction difference

skill and difference

dependency ratio

DSKILLtij*

DDEPtji

Altruism (-)

Cost to remit to home

country

COSTtji Average transaction cost of

sending remittances (%)

Atruism (+)

Self-interest (-)

Disaster in home

country

DIASTERtj Number of people affected natural

and man-made calamities

Atlruism (+)

Self-interest (-)

Political risk in host

country

RISKti Composite index comprising of 6

indicators (0 to 6 with 6 having

least risk)

Altruism (-)

Political risk in home

country

RISKtj Composite index comprising of 6

indicators (0 to 6 with 6 having

least risk)

Altruism (+)

Note: all the variables are expressed in the logrithmic form except dependency ratio.

4.4. Measurement of Bilateral Remittances

IMF (2009) study lists out the caveats in using remittances data highlighting that there is lack of

information of bilateral remittances between countries. Though the Balance of Payment data records

current transfers for a country but data on origin of remittances is far from accurate. It makes a mention of

the importance of migration corridors and stock of migrants in providing an estimate of bilateral

remittances. Ratha and Shaw (2007) develop three allocation rules to estimate bilateral remittances using

different weights. First, weights are allocated based on migrant stocks in host countries, second, weights

are based on migrant incomes which is proxied by migrant stocks multiplied by per capita income in host

countries and third, weights are based on migrants’ incomes in host country and home country incomes.

McCracken et al. (2016) estimate the bilateral remittances between 27 Latin American countries and 18

industrialised countries using the first method. They calculate bilateral remittances by multiplying the

total remittances to a country using the proportion of migrants in host countries.

∑

∑

(6)

Where is remittances from host country i to home country j and is the migrant stock from j to i.

Apart from using the stock of migrants the second approach uses the host country income. Bilateral

remittances are calculated as follows:

∑

∑

(7)

Where is the average per capita income of the host country and is multiplied to the migrant stock in the

host country from country j.

The third method makes another addition by way of including per capita incomes of both home and host

countries. The rationale behind the inclucion of income of home country being that a migrant relocates to

a another country in the expection of higher earnings as compared to the home country2.

( ) {

( )

Where is the average remittance sent by the migrant, is the avergae per capita income of host

country and is the average per capita income of the home country and β is a parameter between 0 and 1.

The bilateral remittances from country i to j are calculated as

2 A detailed explanation can be found in Ratha and Shaw (2009), South-South Migration and Remittances, World Bank Working

Paper No. 102, pg. 43-44.

∑ (8)

The paper uses the bilateral remittances estimated by the World Bank using Ratha and Shaw (2007)

methodology which uses the third method as data on remittances from host countries for the select South

Asian countries. Table 3 provides a comparison between the estimated bilateral remittances and the actual

remittances recorded by Pakistan from Saudi Arabia from 2010 to 2017. The trend is similar for

remittances to Pakistan from other countries as well.

Table 3. Remittances from Saudi Arabia to Pakistan, million US$, 2010-2017.

Year Estimated Data (World

Bank) Official Data

2010 2,215.5 2040.6

2011 2670.1 2596.8

2012 3687 2966.8

2013 4104.7 3848.9

2014 4729.4 4438.6

2015 5630.4 5690.4

2016 5968.3 5808.9

Source: World Bank (2018) and State Bank of Pakistan (2018).

5. Variables, Sample Structure and Sources of Data

Gravity model approach is used to estimate the determinants of bilateral remittances for a sample of four

South Asian countries namely, India, Pakistan, Bangladesh and Sri Lanka. The period of study is 2010-

2016. The host countries include Australia, Bahrain, Belgium, Canada, Denmark, France, Germany,

Ireland, Israel, Italy, Japan, Kuwait, Malaysia, Netherlands, New Zealand, Norway, Oman,Qatar, Saudi

Arabia, Singapore, Spain, Sweden, Switzerland, Thailand, United Arab Emirates, United Kingdom and

United States. India is included as a host country for other three countries as there is large inflow of

migrants from Pakistan, Bangladesh into India and Sri Lanka has considerable inflows of remittances

from India as well. The countries are further categorized on the basis of their geographical location as

Gulf, Euro, Asia and North America and included as dummies to ascertain the region which has the

highest impact on remittances flows to South Asia.

Table 4. Sources of Macroeconnomic Determinants of Bilateral remittances.

Variables Source

Bilateral remittances World Bank, 2017

Migration and Remittances

data

GDP and per capita GDP World Development

Real deposit interest rates Indicators

Real exchange rates

Gross enrolment (tertiary level)

Private sector credit

Dependency ratio

Remittance cost

Political risk Economist Intelligence Unit, 2017

Disaster World Disaster Report (various issues)

Distance CEPII, 2018

Table 5. Summary Statistics

Variable Total No.

of Obs. Mean Std. Dev Min Max

Host country GDP GDPti 776 27.15 1.39 23.97 30.46

Home country GDP GDPtj 777 26.22 1.23 24.76 28.53

Difference per capita income DPCGDPtij 762 3.11 0.86 0.26 4.75

Distance DISTij 777 8.58 0.57 6.53 9.55

Language LANGij 777

Difference interest rate DINTtji 649 1.63 1.17 -0.399 6.19

Difference inflation DINFLji 673 1.47 0.93 -0.88 5.1

Host country exchange rate EXRti 777 0.85 1.6 -1.29 4.88

Home country exchange rate EXRtj 777 4.44 0.31 3.82 4.98

Host country private sector

credit CREDIT

ti 717 4.58 0.46 3.53 5.27

Home country private sector

credit CREDIT

tj 777 3.52 0.432 2.73 3.96

Difference skill DSKILLtij 417 1.32 0.57 -0.79 2.39

Difference dependency ratio DDEPtji 777 9.85 13.26 -13.82 51.9

Interaction difference skill and

difference dependency ratio

DSKILLtij*

DDEPtji

412 9.28 15.97 -29.81 64.18

Cost to remit to home country COSTtji 449 3.71 0.48 1.64 4.96

Disaster in home country DIASTERtj 666 14.66 1.49 10.21 16.83

Political risk in host country RISKti 777 1.43 0.28 0.78 1.75

Political risk in home country RISKtj 777 0.79 0.43 0.38 2.6

6. Empirical Results

The section analyses the estimates of eq. (5) and discusses the motive to remit associated with the

macroeconomic variable thereafter. Table 5 presents the results from pooled regression and a comparison

is made with the Random Effects (REM) estimation technique.

Table 6. Macroeconomic Determinants of Remittances to South Asian Countries.

Pooled

(1)

Pooled

(2)

Pooled

(3)

REM

(1)

REM

(2)

REM

(3)

GDP host 0.43***

(9.01)

0.60***

(10.66)

0.50***

(4.43)

0.55***

(5.57)

0.78***

(5.92)

0.74***

(3.43)

GDP home 0.51***

(10.65)

0.81***

(6.4)

0.60**

(2.28)

0.44***

(4.24)

0.33***

(2.95)

0.04

(0.25)

DGDP pc -0.19**

(-2.11)

0.18

(1.32)

0.85***

(2.75)

-0.37*

(-1.72)

-0.48*

(-1.83)

-0.91***

(-2.61)

Distance -1.61***

(-9.87)

-0.79***

(-2.79)

-0.36

(-0.64)

-0.83*

(-1.79)

-0.92

(-1.26)

-1.72

(-1.44)

Language 1.01***

(7.10)

1.45***

(9.15)

1.71***

(7.15)

1.49***

(4.45)

1.28***

(3.78)

2.18***

(4.04)

Dint -0.25***

(-3.33)

-0.38**

(-2.30)

-0.037

(0.68)

0.03

(0.42)

0.03

(0.45)

Exr host -0.42***

(-12.57)

-0.44***

(-4.87)

-0.39***

(-5.93)

-0.15*

(-1.73)

0.52**

(2.34)

Exr home 2.45***

(4.00)

2.21

(1.51)

-0.16

(-0.52)

-0.69*

(-2.15)

-1.06**

(-2.35)

Credit host 0.84***

(4.17)

1.4***

(3.77)

-0.01

(-0.10)

0.19

(0.90)

0.14

(0.57)

Credit home 0.09

(0.28)

-1.04

(-1.46)

-0.06

(-0.34)

-0.14

(-0.79)

-0.23

(-0.66)

Dskill -0.96**

(-2.22)

-1.72*

(-1.95)

-0.002

(0.01)

-0.39*

(-1.72)

-0.64*

(-2.15)

DDep 0.06***

(3.92)

0.02

(0.50)

0.073***

(5.10)

0.04**

(2.29)

0.08***

(4.62)

Dskill*DDep -0.02

(-1.40)

-0.03

(-0.98)

-0.04***

(-4.86)

-0.02**

(-2.21)

-0.02***

(-2.87)

Cost 0.54*

(1.71)

-0.41

(-0.35)

Political risk host -2.96***

(-3.06)

-1.21**

(-1.97)

-2.02**

(-2.17)

Political risk

home

0.22

(1.00)

-0.07*

(-1.79)

-0.12***

(-2.73)

Disaster home 0.01

(0.10)

0.03

(1.54)

0.04**

(2.22)

Gulf --- ---

Euro -1.41**

(-2.07)

2.21

(0.11)

Asia -2.82***

(-3.39)

-1.4

(-1.04)

Oceania -0.64

(-0.66)

3.03*

(1.68)

North America -0.97

(0.372)

2.71

(1.38)

Intercept 7.67***

(3.85)

-28.5***

(-4.12) -17.51**

5.81

(0.11)

3.26

(0.46)

20.49*

(1.78)

No. of pairs 83 82 52

No of obs. 724 311 156 311 266 156

R sq 0.33 0.72 0.73

Time dummies Yes Yes Yes Yes Yes Yes

Breusch-Pagan

LM test

(chi sq)

223.79*** 99.64***

Hausman test

(chi sq) 15.89 25.25*

Source: Author’s estimation based on equation (5).

Note: * p<0.05; ** p<0.01; *** p<0.001

Heteroscedasticity robust standard errors are used to calculate t-statistic for Pooled OLS and z-statistic for Random

Effects shown in parenthesis.

The relationship between core gravity variables and remittances for South Asian countries (India,

Pakistan, Bangladesh and Sri Lanka) is estimated using Pooled OLS estimation and is presented in the

first column. All the variables are significant and have the excpected sign. The significant negative

coefficient of per capita differencial indicates a self interest motive i.e. with increase in host country

income compared to home country the remittances decline. Distance which is a proxy for cost to remit has

negative impact and commonality of language (which is also an indicator of ease of living for migrants)

between home and host country has positive impact. In the second specification (Pooled 2), the model is

extended by the including economic and demographic variables and in the thrid specification (Pooled 3)

risk variables are added. The LM test from random effects and the Hausan test (between Random effects

and fixed effects) indicated that Random effects estimation was prefered to pooled and fixed effects.

Analysing the coefficients of the REM specifications, among the economic variables, interest rate

differential becomes insignificant which suggests that an increase in home country interest rate as

compared to host country does not lead to increase in remittances implying that remittances are not

affected by higher interest rates in the home country supporting the claim that remittances are altruistic.

The host country’s exchange rates indicate investment or opportunistic motive in REM(1) and REM(2).

But when the cost to remit variable is included in REM(3) exchange rate in the host country shows

altruistic behaviour. The significance of a negative coefficient means that as host country exchange rate

depreciates (against US$) resulting in lesser dollars per host country currency this lowers remittances as a

smaller amount of dollars can be now purchased with given amount of host country currency. But when

the cost to remit in controlled for then it is found that even when host country exchange rates depreciates

(against US$) remittances do not decline. The negative significant coeffecient of home country exchange

rate depicts compenatory behaviour. With depreciation in the home country currency the remittances

decline as lesser remittances (in US$) need to be transferred to maintain the same level of cosumption or

spending. Increasing credit to the private sector in the host country has an altruistic impact of remittances,

increase in the credit to private sector in the host country positively impacts remittances indicating that

increase availability of funds in host country tends to enhance transfers to home country which when

connected to the negative coefficient of interest rate differential further strengthens the argument.

Among the demographic variables, it is found that skill differential between host and home has a

significant negative coefficient, higher the skill difference between host and home lower are the

remittances transferred. This points to the fact that migrants migrating to countries where population is

endowed with higher skills reduces the remittances as they may choose to not return to their home

country hence, less incentive to support the family in the home country. One of the other significant

demographic variable for consideration is dependency ratio. With South Asian countries experiencing a

shift in their age structure profiles, it is observed that with regards to remittances, a higher difference

between the dependency ratios of home and host country, the remittances tend to increase. Hence, higher

dependent population in the home country attracts more remittances. Thus, the large dependent

population in the South Asian countries are key contributors to the increased flow of remittances in the

Sub-Continent. In all the specifications except the Pooled (2), dependent population differential has a

positive and significant impact on remittances. Therefore, the altruistic motive to provide for dependent

family members in the home country exerts considerable influence on transfer flows. The interaction term

between skill and dependency ratio has a significant negative coefficient which indicates that persons

migrating to host countries whose population ie endowed with higher skilled and lower dependency ratios

exhibit self interest motive. Migrants from low skilled countries relocating to countries with low age

dependency ratios transfer lower remittances. According to McCracken et al. (2016) in attempt to increase

ones’ earnings the migrants move to host countries with relatively higher skills and lower dependent

population.

The inclusion of political risk and disaster risk variables in the REM (3) shows that political risk be it in

host or home country impacts remittances. Higher political risk in the host country increases the

remittances, as challenges to the livelihood or lives of migrants will result in them shifting home thereby

leading to transfer of larger portion of their earnings home. The more stable the host country more is the

desire to continue to stay in host country and thereby reducing remittances. A politically stable home

country also depicts a reduction in remittances. Thus, political risk in host country tends to influence

remittances through self interest whereas the home country political stability influences the remittances

through compensatory/altruism motive. South Asian countries are prone to floods, droughts, and other

natural disasters. The Disaster risk variable highlights the positive impact of these diasters on remittances.

Higher intensity of disasters and more number of people affected increase the transfers to home countries,

depicting the altruistic motive.

Previous macroeconomic studies have laid high emphasis on the compensatory aspect of remittances

(Chami et al. 2005) and host country variables (Swamy; 1981, Straubhaar; 1986, El-Sakka and Mcnabb;

1999 and others). This study finds that macroeconomic determinants in host and home country have both

self interest and compensatory influence on remittances. The importance of demographic variables in

influencing remittances have been highlighted by the analysis. South Asian countries which have high

dependent population as compared to host nations influences the remittances positively. The inclusion of

risk variables especially political risk in the host country depicts interesting pattern, i.e. with higher risk in

host country migrants’ remit more to home country with a possibility of returning to their families. Thus,

apart from income and other economic factors demographic and risk factors have a strong impact on

remittances.

7. Conclusions and Policy Implications

The paper uses the bilateral remittance data of the World Bank to analyse the impact of various host and

home country variables on remittances flows to South Asian countries from 27 host countries over the

period 2010-2016. Panel estimations highlight the role of both altruism and self interest motives in

determining the flow of remittances to South Asian region. Apart from core gravity variables such as

income, distance and language, various host and home country factors were included of which

demographic and risk variables have significant impact as compared to core economic variables.

The study highlights some key areas that can be looked into to enhance and support flow of remittances to

South Asian countries in general and India in specific as it has the largest flows of remittances in the

South Asian region (SAR) and globally as well. The distance which is proxy for cost of remitting has a

negative impact on remittances, including regional dummies indicates that Asia, Europe have negative

coefficients. This could highlight two possiblities first, as noted by World Bank (2018), remittances from

countries like Japan and other east Asian countries have the highest costs and second that the remittances

have reduced from Euro nations due to lower economic growth. In the first case, the remittances can be

enhanced by reducing the costs involved in remitting. Newer and efficient technologies can be employed

to make it feasible to remit home and also mainstream informal channels of remittances. Large amount of

remittance flows from Gulf region to South Asia which needs to be secured in the wake of nationalisation

wave sweeping the GCC countries. There is a need for the governments to actively engage in protecting

the employment of migrants in these countries. The political risk faced in host countries contribute

significantly to remittances. Higher risks faced my migrants, be it the anti-migrantion sentiments and

nationalism across advanced nations like UK and US may for a short span increase remittances but will

have adverse impact in the long run.

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