who remits? an examination of emigration by education level and gender

Who Remits? An Examination ofEmigration by Education Level and

GenderArusha Cooray1,2

1School of Economics, University of Wollongong, Wollongong, NSW, Australia and 2Centre for Applied

Macroeconomic Analysis, Australian National University, Canberra, ACT, Australia

1. INTRODUCTION

CURRENTLY, more than 215 million people live outside their country of birth (World

Bank, 2011). The stock of out-migrants for the world stands at 215.8 million or 3.2 per

cent (Ratha et al., 2011).1 Examining the educational attainment of emigrants, in total there

are 43.6 per cent with primary education, 35 per cent with secondary education and 21.5

per cent with tertiary qualifications (Dumont et al., 2010). The number of emigrants with

primary education exceeds the number with secondary and tertiary education in absolute

terms. A disaggregation of migrants by gender indicates that on average, 51 per cent of

migrants are female. The percentage is slightly lower at 49 per cent for non-OECD coun-

tries (Dumont et al., 2010). The benefits of migration accrued in the form of remittances,

comprises a large source of external funding to nations, particularly developing nations, with

receipts having increased phenomenally over the 2000 to 2010 period. The top five remit-

tance receiving countries in 2010 (in absolute terms) were India ($55.0 bn), China

($51.0 bn), Mexico ($22.6 bn), Philippines ($21.3 bn) and France ($15.9 bn). The top five

remittance receiving countries in 2009 as a percentage of GDP were Tajikistan (35.1 per

cent), Tonga (27.7 per cent), Lesotho (24.8 per cent), Moldova (23.1 per cent) and Nepal

(22.9 per cent) (Ratha et al., 2011).

Neoclassical migration theory views emigrants as individual, rational players who decide

to move on the basis of a cost–benefit calculation. This theory perceives migration as

leading to an optimal allocation of resources through which wages are equalised across

countries with the movement of labour from surplus to scarce countries. Structuralists cri-

tique the neoclassical theory stating that individuals do not have a free choice to move as

they are fundamentally constrained by structural forces or alternatively and are forced to

move due to economic and political reasons (de Haas, 2007). Dependency theorists argue

that migration is not necessarily an overall beneficial process as it leads to an extraction

of labour from the periphery to core deepening the vicious cycle of poverty in the periph-

ery and accelerating growth of the core. There are, however, a number of push and pull

forces brought about by demographic change, globalisation, political conflict and climate

I wish to thank an anonymous referee for valuable comments.

1 Note that the Ratha et al. (2011) estimates suffer from several shortcomings. They are based on esti-mations and interpolations. The bilateral migration data are generated by applying weights based onbilateral migrant stocks from population censuses of individual countries. Where the data appear incom-plete or inconsistent, secondary sources have been used (See Ratha and Shaw, 2007 for greater detail).

© 2014 John Wiley & Sons Ltd 1441

The World Economy (2014)doi: 10.1111/twec.12154

The World Economy

change that have increased migration pressures both within and across borders (de Haas,

2007).2

Among the push factors for labour migration are poverty, unemployment, inequality and

political conflict. The pull factors include the attraction of higher wages, better living condi-

tions and networks. Jenkins (1977) in a study of the migration of Mexican agricultural work-

ers to the United States (US) observes that a strong push is exerted by labour market

conditions in rural Mexico and a pull by US agricultural wages. Individuals react to oscilla-

tions in the business cycle. Therefore, they are most likely to emigrate when economic condi-

tions at home decline and are less likely to do so when economic conditions improve

(Jenkins, 1977). Similar views are expressed by Hare (1999). In a study of the emigration of

China’s rural population, Hare (1999) argues that migration occurs due to higher wages in

host regions compared to those in source regions. Economic opportunities such as better liv-

ing conditions, better access to basic services and improvements in socioeconomic status in

host nations are among the many pull factors, which may encourage migration (Chowdhury

et al., 2012). Another pull factor is the increased security provided by networks (Massey and

Basem, 1992; Curran and Rivero-Fuentes, 2003; Roberts and Morris, 2003). Networks reduce

migrant risk and increase migrant employment opportunities. The strength of migration net-

works is highlighted in the work of Stark and Jakubek (2013) who show that networks act as

an informal financial cooperation scheme that spans over time and space. Push factors include

unemployment, underemployment and a mismatch between individual skills and job opportu-

nities available at home (Quinn and Rubb, 2005). Additionally, political, ethnic and religious

conflicts and increasing inequality have created an environment in which current and potential

future migration flows have become increasingly more volatile. When an individual migrates,

implicit is the assumption that the migrant will remit part of his/her income back home.

Remittances into the developing economics have been found to have a number of positive

effects at both the macro and microeconomic level. For example, they have served as insur-

ance policies against risks associated with new production activities (Taylor, 1999), helped

low-income households to smoothen their consumption by reducing vulnerability to adverse

domestic shocks (Yang and Choi, 2007), increased the propensity to save (Adams, 2002) and

helped to reduce poverty (Adams and Page, 2005). Remittances have also been found to

reduce income inequality (Taylor, 1999), promote economic growth (Mundaca, 2009), pro-

mote financial sector development (Aggarwal et al., 2006; Giuliano and Ruiz-Arranz, 2009)

and reduce output volatility (Chami et al., 2009). Studies show that females remit more than

their male counterparts. Vanwey (2004) in a study of migrants from Nang Rong in Thailand

finds that male and female migrants behave both altruistically and contractually, but that

females from low-income households behave more altruistically than males from high-income

households. Similarly, Phongpaichit (1993) notes that females remit more both overall and as

a percentage of their income compared to males.

The significance of out-migration and remittances for nations give rise to a number of

questions. How do remittances contribute to the gross domestic product (GDP) of economies?

How does the education level of the emigrant influence remittances? How does the gender of

the emigrant influence remittances? Answers to these questions are of utmost importance for

2 See de Haas (2007) for a survey of the literature. Much of the theoretical work on remittances hasbeen devoted to the primary motive of migrants to remit. Among the motives put forward are altruism(Banerjee, 1984), insurance (Rosenzweig, 1988), investment (Lucas and Stark, 1985), inheritance(Hoddinott, 1994), risk diversification (Stark and Lehvari, 1982).

© 2014 John Wiley & Sons Ltd

1442 A. COORAY

identifying the role of emigration in the growth trajectory of the countries under study and for

implementing appropriate policies for emigrants and their families.

The education level of the migrant can have important implications for host and home

nations. There is evidence of both positive and negative effects on host and home countries

due to the emigration of tertiary qualified individuals. The benefits of skilled migrants to host

countries in addition to augmenting the stock of human capital include increasing skill diver-

sity of the workforce and making available trained workers to fill vacant jobs. Migration can

thus boost innovation and productivity growth, by reducing the costs of providing labour-

intensive services (Martin, 2003). From the point of view of source countries, there is a brain

drain resulting in a fall in positive externalities created by skilled workers. If, however,

migrants return, then a ‘brain drain’ in the short term could become a ‘brain gain’ in the

longer term (Martin, 2003). This is because migrants return with new skills acquired abroad,

links to scientific and business networks and higher incomes (Beine et al., 2008). Beine et al.

(2008) and Mountford (1997) argue that the return to education is higher in receiving coun-

tries, and hence, the prospect of migration could encourage greater investment in education in

the source country.

Low-skilled migrants are usually concentrated in lower status jobs that natives do not wish

to engage in such as fruit harvesting, manual jobs in manufacturing and construction (Ahn,

2004). Therefore, there is a pull for migrants from poor nations by fast growing developing

nations for jobs that natives do not wish to undertake. For example, there is a demand from

Malaysia for domestic workers from countries such as Bangladesh and Sri Lanka. Evidence

shows that some sectors in host societies may not have survived without migrants. The Snowy

Mountains Hydro-Electric Scheme in Australia, for example, was largely dependent on the

work of migrants – seventy per cent of all workers were migrants (Australian Government,

2008). Another important contribution to host societies by migrants is their access to transna-

tional resources provided by other migrants and nationals living abroad. Studies also show

that low-skilled migrants remit more than high-skilled migrants (Niimi et al., 2010; Cooray,

2014). Adverse effects of emigration include competition with native-born workers in the

labour market, displacing them in employment and bidding down wages. Low-skilled

migrants can lower the pace of structural adjustment and technological progress reducing the

economy’s competitiveness in the international market (Djajic, 1997).

The present study is based on cross-sectional data for 2000/2001 for 103 countries. The

point of departure of the study from the literature is as follows: first, the paper examines

simultaneously the effects of emigration on remittances and remittances on GDP. Second, the

study disaggregates the emigration rate by education level and, consequently examines the

effects of the education level of the migrant on remittances, and remittances on GDP. There

is relatively little evidence in the empirical literature on how the education level of migrants’

affects remittances.3 Third, this estimation is also carried out for males and females sepa-

rately.

The study is structured as follows. Section 2 presents the empirical framework and data.

Section 3 evaluates the empirical results. Section 4 discusses policy implications and

concludes.

3 Docquier and Marfouk (2005) use data on the immigration structure by educational attainment andcountry of birth from all OECD receiving countries to estimates of emigration stocks and rates byeducational attainment. The present study in contrast investigates the effect of the education level of theemigrant on home country remittances and remittances on GDP.


WHO REMITS? 1443

2. THE EMPIRICAL FRAMEWORK AND DATA

A recursive system, where the endogenous variables are determined sequentially, is

employed specifically because it is not possible by definition, to directly estimate the contribu-

tion made by the emigrant labour force to source country GDP. Remittances are estimated as

a mediator between out-migration and the home country’s GDP to permit estimation of the

contribution made by migrants working overseas. The basic equation system that is estimated

is as follows:Ri ¼ bi0 þ b1Mi þ b2wi þ e1i; (1)

Yi ¼ ai0 þ a1Ri þ a2zi þ e2i; (2)

where R is the logarithm of migrant remittances, M is the emigration rate, and Y is the loga-

rithm of gross domestic product, in the origin country i. The vector wit in equation (1)

includes intervening factors such as the ratio of M2 to GDP to capture the level of financial

development of the home country (Giuliano and Ruiz-Arranz, 2009), the degree of trade open-

ness measured by the sum of exports and imports to GDP (Chami et al., 2009), as the more

open an economy, the higher the inflow of remittances, and the stock of physical capital to

GDP to capture infrastructural and technological development (Cooray, 2012).

The vector wit in equation (1) also incorporates the rate of inflation in the home country,

interest rate on deposits and the exchange rate in an attempt to capture the motive of migrants

to remit.4 These variables can have either a positive or negative effect on the volume of

remittances. Katseli and Glytsos (1989) interpret the high level of inflation in the home coun-

try as a measure of the degree of political and economic uncertainty in that country. Higher

inflation may be positively or negatively related to remittance flows depending on the motive

to remit. For example, if the motive to remit was self-interest, higher inflation in the home

country would reduce remittance flows due to increased uncertainty. Conversely, if altruism

were the motive to remit, due to the increased cost of living faced by the migrant workers’

family, the volume of remittances would increase. If high interest rates on deposits at home

are a reflection of high levels of inflation (Elbadawi and Rocha, 1992), they could encourage

or discourage more remittance flows depending on whether they are sent with an altruistic or

self-interested motive. If remittances were sent with an investment motive, high interest rates

at home could increase the volume of remittances (Lianos, 1997). The exchange rate similarly

can have both a positive and negative effect on remittances. A depreciation of the home cur-

rency could lead to an increase in the volume of remittances due to the increased cost of liv-

ing faced by the family at home or expected future exchange rate rises. It could on the other

hand lead to a fall in remittances, if it were a reflection of economic uncertainty. The portfo-

lio diversification or investment motive is closely tied to the concept of risk aversion behav-

iour of migrants. A risk averse migrant would remit less back home in the event of greater

uncertainty.

The vector zi in equation (2) comprises a set of variables that are commonly used in the

growth literature and five regional dummy variables to capture regional heterogeneity. These

variables include the domestic labour force which is a fundamental determinant of economic

growth, the stock of physical capital to GDP, the secondary enrolment ratio to control for the

stock of human capital and the ratio of M2 to GDP to capture financial sector development.

4 A strand of literature examines macroeconomic financial factors in portfolio choice decision-making orthe investment motive of migrants (Swamy, 1981; Katseli and Glytsos, 1989).


1444 A. COORAY

Given that emigration rates and the education level of emigrants differ across regions, five

regional dummy variables are created for Africa, the Middle East and North Africa, Asia and

the Pacific, South America and the Caribbean, and Eastern Europe and Central Asia with the

high-income OECD countries as the benchmark group. e1i and e2i are random disturbance

terms that capture the aggregate effect of all other factors.

Estimation is also carried out by disaggregating the emigrant labour force in equation (1)

by education level, to investigate in greater detail, the degree to which the education level of

the labour outflow contributes to remittances and home country GDP. That is, the right hand

side of equation (1) is decomposed as follows:

Ri ¼ bi0 þ b1MPiþ b2MSi þ b3MTi þ b4wi þ e1i: (3)

where MPi= the emigration rate of primary educated; MSi = the emigration rate of secondary

educated; MTi = the emigration rate of tertiary educated. The vector wit denotes the same con-

trol variables mentioned above. This equation is also estimated separately for males and

females.

The data for emigration are obtained from the Organization for Economic Cooperation and

Development (OECD, 2000/2001) Database on Immigrants in OECD and non-OECD Coun-

tries (DIOC-E) release 2.0 (see http://www.oecd.org/els/mig/dioc.htm.). This database is com-

piled using census data for 2000/2001 for 161 countries. The data for remittances, however,

are available only for 103 countries. The data used in the empirical analysis therefore are

cross-sectional data for 2000/2001 for the 103 countries for which remittance data are avail-

able. Data for the total emigration rate, emigration rate of primary, secondary and tertiary

educated, and emigration rates of primary, secondary and tertiary educated males and females

are taken from the OECD (DIOC-E) database. Data for remittances, GDP, money supply to

GDP, openness to GDP, gross fixed capital formation to GDP, labour force, rate of inflation,

interest rate and exchange rate are taken from the World Development Indicators.

3. EMPIRICAL RESULTS

The empirical estimation is carried out using both the seemingly unrelated regression

(SUR) and three-stage least squares (3SLS) techniques. Equations (1) and (2) comprise a

recursive system and can be estimated using ordinary least squares (OLS) if the error terms

of the two equations are not correlated. If, however, there exists a common set of omitted

variables from the two equations, this could lead to correlation between the error terms. If the

error terms are correlated, the SUR procedure can be used to improve the efficiency of the

OLS estimates. For this purpose, the SUR method is used. Another issue that arises is that

remittances and GDP could be endogenously determined. A country’s level of GDP could also

be a determinant of the amount of remittances it receives. Similarly, remittances by alleviat-

ing liquidity constraints could also influence the emigration rate. Therefore, there could be a

potential endogeneity bias, due to reverse causality. For this reason, the 3SLS estimation

method is also used. The results for the preliminary estimation examining the association

between the main variables of interest are presented in Table 1.

The coefficients on the total emigration rate under both the SUR and 3SLS estimation

methods in columns (1) and (3) are positive and statistically significant at the 1 per cent level,

suggesting that emigration contributes positively to remittances. In column (1), for example,

an increase in the emigration rate by 1 per cent point leads to an increase in remittances by

approximately 0.22 per cent. Similarly, the coefficients on remittances in columns (2) and (4)


WHO REMITS? 1445

TABLE1

SUR

and3SLSEstim

ates

ofRem

ittancesandGDP

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

SUR

3SLS

SUR

3SLS

RY

RY

RY

RY

Independentvariables

R–

0.540

–0.365

–0.509

–0.170

(0.067)***

(0.156)***

(0.070)***

(0.080)**

Mtotal

0.215

–0.113

––

––

–(0.065)***

(0.024)***

MP

––

––

0.118

–0.114

–(0.029)***

(0.017)***

MS

––

––

0.109

–0.113

–(0.040)***

(0.018)***

MT

––

––

0.032

–0.022

–(0.021)

(0.019)

R2

0.45

0.42

0.30

0.35

0.51

0.45

0.48

0.33

Observations

99

99

99

99

86

86

86

86

Notes:

(i)Standarderrors

reported

inparenthesis.

(ii)***and

**,significantat

the1%

and5%

levelsrespectively.


1446 A. COORAY

under both SUR and 3SLS are statistically significant at the 1 per cent level, suggesting that

remittances have a positive and significant effect on GDP. In column (2), a 1 per cent

increase in remittances for instance will lead to a 0.54 per cent increase in GDP. In columns

(5) and (7), emigration is disaggregated by education level. The coefficients on the emigration

categories by education level indicate that primary and secondary educated migrants have a

positive and significant impact on remittances. In the SUR estimates reported in column (5),

for example, an increase in the emigration rate of those with primary education by 1 per cent

point leads to an increase in remittances by 0.12 per cent, and increase in emigration rate of

those with secondary education by 1 per cent point leads to an increase in remittances by

0.11 per cent. The coefficient on the emigration rate of those who are tertiary qualified is

positive, however, not statistically significant. Remittances continue to have a significant and

positive effect on GDP.

Several robustness tests are employed to check the robustness of the results to the estima-

tion procedure and variables used in the study. The 3SLS estimation procedure is used in

addition to the SUR estimation procedure to correct for any potential endogeneity bias in the

estimates. In addition, a number of control variables are used to investigate the robustness of

the estimates and regional dummy variables to account for regional heterogeneity. Table (2)

incorporates additional control variables to the education disaggregated model.

The estimation is carried out by controlling for country characteristics including regional

dummy variables, the domestic labour force, stock of physical capital, human capital, financial

sector development and openness in the GDP equation, and controlling for financial sector

development, infrastructural development, openness, inflation, interest rates and the exchange

rate in the remittance equation. These results are presented in Table 2. The emigration of pri-

mary and secondary qualified individuals continues to have a positive and significant effect on

remittances, while tertiary qualified migrants do not significantly affect remittances. Column (1)

indicates that an increase in the primary emigration rate by 1 per cent point leads to a 0.14 per

cent increase in remittances and a 1 per cent point increase in the secondary emigration rate

leads to a 0.17 per cent increase in remittances. Similarly, remittances continue to have a posi-

tive and significant impact on GDP. A 1 per cent increase in remittances leads to a 0.11 per cent

increase in GDP in column (2). In the remittance equations in columns (1) and (3), the coeffi-

cients on the stock of capital, financial sector development and openness are positive and signifi-

cant suggesting that a well-developed financial system, infrastructure and openness are

important for migrant remittances. The coefficient on inflation is statistically significant at the 10

per cent level under the 3SLS estimation method, suggesting that an increase in home country

inflation leads to an increase in the volume of remittances. There is some support therefore for

the altruistic motive. The coefficients on the interest rate are positive and significant under both

methods providing some evidence in favour of the investment motive, and the coefficients on

the exchange rate are positive and significant implying that a exchange devaluation leads to more

remittances providing evidence in favour of the altruistic motive.

In the GDP equations in columns (2) and (4), remittances have a positive and significant

effect on GDP. The domestic labour force, capital stock, enrolment ratio, financial sector

development and openness influence GDP positively and significantly. The regional dummy

variables for all regions except for the Middle East and North Africa are statistically

significant and negative. This is reasonable considering that on average all these regions have

a lower GDP compared to the high-income OECD economies.

Next, the estimation in Table 1 is replicated for males and females separately. The results

are reported on Table 3.


WHO REMITS? 1447

Note that the magnitude of the coefficients on females with primary and secondary

education is greater than the coefficients on primary and secondary educated males in the

remittance equations, suggesting that females remit more than males consistent with the find-

ings of Vanwey (2004). The coefficients on remittances from primary and secondary educated

females are statistically significant at the 1 per cent level under the SUR estimation method

and statistically significant at the 5 per cent level under the 3SLS method. For males,

TABLE 2SUR and 3SLS Estimates of Remittances and GDP with Control Variables

(1) (2) (3) (4)R Y R Y

Independent variables SUR 3SLSR – 0.111 – 0.159

(0.013)*** (0.032)***MP 0.141 – 0.143 –

(0.032)*** (0.032)***MS 0.171 – 0.167 –

(0.040)*** (0.091)**MT 0.030 – 0.122 –

(0.020) (0.120)Labour Force – 0.053 – 0.052

(0.031)* (0.030)*Capital Stock 0.665 0.523 0.458 0.550

(0.212)*** (0.129)*** (0.140)*** (0.131)***Enrolment Ratio – 0.149 – 0.152

(0.047)*** (0.057)***M2 0.120 0.171 0.142 0.091

(0.060)** (0.100)* (0.091)* (0.041)**Openness 0.019 0.122 0.121 0.222

(0.009)** (0.054)** (0.054)** (0.069)***Inflation 0.012 – 0.029 –

(0.020) (0.018)*Interest Rate 0.002 – 0.002 –

(0.001)** (0.001)**Exchange Rate 0.002 – 0.004 –

(0.001)** (0.002)**Africa – –0.042 – –0.096

(0.016)*** (0.042)**Asia and the Pacific – –0.034 – –0.081

(0.014)*** (0.032)***Eastern Europe and Central Asia – –0.028 – –0.034

(0.012)*** (0.019)*Latin America and the Caribbean – –0.014 – –0.024

(0.007)** (0.012)*Middle East and North Africa – –0.017 – –0.029

(0.011) (0.020)R2 0.54 0.86 0.54 0.88

Observations 58 58 58 58

Notes:(i) Standard errors reported in parenthesis.(ii) ***, **, *, significant at the 1%, 5% and 10% levels respectively.


1448 A. COORAY

the coefficients on primary and secondary educated males are statistically significant at the

10 per cent level under both methods. The coefficients on tertiary qualified males and females

are not statistically significant. Remittances continue to have a positive and significant effect

on GDP. The explanatory power of the equations for females is higher than that of males.

The literature also shows that females are more altruistic compared to males. Therefore,

the estimation in Table 2 is replicated for males and females separately. The results are

reported on Table 4. Only SUR estimates are reported to conserve space. The 3SLS estima-

tion yielded similar results.

In the remittance equations, the coefficients on females at all three levels of education are

higher than that on males corroborating the findings reported on Table 3. The coefficients on

the rate of inflation, and the exchange rate, assume greater statistical significance for females

compared to males. For example, the coefficient on the rate of inflation in column (3) is posi-

tive and statistically significant at the 10 per cent level, suggesting that females remit more

when the rate of inflation is high reflecting the altruistic motive. Similarly the coefficient on

the exchange rate is higher and significant for females at the 5 per cent level, whereas it is

positive and significant at the 10 per cent level for males implying that females remit more

when the exchange rate is high, once again suggesting the altruistic motive. The statistical

significance of the coefficients on the rate of interest, however, also provides some evidence

in favour of an investment motive of both males and females.

4. POLICY IMPLICATIONS AND CONCLUSIONS

The results of the present study provide evidence in favour of the hypothesis that primary

and secondary educated migrants contribute positively and significantly to source country

income. There is no evidence to show that tertiary qualified migrants contribute significantly

to remittances. These conclusions are supported by Faini (2007) who argues that brain drain

TABLE 3SUR and 3SLS Estimates of Remittances and GDP by Gender

(1) (2) (3) (4) (5) (6) (7) (8)SUR SUR 3SLS 3SLS

R Y R Y R Y R Y

Independentvariables

Male Female Male Female

R – 0.572 – 0.592 – 0.172 – 0.160(0.073)*** (0.072)*** (0.068)*** (0.051)***

MP 0.021 – 0.060 – 0.020 – 0.022 –(0.013)* (0.020)*** (0.012)* (0.011)**

MS 0.034 – 0.049 – 0.029 – 0.032 –(0.018)* (0.019)*** (0.016)* (0.014)**

MT 0.027 – 0.018 – 0.010 – 0.040 –(0.110) (0.034) (0.022) (0.035)

R2 0.25 0.36 0.29 0.39 0.20 0.15 0.25 0.26

Observations 86 86 86 86 86 86 86 86



WHO REMITS? 1449

or the migration of skilled workers is associated with a lower propensity to remit because

these migrants are likely to spend more time abroad and be re-united with their families. Sim-

ilar conclusions are put forward by Niimi et al. (2010) who find that remittances fall with the

migrants education level and Cooray (2014) who shows that low-skilled migrants contribute

more to source country income compared to high-skilled migrants. Evidence also shows that

low-skilled migrants remit more as a percentage of their income and also more frequently

TABLE 4SUR Estimates of Remittances and GDP by Gender with Control Variables

(1) (2) (3) (4)R Y R Y

Independent variables Male FemaleR – 0.034 – 0.052

(0.014)*** (0.010)***MP 0.026 – 0.035 –

(0.012)** (0.012)***MS 0.078 – 0.085 –

(0.043)* (0.041)**MT 0.074 – 0.122 –

(0.061) (0.120)Labour Force – 0.099 – 0.065

(0.034)*** (0.030)*Capital Stock 0.247 0.441 0.594 0.321

(0.112)** (0.224)* (0.217)*** (0.125)***Enrolment Ratio – 0.156 – 0.146

(0.032)*** (0.058)***M2 0.161 0.024 0.142 0.024

(0.010)* (0.011)* (0.091)* (0.011)**Openness 0.016 0.125 0.121 0.220

(0.010)* (0.064)* (0.054)** (0.119)*Inflation 0.004 – 0.029 –

(0.014) (0.015)*Interest Rate 0.005 – 0.002 –

(0.002)*** (0.001)*Exchange Rate 0.004 – 0.006 –

(0.002)* (0.002)***Africa – –0.044 – –0.047

(0.013)*** (0.013)**Asia and the Pacific – –0.035 – –0.035

(0.013)*** (0.013)***Eastern Europe and Central Asia – –0.023 – –0.023

(0.011)** (0.011)**Latin America and the Caribbean – –0.010 – –0.016

(0.005)** (0.008)*Middle East and North Africa – –0.010 – –0.019

(0.014) (0.017)R2 0.55 0.89 0.57 0.90

Observations 52 52 52 52



1450 A. COORAY

compared to high-skilled migrants. Datta et al. (2007) in a study of low-skilled migrants to

London find that these individuals emigrate specifically to remit money back home. The

results also show that remittances contribute positively to GDP. The results suggest some evi-

dence of a combination of both the altruistic and self-interested motives to remit. When, how-

ever, the model is estimated for males and females separately, the evidence suggests that

females remit more compared to males and are also more altruistic.

Given the contribution made by primary and secondary educated workers to the GDP of

sending countries, governments should place proper safety nets in place for return migrants,

in particular primary and secondary educated migrants and female migrants. Quinn and Rubb

(2005) show that a push factor for emigration is the mismatch between individual skills and

job opportunities available at home. Therefore, governments of sending countries could strive

to tailor education systems and training programmes to cater to the demands of labour mar-

kets. This would not only reduce the emigration rates of primary and secondary qualified

workers but also tertiary qualified workers and reduce unemployment and underemployment

in sending countries.

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1452 A. COORAY

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who remits? an examination of emigration by education level and gender

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