DP2016-28

Remittances, Growth and Poverty
Reduction in Asia - A Critical Review of the
Literature and the New Evidence from
Cross-country Panel Data*
Katsushi S. IMAI
Wenya CHENG
Bilal MALAEBFabrizio BRESCIANI
Revised December 1, 2016
* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Remittances, Growth and Poverty Reduction in Asia - A Critical Review of
the Literature and the New Evidence from Cross-country Panel Data
Katsushi S. Imai *Economics, School of Social Sciences, University of Manchester, UK and RIEB, Kobe University,Japan
Wenya Cheng
Economics, School of Social Sciences, University of Manchester, UK
Bilal Malaeb
Oxford Department of International Development, University of Oxford, UK
Fabrizio Bresciani
Asia and the Pacific Division of International Fund for Agricultural Development (IFAD), Italy
This Version: 1st December 2016

Abstract
This study provides a critical review of the role of international remittances and migration inpromoting growth and reducing poverty and inequality in developing countries in Asia andthe Pacific. It also uses cross-country panel data and examines the effect of remittances on
economic growth, poverty and inequality after taking into account the endogeneity ofremittances. First, it has been found from our econometric results that remittances promoteeconomic growth and reduce poverty - both national and rural - based on the international
poverty lines of the US$1.25 or US$2 thresholds, while remittances have no inequalityreducing effect. Second, we have suggested the importance of understanding the underlying
factors enabling households to undertake migration and remittances in relation with theunderlying structural transformation of the rural economy, such as its shift to the non-farm
sector, which typically takes place as village infrastructure develops and educational level ofthe households improves. Third, we argue that the risk-coping roles of remittances at both
macro and micro levels are important in understanding the poverty-reducing mechanisms
associated with migration and remittances. Fourth, poor households outside the village
networks should be supported by policy measures. This is important particularly because ourresults suggest that remittances increase inequality in rural areas.

Key Words: Remittances, Migration, Growth, Poverty, Inequality, Asia
JEL classification: F22, O10, O15, O53, I30*Corresponding Author:
Katsushi S. Imai (Dr.), Economics, School of Social Sciences, University of Manchester, Arthur
Lewis Building, Oxford Road, Manchester M13 9PL, UK;
Email: Katsushi.Imai@manchester.ac.uk
Acknowledgements
This study is funded by Asia and the Pacific Division (APR), IFAD (International Fund for Agricultural
Development). We are grateful to Anil Deolalikar, Raghav Gaiha, Pedro Vasconcellos for their usefulcomments. We thank Christian Mina for sharing the data. The views expressed are, however, personal andnot necessarily of the organisations to which we are affiliated or of IFAD.
1

Remittances, Growth and Poverty Reduction in Asia - A Critical Review of
the Literature and the New Evidence from Cross-country Panel Data
Contents
I. Introduction

p.2

II. The Review of Literature

p.9

III. The Review of Cross-country Data and Econometric Analyses

p.25

IV. Concluding Observations

p.46

A list of Acronyms
CALABARZON: Cavite, Laguna, Batangas, Rizal, and Quezon (an administrative region in
the Philippines)
GMM: Generalized method of moments
IFAD: International Fund for Agricultural Development
IV: Instrumental Variable
LAD: Land acquisition and distribution
OCWs: Overseas Contract Workers
PCGDP: Per capita Gross Domestic Product
PSTR: Panel smooth transition regression
RIGA: Rural Income Generating Activities
SGMM: System Generalized method of moments
WDI: World Development Indicators
I.

Introduction

It is now widely recognised that remittances are important not only for economic growth but
also for poverty reduction. In 2009 migrants from developing countries (i.e., low and middleincome countries) sent over US$271 billion to their countries of origin, which decreased from
US$284 billion in 2008 (Figure 1). It reached US$423 billion in 2015, which wasconsiderably larger than remittances received by high-income countries, US$150 billion.1
Remittances have recently played an important role as a stable source of finance at the macrolevel, while they play a key role in poverty reduction at micro levels, in particular after 2000

1

It is noted that the remittance figures for developing countries and high income counties cannot becompared in a strict sense. To developing countries, P2P remittances are the bulk of these flows, tosupport family mainly, whereas to high-income countries, it is rather non-resident employees’ savings
repatriation (in other words, wealthy expatriates bringing back home their money).
2

(Imai, Gaiha, Ali, and Kaicker, 2014), while the remittance flows to low and middle income
countries increased acceleratedly (Figure 1) and poverty declined in many of these countries.
Figure 1 Trends of remittances (received, current US$) by income class, aggregate levels (only

0

100

200

300

400

developing countries)

1970

1980

1990
Year

Low and Middle Income Countries

2000

2010

High Income Countries

Source: World Development Indicators 2015.

It might be useful to further break down the category of developing world into the groups bycountry’s income classes. As can be seen in Figure 2, lower middle income countries haveattracted the highest amount of remittances with a sharp increasing trend, to be followed byupper middle income countries and then by low-income countries.
Figure 2 Trends of Remittances (received, current US$) by income class, aggregate levels

3

250
200
150
100
0

50
1970

1980

1990
Year

Low Income Countries
Upper Middle Income Countries

2000

2010

Lower Middle Income Countries

Source: World Development Indicators 2015.

However, once we derive the shares of remittances in GDP, we find that the macro
remittance inflow has non-negligible shares (above 4% after 2010) in low income and lowermiddle income countries, while there is some degree of heterogeneity as smaller countries
tend to have higher shares (e.g. Nepal, 32.2%, Philippines, 10.3%, Sri Lanka, 8.5% in 2015,
based on WDI 2015). It is interesting to find that the share of remittances in GDP of upper
middle income countries has decreased in recent years. We can safely conclude that theremittance inflow at the macro level has played an important role for lower middle income
countries – in terms of its volume and share in GDP- and for low income countries in termsof its share. 2 This will justify our study that aims to examine the relationships among
remittances, growth and poverty reduction in Asia and the Pacific drawing upon a criticalreview of the literature and econometric analyses based on cross-country panel data.
Figure 3 The share of remittances in GDP by income class, aggregate levels

2

We will further investigate the trends of remittances for each country in the section III.
4

5
4
3
2
1
1970

1980

1990
Year

Low income Countries
Upper middle income

2000

2010

Lower middle income

Source: World Development Indicators 2015.

The objective of this draft - following the above observation that remittances haveincreased in low-income and lower-middle income countries in recent years - is twofold. First,
we will provide a critical review of the recent literature on the role of remittances andinternational migration3 in promoting economic growth and reducing poverty – in particular
rural poverty. In the review, we pay a particular attention to the mechanisms whereby
remittances reduce poverty at micro levels, for instance, how the income structure or the
behaviour of recipients is changed as a result of migration and remittances.4 Second, we will
review the macro statistics of remittances and migration drawing upon the cross-country
panel data and carry out the econometric analyses where we estimate economic growth andvarious poverty measures, including rural poverty measures.

3

It is evident that domestic migration (as well as remittances from migrants in urban areas to those
who are left out) affects growth and poverty in a complex manner, but this draft mainly focuses on therole of international migration and remittances. The role of the domestic migration has been discussed
by Imai, Gaiha and Garbero (2014).
4
An alternative approach to assess the impact of remittances on rural poverty would be to evaluate
their vulnerability to financial shocks and resilience and estimate the role of remittances in reducing
vulnerability in line with IFAD and the World Bank (2015). However, as remittances are endogenous
to the measure of vulnerability and it has been recommended that the vulnerability should be
estimated by exogenous variables, such as household or community characteristics (e.g. Mina andImai, 2016).
5

As can be seen in Figure 1, remittances received in developing countries5 have steadily
increased in the period 1970-2014 across different regions. This reflects (i) the increased
financial openness influencing developing countries over the years (Baltagi et al., 2009), (ii)a large share of remittances in the total financial inflows in some countries, such as Tajikistan
and Kyrgyzstan (IFAD and the World Bank, 2015, p.19), (iii) increased net migrants (migrant
outflows minus inflows) (IFAD and the World Bank, 2013) and (iv) the increased financial
networks through microfinance institutes, post offices and mobile operators (ibid., 2013).
Because the logarithm of remittances in real terms (amount in US$) has been taken, the slope
shows the growth rate of remittances.6

10

logRemittances
15
20

25

Figure 4 Trends of log Remittances (received, current US$) at Regional Levels (only developing
countries)

1970

1980

1990
Year

East Asia & Pacific (developing only)
Latin America & Caribbean
South Asia

2000

2010

Europe & Central Asia (developing only)
Middle East & North Africa (developing only)
Sub-Saharan Africa (developing only)

Source: World Development Indicator 2016.

5
6

Remittances comprise personal transfers and compensation of employees.
The trends of remittances and migration of each country will be reviewed later.
6the years with a very fast growth in the 1970s. South Asia and East Europe & Central Asia
have seen an increase in remittances over the years.
On the contrary, Figure 5 shows that the share of remittances in GDP (derived as an
unadjusted average of countries in the region) does not show increasing trends across regions.
For instance, the share of remittances has been more or less stable over the years in East Asiaand the Pacific (including South East Asia) and in East Europe and Central Asia. In theformer, the share of remittances has been at around 1% of GDP and in the latter the share has
been at around 2%. The share of remittances in GDP has increased from around 1% of GDP
to 4% in South Asia. However, this does not reflect the heterogeneity across different
countries. It is noted that a few countries are characterized by the high share of remittances in
GDP is (e.g. Nepal, 32.2%, Philippines, 10.3%, Sri Lanka, 8.5% in 2015).

2

4

6

8

Figure 5 Trends of the share of Remittances in GDP at Regional Levels (only developing
countries)

0

Remittances_ShareAs Figure 4 indicates, the East Asia & the Pacific region has seen the fastest growth over

1970

1980

1990
Year

East Asia & Pacific (developing)
Latin America & Caribbean
South Asia

2000

2010

Europe & Central Asia
Middle East & North Africa
Sub-Saharan Africa

Source: World Development Indicator 2015.

The trends of net international migration (defined as the total number of immigrants lessthe annual number of emigrants, including both citizens) are summarised in Figure 6. They
were fluctuating and have not seen any clear increasing or decreasing trends over the years.
7However, the figure of net migrants has decreased over the years in the East Asia & Pacific,
while it has decreased since 1990 in South Asia. It is not easy to confirm the trends as thedata are available only once in five years (based on WDI 2015). As the total volume ofremittances has increased, the net migration has decreased since 1990 in these regions,
possibly as a result of the increase in emigration. Continuous increase in emigration tends toincrease the stock of migrants abroad, which will have a positive effect on remittances over
the years. It is noted that IFAD and the World Bank (2013) have shown that migrant outflows
exceeded migrant inflows in many Asian countries.

-1.0e+07

Migration-5.0e+06

0

Figure 6 Trends of the share of remittances in GDP at Regional Levels (only developing
countries)

1970

1980

1990
Year

East Asia & Pacific (development)
Latin America & Caribbean
South Asia

2000

2010

Europe & Central Asia (development)
Middle East & North Africa (development)
Sub-Saharan Africa (development)

Source: World Development Indicator 2015.

8

The rest of the draft is organised as follows. Section II provides a critical review of theempirical literature on remittances and migration. In doing so a particular focus will beplaced on the role of remittances in promoting economic growth and poverty reduction fromdifferent angles, including their role in stabilising economic growth and the effect ofremittances on the income structure of recipient households or the aggregate economy.
Section III uses the cross-country panel data to review the trends of remittances andmigration at country levels. It then provides econometric results on the relationship between
remittances (as well as migration) on economic growth or poverty by taking account of theendogeneity of remittances. The section IV concludes with some policy implications for thecountries in Asia and the Pacific.

II.

The Review of Literature

The Role of Remittances to promote growth and stabilise the economy
There is a large literature that analyses the effect of international remittances on economic
growth. As discussed by Barajas, Gapen, Chami, Montiel, and Fullenkamp (2009), part ofremittance inflows will be used to finance directly physical and human capital investment,such as education. If the firms or the households face credit constraints, the remittances may
be useful to ease them and the access to the remittances would also improve the credit
worthiness of the firms of the households which enables them to borrow more and leads tolower capital costs. Imai, Gaiha, Ali, and Kaicker (2014) used the panel data for 24 Asia andPacific countries and confirmed that remittances have been beneficial to economic growth
after taking into account the endogeneity of remittances in their econometric models. The
present study will extend this study by updating the data and econometric models (e.g. theuse of System GMM) as we will see later.

9

Other empirical works have reached a similar conclusion that remittances will promote
growth. For instance, Pradhan, Upadhyay, and Upadhyaya (2008) examined the effect ofworkers’ remittances on economic growth using panel data from 1980–2004 for 39
developing countries and confirmed a positive impact on growth. Using the data for morethan 100 countries in 1975–2002 and taking into account the endogeneity of remittances andfinancial development by System GMM, Giuliano and Ruiz-Arranz (2009) investigated therelationship between remittances and growth and the interaction of remittances with thefinancial development in the recipient country. They found that remittances have promoted
growth in less financially developed countries.7
Remittances would also make the economy less volatile (Barajas et al., 2009). Chami,
Hakura, and Montiel (2012) have provided evidence that remittance flows have contributed
to the reduction of the volatility of GDP growth in recipient countries after controlling forother determinants of growth volatility and the reverse causality. However, the evidence on
the existence of threshold effects suggests that the stability-enhancing effects of remittances
appear to be achieved rather quickly, so whatever benefits may be associated with very large
remittance flows, enhanced macroeconomic stability may not loom large among them,suggesting the importance of strengthening macroeconomic resilience through other means inrecipient countries (ibid., p.17). As the last section shows, low and lower-middle income
countries tend to be dependent on remittances. While this is likely to enhance and stabilise
growth, the macro policies of these countries should pay attention to (i) longer-term macro
effects of remittance flows and (ii) possible complementary measures to further stabilise theeconomy in the middle to the long run.
Other empirical studies that have shown the negative relationship between remittances andgrowth volatility include Bugamelli and Paterno (2008), Combes and Ebeke (2010), Bettin,
Kelegama’s (2011) review on the relationship between remittances and development in South Asia
is broadly in line with these conclusions.
710

Presbitero, and Spatafora (2015) and Jidoud (2015). Bugamelli and Paterno (2008) used thecross-country panel for 60 emerging economies and showed that remittances help reduce
output growth volatility. Using the dynamic panel model applied to the cross-country panel,
Combes and Ebeke (2010) have shown that recipient countries exhibit on average lower
consumption instability where remittances served as a hedge against various types ofmacroeconomic instability: natural disaster, agricultural shocks, systemic banking crisis, andexchange rate instability. However, the stabilising effect is weakened when the private credit
ratio exceeds 20% of GDP and when the remittance ratio is above 6% of GDP. This may be
because above the thresholds private credit or remittance inflows themselves tend to be more
fluctuating and weaken the stabilising roles of remittances. Most of the studies which arereviewed here are based on the cross-country panel data (typically WDI), but the conclusion
has been confirmed by Bettin, Presbitero, and Spatafora (2015) who used a rich panel dataset
on bilateral remittances from 103 Italian provinces to 79 developing countries over the period
2005–2011. They concluded that remittances contributed significantly to macroeconomic
stability in recipient countries. The stabilising effect is stronger where the migrantcommunities have a larger share of newly arrived migrants who tend to have closer
relationships with foreign countries.
Using a different approach, Jidoud (2015) investigated the mechanisms through which
remittances affect macroeconomic volatility using a general equilibrium framework andshowed that the stabilization impact of remittances (i) depends on the size of the negative
effect on labour supply induced by remittances and (ii) is larger in the country with anunderdeveloped financial sector (where remittances can play a more important role byinducing financial sector development by providing a greater access to financial markets totheir recipients). This underscores the need for recipient countries to further promote
financial development so as to channel remittances through the financial system and help the11poor have greater access to credit. Ahamada and Coulibaly (2011) indeed showed, based on
the panel data and the panel smooth transition regression (PSTR) approach8 that the impact ofremittances on GDP growth volatility is nonlinear, changing over time and across countries infunction of financial development, and that a high level of financial development helps
remittances have a stabilizing role.
To examine further the stabilising role of remittances, Chami, Cosimano, and Gapen,(2006) assess how remittances influence the incentives and decisions of economic agents,
while investigating how these decisions impact the recipient economy at large. Using atheoretical framework drawing upon the literature of business cycle and public finance, theyfound that remittances may increase the macroeconomic risk through higher business cycle
volatility, while they increase consumption and have the ability to smooth household
consumption against income shocks (Chami et al., 2006). 9 10 The presence of remittances
also changes the underlying relationship between labour and output, thereby changing thefunctioning of government policy instruments. If the set of policy instruments is not
sufficiently varied, this may result in an increased reliance on the inflation tax (ibid). It has
been suggested by these authors that governments should examine a wider variety of policy
instruments when the countries rely on a large amount of remittance inflows. Chami et al.
(2008) provide detailed theoretical and empirical examinations on the relationship between
remittances and macro stability and conclude that (i) remittances should not be taxed directly,

8

The model is developed by González, Tersvirta, and van Dijk (2005) and allows regression
coefficients that vary across different countries and over time, by assuming that the (heterogeneous)coefficients are continuous functions of an observable variable through a bounded function of thisvariable and fluctuate between two (or more) “extreme regimes” as an extension of Hansen (1999).
9
The permanent income hypothesis suggests that, if the remittances are perceived by temporary
income by the recipient households, they are more likely to be saved and helps them smoothconsumption (McKenzie and Sasin, 2007, p.6).
10
Using the Vietnamese household data in 2004, Niimi and Reilly (2011) found that women show
more reliable remittance behaviour than men, suggesting that the contribution of female migrants tothe well-being and risk-coping ability of their household at the origin is potentially larger. However,
the gender dimensions of remittance behaviour are under-researched and this conclusion should not be
generalised as in other countries, such as India, male migrants are dominant.
12

as taxation of remittances will cause a decline in remittance activity 11 or increase thetransaction costs of remittances, undermining their poverty-reducing potentials; and (ii) any
loosening of the government budget constraint as a result of positive remittance flows must
be used to channel remittances into activities that promote long-run economic development,
while preserving their poverty-reducing effects in the short run. It should be noted, however,that remittances are private flows normally to support families and cannot be easily
controlled by governments. If the government can help potential recipients of remittances use
the money for investment purposes, for instance, through the easier access of the poorest to
livestock (Bandiera, et al., 2016), this may promote growth and reduce poverty at the sametime.
Even if policymakers recognise the importance of public policies to maximise the povertyreducing potential of remittances, these policies may not be well implemented without thecountry’s institutional settings. Ajide, Raheem, and Adeniyi (2015) examined how
institutional settings will affect the relationship between output growth and volatility by
applying the dynamic panel data model (System GMM) to 71 recipient countries. Theirfindings include: (i) the growth volatility reduction potential of remittances was found to be
more pronounced in the presence of well-functioning institutions and (ii) the interaction ofremittances with six institutional quality measures showed that growth volatility reduced
considerably with better institutions. However, it should be noted that remittances themselves
may have an adverse effect on the macro institutional quality, the point of which is notaddressed by Ajide et al. (2015). By addressing issues of endogeneity and robustness, Abdih,
Chami, Dagher, and Montiel (2012) found a negative and statistically significant effect ofremittance inflows on institutional quality (e.g. control of corruption, government
Effectiveness, and rule of law). That is, if the country tries to increase the total inflows of
11

However, Chami et al. (2008) did not take into account a possible increase in informal remittances
due to the taxation, which may involve additional costs and risks for senders and receivers.
13

remittances by improving the institutional qualities, the increased remittances may have a riskof undermining the institutional qualities. As the relationship among remittances, finance,monetary and fiscal policies, and institutions is likely to be complex, the recipient country
would need to pay a further careful attention to not only policies but also their setups (e.g.
control of corruption) in the face of a surge of remittance inflows. Our overview of the data in
Section I suggests that this point is likely to be more relevant to lower-middle income
countries.
A surge of remittances, while benefiting the macro economy by stabilising the economic
growth, may have an adverse effect in a different way. Using the two-sector dynamic generalequilibrium model for the small economy and adapt it to El Salvador, Acosta, Lartey, andMandelman (2009) showed an illuminating case for the Dutch disease where an increase inremittances leads to a decline in labour supply and to an increase in production costs of the
non-tradable sector which is relatively labour intensive. In their model, this will, in turn, raise
the prices of the non-tradable sector and appreciate the exchange rates, while the non-tradable
sector expands and the tradable sector shrinks. In a similar vein, Amuedo-Dorantes, and Pozo
(2004) argued that remittances may appreciate the real exchange rate in the receiving
economies and may reduce the competitiveness of exported goods in the international
economy. The authors found that doubling remittances results in real exchange rate
appreciation of about 22% in the panel of 13 Latin American and Caribbean nations.

The Role of remittances to reduce Poverty and Inequality
Even if the country receives more inflows of remittances and this promotes and stabilises the
economic growth of the country, there is no guarantee that this will lead to the reduction inpoverty and inequality. It is important in analysing the effects of remittances on poverty toconsider the direct effect of remittances on poverty (e.g. households under the poverty

14threshold have access to remittances and get out of poverty) as well as its indirect effect onpoverty through the economic growth (e.g. remittances facilitate capital acquisitions andlowers the cost of capital and increases output and then poor people will benefit from theincreased outputs indirectly12). Using the panel data for Asian countries, Imai, Gaiha, Ali, andKaicker (2014) decomposed the poverty-reducing effect of remittances into direct andindirect effects and showed that the remittances contributed to poverty reduction monthly
through their direct effects rather than through indirect effects, the result of which is robust to
two measures of international poverty, the cut-off points of $1.25 per capita/day and $2 percapita/day. As reviewed by Imai et al (2014), Vargas Silva, Jha, and Sugiyarto (2009)
examined the impact of remittances on poverty and economic growth in Asia using annual
data. Their econometric results imply that while the impact of remittances on growth ispositive, the impact on poverty is negative in Asia.
There have been some studies to examine the relationship between remittances andpoverty or inequality using the household survey data. Adams (1991) found that international
remittances reduced the number of poor households by 9.8 per cent in rural Egypt, but had anegative impact on the income distribution, because the rich households in the top 20
percentile group disproportionately benefited from remittance inflows. Taylor, Mora, Adams,
and Lopez-Feldman (2005) found that in Mexico international remittances increase rural
income inequalities while they reduce rural poverty because of the relatively larger benefits
from remittance inflows for the relatively poor. Using the village-level microdata fromBurkina Faso and the method of Gini decomposition, WouTerSe (2010) found that a marginalincrease in remittances from intra-African migration reduces inequality, whereas a marginalincrease in remittances from the more costly and risky intercontinental migration has the12

Indirect effects include a positive effect of remittances reception on financial inclusion ofhouseholds. For instance, access to remittance services may serve as an alternative to formal credit to
the poor, by helping them cover basic expenditures, cope with risks or channel a complementarysource of income that can be transformed into savings (IFAD and the World Bank, 2015).
15

opposite effect because the costs incurred to finance these two types of migration are
different and thus only better-off households were able to invest in international migration.
Brown, Connell, and Jimenez-Soto (2014) used household data from Fiji and Tonga anddemonstrated that where formal social protection systems are largely absent, migration andremittances can perform a similar function informally, contributing significantly to poverty
alleviation and wealth creation, while the impacts on Gini coefficients were unclear.

The relevance of remittances in rural household income structure
Covarrubias, Davis, Bakouan, and Di Giuseppe (2012) – in a background paper for the World
Development Report 2013 - present the results of a descriptive analysis of income generating
activities of 19 countries based on the Rural Income Generating Activities (RIGA) database.
The RIGA database has produced the analysis using the rural sample of Living Standards
Surveys.

16

Table 1 Share of Income Generating Activities in Total Income (Means of Shares)- based on RIGA data
Country/
Year

Per
Capita
GDP

(1)

(2)

2005
USD
919

AgricultureCrops
18.6%

Agriculture- Livestock
16.0%

(3)
Agricultural
wage
employment
11.5%

(4)
Non-farm
wage
employment
23.7%

(5)
Non-farm
selfemployment
11.2%

(6)
Transfers
from thegovernment
16.4%

Nepal 2003
Bangladesh
7.4%
2005
1 165
14.7%
7.2%
12.5%
28.8%
17.2%
Tajikistan
8.7%
2007
1 674
46.5%
5.0%
5.3%
26.0%
7.7%
Vietnam
10.7%
2002
1 784
32.2%
4.5%
5.5%
23.7%
20.9%
Pakistan
13.7%
2001
1 843
11.8%
10.5%
6.8%
38.4%
14.2%
Indonesia
22.7%
2000
2 623
13.9%
1.4%
6.7%
31.6%
20.3%
Source: Based on Covarrubias, Davis, Bakouan, and Di Giuseppe (2012, Table 3 on p.5) based on RIGA data.

(1) +
(2)
OnFarm
Total
34.6%

(4) +
(5)
Nonfarm
total
34.9%

(6) + (7)(3) + (4) +(5) + (6) +(7)Transfers& Other
19.1%

Off-farm
Total
65.4%

(7)

(1) + (2) +
(3)

Other
2.7%

Agricultural
total
46.0%

(4) + (5) +(6) + (7)NonAgricultural
Total
54.0%

12.1%

34.5%

65.5%

22.0%

46.0%

19.5%

78.0%

0.7%

56.9%

43.1%

51.6%

33.7%

9.4%

48.4%

2.4%

42.2%

57.8%

36.7%

44.6%

13.1%

63.3%

4.7%

29.1%

70.9%

22.3%

52.6%

18.4%

77.7%

3.5%

22.0%

78.0%

15.3%

51.9%

26.1%

84.7%

Table 2 Participation in Income Generating Activities (share of households undertaking each activity) –based on RIGA data
Country/
Year

Per
Capita
GDP

(1)

(2)

2005
USD
919

AgricultureCrops
88.2%

Agriculture- Livestock
79.9%

(3)
Agricultural
wage
employment
34.8%

(4)
Non-farm
wage
employment
38.7%

(5)
Non-farm
selfemployment
23.7%

(6)
Transfers
from thegovernment
37.3%

Nepal 2003
Bangladesh
37.3%
2005
1 165
74.8%
63.9%
22.8%
43.3%
27.4%
Tajikistan
45.6%
2007
1 674
89.8%
58.1%
21.3%
49.9%
16.4%
Vietnam
83.4%
2002
1 784
78.7%
67.5%
11.2%
38.7%
40.1%
Pakistan
31.5%
2001
1 843
40.1%
49.8%
15.1%
56.0%
21.5%
Indonesia
85.5%
2000
2 623
33.8%
6.8%
13.4%
44.8%
36.6%
Source: Based on Covarrubias, Davis, Bakouan, and Di Giuseppe (2012, Table 2 on p.4) based on RIGA data.

17

(1) +
(2)
OnFarm
Total
91.3%

(4) +
(5)
Nonfarm
total
54.3%

(6) + (7)(3) + (4) +(5) + (6) +(7)Transfers& Other
54.4%

Off-farm
Total
91.7%

(7)

(1) + (2) +
(3)

Other
28.4%

Agricultural
total
93.0%

(4) + (5) +(6) + (7)NonAgricultural
Total
83.7%

57.5%

75.2%

91.9%

71.2%

64.0%

72.9%

96.5%

2.7%

90.8%

81.6%

90.7%

61.4%

47.4%

88.6%

25.3%

78.8%

95.7%

83.1%

64.4%

86.5%

96.4%

14.7%

56.4%

83.6%

52.4%

67.2%

40.4%

88.5%

15.9%

42.1%

94.2%

34.4%

66.5%

87.2%

95.0%

Covarrubias et al. (2012) broke down the household income into several categories to analyse
the income structure of rural households in selected developing countries for which RIGA
data are available. We highlight only Asian countries in Table 1 and Table 2. RIGA data willshow that (i) the share of households which have positive values in transfers - the sum ofremittances and other transfers, such as transfers from the government (the column (6) of
Table 2) and the average share of ‘transfers’ (the column (6) of Table 1) appear to increase
as per capita GDP of the country increases (if Nepal – with a high share of ‘transfers’ isdropped from the sample in case of the latter), and (ii) both non-agricultural and non-farmncome, as well as the share of households participating in the activities related to the nonagricultural/non-farm sector, tend to increase as per capita GDP increases. Using RIGA datafor 19 developing countries including those outside Asia, Covarrubias et al. (2012) conclude:
Despite high levels of participation across GDP levels, the shares of income originatingfrom on-farm and agricultural activities drop with increasing PCGDP. Conversely,
nonfarm, off-farm and non-agricultural income are generally positively related to thelevel of development, driven largely by a greater share of income from nonfarm wage
employment. Moreover, a greater share of income is derived from transfers among
wealthier countries than poorer countries. This last trend could be due to greater
resources in the government (translated into more public transfers) or greater extendedfamily wealth (resulting in greater remittances income). (Covarrubias et al., 2012, p.3)

An important implication is that the change of the effects of household remittances is likely
to be closely associated with the income structure of recipient households at micro levels.
However, Covarrubias et al. (2012) only present the overall pattern of income sources and no
causal inference should be made between remittances and the income structure of recipient
households based on their statistical analyses. One possible extension is to carry out
econometric estimations where, for instance, a measure of diversification of householdincome sources is estimated by the remittances which are instrumented by appropriate
instruments in order to infer any causality from the RIGA data. This is a useful exercise as
most of the empirical literature on remittances relies on cross-country panel data and thestudies based on micro-level household survey data are relatively scarce.
18

Tables 1 and 2 imply that as the country progresses, the income structure of the household
is more diversified, but this change is likely to be associated with a more fundamental
transformation of the rural economy, such as, modernisation of rural infrastructure, expansionof rural non-farm sector, dynamic interactions between rural and urban sectors as well as
rural and foreign sectors, for instance, through the expansion of supermarkets or directcontacting of multi-national corporations with rural farmers, creating high value chains (Imai,
Gaiha, and Bresciani, 2016; Reardon and Timmer, 2014).13 Ramos, Estudillo, Sawada, andOtsuka (2012) have analysed in detail patterns and causes underlying the transformation of
the rural economy in the Philippines in 1988-2006. As can be seen in Table 3, as thehousehold income increased, the income structure gradually changed with higher shares in
non-farm sector income and remittances both at national levels (the upper panel of Table 3)
and in progressive towns (the lower panel of Table 3). That is, the overall pattern of thechanges of income structure we observed from the cross-country analyses for a limited
number of Asian countries in Table 1 can be confirmed in the time-series analysis for thePhilippines.
Table 3 Sources of real per capita income of rural households, the Philippines and its
progressive towns, 1988–2006
Income Sources
Philippines (US$ PPP)
Farm (%)
Nonfarm (%)
Formal salary work (%)
Informal work (%)
Manufacturing (%)
Trade, transportation and communication (%)
Others (%)
Remittances (%)
Domestic (%)
International (%)
Total (%)
Central Luzon, CALABARZON,1 Western
Visayas and Central Visayas (US$ PPP)

1988
578
45
41
28
13
2
10
1
14
8
6
100

1997
857
39
46
33
13
2
10
1
15
9
6
100

2000
826
35
48
35
13
2
10
1
17
9
8
100

2006
943
32
46
32
14
1
11
1
22
12
10
100

286

974

941

1026

13

Reardon and Timmer (2014) argue that agricultural transformation is characterised by (i)urbanization, (ii) growth of the rural non-farm economy, (iii) dietary diversification, (iv) a revolution
in supply chains and retailing; and (v) transformation of the agricultural sector. See Imai, Gaiha, andBresciani (2016) for further discussions about rural transformation.
19

Farm (%)
Nonfarm (%)

38
32
26
26
45
50
54
50
Formal salary work (%)
32
39
40
35
Informal work (%)
13
11
14
14
Manufacturing (%)
2
1
2
1
Trade, transportation and commuication (%)
9
9
10
12
Others (%)
2111
Remittances (%)
17
18
20
24
Domestic (%)
9
10
10
12
8
8
9
12
International (%)
Total (%)
100
100
100
100
Note: 1CALABARZON refers to the provinces of Cavite, Laguna, Batangas and Quezon.
Source: Ramos et al. (2012, Table 2 on p.1633). Emphasis has been added by theauthors.

Using the province data of the Philippines and applying Instrumental Variable (IV, where
infrastructure variables are treated as endogenous), Ramos, Estudillo, Sawada, and Otsuka(2012) found that the infrastructure, such as electricity and roads, and secondary and tertiary
education are important factors for the economic transformation of the rural economy, suchas an increase in non-farm income.14 In the Online appendix, Ramos, Estudillo, Sawada, andOtsuka estimated the determinants of international and domestic remittances using IV where
access to electricity or proportion of paved local road is instrumented by the number ofislands at the province level and a few other variables, such as population density.
Table 4. Determinants of remittances at the provincial level in the Philippines, 1988–2006(Second-stage of the IV regression)Access to electricity(an endogenous
variable,
Instrumented)
Proportion of paved
local road

International Remittances
1988-97
2000-66.02***
1.02

Domestic Remittances
1988-97
2000-62.49*-0.13[0.39][3.03][-0.12][1.83]

-2.24

-2.3

0.47

1.5

14

The results of Ramos, Estudillo, Sawada, and Otsuka (2012) should be interpreted cautiously
because (i) exclusion restrictions are not statistically validated in their main cases where farm or
nonfarm income (and its sub-component) is estimated by IV, that is, there is a possibility that theirinstruments, such as, the number of islands or population density, are correlated with the error terms
in the equation for subcomponent of income (p.7 and p.9 of the Online Appendix) and (ii) F test
statistics of excluded instruments are low (less than 10) in 6 out of 8 cases, suggesting the weak
instrument problem. On the former, there is a possibility that geographic isolations (by having manyislands) or the less concentration of the people would serve as disincentive effects for the farmers toundertake non-farm activities (p.7 and p9). In the case of remittances, tests for over-identification
support the validity of instruments in 7 out of 8 cases. However, the geographical conditions may be
important determinants of the indirect costs of remittances (e.g. how easily the recipients can do banktransactions).
20

(an endogenous
variable,
instrumented)
Proportion of paved
national road
National road density
Local road density[-0.42][-1.33][0.22][1.44]

0.55[0.78]
0.71[0.15]
1.02[1.25]-0.15[-0.31]-1.08[-0.61]
0.09[0.33]-0.14[-0.46]0.5[0.26]
0.47[1.43]

0.13[0.45]
1.64[1.57]
0.06[0.42]

2.3[1.21]

2.05***[3.24]

0.99[1.15]

1.8[0.77]

0.44[0.37]

2.04*[2.04]

0.57[0.21]-1.25[-0.92]-2.04[-0.16]0.05[0.02]-0.62[-0.44]-1.62[-1.23]3.23[0.91]-3.52[-1.15]0.51[0.28]
2.21**[2.42]

70.89[70.56]
3.78**[2.48]-1.32[-0.40]5.83[-1.18]3.85***[3.36]
3.77**[2.03]

2.78*[1.66]
2.71[0.89]-0.22[-0.62]0.12[0.85]-0.26[-1.43]-0.52[-0.87]-0.03*[-1.66]-0[1.48]
0.002[0.44]-0[-0.25]0.003[0.98]
1.73[0.48]
186
0.57

0.05
[0.15]
0.003[0.03]-0[-0.54]0.002[0.58]
0[-0.31]
0.002[1.32]
0.23[0.16]
248
0.59

-0.07
[-0.22]-0.14[-1.40]0[-0.12]0[-0.21]0[0.00]
0.003*[1.85]
1.04[0.65]
186
0.62

Proportion of the labour force:
Female0.24[0.19]
Between 15 and 25
years old
2.83
[1.21]
Between 26 and 35
years old
-3.01[-1.03]
Between 36 and 45
years old
-3.57[-1.45]
Between 46 and 59
years old
-1.24
[0.35]
With primary schooling
71.89[1.05]
With secondary
schooling
2.68[1.04]
With tertiary schooling
0.27[0.06]
Proportion of irrigatedarea
0.2[0.62]
Land acquisition anddistribution (LAD)
0.11[0.18]
Farmland to labour ratio-0.02[-0.09]Distance
0.005[1.48]
Road*distance-0[-0.57]-0.004**
Luzon*distance[-1.99]Visayas*distance0[0.01]
Constant
2.87[1.01]
Number of observations
244
R-squared
0.56***significant at 1 per cent significance level, **significant at 5 per cent, *significant at 5 per cent.
Source: Ramos et al. (2012, Table A4, Online Appendix). Emphasis has been added by the authors.

Table 4 suggests that infrastructure, such as access to electricity, which is treated as
endogenous, is an important determinant of international and domestic remittances in 20002006. Because the infrastructure is an important determinant of non-farm income, it isinferred that, as the rural economy undergoes transformation, the overall access to21

remittances will improve. Interestingly, the household with more female members tends toreceive a larger amount of remittance in 1998-1997, the result consistent with Niimi andReilly (2011). Contrary to our intuitions, the coefficient estimates for variables on educationof recipients are positive and significant for domestic remittances, and not for international
remittances. Because the unit of the regressions is a province, the neglect of the intraprovince variations may explain this counter-intuitive result. Using the different data (theSurvey of Households and Children of Overseas Workers in 1999-2000), Semyonov andGorodzeisky (2005) showed that education (schooling years) of recipients is positively andsignificantly associated with remittances sent from overseas workers to the households in thePhilippines, the results of which are deemed more reasonable.
‘Distance’ (proximity to urban centres affects the sources of household income) interacted
by a dummy variable for the island of Luzon is negative and significant for the international
remittances in 1998-1997. This implies that in Luzon the geographical location of thehousehold – aggregated at the province level – was an important determinant for theinternational remittances. Overall, the results for remittances are similar to those for non-farmincome in Ramos, Estudillo, Sawada, and Otsuka (2012).
Contrary to the results in Table 4, McKay and Deshingkar (2014) used the secondary datafrom household surveys for six countries in Africa and Asia (Nigeria, Rwanda, South Africa,
Uganda, Bangladesh and Vietnam) and found that (i) domestic remittances are more likely tobe received by poorer households, while international remittances tend to be received byricher households and that (ii) if a poor household does receive international remittances,these will have a substantial poverty reduction impact for that household, but few poor
households benefit from such remittances. The latter result implies that the poverty-reducing
effect of international remittances can be statistically significant using the micro-level data,
but it may not be significant at the aggregate level.

22Migration and Remittances: Incentives, Costs and Dynamics
In discussing the role of remittances and migration in economic outcomes of the recipient
households, it is important to consider the underlying determinants of migration. This is notnecessarily easy as each household has a different incentive and costs for migration andremittances and they are normally unobservable to researchers. Empirically, the panel data
will allow a researcher to model unobservable characteristics specific to each household, but
incentives and costs for migration cannot be convincingly disaggregated in the normal datasettings.
A short review article entitled ‘The new economics of labor migration’ published in
American Economic Review almost three decades ago (Stark and Bloom, 1985) is stillinsightful into the theoretical and empirical research on migration. Stark and Bloom pointed
out that a person who is more relatively deprived can be expected to have a stronger incentive
to migrate than a person who is less relatively deprived and emphasized the importance of
empirically modelling the income incentive and the direct and indirect costs – including
physical and psychological costs due to the migration - when modelling migration. In termsof empirical modelling, however, no suggestions were made by Stark and Bloom, (1985) on
how researchers will discern between migrant's incentives and transaction costs, while theyargue that it is important to include the wages of the locations before and after migration tocapture the incentives for migration. In this context, Stark and Taylor (1991) found using thedata from Mexico that, if the absolute income is controlled for, relatively deprived
households are more likely to be engaged in international migration than are households morefavourably situated in their village's income distribution. As far as we are aware, no studies
have convincingly distinguished incentives and transaction costs for migration because suchdata are not easily available.

23Even though people in rural areas migrate to foreign countries or urban areas, they do notnecessarily send money to the household members left out in the rural areas. Lucas and Stark(1985) theoretically modelled the motivations behind the remittances and empirically applied
the model to the data from Botswana. Lucas and Stark argued (1985) that altruism alone does
not appear to be a sufficient explanation of the motivations to remit. 15 Other motivations
include (i) risk-spreading that allows household members left out in the rural areas to takegreater risk – which was evidenced partly by the positive statistical association between the
degree of drought and the amount of remittance in Botswana; and (ii) investment in educationof youngsters who will migrate to town to reap returns and remit to repay the family's outlay.
Taylor, Rozelle, and De Brauw (2003) investigated a complex relationship between
migration, remittances and crop and self-employed income using the household data in China.
They found that (i) the loss of labour in rural areas to migration has a negative effect onhousehold cropping income in source areas, although it does not negatively affect crop yields,
and that (ii) the remittances sent home by migrants partially compensate for this loss due tomigration directly and stimulate crop production indirectly. That is, remittances will easeconstraints on production in the imperfect credit market. Taylor et al. (2003) concluded thatparticipating in migration at the household level increases household per capita income, forthose left behind, by between 16% and 43%, which is substantial.
In a similar context, Taylor and Wyatt (1996) used the data of Mexico and empiricallyshowed that remittances stimulate farm income by relaxing the credit and risk constraints onhousehold-farm production. Here they argue that the distribution of remittances acrossdifferent income groups and the initial distribution of constraints on production will shape theincome distribution over time. Because the shadow value of remittances is higher at therelatively poorer households, the remittances have an income-equalising effect in the long run
15

Admittedly, this is not a recent work, but their theoretical modelling can provide rich empiricalimplications in understanding the motivations behind remittances behaviour.
24

once the indirect effect (e.g. of loosening constraints on investing in income-producing assets)
is taken into account.
In terms of policy implications of these analyses, policymakers should lower both direct
and indirect costs of remittances and migration. Proven measures include the introduction of
the banking via mobile phones or online banking systems16, or the reduction of transaction
costs (e.g. by reducing fees or relevant taxes related to international and domestic remittances;
reducing risk-related and transportation costs; introducing more ATMs and other cash-out
access points through mobile agents in village centres or innovative mechanisms to reach thelast mile). Second, policymakers aiming to promote rural transformation (e.g. supporting therural non-farm sector or the rural infrastructure) should be aware that development of thenon-farm sector and increase in remittances and/or migration occur at the same time. Starting
the new business or shops in rural areas would not only require the initial investment, but also
create the needs for more frequent financial flows. Measures for facilitating financialtransactions, including remittances (e.g. by lowering remittance fees) would be useful toenhance both non-farm business and remittances behaviour at the same time. Third, there
might be some households who do not have access to remittances or migration at all,particularly if they are outside the village networks. Policies supporting these poor
households are equally important as those facilitating remittances and/or migration. These
policies should focus on rural infrastructure and new communication networks.

III.

The Review of Cross-country Data and Econometric Analyses

In the last section we have reviewed cross-country studies as well as country-level studies toinvestigate the role of remittances in promoting and stabilising economic growth as well as in16

See IFAD and World Bank (2013, 2015) for a much broader recommendation related to payment
systems and market competition as well as financial inclusion opportunities. These issues areimportant, but beyond the scope of this paper.
25

reducing poverty and inequality. This section first reviews the country-level data of
remittances and migration data. It then carries out econometric analyses to provide an insight
into the effect of remittances on economic growth, poverty or inequality after taking accountof the endogeneity of remittances.

The Review of Trends of Remittances and Migrations at Country Levels

Figures 7A-7B show the trends of remittances as a share of GDP for South Asia and East
Asia and the Pacific. The selection of the countries and years is based on the availability of
the relevant data in World Development Indicators 2015. Figure 7A indicates that the shareof remittances in GDP gradually increases over the years in most of the South Asian
countries except Pakistan which has experienced a decline of the share in 1980-2000 and
Nepal with a sharp increase of the share in 2000-2014.

30
20
10
0

Remittances, received (% of GDP)

Figure 7A. Remittances, received (% of GDP), South Asia

1970

1980

1990

2000

2010

2020

Year
Bangladesh

BhutanNepal
Sri Lanka

India
Pakistan

Figure 7B shows that in East and South-East Asia there are no common trends - either
increasing or decreasing – across the countries. We found in Table 3 that the share of
remittances in GDP has increased in 1988-2006, which is consistent with the trend of the26

remittances in the Philippines,17 but it decreased in 2006-2014. Though we need to avoid
making a definite conclusion, but it is interesting to find that in 2007-2008, the period where
many Asian countries were influenced by the global financial crisis, the share of remittances
in GDP decreased in the Philippines, while it increased in the Vietnam. The former could be
due to the decline of employment or the wage of Overseas Contract Workers (OCWs) in caseof the Philippines 18 , while the latter (the Vietnamese case) may be due to that fact that
remittances played a risk-insurance role, to insure against the loss of income of family
members left out in rural areas. It can also be found that in 1997-1998, the period of Asianfinancial crisis, the Philippines, Cambodia and Lao PDR experienced an increase in the shareof remittances in GDP which suggests that remittances might have played a risk-insurance
role. These observations, however, should not be generalised as there are some countries (e.g.
Thailand) where the trend of the remittances was more or less stable in these crisis periods.

15
10
5
0

Remittances, received (% of GDP)

Figure 7B. Remittances, received (% of GDP), East Asia and the Pacific

1970

1980

1990

2000

2010

2020

Year
CambodiaFiji
Lao PDR
Micronesia
Philippines
Timor-Leste

China
Indonesia
Malaysia
Papua New Guinea
Thailand
Vietnam

17

The difference of the absolute value of percentages (e.g. 22% in 2006 in Table 4 versus 13% in2006 in Figure 1B) is likely to be due to the difference of definitions of remittances.
18
It is noted that migration from the Philippines to EU and US were likely to be negatively influenced
by financial crisis.
27

Figures 8A and 8B present the trends of the absolute amount of remittances for each region.
In Figure 8A, we find that, reflecting the stable economic growth over the years and thegradually increasing trend of the share of remittances in GDP, the absolute amount ofremittances have increased over the years, except Bhutan. It is noted that remittances haveincreased very sharply in India.

2.0e+10 4.0e+10 6.0e+10 8.0e+100Remittances, received (current US$)

Figure 8A. Remittances, received (current US$), South Asia

1970

1980

1990

2000

2010

2020

Year
Bangladesh
India
Pakistan

BhutanNepal
Sri Lanka

In Figure 8B we can observe that the Philippines and China are the two major countries withthe largest and most increasing volume of international remittances. Indonesia, Vietnam,
Thailand have also had a relatively large amount of remittances from abroad and it has been
increasing in recent years. It is not easily to see the trends due to the scaling, but the othercountries have also had a marginally increasing trend of the amount of remittances.
Figure 8B. Remittances, received (current US$), East Asia and the Pacific28

1.0e+10 2.0e+10 3.0e+100Remittances, received (current US$)

1970

1980

1990

2000

2010

2020

Year
CambodiaFiji
Lao PDR
Micronesia, , Fed. Sts
Philippines
Timor-Leste

China
Indonesia
Malaysia
Papua New Guinea
Thailand
VietnamFigures 9A and 9B present the trends of the absolute amount of outgoing remittances for eachregion. In Figure 9A, we find that the absolute amount of outgoing remittances has anincreasing trend for India and for Sri Lanka. However, it is difficult to find a common trend
among the countries in East and South-East Asia and the Pacific in Figure 9B. We can argue
that Micronesia, Indonesia, Lao PDR and China have seen an increasing trend of outward
remittances.

Figure 9A. Remittances, received (current US$), South Asia29

6.0e+090

2.0e+09

4.0e+09

Remittances, paid (current US$)

1970

1980

1990

2000

2010

2020

Year
Bangladesh
India
Pakistan

BhutanNepal
Sri Lanka

2.0e+094.0e+096.0e+098.0e+09
0Remittances, paid (current US$)

Figure 9B. Remittances, paid (current US$), East Asia and the Pacific

1970

1980

1990

2000

2010

2020

Year
CambodiaFiji
Lao PDR
Micronesia, Fed. Sts.
Philippines
Timor-Leste

China
Indonesia
Malaysia
Papua New Guinea
Thailand
Vietnam

Consistent with an overall trend of inward remittances, net immigration (the total number of
immigrants minus emigrants during the period) has had an overall decreasing trend across
many Asian countries because the number of emigrants generally exceeded the number of
immigrants in a number of countries. The trends are shown in Figures 10A and 10B for South
Asia and East &d South-East Asia. The countries where the net immigration has declined

30

since the late 1970s include India, Bangladesh, Pakistan, China, Philippines, and Indonesia.
In these countries, there have been more emigrants than immigrants over the years. Other
countries have had a relatively stable trend. These trends are broadly consistent with theincreasing trend of inward remittances over the years in these countries.

-4.0e+06-2.0e+06

0

2.0e+06

Figure 10A. Net immigration, South Asia

1970

1980

1990

2000

2010

2020

Year
code1 = 7
code1 = 51
code1 = 81

code1 = 11
code1 = 77
code1 = 99

Notes: Code1: 7-Bangladesh; 11- Bhutan; 51-India; 77- Nepal; 81-Pakistan; 99- Sri Lanka
Net migration is the net total of migrants during the period.

Figure 10B. Net immigration, South Asia, East Asia and the Pacific

31

1
0

migration

-1
-2
1970

1980

1990

2000

2010

2020

Year
code1 = 18
code1 = 39
code1 = 60
code1 = 71
code1 = 86
code1 = 108

code1 = 24
code1 = 52
code1 = 67
code1 = 83
code1 = 107
code1 = 117

Notes: Code1: 18 – Cambodia; 24- China; 39- Fiji; 52-Indonesia; 60- Lao PDR; 67- Malaysia; 71- Micronesia, Fed. Sts.;
83- Papua New Guinea; 86- Philippines; 107- Thailand; 108- Timor-Leste; 117- Vietnam

In Figures 11A and 11B, the trends of emigration rate of tertiary educated based on the twoyears, 1990 and 2000, are summarised. The data are limited and it is better to avoid making
any conclusion based on them. Given the data limitations, the countries with a clearly
increasing trend of emigration rate of tertiary educated include Pakistan, Lao PDR,
Bangladesh, and India. Other countries did not experience a significant change in theemigration rate of tertiary educated.
Figure 11A. Emigration rate of tertiary educated (% of total tertiary educated population),
South Asia

32

30
20
10
0

Emigration rate of tertiary educated (% of total tertiary educated population)

1970

1980

1990

2000

2010

2020

Year
code1 = 7
code1 = 51
code1 = 81

code1 = 11
code1 = 77
code1 = 99

Notes: Code1: 7-Bangladesh; 11- Bhutan; 51-India; 77- Nepal; 81-Pakistan; 99- Sri Lanka

Figure 11B. Emigration rate of tertiary educated (% of total tertiary educated population), East
Asia and the Pacific

33

80
60
40
20
0
1970

1980

1990

2000

2010

2020

Year
code1 = 18
code1 = 39
code1 = 60
code1 = 71
code1 = 86
code1 = 108

code1 = 24
code1 = 52
code1 = 67
code1 = 83
code1 = 107
code1 = 117

Notes: Code1: 18 – Cambodia; 24- China; 39- Fiji; 52-Indonesia; 60- Lao PDR; 67- Malaysia; 71- Micronesia, Fed. Sts.;
83- Papua New Guinea; 86- Philippines; 107- Thailand; 108- Timor-Leste; 117- Vietnam

In Figures 12A and 12B, the trend of the average transaction cost of remittances based on alimited number of data points in 2010-2014. Here the long definition of the averagetransaction cost of remittances is ‘the average of the total transaction cost in percentage forsending the local currency equivalent of US$200 charged by each single remittance service
provider’. Given the data limitations, we can observe that the average transaction costs havedeclined over the years with some fluctuations across Asian countries. This might be partly
associated with the increase of the amount of outward remittance flow in Figures 8A and 8B.
Figure 12A. Average transaction cost of remittances (%), South Asia

34

9
8
7
6
5
4

1970

1980

1990

2000

2010

2020

Year
code1 = 7
code1 = 51
code1 = 81

code1 = 11
code1 = 77
code1 = 99

Notes: Code1: 7-Bangladesh; 11- Bhutan; 51-India; 77- Nepal; 81-Pakistan; 99- Sri Lanka

5

10

15

20

Figure 12B. Average transaction cost of remittances (%), East Asia and the Pacific

1970

1980

1990

2000

2010

2020

Year
code1 = 18
code1 = 39
code1 = 60
code1 = 71
code1 = 86
code1 = 108

code1 = 24
code1 = 52
code1 = 67
code1 = 83
code1 = 107
code1 = 117

Notes: Code1: 18 – Cambodia; 24- China; 39- Fiji; 52-Indonesia; 60- Lao PDR; 67- Malaysia; 71- Micronesia, Fed. Sts.;
83- Papua New Guinea; 86- Philippines; 107- Thailand; 108- Timor-Leste; 117- Vietnam

35

The Econometric Modelling on the Effects of Remittances and Migration on Growth, Poverty andInequality

In this subsection we will estimate the effects of remittances - as well as migration in one
specification – on economic growth, poverty or inequality. We update the cross-country datausing World Development Indicator 2015 and extend the specifications used by Imai, Gaiha,
Ali, and Kaicker (2014). Our sample is dictated by data availability and consists of 21 Asia
and Pacific economies over the period 1980 to 2014. 19 The definition and sources of thevariables are given in Appendix 2. Unless stated otherwise, the data are drawn from World
Development Indicators 2015. Based on the existing literature on remittances and growth,such as Chami, Fullenkamp, and Jahjah (2005) and Imai, Gaiha, Ali, and Kaicker (2014), ourbaseline specification takes the following form:∆𝒍𝒐𝒈 𝒚𝑖𝑡 = 𝛼 + 𝑿′ 𝑖𝑡 𝛽 + 𝛾𝑳𝑹𝑬𝑴𝑖𝑡 + 𝜂𝑖 +𝜀𝑖𝑡(1)

where for country i at time (denoting year) t, ∆log yit denotes the rate of growth of real per
capita GDP, LREMit is the logarithm of remittances expressed as a percentage of GDP,
𝜂𝑖 is unobserved country-specific effect and 𝜀𝑖𝑡 is the idiosyncratic error term. The vector
𝑿′ 𝑖𝑡 contains the lag of real per capita GDP, financial sector development, inflation,
investment, and the intensity of the conflict.
Given that 𝑳𝑹𝑬𝑴𝒊𝒕 may be endogenous, we have used two different models to address
this issue. First, we have estimated the panel IV (or 2SLS) using the fixed-effects estimator(FE-2SLS), the random-effects estimator (RE-2SLS), and the first-difference estimator (FD2SLS).20 In the first stage of 2SLS, we use the two instruments, (i) logarithm of the absolute
latitude of the country interacted by the time trend and (ii) ethnic fractionalization index
multiplied by the time trend. It is assumed that the geographical locations (proxied by theabsolute latitude) or the country’s ethnic profiles would determine the direct or the indirect
19

We have restricted the sample to the period after 2003 and have obtained broadly similar results.
An improvement has been made over Imai, Gaiha, Ali, and Kaicker (2014) who used only FE-2SLS
as we estimate RE-2SLS, FD-2SLS and System GMM to address the endogeneity of remittances.
20

36

costs of remittances. If the country’s latitude is higher, the distance to developed countries
will be closer and the transaction costs for international migration or remittances will besmaller and the amount of the remittances is larger. That is, the expected sign of thecoefficient estimate is positive. It is also noted that in developing countries the network plays
an important role for remittances and the country with a smaller value of ethnic
fractionalization will have a higher value of remittances. That is, the expected coefficientestimate is negative. These are expressed as a vector, 𝑍𝑖𝑡 in the equation (2).21(1st Stage) 𝑳𝑹𝑬𝑴𝑖𝑡 = 𝑎 + 𝑿′ 𝑖𝑡 𝑏 + 𝑍𝑖𝑡 𝑐 + 𝜇𝑖 +𝑒𝑖𝑡

(2)

(2nd Stage) ∆𝒍𝒐𝒈 𝒚𝑖𝑡 = 𝛼 + 𝑿′ 𝑖𝑡 𝛽 + 𝛾𝐿𝑅𝐸𝑀𝑖𝑡 + 𝜂𝑖 +𝜀𝑖𝑡

(1)’

It should be noted here that these are not necessarily ideal instruments because we cannot
deny the possibilities that – even after controlling for the country-fixed effects, the degree oflocations and ethnic fractionalization may influence the productivity and then GDP per capita
growth. However, the data restrictions do not allow us to construct a better instrument. At
least, the statistical tests (e.g. over-identification test) validate the instruments at least
statistically. Furthermore, FD-2SLS, that is, taking the first difference, would further mitigate
the problem of endogeneity. These are estimated by the equations (1)’ and (2)’.
(First Stage) ∆𝑳𝑹𝑬𝑴𝒊𝒕 = 𝑎′ + ∆𝑿′ 𝑖𝑡 𝑏′ + ∆𝒁𝒊𝒕 𝑐′ + ∆𝜇𝑖 +∆𝑒𝑖𝑡(2)’(Second Stage)∆∆𝒍𝒐𝒈 𝒚𝑖𝑡 = 𝛼′ + ∆𝑿′ 𝑖𝑡 𝛽′ + 𝛾′∆𝐿𝑅𝐸𝑀𝑖𝑡 + ∆𝜂𝑖 +∆𝜀𝑖𝑡(1)’’

Given the limitations, we estimate the dynamic panel using System GMM (Blundell andBond, 1998; Blundell, Bond, and Windmeijer, 2000) with Windmeijer’s (2005) finite sample
correction for the variance given that our observations are small.
21

Imai et al. (2014) used the income gap between each remittance receiving country and the US as an
instrument, but this has been criticised in the literature as the income gap can be directly related to thedependent variable in the second stage, such as the economic growth. That is, use of the income gap is
not much different from the income level of the country concerned.
37∆𝒍𝒐𝒈 𝒚𝑖𝑡 = ∑𝑃𝑗=1 𝛼𝑗 ∆𝒍𝒐𝒈 𝒚𝑖𝑡−𝑗 + 𝑿′ 𝑖𝑡 𝛽 + 𝛾𝐿𝑅𝐸𝑀𝑖𝑡 + 𝜂𝑖 +𝜀𝑖𝑡

(3)Here while we include lagged dependent variables ( ∆𝒍𝒐𝒈 𝒚𝑖𝑡−𝑗 ), we treat 𝐿𝑅𝐸𝑀𝑖𝑡 asendogenous by using its own lagged variables as instruments.
Next, we repeat the same estimations for the migration. However, as the number ofobservations is limited for the migration data, we will not estimate FD-2SLS or System
GMM.
Furthermore, we estimate the effect of remittances on poverty or inequality by taking into
account the endogeneity of remittances. This is based on the two-stage estimation describedas follows.
[First Stage]
𝑳𝑹𝑬𝑴𝑖𝑡 = ∑𝑃𝑗=1 𝑎′𝑗 𝑳𝑹𝑬𝑴𝑖𝑡−𝑗 + 𝑿′ 𝑖𝑡 𝑏′ + 𝒁𝑖𝑡 𝑐′ + 𝜂′𝑖 +𝜀′𝑖𝑡(4) 22

Here 𝒁𝑖𝑡 stands for (i) logarithm of absolute latitude of the country interacted by the timetrend and (ii) ethnic fractionalization index multiplied by the time trend.
[Second Stage]
̂𝑖𝑡 + 𝜇′𝑖 +𝑒′𝑖𝑡̂ 𝑖𝑡 + 𝛿′𝜀′𝑷𝑶𝑽𝑖𝑡 = 𝛼′ + 𝑿′ 𝑖𝑡 𝛽′ + 𝛾′𝑳𝑹𝑬𝑴(5)

Here in both equations (4) and (5), exogenous variables include trade openness, the labour
force with secondary education, and the lagged value of GDP per capita. The definition ofpoverty includes poverty headcount ratio or poverty gap at the national level based on theinternational poverty thresholds, US$1.25 or US$2.00 and poverty headcount ratio, poverty
gap or poverty gap squared for rural areas based on the international poverty thresholds,
̂ 𝑖𝑡 is the predicted value of the logarithm of remittances
US$1.25 or US$2.00. Here 𝑳𝑹𝑬𝑴
22

The same notations for coefficients or error terms are repeated for notational convenience, buteach case is estimated separately without any constraint for coefficients being equal, statedotherwise. The same caveat is applied to other equations.
38̂𝑖𝑡 is the predicted value of the error term in the equation (4). In
based on the equation (4). 𝜀′estimating the equation (5), the standard errors are adjusted by bootstrapping. We will thenreplace poverty by the Gini coefficient at the national level, or in the rural or urban areas.

The Econometric Results
The results for the equations (1)-(5) are presented in Tables 5-9. To save the space, we will
mainly report the coefficient estimates associated with remittances or migration.
Table 5 presents the results where the GDP per capita growth rate is estimated byremittances and other covariates. The first two columns show the results of fixed and random
effects models. Here the fixed-effect model is favoured by the result of the Hausman
specification test. In the column (1) the logarithm of remittances is positive and significant
with a coefficient estimate 0.007. This implies that the 10% increase in remittances on
average is associated with 0.07% increase in GDP per capita growth rate, other things being
equal. Other coefficient estimates are broadly intuitive.

Table 5 Effect of Remittances on Economic Growth (Dep. Var. GDP per capita growth)
(1)

(2)

(3)

(4)

(5)

VARIABLES

Case 1 FE

Case 2 RE

Case 3
FE-2SLS

Case 4
RE-2SLS

Case 5
FD-2SLS

GDP per capita (-1)

-

-

-

-

-

Log GDP per capita(-1)-0.0731***(0.0220)-0.00402***(0.00142)-0.00362(0.00762)
0.00687*(0.00340)-0.00037(0.00358)
0.0664***(0.0136)
Yes
0.301(0.130)0.64
Chi2(26)=55.35-0.0108***(0.00376)-0.00380***(0.00119)-0.00393(0.00581)
0.00455*(0.00253)-0.00467(0.00325)
0.0587***(0.0136)
Yes-0.0616(0.0406)-0.0424*(0.0229)0.00739*(0.00399)-0.0666***(0.0165)0.0874***(0.0241)-0.0086(0.00991)
0.103***(0.0301)
Yes

0.0119(0.00758)
0.00194(0.00296)-0.0332***(0.00904)
0.0392***(0.00830)-0.0157(0.00958)
0.0910***(0.0208)
Yes-0.227***(0.0770)-0.826***(0.0664)-0.00011(0.00281)-0.148***(0.0535)0.130*(0.0700)
0.001(0.00911)
0.145***(0.0497)
Yes
0.0349***(0.00671)
0.242

Log inflation
log Financial Development
Log Remittance
Conflict Intensity
log Investment
Year dummies
Constant
R-squared
Hausman-0.38539(6)Case 6
Dynamic
SGMM-0.0534(0.0355)-0.0115(0.0106)-0.00121(0.00171)-0.0212**(0.00948)
0.0150***(0.00463)-0.0129***(0.00380)
0.0760***(0.0181)
Yes-0.0375(0.0733)

P-value=0.0007
In favour of FE
First Stage

First Stage(Dep. Var. log (remittances) or Dlog (remittances)-0.5145*-0.7550***-0.6447***(0.2947)(0.0907)(0.2506)-0.0120-0.04280.0127(0.0339)(0.0328)(0.0139)
0.3904***0.1146-0.0018(0.0901)(0.1212)(0.1635)-0.1948***0.2040*0.0389(0.0712)(0.1130)(0.0472)-0.5718**-0.1275-0.1980(0.2343)(0.1956)(0.1470)log GDP per capita
L1.
log Inflation
log Financial Development
Conflict Intensity
log Investment
Absolute Latitude X Time
trend
Ethnic Fractionalization X
Time Trend
Constant
R-squared
F test
Test of excluded instruments:
Prob>F
Hansen J statistic (overidentification test of all instruments)

0.3396***(0.0593)0.1543***(0.0220)0.4042***(0.1259)0.0102*(0.0057)
0.362
F(2,519)17.49
0.0000-0.0276***(0.0055)
4.9224(0.8813)-0.0412*(0.0223)0.0402(0.0378)
F(2,515)
5.6
0.00390.882
P-val
0.3475
Robust standard errors in parentheses. Statistical significant coefficients are shown in bold.
*** p<0.01, ** p<0.05, * p<0.1

0.00550.8151In the columns (3), (4) and (5) the logarithm of remittance is treated as endogenous in themodels based on FE-2SLS, RE-2SLS and FD-2SLS. First, the instruments are statisticallysignificant, implying that the geographical characteristics and the country’s ethnic
fractionalization influence the transaction costs of remittances and thus their amount. F test of
excluded instrument is significant and greater than 10 (the commonly-used threshold value
with one endogenous variable) for FE-2SLS and significant with the F statistic being 5.6 for
FD-2SLS. Therefore, some doubt may be cast against the latter in the strength of instruments.
However, p-values for over-identification tests are 0.3475 and 0.8151, implying that
exclusion restrictions are likely to be valid in both cases. We find in these cases that theremittances are positive and significant in the second stage irrespective of the specifications.
If we take the case of FE-2SLS, the result suggests that the 10% increase in the remittances
share (from the current share of remittances in GDP, e.g., 10% share to 11%) on average is40

associated with the 0.874% increase in GDP per capita growth rate, other things being equal.
The positive and significant effect of remittances on GDP per capita growth is also confirmedin the final column of Table 5 where the dynamic panel model is estimated by System GMM.
However, once we replace the remittances by the net immigration (the number of
immigrants minus the number of emigrants), the coefficient estimate is not significant in FE,
RE or RE-2SLS as can be seen in Table 6. Only in the column (3) based on FE-2SLS where
the net immigration is treated as endogenous, the coefficient estimate is negative andsignificant, consistent with the results in Table 5. That is, the increase in emigrants (net of
immigrants) is positively associated with the increase in GDP growth. F test statistic of
excluded instruments is 7.67 and though it is statistically significant, the strength of
instrument may not be good enough as it is smaller than the commonly-used threshold of 10.
Over-identification test for instruments suggests that exclusion restrictions are likely to bevalid. Hence with some caveat, we find some evidence that out-migration (net of immigration)
tends to promote the economic growth.
Table 6 Effect of Migration on Economic Growth (Dep. Var. GDP per capita
growth)
VARIABLES
Log GDP per capita(-1)
log Inflation
log Financial
Development
Net immigration
Conflict Intensity
log Investment
Year Dummies
Constant

Observations
R-squared
Number of code1
Hausman(1)Case 1 FE(2)Case 2 RE(3)Case 3 FE-2SLS(4)Case 4 RE-2SLS-0.0449*(0.0242)-0.00674**(0.00311)-0.0126***(0.00423)-0.00730**(0.00357)-0.0252**(0.0120)-0.0249***(0.00659)-0.011(0.00751)-0.0231***(0.00426)

0.0232*(0.0122)-0.00378(0.00482)-0.00658(0.00714)-0.0182(0.0276)
Yes
0.276(0.166)0.0178*(0.00923)-0.000623(0.00392)-0.00795(0.00747)
0.0112(0.0292)
Yes
0.0138(0.0711)0.0283***(0.0101)-0.0277*(0.0143)-0.0211*(0.0115)-0.0561**(0.0282)
Yes

0.0200***(0.00688)-0.00564(0.0132)-0.0231***(0.00895)-0.0279(0.0172)
Yes
0.189***(0.0693)

183
0.308
30
Chi2=34.61

183

111
0.44
22

111

30

41

22

P-value=0.0017
In favour of FE
First Stagelog GDP per capita
L1.
log Inflation

First Stage(Dep. Var. log (remittances) or Dlog
(remittances)
0.09210.0877(0.1679)(0.1023)-0.0024-0.0185(0.0508)(0.0489)

log Financial
Development
Conflict Intensity
log Investment
Absolute Latitude X
Time trend
Ethnic Fractionalization
X Time Trend
Constant
Observations
R-squared
F test
Test of excluded instruments:
Prob>F
Hansen J statistic (overidentification test of all instruments)

0.0733(0.0710)-0.0180(0.1483)-0.0948(0.1935)-0.0237(0.0781)-0.1024(0.1059)-0.1412(0.1937)

0.0319(0.0372)

0.0338(0.0215)0.0379***(0.0097)-

0.0347***(0.0052)-0.0129(0.8329)
111-

111
F(2,82)7.67
0.00092.25
P-val
0.1336
Robust standard errors in parentheses. Statistical significant coefficients are shown in bold.
*** p<0.01, ** p<0.05, * p<0.1

In Table 7 the effect of remittances on poverty at the national level (based on the international
poverty lines) is estimated where the remittances are treated as endogenous. We estimate thepoverty headcount ratio and the poverty gap based on the US$1.25 per day poverty line (inthe columns (1) and (2)) and those based on the US$2 per day poverty line by the predictedvalue of remittances. In all cases, remittances are found to be negative and significant. That is,if the share of remittances increases by 10% (e.g. the share of remittances increase from 10%
to 11%) the poverty headcount based on US$1.25 a day (US$2.00 a day) decreases 3.97%(4.48%) (in comparison with the initial poverty headcount being set at 100%), other things
being equal. On the other hand, if the share of remittances increases by 10%, the poverty gap
on US$1.25 a day (US$2.00 a day) tends to decrease 16.7% (29.3%) (in comparison with the42initial poverty gap being set at 100%), other things being equal. It is thus concluded that theremittances have a substantial poverty-reducing effect.
Table 7 Effect of Remittances on National Poverty (based on theinternational poverty lines)
Second Stage

Dep.Var
VARIABLESP log (Remittance)̂ 𝑖𝑡 )(𝑳𝑹𝑬𝑴
openness

Labour Force with Secondary
Education
GDP per capita (-1)̂𝑖𝑡𝜀′Constant
Observations
R-squared
First Stage
Dynamic Panel (System GMM)
Log(Remittance(-1))
openness
Labour Force with Secondary
Education
GDP per capita (-1)
Absolute Latitude X Time trend
Ethnic Fractionalization X Time Trend
Constant(1)
FE
PovertyHC
US$1.25
Case 1(2)
FE
PovertyGap
US$1.25
Case 2(3)
FE
PovertyHC
US$2.00
Case 3(4)FE
PovertyGap
US$2.00
Case 4-0.397**(0.194)0.314(0.369)-1.670***(0.511)
0.393(0.345)-0.448(0.296)
0.0416(0.521)-2.903**(1.282)0.803(0.866)-0.0433-0.33(0.197)(0.491)0.813-0.76(1.414)(5.520)-1.177**0.00443(0.478)(0.459)-1.2182.89(1.905)(2.340)
211
200
0.113
0.197
Dep. Var. log (remittances)

0.0116(0.204)1.355(1.888)-0.127(0.343)1.742(3.044)211
0.14-0.524(1.298)-2.313(12.53)-1.899(1.339)7.249(6.217)200
0.212

0.826***(0.0397)-0.00259(0.0223)0.826***(0.0397)-0.00259(0.0223)0.826***(0.0397)-0.00259(0.0223)0.826***(0.0397)-0.00259(0.0223)0.00442(0.0155)-0.216(0.207)0.0283*(0.0145)0.00187(0.00364)
0.04(0.166)0.00442(0.0155)-0.216(0.207)0.0283*(0.0145)0.00187(0.00364)
0.04(0.166)0.00442(0.0155)-0.216(0.207)0.0283*(0.0145)0.00187(0.00364)
0.04(0.166)0.00442(0.0155)-0.216(0.207)0.0283*(0.0145)0.00187(0.00364)
0.04(0.166)

Joint siignificance
Wald chi2(6)2635.91***
Arellano-Bond test for zero autocorrelation in first-differenced errors (P-value)
10.00030.00030.00030.00032
0.28250.28250.28250.2825
Sargan test of overidentifying restrictionschi2(426)=
357.34357.34357.34357.34P-value
0.9932
0.9932
0.9932
0.9932
Robust standard errors in parentheses. Statistical significant coefficients are shown in bold.
*** p<0.01, ** p<0.05, * p<0.1
Standard errors in the second stage are bootstrapped.

43

We observe in Table 8 that the effect of the share of remittances on rural poverty (based on
the international poverty lines) is negative and significant in all the cases - regardless of the
choice of poverty lines or definitions of poverty - after the endogeneity of remittances istaken into account. For instance, Column (1) shows that a 10% increase in the share of
remittances in GDP tends to decrease the poverty headcount ration based on the US$1.25 line
by 3.06% on average – with the initial poverty level being set at 100%, other things being
equal. The amount of reduction in response to the 10% increase in the share of remittances in
GDP will be 5.22% for poverty gap (the column (2)) and 7.44% for poverty gap squared ((3))
both of which are based on US$1.25 poverty line. The poverty-reducing effects at the US$2
per day poverty line are getting smaller, though the coefficient estimates are statisticallysignificant (columns (4), (5) and (6)). If the share of remittances in GDP increases by 10%,the poverty headcount (gap; gap squared) based on the US$2 per day will decline by 0.998%(2.62%; 3.46%). It is found that remittances have a substantial poverty-reducing effect not
only on national poverty but also on rural poverty. The signs of other coefficient estimates are
expected, though the trade openness measure is not statistically significant.

44

Table 8 Effect of Remittances on Rural Poverty (based on the international poverty lines)
Second Stage

(1)
FE

(2)
FE

(3)
FE

(4)
FE

Rural Poverty
HC

Rural Poverty
Gap

Rural
Poverty HC

US$1.25

US$1.25

Rural Poverty
Gap
Squared
US$1.25

(5)
FE
Rural
PovertyGap

US$2.00

US$2.00

(6)
FE
Rural
PovertyGap
Squared
US$2.00

VARIABLES

Case 1Case 2

Case 3

Case 4

Case 5

Case 6p log(Remittance)-0.306***-0.522***-0.744***-0.0998***-0.262***-0.346***

̂ 𝑖𝑡 )(𝑳𝑹𝑬𝑴(0.0420)(0.102)(0.196)(0.0375)(0.0484)(0.0707)

Dep.Var

Openness

0.148

0.217

0.155

0.03340.0792

0.0971(0.668)(1.631)(3.111)(0.597)(0.770)(1.125)

Labour Force with
Secondary Education-0.160***-0.151**-0.0574-0.0921***-0.113***-0.125***(0.0257)(0.0628)(0.120)(0.0230)(0.0297)(0.0433)

L.GDP per capita growth-5.219***-6.215***-3.960***-2.473***-3.516***-3.970***(0.0640)(0.156)(0.298)(0.0572)(0.0738)(0.108)0.929***

0.950***

0.740**

0.197***

0.250***

0.314***(0.0709)(0.173)(0.331)(0.0634)(0.0818)(0.120)

2.324

0.76

0.204

3.623

2.344

1.504

(4.446)(10.78)(20.53)(3.966)(5.111)(7.459)Observations

75

73

69

78

78

77

R-squared

0.402

0.453

0.353

0.393

0.487

0.456

̂𝑖𝑡
𝜀′
Constant

First Stage

Dep. Var. log (remittances)

Dynamic Panel (System GMM)
Log(Remittance(-1))

Openness
Labour Force with
Secondary Education

L.GDP per capita growth
Absolute Latitude X Time
trend
Ethnic Fractionalization X
Time Trend

Constant

0.826***

0.826***

0.826***

0.826***

0.826***

0.826***(0.0397)(0.0397)(0.0397)(0.0397)(0.0397)(0.0397)-0.00259-0.00259-0.00259-0.00259-0.00259-0.00259(0.0223)(0.0223)(0.0223)(0.0223)(0.0223)(0.0223)0.004420.004420.004420.004420.004420.00442(0.0155)(0.0155)(0.0155)(0.0155)(0.0155)(0.0155)-0.216-0.216-0.216-0.216-0.216-0.216(0.207)(0.207)(0.207)(0.207)(0.207)(0.207)0.0283*0.0283*0.0283*0.0283*0.0283*0.0283*(0.0145)(0.0145)(0.0145)(0.0145)(0.0145)(0.0145)0.00187

0.00187

0.00187

0.00187

0.00187

0.00187(0.00364)(0.00364)(0.00364)(0.00364)(0.00364)(0.00364)

0.04

0.04

0.04

0.04

0.04

0.04

(0.166)(0.166)(0.166)(0.166)(0.166)(0.166)

Arellano-Bond test for zero autocorrelation in first-differenced errors (P-value)
10.00030.00030.0003

Joint siignificance
Wald chi2(6)22635.91***0.28250.00030.00030.00030.28250.28250.28250.28250.2825

Sargan test of overidentifying restrictionschi2(426)=

357.34357.34357.34357.34357.34357.34P-value

0.9932

0.9932

0.9932

0.9932

0.9932

0.993245

Robust standard errors in parentheses. Statistical significant coefficients are shown in bold. *** p<0.01, ** p<0.05, * p<0.1.
Standard errors in the second stage are bootstrapped.

While the remittances have a substantial and statistically significant poverty-reducing effectfor both rural and urban poverty, our results suggest that they do not have any inequalityreducing effect. Column (1) of Table 9 shows that the predicted value of remittanceŝ 𝑖𝑡 ) is not statistically significant. However, 𝑳𝑹𝑬𝑴
̂ 𝑖𝑡 is significant in Columns (2) and(𝑳𝑹𝑬𝑴(3), implying that the 10% increase in the share of remittances in GDP (e.g. from 10% to11%) is associated with 0.399% increase in rural Gini or 0.466% in urban Gini. Given that
remittances have increased sharply in recent years and they are expected to increase, theaccumulated effect of inequality increase may be large. This is likely to be due to the factthat the remittances benefit disproportionally the relatively rich households. Our findings arein line with Adams’s (1991) in rural Egypt and Taylor, Mora, Adams, Lopez-Feldman’s(2005) in rural Mexico and McKay and Deshingkar (2014) on the cross-country evidence.
However, our results are not consistent with Taylor and Wyatt’s (1996, p.910) result on
Mexico where the remittances were found to marginally decrease Gini. More research basedon the household datasets is necessary to investigate the relation between remittances andinequality.

Table 9 Effect of Remittances on Inequality (National Gini, Rural Giniand Urban Gini)
Second Stage(1)
FE
National
Gini
Case 1

(2)
FE
Rural
Gini
Case 2

(3)
FE
Urban
Gini
Case 3p log(Remittance)

0.02320.0399***0.0446***

̂ 𝑖𝑡 )(𝑳𝑹𝑬𝑴(0.0183)(0.00579)(0.0125)

Openness0.00961

0.0313-0.00273(0.0123)(0.0922)(0.199)-0.0079-0.0178***-0.0152**(0.0107)(0.00355)(0.00766)-0.0195-0.234***-0.223***

Dep.Var

VARIABLES

Labour Force with Secondary
Education.GDP per capita growth (-1)

46

̂𝑖𝑡
𝜀′

(0.0648)(0.00883)

0.0461***0.0164*0.00951(0.00975)(0.00979)(0.0211)3.470***

3.298***

3.596***(0.0939)(0.610)(1.297)

Constant(0.0191)

Observations

215

78

76

R-squared

0.136

0.15

0.165

First Stage: Dynamic Panel Dep. (System GMM) Var. log (remittances)
Log Remittance (-1)

0.826***(0.0397)(0.0397)(0.0397)

Openness-0.00259-0.00259-0.00259(0.0223)(0.0223)(0.0223)0.004420.004420.00442(0.0155)(0.0155)(0.0155)-0.216-0.216-0.216(0.207)(0.207)(0.207)

Labour Force with Secondary
Education.GDP per capita growth (-1)

0.826***

0.826***

0.0283*0.0283*0.0283*(0.0145)(0.0145)(0.0145)0.00187

0.00187

0.00187(0.00364)(0.00364)(0.00364)

0.04

0.04

0.04

(0.166)(0.166)(0.166)

Absolute Latitude X Time trend

Ethnic Fractionalization X Time Trend

Constant

Joint siignificance
Wald chi2(6)2635.91***

Arellano-Bond test for zero autocorrelation in first-differenced errors (P-value)
10.00030.00030.00032

0.28250.28250.2825chi2(426)=

357.34357.34357.34P-value

0.9932

0.9932

0.9932

Sargan test of overidentifying restrictions*** p<0.01, ** p<0.05, * p<0.1. Standard errors in the second stage are bootstrapped.
Robust standard errors in parentheses. Statistical significant coefficients are shown in bold.

IV.

Concluding Observations

In this study, we have first reviewed the literature on remittances with respect to their effecton economic growth, its stability and poverty. Some micro studies on remittances have beenreviewed to understand better what the underlying mechanisms are whereby people indeveloping countries migrate and remit with a focus on the income structure of households,
their incentives and costs. We then use the cross-country panel data and econometricallyexamine the effects of remittances and migration on economic growth, poverty and inequality
47

after taking into account the endogeneity of remittances. Our conclusions are summarised
into the following three points.
First, it is found from our econometric results that remittances will promote economic
growth and reduce poverty - both national poverty and rural poverty based on theinternational poverty lines for the US$1.25 or US$2 thresholds after taking into account theendogeneity of remittances. Consistent with this result, net migration (the number of
immigrants minus the number of emigrants) which is treated as endogenous tends to be
negatively associated with economic growth given that the remittances are likely to bepositively associated with the number of emigrants. However, we have found that theremittances have no inequality-reducing effect. Rather, they will significantly increase rural
Gini and urban Gini, the results which are contradictory to Taylor and Wyatt’s (1996) result
on Mexico. As discussed by Taylor and Wyatt, in order to analyse the effect of remittanceson income distribution, it is necessary to consider (i) the distributions of the amount ofremittances across different income groups, (ii) the distributions of the sizes of potential
benefits/incentives of remittances across different income groups and (iii) the costs and risk
associated with migration and remittances. That is, the distributional effect of remittances
will have to be investigated in the long run. Our use of rural poverty (based on theinternational poverty lines) and rural or urban Gini for the cross-country study nevertheless is
a departure from the existing literature, such as, Imai, Gaiha, Ali, and Kaicker (2014) who
used only poverty data at the national level. Our econometric results are broadly consistent
with the existing literature on remittances and migration in developing countries. In ourreview, many studies have based on the cross-country panel data have shown that (i)remittances tend to promote economic growth, (ii) remittances tend to stabilise economic
growth (ii) remittances tend to reduce poverty.

48

Second, we need to understand the underlying factors, or motivations, behind themigration or remittance behaviour. For instances, our detailed review of the micro-study in
the Philippines (Ramos, Estudillo, Sawada, and Otsuka, 2012) has suggested that (i) the
development of infrastructure as well as a geographical location (e.g. the distance to town
centres) is key to the increase in remittances and (ii) the remittances tend to increase when therural economy undergoes the structural transformation, such as, development of non-farmrural sector where the infrastructure, such as electricity access, or improvement of educationof the residents is deemed important (ibid., 2012). These are broadly consistent with our
statistical summary of the cross-country data where the lower-middle income countries many of which have experienced the structural transformation in rural areas (Imai, Gaiha,
and Bresciani, 2016) - have typically experienced a surge of inward remittances.
Third, it has also been suggested that the risk-coping role of remittances – at the macrolevel to stabilise the economic growth and at the micro level where households can ease thecredit constraints and manage to smooth consumption. The role of networks is important as
the kinship or castes in the South Asian context will affect how easily poor households will
migrate to urban areas or abroad (e.g. Munshi and Rosenzweig, 2016). In this context,
remittances will serve as a risk insurance role. Hence, it is important to consider any effect of
the networks available to households in estimating the determinants of remittances. As
suggested by Taylor, Rozelle, and De Brauw (2003), the remittances will allow household
members left out in the rural area to take some risk and stimulate crop yielding. This willserve as insurance for the migrants’ future income when they come back to the home village
after the temporary migration. The research on remittances or migration and risk-insurance orrisk-coping mechanisms is still scarce and should be an important research topic in the future.
It is important, however, to consider the fact that the very poor households cannot afford
undertaking migration or remittance behaviour because they cannot afford the cost of49

migration (e.g. travel), or they may be outside the network which facilitates migration or
remittances. The experimental study (e.g. the randomised controlled trials) is difficult to
conduct to evaluate the effect of migration on poverty as the opportunity for migration cannot
be easily randomised experimentally, and so the large national household datasets should be
used to carry out rigorous examinations of the poverty-reducing effect of migration or
remittances at household levels.
The study will offer a few useful policy implications. First, given that the remittances haveincreased sharply in recent years and have a substantial share in GDP in many Asian
countries, the government should monitor the trend of remittances along with other financial
inflows and consider the possible macroeconomic consequences in the future. For instance,
the recession of developed countries may result in the reduced amount of international
remittances at the macro level and this may increase the fluctuation of the economic growthin the future. On the other hand, as the surge of the remittances may have a risk of the realexchange rate appreciation and of weakening the competitiveness of exported goods in theinternational economy, the aggregate remittances should be used as one of the macro-level
financial indicators.
Second, policymakers should lower the transaction costs of remittances (the reduction ofwhich have been confirmed by our data) as well as those of migration. The latter includes not
only physical costs but also the costs associated with any legal restrictions on domestic andinternational to migration. The former may include the introduction of the banking via mobile
phones or online banking systems, the reduction of fees or relevant taxes related tointernational and domestic remittances, or the introduction of more ATMs or other equivalentsystems in the village centres. These measures are not only important for promoting
economic growth and reducing poverty at the macro level but also helpful for providing the
agricultural households in rural areas with more options to cope with risk.

50

Third, it is important for policymakers to support the process of rural transformation, e.g.,
providing village infrastructure and communication networks, and promoting education
because these measures will eventually facilitate households’ remittance behaviour.
Finally, as migration or remittances are unlikely to benefit all the households in the rural
economy, it is important for the government to provide enough policy support for those who
do not have access to the opportunity for migration or remittances (e.g. the poor households
in backward castes, ethnic minorities, and/or living in remote areas).

51

References
Abdih, Y., Chami, R., Dagher, J. and Montiel, P. (2012). Remittances and institutions: Are
remittances a curse? World Development, 40(4), pp.657-666.
Acosta, P.A., Lartey, E.K. and Mandelman, F.S. (2009). Remittances and the Dutch disease.
Journal of International Economics, 79(1), 102-116.
Adams, R.H. (1991). The effects of international remittances on poverty, inequality anddevelopment in rural Egypt. IFPRI Research Report 86. International Food Policy
Research Institute, Washington, DC.
Ahamada, I. and Coulibaly, D. (2011). How does financial development influence the impactof remittances on growth volatility?, Economic modelling, 28(6), pp.2748-2760.
Amuedo-Dorantes, C. and Pozo, S. (2004). Workers' remittances and the real exchange rate:a paradox of gifts. World Development, 32(8), 1407-1417.
Ajide, K.B., Raheem, I.D. and Adeniyi, O. (2015). Output growth volatility, remittances andinstitutions. International Journal of Development Issues, 14(3), pp.190-203.
Baltagi, B.H., Demetriades, P.O. and Law, S.H. (2009). Financial development and openness:
Evidence from panel data. Journal of Development Economics, 89(2), pp.285-296.
Labor Markets and Poverty in Village Economies¤
Bandiera, O., Burgess, R., Das, N, Gulesci, S., Rasul, I., Sulaiman, M. (2016). Labor Markets
and Poverty

inVillage Economies,

Quarterly

Journalof

Economics,

forthcoming.
Barajas, A., Gapen, M. T., Chami, R., Montiel, P., & Fullenkamp, C. (2009). Do workers’
remittances promote economic growth? IMF Staff Paper, 52(1), 55–82.
Beck , T., A. Demirguc-Kunt, and R. Levine (2009), ‘Financial institutions and markets
across countries and over time - data and analysis’, Policy Research Working Paper Series
4943 (Washington DC: The World Bank).

52

Bettin, G., Presbitero, A. F. and Spatafora, N. L. (2015). Remittances and vulnerability indeveloping countries. The World Bank Economic Review, p.lhv053.
Blundell, R., and S. Bond (1998).

Initial conditions and moment restrictions in dynamic

panel data models., Journal of Econometrics 87: 115-143.
Blundell, R., S. Bond, and F. Windmeijer, F. (2000), ‘Estimation in dynamic panel data
models: Improving on the performance of the standard GMM estimator’, in Baltagi, B. ed.,Nonstationary Panels, Panel Cointegration, and Dynamic Panels. Advances in
Econometrics Volume 15 (Amsterdam: JAI Press, Elsevier Science).
Bugamelli, M. and Paternò, F. (2011). Output growth volatility and remittances. Economica,
78(311), pp.480-500.
Brown, R.P., Connell, J. and Jimenez‐Soto, E.V. (2014). Migrants' remittances, poverty andsocial protection in the South Pacific: Fiji and Tonga. Population, Space and Place, 20(5),pp.434-454.
Chami, R.; A. Barajas; T. Cosimano; C. Fullenkamp; M. Gapen and P. Montiel (2008).
“Macroeconomic Consequences of Remittances”, IMF Occasional Paper No 259,
(Washington: International Monetary Fund).
Chami, R., T.F. Cosimano, and M.Gapen, (2006), “Beware of Emigrants Bearing Gifts:
Optimal Fiscal and Monetary Policy in the Presence of Remittances,” IMF Working Paper
06/61 (Washington: International Monetary Fund).
Chami, R., Fullenkamp, C., Jahjah, S., (2003). Are migrant remittance flows a source ofcapital for development? IMF Working Paper. International Monetary Fund, Washington,
DC (August).
Chami, R., Hakura, D. S., Montiel, P. J. (2012). Do Worker Remittances Reduce Output
Volatility in Developing Countries? Journal of Globalization and Development 3 (1), 1-23.

53

Covarrubias, K., Davis, B., Bakouan, A. and Di Giuseppe, S., 2012. Household Income
Generation Strategies. Background paper for the WDR 2013.
Combes, J. L. and Ebeke, C. (2011). Remittances and household consumption instability indeveloping countries. World Development, 39(7), pp.1076-1089.
González, A., Teräsvirta, T. and Dijk, D.V. (2005). Panel smooth transition regression
models (No. 604). SSE/EFI Working Paper Series in Economics and Finance.
Giuliano, P., & Ruiz-Arranz, M. (2009). Remittances, financial development and growth.
Journal of Development Economics, 90(1), 144–152.
Hansen, B.E., (1999). Threshold effects in non-dynamic panels: Estimation, testing, andinference. Journal of econometrics, 93(2), pp.345-368.
IFAD and the World Bank (2013). Sending Money Home to Asia: Trends and opportunities
in the world’s largest remittance marketplace, IFAD, Rome.
IFAD and the World Bank (2015). The use of remittances and financial inclusion: A reportby the International Fund for Agricultural Development and the World Bank Group to theG20 Global Partnership for Financial Inclusion, IFAD, Rome.
Imai, K.S., Gaiha, R., and Bresciani, F. (2016). ‘Dynamics of Transformation in Rural Asiaand the Pacific: Policy and Operational Implications’, Draft, IFAD.
Imai, K.S., Gaiha, R., Ali, A. and Kaicker, N., (2014). Remittances, growth and poverty: New
evidence from Asian countries. Journal of Policy Modeling, 36(3), pp.524-538.
Imai, K.S., Gaiha, R. and Garbero, A., (2014). Poverty Reduction during the Rural-Urban
Transformation: Rural Development is still more important than Urbanisation?. Brooks
World Poverty Institute Working Paper.
Jihoud,A. (2015). Remittances and Macroeconomic Volatility in African Countries. IMF
Working paper, WP/15/49.

54

Kelegama,S. (2011). Migration, Remittances and Development in South Asia, Institute of

Policy Studies of Sri Lanka (IPS), Colombo, Sri Lanka.
Lucas, R.E. and Stark, O. (1985). Motivations to remit: Evidence from Botswana. The
Journal of Political Economy, pp.901-918.
McKay, A. and Deshingkar, P. (2014). Internal Remittances and Poverty: Further Evidence
from Africa and Asia. Migrating out of Poverty Research Programme Consortium's
Working Paper, 12.
McKenzie, D. and Sasin, M.J. (2007). Migration, remittances, poverty, and human capital:
conceptual and empirical challenges. World Bank Policy Research Working Paper, (4272).
Munshi, K. and Rosenzweig, M. (2016). Networks and misallocation: Insurance, migration,and the rural-urban wage gap. The American Economic Review, 106(1), pp.46-98.
Niimi, Y. and Reilly, B. (2011). Gender differences in remittance behavior: Evidence from
Vietnam. The Singapore Economic Review, 56(2), 215-237.
Pradhan, G., Upadhyay, M., & Upadhyaya, K. (2008). Remittances and economic growth indeveloping countries. The European Journal of Development Research, 20(3), 497–506.
Ramos, C.G., Estudillo, J., Sawada, Y. and Otsuka, K. (2012). Transformation of the rural
economy in the Philippines, 1988–2006. The Journal of Development Studies, 48(11),pp.1629-1648.
Reardon T and Timmer CP. (2014). Five Inter-Linked Transformations in the Asian Agrifood
Economy: Food Security Implications. Global Food Security 3(2): 108-117.
Semyonov, M. and Gorodzeisky, A. (2005). Labor Migration, Remittances and Household
Income: A Comparison between Filipino and Filipina Overseas Workers1.
International Migration Review, 39(1), 45-68.
Stark, O. and Bloom, D. E. (1985) The new economics of labor migration. The American
Economic Review, 75(2), pp.173-178.

55

Stark, O. and Taylor, J.E. (1991) Migration incentives, migration types: The role of relative
deprivation. The Economic Journal, 101(408), pp.1163-1178.
Taylor, J.E., Mora, J., Adams, R. and Lopez-Feldman, A. (2005) Remittances, inequality andpoverty: evidence from rural Mexico. ARE Working Papers 05-003. University ofCalifornia, Davis, Department of Agricultural and Resource Economics, Davis, CA.
Taylor, J.E., Rozelle, S. and De Brauw, A. (2003). Migration and incomes in source
communities: A new economics of migration perspective from China. Economic
Development and Cultural Change, 52(1), pp.75-101.
Taylor, J.E. and Wyatt, T.J. (1996). The shadow value of migrant remittances, income andinequality in a household‐farm economy. The Journal of Development Studies, 32(6),pp.899-912.
Vargas Silva, C., Jha, S., & Sugiyarto, G. (2009). Remittances in Asia: Implications for theFight against Poverty and thePursuit of Economic Growth, ADB Economics Working
Paper Series No. 182. Manila: Asian Development Bank.
Windmeijer, F. (2005). ‘A finite sample correction for the variance of linear efficient twostep GMM estimators’. Journal of Econometrics, 126: 25-51.
World Bank (2009). Migration and Development Brief 11. World Bank, Washington D.C.
http://library.aua.am/files/2011/06/ATTEBP6Z.pdf.
World Bank (2016). World Development Indicators 2015, http://data.worldbank.org/datacatalog/world-development-indicators.
WouTerSe, F., 2010. Remittances, poverty, inequality and welfare: Evidence from theCentral Plateau of Burkina Faso. The Journal of Development Studies, 46(4), pp.771-789.

56

Appendix 1: Descriptive Statistics and Definitions of Variables
Variables
Log (Remittances)

Definitions of Variables and Data Sources in squared brackets.

Obs

Mean

Std. Dev.

Min

Max

log (Remittances, received (% of GDP) [WDI 2015].
Net migration is the net total of migrants during the perio

882
324

0.2689615-.182445

1.679814.5692244-7.673314-3.570954

3.903869
2.118323

Net Immigration
GDP per capita growthlog GDP per capita
log Inflation

Real GDP Growth Rate (2005 PPP, US$) [WDI 2015].
1,648
0.032527
0.0846911-0.3736391
1.256495
log real GDP per capita (2005 PPP, US$) [WDI 2015].
1,448
6.914186
1.096407
4.281169
9.535043
log of inflation measured by CPI (annual %) [WDI 2015].
1,651
1.579604
2.199332-13.36887
8.112167
Captured by deposit money bank assets / (deposit money + central) bank assets [Beckand Demirgüç-Kunt, 2009] expressed in log-form, updated version in 2013,
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMD
log Financial Development
K:20696167~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html.
1,440
3.157944
0.7902612-0.5845596
5.110294
Absolute Latitude X Time trend
Log of absolute value of latitude multiplied by time trend [WDI 2016].
1,211
0.3468181
0.1845544
0.0555556
0.6555555
Ethnic fractionalization Index multiplied by time trend [Quality of Government
Ethnic Fractionalization X Time Trend
dataset].
1,151-1.146982
0.6382936-3.091495-0.3077025
Conflict Intensity
Intensity of conflict (Internal armed conflicts [UCDP/PRIO Conflict Database].
2,440
0.172541
0.4732969
02Gross capital formation (% of GDP) [WDI, 2015] expressed in log-form.
Log Investment
1,630
3.149867
0.3247958
1.547072
4.090516
Openness
Exports plus imports (% of GDP) [WDI, 2015] expressed in log-form.
2,184
3.573144
3.12678507.145985
Labour Force with Secondary Education The share of labour force with secondary education.
2,184
0.6101827
1.572984
05.384495
povertyhc125
Poverty headcount ratio based on US$1.25 per day, 2005 PPP [WDI, 2015].
347
0.2895066
2.723209-4.60517
4.431055
povertyg125
Poverty gap ratio based on US$1.25 per day, 2005 PPP [WDI, 2015].
327
4.043402
6.950575
039.26
povertyhc200
Poverty headcount ratio based on US$2.00 per day, 2005 PPP [WDI, 2015].
347
1.699737
2.517087-4.60517
4.583027
povertyg200
Poverty headcount gap based on US$2.00 per day, 2005 PPP [WDI, 2015].
327
9.882015
13.9548
0
59.27
Poverty headcount ratio in rural area based on US$1.25 per day, 2005 PPP [SKD, IFAD,
epov_h_rur
2015].
105
2.31634
2.047917-4.60517
4.424847
epov_gap_rur
Poverty gap ratio in rural area based on US$1.25 per day, 2005 PPP [SKD, IFAD, 2015].
103
0.9847825
2.042092-4.60517
4.053523
Poverty gap squared in rural area based on US$1.25 per day, 2005 PPP [SKD, IFAD,
epov_gap2_rur
2015].
97
0.2660669
1.71577-4.60517
3.81903
Poverty headcount ratio in rural area based on US$2.00 per day, 2005 PPP [SKD, IFAD,
mpov_h_rur
2015].
110
3.215607
1.657771-1.660731
4.53303
Poverty gap ratio in rural area based on US$2.00 per day, 2005 PPP [SKD, IFAD, 2015].
mpov_gap_rur
1102.021771
1.978429-4.60517
4.234541
Poverty gap squared in rural area based on US$2.00 per day, 2005 PPP [SKD, IFAD,
mpov_gap2_rur
2015].
108
1.266505
2.005066-3.912023
4.043402
gini
Gini coefficient at the national level [WDI, 2015].
683
3.481273
0.1600894
2.965273
4.112512
gini_rur
Gini coefficient in rural areas [WDI, 2015].
110
3.472021
0.1668328
3.171784
4.158258
gini_urb
Gini coefficient in urban areas [WDI, 2015].
107
3.569651
0.1582924
3.202746
4.272491
Note: We include the countries in South Asia (Bangladesh; Bhutan; India; Nepal; Pakistan; Sri Lanka), East Asia and the Pacific (Cambodia; China; Fiji; Indonesia; Lao PDR; Malaysia; Micronesia, Fed. Sts.; Papua
New Guinea; Philippines; Thailand; Timor-Leste; Vietnam) and Central Asia (Kazakhstan; Tajikistan; Turkmenistan). The selection of the countries is guided by the availability of data in WDI 2015.

57