DP2011-30

Rem ittances, Growth and Poverty:
New Evidence from Asia n C ou ntries*
Katsushi S. IMA I
Raghav GAIH A
A bd ilahi ALI
Ni dhi KAICKER
November 8, 2011

* 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: New Evidence from Asian Countries

Katsushi Imai*
Raghav Gaiha**
Abdilahi Ali*
Nidhi Kaicker ***School of Social Sciences, University of Manchester, UK**Faculty of Management Studies, University of Delhi, India

ABSTRACTThe present study re-examines the effects of remittances on growth of GDP per capita using
annual panel data for 24 Asia and Pacific countries. The results generally confirm that remittanceflows have been beneficial to economic growth. However, our analysis also shows that thevolatility of capital inflows such as remittances and FDI is harmful to economic growth. Thismeans that, while remittances contribute to better economic performance, they are also a sourceof output shocks. Finally, remittances contribute to poverty reduction – especially through theirdirect effects. Migration and remittances are thus potentially a valuable complement to broadbased development efforts.
Keywords: remittances, economic growth, volatility, poverty, Asia.
JEL Codes: C23, F24, I32, O15, O47, O53
Corresponding Author:
Katsushi Imai (Dr)
Department of Economics, School of Social Sciences
University of Manchester, Arthur Lewis Building,
Oxford Road, Manchester M13 9PL, UK
Phone: +44-(0)161-275-4827
Fax: +44-(0)161-275-4928
E-mail: Katsushi.Imai@manchester.ac.uk
andResearch Institute for Economics & Business Administration (RIEB), Kobe University, Japan

Acknowledgement
This study is funded by IFAD (International Fund for Agricultural Development). We are
grateful to Thomas Elhaut and Ganesh Thapa, Asia and the Pacific Division, IFAD, for theirsupport and guidance throughout this study. The views expressed are our personal views and notnecessarily of the organisations to which we are affiliated.
1Remittances, Growth and Poverty: New Evidence from Asian Countries

1. Introduction
In 2010, migrants from developing countries sent over $325 billion to their origin countries,
far exceeding the official development assistance (ODA) received. This does not include theunrecorded flows. The increase in remittances to developing countries has been due to (i) morenumber of people settling abroad, and (ii) easier, faster and cheaper modes of transmitting money
to another country are now available which also facilitate recording by the Central Banks.
The impacts of migration on growth and poverty levels of a country are mixed. While theresulting remittances increase the income of the recipient country and consequently decrease
poverty, there are social costs not accounted for in these higher incomes1. On the one hand,
remittances reduce work efforts and dampen long term growth, and on the other, they improvefinancial sector development and thus stimulate growth. Remittances have a positive impact on
the credit rating of a country, provide a large and stable source of foreign currency that cancurtail investor panic, help deal with balance of payments crisis, and can be used fordevelopment projects (Ratha et al., 2011).
Remittances reduce poverty through increased incomes, allow for higher investments inphysical assets and education and health, and also enable access to a larger pool of knowledge.
Inflow of workers’ remittances results in physical capital accumulation through increased accessto finance, although this depends on the recipients’ marginal propensity to consume. For
1

These (remittances) also come at the risk of psychological stress and adverse emotional impact,

both for the migrant as well as his family.
2

instance, in Nepal, one third to one half of the reduction in the poverty headcount ratio from 42
per cent in 1995-96 to 31 per cent in 2003-04 is attributed to the increases in remittances (World
Bank, 2006). In rural Pakistan, temporary migration is associated with higher female and total
school enrolment (Mansuri, 2006). On the other hand, migration of high skilled workers canresult in a brain drain (Adams, 2003; Docquier et al. 2007) that could have a negative impact on
the growth of the country in the long run2.
Many of Asia and the Pacific countries recently enjoyed a surge of remittances until the
beginning of the global financial crisis and experienced economic growth as well as poverty
reduction at the same time, but no studies, to our knowledge, have assessed the impacts ofremittances on economic growth and poverty in these countries. The present study attempts tofill this gap. The objectives of the present study are (i) to assess the relationship between
remittances and growth of GDP; (ii) whether volatility of remittances is harmful to growth; and(iii) whether remittances reduce poverty. The econometric methods we employed correct for
endogeneity of remittances and other variables, and robust results are obtained, based on a crosscountry panel of a large number of countries in Asia and the Pacific region.
The remainder of the paper is structured as follows. Section 2 discusses the impact of therecent financial crisis of 2008-09 on remittances. Section 3 reviews the recent literature on therelationship between remittances, economic growth and poverty. Section 4 is devoted to a reviewof the data and discussion of the econometric specifications used. The results are discussed in
Section 5. Section 6 concludes with observations from a broad policy perspective.

2

However, the effect of the brain drain could be positive if migration prospects foster

investments in education because of higher expected returns abroad (Beine et al., 2001).
3

2. Financial Crisis and Remittances
The global financial crisis has had a dampening effect on the remittances received bydeveloping countries. A recent ADB (2011) study shows that since the onset of the financial
crisis, remittance flows to Asian countries have declined, primarily due to rising unemployment.
Analysis of household surveys shows that, during the crisis, the number of migrant workers
declined by 7 per cent for Bangladesh, 2 per cent for Indonesia and remained unchanged for thePhilippines. There was a decline in incomes as a result of the crisis. 97% of households in
Bangladesh, 82% in Indonesia, and 64% in the Philippines reported lower incomes. The reasons
include, apart from falling remittances, job losses, wage cuts and depreciation of the peso (in thePhilippines). Both savings and investments (in physical and human capital) declined. As acoping mechanism, households in Bangladesh and Indonesia worked more, and in thePhilippines, borrowed more. Evidence from the Philippines shows that children were removed
from school as a result of the shock.
Although, in most cases, there has been a decline in remittances received by developing
nations (e.g. remittances to Tajikistan decreased by 29 per cent in 2009), in some cases,
remittances have increased due to workers coming back to their home country and bringing backall their savings. This, however, may be just a temporary increase (e.g. Pakistan witnessed a 23
per cent growth in remittances in the first half of 2009). The Philippines received USD 11.34
billion in remittances between January and August 2009, as compared to USD 10.94 billion forthe same period in the previous year. In Bangladesh, remittances increased from USD 471
million in August 2007 to USD 935 in August 2009. That there has not been a steep decline inremittances in some countries may be attributed to (i) permanent oversees migrants not suffering

4from the financial crisis, (ii) many migrants are settled in developing nations which were notseverely affected by the financial crisis, and (iii) migrants are engaged in those jobs or industries
which are relatively untouched by the financial crisis (Jha, Sugiyarto, & Vargas-Silva, 2009).
More recent evidence points to a rise in remittances (IFAD, 2011). Migrant workers aroundthe world began 2011 by sending home significantly more money than they did in 2010. While
Pakistan showed a 34 per cent increase, Bangladesh reported a two per cent increase. This may
be attributed to the rate of recovery in the United States, the largest remitting economy. While
short-term migrant labourers tend to be the first to lose their jobs during an economic downturn,they are often the first to be rehired during a recovery, so there is hope for continued
improvement in global remittances as the U.S. economy continues to emerge from the crisis.
Since the outbreak of the financial crisis, exchange rates have been highly volatile. Accordingly,
over the course of 2010, while 70 per cent of the countries showed an increase in the dollars
remitted, recipients in 60 per cent of the countries experienced an actual decrease in thepurchasing power of the money they received. The rise of the dollar against developing country
currencies at the outset of the global recession initially had a positive effect for families
receiving remittances, effectively delaying the effect of the crisis in those countries with aflexible exchange rate. In 2010, however, that trend began reversing as developing country
currencies rebounded, leaving many recipient families to face the same financial pressures thathave been experienced by migrant workers in more developed economies (ibid, 2011).

3. Remittances, Growth and Poverty
Remittances impact growth in the following three ways: (i) By affecting the rate of capital
accumulation. Remittances not only increase the rate of accumulation of both physical and5human capital, but also lower the cost of capital in the recipient country. Thus, additionalborrowing may increase and lead to greater indebtedness. These may also have a role instabilizing the economy, or reducing volatility, and hence, reducing the risk premium thatinvestors demand; (ii) By affecting the labour force growth: remittance receipts have a negative
impact on labour force participation, by substituting remittance income for labour income, andby consuming more leisure and doing less work; (iii) By affecting TFP growth: remittances
impact the efficiency of investment, depending on who is making the investment decision(Barajas, et al. 2009). If the recipient makes the decision on behalf of the remitter, it is likely that
his decision is not as efficient as the one made by a skilled domestic financial intermediary incase of a formal capital inflow. Remittances may result in greater financial development. It canalso result in exchange rate changes – inflow of funds can result in currency appreciation (or the
Dutch disease) and lower exports.
Barajas et al. (2009) examine the impact of remittances on growth in 84 recipient countries
based on annual observations during 1970–2004. They use the following instruments: the ratio ofremittances to GDP of all other recipient countries that captures the effects of global reductions
in transactions costs and other systematic changes in the microeconomic determinants ofremittances. In most cases, remittances have a negative sign and, in others, there is no robust
relationship between remittances and economic growth.
Chami et al.’s (2005) model shows that remittances are compensatory in nature, rising withthe level of altruism, and falling as the recipient’s wage in the high output stage rises given anegative relationship between the recipient’s income and the level of remittances. This is the
opposite of what would happen if remittances functioned as investment flows. The model also
implies a negative externality on both the immigrant and the recipient. Given the moral hazard

6issue-workers slackening with remittances-there is a negative effect on aggregate output. Basedon data for 113 countries over a 29 year (1970-98) period, Chami et al. (2005) controlled for
lagged income gap and the interest rate gap between the recipient country and US as
determinants of remittances and showed that workers’ remittances have a negative andsignificant effect on growth, which is consistent with the moral hazard issue of workers’
slackening efforts with higher remittances.
Vargas-Silva et al. (2009) examine the impact of remittances on poverty and economic
growth in Asia (using annual data). In their specification, GDP growth rate and poverty gap ratio
are expressed as a function of remittances (log of remittances as per cent of GDP), logarithm ofinitial GDP per capita, primary school completion rate, natural logarithm of gross capitalformation, openness of trade, and GDP deflator. While the impact of remittances on growth inpositive, the impact on poverty is negative. A 10 per cent increase in remittances as a share of
GDP in a given year leads to about a 0.9–1.2 per cent increase in annual growth. A 10 per centincrease in remittances (as a percentage of GDP) decreases the poverty gap by about 0.7–1.4 percent. Pradhan et al. (2008) examined the effect of workers' remittances on economic growthusing panel data from 1980–2004 for 39 developing countries and confirmed a positive impact
on growth.
Adams and Page (2005) study the effect of international migration on poverty in thedeveloping world. Attention is given to endogeneity of migration and remittances by using
instrument variables. The instruments include: distance between remittance-sending andreceiving countries, level of education, and government stability.3 A merit of this study
3

There are a few difficulties. (i) Use of the same set of instruments for both migration andremittances renders identification difficult. Specifically, remittances are likely to be affected by7

(compared with the extant literature) is that the econometric analysis is based on a large data set(71 low income and middle income developing countries, covering migration, remittances,inequality and poverty). Both OLS and IV estimation results are reported. Poverty indices areregressed on per capita GDP, the Gini coefficient of income distribution, share of migrants in the
population, and (alternatively) per capita official remittances. In addition, regional dummies areused. After taking account of the endogeneity of international migration and remittances, thesetwo variables have a significant negative impact on poverty.
Aggarwal et al. (2011) assess the impact of remittances on financial sector development(measured as share of bank deposits or the ratio of bank credit to the private sector expressed as apercentage of GDP) using data for 109 countries over the period 1975-2007. The study uses a
dynamic GMM framework using lagged values of regressors to tackle the problem of reverse
causality. The findings show that remittances are positively related to the measures of financial
development. The coefficient is larger for the bank deposit to GDP ratio than that of bank credit
to GDP ratio. The results hold true even for a smaller sample (42) of countries for which
remittances also include those received using informal or non-bank sources. After instrumenting,using economic conditions in remittances sending countries, and policies and views on
immigration in these countries, the second stage results show a positive association between
remittances and deposit and credit ratios. In line with this study, using the data for more than 100
countries in 1975–2002 and controlling for the endogeneity of remittances and financial

cost of transfers, and exchange rate fluctuations, among others. Also, the degree of altruism is
key to remittances and not necessarily to migration. (ii) Another difficulty is separate use of
migration and remittances in the poverty equation. Semyonov and Gorodzeisky (2005) havedemonstrated that both matter.
8

development by System GMM, Giuliano and Ruiz-Arranz (2009) investigated the relationshipbetween remittances and growth and its interaction with the financial development in therecipient country. They found that remittances have promoted growth in less financiallydeveloped countries.
Remittances also help in reducing consumption instability in developing countries.
Remittances act both as ex-ante risk avoidance tool as well as ex-post risk management
mechanism (e.g. remittances increase after natural disasters affect a region). Combes and Ebeke(2011) use a System-GMM-IV model for a cross-sectional panel of 87 developing countries overthe period 1975-2004 to estimate the impact of remittance on consumption instability. They findthat remittances significantly reduce consumption instability, the impact being stronger infinancially less developed countries. However, the stabilizing impact of remittances decreases athigher levels of remittances. Remittances also increase resilience to shocks, such as natural
disasters and macroeconomic shocks.

4. Data and empirical strategy
Data
Our sample is dictated by data availably and consists of 24 Asia and Pacific economies over theperiod 1980 to 2009. 4 A list of the countries as well as the definition and sources of all thevariables are given in Appendices 1 and 2. Unless stated otherwise, the data are drawn fromWorld Development Indicators 2011 (World Bank, 2011). Based on the existing literature on
4

As our panel has a relatively small n (individual units) and large t (time), the first difference orsystem GMM estimators cannot be adequately applied to our sample as these methods weredeveloped for panel data with a large n and a relatively smaller t (e.g. Blundell et al. 2000).
9remittances and growth, such as Chami et al. (2005), our baseline specification takes thefollowing form:

where for country i at time (denoting year) t,

denotes rate of growth of real per capita GDP,is logarithm of workers’ remittances expressed as a percentage of GDP,country-specific effect andis the idiosyncratic error term. The vector

is unobserved

contains a standard

set of determinants of economic growth, such as lag of real per capita GDP5, financial sector
development, inflation, civil war, resource abundance, capital account openness, and investment.
Following the empirical literature of economic growth, we include lagged real per capita GDP
to allow for convergence. Here a negative coefficient is expected given the predictions of thestandard neoclassical model. In line with Levine et al. (2000), we use deposit money bank assets
as a share of deposit money and central bank assets (defined by Beck et. al., 2009) as a measureof financial sector development to account for the fact that the relationship between remittancesand growth may work through the financial sector (Guiliano and Ruiz-Arranz, 2009).6 To
capture the macroeconomic and political environments, we account for inflation and civil

5

A 2 year lag has been taken in the present study, but use of a 1 year or longer lag will notchange the results significantly.
6

Unlike Giuliano and Ruiz-Arranz (2009), our sample consists of only 24 countries and wecannot apply System GMM and thus we do not address the endogeneity of financial
development, focusing only on the endogeneity of remittances in the static panel model
framework for simplicity.
10

conflicts measured by internal armed conflicts from UCDP/PRIO Conflict Database (2009)7. In
addition, we consider the role of resource abundance captured by fuel exports as a percentage ofmerchandise exports sourced from the Quality of Government dataset (2011)8. We also use thecapital account openness measure, first introduced by Chinn and Ito (2006), which measures acountry’s degree of openness based on restrictions on cross-border transactions. Following
Barajas et al. (2009), we check the sensitivity of the remittances-growth nexus to the inclusion of
investment as a conditioning variable recognising that it may be one of the most importantchannels through which remittances influence economic growth.
To further check the robustness of the baseline regressions, we also utilise an extended set ofcontrol variables, including trade, foreign direct investment (FDI), foreign aid, government
expenditure and regime durability - measured by the number of years since the most recent
regime change (from Quality of Government dataset, 2011). Finally, we control for property
rights protection which is captured by ‘constraint on the executive’ from the Polity IV dataset.
This follows Acemoglu and Johnson (2005) who make a strong case for the appropriateness of
this indicator as a measure of property rights protection. According to them, because this variable
captures procedural rules which constrain political leaders and other powerful elites, it is closely
linked with the security of private property rights.

Model of Remittances and Economic Growth7

It is available from http://www.prio.no/CSCW/Datasets/Armed-Conflict/UCDP-PRIO/(accessed on 5th November 2011).
8

It is available from http://www.nsd.uib.no/macrodataguide/set.html?id=37&sub=1 (accessed on

5th November 2011).
11

To explore the effects of remittances on growth, we first use static panel data methods, such as
fixed or random effects model. However, as some of the explanatory variables, including
remittances, are likely to be endogenous, we also use the panel two-stage least squares (2SLS).
Here, lagged per capita GDP, financial development, and investment are instrumented by theirown lags since these are orthogonal to the error term. Our main variable of interest – remittances– is also instrumented by its own lag. In line with Chami et al. (2005), we use the income gap
between each remittance receiving country and the US as an additional instrument.

Volatility of capital inflows and growth
It is generally accepted that most sources of foreign exchange for poorer countries tend to follow
global economic trends, increasing in good times and decreasing in bad times. Here, we
empirically test whether the volatility of two types of inflows – namely, FDI and remittances- isharmful, or beneficial to economic growth. To measure volatility, we have used the standard
deviation of each variable measured over a non-overlapping 5-year period as we are interested in
the steady state link between the volatility of capital inflows and growth.
For this purpose, following Love and Zicchino (2006), we estimate a trivariate panel vector
autoregression (PVAR) in the following form:

where for country i at time t,

is a vector of three endogenous variables (i.e., the logarithm ofreal per capita income and the standard deviations of FDI and remittances),
specific fixed effect anddenotes a country-

is the error term. Since by construction the lagged dependent

variables are correlated with the unobserved country-level fixed effect,12, we use forward mean-differencing which validates the use of lagged right hand side variables as instruments for theendogenous variables via system generalised method of moments (GMM) procedure.
Our interest lies in generating impulse response functions which depict the reaction of one
variable in the system to innovations in another variable while keeping all other shocks at zero.
To make the variance-covariance matrix of the errors orthogonal, Cholesky decomposition isused where variables that come early in the ordering of the VAR system are assumed to affectthe other variables contemporaneously and those that come last in the ordering are assumed toinfluence those listed earlier only with a lag. In our estimations, we assume that innovations in
the volatility of remittances influence the other variables contemporaneously and hence thestandard deviation of remittances appears first in the ordering. On the other hand, we assume thatthe performance of real per capita GDP in resource receiving countries does not influence thevolatility of inflows within the same year. Hence, it comes last in the ordering. The matrix of theimpulse response functions is based on the estimated VAR estimates and their standard errors
and the confidence intervals are produced with Monte Carlo simulations.

5. Empirical results
Remittances and Growth
The baseline results are reported in Table 1. In columns [1] – [4], we exclude investment fromthe regressions. The results show that the coefficient on lagged GDP carries the expected
negative sign and it is significant at the 1% level. Financial development is found to be positively
related to growth, but it is not statistically significant once investment is included in thespecification in columns [5] – [8].

13The results show that macroeconomic instability in the form of high inflation is detrimental toeconomic growth as found in all the columns. This is in line with the conventional wisdom that astable macroeconomic environment reduces the risks and uncertainties associated withinvestment projects and thus results in economics growth. Similarly, we find that civil wars arenegatively related to growth presumably because of their disruptive effects on economic activity.
The coefficient estimate is negative and significant except in the columns [5] and [7].
It is consistently found across different specifications and estimation methods that remittances
are positively associated with better economic performance. The results are important because
the coefficient estimate of remittances is positive and significant even if the endogeneity concern
is addressed (in columns [3], [4], [7] and [8]). The existing literature (for example, Barajas et al.
2009) identifies various channels through which remittances enhance growth, including theboosting of capital accumulation, labor force growth, and total factor productivity. While we are
not exploring these channels empirically, our results are in sharp contrast with Barajas et al.
(2009), which finds no relation between remittances and growth, or Chami et al. (2005) claimingthat remittances negatively affect growth. The reason why we have obtained different results
remains unclear, but it is surmised that focusing only on Asian countries and more recent periods(1980-2009) may have overturned the sign of the coefficient estimate.
The results indicate that, on average, countries with open capital account regimes registerhigher rates of growth. This is in line with the new evidence which indicates that financial
openness is likely to be associated with higher factor productivity and greater efficiency, andhence better economic performance (Bekaert et al. 2010). The estimated coefficients also suggestthat both investment and natural resources are positively related to growth.

14

The results in Table 2 check the sensitivity of the baseline results by considering the effects ofan extended set of control variables using panel-2SLS. In columns [1] and [2], we augment thebaseline specification with trade openness (proxied by the share of imports and exports in GDP)
which enters with the expected positive sign. Columns [3] and [4] incorporate property rights
protection which is found to boost growth. This is in line with the results of a broader research
agenda showing the positive effects of institutions on economic performance (see e.g., Acemoglu
and Johnson, 2005).
The impact of regime durability on growth is generally found to be positive (in columns [5]and [6]), suggesting that countries with stable governments tend to enjoy a higher level of
economic growth. This variable has previously been used as an indicator of political stability(e.g., Collier et al. (2004)). The main results remain unchanged when we include additionalvariables such as FDI, government expenditure and foreign aid. The results suggest that both aid
and government expenditure are inversely related to growth, for example, because aid may
encourage corruption (as found by Knack, 2001), while increased government expenditures may
crowd out the private sector. Finally, FDI generally carries the expected positive sign eventhough it is mostly non-significant at the 10% level.
The positive and statistically significant coefficient estimate of remittances is unchanged inTable 2 after adding various control variables. The results are robust as they are either significant
at the 1% or 5% level. The magnitude of coefficient estimates varies from 0.667 to 3.248
depending on which model or specification is applied. In all cases, remittances are instrumented
by their own lag and the income gap between each country and the US.
Throughout the estimations, the Hansen J statistic fails to reject the validity of the overidentifying restrictions assumed for the estimation, suggesting that the instruments are valid. The

15

Kleibergen-Paap rk Wald F statistic is almost always above 10, the critical value proposed by
Stock and Yogo (2005), indicating that the instruments are indeed relevant. Finally, theKleibergen–Paap rk LM statistic indicates that the regressions are not underidentified, suggestingthat the excluded instruments are correlated with the endogenous variables.
To sum up, our findings from Table 1 and Table 2 indicate that remittances (as a share ofGDP) have promoted economic growth in our sample countries. This result is robust toendogeneity issues and omitted variable bias. In what follows, we investigate the related issue of
how the volatility of remittances inflows influences economic growth relative to other types ofcapital inflows, such as FDI.

The volatility of capital inflows and growth
An attractive feature of the PVAR is that it sidesteps endogeneity concerns by treating all thevariables in the system as endogenous. Table 3 summarises the results9. As may be seen from
Table 3, the volatility of both remittances and FDI is inversely related to economic performance.
The coefficient estimates indicate that the negative effects of volatility are little larger with FDI
than with remittances. It is postulated based on this finding and our previous results that, while
remittance flows may alleviate financial constraints and thus stimulate economic development,
they may also be a source of output shocks, e.g. arising from the situations where countries areunable to buffer against sudden swings in inflows.
9

An important caveat to our results is that the sample size is reduced significantly with 5-year

averages when calculating the volatility measures. So we have also estimated models with 4 and3-year averages and the results remain largely unchanged. These alternative results are available
on request from the corresponding author.
16

[Table 3 to be inserted around here]

To get a better feel of the response of income to changes in the volatility of capital inflows,
we also show the impulse response functions for our variables of interest – namely, the volatilityof remittances and FDI, as illustrated in Figures 1a and 1b. The confidence intervals of theimpulse response functions are obtained using Monte Carlo simulations with 1000 repetitions.
Impulse response functions show that an exogenous shock to the volatility of both types ofcapital inflows contracts economic growth- especially in the short run (i.e. in 2 to 3 years after
the shock), where countries may find it harder to adjust to unexpected changes in capital inflows.
[Figures 1a and 1b to be inserted around here]

Remittance and Poverty in Asia
In this sub-section, we examine how remittances would affect poverty in Asian countries as anextension of the growth regressions in the previous sections along the lines of Imai et al. (2010).
Among various poverty measures including both income and non-income indicators, we useinternational poverty headcount measures based on US$1.25 or US$2 a day, estimated by theWorld Bank (Ravallion et al. 2008),

as they cover a wide range of countries and years.

However, as these poverty data are usually based on household surveys which take place once in
few years, the corresponding panel is highly unbalanced. Constrained by limited data, we haveused a parsimonious specification in which log of growth rate of GDP per capita is estimated bya smaller number of explanatory variables, that is, (a 2 period) lagged growth of agriculturalvalue added per worker (or lagged (level of) agricultural value added per worker, or lagged (levelof) GDP per capita as an instrument), investment, financial development, remittances, trade in17

the first stage of Fixed-effects 2SLS. In the second stage, the poverty head- count ratio (based oneither US$1.25 or US$2 a day poverty line) is estimated by the same set of variables except the
instrument (i.e. GDP growth rate from the first stage). The growth of two-year lagged
agricultural value added per worker is used as an instrument for economic growth rate to capture
the long-run effect of agricultural productivity on growth in our sample countries in Asia.
Tables 4a, 4b and 4c give the FE-2SLS results for poverty (Table 4a is for lagged agricultural
growth per worker, 4b for lagged agricultural value added (in level) per worker and 4c for lagged
GDP per capita). The first two columns of each table show the results for poverty headcountbased on US$1.25 and the second two columns on US$2. Both cases, however, yield broadly
similar results.
The results of the first stage equation for growth rate are largely in line with those in
Table 1. There is a striking difference in the effect of agricultural production on growthdepending on whether we use the level or growth. In Table 4a, we observe a strong andstatistically highly significant effect of lagged agricultural growth on economic growth(consistent with a key role of agricultural sector as an engine of economic growth). However, in
Table 4b, the coefficient estimate of the level of agricultural value added per worker becomes
negative and statistically significant. This presumably reflects the convergence effect ofagricultural production, that is, a country with low initial agricultural production tends to have ahigher growth than those with high initial production. If we replace lagged agricultural value
added per worker by lagged GDP per capita in Table 4c, another and more conventional
specification to check for growth convergence, we find a similar pattern of results. The results ofother variables are the same as before – investment, financial development, and remittances havepositive and significant coefficients. However, trade openness is positive but non-significant.

18

[Tables 4a, 4b and 4c to be inserted around here]

In the second stage, the share of remittances in GDP is negatively associated with poverty in
Tables 4b and 4c. It follows that remittances not only promote economic growth, as evidenced
by the results in Tables 1, 2, 4a, and 4b, but also reduce poverty (on the two criteria of US$1.25
and US$2). The underidentification test suggests that the equations are not underidentified, i.e.,
the instruments are relevant and correlated with the endogenous variable. However, in Table 4a,
the coefficient estimate of remittances is negative and not significant in the second stage ofpoverty equation. Simulation requires significant coefficient estimates and thus we will use
Table 4b for poverty simulations.
As both dependent and explanatory variables are in logarithms, the coefficient estimates inTable 4b are elasticities. Table 5 discusses in detail the magnitude of the effects of remittanceson poverty. In the case of headcount ratio (US$1.25), the indirect effect of remittances onpoverty (0.061) is obtained by multiplying 0.309 (the elasticity of economic growth with respect
to remittances) and 0.198 (the elasticity of poverty with respect to economic growth) assumingthat other factors are unchanged. With regard to the direct effect, the elasticity of poverty withrespect to remittances is -0.500. This is much larger than the indirect effect in absolute term andthe total effect is -0.439. This implies that a 1% increase in the share of remittances in GDP (e.g.
10% to 10.1%) leads to a 0.439% decrease in the headcount ratio (from 10% to 9.956%) ceterisparibus. Likewise, in the case of the US$2 poverty, the indirect effect of remittance is obtained
as 0.040 and the direct effect is -0.280, leading to the total effect of -0.240 ceteris paribus.1010

Our results are consistent with Adams (2011) who surveyed 50 studies on the economic

impact of international remittances and concluded that remittances generally have a positive
19

[Table 5 to be inserted around here]

We have estimated the change in the poverty headcount ratio for 10 selected countries using
these elasticity estimates. Three cases have illustrative value – a 10%, 20%, or 50% increase in
the current remittance ratio and their poverty effects. For example, in Bangladesh, a 50%
increase of the share of remittances in GDP (from 11.78% to 17.67%) would increase GDP per
capita growth rate from 4.30% to 4.97% and reduce the poverty headcount (on US$1.25 a day)from 49.60% to 38.69% and that on the higher cut-off (US$2.00 a day) from 81.30% to 71.54%.
These results imply that remittances reduce poverty significantly, especially extreme poverty.
A few other cases further corroborate these results. In India, a 50% increase in the share of
remittances in GDP (3.59% to 5.39%) accelerates economic growth (from 7.65% to 8.84%) andreduces the US$1.25 poverty from 41.6% to 32.45%, and the US$2 poverty from 75.60% to66.53%. Again, a potential reduction in poverty arising from increased remittances is substantial.
Similar results are obtained for Nepal, the Philippines and Sri Lanka. In Nepal, where theremittance share has increased significantly in recent years, a 50% increase in it-a rise in theshare from 23.83% to 35.75%- leads to a substantial poverty reduction from 55.10% to 42.98%(US$1.25 a day) and from 77.60% to 68.29% (US$2.00 a day). If Sri Lanka sees a rise in theshare of remittance from 8.01% to 12.02% (i.e by 50%), the headcount ratio (on US$2.00) will
reduce from 29.1% to 25.61%. These results will, however, have to be interpreted with somecaution as the same elasticity estimates are applied to all countries in the sample. However, it

impact on poverty and health, while they can have negative effects on economic growth drawing
mainly on Chami et al. (2005).
20

would be safe to conclude that increase in remittances not only promotes economic growth but
also reduces poverty.

6. Concluding Observations
The present study re-examined the effects of remittances on growth of GDP per capita using
annual panel data for 24 Asia and Pacific countries. The results confirm that remittances flows
have been beneficial to economic growth. This finding is robust to endogeneity concerns.
However, the paper also presents some new evidence that the volatility of remittance and FDI isharmful to economic growth. This means that, while remittances contribute to better economic
performance, they are also a source of output shocks. Finally, remittances contribute to poverty
reduction – especially through their direct effects. This result is robust to two measures ofpoverty, estimated using the cut-off points of $1.25 per capita/day and $2 per capita/day.
Migration and remittances are thus potentially a valuable complement to broad-based
development efforts. However, we argue that they should not be seen as a panacea for growthand poverty reduction as they have been linked with, among other things, lower work effort,
brain drain and Dutch disease. Also, remittances cannot act a substitute for official sources ofcapital such as aid, as private money cannot be expected to contribute towards public projects.
Moreover, not all poor households receive remittances, and public funds are meant to alleviate
poverty. Nonetheless, in tandem with both the theoretical and empirical literature our results
suggest that remittances can have a positive effect on growth and poverty reduction. A
supplementary conclusion emanating from this study is that policy makers should adopt policies
that encourage the use of remittances for physical and human capital investments so as to harness
its full potential for economic development.

21

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Semyonov, M. and A. Gorodzeisky (2005), ‘Labor migration, remittances and householdincome: A comparison between Filipino and Filipina overseas workers’, International
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25

TABLE 1
Remittances and growth – baseline models
FE

Lagged GDP1
Inflation1
Fin dev / GDP1
Remittance / GDP1
Resource abundance
Cap acc openness
Civil war[1]
-3.014[1.046]***-0.801[0.524]
4.184[2.287]*1.220[0.529]**0.096[0.024]***0.964[0.505]*-0.534[0.276]*RE

FE-2SLS
Without investment[2][3]-1.531-6.232[0.697]**[1.601]***-0.926-0.812[0.501]*[0.325]**
4.435
6.353
[1.730]**[2.967]**
0.841
2.011
[0.447]*[0.488]***
0.082
0.091
[0.020]***[0.035]***
0.770
0.905
[0.292]***[0.428]**-0.657-0.644[0.301]**[0.336]*

RE-2SLS

FE[4]-2.597[0.851]***-0.988[0.312]***5.180[2.044]**1.304[0.397]***0.095[0.022]***0.823[0.318]***-0.756[0.305]**[5]-4.379[1.454]***-1.069[0.517]*
2.159[2.355]1.078[0.548]*0.084[0.026]***0.746[0.469]-0.421[0.272]0.219[0.078]**303

Investment / GDPRE

FE-2SLS
With investment[6][7]-2.503-8.145[0.870]***[1.791]***-1.143-1.044[0.496]**[0.322]***
2.508
4.243
[1.774][2.969]0.805
1.702[0.464]*[0.475]***
0.071
0.077
[0.026]***[0.034]**
0.652
0.767
[0.292]**[0.411]*-0.534-0.434[0.285]*[0.324]0.204
0.166[0.071]***[0.069]**303
298

RE-2SLS[8]-3.447[0.940]***-1.137[0.310]***3.619[2.157]*1.196[0.392]***0.087[0.022]***0.760[0.313]**-0.629[0.299]**0.127[0.061]**
298

Observations
303
303
299
299
Specification tests2
Hausman test (chi-squared)
1.69
3.72
Overidentification
0.25
0.87
Underidentification
0.00
0.00
F-statistic (weak inst.)
24.27
19.82
Notes: Dependent variable is GDP per capita growth. Robust standard errors in brackets. ***, ** and * indicate significance at the 1, 5 and 10% levels,
respectively. 1 Variables are in log form. Lagged GDP, financial development and investment are instrumented with their own lags. Remittance is instrumented
with its 1st lag and the income gap between each country and the US. 2The specification tests are (i) the overidentification test which displays the p-values for the
Hansen J-statistic for the null that instruments are uncorrelated with the error term and thus valid; (ii) the underidentification test shows the p-values of theKleibergen–Paap rk LM-statistic for the null that the excluded instruments are uncorrelated with the endogenous variables; (iii) the weak identification test is the
Kleibergen-Paap rk Wald F statistic for the null of weak correlation between the endogenous variables and the instruments.

26

TABLE 2
Remittances and growth – extended models

Lagged GDP1
Investment/GDP
Fin dev / GDP1
Remittance/GDP1
Inflation1
Resource
abundance
Cap acc openness
Civil war
Trade

FE-2SLS[1]-9.412[1.945]***0.191[0.071]***4.036[2.970]1.518[0.488]***-1.064[0.321]***
0.057[0.036]0.908[0.416]**-0.251[0.335]0.042[0.019]**RE-2SLS[2]-3.131[0.923]***0.129[0.060]**3.513[2.054]*1.085[0.377]***-1.150[0.311]***
0.083[0.021]***0.723[0.299]**-0.630[0.302]**0.006[0.012]

FE-2SLS[3]-10.757[2.072]***0.220[0.072]***
4.977[3.133]1.406[0.493]***-1.065[0.335]***0.049[0.036]0.791[0.427]*-0.163[0.339]0.045[0.019]**0.313[0.171]*

RE-2SLS[4]-3.143[0.914]***0.137[0.060]**3.318[2.063]1.000[0.374]***-1.176[0.322]***0.080[0.021]***0.711[0.298]**-0.621[0.302]**0.007[0.012]0.019[0.149]FE-2SLS[5]-11.066[2.137]***0.220[0.072]***
4.265[3.254]1.467[0.491]***-0.998[0.336]***0.040[0.037]0.649[0.437]-0.193[0.338]0.039[0.019]**0.377[0.179]**0.069[0.049]RE-2SLS[6]-1.037[0.634]0.112[0.053]**2.042[1.625]0.867[0.305]***-1.238[0.323]***0.061[0.017]***0.603[0.241]**-0.984[0.284]***-0.013[0.009]-0.111[0.143]0.056[0.022]***

FE-2SLS[7]-9.426[2.041]***
0.191[0.071]***
5.913[3.280]*2.123[0.481]***-0.750[0.333]**0.046[0.036]0.450[0.430]0.076[0.341]
0.045[0.018]**0.365[0.184]**-0.019[0.053]0.238[0.250]RE-2SLS[8]-1.081[0.651]*0.116[0.053]**2.690[1.709]0.899[0.304]***-1.033[0.323]***0.058[0.018]***0.481[0.251]*-0.718[0.298]**-0.014[0.010]-0.141[0.145]0.040[0.022]*0.317[0.205]

FE-2SLS[9]-9.937[2.090]***0.177[0.071]**
6.858[3.319]**2.212[0.489]***-0.813[0.337]**0.037[0.037]0.635[0.445]0.171[0.348]0.045[0.019]**0.512[0.205]**-0.005[0.054]0.233[0.252]-3.329[1.901]*

RE-2SLS[10]
0.009[0.510]0.109[0.048]**1.568[1.531]
0.667[0.272]**-1.141[0.322]***0.042[0.015]***0.463[0.211]**-0.810[0.273]***-0.028[0.008]***-0.200[0.142]0.055[0.018]***0.388[0.214]*-0.743[0.888]

298

298

295

295

295

295

283

283

283

283

Property rights
Regime durability
FDI1
Gov exp / GDP1
ODA / GNP1
Observations
Specification tests2
Overidentification
Underidentification
F-statistic (weak
inst)

0.66
0.00

0.29
0.00

0.57
0.00

0.88
0.00

0.78
0.00

25.60

29.85

29.46

22.52

25.74

FE-2SLS[11]-14.657[2.999]***0.232[0.080]***10.719[3.868]***3.284[0.636]***-0.527[0.375]0.030[0.040]0.355[0.498]0.061[0.364]0.034[0.020]*0.676[0.234]***-0.006[0.057]-0.008[0.289]-4.857[2.145]**-0.982[0.512]*
265

RE-2SLS[12]-2.920[0.953]***0.117[0.049]**1.144[1.519]1.065[0.299]***-0.607[0.340]*0.053[0.016]***0.729[0.217]***-1.089[0.276]***-0.011[0.010]-0.242[0.141]*0.039[0.018]**0.604[0.220]***0.460[0.946]-1.221[0.281]***
265

0.67
0.00
7.93
1

Notes: Dependent variable is GDP per capita growth. Robust standard errors in brackets. ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively. Variables are in log form. Lagged real GDP,financial development and investment are instrumented with their own lags. Remittance is instrumented with its 1st lag and the income gap between each country and the US. 2The specification tests are (i) the
overidentification test which displays the p-values for the Hansen J-statistic for the null that instruments are uncorrelated with the error term and thus valid; (ii) the underidentification test shows the p-values of
the Kleibergen–Paap rk LM-statistic for the null that the excluded instruments are uncorrelated with the endogenous variables; (iii) the weak identification test is the Kleibergen-Paap rk Wald F statistic for thenull of weak correlation between the endogenous variables and the instruments.

27

TABLE 3
PVAR results: Effects of Volatility of Capital Inflows on Economic Growth
Income

FDI volatility

Rem volatility

Rem volatility (t-1)-0.0270.130[2.010]**[1.822]
FDI volatility (t-1)-0.0490.196[-2.882]**[2.194]**
Income (t-1)
0.591
0.027
[21.872]**[0.211]
Notes: the trivariate panel VAR model is generated via GMM.
Robust t-statistics are in parentheses and ** indicates significance at the 5% level.

0.002[0.010]-0.001[-0.014]-0.090[-0.998]

TABLE 4aRemittances, growth and poverty (with lagged growth of agricultural VA per worker)
FE-2SLS
Dep VarGrowthRate

1

FE-2SLS

1st Stage

2nd Stage

1st Stage

2nd Stage

GrowthRate

Poverty
Headcount

GrowthRate

Poverty
Headcount(GDP pc)(US$1.25)(GDP pc)(US$2.00)--0.140--0.100

19.25

17.71
[7.015]**[0.062]-Lagged growth of Ag
VA per worker 1[6.224]***[0.079]*-Investment/GDP

0.255-0.0060.326-0.0021[0.069]***2.891[0.026]-0.645[0.074]***2.491[0.023]-0.110[2.350][0.619][2.649][0.442]

1.169[0.499]**0.017-0.010[0.166]-0.0131.026[0.562]*0.0126-0.008[0.117]-0.006[0.026][0.006]**[0.028][0.004]

101

101

103

103

Fin dev / GDP

1

Remittance/GDP1
Trade
Observations
Specification tests
Overidentification

0.000

0.000

Underidentification

0.00260.0123F-statistic(weak 9.561
6.375
identification test)
Notes: Robust standard errors in brackets. ***, ** and * indicate significance at 1, 5 and
10% levels, respectively.
1
Variables are in log form.

28

TABLE 4b
Remittances, Growth and Poverty (with lagged agricultural VA per worker (level))
FE-2SLS

FE-2SLS

1st Stage

2nd Stage

1st Stage

2nd Stage

GrowthPoverty

GrowthPoverty

Rate(GDP pc)

Headcount(US$1.25)

Rate(GDP pc)

Headcount(US$2.00)

GrowthRate 1-

0.198[0.093]**-Lagged Ag VA per
worker (level) 1

-9.86

-

-11.090.110[0.052]**-[2.935]***-[3.058]***

-

0.309
[0.069]***-0.094[0.033]***0.361[0.072]***-0.067[0.021]***

Dep VarInvestment/GDP
Fin dev / GDP1

5.434

-1.64

5.431

-0.648[2.440]**[0.785]**[2.671]**[0.495]

Remittance/GDP1

1.878-0.5005

1.796-0.2804Trade[0.502]***0.031[0.026][0.205]***-0.0174[0.008]**[0.549]***0.0355[0.028][0.120]**-0.009[0.005]*Observations

101

101

103

103

Specification tests
Overidentification

0.000

0.000

Underidentification

0.00120.0005F-statistic (weak11.29813.165
identification test)
Notes: Robust standard errors in brackets. ***, ** and * indicate significance at 1, 5 and
10% levels, respectively.
1

Variables are in log form.

29

TABLE 4c
Remittances, Growth and Poverty (with lagged GDP per capita (level))
FE-2SLS
Dep VarGrowthRate 1
Lagged GDP
capita (level) 1

perInvestment/GDP

FE-2SLS

1st Stage

2nd Stage

1st Stage

2nd Stage

GrowthPoverty

GrowthPoverty

Rate(GDP pc)

Headcount(US$1.25)

Rate(GDP pc)

Headcount(US$2.00)-

0.103[0.051]**-

0.054[0.029]*-8.479--9.534-[1.548]***-[1.657]***

-

0.312

-0.0690.364-0.048[0.063]***[0.022]***[0.065]***[0.014]***5.654[2.170]**-1.361[0.608]**5.699[2.376]**-0.504[0.394]

Remittance/GDP1

2.511-0.3622.519-0.207Trade[0.481]***0.048[0.143]**-0.0162[0.527]***
0.0537[0.089]**-0.008[0.023]**[0.006]**[0.026][0.004]**

101

101

103

103

Fin dev / GDP

1

Observations
Specification tests
Overidentification

0.000

0.000

Underidentification

0.000

0.000

F-statistic(weak 30.010
33.111
identification test)
Notes: Robust standard errors in brackets. ***, ** and * indicate significance at 1, 5 and
10% levels, respectively.
1

Variables are in log form.

30

TABLE 5
Magnitude of the effect of remittances on poverty
Case (1) Headcount Ratio based on US$1.25$∂log gdp pc ∂log
growth *poverty∂log∂log gdp pc
indirect
remittances
growtheffect0.309 *
0.198
0.061
% increase in remittance
10.0
ratio
% increase in remittance
20.0
ratio
% increase in remittance
50.0
ratio
Case (2) Headcount Ratio based on US$2
∂log gdp pc ∂log
growth *poverty∂log∂log gdp pc
indirect
remittances
growtheffect0.361 *
0.110
0.040
% increase in remittance
10.0
ratio
% increase in remittance
20.0
ratio
% increase in remittance
50.0
ratio

Bangladesh

China

India

Indonesia∂log gdp pc
growth∂log
remittances-0.439+directeffect(-0.500)

=

→

4.4

% reduction of poverty head count ratio ($1.25 a day)→8.8

% reduction of poverty head count ratio ($1.25 a day)

→

22.0

% reduction of poverty head count ratio ($1.25 a day)∂log gdp pc
growth∂log
remittances-0.240+directeffect(-0.280)

=

→

2.4

% reduction of poverty head count ratio ($2 a day)→

4.8

% reduction of poverty head count ratio ($2 a day)

→

12.0

% reduction of poverty head count ratio ($2 a day)

%Change in Remittance Ratio(% in GDP)

%Change in Growth Rate
per capita

Remittance Ratio (% in GDP)
2009
11.78
%
10%
12.96
%
increase
20%
14.14
%
increase
50%
17.67
%
increase
2009
0.98
%
10%
1.08
%
increase
20%
1.18%
increase50%
1.47%
increase
2009
3.59
%
10%
3.95
%
increase
20%
4.31%
increase
50%
5.39%
increase
2009
1.26
%
10%
1.39
%
increase

Growth Rate
2009
4.30
10%
4.43
increase
20%
4.57
increase
50%
4.97
increase
2009
8.54
10%
8.80
increase
20%
9.07
increase
50%
9.86
increase
2009
7.65
10%
7.89
increase
20%
8.12
increase
50%
8.84
increase
2009
3.35
10%
3.45
increase

31

%
%
%
%
%
%
%
%
%
%
%
%
%
%

%Change in Poverty
Headcount Ratio
US$1.25 aday
Poverty Headcount Ratio
2005
49.60
%
10%
47.42
%
increase
20%
45.24
%
increase
50%
38.69
%
increase
2005
15.90
%
10%
15.20
%
increase
20%
14.50
%
increase
50%
12.40
%
increase
2005
41.60
%
10%
39.77
%
increase
20%
37.94
%
increase
50%
32.45
%
increase
2005
18.70
%
10%
17.88
%
increase%Change in Poverty
Headcount Ratio
US$1.25
a day
Poverty Headcount Ratio
2005
81.30
%
10%
79.35
%
increase
20%
77.40
%
increase
50%
71.54
%
increase
2005
36.30
%
10%
35.43
%
increase
20%
34.56
%
increase
50%
31.94
%
increase
2005
75.60
%
10%
73.79
%
increase
20%
71.97
%
increase
50%
66.53
%
increase
2005
50.60
%
10%
49.39
%
increase

20%
increase
50%
increase
Kazakhstan

Lao PDR

Nepal

Philippines

Sri Lanka

2008
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase
2008
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase

1.51

%

1.89

%

2.05

%

2.26

%

2.46

%

3.08

%

0.63

%

0.69

%

0.76

%

0.95

%

23.83

%

26.21

%

28.60

%

35.75

%

11.19

%

12.31

%

13.43

%

16.79

%

8.01

%

8.81

%

9.61

%

12.02

%

20%
increase
50%
increase
2008
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase
2008
10%
increase
20%
increase
50%
increase
2009
10%
increase
20%
increase
50%
increase

3.56

%

3.87

%

0.14

%

0.14

%

0.15

%

0.16

%

4.49

%

4.63

%

4.77

%

5.19

%

2.80

%

2.89

%

2.97

%

3.23

%

1.86

%

1.92

%

1.98

%

2.15

%

2.79

%

2.88

%

2.96

%

3.22

%

32

20%
increase
50%
increase
2007
10%
increase
20%
increase
50%
increase
2005
10%
increase
20%
increase
50%
increase
2004
10%
increase
20%
increase
50%
increase
2006
10%
increase
20%
increase
50%
increase
2007
10%
increase
20%
increase
50%
increase

17.05

%

14.59

%

0.17

%

0.16

%

0.16

%

0.13

%

33.90

%

32.41

%

30.92

%

26.44

%

55.10

%

52.68

%

50.25

%

42.98

%

22.60

%

21.61

%

20.61

%

17.63

%

7.04

%

6.73

%

6.42

%

5.49

%

20%
increase
50%
increase
2005
10%
increase
20%
increase
50%
increase
2005
10%
increase
20%
increase
50%
increase
2005
10%
increase
20%
increase
50%
increase
2006
10%
increase
20%
increase
50%
increase
2005
10%
increase
20%
increase
50%
increase

48.17

%

44.53

%

1.48

%

1.44

%

1.41

%

1.30

%

66.00

%

64.42

%

62.83

%

58.08

%

77.60

%

75.74

%

73.88

%

68.29

%

45.00

%

43.92

%

42.84

%

39.60

%

29.10

%

28.40

%

27.70

%

25.61

%

FIGURE 1a
Impulse response function: Response of income to remittancevolatility shock

FIGURE 1b
Impulse response function: Response of income to FDI volatility shock

33

APPENDIX 1
List of countries
1. Armenia
4. Azerbaijan
7. Bangladesh
10. Cambodia
13. China
16. Fiji
19. India
22. Indonesia

2. Iran
5. Kazakhstan
8. Korea, Rep.
11. Kyrgyz Republic
14. Lao PDR
17. Malaysia
20. Maldives
23. Mongolia

3. Nepal
6. Pakistan
9. Papua New Guinea
12. Philippines
15. Sri Lanka
18. Thailand
21. Tonga
24. Vanuatu

APPENDIX 2
List of Variables
Variable
GrowthLagged GDP
Remittance

Financial development
Investment
Inflation
Resource abundanceCapital account openness

Civil war
Trade

Property rights protection

Regime durability
FDI
Government size
Aid

SourceReal per capita growth (WDI, 2010)
Lagged real per capita income (WDI, 2011 April]
expressed in log form
Workers' remittances and compensation of employees,
received (% of GDP) [WDI, 2011 April] expressed in
log-form.
Captured by deposit money bank assets / (deposit money+ central) bank assets [Beck and Demirgüç-Kunt, 2009]
expressed in log-form
Gross capital formation (% of GDP) [WDI, 2011 April]
expressed in log-form.
Measured by CPI (annual %) [WDI, 2011 April]
Proxied by fuel exports (% of Merchandise Exports)[Quality of government dataset, 2011 April]A measure of a country’s degree of capital accountopenness based on the existence of multiple exchange
rates, current account and capital account transaction
restrictions [Chinn and Ito, 2008]
Internal armed conflicts [UCDP/PRIO Conflict
Database, 2009]
Exports plus imports (% of GDP) [WDI, 2011 April]
expressed in log-form
A measure of property rights protection or institutional
quality: measured by ‘constraint on the executive’ fromthe Polity IV dataset. A 7-point scale where higher
values imply strong property rights (Marshall et al.,2009).
The number of years since the most recent regimechange [Quality of Government dataset, 2011)
Foreign direct investment (% of GDP) [WDI, 2011
April]
General government final consumption expenditure (%of GDP) [WDI, 2011 April]Oversees development aid (% of GNP) [WDI, 201134

Poverty head countAgricultural value added per worker

April]The percentage of the population living on less than
$1.25 a day at 2005 international prices (World Bank,
2011).
The net output of the agricultural sector (after adding up
all outputs and subtracting intermediate inputs) devided
by the labour force (World Bank, 2011).

35