15
OCCASIONAL PAPERS
Knowledge for development effectiveness
Enabling poor rural people to overcome poverty
Remittances, growth and poverty
New evidence from
Asian countries
Remittances, growth and poverty.
New evidence from Asian countries
by
Katsushi S. Imai Economics Department School of Social Sciences, University of Manchester United Kingdom Raghav Gaiha
Department of Urban Studies and Planning Massachusetts Institute of Technology (MIT) United States
Abdilahi Ali
Economics Department School of Social Sciences, University of Manchester United Kingdom and
Nidhi Kaicker
Faculty of Management Studies University of Delhi
India
The fifteenth in a series of discussion papers produced by the Asia and the Pacific Division, IFAD
15
OCCASIONAL PAPERS
Knowledge for development effectiveness
Enabling poor rural people to overcome poverty
country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The designations ‘developed’ and ‘developing’ countries are intended for statistical convenience and do not necessarily express a judgement about the stage reached by a particular country or area in the development process.
All rights reserved
ISBN 978-92-9072-346-2 Printed October 2012
3
Table of contents
Acknowledgements 4
Acronyms 5
Foreword 6
Abstract 7
Introduction 8
I. Financial crisis and remittances 10
II. Remittances, growth and poverty 12
III. Remittances and rural poverty 15
IV. Data and empirical strategy 17
V. Empirical results 19
IV. Concluding observations 29
Annex A
List of countries 30
Annex B
List of variables 31
Annex C
Models 32
References 34
Tables
1 Remittances and growth 20
2 PVAR results: Effects of volatility of capital inflows on economic growth 21 3a Remittances, growth and poverty (with lagged growth of agricultural VA per worker) 23 3b Remittances, growth and poverty (with lagged agricultural VA per worker (level)) 24 3c Remittances, growth and poverty (with lagged GDP per capita (level)) 25
4 Effect of remittances on poverty 27
This study was funded by the International Fund for Agricultural Development (IFAD).
We are grateful to Thomas Elhaut and Ganesh Thapa, Asia and the Pacific Division, IFAD, for their support and guidance throughout the study. The present version also benefited from the constructive suggestions of two anonymous reviewers. The views expressed are our personal views and not necessarily those of the organizations to which we are affiliated.
5
Acronyms
AsDB Asian Development Bank FDI foreign direct investment
FE-2SLS fixed effects – two stage least squares
FFR Financing Facility for Remittances for Rural Sector Development GDP-PC gross domestic product per capita
MFI microfinance institution ODA official development assistance PVAR panel vector autoregression
RE-2SLS random effects – two stage least squares VAR vector autoregression
WDI World Development Indicators (World Bank)
The flow of remittances to countries in the Asia and Pacific region reached US$204 billion in 2011, which constitutes about 55 per cent of the global remittance flow. By 2014, this is projected to increase to US$257 billion. For many countries in the region, remittances constitute the largest source of foreign exchange earnings and represent more than 10 per cent of gross domestic product. Thus, a better understanding of their impact on economic growth and poverty reduction could help policymakers design appropriate remittance policies.
Many studies have examined the relationship between the flow of remittances and economic growth. However, most past studies are based on cross-sectional analysis, which assumes homogeneity in the observed relationship across countries.
Such an assumption is very restrictive, as countries vary widely in terms of structural characteristics and institutions, which can have an important bearing on the remittance/growth relationship. The first innovative dimension of this study is that it uses panel data from 24 developing Asia/Pacific countries, covering the period 1980-2009, to examine the impact of remittance flows on economic growth and poverty reduction.
Second, the study assesses both the direct impact of remittances on poverty reduction and the indirect impact through their effect on overall economic growth.
Third, it analyses the effect of the volatility of capital inflows such as remittances and foreign direct investment on economic growth. This represents an important dimension, as some recent studies show that remittance flows to several Asian countries have declined substantially owing to the global financial crisis.
The study confirms the positive contribution of remittance flows to economic growth, as well as to poverty reduction. However, it also shows that they can be a source of output shocks during times of economic uncertainty. Based on the findings, it identifies several important policy implications and recommendations that can contribute to harnessing the full potential of remittance flows.
It is hoped that the findings of this study will be useful to policymakers, development practitioners, academics and civil society.
Ganesh Thapa Regional Economist
Asia and the Pacific Division
Foreword
7
Abstract
The present study re-examines the effects of remittances on the growth of GDP per capita using annual panel data for 24 Asia/Pacific countries. The results generally confirm that remittance flows have been beneficial to economic growth. However, our analysis also shows that the volatility of capital inflows such as remittances and foreign direct investment is harmful to economic growth. In other words, 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. Migration and remittances are thus potentially a valuable complement to broad-based development efforts.
Introduction
In 2010, migrants from developing countries sent more than US$325 billion to their countries of origin, far exceeding the official development assistance (ODA) received.
And this does not include unrecorded flows. The increase in remittances to developing countries has been due to two factors: (i) more people are settling abroad; and (ii) easier, faster and cheaper modes of transmitting money to another country are now available, which also facilitates recording by central banks.
Evidence of the impacts of migration on the growth and poverty levels of any country is mixed. While the resulting remittances increase the income of the recipient country, and consequently decrease poverty, there are social costs not accounted for in these effects.1On the one hand, remittances reduce work effort and dampen long-term growth, and on the other, they improve financial-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 can curtail investor panic, help deal with a balance-of-payments crisis, and can be used for development projects (Ratha, Mohapatra and Scheja 2011).
Remittances reduce poverty through increased incomes, allow for greater investment in physical assets and in education and health, and also enable access to a larger pool of knowledge (Adams 2011). Inflow of workers’ remittances results in physical capital accumulation through increased access to finance, although this depends on the recipients’ marginal propensity to consume. In Nepal, for instance, 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 largely to 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 highly skilled workers can result in a brain drain (Adams 2003; Docquier, Lohest and Marfouk 2007) that could have a negative impact on the growth of the country in the long run.2
Many Asia/Pacific countries enjoyed a surge of remittances until the beginning of the global financial crisis and experienced economic growth and poverty reduction at the same time, but no studies, to our knowledge, have assessed the impacts of remittances on economic growth and poverty in these countries. The present study attempts to fill this gap. Its objectives are to assess: (i) 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 have employed here correct for endogeneity of remittances and other variables. Robust results are obtained, based on a cross-country panel of a large number of countries in the Asia and Pacific region.3
1 Remittances also involve risk of psychological stress and adverse emotional impact, both for the migrant and the family.
2 However, the effect of the brain drain could be positive if migration prospects foster investment in education because of higher expected earnings abroad (Beine, Docquier and Rapoport 2001).
3 For example, if remittances depend on the costs of sending them, then remittances are considered an endogenous variable. Under certain conditions, the costs could then be used as an instrument to correct this endogeneity. For details, see Greene (2008).
9 The remainder of this study is structured as follows. Section 1 discusses the impact of
the financial crisis of 2008/09 on remittances, and section 2 reviews the recent literature on the relationship of remittances to economic growth and poverty. Section 3 examines the potential contribution of remittances to community development and rural poverty, drawing on a recent review of IFAD initiatives. Section 4 is devoted to a review of the data and a brief discussion of the econometric specifications used.
Results are discussed in section 5, while section 6 concludes with observations from a broad policy perspective.
I. Financial crisis and remittances
The global financial crisis has had a dampening effect on the remittances received by developing countries. A recent study by the Asian Development Bank (AsDB 2011) shows that remittance flows to Asian countries have declined since the onset of the financial crisis, 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 the Philippines. There was a decline in incomes as a result of the crisis. Ninety-seven per cent of households in Bangladesh, 82 per cent in Indonesia, and 64 per cent in the Philippines reported lower incomes. Apart from falling remittances, the reasons include job losses, wage cuts and depreciation of the peso (in the Philippines). Both savings and investments (in physical and human capital) declined. As a coping mechanism, households in Bangladesh and Indonesia worked more, and in the Philippines, 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 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 US$11.34 billion in remittances between January and August 2009, as compared with US$10.94 billion for the same period in the previous year. In Bangladesh, remittances increased from US$471 million in August 2007 to US$935 million in August 2009. The absence of a steep decline in remittances in some countries may be attributed to: (i) some permanent oversees migrants have not suffered from the financial crisis; (ii) many migrants are settled in developing nations that were not severely affected by the crisis; and (iii) migrants are engaged in those jobs or industries that are relatively untouched by the crisis (Jha, Sugiyarto and Vargas-Silva 2009).
More recent evidence points to a rise in remittances (IFAD 2011).
Migrant workers around the 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 2 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, during 2010, while 70 per cent of the countries showed an increase in dollars remitted, recipients in 60 per cent of the countries experienced an actual decrease in the purchasing power of the money they received.
The rise of the dollar against developing country currencies at the outset of the
11 global recession initially had a positive effect on families receiving remittances,
effectively delaying the effect of the crisis in those countries with a flexible exchange rate. In 2010, however, that trend began reversing as developing country currencies rebounded, leaving many recipient families to face the same financial pressures experienced by migrant workers in more developed economies (ibid.).
II. 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 and human capital, but also lower the cost of capital in the recipient country. Thus, additional borrowing may increase and lead to greater indebtedness. They may also have a role in stabilizing the economy or reducing volatility, and hence in reducing the risk premium that investors demand.
(ii) By affecting labour force growth. Remittance receipts have a negative impact on labour force participation by substituting remittance income for labour income – and by creating more leisure, with the performance of less work.
(iii) By affecting total factor productivity 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 the decision is not as efficient as one made by a skilled domestic financial intermediary in the case of formal capital inflow. Remittances may result in greater financial development. But they can also result in exchange rate changes – inflow of funds can result in ‘the Dutch disease’, i.e. currency appreciation and thus lower exports.
Barajas et al. (2009) examine the impact of remittances on growth in 84 recipient countries, based on annual observations in the period 1970-2004. They use the following instruments:4 the ratio of remittances to the GDP of all other recipient countries, which captures the effects of global reductions in transaction costs and other macroeconomic determinants of remittances. In most cases, remittances have a negative sign and, in others, there is no robust relationship between remittances and economic growth.
Chami, Fullenkamp and Jahjah’s model (2005) shows that remittances are compensatory in nature, rising with the level of altruism, and falling as the recipient’s wage in the high output stage rises, given a negative 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 issue – workers slackening with the receipt of higher remittances – there is a negative effect on aggregate output. Based on data for 113 countries over a 29-year period (1970-1998), Chami, Fullenkamp and Jahjah (ibid.) controlled for lagged income gap and the interest rate gap between the recipient country and the United States as determinants of remittances, and showed that workers’ remittances have a negative and significant effect on growth, which is consistent with the moral hazard issue.
4 As explained in footnote 3, certain conditions must be met for a valid instrument. For details, see Greene (2008), as well as Imai et al. (2011) for tests of the validity of instruments.
13 Vargas-Silva, Jha and Sugiyarto (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 (logarithm of remittances as a per cent of GDP), logarithm of initial GDP per capita, primary school completion rate, logarithm of gross capital formation, openness of trade and GDP deflator. While the impact of remittances on growth is positive, 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 cent increase in remittances (as a percentage of GDP) decreases the poverty gap by about 0.7- 1.4 per cent. Pradhan, Upadhyay and Upadhyaya (2008) examined the effect of workers’ remittances on economic growth using panel data from 1980-2004 for 39 developing countries; they confirmed a positive impact on growth.
Adams and Page (2005) study the effect of international migration on poverty in the developing world. Attention is given to endogeneity of migration and remittances by using instrument variables. The instruments include: distance between remittance- sending and receiving countries, level of education, and government stability.5 A merit of this study (compared with the extant literature) is that the econometric analysis is based on a large data set (71 low- and middle-income developing countries, covering migration, remittances, inequality and poverty).
Poverty indices are regressed on GDP per capita, the Gini coefficient of income distribution, share of migrants in the population and (alternatively) per capita official remittances. In addition, regional dummies are used. After taking account of the endogeneity of international migration and remittances, these two variables have a significant negative impact on poverty.
Aggarwal et al. (2011) assess the impact of remittances on financial-sector development, using data for 109 countries over the period 1975-2007. Financial-sector development is measured as the share of bank deposits or the ratio of bank credit to the private sector, expressed as a percentage of GDP. This study addresses 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 for that of bank credit to GDP. The results hold true even for a smaller sample of countries (42) for which remittances also include those received using informal or non-bank sources. After correcting for the endogeneity of remittances – using economic conditions in remittance-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 data for more than 100 countries over 1975-2002 and controlling for the endogeneity of remittances and financial development, Giuliano and Ruiz-Arranz (2009) investigated the relationship between remittances and growth. They found that remittances promoted growth in less financially developed countries.
Remittances also help in reducing consumption instability in developing countries.
They act both as an ex-ante risk avoidance tool and an ex-post risk management
5 There are a few difficulties. (i) Use of the same set of instruments for both migration and remittances is problematic. Specifically, remittances are likely to be affected by cost of transfers and exchange rate fluctuations, among other factors. Also, the degree of altruism is key to remittances and not necessarily to migration.
(ii) Another difficulty is the separate use of migration and remittances in the poverty equation. Semyonov and Gorodzeisky (2005) show that both matter.
mechanism (e.g. remittances increase after natural disasters affect a region).
Combes and Ebeke (2011) analyse a cross-sectional panel of 87 developing countries over the period 1975-2004 to estimate the impact of remittances on consumption instability. They find that they significantly reduce such instability, the impact being stronger in less financially developed countries. However, the stabilizing impact decreases at higher remittance levels. Remittances also increase resilience to shocks, such as natural disasters (e.g. floods) and macroeconomic events (e.g. a collapse of export markets).
15 Our econometric analysis focuses on the role of remittances in reducing an overall measure
of poverty. The main reason for not examining the impact of remittances on rural poverty through agricultural growth is paucity of cross-country estimates of rural poverty – especially in the context of the panel data models used here. However, some useful insights on enhancing the impact of remittances on rural development and poverty can be found in a recent account of IFAD’s initiatives (IFAD 2012). A selective summary is given below.
IFAD has been in the forefront of innovative practices to enhance the development impact of remittances. A major endeavour is the creation of the multi-donor Financing Facility for Remittances (FFR) for Rural Sector Development. Its goal is to leverage the development impact of remittances and enable migrants and their families to achieve financial independence.
The FFR has co-funded 50 innovative projects in more than 40 countries worldwide, implemented by over 200 partner institutions on the ground. The majority of these projects target: (i) governments, to encourage financial access to remittance senders and recipients, and to empower migrant workers to realize their financial goals; (ii) the private sector, to develop profitable services tailored to the needs of migrant workers and their families; and (iii) diaspora organizations, to help them empower themselves, investing in their members’ families and rural communities.
Up to 40 percent of remittances are sent to rural areas. But these areas pose specific challenges that must be overcome to ensure access to finance by rural recipients. A few illustrations will suffice (IFAD 2012):
Postal networks offer a unique combination of a broad global presence of brick- and- mortar locations and a long tradition of procurement of financial services.
Through the modernization of rural branches and their scaling up, cheaper and faster remittance services are being offered in Central and South Asia. The greater number of clients served also allows post offices to develop strategies to increase revenues through the cross-selling of financial services (e.g. postal savings accounts), together with an array of other financial services tailored to the needs of clients.
Microfinance institution (MFI) networks are potentially the best equipped to provide financial services to rural clients. By enhancing their capacity to offer remittance services, MFIs stand to gain from both revenues and deposits, which can be used to make loans to local community members. Training MFIs to become agents of remittance companies has proved successful in helping agents serve their rural clients more efficiently – for example, FFR projects in Nepal and Tajikistan, among others.
Working with the private sector encourages attention to market data and customer- oriented approaches. In Sri Lanka, the remittance-linked savings product designed by Hatton National Bank provided access to financial products aligned to the needs of migrant workers and their families, in particular insurance and loans for production. The results of this initiative illustrate the value of scaling up successful operations, where private-sector companies can have an impact far beyond the envisaged original scope of a pilot.
III. Remittances and rural poverty
Insurance products help protect migrant workers and their families against financial risks. They are especially important when lack of access to formal financial services forces migrants to make use of informal lenders. FFR experience in Nepal and Sri Lanka demonstrates the untapped market potential of these products for migrants’ families, even when a close-knit culture does not give much prominence to insurance.
Even if remittances can relieve the poverty of recipient households, and sometimes have wider positive effects on communities, they do not automatically generate development. In some specific contexts, remittances may enhance households’
dependence. FFR is exploring how recipients could be enabled, as entrepreneurs, to lessen their dependence on remittances.
17
Data
Our sample is dictated by data availability and consists of 24 Asia and Pacific economies over the period 1980-2009. A list of countries, as well as definitions and sources of all the variables used, are given in annexes A and B. Unless stated otherwise, the data are drawn from World Development Indicators 2011 (World Bank 2011).
Remittances and economic growth
Based on the existing literature on remittances and growth, such as Chami, Fullenkamp and Jahjah (2005), in our baseline specification growth rate of GDP per capita is posited to depend on workers’ remittances as a share of GDP, a standard set of determinants of growth such as lag of real GDP per capita, financial-sector development, inflation, civil war, resource abundance, capital account openness and investment, and unobserved country effects.6
Following the empirical literature on economic growth, we include lagged real GDP per capita to allow for convergence. Here, a negative coefficient is expected. In line with Levine, Loayza and Beck (2000), we use deposit-money bank assets as a share of deposit money and central bank assets (defined by Beck, Demirgüç-Kunt and Levine 2009) as a measure of financial-sector development to account for the fact that the relationship between remittances and growth may work through the financial sector (Guiliano and Ruiz-Arranz 2009). To capture the macroeconomic and political environments, we account for inflation and civil conflicts measured by internal armed conflicts from the UCDP/PRIO Armed Conflict Dataset (2009).7 In addition, we consider the role of resource abundance captured by fuel exports, as a percentage of merchandise exports, sourced from the Quality of Government Dataset (2011).8 We also use a capital-account openness measure, first introduced by Chinn and Ito (2006), who take into account 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, recognizing that it may be one of the most important channels through which remittances influence economic growth.
In an expanded specification, we add a few other control 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 the Quality of Government Dataset, 2011). Finally, we control for property rights protection, captured by ‘constraint on the executive’ from the Polity IV Dataset.9 This follows Acemoglu and Johnson (2005), who make a strong case for the appropriateness of this indicator as a measure of property rights protection. As this
6 For details of specification of the variables used, see annex C.
7 Available at www.prio.no/CSCW/Datasets/Armed-Conflict/UCDP-PRIO/ (accessed 5 November 2011).
8 Available at www.nsd.uib.no/macrodataguide/set.html?id=37&sub=1 (accessed 5 November 2011).
9 Available at www.systemicpeace.org/polity/polity4.htm.
IV. Data and empirical strategy
10 Two different versions of two stage least squares (2SLS) are used, in which country effects are taken to be either random (RE-2SLS) or fixed (FE-2SLS). For details, see annex C.
11 For details, see annex C.
variable captures procedural rules that constrain political leaders and other powerful elites, it is closely linked to the security of private property rights.
We use a two-stage estimation procedure that first corrects for the endogeneity of GDP per capita, financial development, investment and remittances, and then estimates the effects of remittances on growth and poverty, allowing for unobservable country effects.10For remittances we use lagged remittances and income gap between the United States and each country as instruments.
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 ones. Here, we empirically test whether the volatility of two types of inflows – FDI and remittances – is harmful or beneficial to economic growth. To measure volatility, we use the standard deviation of each variable measured over a non-overlapping five-year period, as we are interested in a stable link between the volatility of capital inflows and growth.
The model used has three dependent variables (i.e. real per capita income and standard deviations of FDI and remittances), which are regressed on appropriate instruments, and unobservable (fixed) country effects.11
19
Remittances and growth
Our comments are confined to Table 1, which contains selected results of the expanded specification.12The results show that macroeconomic instability in the form of high inflation is detrimental to economic growth. This is in line with the conventional wisdom that a stable macroeconomic environment reduces the risks and uncertainties associated with investment projects and thus results in economic growth. Similarly, we find that civil wars are negatively related to growth, presumably because of their disruptive effects on economic activity.
Remittances are positively associated with better economic performance (the coefficient ranges from 0.67 to 3.28). The existing literature (for example, Barajas et al.
2009) identifies various channels through which remittances are likely to enhance growth, including the boosting of capital accumulation, labour force growth and total factor productivity. While we did not explore these channels empirically, our results are in sharp contrast with Barajas et al., whose empirical analysis finds no relationship between remittances and growth, or Chami, Fullenkamp and Jahjah (2005) claiming that 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. As the results are robust to different specifications and the favourable effects of remittances on growth persist, the lack of a positive relationship between them in a few studies reviewed here is suspect.
The results indicate that, on average, countries with open capital-account regimes register higher 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, and hence better economic performance (Bekaert, Lundblad and Harvey 2010). The estimated coefficients also suggest that both investment and natural resources are positively related to growth.
The impact of regime durability on growth is positive, 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, Hoeffler and Pattillo 2004). Both aid and government expenditure are inversely related to growth, because aid may encourage corruption (as found by Knack 2001), while increased government expenditures may crowd out private investment. Finally, FDI generally carries the expected positive sign, even though it is mostly non-significant at the 10 per cent level.
To sum up, our findings indicate that remittances (as a share of GDP) have promoted economic growth in our sample countries. In what follows, we investigate the related issue of how volatility of remittance inflows influences economic growth relative to other types of capital inflows, such as FDI.
12 For the omitted results, see Imai et al. (2011).
V. Empirical results
Table 1
Remittances and growth
-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
0.67 0.00 7.93 Lagged GDPa
Investment/GDP
Fin. dev./GDPa
Remittance/GDPa
Inflationa
Resource abundance
Cap. acc. openness
Civil war
Trade
Property rights
Regime durability
FDIa
Gov. exp./GDPa
ODA/GNPa
Observations Specification testsb Overidentification Underidentification F-statistic (weak inst.)
FE-2SLS (1)
-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 RE-2SLS (2)
Notes: Dependent variable is GDP per capita growth. Robust standard errors in parentheses. ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
aVariables are in log form. Lagged real GDP, financial development and investment are instrumented with their own lags. Remittance is instrumented with its 1stlag and the income gap between each country and the United States.
bThe specification tests are: (i) the overidentification test, which displays 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, which 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, which is the Kleibergen-Paap rk Wald F-statistic for the null of weak correlation between the endogenous variables and the instruments.
21
Volatility of capital inflows and growth
Table 2 summarizes the results.13Volatility of both remittances and FDI is inversely related to economic performance. The negative effects of volatility are marginally greater with FDI than with remittances. While remittance flows may alleviate financial constraints and thus stimulate economic development, they may also be a source of output shocks, for example in situations where countries are unable to buffer against sudden swings in inflows.
To understand better the response of income to changes in the volatility of capital inflows, we also show the impulse response functions for our variables of interest – the volatility of remittances and FDI. Impulse response functions show that an exogenous shock to the volatility of both types of capital inflows contracts economic growth – especially in the short run (i.e. in two to three years after the shock) in which countries may find it harder to adjust to unexpected changes in capital inflows.14
A related issue has been volatility of public expenditure in agriculture, and consequently in capital formation, in some of the poorest countries in this region (Cambodia and Laos, for example) as foreign flows declined in recent years (ODA and other). This renders it difficult to accelerate agricultural productivity sustainably. As our review shows, while remittances also declined during the initial phase of the financial crisis, they recovered quickly, and to that extent acted as a buffer against declining foreign flows and their dampening effects on living standards in rural areas.
Remittances and poverty in Asia
Here, we examine how remittances affect poverty in Asian countries as an extension of the growth regressions in the previous sections, along the lines of Imai, Gaiha and Thapa (2010). Among various poverty measures – including both income and non- income indicators – we use international poverty headcount measures based on the US$1.25 or $2-a-day poverty line, estimated by the World Bank (Ravallion, Chen and Sangraula 2008), as they cover a large number of countries and years. However, as Table 2
PVAR results: Effects of volatility of capital inflows on economic growth
0.130 (1.822) 0.196 (2.194)**
0.027 (0.211) Rem volatility (t-1)
FDI volatility (t-1)
Income (t-1)
FDI volatility -0.027
(2.010)**
-0.049 (-2.882)**
0.591 (21.872)**
Income
0.002 (0.010) -0.001 (-0.014) -0.090 (-0.998) Rem volatility
Notes: The trivariate panel vector autoregression (PVAR) model is generated via generalized methods of moments (GMM). Robust t-statistics appear in parentheses and ** indicates significance at the 5% level.
13 An important caveat to our results is that the sample size is reduced significantly with five-year averages when calculating the volatility measures. Thus, we have also estimated models with four- and three-year averages and the results remain largely unchanged. These alternative results are available on request from the first-listed author, K.S. Imai (Katsushi.Imai@manchester.ac.uk).
14 For a brief exposition of impulse response function, see annex C.
these poverty estimates are usually based on household surveys that take place every few years, the corresponding panel is highly unbalanced (i.e. the number of observations for different countries varies a great deal). Constrained by limited data, we have used a parsimonious specification in which growth rate of GDP per capita is estimated by a small number of explanatory variables: a two-period, lagged growth of agricultural value added (VA) per worker (or lagged level of agricultural VA per worker, or lagged level of GDP per capita as an instrument), investment, financial development, remittances and trade in the first stage of FE-2SLS.15In the second stage, the poverty headcount ratio (based on either US$1.25 or $2 a day) 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 VA per worker is used as an instrument for economic growth rate to capture the long-term effect of agricultural productivity on growth in our sample countries in Asia.
Tables 3a, 3b and 3c give the FE-2SLS results for poverty. Table 3a is for lagged agricultural growth per worker, 3b for lagged agricultural VA (in level) per worker and 3c for lagged GDP per capita. The first two columns of each table show the results for poverty headcount based on US$1.25, and the second two columns on US$2.
Both cases, however, yield broadly similar results.
There is a striking difference in the effect of agricultural production on growth depending on whether we use its level or growth. In Table 3a, we observe a strong and statistically highly significant effect of lagged agricultural growth on economic growth (consistent with a key role of the agriculture sector as an engine of economic growth).
However, in Table 3b, the coefficient of the level of agricultural value added per worker is negative and statistically significant. This presumably reflects the convergence effect of agricultural production, that is, a country with low initial agricultural production tends to have a higher growth than those with high initial production. If we replace lagged agricultural value added per worker by lagged GDP per capita in Table 3c, we find a similar pattern of results. The results of other variables are the same as before – investment, financial development and remittances have positive and significant effects. However, the effect of trade openness is positive, but non-significant.
In the second stage, the share of remittances in GDP is negatively associated with poverty in Tables 3b and 3c. It follows that remittances not only promote economic growth, but also reduce poverty (on the two criteria of US$1.25 and US$2).
However, in Table 3a, the coefficient of remittances is negative, but not significant, in the second stage of the poverty equation. Simulation requires significant coefficient estimates and thus we will use Table 3b for poverty simulations.
15 See footnote 10.
23 Table 3a
Remittances, growth and poverty
(with lagged growth of agricultural VA per worker)
2nd stage Poverty headcount ($1.25) -0.140 (0.079)*
- - -0.006 (0.026) -0.645 (0.619) -0.010 (0.166) -0.013 (0.006)**
101
1st stage Growth rate (GDP-PC) - - 17.71 (7.015)**
0.326 (0.074)***
2.491 (2.649) 1.026 (0.562)*
0.0126 (0.028) 103
0.000 0.0123 6.375 Dep. Var
Growth ratea
Lagged growth of Ag.
VA per workera Investment/GDP
Fin. dev./GDPa
Remittance/GDPa
Trade
Observations Specification tests Overidentification Underidentification F-statistic (weak identification test)
1st stage Growth rate (GDP-PC) - - 19.25 (6.224)***
0.255 (0.069)***
2.891 (2.350) 1.169 (0.499)**
0.017 (0.026) 101
0.000 0.0026 9.561 FE-2SLS
2nd stage Poverty headcount ($2.00) -0.100 (0.062) - - -0.0021 (0.023) -0.110 (0.442) -0.008 (0.117) -0.006 (0.004) 103 FE-2SLS
Notes: Robust standard errors in parentheses. ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
aVariables are in log form.
Table 3b
Remittances, growth and poverty
(with lagged growth of agricultural VA per worker (level))
2nd stage Poverty headcount ($1.25) 0.198 (0.093)**
- - -0.094 (0.033)***
-1.64 (0.785)**
-0.5005 (0.205)***
-0.0174 (0.008)**
101
1st stage Growth rate (GDP-PC) - - -11.09 (3.058)***
0.361 (0.072)***
5.431 (2.671)**
1.796 (0.549)***
0.0355 (0.028) 103
0.000 0.0005 13.165 Dep. Var
Growth ratea
Lagged Ag. VA per worker (level)a Investment/GDP
Fin. dev./GDPa
Remittance/GDPa
Trade
Observations Specification tests Overidentification Underidentification F-statistic (weak identification test)
1st stage Growth rate (GDP-PC) - - -9.86 (2.935)***
0.309 (0.069)***
5.434 (2.440)**
1.878 (0.502)***
0.031 (0.026) 101
0.000 0.0012 11.298 FE-2SLS
2nd stage Poverty headcount ($2.00) 0.110 (0.052)**
- - -0.067 (0.021)***
-0.648 (0.495) -0.2804 (0.120)**
-0.009 (0.005)*
103 FE-2SLS
Notes: Robust standard errors in parentheses. ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
aVariables are in log form.
25 Table 3c
Remittances, growth and poverty (with lagged GDP per capita (level))
2nd stage Poverty headcount ($1.25) 0.103 (0.051)**
- - -0.069 (0.022)***
-1.361 (0.608)**
-0.362 (0.143)**
-0.0162 (0.006)**
101
1st stage Growth rate (GDP-PC) - - -9.534 (1.657)***
0.364 (0.065)***
5.699 (2.376)**
2.519 (0.527)***
0.0537 (0.026) 103
0.000 0.000 33.111 Dep. Var
Growth ratea
Lagged GDP per capita (level)a Investment/GDP
Fin. dev./GDPa
Remittance/GDPa
Trade
Observations Specification tests Overidentification Underidentification F-statistic (weak identification test)
1st stage Growth rate (GDP-PC) - - -8.479 (1.548)***
0.312 (0.063)***
5.654 (2.170)**
2.511 (0.481)***
0.048 (0.023)**
101
0.000 0.000 30.010 FE-2SLS
2nd stage Poverty headcount ($2.00) 0.054 (0.029)*
- - -0.048 (0.014)***
-0.504 (0.394) -0.207 (0.089)**
-0.008 (0.004)**
103 FE-2SLS
Notes: Robust standard errors in parentheses. ***, ** and * indicate significance at the 1, 5 and 10% levels, respectively.
aVariables are in log form.
Table 4 discusses in detail the magnitude of the effects of remittances on poverty.
In the case of the headcount ratio (US$1.25), the indirect effect of remittances on poverty (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), assuming that other factors are unchanged. With regard to the direct effect, the elasticity of poverty with respect to remittances is -0.500. This is much larger than the indirect effect in absolute terms, and the total effect is -0.439.
This implies that a 1 per cent increase in the share of remittances in GDP leads to a 0.44 per cent decrease in the headcount ratio, other things being equal. Likewise, in the case of the US$2 poverty, the indirect effect of remittances is obtained as 0.040 and the direct effect is -0.280, leading to a total effect of -0.240, other things being equal.16
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 per cent increase in the current remittance ratio and their poverty effects.
For example, in Bangladesh, a 50 per cent increase of the share of remittances in GDP (from 11.78 to 17.67 per cent) would increase the GDP per capita growth rate from 4.30 to 4.97 per cent and reduce the poverty headcount (on US$1.25 a day) from 49.60 to 38.69 per cent – and that on the higher cut-off (US$2.00 a day) from 81.30 to 71.54 per cent. These results imply that remittances reduce poverty significantly, especially extreme poverty.
A few other cases further corroborate these results. In India, a 50 per cent increase in the share of remittances in GDP (3.59 to 5.39 per cent) accelerates economic growth (from 7.65 to 8.84 per cent) and reduces US$1.25 poverty from 41.6 to 32.45 per cent, and US$2 poverty from 75.60 to 66.53 per cent. 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 the remittance share has increased significantly in recent years, a 50 per cent increase – a rise in the share from 23.83 to 35.75 per cent – leads to a substantial poverty reduction, from 55.10 to 42.98 per cent (US$1.25 a day), and from 77.60 to 68.29 per cent (US$2.00 a day). If Sri Lanka sees a rise in the share of remittances from 8.01 to 12.02 per cent (i.e. by 50 per cent), the headcount ratio (on US$2.00) will decline from 29.1 to 25.61 per cent. However, these results will have to be interpreted with some caution, as the same elasticity estimates are applied to all countries in the sample. Nevertheless, it would be safe to conclude that an increase in remittances not only promotes economic growth, but also reduces poverty.
16 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 favourable impact on poverty and health. But negative effects of remittances on growth, based on Chami, Fullenkamp and Jahjah (2005), are contentious.
27
∂log gdp pc growth *
∂log remittances 0.361 * 10.0 20.0 50.0
∂log poverty
∂log gdp pc growth 0.110
% increase in remittance ratio→ 2.4 % reduction of poverty headcount ratio ($2 a day)
% increase in remittance ratio→ 4.8 % reduction of poverty headcount ratio ($2 a day)
% increase in remittance ratio→ 12.0 % reduction of poverty headcount ratio ($2 a day)
∂log poverty
∂log gdp pc growth 0.198
% increase in remittance ratio→ 4.4 % reduction of poverty headcount ratio ($1.25 a day)
% increase in remittance ratio→ 8.8 % reduction of poverty headcount ratio ($1.25 a day)
% increase in remittance ratio→ 22.0 % reduction of poverty headcount ratio ($1.25 a day) Table 4
Effect of remittances on poverty
indirect effect 0.061 +
direct effect (-0.500) =
∂log gdp pc growth *
∂log remittances 0.309 * 10.0 20.0 50.0
Case 1. Headcount ratio based on US$1.25
∂log gdp pc growth
∂log remittances -0.439
direct effect (-0.280) =
∂log gdp pc growth
∂log remittances -0.240 indirect
effect 0.040 +
2009 10%
increase 20%
increase 50%
increase 2009 10%
increase 20%
increase 50%
increase 2009 10%
increase 20%
increase 50%
increase
11.78 % 12.96 % 14.14 % 17.67 % 0.98 % 1.08 % 1.18 % 1.47 % 3.59 % 3.95 % 4.31 % 5.39 % Case 2. Headcount ratio based on US$2
Remittance ratio (% in GDP)
2005 10%
increase 20%
increase 50%
increase 2005 10%
increase 20%
increase 50%
increase 2005 10%
increase 20%
increase 50%
increase
81.30 % 79.35 % 77.40 % 71.54 % 36.30 % 35.43 % 34.56 % 31.94 % 75.60 % 73.79 % 71.97 % 66.53 % Poverty headcount ratio
2005 10%
increase 20%
increase 50%
increase 2005 10%
increase 20%
increase 50%
increase 2005 10%
increase 20%
increase 50%
increase
49.60 % 47.42 % 45.24 % 38.69 % 15.90 % 15.20 % 14.50 % 12.40 % 41.60 % 39.77 % 37.94 % 32.45 % Poverty headcount ratio
2009 10%
increase 20%
increase 50%
increase 2009 10%
increase 20%
increase 50%
increase 2009 10%
increase 20%
increase 50%
increase
4.30 % 4.43 % 4.57 % 4.97 % 8.54 % 8.80 % 9.07 % 9.86 % 7.65 % 7.89 % 8.12 % 8.84 % Growth rate
Bangladesh
China
India
% change in remittance ratio (% in GDP)
% change in poverty headcount ratio US$2 a day
% change in growth rate per capita
% change in poverty headcount ratio US$1.25 a day
2009 10%
increase 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
1.26 % 1.39 % 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 % Remittance ratio (% in GDP)
2005 10%
increase 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
50.60 % 49.39 % 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 % Poverty headcount ratio
2005 10%
increase 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
18.70 % 17.88 % 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 % Poverty headcount ratio
2009 10%
increase 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
3.35 % 3.45 % 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 % Growth rate
Indonesia
Kazakhstan
Lao PDR
Nepal
Philippines
Sri Lanka
% change in remittance ratio (% in GDP)
% change in poverty headcount ratio US$2 a day
% change in growth rate per capita
% change in poverty headcount ratio US$1.25 a day
29 The present study re-examined the effects of remittances on growth of GDP per capita
using annual panel data for 24 Asia/Pacific countries. The results confirm that remittance flows have been beneficial to economic growth. However, the present study also offers new evidence suggesting that the volatility of remittances and FDI is harmful 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 of poverty, estimated using the cut-off points of US$1.25 and US$2 a day per capita.
If our review of IFAD initiatives is anything to go by, remittances have potential in community development and in the reduction of rural poverty over time.
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 growth and poverty reduction, as they have been linked, among other things, to lower work effort, brain drain and ‘the Dutch disease’. Also, remittances cannot act as a substitute for official sources of capital such as aid, because 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 extant 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 policymakers should adopt policies that encourage the use of remittances for physical and human capital investments, so as to harness their full potential for entrepreneurial and economic development.
VI. Concluding observations
Armenia Azerbaijan Bangladesh Cambodia China Fiji India Indonesia
Iran (Islamic Rep. of) Kazakhstan Korea (Rep. of) Kyrgyzstan
Lao People’s Democratic Republic Malaysia
Maldives Mongolia Nepal Pakistan
Papua New Guinea Philippines Sri Lanka Thailand Tonga Vanuatu
Annex A
List of countries
31
Variable Source
Growth Real per capita growth (World Bank 2010)
Lagged GDP Lagged real per capita income (WDI 2011 April) expressed in log form
Remittances Workers’ remittances and compensation of employees, received (% of GDP) (WDI 2011 April) expressed in log form Financial development Captured by deposit-money bank assets/(deposit money +
central bank assets) (Beck, Demirgüç-Kunt and Levine 2009) expressed in log form
Investment Gross capital formation (% of GDP) (WDI 2011 April) expressed in log form
Inflation Measured by CPI (annual %) (WDI 2011 April) Resource abundance Proxied by fuel exports (% of merchandise exports)
(Quality of Government Dataset 2011 April – footnote 8) Capital account openness A measure of a country’s degree of capital account
openness based on the existence of multiple exchange rates, current account and capital account transaction restrictions (Chinn and Ito 2006)
Civil war Internal armed conflicts (UCDP/PRIO Armed Conflict Database 2009 – footnote 7)
Trade Exports plus imports (% of GDP) (WDI 2011 April) expressed in log form
Property rights protection A measure of property rights protection or institutional quality: measured by ‘constraint on the executive’ from the Polity IV Dataset. A 7-point scale where higher values imply strong property rights (Marshall, Gurr and Jaggers 2009) Regime durability Number of years since the most recent regime change
(Quality of Government Dataset 2011)
FDI Foreign direct investment (% of GDP) (WDI 2011 April) Government size General government final consumption expenditure
(% of GDP) (WDI 2011 April)
Aid Oversees development aid (% of GNP) (WDI 2011 April)
Poverty headcount Percentage of population living on less than US$1.25 a day at 2005 international prices (World Bank 2011)
Agricultural value added per worker Net output of agriculture sector (after adding up all outputs and subtracting intermediate inputs) divided by the labour force (World Bank 2011)
Annex B
List of variables
32
(a) Model of remittances and economic growth
Our baseline specification takes the following form:
(1)
where for country i at time (denoting year) t, ∆y denotes rate of growth of real GDP per capita, LREM is logarithm of workers’ remittances expressed as a percentage of GDP, niis unobserved country-specific effect and εitis the idiosyncratic error term.
The vector X contains a standard set of determinants of economic growth, such as lag of real GDP per capita,17financial-sector development, inflation, civil war, resource abundance, capital account openness and investment.
As some of the explanatory variables, including remittances, are likely to be endogenous, we use the panel two stage least squares (2SLS). Here, lagged GDP per capita, financial development and investment are instrumented by their own lags.
Our main variable of interest – remittances – is also instrumented by its own lag.
In line with Chami, Fullenkamp and Jahjah (2005), we use the income gap between each remittance-receiving country and the United States as an additional instrument.
(b) Model of volatility of capital inflows and growth
Following Love and Zicchino (2006), we estimate a trivariate PVAR in the following form:
(2)
where for country i at time t, Yit is a vector of three endogenous variables (i.e. the logarithm of real per capita income and the standard deviations of FDI and remittances), nidenotes a country-specific fixed effect and εitis the error term. Since by construction the lagged dependent variables are correlated with the unobserved country-level fixed effect, ni, we use forward mean-differencing, which validates the use of lagged right-hand-side variables as instruments for the endogenous variables.
Our interest lies in generating impulse response functions that show the effects of shocks on the adjustment path of the variables. The matrix of the impulse response functions is based on the estimated VAR estimates and their standard errors, and the confidence intervals are produced with Monte Carlo simulations.18
Annex C
Models
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17 A two-year lag has been taken in the present study, but use of a one-year or a longer lag will not change the results significantly.
18 For further details, see Imai et al. (2011).
33
(c) Models of remittance and poverty in Asia
The 2SLS panel model is estimated. Using a parsimonious specification, in the first stage, growth of GDP per capita is estimated by a two-period, lagged growth of agricultural value added per worker, or lagged (level of) agricultural value added per worker or lagged GDP per worker as instruments, remittances, other controls including investment, financial development and trade, and fixed country effects. In the second stage, the poverty headcount ratio (US$1.25 or US$2-a-day poverty line) is estimated by the same set of variables except the instrument (i.e. lagged GDP per capita).
The growth of two-year lagged agricultural value added per worker is used as an instrument for GDP growth rate to capture the long-term effect of agricultural productivity on growth in our sample of countries in Asia.19
19 For specification of the variables used, see Tables 3a, 3b and 3c.
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