International Journal of Economic Issues, Vol. 7, No. 1 (January-June, 2014) : 51-64 © International Science Press

REMITTANCES AND ECONOMIC GROWTH IN BANGLADESH: AN ARDL COINTEGRATION APPROACH KANCHAN DATTA Associate Professor of Economics, University of North Bengal E-mail: [email protected] BIMAL SARKAR Assistant Professor of Economics, Bangabasi Morning College, Kolkata E-mail: [email protected]

Bangladesh is an important supplier of migrant workers to labour-deficient countries. Migrant workers in large numbers are going to almost all over the world especially to the oil-rich countries andsending remittances. They are likely to play an important role in promoting economic development in many ways. In this paper, an attempt has been made to investigate the impact of concomitant remittances on economic growth in Bangladesh. The data are obtained from the Bangladesh Bank (The Central Bank of Bangladesh)covering the time period of 1975-76 to 2011-12. Applying ARDL model in this study, we find weak long-run relationship between the variables.Also, there is no predictable causal relation in the short-run or in the long-run for the study period. Key Words: Remittances, Economic Growth, Bangladesh, ARDL Model, ECM JEL Codes: O40; O53; C32

I.

INTRODUCTION

Migration from Bangladesh to the rest of the world is not a new incidence. Bangladesh has a long history of migration and overseas remittances since 1942(Mahmood, 1991).Remittances inflow to Bangladesh has increased at an average annual rate of 19 per cent in the last 30 years from 1979 to 2008 (Hussain, Naeem, 2009). Between 1976 and 2010 about a total of 71.5 Lakhs people emigrated temporarily from Bangladesh sending a total of 5 Lakhs crore Taka to their families or relatives or their friends in 2009, total remittances to Bangladesh was about 11.8 per cent of the country’s GDP (BBS, 2010). Bangladesh had become one of the top 10 remittance-recipient countries in the world. Bangladesh’s position in 2008 was ninth in that of ‘top 10’ list after India, China, Mexico, Philippines, Poland, Nigeria, Egypt and Romania (Ratha et al., 2008). It is very interesting to note here that a large part of remittances remains unrecorded and it is about 50 to 200 per cent of the officially recorded remittances (Aggarwal et al., 2006). At least two factors can be responsible for faster growth in remittances in developing countries. First, in the past 20 years, immigration has increased dramatically

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between developing and developed countries (World Bank, 2007). Second, due to technological improvements, the transaction costs for the international transfer of payments between individuals have declined (Guiliano & Ruiz-Arranz, 2006). Also some credits go to the successive government of Bangladesh since late 1990s for growing flow of remittance to Bangladesh as the government of Bangladesh was taken some macroeconomic reforms(Bangladesh Bank’s Annul Report, 2003-04) like opening of new exchange house in source countries, expansion of drawing arrangement, setting an annual remittance threshold, close monitoring and supervision of banks, speeding up of delivery to the beneficiaries, surveillance measures under the Money Laundering Prevention Act. Also we can observe from the trend of remittances in Bangladesh that remittances were increased during the late 1970s in the line with the sharp rise in oil prices of the time. There was decaling in flow remittances during Gulf war but there was steady remittance growth in post-war reconstruction (Siddique, 2004) in which so many development programmes like construction of roads, schools, hospitals, houses and other commercial complexes etc. were taken (Kuthiala, 1986). Hence demand for semi skilled and unskilled workers were increased so many folds. Migration flow to USA and Europe from Bangladesh was declined after 9/11 tragedy. The flows of remittance are more stable than other types of private capital inflows like official development aids (ODA) and foreign direct investment (FDI) and are counter cyclical (World Bank, 2006) as the flows increase during downturns as emigrant workers went to provide financial support to the family members in the country of their origin (Sayan, 2006).That is remittance act as a significant macroeconomic stabilizer in the developing countries. II. IMPORTANCE OF REMITTANCES IN BANGLADESH In any developing countries the common problem is shortages of foreign exchange reserve which is very essential to pay the import bills. Bangladesh is not an exceptional country but Bangladesh depends more on remittances to meet the problem of payment of the import bills. Remittances promote growth through smoothing the investment constraint as it act as a substitute for in-efficient or non-existent credit markets (Giuliano & RuizArranz, 2006). There may be positive impact of remittances on economic growth if remittances are used for the purpose of children’s education and welfare expenses such as health care because in the long run there may be a positive impact on labour productivity and hence output of the home country. Even if remittances are spent on consumption or real estate there will be a positive multiplier effects on GDP (Chimhowu et al. 2005). Giuliano and Ruiz-Arranz (2006) suggest in their paper that remittances may act as an alternative way to promote investment in those countries in which there is poor financial sector. Stahl and Arnold (1986) pointed out that there may be negative impact of remittances on growth in the host country if there exist a “demonstration effect”. This demonstration effect can motivate the remittance recipients to consume imported

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goods. Hence if this effect becomes wide-spread this can reduce savings and investments that may be sufficient to reduce the growth rate of the home country. But Glytos (2005) suggests that remittances can import more capital goods into the host country for domestic production which can help to increase the growth rate of the home country. Use of remittances is not same for different remittance receiving countries. Generally most of the receiving countries use remittances for their living expenses, education, health and investments purposes (Carrasco and Ro, 2007). In any developing countries there are some basic problems like inequality in income, income volatility, credit crunch, and poverty and employment opportunity. Remittances can help prevent balance of payment crisis providing a constant source of foreign currency (Lopez-Cordova and Olmedo, 2006). Remittances behave as a very stable source of foreign exchange (Ratha, 2005). If remittances are used for conspicuous consumption or unproductively by an excessive degree of capital intensity in the agricultural sector there would have negative impact on development (Oberoi and Singh, 1980). Again if recipients become highly dependent on the “easy money” which causing them to reduce labour market participation, there may exists the problem of moral hazard between remitters and recipients. That is remittances do effect economic growth negatively (Chami et al., 2003). III. OBJECTIVE OF THE STUDY Observing the increasing remittance income many researchers or policy makers have been showing interest to examine its impact on economic growth in both the host country and the home country of the migrant workers. Regarding the impact of remittances in the home country of migrant workers, there is debate; some argue that remittances have a positive impact on economic growth and some argue it has negative impact on economic growth. In most of the previous studies have been established the null hypothesis as a statement of correlation and not causation. Now question is whether remittances are a statistically significant factor in determining economic growth and whether the relation between remittances and economic growth is causal. Large number of studies is qualitative in nature regarding the impact of remittances on an economy in terms of social measures such as health, education and demoralization (Rahman et al., 2006) and also most of the previous studies are based on panel-data consisting of number of countries. From the panel-data analysis we can answer important questions on average but that may not be suitable for individual countries that seeking to manage domestic policies. So it is very important to study further in a particular country regarding the linkage between remittance income and economic growth. So here in this paper Bangladesh has been chosen to find out the relation between remittances and Gross Domestic Product (GDP). In Bangladesh, remittances sent by the migrant workers to their home country have played an important role to promote economic development in their home

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countries in different ways. This paper has tried to find out the impact of remittances on economic growth in the economy of Bangladesh. The findings of the study have important policy implications not only for Bangladesh but also for other developing countries that depend on remittance income. IV. REVIEW OF LITERATURES There are many macro and micro levels of studies have been published. The following studies are related with this study. Ali (1981) identifies that remittances help for favorable balance of payment.Stahl and Habib (1989) showed the multiplier effect of remittances in economics. They explain that remittances increase savings which increase the growth through multiplier. Even they calculated the multiplier for Bangladesh for the period of 1976-1988. The value of multiplier is 1.24. Chami et al. (2000) find that remittances have negative effects on growth in their study on 113 countries. Mahamud (2003) claims that remittances faster growth in Bangladesh. Adams and Page (2005) studing 71 developing countries finds that remittances significantly reduce the level, depth and severity of poverty in developing world. An IMF (2005) finds no statistical link between remittances and per capita output growth studying on 101 developing countries. Hasan (2006) explains remittance has significant macroeconomic impact at household level and the poorer the household, the more impact or benefits remittance income can have alleviating poverty.Zeisemer (2006) argues that remittances increase savings which intern increase investment by decreasing interest rate and also increases the rate of literacy. Finally remittances increase the rate of growth. Jongwanich (2007) finds that remittances have a positive but marginal impact on economic growth in Asia and Pacific countries. On the other hand Pradhan et al. (2008) find a positive impact on growth in their work with 39 developing countries over the 1980-2004 periods. Fayissa (2008) using an unbalanced panel data from 1980 to 2004 for 37 African countries shows that remittances boost growth in countries where the financial systems are less developed by providing an alternative way to finance investment and helping overcome liquidity constraints. Vargas-Silva et al. (2009) using data for more than 20 Asian countries for the 19882007 sample find that a 10 per cent increase in remittances as ashare of GDP leads to a 0.9-1.2 per cent increase in GDP growth. On the other hand Barajas et al. (2009) find that remittances have no impact on economic growth.According to Catrinescu et al. (2009) although remittances have increased so many times but research has not come to a conclusion whether remittances have a positive or negative impact on long run growth. Hence country specific studies become necessary. The studies on Bangladesh in particular are few in numbers. Raihan et al. (2009) show that remittances have positive effects on the economy and they reduce poverty but Rahman (2009) concludes that remittance seems to have insignificant and ambiguous effects on Bangladesh’s GDP. Ahmed (2010) claims remittances flow to Bangladesh has beenstatistically significant but has negative impact on growth.Siddique et al. (2010) investigate the causal link between remittances and economic growth in three countries, Bangladesh,

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India and Sri Lanka, by employing the Granger causality test under a VAR framework using time series data over a 25 year period;they found that growth in remittances does lead to economic growth in Bangladesh. Paul and Das (2011) find a long run positive relationship between remittance and GDP.But there is no evidence on remittance-led growth in the short run.Ahmed et al. (2011) empirically examine the impact of remittances, exports, money supply on economic growth in the context of Pakistan using bounds testing approach and find that remittances have a positive impact on economic growth of Pakistan in both the long run and short run. Das and Chowdhury (2011) using panel cointegration and pooled mean group (PMG) approach they find a positive long run relationship between remittances and GDP in 11 developing countries. However, the magnitude of the remittance-GDP coefficient is rather quite small. Das (2012) studies on Bangladesh, Egypt, Pakistan, and Syria over the period 19752006 and suggest that remittances have a positive impact on economic growth in Pakistan and Syria but a negative impact in Bangladesh and Egypt. Negative remittance-growth coefficients in those two countries suggest a counter-cyclical relationship. Also, Khathlan (2012) and Dilshad (2013)finda positive and significant relationship between worker remittances and economic growth in the long-run and short-run in Pakistan during the period 1976-2010. V. DATA, VARIABLES AND METHODOLOGY OF THE STUDY The data are collected from the Central Bank of Bangladesh; it covers the time period 1975-76 to 2011-12. The GDP and Remittances are deflated by GDP deflator with base 1995-96=100 and then converted in to logarithmic form. So both the variables are used here are real variables denoted by LRemit and LGDP that is Remittances and Income respectively. In terms of methodology, the paper adopts the recently developed auto regressive distributed lag (ARDL)framework by Pesaran and Shin (1995, 1999). There are advantages of using this approach instead of the conventional Johansen (1998) and Johansen and Juselius (1990). While the conventional cointegrating method estimates the long-run relationships with in a context of a system of equations, the ARDL method employs only a single reduced form equation (Pesaran, and Shin, 1995). Moreover, the ARDL approach does not involve pre-testing variables, which means that the test on the existence relationship between variables in levels is applicable irrespective of whether the underlying regressors are purely I(0), purely I(1) or mixture of both. This feature alone, given the characteristics of the cyclical components of the data, makes the standard of cointegration technique unsuitable and even the existing unit root tests to identify the order of integration are still highly questionable. Furthermore, the ARDL method avoids the larger number of specification to be made in the standard cointegration test. These include decisions regarding the number of endogenous and exogenous variables (if any) to be included, the treatment of deterministic elements, as well as the optimal number of lags to be specified. The empirical results are generally very sensitive to the method and various alternative choices available in the estimation procedure (Pesaran and Smith, 1998).

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With ARDL, it is possible that different variables have different optimal lags, which is impossible with the standard cointegration test. Most importantly the model could be used with limited sample data (30 to 80 observations) in which the set of critical values were developed originally by Narayan (2004). Basically the ARDL approach to cointegration involves estimating the conditional error correction version of the ARDL model for Remittance and GDP of Bangladesh. Two sets of critical values are generated which one set refers to the I(1) series and the other for the I(0) series. Critical values for the I(1) series are referred to as upper bound critical values; while the critical values for I (0) series are referred to as the lower bound critical values. If the F test statistic exceeds their respective upper critical values, we can conclude that there is evidence of a long-run relationship between the variables regardless of the order of integration of the variables. If the test statistic is below the upper critical value we can not reject the null hypothesis of no-cointegration, and if it lies between the two bounds, a conclusive inference can not be made without knowing the order of integration of the underlying regressors. The ARDL Model k

k

i� 1

i�1

DLGDPt = �1 + � �i1DLGDP t-i+ � �i2DLRemit t-i+ �1LGDP t-1+ �2LRemitt-1+µ2t (1) DLRemitt = �1 +

k

k

i�1

i� 1

� �i1DLRemit t-i + � �i2DLGDPt-i+ �1LRemit t-1+ �2 LGDPt-1+µ1t

(2) The orders of the lags in the ARDL model are selected by either the AIC or BIS a criterion before the selected model is estimated by OLS. For annual data, Pesaran and Shin (1999), recommended choosing a maximum of 2 lags. From this the lag length that minimizes (SchwazBaysian Criteria) is selected. Then the F test is used for testing the existence of long-run relationship. When long-run relationship exists, F test indicates which variable should be normalized. The null hypothesis for no-cointegration among variables in equation (1) is, H0: �1= �2 = 0 against H1: �1 � �2 � 0 The F test has a non-standard distribution which depends on (i) whether variables included in the model are I(0) or I(1) (ii) the number of regressors and (iii) whether the model contains an intercept and or a trend If there is evidence of long-run relationship (cointegration) of the variables, the following long-run model is estimated. p

LGDPt � �1 � � �1i LGDPt � i � � i �1 �1i L Re mitt � i � � t i �1

p

(3)

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The ARDL specification of the short-run dynamics can be derived by constructing an error correction model (ECM) of the following form p

DGDPt � � 2 � � �2 i DLGDPt � i � � i � 1 � 2 i DL Re mitt �i � �ECMt �1 � � t p

i �1

(4)

Where DLGDP= is the 1st difference of log GDP,DLRemit is the 1st difference of log Remittance ECM is the Error Correction Mechanism defined as, ECM � LGDPt � � 1 � � i � 1 �1i LGDPt � i � � i �1 � 1i L Re mitt � i p

p

(5)

All coefficients of the short-run equation are coefficients relating to the short-run dynamics of the model’s convergence to equilibrium and ö represents the speed of adjustment. VI. EMPIRICAL FINDINGS Prior to the testing of cointegration we conducted a test of order of integration for each variable, using Augmented Dickey-Fuller (ADF) and Philip-Perron (PP) technique. Even though the ARDL frame work does not require pre testing variables to be done, the unit root test could convince us whether or not the ARDL model should be used. The results in Table 1 show that there is a mixture of I(1) and I(0) of underlying regressors and therefore the ARDL testing could be proceeded. However KPSS test result shows both are I (1) variable (shown in table 2). Table 1 Results of the ADF and PP Unit Root Test Variable

Exogenous

LREMIT LREMIT LGDP LGDP DLGDP DLGDP

Constant Constant + trend Constant Constant + trend Constant Constant + trend

ADF Statistic

Prob. *

PP statistic

Prob.

-3.396896 -5.378830 0.332852 -3.016009 -6.892125 -6.845833

0.0177 0.0005 0.9769 0.1420 0.0000 0.0000

-2.854790 -4.738733 0.526483 -3.016009 -6.946404 -6.947532

0.0608 0.0028 0.9854 0.1420 0.0000 0.0000

*MacKinnon (1996) one-sided p-values Table 2 Results of KPSS Unit Root Test Variable

Exogenous

KPSS statistic (LM Stat.)

Decision

LREMIT DLREMIT LGDP DLGDP

Constant Constant Constant Constant

0.741255 0.356180 0.746241 0.441919

Non-stationary Stationary Non-stationary Stationary

Our next step is to investigate the long-run relationship between the two variables. For this purpose we apply OLS on equation (1) (Unrestricted Regression). The number of lag is selected by Pesaran and Shin (1999) and Narayan’s (2004) suggestion. Since

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our observations are annual in nature, we choose 2 as maximum order of lags in the ARDL and estimate for the period of 1975 to 2011. The calculated F-statistics for the cointegration test is shown in Table 2. The critical value is reported together in the same table which based on critical value suggested by Narayan (2004) using small sample size between 30 and 80. The calculated F-statistic is 6.25(F=6.25) with degrees of freedom (2,28) is higher than the upper bound critical value at 1% level of significance using restricted intercept and trend. This implies that the null hypothesis of no cointegration can be rejected at 1% level of significance and therefore, there is a cointegration relationship among the variables. Table 3 F-statistic of Cointegration Relationship Test Statistic

F Statistic

value

Lag

Significance level

6.25

2

1% 2% 10%

Bound critical values (restricted intercept and no trend)

Bound critical values (restricted intercept and trend)

I(0)

I(1)

I(0)

I(1)

4.614 3.272 2.676

5.966 4.306 3.586

5.333 3.710 3.008

7.063 5.018 4.150

The empirical results of the long-run model are obtained by estimating the equation (3)which is shown in the table (4) below. The results clearly show that though there exist long-run relationship between the variables but there does not havelong-run causal relation between the two variables. Table 4 Results of Estimation of Equations (3) Dependent Variable: LGDP Variable

Coefficient

Std. Error

t-Statistic

Prob.

LGDP(-1)

0.805339

0.180451

4.462921

0.0001

LGDP(-2)

0.161754

0.183922

0.879471

0.3861

LREMIT(-1)

0.021205

0.059332

0.357393

0.7233

LREMIT(-2)

-0.000891

0.045365

-0.019651

0.9845

0.121011

0.191031

0.633462

0.5312

�1

The result of the Error Correction Model that is short-run dynamics is shown through the table below. Here lag 4 is selected by using SIC criterion. From the above results it is clear that though the ECM coefficient is negative but it is not statistically significant, it implies the short-run deviations are not significant. This implies the stability of long-run relation. Since no short-run coefficients are statistically significant(though 1st three lags remittance growth is showing negatively related with GDP growth but the estimated coefficients are not statistically significant) it implies there is no short-run causality between the two variables. The residuals of

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Table 5 Results of the Estimation of Error Correction Model [equation (4)] Dependent Variable D(LGDP) Variable

Coefficient

Std. Error

t-value

ECM D(LGDP(-1) DLGDP(-2) DLGDP(-3) DLGDP(-4) DLREMIT(-1) DLREMIT(-2) DLREMIT(-3) DLREMIT(-4) �2

-0.050088 -0.196063 -0.094361 -0.011976 -0.046938 -0.057360 -0.061635 -0.022684 0.038371 0.037030

0.08312 0.22078 0.22541 0.22282 0.21526 0.08367 0.07355 0.07105 0.06105 0.01642

-0.60264 -0.88805 -0.41862 -0.05375 -0.21805 -0.68552 -0.83801 -0.31926 0.62852 2.25457

the error correction model are not serially correlated. This shown by Portmanteau Tests for Autocorrelations (shown in Appendix, Table-A). This diagnostic checking increases the reliability of our estimation. VII. SUMMARY AND CONCLUSION Remittance is a very important factor for the economic development of developing countries. Bangladesh is a huge labour-surplus country. Bangladesh is an important supplier of migrant workers to those countries which are suffering from labour shortages. Migrant workers in large numbers are going to almost all over the world especially to the oil-rich countries. At least two factors can be responsible for faster growth in remittances in developing countries. First, in the past 20 years, immigration has increased dramatically between developing and developed countries .Second, due to technological improvements, the transaction costs for the international transfer of payments between individuals have declined. In this paper an attempt has been made to investigate the impact of remittances on economic growth in Bangladesh. The data are obtained from the Bangladesh Bank and the time series econometric technique especially ARDL model is applied in this study. Since the two variables do not have the same order of integration, ARDL technique is appropriate for estimation. The findings of this study show that there exist weak long-run causal relationship between remittances and GDP growth in Bangladesh. However, no predictive causal relation is found either in the short-run or in the long-run for Bangladesh. Hence remittance growth can not be used for forecasting GDP growth. Conversely, GDP growth cannot be used for predicting remittances growth in Bangladesh. References Adams, R., Page, J. (2005), “Do International Migration and Remittances Reduce Poverty in Developing Countries?”World Development, 33(10), pp. 1645-1669.

60 / KANCHAN DATTA & BIMAL SARKAR Aggarwal, R., Demirguc-Kunt, A. and M. Peria (2006), “Do Remittances Promote Financial Development?” World Bank Policy Research Working Paper No. 3957, Washington: World Bank Ahmed, Md.S. (2010), “Migrant Workers Remittance and Economic Growth: Evidence from Bangladesh”, ASA University Review, Vol. 4 No. 1, January–June, 2010. Ahmed, J., K. Zaman and I. A. Shah (2011), “An Empirical Analysis of Remittances-growth Nexus in Pakistan using Bounds Testing Approach”, Journal of Economics and International Finance Vol. 3(3), pp. 176-186, March 2011. Ali, S. A. (1981) “An Analysis of the Institute of Home Remittance by Bangladeshi Workers Abroad on the National Economy in Labour Migration from Bangladesh to Middle East”, World Bank Staff Working Paper, 454, Washington, DC. Bangladesh Bank’s Annual Report, 2003-04. Barajas, A., Chami, R., Fullenkamp, C., Gapen, M., Montiel, P., (2009), “Do Workers’ Remittances Promote Economic Growth?” International Monetary Fund, WP/09/153, 2009, pp. 1-23. Barua, S., Majumder, M. and Akhtaruzzaman, M. (2007), “Determinants of Workers’ Remittances in Bangladesh: An Empirical Study”, Working Paper No. WP 0713, Policy Analysis Unit, Bangladesh Bank. BBS (2010), Gross Domestic Product (GDP) of Bangladesh for 2004-2009, Bangladesh Bureau of Statistics, http://www.bbs.gov.bd/Home.aspx Catrinescua, N., Leon-Ledesmab, M., Pirachac, M., Quillind, B. (2009), “Remittances, Institutions and Economic Growth”, World Development, 37(1): 81-92. Chami, R., Fullenkamp, C., Jahjah, S. (2003), “Are Immigrant Remittance Flows a Source of Capital for Development?” IMF Working Paper 01/189, International Monetary Fund, Washington DC. Das, A. and M. Chowdhury (2011), “Remittances and GDP Dynamics in 11 Developing Countries: Evidence from Panel Cointegration and PMG Techniques”, The Romanian Economic Journal, Year XIV, No. 42 December 2011. Das, A. (2012), “Remittance Behavior of Migrants and its Macroeconomic Effects in Four Developing Countries” International Journal of Applied Behavioral Economics, 1(1), January-March, pp. 41-59. Dilshad, W. B. (2013), “Impact of Workers’ Remittances on Economic Growth: An EmpiricalStudy of Pakistan’s Economy”, International Journal of Business and Management; Vol. 8, No. 24; 2013. Fayissa, B. (2008), “The Impact of Remittances on Economic Growth and Development in Africa”, Department of Economics and Finance, Middle Tennessee StateUniversity, Murfreesboro, Working Paper Series, February, 2008. Glytos, N. P. (2005), “The Contribution of Remittances to Growth: a Dynamic Approach and Empirical Analysis”, Journal of Economic Studies, No. 5-6, pp. 267-287. Hasan, R.A. (2006), “Harnessing Remittances for Economic Development of Bangladesh”, Presented at INAFI Asia International Conference on Migration and Development,Date: 23-27 May, Development Academy of the Philippines, Tagaytay City, Philippines. Hussain, Z., Naeem, F. (2009), “Remittances in Bangladesh: Determinants and 2010 Outlook”, http:/ /blogs.worldbank.org/endpovertyinsouthasia/print/514 IMF (2005), “World Economic Outlook”, International Monetary Fund, Washington DC. Jongwanich, J. (2007), “Workers’ Remittances, Economic Growth and Poverty in Developing Asia and the Pacific Countries”, UNESCAP Working Paper, WP/07/01. Khathlan, K. A. (2012), “The Link between Remittances and EconomicGrowth in Pakistan: A Boon to Economic Stability”, British Journal of Economics, Management & Trade 2(3): 167-185, 2012.

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Mahmood, R. A. (1991), ”International Migration, Remittances and Development: Untapped Potentials for Bangladesh, BIISS Journal, Vol. 12, No. 4, Bangladesh Institute of International Strategic Studies, Dhaka. Mahmud and Osmani, S. R. (1980), “Impact of Emigrant Workers’ Remittances on the Bangladesh Economy”, Bangladesh Development Studies, Vol. VIII, No. 3. Mahmud, W., (2003), “Bangladesh: Development Outcomes and Challenges in the Context of Globalization”, Conference on the Future of Globalization, October, 10-11, 2003, Yale University. www.ycsg.yale.edu/activities/files/Wahid.doc Murshid, K., Iqbal, K., and Ahmed, M. (2002), “A Study on Remittance Inflows and Utilization”, International Organization for Migration (IOM). Oberoi, A. S., and H. K. Manmohan Singh (1980), “Migration, Remittances and Rural Development: Findings of a case study in the Indian Punjab”, International Labour Review, Vol. 119, pp. 229-241. Pradhan, G., Upadhyay, M., Upadhyaya K. (2008), “Remittances and Economic Growth in Developing Countries”, European Journal of Development Research, 20(3): 497-506. Paul, B. P., Das, A., (2011), “The Remittance-GDP Relationship in the Liberalized Regime of Bangladesh: Cointegration and Innovation Accounting”, Theoretical and Applied Economics, Vol. XVIII (2011), No. 9(562), pp. 41-60. Quibra, M. G. (1986), “Migrant Workers and Remittances: Issues for Asian Developing Countries”, Asian Development Review, Vol. 4. Quibra, M. G. (1997), “International Migration, Remittances, and Income Distribution in the Source Country: A Synthesis”, Bulletin of Economic Research, Vol. 49, No. 1. Rahman, M. (2009), “Contribution of Exports, FDI, and Expatriates’ Remittances to Real GDP of Bangladesh, India, Pakistan and Sri Lanka”, South West Economic Review, 36: 141-153. Rahman, M., Mustafa, M., Islan, A., Guru-Gharana, K. K., (2006), “Growth and Employment Empirics of Bangladesh”, Journal of Developing Areas, 40(1), 2006, pp. 99-114. Raihan, S., Khondker, B. H., Sugiyarto, G., Jha, S., “Remittances and Household Welfare: A Case Study of Bangladesh”, ADB Economics Working Paper Series, 189, 2009, pp. 1-36. Ratha, D. (2007), “Leveraging Remittances for Development” Policy Brief, Migration Policy Institute, Washington DC.(paper presented at the leading Group on Solidarity Levies conference on Innovative Finance, Oslo, February 6-7, 2007). Ratha, D., Mohapatra, S., XU, Z. (2008), “Outlook for Remittances Flows 2008-2010”, Migration and Development Brief No. 8, Development Prospect Group, World Bank, h ttp :// siteresources.worldbank.org/INTPROSPECTS/Resources/334934-1110315015165/MD_Brief8.pdf Sayan, S. (2006), “Business Cycles and Workers’ Remittances: How Do Migrant Workers Respond to Cyclical Movements of GDP at Home?” IMF Working Paper 06/52. Siddique, T. (2004), “Efficiency of Migrant Workers’ Remittances: The Bangladesh Case”, Asian Development Bank. Siddique, A., E. A. Selvanathan and S. Selvanathan (2010), “Remittances and Economic Growth: Empirical Evidence from Bangladesh, India and Sri Lanka”, The University of Western Australia, August, Discussion Paper, 10.27. Stahl, Charles W., and Ahsanul Habib (1989), “The Impact of Overseas Workers’ Remittances on Indigenous Industries: Evidence from Bangladesh,” The Developing Economies, Vol. 27, pp. 269285. Vargas-Silva, C., Jha, S., Sugiyarto, G., (2009), Remittances in Asia: Implications for the Fightagainst Poverty and the Pursuit of Economic Growth, Asian Development Bank (ADB) Economics Working Paper Series 182, pp. 1-37.

62 / KANCHAN DATTA & BIMAL SARKAR World Bank (2006), “Global Economic Prospects 2006”, WashingtonD.C.: The World Bank. World Bank (2007), “Remittances Trends 2007”, www.worldbank.org. Zeisemer, T. (2006), “Worker Remittances and Growth: The Physical and Human Capital Channel”, UnitednationsUniversity, 020.

Appendix Figure 1: Time Plots of GDP and Remittances and their Growth

Figure 2: Time Plots of Remittances as % of GDP

REMITTANCES

AND

E CONOMIC GROWTH

IN

BANGLADESH / 63

Figure 3 : Workers Remittances in Various Countries

Table A VEC Residual Portmanteau Tests for Autocorrelations H0: no residual autocorrelations up to lag h Lags

Q-Stat

Prob.

Adj Q-Stat

Prob.

df

1

0.603113

NA*

0.622568

NA*

NA*

2

5.372691

NA*

5.710118

NA*

NA*

3

7.327517

NA*

7.867167

NA*

NA*

4

8.863867

NA*

9.622996

NA*

NA* 4

5

18.47484

0.001

21.01378

0.0003

6

25.68293

0.0012

29.88527

0.0002

8

7

30.07829

0.0027

35.51133

0.0004

12

8

31.42932

0.0119

37.31271

0.0019

16

9

32.06987

0.0426

38.2039

0.0084

20

10

32.20164

0.1221

38.39558

0.0316

24

11

32.75787

0.2448

39.24317

0.0771

28

12

36.51104

0.267

45.24824

0.0604

32

*The test is valid only for lags larger than the VAR lag order. df is degrees of freedom for (approximate) chi-square distribution

64 / KANCHAN DATTA & BIMAL SARKAR This study is based on the following data Table B Logarithm of GDP and Remittances year 1975-76 1976-77 1977-78 1978-79 1979-80 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 Source: Bangladesh Bank

log gdp

log remit

1.243595 1.22928 1.34465 1.397447 1.450832 1.493556 1.545673 1.566815 1.63275 1.693022 1.733647 1.77901 1.810955 1.843354 1.864009 1.903056 1.921073 1.921495 1.939726 1.976121 2 2.007453 2.026354 2.055303 2.063307 2.070134 2.083796 2.103027 2.121064 2.142562 2.164465 2.19298 2.229565 2.257 2.284241 2.31578 2.349194

1.390935 1.862728 2.188084 2.276002 2.586024 2.792181 2.924124 3.17032 3.173478 3.059374 3.220396 3.329662 3.362464 3.393996 3.397262 3.435462 3.510746 3.567838 3.638978 3.682548 3.69636 3.799343 3.841021 3.913696 3.991538 4.007321 4.157275 4.248679 4.298294 4.373365 4.50874 4.615935 4.734761 4.823973 4.880876 4.919125 5.008101

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