remittances and economic growth in bangladesh… · remittances and economic growth in bangladesh:...

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

REMITTANCES AND ECONOMIC GROWTH INBANGLADESH: AN ARDL COINTEGRATION APPROACH

KANCHAN DATTAAssociate Professor of Economics, University of North Bengal

E-mail: [email protected]

BIMAL SARKARAssistant 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 tothe oil-rich countries andsending remittances. They are likely to play an importantrole in promoting economic development in many ways. In this paper, an attempt hasbeen made to investigate the impact of concomitant remittances on economic growthin Bangladesh. The data are obtained from the Bangladesh Bank (The Central Bank ofBangladesh)covering the time period of 1975-76 to 2011-12. Applying ARDL model inthis study, we find weak long-run relationship between the variables.Also, there is nopredictable causal relation in the short-run or in the long-run for the study period.Key Words: Remittances, Economic Growth, Bangladesh, ARDL Model, ECMJEL Codes: O40; O53; C32

I. INTRODUCTION

Migration from Bangladesh to the rest of the world is not a new incidence. Bangladeshhas a long history of migration and overseas remittances since 1942(Mahmood,1991).Remittances inflow to Bangladesh has increased at an average annual rate of 19per cent in the last 30 years from 1979 to 2008 (Hussain, Naeem, 2009). Between 1976and 2010 about a total of 71.5 Lakhs people emigrated temporarily from Bangladeshsending 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 theworld. 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 veryinteresting to note here that a large part of remittances remains unrecorded and it isabout 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 indeveloping countries. First, in the past 20 years, immigration has increased dramatically

52 / KANCHAN DATTA & BIMAL SARKAR

between developing and developed countries (World Bank, 2007). Second, due totechnological improvements, the transaction costs for the international transfer ofpayments between individuals have declined (Guiliano & Ruiz-Arranz, 2006). Alsosome credits go to the successive government of Bangladesh since late 1990s for growingflow of remittance to Bangladesh as the government of Bangladesh was taken somemacroeconomic reforms(Bangladesh Bank’s Annul Report, 2003-04) like opening ofnew exchange house in source countries, expansion of drawing arrangement, settingan annual remittance threshold, close monitoring and supervision of banks, speedingup of delivery to the beneficiaries, surveillance measures under the Money LaunderingPrevention Act.

Also we can observe from the trend of remittances in Bangladesh that remittanceswere increased during the late 1970s in the line with the sharp rise in oil prices of thetime. There was decaling in flow remittances during Gulf war but there was steadyremittance growth in post-war reconstruction (Siddique, 2004) in which so manydevelopment programmes like construction of roads, schools, hospitals, houses andother commercial complexes etc. were taken (Kuthiala, 1986). Hence demand for semiskilled and unskilled workers were increased so many folds. Migration flow to USAand Europe from Bangladesh was declined after 9/11 tragedy.

The flows of remittance are more stable than other types of private capital inflowslike official development aids (ODA) and foreign direct investment (FDI) and arecounter cyclical (World Bank, 2006) as the flows increase during downturns as emigrantworkers went to provide financial support to the family members in the country oftheir origin (Sayan, 2006).That is remittance act as a significant macroeconomicstabilizer in the developing countries.

II. IMPORTANCE OF REMITTANCES IN BANGLADESH

In any developing countries the common problem is shortages of foreign exchangereserve which is very essential to pay the import bills. Bangladesh is not an exceptionalcountry but Bangladesh depends more on remittances to meet the problem of paymentof the import bills.

Remittances promote growth through smoothing the investment constraint as itact as a substitute for in-efficient or non-existent credit markets (Giuliano & Ruiz-Arranz, 2006). There may be positive impact of remittances on economic growth ifremittances are used for the purpose of children’s education and welfare expensessuch as health care because in the long run there may be a positive impact on labourproductivity and hence output of the home country. Even if remittances are spent onconsumption or real estate there will be a positive multiplier effects on GDP (Chimhowuet al. 2005).

Giuliano and Ruiz-Arranz (2006) suggest in their paper that remittances may actas an alternative way to promote investment in those countries in which there is poorfinancial sector.

Stahl and Arnold (1986) pointed out that there may be negative impact ofremittances on growth in the host country if there exist a “demonstration effect”. Thisdemonstration effect can motivate the remittance recipients to consume imported

REMITTANCES AND ECONOMIC GROWTH IN BANGLADESH / 53

goods. Hence if this effect becomes wide-spread this can reduce savings andinvestments that may be sufficient to reduce the growth rate of the home country. ButGlytos (2005) suggests that remittances can import more capital goods into the hostcountry for domestic production which can help to increase the growth rate of thehome 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 inincome, income volatility, credit crunch, and poverty and employment opportunity.Remittances can help prevent balance of payment crisis providing a constant sourceof foreign currency (Lopez-Cordova and Olmedo, 2006). Remittances behave as a verystable source of foreign exchange (Ratha, 2005).

If remittances are used for conspicuous consumption or unproductively by anexcessive degree of capital intensity in the agricultural sector there would have negativeimpact on development (Oberoi and Singh, 1980). Again if recipients become highlydependent on the “easy money” which causing them to reduce labour marketparticipation, there may exists the problem of moral hazard between remitters andrecipients. 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 havebeen showing interest to examine its impact on economic growth in both the hostcountry 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 growthand some argue it has negative impact on economic growth. In most of the previousstudies have been established the null hypothesis as a statement of correlation andnot causation. Now question is whether remittances are a statistically significant factorin determining economic growth and whether the relation between remittances andeconomic growth is causal.

Large number of studies is qualitative in nature regarding the impact of remittanceson an economy in terms of social measures such as health, education anddemoralization (Rahman et al., 2006) and also most of the previous studies are basedon panel-data consisting of number of countries. From the panel-data analysis we cananswer important questions on average but that may not be suitable for individualcountries that seeking to manage domestic policies.

So it is very important to study further in a particular country regarding the linkagebetween remittance income and economic growth. So here in this paper Bangladeshhas been chosen to find out the relation between remittances and Gross DomesticProduct (GDP).

In Bangladesh, remittances sent by the migrant workers to their home countryhave played an important role to promote economic development in their home


countries in different ways. This paper has tried to find out the impact of remittanceson economic growth in the economy of Bangladesh.

The findings of the study have important policy implications not only forBangladesh 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 followingstudies are related with this study.

Ali (1981) identifies that remittances help for favorable balance of payment.Stahland Habib (1989) showed the multiplier effect of remittances in economics. They explainthat remittances increase savings which increase the growth through multiplier. Eventhey calculated the multiplier for Bangladesh for the period of 1976-1988. The value ofmultiplier is 1.24. Chami et al. (2000) find that remittances have negative effects ongrowth in their study on 113 countries.

Mahamud (2003) claims that remittances faster growth in Bangladesh. Adams andPage (2005) studing 71 developing countries finds that remittances significantly reducethe level, depth and severity of poverty in developing world. An IMF (2005) finds nostatistical link between remittances and per capita output growth studying on 101developing countries. Hasan (2006) explains remittance has significant macroeconomicimpact at household level and the poorer the household, the more impact or benefitsremittance income can have alleviating poverty.Zeisemer (2006) argues that remittancesincrease savings which intern increase investment by decreasing interest rate and alsoincreases the rate of literacy. Finally remittances increase the rate of growth.

Jongwanich (2007) finds that remittances have a positive but marginal impact oneconomic 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 the1980-2004 periods. Fayissa (2008) using an unbalanced panel data from 1980 to 2004for 37 African countries shows that remittances boost growth in countries where thefinancial systems are less developed by providing an alternative way to financeinvestment and helping overcome liquidity constraints.

Vargas-Silva et al. (2009) using data for more than 20 Asian countries for the 1988-2007 sample find that a 10 per cent increase in remittances as ashare of GDP leads to a0.9-1.2 per cent increase in GDP growth.

On the other hand Barajas et al. (2009) find that remittances have no impact oneconomic growth.According to Catrinescu et al. (2009) although remittances haveincreased so many times but research has not come to a conclusion whether remittanceshave a positive or negative impact on long run growth. Hence country specific studiesbecome necessary. The studies on Bangladesh in particular are few in numbers. Raihanet al. (2009) show that remittances have positive effects on the economy and they reducepoverty but Rahman (2009) concludes that remittance seems to have insignificant andambiguous effects on Bangladesh’s GDP.

Ahmed (2010) claims remittances flow to Bangladesh has beenstatisticallysignificant but has negative impact on growth.Siddique et al. (2010) investigate thecausal link between remittances and economic growth in three countries, Bangladesh,


India and Sri Lanka, by employing the Granger causality test under a VAR frameworkusing time series data over a 25 year period;they found that growth in remittancesdoes lead to economic growth in Bangladesh.

Paul and Das (2011) find a long run positive relationship between remittance andGDP.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 oneconomic growth in the context of Pakistan using bounds testing approach and findthat remittances have a positive impact on economic growth of Pakistan in both thelong run and short run. Das and Chowdhury (2011) using panel cointegration andpooled mean group (PMG) approach they find a positive long run relationship betweenremittances and GDP in 11 developing countries. However, the magnitude of theremittance-GDP coefficient is rather quite small.

Das (2012) studies on Bangladesh, Egypt, Pakistan, and Syria over the period 1975-2006 and suggest that remittances have a positive impact on economic growth inPakistan and Syria but a negative impact in Bangladesh and Egypt. Negativeremittance-growth coefficients in those two countries suggest a counter-cyclicalrelationship. Also, Khathlan (2012) and Dilshad (2013)finda positive and significantrelationship between worker remittances and economic growth in the long-run andshort-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 period1975-76 to 2011-12. The GDP and Remittances are deflated by GDP deflator with base1995-96=100 and then converted in to logarithmic form. So both the variables are usedhere are real variables denoted by LRemit and LGDP that is Remittances and Incomerespectively.

In terms of methodology, the paper adopts the recently developed auto regressivedistributed lag (ARDL)framework by Pesaran and Shin (1995, 1999). There areadvantages of using this approach instead of the conventional Johansen (1998) andJohansen and Juselius (1990). While the conventional cointegrating method estimatesthe long-run relationships with in a context of a system of equations, the ARDL methodemploys only a single reduced form equation (Pesaran, and Shin, 1995). Moreover,the ARDL approach does not involve pre-testing variables, which means that the teston the existence relationship between variables in levels is applicable irrespective ofwhether the underlying regressors are purely I(0), purely I(1) or mixture of both. Thisfeature alone, given the characteristics of the cyclical components of the data, makesthe standard of cointegration technique unsuitable and even the existing unit roottests to identify the order of integration are still highly questionable. Furthermore, theARDL method avoids the larger number of specification to be made in the standardcointegration test. These include decisions regarding the number of endogenous andexogenous 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 variousalternative choices available in the estimation procedure (Pesaran and Smith, 1998).


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

The ARDL Model

DLGDPt = �1 +1

k

i��i1DLGDP t-i+

1

k

i��i2DLRemit t-i+ �1LGDP t-1+ �2LRemitt-1+µ2t (1)

DLRemitt = �1 + 1

k

i��i1DLRemit t-i +

1

k

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 andShin (1999), recommended choosing a maximum of 2 lags. From this the lag lengththat minimizes (SchwazBaysian Criteria) is selected.

Then the F test is used for testing the existence of long-run relationship. Whenlong-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 againstH1: �1 � �2 � 0The 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 trendIf there is evidence of long-run relationship (cointegration) of the variables, the

following long-run model is estimated.

1 1 111

Rep

p

t i t i i t i tii

LGDP LGDP L mit� ��

� � � � � � � �� (3)


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

2 2 2 111

Rep

p

t i t i i t i t tii

DGDP DLGDP DL mit ECM� � ��

� � � � � � � � � �� (4)

Where DLGDP= is the 1st difference of log GDP,DLRemit is the 1st difference oflog Remittance ECM is the Error Correction Mechanism defined as,

1 1 11 1Re

p p

t i t i i t ii iECM LGDP LGDP L mit� ��

� � � � � � �� (5)

All coefficients of the short-run equation are coefficients relating to the short-rundynamics of the model’s convergence to equilibrium and ö represents the speed ofadjustment.

VI. EMPIRICAL FINDINGS

Prior to the testing of cointegration we conducted a test of order of integration foreach 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 underlyingregressors and therefore the ARDL testing could be proceeded. However KPSS testresult shows both are I (1) variable (shown in table 2).

Table 1Results of the ADF and PP Unit Root Test

Variable Exogenous ADF Statistic Prob. * PP statistic Prob.

LREMIT Constant -3.396896 0.0177 -2.854790 0.0608LREMIT Constant + trend -5.378830 0.0005 -4.738733 0.0028LGDP Constant 0.332852 0.9769 0.526483 0.9854LGDP Constant + trend -3.016009 0.1420 -3.016009 0.1420DLGDP Constant -6.892125 0.0000 -6.946404 0.0000DLGDP Constant + trend -6.845833 0.0000 -6.947532 0.0000

*MacKinnon (1996) one-sided p-values

Table 2Results of KPSS Unit Root Test

Variable Exogenous KPSS statistic (LM Stat.) Decision

LREMIT Constant 0.741255 Non-stationaryDLREMIT Constant 0.356180 StationaryLGDP Constant 0.746241 Non-stationaryDLGDP Constant 0.441919 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 numberof lag is selected by Pesaran and Shin (1999) and Narayan’s (2004) suggestion. Since


our observations are annual in nature, we choose 2 as maximum order of lags in theARDL and estimate for the period of 1975 to 2011. The calculated F-statistics for thecointegration test is shown in Table 2. The critical value is reported together in thesame table which based on critical value suggested by Narayan (2004) using smallsample size between 30 and 80.

The calculated F-statistic is 6.25(F=6.25) with degrees of freedom (2,28) is higherthan the upper bound critical value at 1% level of significance using restricted interceptand trend. This implies that the null hypothesis of no cointegration can be rejected at1% level of significance and therefore, there is a cointegration relationship among thevariables.

Table 3F-statistic of Cointegration Relationship

Test Statistic value Lag Significance Bound critical values Bound critical valueslevel (restricted intercept and (restricted intercept

no trend) and trend)

I(0) I(1) I(0) I(1)

F Statistic 6.25 2 1% 4.614 5.966 5.333 7.0632% 3.272 4.306 3.710 5.018

10% 2.676 3.586 3.008 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 thereexist long-run relationship between the variables but there does not havelong-runcausal relation between the two variables.

Table 4Results 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

�1 0.121011 0.191031 0.633462 0.5312

The result of the Error Correction Model that is short-run dynamics is shownthrough 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 itis 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 arestatistically significant(though 1st three lags remittance growth is showing negativelyrelated 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


Table 5Results of the Estimation of Error Correction Model [equation (4)]

Dependent Variable D(LGDP)

Variable Coefficient Std. Error t-value

ECM -0.050088 0.08312 -0.60264D(LGDP(-1) -0.196063 0.22078 -0.88805DLGDP(-2) -0.094361 0.22541 -0.41862DLGDP(-3) -0.011976 0.22282 -0.05375DLGDP(-4) -0.046938 0.21526 -0.21805DLREMIT(-1) -0.057360 0.08367 -0.68552DLREMIT(-2) -0.061635 0.07355 -0.83801DLREMIT(-3) -0.022684 0.07105 -0.31926DLREMIT(-4) 0.038371 0.06105 0.62852�2 0.037030 0.01642 2.25457

the error correction model are not serially correlated. This shown by PortmanteauTests for Autocorrelations (shown in Appendix, Table-A). This diagnostic checkingincreases the reliability of our estimation.

VII. SUMMARY AND CONCLUSION

Remittance is a very important factor for the economic development of developingcountries. Bangladesh is a huge labour-surplus country. Bangladesh is an importantsupplier of migrant workers to those countries which are suffering from labourshortages. Migrant workers in large numbers are going to almost all over the worldespecially to the oil-rich countries. At least two factors can be responsible for fastergrowth in remittances in developing countries. First, in the past 20 years, immigrationhas increased dramatically between developing and developed countries .Second, dueto technological improvements, the transaction costs for the international transfer ofpayments between individuals have declined. In this paper an attempt has been madeto investigate the impact of remittances on economic growth in Bangladesh. The dataare obtained from the Bangladesh Bank and the time series econometric techniqueespecially ARDL model is applied in this study. Since the two variables do not havethe same order of integration, ARDL technique is appropriate for estimation.

The findings of this study show that there exist weak long-run causalrelationship between remittances and GDP growth in Bangladesh. However, nopredictive causal relation is found either in the short-run or in the long-run forBangladesh. Hence remittance growth can not be used for forecasting GDP growth.Conversely, GDP growth cannot be used for predicting remittances growth inBangladesh.

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Appendix

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

Figure 2: Time Plots of Remittances as % of GDP


Table AVEC 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*

5 18.47484 0.001 21.01378 0.0003 4

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

Figure 3 : Workers Remittances in Various Countries


This study is based on the following data

Table BLogarithm of GDP and Remittances

year log gdp log remit

1975-76 1.243595 1.3909351976-77 1.22928 1.8627281977-78 1.34465 2.1880841978-79 1.397447 2.2760021979-80 1.450832 2.5860241980-81 1.493556 2.7921811981-82 1.545673 2.9241241982-83 1.566815 3.170321983-84 1.63275 3.1734781984-85 1.693022 3.0593741985-86 1.733647 3.2203961986-87 1.77901 3.3296621987-88 1.810955 3.3624641988-89 1.843354 3.3939961989-90 1.864009 3.3972621990-91 1.903056 3.4354621991-92 1.921073 3.5107461992-93 1.921495 3.5678381993-94 1.939726 3.6389781994-95 1.976121 3.6825481995-96 2 3.696361996-97 2.007453 3.7993431997-98 2.026354 3.8410211998-99 2.055303 3.9136961999-00 2.063307 3.9915382000-01 2.070134 4.0073212001-02 2.083796 4.1572752002-03 2.103027 4.2486792003-04 2.121064 4.2982942004-05 2.142562 4.3733652005-06 2.164465 4.508742006-07 2.19298 4.6159352007-08 2.229565 4.7347612008-09 2.257 4.8239732009-10 2.284241 4.8808762010-11 2.31578 4.9191252011-12 2.349194 5.008101

Source: Bangladesh Bank

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