Pradeep Agrawal a,∗, Pravakar Sahoo b, Ranjan Kumar Dash a
a Reserve Bank of India Unit, Institute of Economic Growth, Delhi University Enclave, Delhi 110007, Indiab Indian Council for Research on International Economic Relations, New Delhi, India
Received 1 May 2008; received in revised form 1 August 2008; accepted 1 November 2008Available online 7 December 2008
Keywords: Savings behaviour; Growth; South Asia; Co-integration
1. Introduction
Economic growth is among the most important factors affecting the lives of people in a country.Thus, given the close relation between savings and growth, the analysis of savings behaviourbecomes naturally important for policy analysis. Yet, a comprehensive analysis of the savingsbehaviour is missing for many of the South Asian countries. Thus, in this paper we undertakeindividual country analyses of the savings behaviour in five main South Asian countries, namely,India, Pakistan, Bangladesh, Sri Lanka and Nepal using modern time series analysis.
The savings behaviour has received considerable attention from previous authors as well.This includes the works of Lahiri (1989), Edwards (1996), Dayal-Gulati and Thimann (1997),Schmidt-Hebbel and Serven (1999), Schmidt-Hebbel, Servan, and Solomamo (1996), Loayza,Schmidt-Hebbel, and Serven (2000), Baharumshah et al. (2003), Ramajo, García, and Ferré (2006)
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and Mohan (2006). But the existing literature on savings is dominated by cross-section or paneldata on a large number of countries. However, savings behaviour shows considerable variationacross countries depending upon level of development and socio-economic structure and onecannot be sure whether the results of such pooled studies, which are applicable to the averagecountry in the sample, apply to the particular country or region of interest. Thus, cross-countryregression analysis based on the assumption of homogeneity cannot be used as definitive studyfor any specific country of interest. For this reason, country studies have an importance of theirown.
In the present study we have chosen to focus on the South Asia region as it is increasingly beingrecognized as an important emerging economic area. Furthermore, (with the possible exceptionof India), the South Asian countries remain relatively under-researched with many of them (suchas Nepal and Bangladesh) having very limited research done on the savings behaviour, even fewerthat use modern time series analysis. Thus, a modern time series analysis of savings behaviour inSouth Asia that is provided in this paper should be a useful contribution to the literature and topolicy makers.
Further, some controversies still remain in the literature regarding the savings behaviour—forexample, regarding the impact of raising real interest rates on the savings rate. While some authors(see, for example, Fry, 1995) have found the effect to be positive, many others have found it to beinsignificant (see for example, Giovaninni, 1985). On the other hand, the impact of factors such asfinancial development in the form of greater access to banking institutions in improving savingsmobilization has hardly been analyzed except for a few countries. This factor is expected to beimportant for developing countries but has been largely ignored in most previous studies.1 Also,most of the South Asian countries have comparatively low rates of savings (with the possibleexception of India), so that a proper analysis of savings behaviour would help better understandthe determinants of savings in South Asia and thereby suggest ways to improve the savings rates.
The present paper takes into account a comprehensive set of relevant explanatory variablesthat can be expected to be the main determinants of savings behaviour. This includes income,growth rate, dependency rate, foreign savings rate, share of agriculture in the GDP (to accountfor the possibility of a different savings rate for the agriculture sector), real interest rate, inflationrate, banking density (a measure of access to banking) and financial sector development. It alsoprovides the estimation of both total and private savings rates over a relatively longer periodthan most previous studies of these countries. Moreover, most of the previous studies relatingto savings behaviour have concentrated on specific regions like East Asia (Agrawal, 2001; Fry,1995; Lahiri, 1989), North Africa (Jbili, Enders, & Treichel, 1997), and Latin America (Melo &Tybout, 1986). However, there is no existing study for South Asian countries together. This studyfills the gap by doing a systematic study on savings behaviour for South Asia. Thus, we hope thatour study would be a useful contribution to the literature on savings behaviour and might providenew insights into the savings behaviour in South Asia.
The rest of the paper is structured as follows: In Section 2, we report the determinants ofsavings in South Asia along with a brief review of the trends in savings in South Asia over theperiod 1960–2005; in Section 3, we briefly explain the econometric procedures used in the paper;in Section 4, we present the results of our empirical analysis; in Section 5, we summarize themain conclusions and policy implications of our analysis.
1 Fry (1995) has considered it previously in the context of some Asian countries, but there are hardly any previousstudies that use this variable for the South Asian countries.
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Fig. 1. Trends in total savings ratio in India, Pakistan, Bangladesh, Sri Lanka and Nepal. Source: Authors chart based ondata from WDI 2006 CD-ROM.
2. Determinants of savings in South Asia
In this section, we briefly consider the trends in savings and various determinants of savingsfor South Asian countries and specify the savings functions for total and private savings rates.
2.1. Trends in savings in South Asia
Trends in total savings ratio for South Asian countries are presented in Fig. 1. It is seen thatgenerally, savings rate in South Asia is in the low to medium range and comparatively lower thanin some other developing countries, particularly China and countries of in East/South East Asiawhere the savings rats are in the range of 30–40%. The figure shows that among the South Asiancountries, the total savings rate is highest for India for which it increased from 12% in 1960 to23% in 1990 and further increased to 29% by the end of 2004–2005.
The savings rate of Pakistan was among the highest in South Asia2 in the 1970s but has had adeclining trend from 1983 to about 2000. It shows some recovery since 2000 and by 2004 it wasthe second highest in South Asia. For Sri Lanka, the savings rate was almost equal to that of Indiaduring 1960s, but since the mid-1970s, it has come down and been close to the average or middlerange (about 15%) among the South Asian countries. It has dipped further over the last 10 yearsand is now the second lowest in South Asia.
It is seen that while Bangladesh had the lowest savings rate in South Asia over the 1970s,its savings rate has risen rapidly since then and was the third highest in South Asia (after Indiaand Pakistan) over the years 1999–2004. Nepal’s savings rate was relatively high in comparisonto Bangladesh during 1970s. But the rate has remained stagnant over the 1980s and shows adeclining trend over the last 5–6 years, when it also had serious political disturbances. As a result,
2 The savings rate of Pakistan is measured as gross national savings divided by GDP while that of all other countries isdomestic savings divided by GDP. This means that the numbers for Pakistan are not completely comparable with otherSouth Asian countries. This was done due to our perception that the domestic savings series for Pakistan was not fullyreliable.
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it now stands lowest in South Asia. Overall, the savings rate in South Asia is seen to be in the lowto medium range.
2.2. Determinants of savings rate in South Asia
A proper estimation of the savings function for South Asian countries requires that othermajor factors affecting the savings rate are taken into account. Since, the time series of a lengthsufficient to permit a proper econometric analysis has become available only recently for countrieslike Bangladesh, Nepal and Pakistan, it should be of interest to estimate the savings function forthese countries taking all the relevant factors into account. The various factors affecting the savingsrate and how these factors are measured and used for the purpose of undertaking econometricestimations and the likely signs of their coefficients3 are described below. This is followed by thespecification of the savings function to be estimated.
2.2.1. Savings rate (GDSY)The savings rate (GDSY) is measured as the ratio of gross domestic savings4 (GDSY) in
current prices to gross domestic product (Y) in current prices. Similarly, the private savings rateis measured as the ratio of private gross domestic savings (GDSpvt) to gross domestic product.Gross domestic private savings (GDSpvt) is calculated by deducting gross domestic public savingsfrom gross domestic savings. Finally, gross domestic public savings (GDSpb) is calculated as totalgovernment revenue less current government expenditure.
2.2.2. Real income per capita (RYPC) and GROWTH rateThe neoclassical growth models (Solow, 1956) imply that higher savings rate will lead to
higher steady state levels of income (or output) per capita, while the endogenous growth theorymodels imply that higher savings rates would lead to higher levels of growth of income per capita.The “growth theorists” (such as Harrod, 1939; Domar, 1946; Romer, 1986; Lucas, 1988) assumethat all savings is automatically invested and translated to growth. There are empirical studiessuch as Mamingi (1997) and Pelagidis and Mastroyiannis (2003), which prove that savings is verystrongly correlated with investment and capital creation thereby positively affecting growth. Thus,savings leads to growth. On the other hand, the consumption theorists (Modigliani, 1970, 1986;Deaton & Paxson, 1994, 2000; Carroll & Weil, 1994) argue that income and its growth determinesconsumption and hence, savings. The real (in constant domestic prices) gross domestic product(GDP) per capita is used as a measure of real income. The growth rate of GDP per capita is usedas a measure of the growth rate (denoted by GROWTH). As discussed previously, the expectedsign of the coefficient of income per capita, YPC, is positive, that of GROWTH is analyticallyambiguous, though more likely to be positive.
2.2.3. Demographics (DEPEND)Aggregate savings is affected by the age distribution of the population if the share of inactive or
dependent population is high, the savings ratio will be low. According to the life-cycle hypothesis
3 There is by now an abundant literature on the determinants of savings rate—see for example, Edwards (1996), Fry(1995), Schmidt-Hebbel and Serven (1999) and Schmidt-Hebbel et al. (1996).
4 Only in the case of Pakistan we have used gross national savings instead of the gross domestic savings as the latterappeared to have some problems and did not seem reliable.
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a larger working population relative to the older population contributes to raise the savings rate.We use the age dependency ratio (DEPEND), the share of dependent age population (aged below15 or over 64 years) to the working age population (aged 15–64 years), as a reasonable proxyto capture this effect. The expected sign of the coefficient of DEPEND is negative (Ram, 1982;Lahiri, 1989; Bosworth, 1993; Loayza et al., 2000; Ramajo et al., 2006; Guest, 2006).
2.2.4. Foreign savings as share of GDP (FSY)Greater availability of foreign savings may encourage more consumption, and reduce savings.
Thus in case of foreign borrowing constraints, additional foreign savings is likely to lead to higherconsumption and lower domestic savings. That is, foreign and domestic savings are likely to besubstitutes (see Fry, 1995; Schmidt-Hebbel & Serven, 1999; Payne, 2005). The variable FSY usedin the relation to be estimated is the negative of the current account balance as a proportion of theGDP. The expected sign of its co-efficient is negative.
2.2.5. Share of agriculture in GDP (AGRY)The rural or agricultural sector of the economy can display different savings behaviour than
the urban/industrial sector, especially in the case of developing countries like the South Asiancountries, with large agricultural sectors. The agricultural sector could have a different savingsrate due to a lower access to the banking system and because of lower and unstable incomes in theagricultural sector. While the first two facts should lower the savings rate, the greater instabilityof income could increase the savings rate to cope with the greater instability. Thus, the overalleffect of this variable could be either negative or positive.
2.2.6. Financial sector developmentThe previous literature on savings functions (Dayal-Gulati & Thimann, 1997; Edwards, 1996;
Johansson, 1996) has found a positive impact of financial sector reforms on savings. Deeperfinancial markets and prudential regulations of financial institutions improve savings rate byoffering a wider variety of financial instruments to channel savings and also providing moresecurity to savers. Moreover, bank deposits are the principal financial instruments in developingcountries of South Asia. In this context, we have included some analysis of the impact of financialsector development on the savings rate using variables like bank density {bank branches permillion of population (BOPM)} and ratio of broad money to GDP (M2Y). We also consider thereal interest rates and inflation rates.
2.2.7. Bank branch density (BOPM)In order to enable a person to save the full amount he wants to save, access to appropriate
financial instruments, such as bank time deposits at reasonable interest rates, is obviously impor-tant. Such access is not always available in developing countries such as South Asian countries,especially in the rural areas. Here the access to banking is proxied by the number of bank officesper million inhabitants. A priori we expect this variable to have a positive co-efficient and to bemore important in determining the private than the total savings rate.
2.2.8. Real interest rates (RD)Analytically, an increase in interest rates will have an ambiguous effect on savings because
of a positive substitution effect towards future consumption and a negative income effect due toincreased real returns on saved wealth. Empirically, Fry (1995) has found a small but positiveinterest rate elasticity of savings while Giovaninni (1985) has found savings to be insignificantly
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related to real interest rates. The empirical evidence on the effects of interest rates on savingshas proven to be inconclusive (see Schmidt-Hebbel & Serven, 1999). In view of this controversyin the literature, it is of interest to evaluate the interest elasticity of savings in South Asia. Wehave proxied the real expected interest rate on savings by the real interest rate on 1-year timedeposits with banks. The real interest rate on bank deposits is the relevant rate of real return formost households and even firms in developing countries.
2.2.9. Inflation rate (INFY)An increase in the inflation rate can impact income or wealth negatively, which can lower
savings. It can also lower the real interest rate, which can have an ambiguous effect on the savingsrate. Further, an increase in variability of inflation rate (which usually accompanies a higher levelof inflation) is often treated as a proxy for macroeconomic uncertainty. The increased macrouncertainly due to increase in inflation rate may induce people to save more for precautionarymotives. Therefore, analytically, the overall impact of an increase in inflation on the savings rate isambiguous. We include expected inflation rate as an explanatory variable to empirically examineits impact on the savings rate.
2.2.10. Other variables not included here5
The basic life cycle model suggests that the net wealth per capita of the private sector (andvariations in its value due to capital gains and losses) should be a determinant of savings. However,data on proper measures of wealth were not available for South Asian countries. Another variablethat is often considered relevant in savings functions is the ratio of expenditure on social securityand welfare as a proportion of GDP (higher social security and welfare expenditures of thegovernment reduce the private savings due to reduced need to save for old age, etc.). However, incase of South Asian countries, the social security and welfare system are poorly developed andcover a very small fraction of the population. Further, systematic data is generally not available.Hence this variable was not included in our analysis.
2.2.11. The proposed savings functionsThe relation between the total domestic savings rate, growth and other relevant variables6 is
In the case of private savings function also, the above variables are important. In addition, thepublic savings rate is also included and is expected to have a negative coefficient (for example, anincrease in government tax revenue would increase public savings but decrease private savings).
5 We recognize that the social and cultural factors (which are not included in our econometric model for lack ofquantitative data) can also affect the savings rate in a country. However, South Asian countries in our empirical analysishave more or less similar social and cultural habits so that differences in savings rates because of such factors are notlikely to be large. Further, as will be seen in Section 4 below, although we have used individual country analyses, theresults are very similar for all the five countries analyzed. Therefore, we can reasonably claim that our analysis of thesavings behaviour in South Asia provides reliable results, at least regarding the economic determinants of savings.
6 We also considered the possibility of using logarithmic version of the above variables but decided against it primarilybecause the foreign savings (as well as real interest rates and growth rates of real income per capita) can take negativevalues.
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Also of interest here is whether an increase in public savings leads to an equal, larger or smallerdecrease in private savings. Thus, the proposed private savings function is:
The empirical analysis being undertaken here has two goals. The first objective is to estimatethe savings functions for South Asian countries. It is well known by now (see Engle & Granger,1987; Banerjee, Dolado, Galbirth, & Hendry, 1993) that when the variables of interest exhibitunit roots, the procedures of classical econometrics break down. Thus, in order to decide on theappropriate procedure of estimating the savings function, one needs to first consider the order ofintegration of the relevant variables and then decide on the appropriate procedure to use.
3.1. Order of integration of the variables
We use augmented Dickey Fuller (ADF) test (see Dickey & Fuller, 1981) to examine whetherthere exists unit root in each variable. The results of our testing are shown in Table 1. It is seenthat all savings rates (the dependent variables in various estimations) are integrated of order 1{denoted, I(1)} except in the case of Bangladesh. Most of the explanatory variables are also I(1)variables, with a few stationary or I(0) variables mixed in. However, in the case of Bangladesh,practically all variables are stationary in levels.
3.2. The estimation procedures
Given this dominance of I(1) variables, at least in the case of India, Pakistan, Sri Lanka andNepal, the co-integration procedures are appropriate. For more reliable results we have used twoseparate estimation procedures: (1) the ECM co-integration procedure proposed by Banerjee,Dolado, and Mestre (1998), and (2) the dynamic OLS (DOLS) procedure of Stock and Watson(1993). The ECM procedure provides a more reliable test of co-integration as well as an unbiasedestimate of the long run relation when the explanatory variables are weakly exogenous for theparameters of interest. The dynamic OLS procedure has been shown to provide unbiased andasymptotically efficient estimates of the long run relation, even in the presence of endogenousregressors. Further, comparing the estimates obtained by the above two procedures would providesome information about whether any explanatory variables are actually endogenous or not. SinceGROWTH is essentially the first difference of income per capita, and the lags and leads of the firstdifference variables are already included in the ECM and DOLS procedures, we did not includeGROWTH as a separate variable in the estimation of savings functions in these countries.
However, in the case of Bangladesh, the dependent variables, both total and private savingsrates, are stationary at level. Thus in this case, both these econometric procedures could not beapplied to estimate Bangladesh savings behaviour as they are applicable only for non-stationarydependent variables. Thus, we have employed autoregressive distributed lag model (ARDL) incase of Bangladesh. In this case, since practically all variables are stationary while income isnot, income seems unlikely to form long run equilibrium with the other variables. Thus we onlyincluded GROWTH in the estimation in this case.
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Table 1Results of ADF test for unit roots in various variables.
Variables India Pakistan Sri Lanka Bangladesh Nepal
Notes: (1) * denotes that ADF test statistic rejects the null hypothesis of non-stationarity of the variable at the 5% confidence level (5% critical value for ADF test is −2.86). (2)# denotes that the Perron (1997) test statistic rejects the null hypothesis of non-stationarity of the variable at the 5% confidence level We have used Perron endogenous unit roottest (Perron, 1997) in the case of this variable as it has a structural break in the series. (3) A series DX denotes the first difference of series X. If the variable itself is non-stationarybut its fist difference is stationary, it is integrated of order one, denoted, I(1).
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A brief description of the ECM & DOLS procedures follows. The description of the ARDLprocedure, used for only one country, is relegated to Appendix A.
3.3. The ECM procedure
It provides a more reliable test of co-integration (than the static OLS procedure) and simul-taneously yields less biased estimates of the long run relationship among the variables—weakexogeneity of the regressors for the parameters of interest being a sufficient condition for OLSto provide asymptotically efficient estimates. Let Y (a scalar) and X (a k-dimensional vectorin general) be I(1) processes that are co-integrated. Then in the static OLS (first step of Engleand Granger, 1987), the Dickey–Fuller test of co-integration is based on the t-statistic of thecoefficient of β in the regression:
�Y − λs′ �X = β(Y−1 − λs′ X−1) + e1 (3)
where λs is a k-dimensional vector of coefficients of X, estimated by the static OLS and a prime(′) on a vector denotes its transpose. Banerjee, Dolado, Galbirth, and Hendry (1998) point outthat the ECM regression:
�Y = α′ �X + β(Y−1 − λ′ X−1) + e2 (4)
is the more general form of Eq. (3) that does not impose the potentially invalid common factorrestriction, α = λ, and is therefore likely to yield more accurate results. More Generally, when Xmay be only weakly exogenous to the parameters of interest, Banerjee et al. suggest estimating thefollowing (unrestricted) ECM regression by OLS (inclusion of �Xt+j terms take care of the pos-sibility of endogeneity of X, i.e., feedback from Y to future values of X (see Banerjee et al., 1998):
where γ(L) and α(L) are polynomials in the lag operator, L. When β exceeds the critical values(which depend upon the number of I(1) variables and deterministic components but is independentof the number of dynamic terms), the null hypothesis of non-co-integration is rejected.
3.4. The dynamic OLS (or DOLS) procedure
This procedure, developed by Saikkonen (1991) and Stock and Watson (1993), has the advan-tage that the endogeneity of any of the regressors has no effect, asymptotically, on the robustnessof the estimates. Further, statistical inference on the parameters of the co-integrating vector isfacilitated by the fact that the t-statistics of the estimated co-efficient have asymptotic normal dis-tribution, even with endogenous regressors (Stock & Watson, 1993). This procedure also allowsfor direct estimation of a mixture of I(1) and I(0) variables. The DOLS procedure incorporatesthe lags and leads of the first differences of the I(1) variables. Thus estimation of the long runrelation between Y and X is carried out with a regression of the type:
Y = λd′X +n∑
−n
ai �Xt−i (6)
where λd denotes the vector of long run coefficients of X using the DOLS procedure. The inclusionof �Xt+j terms take care of the possibility of endogeneity of X, i.e., feedback from Y to futurevalues of X—see Stock and Watson (1993).
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3.5. Data considerations
The data on gross domestic savings (GDSY), real per capita income (RYPC), foreign savings(negative of current account balance), rate of inflation (INFY), age dependency ratio (Depend) andthe share of agriculture (AGRY) are taken from the World Bank’s World Development Indicators(CD version, 2005 and 2006). However, the data on gross domestic savings (GDSY), gross privatedomestic savings (GDSpvtY) and gross public domestic savings (GDSpbY) for India is takenfrom Economic Survey of India 2005–2006. Similarly, data on gross private domestic savings(GDSpvtY) and gross public domestic savings (GDSpbY) for Bangladesh, Pakistan, Sri Lanka,and Nepal is taken from their respective National Statistics.
The data on real interest rates (RD), bank branches per million (BOPM), and fiscal variablesare taken from their respective Central Bank publications.
4. Empirical results
As noted in the previous section we have used different procedures for estimating the savingsfunctions depending on whether the savings rate is integrated of order one or zero. The estimationhas been carried out using the Error Correction Model (ECM) procedure of Banerjee et al. (1998)and dynamic OLS (DOLS) of Stock and Watson (1993) where most of the variables are non-stationary (as is the case with all countries except Bangladesh) and using the auto regressivedistributed lag (ARDL) procedure of Pesaran and Shin (1998) in the case of Bangladesh. It wasfound that the ECM test for co-integration rejects the null hypothesis of non-co-integration andsuggests the presence of a long term relation among the variables for each of the four South Asiancountries, viz., India, Pakistan, Sri Lanka and Nepal.7
The long run relations obtained using these procedures are shown in Tables 2 and 3.8 TheECM procedure involved up to second order lags of the dynamic terms and first order leads ofdynamic terms—a higher order was usually not feasible given that we usually had 30–45 annualobservations available for each country. Similarly, the dynamic OLS was carried out with up tosecond order of lags and leads in dynamic terms. Though we began the estimation procedure byincluding all the relevant variables for estimating savings functions, the insignificant terms weredropped. Thus, some of the variables dropped out in the final estimations reported in Tables 2 and 3.
In the case of Bangladesh, we used the ARDL procedure because practically all variables werestationary. In this procedure the F-test statistic is used to examine the existence of stable long-run equilibrium relationship. The estimated F-statistic for the appropriate parsimonious form inthe case of the total savings function, denoted, FGDSY, was found to be 82.36, which is muchhigher than the upper bound critical value at 1% level (see Pesaran and Shin, 1998). Thus the nullhypothesis of non-existence of a stable long-run relationship was rejected. The optimum order ofARDL was found to be (1, 1, 1, 1, 2), selected on the basis of Akaike Information Criteria (AIC).Similarly for the private savings relation, order of ARDL for private savings was found to be (1,1, 1, 1, 2, 0) on the basis of the AIC criterion and the estimated F-statistics FGDSpvtY = 74.97
7 Various diagnostic tests of the full estimations, including the Lagrange multiplier test of residual serial correlation,the Ramsey RESET test for functional form mis-specification, the Jarque–Bera test for the normality of residuals andEngle’s autoregressive conditional heteroscedasticity (ARCH) test were also carried out. These tests suggest that we hadrobust and reliable estimations.
8 The details of full ECM, DOLS and ARDL estimations are available from the authors on request.
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Table 2Co-integration and long run coefficients of total savings.Variable India Pakistan Sri Lanka Nepal Bangladesh
Notes: (1) ** denotes significant 1% level and * denotes significance at 5% level. Critical values are 2.57 and 1.96, respectively, for the t-statistics and 5.04 and 4.30, respectively, for the ECM test (critical values for the case of 2 I(1)and 3 deterministic or I(0) variables from Ericsson and MacKinnon, 2002). (2) t-Statistics of coefficients are given in parentheses. (3) DOLS refers to estimation by the dynamic OLS procedure and ECM refers to estimation by errorcorrection mechanism procedure. The long-term for explanatory variables in the ECM procedure are obtained from solving full ECM estimations using Eq. (5). The t-statistics for these are provided by the Microfit program using anon-linear procedure.
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Table 3Co-integration and long run coefficients of private savings.Variable India Pakistan Sri Lanka Nepal Bangladesh
Notes: (1) ** denotes significant 1% level and * denotes significance at 5% level. Critical Values are 2.57 and 1.96, respectively, for the t-statistics and 5.04 and 4.30, respectively, for the ECM test (critical values for the case of 2 I(1)and 3 deterministic or I(0) variables from Ericsson and MacKinnon, 2002). (2) t-Statistics of coefficients are given in parentheses. (3) DOLS refers to estimation by the dynamic OLS procedure and ECM refers to estimation by errorcorrection mechanism procedure. The long-term for explanatory variables in the ECM procedure are obtained from solving full ECM estimations using Eq. (5). The t-statistics for these are provided by the Microfit program using anon-linear procedure.
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is significant at the 1% level, which rejects the null hypothesis of the non-existence of a stablelong-run relationships.
The long run relations obtained for total and private savings rates for each of the five SouthAsian countries are reported in Tables 2 and 3, respectively. A brief discussion of the impact ofthe main variables found to have considerable role in determining the savings rate is providedbelow.
The coefficient of real per capita income (RYPC in constant US Dollars, the base year being2000) is significantly positive for all the five countries.9 Though the coefficients are small, theyare positive and significant at the 5% level for India, Pakistan, Sri Lanka and Nepal. As discussedearlier, in the case of Bangladesh, we have used Growth of per capita income instead of incomeitself. The coefficient of GROWTH is rather large (0.92), positive and significant, suggesting a1% increase in the growth rate is likely to increase savings by about 0.92%. Overall, we find thatthe increase in real per capital income increase savings rate in South Asian countries supportingconsumption theories and previous studies like Schmidt-Hebbel and Serven (1999) and Carrolland Weil (1994). Therefore, it is important for South Asian countries to maintain the growthmomentum thereby increasing per capita income which would contribute to domestic savingsmobilization.
As mentioned earlier, the theoretical and empirical literature is not clear about the impact ofthe real interest rate on the savings rate. We too find mixed results for South Asian countries. Thereal interest rate, Rd, has negative and significant coefficient for India and Pakistan; insignificantcoefficients for Sri Lanka and positive and significant coefficient for Bangladesh and Nepal. Thecoefficients are relatively small for South Asian countries except Bangladesh (a 1% increase inreal interest rate will increase savings rate by about 0.2–0.3% of GDP). The fact that the coefficientof interest rates are so small suggests that interest rates are not likely to be a significant influenceon the savings rates since it is rarely possible for the government or central bank to change themby more than 1 or 2%.
We have also analyzed the effect of improved access to banking facilities. This is particularlyimportant in rural areas as it provides an instrument for saving to a population a good part ofwhich previously had no safe savings instrument (except “stuffing the money in the mattress”).While access to banking may also increase access to credit and may also induce some householdsto borrow from banks, but on the whole, a vast majority of households are net savers and willbe able to save more as a result of increased access to banking. Thus, the net result of increasedaccess to banking on the savings rate should be positive.10 This argument finds empirical supportfrom our results as the coefficients of banking density (BOPM)—a measure of the access tobanking facilities is positive and significant for all the countries (except Nepal, for which thecoefficient is insignificant). Thus it can be said that well developed banking system throughoutthe country improves savings rate in South Asian countries. The above results suggest that insteadof focussing on interest rate policy to generate higher domestic savings, policy makers shouldfocus on providing banking facilities to everyone, especially in rural areas and other regionslacking adequate access to formal financial institutions. This is especially applicable to developingcountries such as those of South Asia where majority of the population is still living in rural areasand has limited access to banking facilities.
9 We have also included variable GDP growth as one of the explanatory variables. However, GDP growth and real percapita income are strongly correlated and GDP growth has turned out to be insignificant.10 It will also be welfare enhancing for the population concerned as it will facilitate greater capital formation and thus
increased productivity as argued by McKinnon (1973).
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Foreign savings rate (FSY) is seen to have significant negative impact on each of savings ratesfor both procedures as expected. This is probably due to larger availability of foreign savingsleading to greater consumption, especially of imported goods. Many developing countries ofSouth Asia have foreign credit constraints to manage their macroeconomic stability and continuedreforms. Thus, additional foreign savings lead to higher consumption and, ceteris paribus, lowerdomestic savings.
Though demographic or age structure of the population, as captured by the dependency ratio isgenerally found to be an important determinant of savings in cross-section or panel studies, it oftenpresents unreliable results in time series analysis because of its strong and negative correlationwith income per capita. We too found the dependency ratio (DEPEND) to be strongly correlatedwith income per capita in the case of India, Pakistan and Sri Lanka. We therefore dropped thisvariable from our estimations in the case of these countries, but given its strong and negativecorrelation with income per capita, its co-efficient can be assumed to be negative. Further, in thecase of Bangladesh and Nepal, where it was NOT highly correlated with real per capita income,we did include it in our estimations and found the coefficient to be negative. Thus the long-runcoefficients of the dependency ratio can be thought to be negative for all countries of South Asiaimplying that a decline in the dependency rate increases both the total and private savings rates.Indeed the sharp increase in the savings rate in Bangladesh is largely due to the rapid decline inthe dependency rate there. All the south Asian countries are getting the dividend of huge workingage population. Further, if these countries can make use of these huge active manpower, it wouldcontribute to savings and also growth.
Other variables like share of agriculture in GDP, inflation, and the ratio (M2Y) of broad moneyto GDP were included in the estimations of each of the countries. However, they were found tobe insignificant and dropped from the final estimations.
A similar exercise was also carried out for private savings and the results are reported in Table 3.It is seen that the results about the impact of the various variables are essentially same as in thecase of total savings. The only difference is that in this case we also include public savings rateas an additional determinant of the private savings rate. This is sensible since policy relating totaxation and expenditure (especially on social security, health and education), which affect publicsavings, also affect the level of private savings. As expected, we find that public savings rate hasnegative and statistically significant relationship with private savings for all the countries (exceptNepal where it is insignificant). The coefficients are quite large for India, Pakistan, Sri Lankaand Bangladesh. This is expected because higher tax collections and lower expenditure on socialsectors, which will increase public savings will have an opposite effect on the private savings byreducing the disposable income per capita and increasing the need to save for old age, sicknessad children’s education, etc.
5. Conclusions and policy implications
Long-term sustainable growth in developing South Asian countries require high level of invest-ment and thereby domestic savings. This has motivated us to analyze the savings behaviour in fiveSouth Asian countries—India, Pakstan, Bangladesh, Sri Lanka and Nepal. Our results provideimportant policy insights for increasing domestic savings rates in these countries. Our economet-ric analysis shows that the main factors positively affecting total savings rate in these countries areincome per capita or its growth rate and access to banking facilities. We also find that dependencyratio and availability of foreign savings have statistically significant negative effect on savings.Thus the recent increase in savings rates in South Asia is largely explained by the increasing per
222 P. Agrawal et al. / Journal of Policy Modeling 31 (2009) 208–224
capita income or growth, declining dependency rates (fewer children per couple) and improvedavailability of banking facilities. The changes in real interest rate have only a small effect on thesavings rate and even the direction of change is unclear—it was found to be positive but negligiblein Bangladesh and Nepal and negative in India, Pakistan and Sri Lanka. These results suggest thattrying to influence the savings rates by manipulating interest rates is not likely to be a practicalpolicy option in these countries as interest rates changes have only minor impact on savings rates.However, greater use of foreign savings (capital account deficits) can reduce savings and do needto be controlled, especially if it is due to increases in imports of consumption goods. Similarresults were obtained for private savings with an added variable, public savings rate, which isfound to have a negative and significant impact on private savings in most South Asian countries.
Appendix A. Appendix A
The ARDL Estimation Procedure: For determining the long-run relationship, Pesaran andShin (1998) have developed the ARDL method. This procedure is valid for stationary variablesas well as for a mixture of I(0) and I(1) variables. The existence of the long run relationship isconfirmed with the help of an F-test that tests that the coefficients of all explanatory variables arejointly different from zero (see Pesaran and Shin, 1998).
The augmented ADRL model can be written as follows:
α(L)yt = μ0 +k∑
i=1
βi(L)xit + ut (A.1)
where
α(L) = α0 + α1L + α2L2 + · · · + αtL
t
and
β(L) = β0 + β1L + β2L2 + · · · + βtL
t
where μ0 is a constant; yt is the dependent variable; L is the lag operator such that Lixt = xt−i. Inthe long-run equilibrium yt = yt−1 = yt−2 = · · · = y0 and xit = xit−1 = xit−2 = · · · = xi0. Solving for ywe get the following long run relation:
y = a +k∑
i=1
bixi + γt (A.2)
where
a = μ0
α0 + α1 + · · · + αt
bi = βi0 + βi1 + βi2 + · · · + βit
α0 + α1 + α2 + · · · + αt
γt = ut
α0 + α1 + α2 + · · · + αn
P. Agrawal et al. / Journal of Policy Modeling 31 (2009) 208–224 223
The error correction (EC) representation of the ARDL method can be written as follows:
�yt = �α̂0 −p∑
j=2
α̂j �yt−j+k∑
i=1
β̂i0 �xit−k∑
i=1
q∑
j=2
βi,t−j �xi,t−j − α(1, p) ECMt−1+μt
(A.3)
where
ECMt = yt − α̂ −k∑
i=1
β̂i0 �xit (A.4)
where � is the first difference operator; αj,t−j and βi,t−j are the coefficients estimated from Eq.(A.1) and α(1,p) measures the speed of adjustment.
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