Role of International Financial Institutions in theEconomic
Development of Indian Economy: A Time-Series Analysis
Role of International Financial Institutions in the Economic Development of Indian Economy: A Time-Series Analysis
Dr. Rajni Dogra[1]
Abstract
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India is home to half of the world’s poor because of wide spread poverty and high population. On the other hand, making the world free of poverty is the aim of World Bank, and the Asian Development Bank (ADB) whose mission is “Working for a world free of poverty” and ''to help its Developing Member Countries (DMC's) reduces poverty and improve the quality of life of their people''. Therefore, present study is an endeavor to assess the significance of assistance provided by the World Bank and the Asian Development Bank to uplift development process of Indian economy over the period 1981 to 2008. Using auto-regressive distributed lag (ARDL) modeling, the long-run relationship between the World Bank and the Asian Development Bank’s assistance in economic development of Indian economy has been analyzed. The estimates indicate that the World Bank’s assistance has significant impact in the development of Social sector of Indian economy, whereas the lending is still to be targeted properly in other sectors like Agriculture, Energy, Industry, and Infrastructure to reap significant benefits of assistance at development front.
Keywords: Economic Development, World Bank, Auto Regressive DistributedLag (ARDL).
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Role of International Financial Institutions in the Economic Development of Indian Economy: A Time-Series Analysis
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India is home to one third of the world’s poor because of wide spread poverty and high population. On the other hand, making the world free of poverty is the aim of World Bank, and Asian Development Bank whose mission are “Working for a world free of poverty” and ''to help its Developing Member Countries (DMC's) reduces poverty and improve the quality of life of their people''. Therefore, present study is an endeavor to assess the significance of the assistance provided by the World Bank and the Asian Development Bank to uplift development process of Indian economy over the period 1981 to 2008. Using auto-regressive distributed lag (ARDL) modeling, the long-run relationship between World Bank and Asian Development Bank’s assistance in economic development of Indian economy has been analyzed. The estimates indicate that the World Bank’s assistance has significant impact in the development of Social sector of Indian economy, whereas the lending is still to be targeted properly in other sectors like Agriculture, Energy, Industry, and Infrastructure to reap significant benefits of assistance at development front.
Keywords: Economic Development, World Bank, Auto Regressive DistributedLag (ARDL).
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1. Introduction
India has been the founder member of International Financial Institutions (IFIs) like the World Bank and of the Asian Development Bank (ADB) whose mission are “Working for a world free of poverty” and ''to help its Developing Member Countries (DMC's) reduces poverty and improves the quality of life of their people''.Presently, India is home to one third of the world’s poor and being a transitional economy[i]it faces hurdles in the form of shortage of capital, limited technology, expertise and inadequacy of institutions necessary for fostering its rapid economic development. Furthermore, due to the presence of Savings-Investment gap, Export-Import gap, and Technological gap, Indian economy. Over the years, both the World Bank and the ADB have increased their involvement in India by raising and diversifying the lending in the different sectors of the economy. Both the World Bank and the ADB have provided lending by keeping in view India’s Five-Year planning priorities. The present paper makes an endeavourto analyse the role of International Financial Institutions (IFIs) in the economic development ofIndian economy. For conducting this exercise, some important sectors of Indian economy have been undertaken as an indicator of economic develpoment. To pursue these objectives, the paper has been divided into following three sections. Section 2 presents the theoretical and empirical background to this research. Section 3 outlines the research design used for obtaining empirical results. In Section 4 empirical results are presented and section 5 concludes the paper.
2. Effectiveness of International Financial Institutions: A Literature Review
Theoretically, Foreign Capital flows in the form of loans and grants through inter-governmental flows (bilateral flows), foreign assistance through institutions (IFIs), foreign equity capital through capital market transactions and Foreign Direct Investments (FDI). In addition, there is flow of foreign capital in the form of transfer of technology and technical know-how.Earlier,foreign Capital was affirmed as an engine of economic growth (Harrod-Domar model, Papanek 1972, Newlyn 1973, Chenery and Strout,1966). The main aim of foreign capital was to augment the economic development of an economy, and it was held that, foreign capital may bridge the set of gaps like Savings-Investment gap, Export-Import gap, and Technological gap, which constrain the development of the developing economies.
From the exiting review of literature, there exists no unanimity among the economists regarding the effectiveness of IFIs lending on the economic development of member countries. The studies by Balassa (1988), Zafar (1994), Wang (1999), Zafar, et al. (2000), Parkinson and McKissack (2003), Bordo, et al. (2004), Klaus (2004), Yilamaz (2005), Shirazi, et al. (2010) show a significant positive impact of IFIs programs on the macroeconomic indicators of member countries. While the studies of Aziz (2001), Butkiewicz and Yanikkaya (2005), Kapur and Webb (2007), Anas and Friawan (2008) shows negative impact of IFIs programs on the macroeconomic indicators. Lastly, the study conducted by Faini, et al. (1990), Dreher (2004), found insignificant impact of IFIs lendin on the economic growth. In addition, Krueger (1998) has put forward the view that the IFIs original rationale no longer fits and their activities have altered as the world economy has grown. Regarding future role of the World Bank, the paper suggests that the World Bank should refocus on development with emphasis on the poorest countries. Buiter and Lankes (2001) suggest that in the 21st century, IFIs will have to frame a strategy to support private sector development along with the existing strategy to work with the governments of member countries for their development. Regarding the future of the IMF and World Bank, Allan H. Meltzer (2003) has suggested that the future of IMF and World Bank depends on how they change and how they achieve the goals of enhancing incentives for member countries for growth, and for reduction of poverty. Mumtaz (2006) analyzed the determinants of multilateral aid from World Bank, IMF and Asian Development Bank (ADB) to Pakistan economy and found the presence of political-economic and bureaucratic determinants affecting the lending decisions of multilateral lending by World Bank, IMF and Asian Development Bank.
Summing up the review of literature, it has been found that, there emerges no definite conclusion regarding the role of IFIs in the economic development of member countries. Moreover, while finding the relationship between IFIs and economic development, a common feature which appears in these studies is that either GDP or saving has been considered as an indicator of economic development which does not go well with the present day definition of economic development. Nevertheless, the result of the present study are more likely to be free from these distortions because some important sectors of Indian economy have been included as an indicator of economic development in the present empirical setting to find the impact of IFIs lending.
3. Research Design
To test the presence of the long-run relationship between the IFIs' lending (the World Bank and the ADB) on the economic development of Indian economy some important sectors of Indian economy have been undertaken as an indicator of economic development. The time-series data has been collected over the period of 28 years spanning over 1981-2008 and have been further divided into two phases namely Pre-reform (1981-1990) and Post-reform (1991-2008). The economic reforms in India were initiated in June 1991[ii]; hence, the fiscal 1991 has been utilized as a cut-off year for Pre-and-Post reform. This division has been undertaken to consider the impact ofinternal policies on the economic development.
Step 1: Obtaining the Data and Construction of Variables
For conducting the exercise, total 25 time-series variables, have been used. The variable LNGDPA is the log of GDP by Agriculture sector, which consists of Agriculture, Forestry and Fishing. The variable LNGDPEN is the log of GDP by Energy sector, which consists of Electricity, Gas and Water Supply. The variable LNGDPID is the log of GDP by Industry sector, which consists of Mining, Quarrying, Manufacturing and Construction. The variable LNGDPIN is the log of GDP by the Infrastructure Sector, which consists of Railways, Transport, Storage and Construction. It is worth mentioning here that the data of GDP by Agriculture sector, Industry sector, Energy sector and Infrastructure sector from 1981-2008 have been obtained from the National Accounts Statistics (NAS) maintained by the National Statistics Commission of India and is measured in Rs. Crore.
The variable LNSSI is the log of Social Sector Index, framed by using four indicators namely GDP per capita, Life expectancy, Labour force participation rate, and Gross Primary Enrollment Ratio. The framing of SSI is justified on the ground that it is impossible to measure the GDP by the Social Sector; hence, an index has been framed and used as a proxy for the development of Social Sector. The data for the GDP per capita, Life expectancy, Labour force participation rate and Gross Primary Enrollment Ratio from 1981-2008 have been obtained from the World Bank Data Catalog, the National Accounts Statistics (NAS) and Ministry of Human Resources. All the above stated variables are entered as Dependent Variables, as a proxy for the economic development.
The variables LNWBA, LNWBEN, LNWBID, LNWBIN, and LNWBSS are defined as log of loans disbursed by the World Bank to Agriculture Sector, to Energy sector, to Industry sector, to Infrastructure sector, and to Social sector respectively. The variables LNADBA, LNADBEN, LNADBID, LNADBIN, and LNADBSS are defined as log of loans, disbursed by the ADB to Agriculture sector, to Energy sector, to Industry sector, to Infrastructure sector, and to Social sector respectively. The variables LNOTA, LNOTEN, LNOTID, LNOTIN, and LNOTSS are defined as log of foreign loans disbursed by others to Agriculture sector, to Energy sector, to Industry sector, to Infrastructure sector, and to Social sector respectively. It is worth mentioning here that the loans by the World Bank, ADB, and Others' loans to various sectors of India from 1981-2008 have been obtained from Ministry of Finance, Department of Economic Affairs, Controller of Aid, Accounts and Audit (CAAA).
Lastly, the variables LNGCFA, LNGCFEN, LNGCFID, LNGCFIN, LNSSE is defined as log of Gross Capital Formation[iii] by Agriculture sector, Energy sector, Industry sector, Infrastructure sector and Social sector Expenditure by the Central Government. Data for all the variables except LNSSE have been obtained from the National Accounts Statistics (NAS) maintained by the National Statistics Commission of India and is measured in Rs. Crore. The data for the Social Sector Expenditure from 1981-2008 has been obtained from the Handbook of Statistics on Indian Economy 2010, Reserve Bank of India (RBI). All the above stated variables have been entered as explanatory variables.
Step 2: Time-series Sectoral specific analysis:
To check the presence of long-run relationship between the indicators of IFIslending and economic development, the cointegration methodology has been used. For this, the presence of unit root has been examined usingAugmented Dicky Fuller (ADF) test statistics.After testing for the presence of unit root, appropriate econometric techniques have been used to estimate the short-run and long-run dynamics. To deal with the problem of unit root in the time-series data, Engle and Granger (1987), Johansen (1988), Johansen, and Juselius (1991) developed and suggested the use of cointegration techniques. These techniques deal with time-series having similar properties, means time-series integrated of same order. On the other hand,Auto Regressive Distributed Lag (ARDL) cointegration model by Pesaran and Shin, 1995, 1998; Pesaran et al., 1996, Pesaran et al., 2001 can estimate the long-run relationship involving time-series integrated of different order. Hence, ARDL cointegration has been used. The optimal lag length for the ARDL conitegration technique is decided by the Schwartz Bayesian Criterion (SBC). The long-run model estimates using ARDL procedure,provides following model.
(1.1)
Where ‘K’ represents sector under evaluation[iv].However, the model (7.1) is incapable ofbifurcate the long-run and short-run elasticities. One way to estimate the long-run and short-run elasticitiesis to estimate ARDL for two sub-periods separately.However, separate estimation may lead to the loss of huge degree of freedom.Thus, following model is estimated to evaluate Pre-and Post-reform elasticities.
(1.2)
Where t* is the year of reforms. From model (1.2), coefficients and are the Pre-reform parameter estimates whereas, provides the Post-reform measure of intercept and provides the Post-reform measure of elasticities. The usualt-statistics has been utilised to check the significance of Pre-reform parameters, whereas, Wald F-statistics has been used to check the joint significance of and.
Finally, to find out whether the model converges to its long-run equilibrium path, the short-term parameters have been estimated using the Error Correction Model (ECM).
(1.3)
where ‘’ are the short-term impact coefficients, ‘’ is the speed of convergence and ‘et-1’ is the residual of cointegration relationship and also represents the error correction term.
Step 3: Hypotheses
Using the aforementioned methodology, the study endeavours to test following hypotheses:
Sr. No. / Null Hypothesis: / Alternative Hypothesis:I / No relationship between any IFIs’ lending and the sectoral growth of Indian economy. / Significant relationship between World Bank's lending and the sectoral growth of Indian economy.
Source: Framed Elaborations.
4 Empirical Results
Table 1.1, 1.2, 1.3 covers the results of inter-sector analysis of Indian economy. In the first step,presence of the unit-root problemin aforementioned variables has been tested using the Augmented Dicky Fuller (ADF) test. Table 1.1presents the results pertaining to ADF unit root test. The variables LNGDPEN, LNOTEN LNOTID,LNWBIN, LNSSI and LNOTIN are stationary at the level. However,other variables are found to be stationary at first difference. Given that, the variables are stationary at different orders of integration, application of ARDL model has been found suitable to analyse the long-run relationship between the target variables. The application of bound test validates the existenceof cointegration among the variables under evaluations.
Table 1.1: Results of Unit Root/Stationary Tests: Various Sectors of Indian Economy
AGRICULTURE SECTORVariable / At All Levels / At First Difference
WODT / WID / WDT / WODT / WID / WDT
LNGDPA
/ 4.1423(10.099) / -0.4902
(0.877) / -2.7253
(0.230) / -1.7824*
(0.0712) / -8.6094***
(0.000) / -8.4255***
(0.0000)
LNWBA / 0.0154
(0.677) / -3.70663
(0.009) / -3.8631
(0.028) / -6.6982***
(0.000) / -6.5048
(0.000) / -6.6413***
(0.0000)
LNADBA# / N.A / N.A / N.A / N.A / N.A / N.A
LNOTA / 0.0632
(0.694) / -2.203
(0.209) / -2.785
(10.214) / -5.216***
(0.000) / -5.129***
(0.000) / -5.0187***
(0.002)
LNGCFA / 2.2101
(0.991) / 1.058
(0.946) / -1.859
(0.542) / -6.354***
(0.000) / -7.140***
(0.000) / -7.554***
(0.000)
ENERGY SECTOR
Variable / At All Levels / At First Difference
WODT / WID / WDT / WODT / WID / WDT
LNGDPEN / 4.877
(1.00) / -2.369*
(0.097) / -1.2780
(0.871) / -1.5276*
(0.100) / -4.8260***
(0.000) / -5.9165***
(0.000)
LNWBEN / -1.52825
(0.115) / 3.267212
(1.000) / 2.32040
(1.000) / 0.90813
(0.8959) / 0.786018
(0.9910) / -0.492089
(0.974)
LNADBEN / -0.3515
(0.548) / -1.8631
(0.3437) / -2.038
(0.5548) / -6.19870***
(0.0000) / -6.2391***
(0.0000) / -6.2469***
(0.000)
LNOTEN / 0.212
(0.740) / -2.727*
(0.082) / -2.1960
(0.472) / -6.651***
(0.000) / -6.553***
(0.000) / -2.196
(0.472)
LNGCFEN / 2.5931
(0.996) / 0.2540
(0.971) / -2.102
(0.520) / -3.029***
(0.003) / -3425**
(0.019) / -3.4310*
(0.069)
INDUSTRY SECTOR
Variable / At All Levels / At First Difference
WODT / WID / WDT / WODT / WID / WDT
LNGDPID / 9.125
(1.000) / 1.772
(10.999) / -1.674
(0.733) / -1.467
(0.129) / -3.379**
(0.021) / -3.548*
(0.054)
LNWBID / -0.7878
(0.363) / -0.9850
(0.740) / -3.1320
(0.122) / -1.6864
(0.0862) / -1.69218
(0.421) / -1.79250*
(0.025)
LNADBID / -1.7465
(0.007) / -2.1922
(0.213) / -2.4071
(0.367) / -6.1354
(0.000) / -4.53804
(0.001) / -4.8495
(0.003)
LNOTID / -1.2193
(0.1985) / -1.6522
(0.443) / -3.6371**
(0.0452) / -6.6414***
(0.000) / -6.6084***
(0.000) / -3.3063*
(0.093)
LNGCFID / 1.4803
(0.962) / -0.3994
(0.895) / -0.54982
(0.478) / -4.1496***
(0.000) / -4.4175***
(0.0019) / -4.31086**
(0.011)
Continue….
INFRASTRUCTURE SECTOR
Variable / At All Levels / At First Difference
WODT / WID / WDT / WODT / WID / WDT
LNGDPIN / 20.7291
(0.999) / 0.0657
(0.95681) / -2.2801
(0.42998) / -0.2925
(0.5698) / -4.8454***
(0.0006) / -4.423***
(0.0090)
LNWBIN / 0.35591
(0.7805) / -2.0269
(0.2742) / -3.3088*
(0.0921) / -4.65370***
(0.0000) / -4.5563**
(0.0013) / -4.4589*
(0.00801)
LNADBIN / 0.180136
(0.7303) / -1.49197
(0.5217) / -1.9134
(0.6189) / -2.8607*
(0.0059) / -3.100215**
(0.00391) / -3.0547
(0.1375)
LNOTIN / 0.02021
(0.6805) / -1.9982**
(0.0477) / -3.0551
(0.1366) / -5.6912***
(0.0000) / -5.5573***
(0.0000) / -5.4032***
(0.0009)
LNGCFIN / 2.3708
(0.9942) / -0.2308
(0.9229) / -2.957
(0.1623) / -3.7201***
(0.0000) / -4.71884***
(0.0009) / -4.6210***
(0.0055)
SOCIAL SECTOR
Variable / At All Levels / At First Difference
WODT / WID / WDT / WODT / WID / WDT
LNSSI / -0.5727
(0.4597) / -1.06087
(0.1760) / -9.5703***
(0.000) / -13.338***
(0.000) / -17.2256***
(0.0001) / -16.1427***
(0.0000)
LNWBSS / -0.6404
(0.4304) / -1.33809
(0.5968) / 1.2592
(0.999) / -1.9525**
(0.050) / -1.7394
(0.400) / -2.5210
(0.316)
LNADBSS / 0.2221
(0.743) / -1.1678
(0.673) / -2.8889
(0.181) / 6.1475***
(0.000) / -6.4435***
(0.000) / -4.1340**
(0.017)
LNOTSS / 0.03527
(0.685) / -1.2612
(0.632) / -2.2142
(0.4633) / -6.7796***
(0.000) / -7.02697***
(0.000) / -6.8797***
(0.000)
LNSSE / 2.6730
(0.997) / 0.6410
(0.988) / -1.8347
(0.659) / -5.3303***
(0.000) / -6.8846***
(0.000) / -7.6119***
(0.000)
Source: Calculated.
Notes: (i) WODT represnts the Without Drift & Trend
(ii) WID represnts the With Drift
(iii) WDT represnts the With Drift & Trend
(iv) ***,*, and * indicates significance at 1%,5%, and 10% level respectively.
(v) Lag lengths are determined by using SBC criteria.
(vi) # ADB has started providing loans in Agriculture sector since 2006 only.
4.1 Long–run analysis
From Table 1.2 it is observed that the estimated coefficients of the long-run relationship show the insignificant impact of any independent variable on thegrowth of Agriculture sector, Energy sector, Industrial sector, and Infrastructure sector for the entire period. However, the variables namely LNWBSSand LNSSE bear the significant impact on the growth of Social sector. The results about the significant impact of LNSSE on Social sector development can be justified on the basis of increasing government expenditure on Social sector development since the inception of Sixth five-year plan (1980-85). The Sixth five-year plan was launched with the slogan of poverty alleviation (i.e. Garibi Hatao). The twenty-point programme was launched in the fiscal 1975 during the Fifth five-year plan, with the objective to improve the standard of living of the poor masses. The major issues addressed by the Sixthfive-year plan were on socio-economic infrastructure development in the rural areas and elimination of rural poverty. A number of national-level programmes and schemes were launched with the specific concerns of socio-economic development. Various programmes like National Rural Employment Programme (NREP) fiscal 1980, Restructured Twenty Point Programme in fiscal 1982, Rural Landless Employment Guarantee Programme (RLEGP) in fiscal 1983, Self-Employment to Educated Unemployed Youth Programme (SEEUP) in fiscal 1983, and Tribal Development Programme (TDP) in fiscal 1983 were launched.