World Bank Lending- Boon or Bane? A case study of India
Zeenia Singh Ahluwalia,
Research Scholar,
Department of Commerce,
Punjabi University, Patiala.
Dr. Rajinder Kaur
Professor,
Department of Commerce,
Punjabi University, Patiala
Abstract
In order to sustain and escalate the process of economic development, adequate finance must be made available as and when required. Developing countries like India rely substantially on foreign capital assistance. One such source of assistance is the World Bank, that has been providing loans and grants to India since 1948. The present study is an attempt to analyze the impact of World Bank lending on India's economic growth. Patterns of lending, both nature and sector wise, have also been studied. Statistical tools such as mean, coefficient of variation, compounded annual growth rate, correlation and regression have been used for the purpose of study. The study found that India has been receiving loans from both IBRD and IDA and transport and agriculture are the two sectors which have received the majority of loan over the period of time, i.e. 1991-2018. Correlation between economic growth and World bank lending was found to be positive, moderate and statistically significant. The results of the multivariate regression revealed that World bank lending had a negative impact on economic growth.
Introduction
Finance is said to be the backbone of an economy, adequacy of which is quintessential for the economic development of any country. Different countries around the world are at different levels of development, moving forward at their own respective paces. No country, especially, developing country has adequate finance to fuel it's process of development entirely. For this purpose, international financial institutions like International Monetary Fund (IMF), World Bank, Asian Development Bank (ADB), etc were set up, so as to give the necessary boost to the needy countries. Financial aid influences the process of growth by reducing the saving investment gap, increasing productivity and transferring the modern technology (Khan and Ahmed 2007). These institutions provide loans to those countries who are in need of it. The loans are given for a variety of purposes, some are project specific while others pertain to a particular sector as a whole. Further, the conditions of the lendings also vary from country to country and project to project.
India and the World Bank
The World Bank group is an important source of finance to the developing countries around the world. It consists of five organisations, namely, International Bank for Reconstruction and Development (IBRD), International Development Association (IDA), International Finance Corporation (IFC), International Centre for the Settlement of Investment Disputes (ICSID) and Multinational Investment Gaurantee Agency (MIGA). However, IBRD and IDA together are known as the World Bank
India has never been a self sufficient country as far as finance is concerned. India was one of the founding members of the World Bank and received it's first assistance in the year 1948[i]. Since then, India has been one of the largest recipients of the World Bank lending.
This paper is an attempt to analyze the relationship between India and the World Bank.
Review of Literature
A lot of studies have been conducted in to analyze the impact of foreign aid inn general but very few has been conducted to analyze the impact of World Bank lending specifically. Hence, review of studies done on foreign aid have also been included to get a broader perspective.
Islam (1992) developed a theoretical framework in his paper, specifying the relationship between foreign capital and economic growth in Bangladesh, which was tested empirically by employing time series analysis. Time series data for the period 1972-1988 was taken into consideration. The study concluded that foreign capital was instrumental in economic development of Bangladesh. However, not all categories of foreign capital were found to be equally vital. The results showed that loans and food aid had a stronger influence on economic growth as compared to the other categories. Further, it was found that the domestic resources had far greater effect on growth in comparison to foreign resources. The study suggested that in order to transfigurate Bangladesh economically , mobilization of domestic resources should be the centre of attention for the government rather than reliance on foreign resources.
Mallick and Moore (2005) conducted a study to analyze the impact of World Bank lending on the economic growth of 30 countries using the Panel Data approach. Both, concessional and non-concessional loans, for the period from 1970 to 2001, were taken into account. The study concluded that World Bank lending exerted positive impact on the rate of growth of GDP Per Capita in both, the long run as well as short run.
Moreira (2005) scrutinized the macroeconomic impact of foreign aid on the economic growth of 48 developing countries. A cross country growth regression was carried out for the period 1970-1988 by using 6 sub period averages instead of yearly data. The study concluded that foreign aid was beneficial to the economic growth of the countries. It was also found, that Programme aid had more rapid impact than project aid, which in turn was expected to have a more rapid impact than technical cooperation aimed at raising the level of human skill.
Shirazi (2009) tried to assess the impact of foreign aid on human development in Pakistan, found out that former had a positive impact on the latter. The study was conducted for a period of 31 years, from 1975 to 2006. The hypothesis was tested using vector error correction approach. The paper concluded by suggesting that proper management of foreign aid will contribute to human development in Pakistan.
Chatrna and Ekanayake (2010), took a sample of 85 developing countries covering Asia, Africa and Latin America and the Caribbean for the period 1998-2007. By using regression analysis, they examined the effect that foreign aid has on economic growth of the sample countries. Three separate models for shorter time periods, i.e. 1980-89, 1990-99 and 2000-07 were estimated. Also the models were estimated for different regions, namely, Asia, Africa, and Latin America and the Caribbean and different income levels i.e. low income, low middle income and upper middle income. Mixed impact of foreign aid on the economic growth was found. First, when the model was estimated for different time periods, except for 2000-2007, foreign aid variable had a negative impact in three out of four cases. Second, except for Africa, foreign aid showed a negative impact in all the other regions. Finally, when the model was estimated for different income levels, foreign aid showed a positive impact in all the cases, except for the low-middle income countries.
Moyo and Mafuso (2017) did an extensive review of the studies that had already been undertaken to analyze the impact of foreign aid on the economy of Zimbawe. The study found that albeit Zimbawe had been receiving foreign aid in substantial quantities, the aid had not been effective. It was further found that the reason for ineffectiveness of aid was the futile policies of the country, ultimately leading to the stopping of the aid by the donors, which in turn, further led to the collapse of the economy of Zimbawe, as the country was majorly dependent on foreign aid. The authors also concur with the viewpoint that the aids given to the developing nations are not based on their needs, but primarily on the interests of the donors.
In the study conducted by Seng (2018) to assess the impact of foreign aid on economic growth of Cambodia from 1980-2014, it was found that foreign aid has a positive impact on growth, but only in the short run. In the long run, it was found to have a negative impact on both investment and growth. The author employed auto regressive distribution lag (ARDL) model for the analysis. It was further suggested that sustainable growth could be achieved and industrialization could be enhanced, by shifting from aid dependence to promoting investment through elevating domestic and foreign capital in the country.
Sethi et. al (2019) in their paper made an attempt to analyze the impact of foreign aid on the economic growth of India and Sri Lanka. The data was collected for the period 1960-61 to 2014-15 and time series analysis was conducted, for both, long run and short run. The results revealed that, as far as India is concerned Foreign aid has positive impact on the economic growth in both, long run and short run. But in case of Sri Lanka, on the other hand, the impact of foreign aid on growth was found to be positive and significant only in the long run and not in the short run. The paper concluded by suggesting that rather than focusing on the quantity of aid, the governments of both the countries should focus on the efficient utilization of the same. The authors also proposed that sound and stable macroeconomic policies along with outward looking development strategies needed to be brought into force in order to ensure optimum utilization of foreign aid.
Objective
The objective of the study are as follows :
Research Methodology
The study is completely secondary in nature. Data for a period of 28 years, from 1991 to 2018, has been taken. The data has been made available from World Bank's World Development Indicators. For studying the patterns of lending, both nature wise and sector wise, statistical tools of Mean, Coefficient of Variation (C.V.) and Compounded Annual Growth Rate (CAGR) has been computed. For the purpose of assessing the impact of World Bank lending, Correlation and Multiple Linear Regression have been employed. GDP per capita has been taken as the proxy of economic growth (GDP) and the dependent variable as well. Per capita lending by World Bank (PCL), Gross Domestic Savings as a % of GDP (GDS) and Consumer Price Index (annual % change)(CPI) has been taken as independent variables. Statistical software, SPSS 16 was used for the purpose of analysis.
Analysis And Findings
Nature of lending by the World Bank
Foreign capital assistance by the World Bank is provided via it's two main branches i.e. International Bank for Reconstruction and Development (IBRD) and International Development Association (IDA). The loans provided by IBRD are to be repaid by the borrower country within 12-15 years[ii]. The interest rate charged by IBRD is revised every six months and is slightly (around 0.5 percent) above it's own borrowing cost on the international market in order to cover it's operating cost[iii]. IDA, on the other hand, lends money on concessional terms, where the interest charged is zero percent or minimalistic. The loans are to be repaid by the countries over a stretch of 30-38 years, including a grace period of 5-10 years.[iv] For these reasons, IDA loans are also known as Concessional loans.[v]
Table 1 contains the details of the nature of lending made by the World Bank to India for the period 1991-2018 in absolute and relative terms.
As per the tables, World Bank has lend a total of US$ 76,442.58 million to India over the period 1991-2018, of which, 55.55% had been lent by IRBD and the rest 44.44% through IDA. The highest amount lent by the bank was in the year 2010, as high as US$ 9266 million, majority of which was to help the Indian capital market in insulating itself from the global recession. On the other hand, the minimum amount of aid (US$ 928.8 million) received by India from the bank was in the year 1994, which also corresponded to the least amount (US$ 94 million) sanctioned through IBRD as well, over the period of study. The analysis revealed that where on one hand, the percentage share of lending by the IBRD increased from 54.26% in 1991 to 100% in 2018, on the other hand that of IDA decreased from 45.74% to 0% over the period of study. It can be well inferred that on the whole, the pattern of lending had been irregular. It can be seen, that prior to the year 2000, majority of the lending came as interest free credits from IDA. In the year 1994, about 89.8% (US$ 834.8 million) of the total aid came in the form of IDA credits. Between the years 2000 and 2011, IBRD loans constituted the greater part in the total lending except for the years 2002, 2004 and 2007, where the case was the other way around. IDA again had the lion's share for few of the years to follow, up till the late 2010s, where it's share could be seen as declining from 44.57% in 2015 to 26.65% in 2016 and ultimately reaching zero percent in the year 2018.
On an average, the analysis indicates, the World Bank has lent US$ 2730.09 million to India, over the years, of which US$ 1516.67 million were channeled via IBRD loans and US$ 1213.42 million via IDA credits. The coefficient of variation (C.V.) was 87.64% for IBRD, 60.36% for IDA and 62.54% for the total World Bank lending, indicating enormous year-on-year disparities. CAGR revealed that although IBRD loans grew at the rate of 4.29%, IDA credits witnessed a negative growth of 2.33%. In totality, World Bank lending grew at the rate of 1.95%.
Table 1 : Nature of lending by World Bank to India (1991-2018)
YEAR |
IBRD (US $ Million) |
IBRD (% of total) |
IDA (US $ Million) |
IDA (% of total) |
TOTAL (US $ Million) |
TOTAL (%) |
1991 |
1112 |
54.26 |
937.4 |
45.74 |
2049.4 |
100.00 |
1992 |
1168 |
53.30 |
1023.5 |
46.70 |
2191.5 |
100.00 |
1993 |
1145 |
42.76 |
1532.7 |
57.24 |
2677.7 |
100.00 |
1994 |
94 |
10.12 |
834.8 |
89.88 |
928.8 |
100.00 |
1995 |
1118.8 |
54.22 |
944.7 |
45.78 |
2063.5 |
100.00 |
1996 |
776.6 |
37.38 |
1301.1 |
62.62 |
2077.7 |
100.00 |
1997 |
626.5 |
40.96 |
903 |
59.04 |
1529.5 |
100.00 |
1998 |
1068 |
49.87 |
1073.64 |
50.13 |
2141.64 |
100.00 |
1999 |
400 |
37.92 |
654.8 |
62.08 |
1054.8 |
100.00 |
2000 |
934.27 |
51.88 |
866.48 |
48.12 |
1800.75 |
100.00 |
2001 |
2035 |
79.64 |
520.3 |
20.36 |
2555.3 |
100.00 |
2002 |
893 |
40.79 |
1296.5 |
59.21 |
2189.5 |
100.00 |
2003 |
836 |
54.91 |
686.62 |
45.09 |
1522.62 |
100.00 |
2004 |
389.5 |
27.38 |
1033.03 |
72.62 |
1422.53 |
100.00 |
2005 |
1748.5 |
60.58 |
1137.83 |
39.42 |
2886.33 |
100.00 |
2006 |
916 |
64.69 |
500 |
35.31 |
1416 |
100.00 |
2007 |
1499.5 |
39.98 |
2251.33 |
60.02 |
3750.83 |
100.00 |
2008 |
1317 |
61.14 |
837 |
38.86 |
2154 |
100.00 |
2009 |
1286 |
57.35 |
956.4 |
42.65 |
2242.4 |
100.00 |
2010 |
6688.74 |
72.18 |
2577.6 |
27.82 |
9266.34 |
100.00 |
2011 |
3469 |
62.61 |
2072.04 |
37.39 |
5541.04 |
100.00 |
2012 |
445 |
14.00 |
2733.3 |
86.00 |
3178.3 |
100.00 |
2013 |
377.7 |
28.49 |
948 |
71.51 |
1325.7 |
100.00 |
2014 |
1975 |
38.65 |
3134.4 |
61.35 |
5109.4 |
100.00 |
2015 |
2098 |
55.43 |
1686.9 |
44.57 |
3784.9 |
100.00 |
2016 |
2820 |
73.35 |
1024.5 |
26.65 |
3844.5 |
100.00 |
2017 |
1776.4 |
77.76 |
508 |
22.24 |
2284.4 |
100.00 |
2018 |
3453.2 |
100.00 |
0 |
0.00 |
3453.2 |
100.00 |
TOTAL |
42466.71 |
|
33975.87 |
|
76442.58 |
|
MEAN |
1516.67 |
|
1213.42 |
|
2730.09 |
|
S.D. |
1329.22 |
|
732.36 |
|
1707.46 |
|
C.V. (%) |
87.64 |
|
60.36 |
|
62.54 |
|
CAGR |
4.29 |
|
-2.33 |
|
1.95 |
|
Source: World Development Indicators, World Bank.
Sectoral Pattern of Lending by the World Bank
Table 3 and Table 4 illustrate the details of the World Bank's sector wise lending to India for the period 1991-2018, in absolute and relative forms, respectively. A perusal of the tables reveals that over the period of time, the largest amount (US$ 17,187 million) has been lent to the Transport sector, forming 22.48% of the total lending, followed by the sector of Agriculture (US$ 11,511 million) with, Energy and Extractives (US$ 10,624 million) and Water and Sanitation (US$ 9194 million). The least amount of money was lent to the sector of Information and Communication (US$ 318.7 million) followed by Social Protection (US$ 1355 million), Industry and Trade (US$ 3209 million) and Public Administration (US$ 3917 million). On an average, per year, the World Bank lent US$ 614 million to the Transport sector, US$ 411 million to the Agriculture sector, US$ 379 million to the sector of Energy and Extractives, US$ 328 million to the Water and Sanitation sector, US$ 267 million to the Education sector, US$ 212 to the Financial sector, US$ 199 million to the Health sector, US$ 140 million to Public Administration, US$ 115 million to Industry and Trade and only US$ 48 million and US$ 11 million to Sectors of Social Protection and Information and Communication, respectively.
It is evident from the table that the coefficient of variation was very high for all the sectors, highest being for Financial sector (312.64%) followed by Information and Communication (277.38%) and Social Protection (233.16%). The lowest variation was seen in the Agriculture sector, with C.V. being 88.67% closely followed by Health sector (88.79%). CAGR showed a growth of lending by 8.32% in the sector of Public Administration, being the highest among all the sectors. Lowest was recorded by the sector of Social Protection ,a negative growth of 7.79%. Other sectors which recorded negative growth of lending were, Information and Communication (5.38%r ), Energy and Extractives (2.82%), Financial sector (1.66%) and Education sector (0.40%).
Table 2 : Sector wise World Bank lending to India : 1991-2018 (US $ Million)
YEAR |
AGRCULTURE |
EDUCATION |
ENERGY AND EXTRACTIVES |
FINANCIAL |
HEALTH |
INDUSTRY AND TRADE |
INFORMATION AND COMMUNICATION |
PUBLIC ADMINISTRATION |
SOCIAL PROTECTION |
TRANSPORT |
WATER AND SANITATION |
TOTAL |
1991 |
322.8 |
307.1 |
650 |
0 |
106 |
400.6 |
0 |
0 |
0 |
0 |
262.9 |
2049.4 |
1992 |
243 |
0 |
765 |
0 |
377.5 |
500 |
0 |
0 |
0 |
306 |
0 |
2191.5 |
1993 |
252.7 |
165 |
972 |
0 |
279 |
300 |
0 |
0 |
500 |
0 |
209 |
2677.7 |
1994 |
77.4 |
0 |
0 |
0 |
206.4 |
0 |
0 |
47 |
246 |
94 |
258 |
928.8 |
1995 |
466.9 |
319.8 |
0 |
710 |
133 |
0 |
0 |
0 |
0 |
0 |
433.8 |
2063.5 |
1996 |
290.9 |
425.2 |
413 |
0 |
350 |
0 |
0 |
142 |
0 |
205 |
251.6 |
2077.7 |
1997 |
325 |
0 |
0 |
0 |
555.5 |
0 |
0 |
279.5 |
19.5 |
350 |
0 |
1529.5 |
1998 |
831.94 |
211.4 |
592 |
0 |
376.4 |
0 |
0 |
129.9 |
0 |
0 |
0 |
2141.64 |
1999 |
329.1 |
85.7 |
210 |
0 |
325 |
0 |
0 |
0 |
0 |
0 |
105 |
1054.8 |
2000 |
0 |
182.4 |
280 |
0 |
252.6 |
0 |
62 |
296.27 |
211.48 |
516 |
0 |
1800.75 |
2001 |
210.5 |
139.3 |
450 |
0 |
30 |
0 |
0 |
330 |
0 |
1330 |
65.5 |
2555.3 |
2002 |
238.9 |
0 |
399.2 |
0 |
0 |
0 |
0 |
0 |
0 |
1399.8 |
151.6 |
2189.5 |
2003 |
370.59 |
250 |
0 |
0 |
66.03 |
0 |
0 |
0 |
0 |
836 |
0 |
1522.62 |
2004 |
69.62 |
500 |
0 |
0 |
172.41 |
0 |
0 |
220 |
0 |
240 |
220.5 |
1422.53 |
2005 |
719.02 |
0 |
0 |
120 |
178.83 |
465 |
0 |
229.98 |
0 |
1173.5 |
0 |
2886.33 |
2006 |
380 |
0 |
400 |
0 |
0 |
0 |
0 |
120 |
0 |
300 |
216 |
1416 |
2007 |
674 |
280 |
0 |
741.83 |
610 |
63 |
0 |
620 |
0 |
488 |
274 |
3750.83 |
2008 |
129 |
600 |
1000 |
0 |
0 |
0 |
0 |
425 |
0 |
0 |
0 |
2154 |
2009 |
391.4 |
0 |
580 |
400 |
521 |
100 |
0 |
0 |
0 |
250 |
0 |
2242.4 |
2010 |
900.6 |
1050 |
1330 |
3495 |
117.7 |
0 |
0 |
307 |
0 |
905.93 |
1160.11 |
9266.34 |
2011 |
23.34 |
0 |
648 |
0 |
0 |
262.7 |
150 |
200 |
154 |
2883 |
1220 |
5541.04 |
2012 |
1811 |
500 |
0 |
0 |
152 |
50 |
0 |
60 |
130 |
320 |
155.3 |
3178.3 |
2013 |
333 |
0 |
100 |
100 |
325 |
0 |
0 |
190 |
0 |
277.7 |
0 |
1325.7 |
2014 |
481.2 |
1006.2 |
0 |
0 |
100 |
353 |
0 |
0 |
84 |
2292 |
793 |
5109.4 |
2015 |
328.5 |
550 |
0 |
500 |
0 |
0 |
0 |
0 |
0 |
1450 |
956.4 |
3784.9 |
2016 |
425 |
314.5 |
1220 |
0 |
0 |
0 |
35 |
0 |
0 |
250 |
1600 |
3844.5 |
2017 |
100 |
325 |
315 |
0 |
148 |
125 |
50 |
320.2 |
0 |
610 |
291.2 |
2284.4 |
2018 |
786 |
275.5 |
300 |
0 |
200 |
590 |
21.7 |
0 |
0 |
710 |
570 |
3453.2 |
TOTAL |
11511 |
7487.1 |
10624.2 |
6066.8 |
5582.37 |
3209.3 |
318.7 |
3916.85 |
1344.98 |
17186.93 |
9193.91 |
76442.58 |
MEAN |
411.12 |
267.4 |
379.436 |
216.67 |
199.3704 |
114.6179 |
11.38 |
139.88 |
48.04 |
613.81 |
328.35 |
2730.092 |
S.D. |
364.56 |
285.84 |
399.05 |
677.39 |
177.0223 |
184.86 |
31.57 |
163.68 |
111.99 |
716.02 |
429.99 |
1707.46 |
C.V. (%) |
88.674 |
106.9 |
105.17 |
312.64 |
88.79066 |
161.2837 |
277.38 |
117.00 |
233.16 |
116.65 |
130.96 |
62.54 |
CAGR |
3.35 |
-0.40 |
-2.82 |
-1.66 |
2.38 |
1.44 |
-5.38 |
8.32 |
-7.79 |
3.29 |
2.91 |
1.95 |
Source: World Bank annual reports, Various issues : 1991-2018
Table 3 : Sector wise World Bank lending to India : 1991-2018 (%)
YEAR |
AGRCULTURE |
EDUCATION |
ENERGY AND EXTRACTIVES |
FINANCIAL |
HEALTH |
INDUSTRY AND TRADE |
INFORMATION AND COMMUNICATION |
PUBLIC ADMINISTRATION |
SOCIAL PROTECTION |
TRANSPORT |
WATER AND SANITATION |
TOTAL |
1991 |
15.75 |
14.98 |
31.72 |
0.00 |
5.17 |
19.55 |
0.00 |
0.00 |
0.00 |
0.00 |
12.83 |
100.00 |
1992 |
11.09 |
0.00 |
34.91 |
0.00 |
17.23 |
22.82 |
0.00 |
0.00 |
0.00 |
13.96 |
0.00 |
100.00 |
1993 |
9.44 |
6.16 |
36.30 |
0.00 |
10.42 |
11.20 |
0.00 |
0.00 |
18.67 |
0.00 |
7.81 |
100.00 |
1994 |
8.33 |
0.00 |
0.00 |
0.00 |
22.22 |
0.00 |
0.00 |
5.06 |
26.49 |
10.12 |
27.78 |
100.00 |
1995 |
22.63 |
15.50 |
0.00 |
34.41 |
6.45 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
21.02 |
100.00 |
1996 |
14.00 |
20.46 |
19.88 |
0.00 |
16.85 |
0.00 |
0.00 |
6.83 |
0.00 |
9.87 |
12.11 |
100.00 |
1997 |
21.25 |
0.00 |
0.00 |
0.00 |
36.32 |
0.00 |
0.00 |
18.27 |
1.27 |
22.88 |
0.00 |
100.00 |
1998 |
38.85 |
9.87 |
27.64 |
0.00 |
17.58 |
0.00 |
0.00 |
6.07 |
0.00 |
0.00 |
0.00 |
100.00 |
1999 |
31.20 |
8.12 |
19.91 |
0.00 |
30.81 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
9.95 |
100.00 |
2000 |
0.00 |
10.13 |
15.55 |
0.00 |
14.03 |
0.00 |
3.44 |
16.45 |
11.74 |
28.65 |
0.00 |
100.00 |
2001 |
8.24 |
5.45 |
17.61 |
0.00 |
1.17 |
0.00 |
0.00 |
12.91 |
0.00 |
52.05 |
2.56 |
100.00 |
2002 |
10.91 |
0.00 |
18.23 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
63.93 |
6.92 |
100.00 |
2003 |
24.34 |
16.42 |
0.00 |
0.00 |
4.34 |
0.00 |
0.00 |
0.00 |
0.00 |
54.91 |
0.00 |
100.00 |
2004 |
4.89 |
35.15 |
0.00 |
0.00 |
12.12 |
0.00 |
0.00 |
15.47 |
0.00 |
16.87 |
15.50 |
100.00 |
2005 |
24.91 |
0.00 |
0.00 |
4.16 |
6.20 |
16.11 |
0.00 |
7.97 |
0.00 |
40.66 |
0.00 |
100.00 |
2006 |
26.84 |
0.00 |
28.25 |
0.00 |
0.00 |
0.00 |
0.00 |
8.47 |
0.00 |
21.19 |
15.25 |
100.00 |
2007 |
17.97 |
7.47 |
0.00 |
19.78 |
16.26 |
1.68 |
0.00 |
16.53 |
0.00 |
13.01 |
7.31 |
100.00 |
2008 |
5.99 |
27.86 |
46.43 |
0.00 |
0.00 |
0.00 |
0.00 |
19.73 |
0.00 |
0.00 |
0.00 |
100.00 |
2009 |
17.45 |
0.00 |
25.87 |
17.84 |
23.23 |
4.46 |
0.00 |
0.00 |
0.00 |
11.15 |
0.00 |
100.00 |
2010 |
9.72 |
11.33 |
14.35 |
37.72 |
1.27 |
0.00 |
0.00 |
3.31 |
0.00 |
9.78 |
12.52 |
100.00 |
2011 |
0.42 |
0.00 |
11.69 |
0.00 |
0.00 |
4.74 |
2.71 |
3.61 |
2.78 |
52.03 |
22.02 |
100.00 |
2012 |
56.98 |
15.73 |
0.00 |
0.00 |
4.78 |
1.57 |
0.00 |
1.89 |
4.09 |
10.07 |
4.89 |
100.00 |
2013 |
25.12 |
0.00 |
7.54 |
7.54 |
24.52 |
0.00 |
0.00 |
14.33 |
0.00 |
20.95 |
0.00 |
100.00 |
2014 |
9.42 |
19.69 |
0.00 |
0.00 |
1.96 |
6.91 |
0.00 |
0.00 |
1.64 |
44.86 |
15.52 |
100.00 |
2015 |
8.68 |
14.53 |
0.00 |
13.21 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
38.31 |
25.27 |
100.00 |
2016 |
11.05 |
8.18 |
31.73 |
0.00 |
0.00 |
0.00 |
0.91 |
0.00 |
0.00 |
6.50 |
41.62 |
100.00 |
2017 |
4.38 |
14.23 |
13.79 |
0.00 |
6.48 |
5.47 |
2.19 |
14.02 |
0.00 |
26.70 |
12.75 |
100.00 |
2018 |
22.76 |
7.98 |
8.69 |
0.00 |
5.79 |
17.09 |
0.63 |
0.00 |
0.00 |
20.56 |
16.51 |
100.00 |
TOTAL |
15.06 |
9.79 |
13.90 |
7.94 |
7.30 |
4.20 |
0.42 |
5.12 |
1.76 |
22.48 |
12.03 |
100.00 |
Source : Constructed on the basis on Table-2.
Impact of World Bank lending on India's Economic Growth
To find out the impact of World Bank lending on India's economic growth correlation and regression analysis was done. the results of the same are given as follows:
Table 4 : Results of Correlation between GDP and the independent variables
|
PCL |
GDS |
CPI |
Pearson Correlation |
0.481* |
0.669** |
0.993** |
Sig. Value |
0.042 |
0.000 |
0.000 |
N |
28 |
28 |
28 |
Result |
Positive, moderate and significant |
Positive, moderate and significant |
Positive, strong and significant |
* Correlation significant at 0.05 level (2-Tailed) ** Correlation significant at 0.01 level (2-Tailed) |
A correlation analysis was conducted to find the degree of association of GDP per capita with the rest of the independent variables, i.e. Per capita lending by World Bank (PCL), Gross Domestic Saving (GDS) and Consumer price index (CPI). The results of the same that have also been tabulated in Table 4, are as follow :
To find out the impact of World Bank lending on the economic growth of India a regression analysis was carried out using the following model:
Y = c + β1PCL + β2GDS + β3CPI
Where,
Y = GDP Per Capita
PCL = Per Capita Lending (by World Bank)
GDS = Gross Domestic Savings (as a % of GDP)
CPI = Consumer Price Index
Table 5: Summary statistics of Regression Analysis |
||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.995a |
.989 |
.988 |
49.921872076437370 |
a. Predictors: (Constant), PCL, CPI, GDS b. Dependent variable : GDP |
Table 5 contains the summary statistics of the regression analysis. R square value is 0.989,meaning thereby, that 98.9% of the variations in the dependent variable (GDP per capita) can be explained by the chosen regression model.
Table 6: Results of ANOVA |
|
||||||||||||
Model |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
|
|||||||
|
Regression |
5460595.379 |
3 |
1820198.460 |
730.360 |
.000b |
|
||||||
Residual |
59812.639 |
24 |
2492.193 |
|
|
|
|||||||
Total |
5520408.019 |
27 |
|
|
|
|
|||||||
a. Dependent Variable: GDP |
|
||||||||||||
b. Predictors: (Constant), PCL, CPI, GDS |
|
||||||||||||
As depicted by Table 6, the ANOVA resulted in F = 730.360 and F is significant at less than 0.001 level indicating that the relationship between dependent and independent variables as modeled in this paper is statistically significant. Table 7: Coefficients of the variables |
|||||||||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
|||||||||
B |
Std. Error |
Beta |
|||||||||||
|
(Constant) |
99.220 |
75.093 |
|
1.321 |
.199 |
|||||||
GDS |
8.810 |
3.112 |
.079 |
2.831 |
.009 |
||||||||
CPI |
9.683 |
.280 |
.945 |
34.543 |
.000 |
||||||||
PCL |
-2.473 |
7.858 |
-.007 |
-.315 |
.756 |
||||||||
a. Dependent Variable: GDP b. Predictors : (Constant), PCL, CPI, GDS |
|||||||||||||
It can be observed from Table 7, PCL has a negative but statistically insignificant effect on GDP. On the other hand, a positive and statistically significant impact of GDS and PCI can be seen on the economic growth of the country. The results of the estimated model are, thus, as follows :
GDP = 99.22 + 9.68(CPI) + 8.81(GDS) - 2.47(PCL)
Where,
Y = GDP Per Capita
PCL = Per Capita Lending (by World Bank)
GDS = Gross Domestic Savings (as a % of GDP)
CPI = Consumer Price Index
Conclusion
Impact of foreign aid has always been a debatable issue. The present study was undertaken to get an insight into the impact that foreign aid in the form of World Bank lending have had on India's economic growth. The data was taken for a period of 28 years, from 1991 to 2018. The results revealed that lending and economic growth are positively correlated. Further, regression analysis revealed that foreign aid in the form of World Bank lending have a negative impact on India's economic growth, which also coincides with the results of Moyo and Mafuso (2017) and Abouharb and Duchesne (2019). Effective utilization of foreign assistance through proper policy making is the need of the hour as it can help generate a positive impact of aid on India's economic growth.
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