Factors taking economic growth as subject of study-

 

 

 

 

Factors Affecting Economic Growth: A Study of Indian States

                                                                                                                   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Divya Budhia Gupta

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Assistant Professor in Economics

PCM S.D. College for Women, Jalandhar

Email:[email protected]

Mobile: +919781673773

 

Abstract

The study of economic growth has always been a topic of huge interest among economists, researchers and policy makers alike. There are numerous works taking economic growth as subject of study- at national as well as international level. But, there has been limited work in this direction at the state level. This paper tries to identify the factors which influence the growth in Indian states significantly. The study is related to the decade from 2002-03 to 2012-13. Factors included in the multiple regression analysis are- base level per capita income, gross fixed capital formation, openness index, literacy rate, life expectancy and population growth rate. Out of these, only base level per capita income, capital formation and literacy levels have been found to be significant.

 

Keywords: Economic growth, Indian states, per capita income, capital formation and literacy

 

1.      Introduction

There is no dearth of growth literature pertaining to theorising on factors affecting economic growth at the national and international level. There have been numerous regressions and correlations explaining the linkages between growth and the variables of interest. Rise of India as an economic superpower and considerable shift in the macro parameters after the introduction of economic reforms in 1991 have kept the researchers interested in the study of its various facets. Much work has been done in the past relating to growth of the Indian economy and allied fields. But, India has a federal system of governance with both the state or provincial and the Central governments responsible for the development of the nation as a whole (Kalirajan et al., 2009). India’s growth performance, especially across the states within the country, since the take-off in the late 1970s/early 1980s has been the subject of considerable research interest. Different authors emphasise different aspects of growth performance (Kumar & Subramanian, 2012). Even after 70 years of independence, there is wide regional disparity in the country in terms of development. The reasons are differences in the base level income of the states and differences in their growth rates.

As per Economic Times (26 January, 2017), India’s states broadly follow two distinct paths of development. One path often followed by less developed states like UP, focus on the state government driving development often to the detriment of the private sector. Another path finds the state government playing an enabling role, fostering entrepreneurship and providing good infrastructure and relatively efficient administrative services, like Tamil Nadu and Gujarat. As per data from Economic & Statistical Organization Punjab and Central Statistical Organization, average growth rate during 11th Five Year Plan in Assam and West Bengal was 5.88 percent and 6.24 percent respectively. However, it was 13.66 percent and 10.15 percent respectively for Uttrakhand and Bihar. Similarly, in 2014-15 growth rate of gross state domestic product (at 2011-12 prices, in percent) was -0.31 for Jammu & Kashmir, 4.20 for Punjab and 5.44 for Madhya Pradesh. But, it was 13.02 for Bihar, 12.47 for Jharkhand and 8.82 for Telangana.

Such wide gap in regional growth rates is not good for the economic and overall health of the country. As per Economic Survey 2016-17, spatial     dispersion in income is still rising in India in the last decade (2004-14), unlike the rest of the world and even China. When such differences are narrowing down world over, there is a need to check as to why this is not happening in our country. For this, first of all we need to know what factors underlie growth of state gross domestic product. Then a comparison of these across states can be done to know where the differences actually lie and what needs to be boosted to spur growth in the states that are lagging behind. Present paper attempts to locate the factors that affect growth in states’ economies. It considers many relevant variables but finds only a few to be significant.

2.      Objectives

Present paper intends to identify the factors which significantly affect the growth of net state domestic product.

3.      Research Methodology

Paper evaluates the effect of per capita net state domestic product (PCNSDP) (2002-03), openness index values of Indian states (2002-03), decadal growth rate of population growth (2001-11), gross fixed capital formation (GFCF) (2002-03), literacy rates (2001) and life expectancy (2000-04) on PCNSDP (2012-13) using multiple regression technique. Data for 15 major states has been used for this purpose. Calculations have been performed using Statistical Package for Social Sciences (SPSS) version 21.

4.      Analysis & Discussion

Firstly, all the variables mentioned above were simultaneously used in the multiple regression analysis using “enter” method. The results obtained were, however, not very promising, as shown below:

Table1: Model Summary

 

Value

R

.974

R Square

.948

Adjusted R Square

.909

 

The above table suggests that approximately 91 percent of variations in dependent variable are being explained by the independent variables, which is satisfactory.

 

Table2:  ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

4003977440.343

6

667329573.390

24.258

.000b

Residual

220079553.390

8

27509944.174

 

 

Total

4224056993.733

14

 

 

 

a. Dependent Variable: PCNSDP12

b. Predictors: (Constant), LE04, OIValues2, GFCF2, PGR1, PCNSDP2, LR2

 

In table no. 2, significant value of .000 corresponding to F-value of 24.258 means that model is fit.

Table3: Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-66949.649

39248.095

 

-1.706

.126

PCNSDP2

1.616

.378

.575

4.273

.003

OIValues2

246.338

562.966

.038

.438

.673

PGR1

469.062

456.822

.131

1.027

.335

GFCF2

.234

.072

.317

3.232

.012

LR2

488.621

262.390

.281

1.862

.100

LE04

480.733

580.692

.106

.828

.432

a. Dependent Variable: PCNSDP12

Table no. 3 reveals that out of all the variables included in the model, coefficients only per capita net state domestic product (2002-03), gross fixed capital formation (2002-03) and literacy rate (2001) are significant. While coefficient of PCNSDP is significant at 1 percent and GFCF at 5 percent, coefficient of literacy rate is low on significance at 10 percent.

To get better results, backward multiple regression technique was run on same variables. The results obtained are discussed below:

Table4: Model Summary

 

Model 1

Model 2

Model 3

Model4

R

.974

.973

.971

.968

R Square

.984

.947

.942

.937

Adjusted R Square

.909

.917

.919

.920

 

Above results of backward multiple regressions show that as variables have been removed from the model, its explanatory power has increased from 91 percent to 92 percent. Thus, model has become better with removal of some variables, which were not explaining much of the variations in the dependent variable or were redundant. Retained variables are PCNSDP, GFCF and literacy rate whereas excluded variables are life expectancy, openness values and population growth rate.

 

 

 

 

Table5: ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

4003977440.343

6

667329573.390

24.258

.000b

Residual

220079553.390

8

27509944.174

 

 

Total

4224056993.733

14

 

 

 

2

Regression

3998710144.961

5

799742028.992

31.940

.000c

Residual

225346848.773

9

25038538.753

 

 

Total

4224056993.733

14

 

 

 

3

Regression

3980718629.108

4

995179657.277

40.897

.000d

Residual

243338364.626

10

24333836.463

 

 

Total

4224056993.733

14

 

 

 

4

Regression

3958848018.077

3

1319616006.026

54.733

.000e

Residual

265208975.656

11

24109906.878

 

 

Total

4224056993.733

14

 

 

 

a. Dependent Variable: PCNSDP12

b. Predictors: (Constant), LE04, OIValues2, GFCF2, PGR1, PCNSDP2, LR2

c. Predictors: (Constant), LE04, GFCF2, PGR1, PCNSDP2, LR2

d. Predictors: (Constant), GFCF2, PGR1, PCNSDP2, LR2

e. Predictors: (Constant), GFCF2, PCNSDP2, LR2

In table no. 5, all of the four regression models are fit with significant value .000. But, it is clear that F-value has increased in each subsequent model and is the highest for the last model at 54.733.

 

Table6: Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-66949.649

39248.095

 

-1.706

.126

PCNSDP2

1.616

.378

.575

4.273

.003

OIValues2

246.338

562.966

.038

.438

.673

PGR1

469.062

456.822

.131

1.027

.335

GFCF2

.234

.072

.317

3.232

.012

LR2

488.621

262.390

.281

1.862

.100

LE04

480.733

580.692

.106

.828

.432

2

(Constant)

-63584.587

36717.834

 

-1.732

.117

PCNSDP2

1.614

.361

.574

4.473

.002

PGR1

464.928

435.727

.130

1.067

.314

GFCF2

.226

.067

.306

3.384

.008

LR2

484.049

250.128

.278

1.935

.085

LE04

469.117

553.416

.104

.848

.419

3

(Constant)

-37305.947

19397.188

 

-1.923

.083

PCNSDP2

1.749

.319

.622

5.480

.000

PGR1

401.105

423.090

.112

.948

.365

GFCF2

.228

.066

.309

3.465

.006

LR2

526.909

241.493

.303

2.182

.054

4

(Constant)

-21281.329

9471.430

 

-2.247

.046

PCNSDP2

1.731

.317

.616

5.459

.000

GFCF2

.250

.061

.338

4.067

.002

LR2

379.895

184.272

.218

2.062

.064

a. Dependent Variable: PCNSDP12

 
It is clear from above that it is only in the last model that all the explanatory variables have significant coefficients. Not only this, coefficient of literacy rate has gained on significance. Now it is clearly significant at 10 percent.
 
Table7: Excluded Variablesa

 

Model

Beta In

t

Sig.

Partial Correlation

Collinearity Statistics

 

Tolerance

 

2

OIValues2

.038b

.438

.673

.153

.872

 

3

OIValues2

.035c

.407

.693

.135

.874

 

LE04

.104c

.848

.419

.272

.397

 

4

OIValues2

.034d

.399

.698

.125

.874

 

LE04

.081d

.669

.519

.207

.409

 

PGR1

.112d

.948

.365

.287

.411

 

a. Dependent Variable: PCNSDP12

 

b. Predictors in the Model: (Constant), LE04, GFCF2, PGR1, PCNSDP2, LR2

 

c. Predictors in the Model: (Constant), GFCF2, PGR1, PCNSDP2, LR2

 

d. Predictors in the Model: (Constant), GFCF2, PCNSDP2, LR2

 

 

 

5.      Conclusion

It becomes evident from above analysis that out of all the explanatory variables considered in the multiple regression model having per capita net state domestic product (2012-13) as the dependent variable, coefficients of only per capita net state domestic product (2002-03), gross fixed capital formation and literacy rate were found to be significant. But, life expectancy, openness values and population growth rates were not found to be significant. Thus, it can be said that economic growth in Indian states is affected by growth of both physical capital as well as human capital. This outcome has important policy implications. These variables can further be studied for inter regional variations to locate the causes of regional disparity in economic growth, which is taking severe forms and is hampering the development of the country as a whole.

 

References

Health, and Fertility: Convergence Puzzle, Economic Survey (20016-17).

Kalirajan, K., Bhide, S., & Singh, K. (2009). Development Performance across Indian States and  The Role of the Governments. ASARC Working Paper 2009

(Retrieved from: https://crawford.anu.edu.au/acde/asarc/pdf/papers/2009/WP2009_05.pdf)

Kumar, U., & Subramanian, A. (2012). Growth in India’s States in the First Decade of the 21st Century: Four Facts. Economic & Political Weekly, 40 (3), 48-57.

State Wise Data (2017)

(Retrieved from: http://www.esopb.gov.in/Static/PDF/GSDP/Statewise-Data/StateWiseData.pdf)

The Economic Times (2017). India’s growth depends on states. (26 January, 2017)

Websites:

 

https://rbi.org.in/scripts/AnnualPublications.aspx?head=Handbook%20of%20Statistics%20on%20Indian%20States

 

http://niti.gov.in/state-statistics

 

 

 

APPENDIX

(Data Table)

States

PCNSDP (2012-13)

PCNSDP (2002-03)

Openness Index Values of Indian States (2002-03)

Decadal Growth Rate of Population (2001-11)

GFCF (2002-03) (Million Rs.)

Literacy Rates (2001)

Life Expectancy (2000-04)

Andhra Pradesh

39645

17340

10

10.98

30081.3

60.47

64.6

Assam

22273

13072

12.5

17.07

4118.2

63.25

58.8

Bihar

14356

6658

11.5

25.04

16411.0

47.00

64.1

Gujarat

59157

19509

8

19.28

64003.8

69.14

65.6

Haryana

64052

26748

9.5

19.90

25942.2

67.91

66.1

Karnataka

43266

18115

7.5

15.60

30994.9

60.47

65.8

Kerala

55643

21944

10.5

4.91

6471.8

90.86

73.2

Madhya Pradesh

24867

10880

7.5

20.35

10368.1

63.74

59.3

Maharashtra

65095

23447

8.5

15.99

78486.6

76.88

67.5

Odisha

25163

10500

7

14.05

14361.9

63.08

60.4

Punjab

47854

25955

5

13.89

23661.7

69.65

68.3

Rajasthan

30839

12054

5.5

21.31

11257.3

60.41

64.1

Tamil Nadu

58360

19662

2.5

15.61

70028.9

73.45

66.7

Uttar Pradesh

18635

9806

9

20.23

29690.0

56.27

60.5

West Bengal

34177

17568

5.5

13.84

15173.3

68.64

66.8

Source: rbi.org.in & niti.gov.in                                  

(Abbreviations for Variables)

Per capita net state domestic product (2012-13)

PCNSDP12

Per capita net state domestic product (2002-03)

PCNSDP2

Openness Values of Indian States (2002-03)

OIValues2

Life Expectancy

LE04

Literacy Rates

LR2

Gross Fixed Capital Formation (2002-03)

GFCF2

Decadal Population Growth Rate (2001-11)

PGR1