Assessing Policy Initiatives to Accelerate Economic Growth: An Illustration Using a Macroeconometric Model for India

Murty, K. N.

doi:10.1007/978-981-19-0185-0_7

K. N. Murty⁴

Part of the book series: India Studies in Business and Economics ((ISBE))

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Abstract

This paper attempts to assess the effects of devaluation, demonetization and demand management through public investment (3Ds) on Indian economy using a macroeconometric model. The model is estimated using annual time series data for 1985–86 to 2009–10 at 2004–05 prices. A 30% devaluation of India-US bilateral nominal exchange rate re-confirmed the inverse ‘J-curve’ hypothesis for India. Similarly, the demonetization scenario consisting of 5% reduction in reserve money, 50% increase in deposits with commercial banks and 30% increase in direct taxes seems to suggest 5.3% decline in nominal GDP, but 0.3% increase in real GDP in 1991–92. In a third scenario, Rs. 30,000 crore increase in real public investment in conjunction with devaluation, demonetization and reduction in corporate tax (3Ds) seems to suggest quite beneficial impacts in the Indian economy. Due to shock-nature of these policy measures, the dynamic effects are found to dampen and vanish over time, confirming the long-run dynamic stability of the model. Since the financial sector has strong linkages with real sector, the former also contributed to these observed effects. It may be mentioned that this study is not meant to predict growth scenario, but it is methodological in nature to assess a given policy option to improve growth acceleration in India. In all, this study suggests that it is possible to stimulate real economic growth to a desired level with appropriate monetary and fiscal initiatives.

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Notes

1.
The preliminary estimates of GDP growth in the economy after ‘demonetization’ seem to conform this.
2.
A comprehensive review of macroeconometric models and policy modeling for India can be found in Klein and Palanivel (1999), Radhakrishna et al. (2001), Krishnamurty (2002), Pandit and Krishnamurty (2004) and Bhattacharya and Kar (2005). A good review of monetary sector models was provided by Jadhav (1990). There has also been some published work by the author earlier (e.g., Murty & Soumya, 2007, 2011) on macroeconometric model for India, with similar methodology. The bibliography given here is somewhat dated and needs more recent references.
3.
In present day research, requirement of stationarity of variables in regression equations is a much debated issue. However, this issue is somewhat side stepped in macroeconometric model estimation, because capturing the structure of the economy in level variables and interpretation of the coefficients are considered more important than correcting for non-stationarity of the underlying variables. Further, despite non-stationarity of variables, in simultaneous equation systems, the 2SLS but not the OLS estimators is shown to produce consistent estimates (see, e.g., Hisio, 1997). Since we use 3SLS method, the parameter estimates will be consistent and asymptotically efficient. The author is grateful to Professor V. N. Pandit for bringing this literature to his notice.
4.
Given the shortness of annual time series data in systems estimation context, estimating regime specific models to address the well-known Lucas critique is somewhat impracticable. For the same reason, testing for stability of coefficients across policy regimes may not be possible either.
5.
Despite the best efforts to get economically meaningful signs for all coefficients and ‘good’ overall measures of goodness of fit, there are still some worrisome aspects like inappropriate Durbin-Watson statistic for quite some equations. Re-estimation of the model with longer time series may be needed.
6.
In order to assess the symmetry of impacts for each exogenous variable, both an increase and a decrease in that variable have been attempted separately. It is found that the impacts are numerically identical except for the sign change. Further, the simulation impacts due to different exogenous variables are also fully additive.
7.
Although, ‘devaluation’ was a bygone policy measure, whose effects were already debated and documented, it is being revisited here for comparative analysis with two other policy instruments, ‘demonetization’ and ‘public investment’ using a common macro econometric model framework.
8.
Thus, this study seems to re-confirm the presence of inverse ‘J-curve’ in India.
9.
As per RBI annual report for 2016–17, the Central Bank had issued Rs. 15.44 lakh crores worth of demonetized currency over time. This constituted 87% of total currency in circulation. Post-demonetization, Rs. 15.28 lakh crores, nearly 99% of total demonetized currency has been returned to the Central Bank. It is reported that old notes worth only Rs. 5,000 crores have not been returned back, which probably constitute ‘black money’ and or ‘fake’ currency.
10.
There has been lot of debate in India since its implementation on the efficacy and impact of ‘demonetization’. In particular, its adverse effect on small-scale industry, petty trade as well as wage earners was much in focus. There were also serious doubts on its modus-operandi, particularly the manipulation made by bankers and big players. Clearly, an aggregate macro model like the present one cannot capture these complex issues.
11.
Post-demonetization, recent estimates show that the direct tax collections have increased by 17.5% in the first quarter of FY2017-18.
12.
Despite some possible economic benefits, critics contend that the short-run costs of demonetization totally outweigh the long-run benefits, if any (among others, e.g., Dr. Raghuram Rajan, former Governor, RBI).

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Acknowledgements

Revised version of an ‘Invited paper’ presented at the 6th Annual Conference on ‘Globalization at Crossroads’ by SIMSR, Mumbai, during October 11–12, 2019. I would like to thank the Director, CESS, Hyderabad, for permitting me to update the data-set while I was associated with them on a different project. I would like to thank Professors V. N. Pandit and R. Radhakrishna for their incisive comments in improving this paper. But, I only am responsible for the remaining errors, if any.

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School of Economics, University of Hyderabad, Hyderabad, India
K. N. Murty

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K. N. Murty
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Editors and Affiliations

Institute for Human Development, New Delhi, Delhi, India
Saiyed Raza Hashim
South Asia Institute (SAI), Heidelberg University, Heidelberg, Baden-Württemberg, Germany
Rahul Mukherji
ICFAI Business School, Hyderabad, Telangana, India
Brajaraja Mishra

Appendices

Appendix 1

See Fig. 1.

Appendix 2

See Tables 1 and 2.

Table 1 Annual average compound growth rate (%) of important variables used in the model

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Table 2 Annual average for important variables

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Appendix 3

Estimated model: Period: 1985–86 to 2009–10 Method: 3SLS

Real sector:

Production:

Real GDP:

1. YR	−	35.114	+	0.6097 ADD	+	0.0639 KACR_-1	+	3.9023 MFLIMPTR_-1
		(2.17)		(18.41)		(4.18)		(8.63)
EL	:			0.65		0.18		0.16
							–	0.1054 AR (1)
								(−0.77)
				R² = 0.999		DW = 2.34

Capital formation:

Real gross investment: private.

2. PITOTR	=	−273.506	+	0.3277 YR	+	0.5743 PCFTOTR
		(−10.25)		(16.68)		(3.02)
EL	:			1.52		0.25
	–	0.3685 (RGPUB/PGKE)	–	5.5376 (PLR₋₁–INFL₋₁)	+	0.3699 AR(1)
		(−4.37)		(−5.66)		(2.94)
EL	:	−0.13		−0.07
		R² = 0.987		DW = 1.92

Adjusted total investment: private.

3. PIADJR	=	−15.7076	+	1.0633 PITOTR	–	0.3031 AR(1)
		(−2.80)		(110.35)		(−1.71)
EL	:			1.03
				R² = 0.996		DW = 1.48

Real consumption: private.

4. PVCR/NTOT	=	0.1338	+	0.3807 (PYDR/NTOT)	+	0.4356 (PVCR₋₁/NTOT₋₁)
		(3.18)		(6.15)		(4.28)
SR EL	:			0.42
LR EL	:			0.86
			+	0.0448 D81t04	+	0.447 AR (1)
				(3.82)		(2.94)
				R² = 0.997		DW = 1.80

Real depreciation:

5. DEPAC	=	−89.5902	+	0.0585 KACR_-1	–	0.3890 AR(1)
		(−4.91)		(18.09)		(−4.08)
EL	:			1.36
				R² = 0.741		DW = 1.09

Price behavior:

Wholesale price index:

6. PGDP	=	0.4136	+	0.7656 (M3 /YR)	+	0.0005 D81t96	+	0.8855 AR(1)
		(5.69)		(13.57)		(0.04)		(25.32)
EL	:			0.53
				R² = 0.997		DW = 1.08

7. P	=	0.0079	+	0.9761 PGDP	+	0.6104 AR(1)
		(0.65)		(67.49)		(6.29)
EL	:			0.99
				R² = 0.998		DW = 1.17

Implicit price deflators:

Gross investment: public.

8. PGKE	=	−0.0464	+	0.8475 P	+	0.0072 TREND	+	0.3815 AR(1)
		(−2.57)		(11.58)		(2.27)		(3.00)
EL	:			0.85
				R² = 0.998		DW = 1.20

Gross investment: private.

9. PPIE	=	0.0558	+	0.9364 P	+	0.4180 AR(1)
		(6.48)		(89.49)		(4.29)
EL	:			0.92
				R² = 0.999		DW = 1.98

Fiscal sector:

Revenue from direct taxes of (ADORC) (nominal).

10. DT	=	−46.5644	+	(0.0821	+	0.0058 D81t02) (k3T*Y)	+	0.8861 AR(1)
		(−1.36)		(9.30)		(1.40)		(10.55)
EL	:					1.72
						R² = 0.988		DW = 1.68

Revenue from indirect taxes (ADORC) (nominal).

11. IDT	=	17.1515	+	0.1004 YM	+	15.8325 D81t02	+	0.1877 AR(1)
		(2.33)		(28.49)		(1.54)		(0.89)
EL	:			0.92
						R² = 0.988		DW = 0.87

Non-tax revenue (ADORC) (nominal).

12. NTX	=	−24.6338	+	0.0463 Y	–	14.2886 D81t97
		(−6.58)		(25.35)		(−3.34)
EL	:			1.55
				R² = 0.968		DW = 0.38

Govt. final consumption expenditure (ADORC) (nominal).

13. GFCE	=	19.9803	+	0.4726 TR	+	4.5751 D81t00
		(2.12)		(21.73)		(0.38)
EL	:			0.88
				R² = 0.962		DW = 0.72

14. GFD	=	0.7893 RGPUB	+	1.1066 AR(1)
		(22.84)		(19.59)
EL	:	0.71
		R² = 0.981

Monetary sector:

Money supply (nominal).

15. M₃	=	16.199	+	4.8102 RM	−	15.0075 CRR	−	0.0527 TREND²	+	0.0251 AR(1)
		(0.22)		(31.65)		(−3.47)		(0.33)		(0.12)
EL	:			1.08		−0.11
				R² = 0.995		DW = 1.79

External sector:

Real exports.

16. EXPTR	=	−221.238	−	69.847 (UVEXP/WPEXP)	+	0.003 WYR	+	0.6993 EXPTR₋₁
		(−4.02)		(−2.70)		(4.03)		(7.99)
SR EL	:			−0.23		1.53
LR EL	:			−0.76		5.09
				R² = 0.993		DW = 2.19

Unit value of exports.

17. UVEXP	=	1.842	+	0.004 EXPTR	−	0.195 (WPEXP/P)	−	0.000012 WYR
		(2.74)		(4.56)		(−2.70)		(−2.15)
EL	:			1.16		−0.41		−2.27
							+	0.558 AR(1)
								(3.91)
				R² = 0.966		DW = 1.65

Real imports.

18. IMPTR	=	0.031 AD	−	0.776 ((UVIMP*(EXR + TARRT)/P)	+	4.619 (RBFA/EXR)
		(2.01)		(−0.93)		(3.28)
SR EL	:	0.24		−0.07		0.10
LR EL	:	1.34		−0.39		0.54
			–	0.817 IMPTR_-1	–	0.353AR(1)
				(7.25)		(−1.77)
				R² = 0.988		DW = 2.19

Poverty ratios:

Head count ratio: rural.

19. HCRRUR	=	53.368	−	5.816 (PYDR/NTOT)
		(39.36)		(−8.77)
EL	:			−0.27
		R² = 0.732		DW = 2.30

Head count ratio: urban.

20. HCRURB	=	−1.747 (PYDR/NTOT)	+	0.982 AR(1)
		(−1.59)		(220.59)
EL	:	−0.11
		R² = 0.992		DW = 3.03

Financial sector: Period: 1990–91 to 2009–10 Method: 3SLS.

Prime lending rate.

21. PLR	=	18.0665	−	0.0008 YR	−	0.0045 M3	−	0.1245 INFL
		(4.64)		(−0.35)		(−2.54)		(−1.24)
EL	:			−0.16		−0.64		−0.07
			+	194.8659 DEBTSEC	−	0.3815 81t01	+	0.1107 AR(1)
				(6.29)		(−0.42)		(0.69)
EL	:			0.34
				R² = 0.764		DW = 2.2

22. LOANS	=	−311.3818	+	0.7711 DEPOSITS	+	15.6948 PLR	+	0.7045 AR(1)
		(−3.48)		(34.95)		(3.06)		(3.44)
EL	:			1.1		0.18
				R² = 0.997		DW = 1.94

23. SENSEX	=	3.5948	+	0.0807 PLR₋₁	+	0.0028 LOANS	+	0.0002 MERGACQ₋₁
EL	:	(3.07)		(3.9)		(7.59)		(1.29)
				1.01		2.84		0.04
			+	0.007 FII₋₁	–	0.0433 DEBTGDPRATIO	+	0.7382 (M3/YR)
				(2.57)		(−5.77)		(1.01)
EL	:			0.16		−3.16		0.43
			+	0.0004 TURNOVERRATIO	–	0.043 FDI	–	12.1055 ((EXPT + IMPT)/Y)
				(0.69)		(−6.02)		(−2.7)
EL	:			0.04		−0.92		−2.91
			–	0.6324 AR(1)
				(−2.5)
				R² = 0.78		DW = 1.57

24. EXR	=	58.3978	+	0.0271 PLR₋₁	−	0.0207 CAB	−	3.7639 SENSEX
		(6.73)		(0.21)		(−1.7)		(−3.3)
EL	:			0.01		−0.02		−0.10
			+	1.9326 (UVEXP/UVIMP)	−	1.4013 D81t91	+	0.8983 AR(1)
				(0.55)		(−0.9)		(24.94)
EL	:			0.06
				R² = 0.912		DW = 1.95

25. MKTCAPTGDP	=	3.2001	+	53.0598 SENSEX	+	0.0586 FII	−	0.6017 FDI
		(1.52)		(15.31)		(1.35)		(−6.87)
EL	:			1.2		0.03		−0.29
							−	0.5396 AR (1)
								(−2.9)
				R² = 0.908		DW = 1.89

The t-ratios are given in parenthesis below the respective coefficient. For important variables, the short- and long-run mean partial elasticities also given below the t-ratios

Identities:

26. absp = pvcr + piadjr

27. add = absp + (gfce/p) + pcftotr + exptr − imptr

28. ad = add + imptr

29. pyd = ym − tr + sub + otp

30. pydr = pyd/pgdp

31. infl = (p − p(−1)) * 100/p(−1)

32. y = pgdp * yr

33. ym = y + idt − sub

34. kacr = kacr(−1) + pitotr + pcftotr − depac

35. pcftot = pcftotr * pgke

36. gcfadj = (piadjr * ppie) + pcftot + em2

37. gds = gcfadj + cab + em3

38. gce = gfce + sub + ogce

39. gxp = gce + pcftot

40. tr = dt + idt + ntx

41. gdsadorc = tr − gce

42. gdspub = gdsadorc + gdsrcndqg

43. rgpub = pcftot − gdspub

44. rm = rcg + rbcs + rbfa + gcl + rbcb − rbnml

45. d (rcg) = gfd − d (bcg) − dnb − eb − miscr

46. expt = exptr * uvexp

47. impt = imptr * uvimp

48. tb = expt − impt

49. tbbop = k1t * expt − k2t * impt

50. cab = tbbop + invsb

51. rbfa = rbfa(−1) + cab + ncif

Description of variables:

Data source: National Account Statistics (NAS) compiled by Central Statistical Organization, Government of India.

Endogenous variables (Rs. ‘000 crores):

Data source: Reserve Bank of India (RBI).

Endogenous variables (Rs. ‘000 crores):

AD::: Real aggregate absorption

ADD::: Real aggregate demand for domestically produced goods

CAB::: Current account balance

EXPT::: Exports (merchandise, DGCI&S)

EXPTR::: Real exports (DGCI&S)

EXR::: Exchange rate of Indian rupee against US (nominal, Rs. /)

IMPT::: Imports (merchandise, DGCI&S)

IMPTR::: Real imports (DGCI&S)

LOANS::: Commercial bank loans to private sector

MKTCAPTGDP::: Market capitalization to GDP (%)

M3::: Money supply

P::: Wholesale price index

INFL::: Rate of inflation

PLR::: Prime lending rate

RBFA::: Net foreign exchange assets of RBI

RCG::: Reserve bank credit to the government

RM::: Reserve money

SENSEX::: BSE Sensex (2004–05 = 1.0)

TB::: Trade balance (DGCI & S)

TBBOP::: Trade balance (Merchandise)

UVEXP::: Unit value of exports

Exogenous variables (Rs. ‘000 crores):

BCG::: Commercial bank credit to government

CRR::: Cash reserve ratio

DEBTGDPRATIO::: Debt to GDP ratio

DEBTSEC::: Debt securities

DEPOSITS::: Aggregate deposits with commercial banks

DNB::: Non-market borrowings of both central and state governments

EB::: External borrowings by the government

FDI::: Foreign direct investment in India

FII::: Foreign institutional investment in India

GCL::: Government’s currency liabilities to public

GFD::: Gross fiscal deficit of both central and state governments

INVSB::: Invisibles in current account balance

K1T::: Export share of trade balance in BOP

K2T::: Import share of trade balance in BOP

K3T::: Share of non-agricultural income in GDP

MERGACQ::: Merger and acquisitions

MFLIMPTR::: Real imports of mineral fuel and lubricants

MISCR::: Other capital receipts of the government

NCIF::: Net capital inflows including net capital account in the balance of payments and errors and omissions

RBCB::: Reserve bank credit to commercial banks

RBCS::: RBI credit to the commercial sector

RBNML::: Reserve bank non-monetary liabilities

TARRT::: Average tariff rate

TRADE::: Extent of total trade (exports + imports)

TURNOVERRATIO::: Turnover ratio

UVIMP::: Unit value of imports

WPEXP::: World export price index (IFS)

WYR::: Real world income (IFS)

Data source: generated by the author.

D81t96: Dummy variable for 1981–96

D81t97: Dummy variable for 1981–97

D81t00: Dummy variable for 1981–2000

D81t02: Dummy variable for 1981–2002

EM2: Errors and omissions in gross capital formation between adjusted and un-adjusted by using sectors

EM3: Errors and omissions in gross domestic savings

TREND: Time trend

Note: Variables which are specified as ‘real’ are in 2004–05 prices, and all others are nominal, i.e., in current prices. All indices are with 2004–05 value as unity.

Appendix 4

See Figs. 2, 3, 4 and 5.

Appendix 5

Tables 3, 4, 5 and 6.

Table 3 Immediate impacts of alternative policy simulations in India

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Table 4 Short-run (after 3 years) impacts of alternative policy simulations in India

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Table 5 Medium-term (after 10 years) impacts of alternative policy simulations in India

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Table 6 Long-term (after 18 years) impacts of alternative policy simulations in India

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Murty, K.N. (2022). Assessing Policy Initiatives to Accelerate Economic Growth: An Illustration Using a Macroeconometric Model for India. In: Hashim, S.R., Mukherji, R., Mishra, B. (eds) Perspectives on Inclusive Policies for Development in India. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-19-0185-0_7

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