Abstract
Investment is universally acknowledged as one of the chief drivers of economic growth; private corporate investment is a key component of aggregate demand as well as an important driver of productive capacity, on the supply side. In India, aggregate investments touched a peak of nearly 36% of GDP in 2007–2008, declining to 34% in 2012 and further to around 28% of GDP in 2016. Even at the current conjuncture, lacklustre recovery in Indian business investments remains puzzling, especially with recovery well under way in the global economy. This paper attempts to analyse the slowdown in private corporate investments in India in the post-2008 crisis phase, taking into account both demand as well as supply-side factors. The first part of the paper attempts to identify the main determinants of business investments by drawing on economic theory and relevant literature in the Indian context. Thereafter, an expository data analysis is carried out on key correlates of corporate investments in India with a view to identifying emerging trends especially since the middle of the 2000s decade. A simple empirical model is also estimated, using annual aggregate data for the period 1995–96 to 2016–17, in an attempt to confirm some of the insights emerging from the literature. Results indicate that uncertainty in the overall macroeconomic and business environment, demand-side factors, especially external demand and real interest rates, and the pace of public investments plays an important role in affecting private business investments in India.
We are deeply honoured to be able to contribute to this volume in honour of Professor Sarmila Banerjee, who is a very fine economist, a wonderful human being and a superb teacher. She has been an inspiration to generations of students. We would like to thank the reviewers for their valuable comments and suggestions and the editors for providing this opportunity. The usual disclaimer applies.
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Notes
- 1.
A recent study by the RBI showed a steady decline in phased capital expenditure plans of the private corporate sector in absolute terms from 2010–11 down to 2017–18 (reported in The Indian Express, dated 13 March 2019).
- 2.
Analysis of firm-level data does indicate signs of a turnaround in net sales of listed firms since the last quarter of 2017 after a long period poor performance (quarter on quarter growth in net sales of listed firms were either negative or below 1%, from 2011 up to the third quarter of 2017) (Patnaik 2019). However, it is too early to say to what extent this would usher in a pickup in business investments.
- 3.
- 4.
See Goldar (1997) for a survey of early studies on investment demand function estimation for India.
- 5.
See Dastidar (2015) for the argument that this episode is better described as ‘export induced’ growth since it resulted from favourable world market conditions, rather than from directed state policies as in case of the East Asian episode of ‘export-led’ growth.
- 6.
The repo rate touched a high of 8.5% in the third quarter of 2011–12 (Rakshit 2016).
- 7.
The following discussion draws heavily on Rakshit (2016).
- 8.
Till 2014, 60% of stalled projects were in infrastructure (Rakshit 2016).
- 9.
Sen and Dasgupta’s (2018) analysis of firm-level data shows the declining importance of equity finance, stagnant reserves (especially since 2008) and increasing importance of borrowings, with a rising share of overseas credit among Indian private (non-financial) firms over the 2000s decade. There is even evidence of Indian firms taking on more debt to meet interest liabilities on a significant scale.
- 10.
Despite comparability issues of the new GDP series (2011–12 base year) with previous 2004–05 data, the trends based on the spliced data reveal a few interesting insights. Without splicing the two series, it is not possible to generate a continuous time series data including the period post-2012–13. While the exact values in the spliced series differ from the 2004–05 series, the basic pattern regarding year-on-year growth is the same. Also refer to Footnote 17 in this regard.
- 11.
From the third quarter of 2016–17 to the first quarter of 2017–18, quarterly GDP growth slipped below 7%, growing at 6.8, 6.1 and 5.6% respectively over these three quarters that witnessed major policy shocks by way of demonetisation (in November, 2016) and introduction of the GST regime (in July, 2017) (Ahmad et al. 2018).
- 12.
Quarterly data is available only on total gross fixed capital formation, which includes investments by the government, private businesses as well as households. This is used by Anand and Tulin (2014).
- 13.
Similar results are obtained by using the mean value as the cut-off for both the series.
- 14.
Details of the test for endogeneity following 2SLS are as follows:
Value of the HAC score chi2(1) test statistic = 0.85918 (p-value = 0.3540).
Optimal GMM method was also used, and details of the test for endogeneity following optimal GMM are as follows: Value of the GMM C statistic chi2(1) = 0.00615 (p-value = 0.9375).
The high p-values (based on HAC robust standard errors) indicated the null hypothesis that the regressor is exogenous could not be rejected.
- 15.
The null hypothesis is that sum of the coefficients of GDP growth and its lag is zero; and the value of the test statistic, based on t-distribution (using Newey–West HAC standard errors): (a) in Model 1 was 2.57, statistically significant at 11% level; (b) in Model 2 was 2.71, statistically significant at 10.3% level. This provides limited support for the hypothesis that a permanent increase in GDP growth rate by 1 percentage point, ceteris paribus, is associated with more than 2.5 percentage point increase in private investment growth after one year.
- 16.
The estimated coefficient of growth in government consumption is −5.49 with p-value of 0.184 when it is included in Model 1.
- 17.
Ahmad et al. (2018) splice the 2004–05 and 2011–12 series backward using the following back-casting method that maintains a growth rate in the new series that is consistent with the old series so that the resulting spliced series is essentially a level shift to the old series with equivalent growth rates:
For the variable Xt that needs to be spliced, the new series is denoted as X* and X is the variable value in the old series. With data in the new series starting from period t, the value of X*t−1 is calculated as X*t−1 = (Xt−1/Xt)X*t−1.
- 18.
This is in line with the methodology adopted in RBI, KLEMS India Project (https://rbi.org.in/Scripts/PublicationReportDetails.aspx?UrlPage=&ID=894).
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Appendix
Appendix
The data on the real interest rate is accessed from the World Development Indicators (WDI), 2018. For the remaining variables, i.e. Real GFCF in the private corporate sector, Real GFCF in the public sector, Real GDP, Real exports (in Rupees), NAS data is accessed through the CEIC India Database.
For real GDP at 2011–2012 prices, we spliced the two GDP series at different base years, i.e. 2004–2005 and 2011–2012, to get one long GDP series at 2011–2012 prices. There are methodological differences between the 2011–12 and 2004–05 aggregate macroeconomic series, and therefore, to check the consistency of the data, the growth rates in the series generated through simple splicing were compared to the methodology used in Ahmad et al. (2018) and the results were very similar.Footnote 17 Although this method is imperfect, there are no other options for generating a long enough time series for carrying out an estimation exercise. Similarly, the data series on real public gross fixed capital formation, available in 2011–12, 2004–05, 1999–2000 and 1993–94 base years and on real exports (in rupees) available in 2004–05 and 2011–12 base years, were spliced to generate a single series at 2011–12 prices. These series were used to calculate year-on-year growth in each of the variables for the period 1989–2017. However, estimation could be carried out only for the sub-period 1995–96 to 2016–17 owing to limitation related to the measure of business expectations.
The measure of business expectation was calculated based on monthly data on the CEIC Leading Indicator Index available from November 1995 onwards. Standard deviation in this index, calculated for each fiscal year (from April, current year to March, next year), is taken as a measure of uncertainty in the business and overall macroeconomic environment. A high value of this variable indicates greater uncertainty with potentially negative impact on business expectations.
The export growth dummy takes the value one for the years when export growth was higher than 11% (its median value during this period). The public investment growth dummy takes the value one for the years when growth in public investment was higher than 4.5% (its median value during this period).
The user cost of capital (UC) is calculated as UC = external nominal rate of return + nominal depreciation (i.e. real rate of return * investment price t − 1 + depreciation rate * investment price t).Footnote 18 Nominal depreciation was estimated by calculating capital consumption allowance as a percentage of capital stock and the implicit deflator for GFCF was used to denote investment price—all NAS data accessed from CEIC database.
The table below shows all the growth series are stationary at standard levels of significance, except for real interest rates and user cost, which were therefore first-differenced to render them stationary.
Variable | ADF test of H0: I(1) versus H1: I(0) | Whether I(0) or I(1) |
|---|---|---|
Pvt_Inv_Gr | −3.595*** (0.0059) | I(0) |
R_GDP_Gr | −4.100*** (0.0010) | I(0) |
LI_SD | −3.500*** (0.0080) | I(0) |
R_Int | −2.319 (0.1658) | I(1) |
R_Int_Ch | −7.370*** (0.0000) | I(0) |
UC | −1.126 (0.7045) | I(1) |
UC_Ch | −3.235** (0.0181) | I(0) |
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Dastidar, A.G., Ahuja, R. (2019). A Perspective on the Slowdown in Private Corporate Investments in India. In: Bandyopadhyay, S., Dutta, M. (eds) Opportunities and Challenges in Development. Springer, Singapore. https://doi.org/10.1007/978-981-13-9981-7_3
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