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Investment, Uncertainty and Credit Market Imperfection in India

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Abstract

The paper attempts to make a timely contribution to the debate on the status of business fixed investments in Indian private manufacturing firms. There are two key issues on which the debate hinges: lower presence of formal credit and, procedural and contractual rigidities. Lower presence of formal credit restricts or makes it costlier for a group of firms to incur investment expenditure that they would have incurred otherwise. Such firms predominantly rely on their internal funds for investment. Procedural and contractual rigidities, on the other hand, make almost all the investment projects undertaken, partially or completely, irreversible. Firms respond to such irreversibilities by aligning their investment to a relatively favorable time which, in turn, depends on the way firms process future uncertainty. The analytical exercise endogenously distinguishes between two investment regimes based on the access to external credit and uses a set of characteristics, along with different measures of uncertainty, to explain fluctuations in investment. The results provide three important observations. First, in the post-reform period there has been an adverse shift in the investment financing policy. Second, firms with inferior access to external credit are smaller, younger, pay less dividend, export less and belong to an industry with inferior demand than others. Such firms invest by running down their available cash flows and selling assets. Third, macroeconomic uncertainty depresses investment whereas microeconomic uncertainty has no impact on investment.

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Notes

  1. Besides the trade-off between non-performing assets (NPA) and lending portfolio of banks, the role of covenants and their violations are also important in extension of formal credit to the private sector. The presence of covenants in financial contracts is motivated by their ability to mitigate agency problems. Their violations, in general, lead to transfer of control rights which can impact investment (see, for example, Chava and Roberts 2008). Due to data unavailability on such covenants, we are unable to include them in our purview. Also note that non-banking financial companies (NBFC) alongside banks and stock markets also lend to the businesses. Due to lack of time series data on NBFC loan portfolio, we restrict out discussion to banks and stock markets only.

  2. In 1985, the first ever system of classification of assets for the Indian banking system was introduced on the recommendations of Ghosh Committee on final accounts called the ‘Health Code System’. It involved classification of bank advances into eight categories ranging from 1 (satisfactory) to 8 (bad and doubtful debt). In 1991, the Narasimhan Committee on the financial system suggested that banks should classify their advances into four broad groups, viz. (i) standard assets; (ii) substandard assets; (iii) doubtful assets; (iv) loss assets. In 1998, the Narasimhan Committee on Banking Sector Reforms recommended a further tightening of prudential standards in order to bring them at par with the international best practices. As a consequence, a 90-days norm for classification of NPAs was introduced in 2001.

  3. Priority sector lending program started in early 1970s to disburse credit to those key sectors which were unable to get adequate institutional credit due to lower creditworthiness. Currently, besides the four sectors mentioned above, priority sectors also include medium scale enterprises, education, housing, social infrastructure and renewable energy. However, the share of priority sector still remains 40 percent of ANBC (defined in footnote 4). For further details See RBI circular on “Priority Sector Lending-Targets and Classification” dated April 23, 2015.

  4. ANBC is Bank Credit in India minus bills rediscounted with Reserve Bank of India (RBI) and other approved financial institutions plus investments eligible to be treated as priority sector minus exemptions on issuance of long-term bonds for infrastructure and affordable housing minus CRR/SLR exempted advances extended in India against the incremental foreign currency non-resident deposits to non-resident rupee account ratio.

  5. Gross bank credit here does not include credit for food business and credit for exporting.

  6. For further details see, RBI Master Circular on “Priority Sector Lending”, July 01, 2014.

  7. The proportion is compiled from Love and Peria (2005), Allen et al. (2012) and the sample used in this study.

  8. Data for Table 1 is availed from World Bank.

  9. For literature survey on investment under uncertainty see Carruth et al. (2000) and Lensink et al. (2001) and; for investment under credit market imperfection see Hubbard (1998), Lensink et al. (2001) and Calcagnini and Saltari (2010).

  10. The selection equation in (3) is akin to a probit model where variance of \(\varepsilon _{it} \) is normalised to 1 to identify the model.

  11. The second equality represents joint density as the product of conditional density and marginal density. For details see Maddala (1983).

  12. The dummy specification of D is overwritten in estimating endogenous regime switching model based on a numerical maximisation technique.

  13. Following Salinger and Summers (1983), we use perpetual inventory stock method for constructing the replacement value of capital stock.

  14. The use of ICFS to identify financially constrained firms was first suggested by Fazzari et al. (1988).

  15. These results are omitted to save space. They can be furnished on request.

  16. These results are omitted to save space. They can be furnished on request.

  17. The Government of India, in a phased way, started replacement of automatic deficit financing role of ad-hoc treasury bills with a system of ways and means advances only from 1997–1998. Moreover, high and compulsory SLR (Statutory Liquidity Ratio) holding requirement by Indian banks also ensured a captive market for such securities. For instance, in 1997–1998, banks had invested in excess of 25 % in such securities (Darbha et al. 2003). Since our sample period starts from 1994, method 4 is of limited use.

  18. We evaluate 300 one-step-ahead forecasts using a rolling window of 1000 observations for the mean and the variance equation. The forecasts we obtain are evaluated using five different measures: mean squared error, median squared error, mean absolute error, adjusted mean absolute percentage error and Theil’s inequality coefficient. Our model is consistent with several other works that find support for APARCH model and its ability to capture properties like, fat-tails, persistence in volatility, asymmetry and leverage effect (e.g., Laurent 2004).

  19. The use of stock prices is also a potential candidate for constructing a measure of microeconomic uncertainty. However, a large number of firms are unlisted in our sample restricting the use of stock prices for our purpose.

  20. Choice of top four-firm market share dispersion is ad-hoc. We also used top three-firm and top five-firm market share dispersion in alternative setups. The results remain consistent.

  21. These arguments can be contested if firms face different credit availability conditions. However, we control for such conditions in our analysis.

  22. The variables are chosen to qualify investment from sources (of funds) side. We are not using funds mobilised from stock market to avoid multicollinearity. The econometric model is similar to Hovakimian and Titman (2006).

  23. We allow up to a ten-fold jump if the manufactured sales is up to Rs. (Indian Rupees) 10 million; five-fold if the manufactured sales is between Rs. 10 million and Rs. 50 million; four-fold if the manufactured sales is between Rs. 50 million and Rs. 100 million; three-fold if the manufactured sales is between Rs. 100 million and Rs. 250 million and; two-fold if the manufactured sales is above Rs. 250 million. These cutoffs are chosen to include maximum possible number of observations in the sample and yet putting a restriction on the restructuring firms.

  24. Distressed firms are those firms whose claims to the creditors are broken or honored with difficulty. Treatment of distressed firms draws importance over the contradictory findings of Fazzari et al. (1988) and Kaplan and Zingales (1997). Fazzari et al. (2000) show that the results obtained by Kaplan and Zingales (1997) are erroneous as they have distressed firm observations in their sample.

  25. Figures for the unconstrained regime also show a pattern consistent with the results in Table 3. They are omitted to save space.

  26. See Carruth et al. (2000) for a survey of results.

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Authors and Affiliations

  1. Centre for International Trade and Development, Jawaharlal Nehru University, New Delhi, India

    Vikash Gautam

  2. Indira Gandhi Institute of Development Research, Mumbai, India

    Vikash Vaibhav

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  1. Vikash Gautam
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Correspondence to Vikash Gautam.

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Gautam, V., Vaibhav, V. Investment, Uncertainty and Credit Market Imperfection in India. J. Quant. Econ. 15, 265–289 (2017). https://doi.org/10.1007/s40953-016-0048-1

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