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Financing constraints and exports: Evidence from manufacturing firms in India

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Abstract

This paper examines the relationship between external financing constraints and the intensive margin of exports for manufacturing firms in India. We use a sample of nearly 3200 firms over the period: 2000–2015 and construct a multivariate index proposed by Musso and Schiavo (J Evol Econ 18(2):135–149, 2008) to estimate the degree of external financing constraints. We find that an increase in the degree of external financing constraints faced is associated with lower firm-level exports and this result holds even after accounting for endogeneity issues. We next examine whether business group-affiliated firms are less dependent on external finance to support their overseas sales. We find that financing constraints are a significant binding factor even for firms with access to internal capital markets. Moreover, we find that firm size matters, as a decline in the financial health of small- and medium-sized firms is associated with a significantly larger decline in their export levels. Finally, we find some evidence of industry-level heterogeneity, as financing constraints lead to a more pronounced decline in the exports of firms in industries with greater dependence on external finance.

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Notes

  1. India’s manufactured exports contributed 2% to world manufacturing exports in 2014 (UNCTAD statistics).

  2. Prior to 1991, the cash reserve ratio was 25% and the statutory liquidity ratio was 40%, and these ratios currently stand at 4% and 19.5%, respectively (as of December, 2019).

  3. The SEBI is the apex regulatory authority governing capital markets in India and was formed in 1992.

  4. The priority sector guidelines do not lay down preferential rates of interest for loans under this category. https://www.rbi.org.in/scripts/FAQView.aspx?Id=87.

  5. Refer to the table in “Appendix 1” for variable definitions.

  6. See “Appendix 1” for variable definitions. The variables that constitute this index are chosen based on their performance in prior studies and their expected role in influencing the firm’s ability to raise external finance (Musso and Schiavo (2008)).

  7. Each variable is defined such that an increase in the value represents an improvement in that financial metric of the firm. Sectoral averages are used to account for industry-specific differences in the variables.

  8. Our results are also robust to other ways of combining the scores from the seven variables.

  9. This pattern is consistent with Musso and Schiavo (2008) and Bellone et al. (2010), who report a correlation (between Score A and Score B) of 0.78 and 0.91, respectively.

  10. Refer to “Appendix 1” for all variable definitions. We follow Levinsohn and Petrin (2003) to estimate firm-specific productivity for each two-digit industry uniquely.

  11. We refrain from defining export sales (the dependent variable) in terms of annual (percentage) growth as this can introduce autocorrelation in the error terms.

  12. A firm is recognized as a continuous exporter if it has exported in all periods (observed in the sample). Firms which exported only in some of the (reported) years are classified as occasional exporters, and firms which did not export in any year (over the sample period) are classified as non-exporters.

  13. This segmentation is based on the industry-specific median of the multivariate FC index (Score A) at the 3-digit industry-level classification.

  14. One possible explanation for this could be that firms may reduce investments in productivity-enhancing activities after becoming exporters, which can happen even if they face borrowing constraints.

  15. These can include a range of activities such as investment in better technology, import of capital goods or research and development-related expenses, all of which have implications for product quality and subsequently for the future demand for the products exported.

  16. We use the CMIE Prowess database’s group classification for identifying the group affiliation for all firms in our sample. This approach follows existing studies on Indian business groups, including Khanna and Palepu (2000), Bertrand et al. (2002) and Gopalan et al. (2007), among others. The Prowess classification is appropriate for our empirical analysis as it is based on a continuous monitoring all corporate announcements as well as a qualitative interpretation of group-specific behavior of all affiliated firms. In our sample, 38% of all exporting firms are affiliated to an Indian or foreign-owned business group [Table 2 (Summary Statistics)].

  17. Our empirical approach is not designed (or intended) to test for the existence of internal capital markets. However, prior studies by Gopalan et al. (2007) and Manos et al. (2007) provide strong evidence in support of the existence of internal capital markets among Indian business groups and our findings build on these well-established results.

  18. Gopalan et al. (2007) find limited evidence of tunneling among Indian affiliated firms, in which intra-group flows are used to divert resources away from group firms with low insider holding and toward firms with high insider holding. This leads the authors to conclude that there is no evidence of intra-group loans being used by Indian firms to finance investment activities or to divert cash.

  19. The World Bank report (2014) on financial inclusion documents that SMEs usually face greater shortages of formal credit, especially in low- and middle-income countries. Based on the World Bank Enterprise Survey, they find that nearly 44% of SMEs are involuntarily denied loans in low-income countries, whereas a comparatively smaller share of the large firms (25%) experience this issue.

  20. Numbers are based on the Fourth Census of MSMEs conducted between 2006 and 2009.

  21. The corresponding GMM estimates are reported in Table 2.1 (Appendix 2 in Electronic Supplementary Material).

  22. The classification of industries based on our sample is similar (but not identical) to the classification listed in Rajan and Zingales (1998). We obtain qualitatively similar results following the original classification, which are not reported for brevity.

  23. GMM estimates are reported in Table 2.2 (Appendix 2 in Electronic Supplementary Material).

  24. We obtain similar results using industry-specific capital intensity (as an alternative measure of external financial dependence). These results are not reported for brevity.

  25. We obtain qualitatively similar results using the GMM estimator, which are not reported for brevity.

  26. See Levinsohn and Petrin (2003) for more information on the methodology.

  27. We use the “levpet” command in Stata to obtain these estimates.

  28. We use the total wage bill to represent labor as most firms over the sample period do not report information on the number of employees.

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Authors and Affiliations

  1. Bangalore, India

    Shahana Mukherjee & Rupa Chanda

Authors
  1. Shahana Mukherjee
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  2. Rupa Chanda
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Correspondence to Shahana Mukherjee.

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Supplementary material 1 (DOCX 42 kb)

Appendix 1

Appendix 1

  1. a.

    Definitions

Control variables

 

Firm size

Log (assets)

Productivity

LOG (TFP) (based on the Levinsohn–Petrin method described below)

Capital intensity

Fixed capital/total assets

Business group affiliation

A binary variable equal to 1 if firm belongs to an Indian or foreign-owned business group and 0 otherwise

Demand

LOG (total industry exports)

(proxy for external demand conditions, defined at the 2-digit NIC level)

Measures of financing constraints

 Musso and Schiavo (2008) Index

  Firm size

Log (assets)

  Profitability

Return of assets (net income/total assets)

  Liquidity

Current assets/current liabilities

  Cash flow

Cash flow from operations

  Solvency

(Profit after tax + depreciation)/(short-term liabilities + long-term liabilities)

  Trade credit

Trade credit/total assets

  Repaying ability (represents the coverage ratio)

Cash flow from operations/debt

 Other measures of financing constraints

  Liquidity

(Current assets-short-term debt)/total assets

  Leverage

Short-term debt/current assets

  1. b.

    Measuring firm-level productivity (Levinsohn–Petrin method)

Following Levinsohn and Petrin (2003), the firm-specific, time-varying estimates of TFP are obtained by estimating the following production function:

$$y_{it} = \beta_{0} + \beta_{1} k_{it} + \beta_{2} w_{it} + \beta_{3} n_{it } + \mu_{it} + \varepsilon_{it}$$
(3)

where yit denotes firm revenue, kit denotes capital or fixed assets, wit represents the number of employees and nit denotes expenditure on intermediate inputs. The unexplained variation in output (yit) comprises of the unobserved efficiency term (μit) and the error component (εit). Estimating Eq. (3) above by Ordinary Least Squares (OLS) can be problematic as firms are likely to choose their factor inputs each period contingent on their contemporaneous productivity levels (which are unobservable to the econometrician). This may give rise to biased coefficient estimates of the production function and consequently biased estimates of firm productivity. Levinsohn and Petrin (2003) account for this possibility and propose the use of intermediate inputs to correct the simultaneity problem.Footnote 26 Their method (referred to as the LP method, henceforth) comprises of a semi-parametric approach to obtain consistent estimates of β, following which, TFP is obtained using the following equation:

$$\mu_{it} = y_{it} - \beta_{1} k_{it} - \beta_{2} w_{it} - \beta_{3} n_{it }$$
(4)

We follow the LP method to obtain consistent estimates of firm-specific productivity by estimating Eq. (4) for each industry at the two-digit NIC level.Footnote 27 We use annual sales as our measure of firm revenue, fixed assets as a measure of capital [(kit), total wage bill as a proxy for labor (wit) and raw material expenses as a measure of intermediate inputs (nit)]. All variables used are in real terms and enter the regression equation in natural logarithm.Footnote 28

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Mukherjee, S., Chanda, R. Financing constraints and exports: Evidence from manufacturing firms in India. Empir Econ 61, 309–337 (2021). https://doi.org/10.1007/s00181-020-01865-9

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