Sankar De Manpreet Singh Centre for Analytical Finance Centre for Analytical Finance

1. Credit Rationing in Informal Markets: The Case of Small Firms in India Sankar De Manpreet Singh Centre for Analytical Finance Centre for Analytical Finance Indian School of Business Indian School of Business December 2010

2. Presentation scheme • Background • Summary of findings • Data and empirical variables • Methodology: identification issues • Results: Rationing of relationship-based credit • Results: Identification of credit-rationed firms • Significance of results • Conclusions

3. Background • This research is part of the research agenda on the Role of Institutions in Emerging Capital Markets at the Centre for Analytical Finance (CAF) , Indian School of Business (ISB).

4. Main findings at a glance • We find evidence of rationing of credit within informal relationships for the firms in our sample. • Credit rationing is correlated with firm size (assets) • Creditors resort to rationing to prevent involuntary default by small firms in the presence of debt overhang. Since direct monitoring is not feasible, the creditors do not let the interest rate rise to an arbitrarily high level and ration credit. • The bottom 20% - 30% of the firms in our sample by asset size are at risk of credit rationing. • The critical interest rates are in 50% - 58% range. • Rationing triggers at higher rate for credit from social than from business relationships.

5. Theoretical Support • Our findings are consistent with Moral Hazard model of credit rationing (Ghosh, Mookherjee, and Ray, 1999). • They are not consistent with an alternative theory of credit rationing to prevent voluntary default (in the presence of outside options). Normally bigger firms would have more outside options. We do not see that.

6. Significance of findings:rationing of formal credit • This paper is the first to provide evidence of rationing of informal credit. • Voluminous evidence exists on formal credit rationing in India, especially for smaller firms (Banerjee and Duflo, 2001; Banerjee and Duflo, 2004; Banerjee, Cole, and Duflo, 2003; Gormley, 2007). • Similar evidence exists for other emerging countries. • Taken together, a firm may be excluded from formal and informal credit markets at the same time. • Important policy implication: strengthen market institutions.

7. Significance of findings:finance and growth • Our findings also throw light on the literature on financial development and growth. • Rajan and Zingales (98): industries dependent on external finance grow disproportionately faster in countries with developed financial markets. RZ consider only formal finance. • Fisman and Love (2003): industries with higher dependence on trade credit financing achieve higher rates of growth in countries with weaker financial institutions. They do not consider informal finance. • AQQ (2005) suggest that informal finance can foster economic growth. • Our findings indicate that informal finance is unlikely to spur growth.

8. Significance of findings:formal versus informal institutions • The findings have implication for a much bigger issue. • Can informal private arrangements substitute for formal public institutions, such as markets and banks? Inter-firm credit is sometimes cited as an example of such private arrangements. • If yes, this would indeed be a very desirable outcome, especially for countries with weak or ineffective formal institutions. • However, empirical studies are few and far between. Studies with firm-level analysis are even fewer.

9. Data • Unique dataset Combines survey responses of a sample of Indian SMEs with the panel data of corporate finance activities of the same firms for five years (2001-2005) collected from CMIE Prowess. The dataset permits • Use of survey data for qualitative information and Prowess data for hard quantitative information • Partitioning the data in many different ways and constructing a variety of indices for a given firm in the sample • Separate indices for credit for business relationship and credit from social relationship s.

10. Data • Survey data • Conducted in late 2006 • Survey administered in Personal interviews with company owners and/or CEO/CFO • Survey instrument had 108 questions in four parts • Focused on company history, corporate financing, relations with banks and financial institutions, informal relationships and trade credit transactions, business and social networks, and factors affecting corporate performance. • Out of the Prowess population of 680 SMEs with complete 5-year financial data, after excluding firms with any kind of financial business, we were able to survey 141 firms. • The sample spans a variety of industries and all geographic locations in India.

11. Sample representativeness • The sample firms account approximately 21% of the population of 680 SMEs with complete 2001 – 2005 financial history in Prowess • For year 2005 (the last year before the survey was conducted), we conduct large sample mean difference tests between the sample firms and the Prowess SME population for important firm-specific variables, including total assets, sales, trade credit received and extended. • In each case, the difference is insignificant.

12. Summary statistics • Summary of survey data • Chemicals and chemical products-15% • Construction companies- 9% • Basic metals-8% • Food products & beverages-7% • For 2/3rd of the firms’ manager belongs to founding family • For 63% of the firms owners are actively involved in day-to-day management • Summary statistics of firm characteristics from panel data (Median Firm-year) • Assets: 3.16 Mn. $ • Trade Credit Received: 0.41 Mn. $ • Average payment period : 87 days • Bank Credit Received: 0.43 Mn. $

13. Inter-firm credit classification:our approach

14. Empirical measures • Proportion of credit from relationships (ranges from 0 to 1) • Sample Average • Business Relationships • Reliable Industry Sources 0.069 • Met in Professional Setting 0.064 • Location in same City/Proximity 0.067 Credit from Business Relationships Credit from All Relationships • Social Relationships • Extended Family 0.041 • Social Acquaintances 0.054 • Same Caste 0.051 • Same Native Language 0.055 • We use two approaches: • Simple addition, with equal weights • PCA to calculate the weights Credit from Social Relationships

15. Summary statistics of relationship-based inter-firm credit

16. Methodology • Creditit = Trade Credit from relationship-based sources scaled by firm assets; for firm i in year t • Costi= Annualized cost of credit (using discount rate and free credit period reported by survey firms) • Controls • Financing Sources: Bank Credit and Internal Sources , scaled by firm assets • Firm Characteristics: Total Assets, Net Sales, Age (all log transformed) • Industry fixed effects to control for heterogeneity in use of trade credit across industries • Time fixed effects to control for any change in macroeconomic environment • We estimate equation (1) for credit from all relations, business relations, and social relations.

17. Identification strategy • The observed level of relationship-based credit for a given firm is determined simultaneously by the both the credit extended to the firm by its suppliers as well as the firm’s demand for credit. • We use Cost of Goods Sold by the firms as a proxy for its demand for trade credit after adjusting for labor cost. It is free credit during a typical trade credit contract period (equation 2). • Analytically, our procedure estimates the firm’s true demand for credit independently of any supply-side factors. This demand estimate serves as an instrument for credit demand when estimating the credit supply function (equation 1).

18. Evidence of credit rationing

19. Robustness checks • We recognize the overlap between different types of business and social relationships in survey questions. • Use PCA to correct for over-weighting of the proportions of credit received from a particular relationship-based source. • All results continue to hold (Table 4, Panel B) • We also scale credit by total borrowings instead of total assets. • Results continue to hold (Table 4, Panel C) • We also do the analysis for various lags of total assets. • Results are robust to such changes (Table 5)

20. Economic significance • Credit/Total Assets from All Relations • Regression Coefficient of • Cost:0.22 • Cost2: (-) 0.20 • Median cost of credit : 22% • Cost at Maximum Credit: 55% • Credit at Median Cost (in Mn. $): 0.43 • Maximum Credit (in Mn. $): 0.88 • Credit at higher cost (in Mn. $): 0.67 • Similar results for Credit/Total Borrowings

21. Identifying prospective credit-rationed firms • How to identify the likely candidates for credit rationing? • Demand for collateralizable assets is the fundamental cost of financing in many existing models of financial constraints (Bernanke and Gertler, 89; Banerjee and Newman, 93; Liberti and Mian (JF, 10) • In our tests, the dependent variable Credit from Relationship-based Sources is scaled by assets. • We classify the firms in our sample by their assets and run the tests for each class.

22. Identifying credit-rationed firms • We augment the Price variables in the previous model with TOP(j)dummy where • TOP(j)is a dummy variable taking value 1 if the firm belongs to top j percentile in terms of average assets and zero otherwise, j=10 to 90 • Using this model we identify firms which are most likely to face credit rationing • To run this test, we use two different types of asset distributions: • Average assets over the sample period 2001-5 • Assets in each year (dynamic classification)

23. Results

24. Results • We find that the bottom 20/30 percent of firms by asset size are at risk of credit-rationing. • They are firms with assets less than $1.8-2 mn. • Size of a median firm in our full sample is $3.15 mn. • The results for the two types of asset distributions are very similar (Table 7)

25. Industry classification of firms at risk of credit rationing • Credit rationing is not endemic to particular industries. • For example, manufacturing of chemicals and chemical products industry • Accounts for 3.3% of the bottom 20% and 5.1% of the bottom 30%, • Accounts for 65 firm-year observations in our full sample of 455. • Hence the reasons must be firm-specific.

26. Industry Classification of Firms at Risk of Credit Rationing

27. Further analysis • Firms at risk of rationing vis-à-vis other sample firms • Have lower assets (by construction ) • Receive less trade credit from all sources and relationship-based sources • Have higher average payment period • Receive less bank credit • Are of same age (as on 2005) • Have lower profitability • Have more outstanding debt in relation to assets (debt overhang)

28. Conclusions • Informal credit is rationed. • Overall, we find that firm assets play an important role in credit decisions of the lenders. • Creditors appear to ration credit to contain moral hazard problems on the part of borrowers.