Introduction There are longstanding concerns that entrepreneurs experience binding financial constraints. A situation where increasing the amount of finance available to the entrepreneur would raise the value of the venture (i.e., the supply of finance is sub-optimal). The basis for these concerns is that entrepreneurs are better informed about their venture s prospects than finance providers (information asymmetries). In essence lenders face uncertainty about borrowers default risk. This leads to problems of adverse selection and moral hazard in credit markets. In these circumstances it may be optimal for banks to under-supply credit in equilibrium (credit rationing: Stiglitz & Weiss, 1981). Entrepreneurs may be able to signal their type with collateral (Bester, 1985): low risk businesses will select loan contracts with lower interest rates and higher collateral requirements (vice-versa for high risk businesses). But this leads to the possibility that low risk firms with insufficient tangible assets may face financial constraints. Assistance for creditworthy businesses with insufficient collateral e.g., Enterprise Finance Guarantees (UK). In the context of the financial crisis, related research suggests that both increased default risk uncertainty and lender risk aversion have reduced credit availability following the financial crisis (Fraser, 2009: see appendix).
Introduction The focus of the mainstream finance literature has been on inefficient outcomes caused by imperfect capital markets. That is, uncertainty and/or risk aversion, causes finance providers to supply businesses with sub-optimal amounts of finance. However increasingly the small business/entrepreneurship literature has looked at the behavioural psychology of the business owner/entrepreneur. This is related to the success of the behavioural economics literature (e.g., Tversky and Kahneman, 1974; Kahneman and Tversky, 1979). In particular entrepreneurs may be especially prone to positive illusions (Taylor, 1989): Optimism bias Illusion of control Illusory superiority In the context of entrepreneurial finance, optimism bias may lead to excessive finance demands if not over-investment (de Meza and Southey, 1996).
Introduction In this context, the principal contribution of the paper is to provide a new test of financial constraints based on the relationship between finance gaps (the difference between the amount of finance requested (demand) and the amount received (supply)) and the value of the firm. The paper shows that the impact of finance gaps on the value of the firm varies depending on whether: Finance gaps are caused by finance providers offering sub-optimal amounts of finance ( financial constraints ). Finance gaps are caused by entrepreneurs seeking too much finance ( over-optimism ). This variation forms the basis of a test for financial constraints based on the relationship between firm performance and finance gaps (holding other relevant characteristics of the business/entrepreneur constant).
Introduction In a related contribution, the paper looks at discouraged borrowers i.e., individuals who don t apply for loans because they believe they will be rejected (e.g., Kon and Storey, 2003; Han, Fraser and Storey, 2009). In particular, the paper develops a test for bias in entrepreneurs perceptions of their likelihood of loan rejection ( discouraged borrower test ). This provides a complementary test of market inefficiency due to imperfectly informed entrepreneurs. The paper applies the tests to a large small business finance data-set (UK Survey of SME Finances UKSMEF) to examine the evidence for credit market inefficiency before and after the collapse of Lehman Bros in 2008.
Testing financial constraints: the internal finance approach Evans and Jovanovic (1989) proposed a test of financial constraints based on the relationship between the entrepreneur s personal wealth (availability of collateral) and venture creation/performance. They predicted that venture creation/performance is positively related to wealth iff there are binding financial constraints. In the corporate finance literature cash-flow is often used as a measure of the availability of internal finance (Carpenter and Petersen, 2002). In perfect capital markets investment decisions/growth should only be affected by investment opportunities the availability of internal finance is irrelevant. In the presence of binding financial constraints * k W k, W personal wealth dk w 0. dw In other words an increase in wealth relaxes the constraint allowing the business to increase capital towards k * and grow towards its optimal scale. In the absence of financial constraints dk 0 dw implying wealth will have no affect on firm performance. In terms of the investment rate: idt dk k under financial constraints we may write 1 idt w dw k In other words, under financial constraints, the investment rate depends on the flow of wealth/internal finance.
Empirical evidence Holtz-Eakin et al (1994) estimated a joint model of entrepreneurial survival and growth for a sample of sole proprietorships in the US. They found that the receipt of an inheritance increased entrepreneurial survival and sales consistent with the notion that liquidity constraints exert a noticeable influence on the viability of entrepreneurial enterprises. Carpenter and Petersen (2002) tested for financial constraints on a sample of small (assets < $100m) incorporated manufacturing firms in the US. The test involved regressing asset growth on Tobin s Q (investment opportunities) and the ratio of cash flow-assets. They found that growth was positively related to internal finance and inferred from this the presence of financial constraints. Tahmiscioglu (2001) estimated a time varying parameter model for the relationship between investment, Tobin s Q and cash-flow. Financial constraints are at their most binding about 1-2 years after the trough of a business cycle (i.e., just when capital demands are starting to pick up again).
Criticisms of internal finance tests Personal wealth may proxy more than just liquidity: Wealth and human capital are positively correlated (Cressy, 1996): Wealthier individuals may be better at running high growth businesses regardless of the availability of finance. Diminishing absolute risk aversion (Cressy, 2000): Wealthier individuals may be more willing to take on the risk of running a high growth firm. Wealthier, over-optimistic entrepreneurs may be able to go ahead with projects that banks would be unwilling to finance (de Meza and Southey, 1996). Similarly, cash flows may signal investment opportunities/future profitability rather than financial constraints. The problem is that Tobin s Q (ratio of market-book value of the firm) may be a poor measure of investment opportunities (e.g., where stock prices are driven by fads/fashions rather than fundamentals). The point is that internal finance tests have low power against a range of plausible alternatives.
Finance gaps test Finance gaps may be due to: 1) Credit rationing by imperfectly informed/risk averse banks. Businesses get less funding than required to reach optimal scale. If the business were able to reduce their finance gap then the value of the firm would increase. 2) Excessive finance demands by imperfectly informed entrepreneurs. Bank offers a smaller loan than requested to rein in excessive funding demands (due e.g., to entrepreneurial over-optimism). If the business were able to reduce their finance gap then the value of the firm would not increase. Accordingly, finance gaps are negatively related to the value of the firm only in the presence of binding financial constraints. This is a more direct approach to testing for financial constraints and has power against alternative explanations for finance gaps (such as over-optimism).
Finance gaps test These two explanations/hypotheses for finance gaps give the following equilibrium relationship between the supply of loans ( ), the demand for loans ( ) and the optimal level of borrowing ( ) Accordingly we may write where. No financial constraints implies Note that depends (broadly speaking) on entrepreneurial talent, interest rates and the perceived likelihood of loan rejection. A higher perceived likelihood of loan rejection increases the real cost of loan applications thus reducing (i.e., it increases the likelihood of discouragement). Misperceptions of the likelihood of loan rejection (and talent) may cause to deviate from (see below).
Finance gaps test The paper derives the following relationships (testing equations) between finance gaps and firm performance. 1) Probability of business closure Optimal capital stock Entrepreneurial talent Total costs of the venture Accordingly the probability of closure is increasing in finance gaps if and only if financial constraints are binding:
Finance gaps test 2) Sales growth Speed of adjustment of capital stock Accordingly sales growth is decreasing in finance gaps if and only if financial constraints are binding:
Discouraged borrower test The test is based on the relationship between the probability of discouragement and the perceived probability of loan rejection. Suppose actual lending decisions are based on a weighted combination of observed risk factors (a risk index):. In practice the risk factors will relate to small business lending technologies: collateral-based lending; credit-scoring; and relationship lending. Mapping the risk index onto [0,1] (e.g., wlog, using a normal cdf) gives the actual probability of loan rejection:. The entrepreneur s perceived probability of loan rejection is (by assumption): where is a perceptions parameter. Note that if then the entrepreneur misperceives his/her level of risk/probability of loan rejection.
Discouraged borrower test Now an entrepreneur is a discouraged borrower if is greater than or equal to a threshold. Equivalently, ଵ ଵ ; otherwise. The latent discouragement propensity may therefore be written ଵ ଵ Assuming then the probability of discouragement may be written This shows the relationship between the probability of discouragement and the entrepreneur s perceived risk/probability of rejection. The DB test is an important complement to the FG test because misperceptions about may also lead to inefficient outcomes in the credit market (since loan demands depend on ). ଵ
Data: UK Survey of SME Finances (UKSMEF) Surveys conducted on representative samples of (typically) 2,500 private sector SMEs (<250 employees and sales< 50m) located in the UK. Public sector and not for profit organisations are excluded, together with the Financial Services, Mining and Quarrying, Electricity, Gas and Water Supply sectors (the latter due to the very small numbers in the population). Main surveys conducted in 2004, 2008 and 2009. UKSMEF has involved strong collaboration with policy makers (Bank of England (2004); Dept. for Business Innovation and Skills (BIS) (2004-9)). Respondents interviewed in detail about: The characteristics of the business and its owner. Finances used/applied for recently. Reasons for not applying for finance (discouraged borrowers). Financial relationships. The data and related reports can be downloaded from the UK Data Archive: www.data-archive.ac.uk
General structure of UKSMEF Business/Owner Characteristics (Risk Profile) Applicant Successful Amount, Terms and Conditions Rejected Amount, Reasons for Rejection Non-Applicant: Discouraged Borrower Non-Applicant: No Need for External Finance
Finance gaps test The survey is structured such that respondents can be in one of 4 mutually exclusive states for each type of finance they are asked about (see previous slide): 1) Applied and received 100% of the amount requested. 2) Applied and received <100% of the amount requested (outright/partial financial rejection finance gap). 3) Did not apply because the business owner thought they would be turned down (discouraged borrowers). 4) Did not apply because there was no need for the type of finance. The finance gaps tests therefore include 3 dummy variables corresponding to states 2), 3) and 4) with effects measured relative to state 1).
Interpretation Overdraft rejection in 2008 increased the probability of business closure in 2008-9 by 1.5% points. There are also significant negative effects of overdraft rejection on sales growth in 2007-8 and 2008-9. Holding sales in 2007 (resp. 2008) constant, overdraft rejection is associated with a 35% (resp. 68%) reduction in sales by the end of 2008 (resp. 2009) relative to a firm which had all of its overdraft needs met. For an average firm this means a reduction in sales from 3.2m in 2007 to 2.1m in 2008 and from 3.3m in 2008 to just over 1m in 2009. These findings are consistent with financial constraints due to a lack of working capital since the early stages of the financial crisis (20078) but with more severe constraints in 2008-9. However, no evidence of financial constraints (on survival or growth) prior to the financial crisis.
Interpretation There are no effects of term loan rejection on business closure rates or growth in any of the periods analysed. No evidence of financial constraints due to a lack of term lending before or after the financial crisis. However, term loan discouragement in 2004 increased the likelihood of business closure in 2005-8 by about 15% points (relative to a business which applied for a term loan and received all of the amount requested). Also, holding sales in 2007 constant, term loan discouragement is associated with a 57% reduction in sales by the end of 2008. In addition, businesses with no need for term loans grew more slowly in 2003-4 and 2007-8. Lower loan demands lower investment/growth.
Discouraged borrower test Estimate a model for the probability of loan rejection using a probit model with sample selection and obtain the risk index. 2) Estimate a probit model for the probability of discouragement conditional on the risk index obtained at stage 1). 3) Test whether the perceptions parameter (the parameter on the risk index at stage 2)) is different from unity ( implies unbiased perceptions). 1)
Interpretation The average business owner misperceives their probability of loan rejection ( ). Given the average value of the risk index is negative (implying an average probability of rejection< 50%), is consistent with over-estimation of the probability of rejection. In this case loan demands are sub-optimal ( )which can be shown to ) on the firm. exacerbate the effects of supply-side constraints ( Businesses with better (minimal/low risk) credit ratings seem (in 2004/9) to perceive their probability of rejection more accurately. However, among businesses with poor credit ratings the actual probability of rejection (in 2004/9) has no bearing on perceptions ( ). These businesses seem to feel they have only a 50/50 chance of a successful loan application ( actual risk profile. ) and the outcome has nothing to do with their
Conclusions Evidence of financial constraints due to a lack of working capital. Supports widening of govt. assistance to include working capital (EFGs) and moves to promote non-bank sources of finance (Breedon Review). Apparent absence of financial constraints relating to a lack of term loans may be due to recent low demand for these types of loan. Financial constraints may yet emerge here when demand begins to pick up (Tahmiscioglu, 2001). The discouraged borrower test points to entrepreneurial misperceptions as another source of inefficiency in the credit market. Suggests a need for better financial education in general and, specifically, promoting a better understanding of the factors which affect bank lending decisions.
Appendix Related research by the author has shown that the likelihood of bank loan (overdraft or term loan) denial increased in 2008 due to: H1: A tightening of lending criteria (increased lender risk aversion). Loan approval thresholds H2 H2: An upward shift in the default risk distribution (an increase in default risk uncertainty). The likelihood of bank loan denial doubled as a result of tighter loan approval thresholds from 6% pre-2008 to 12% in 2008 (holding the default risk distribution constant). Default Risk Distribution The likelihood of bank loan denial more than doubled, from 6% pre-2008 to 13% in 2008, due to the upward shift in the default risk distribution (holding loan approval thresholds constant). In other words there was a total increase in the likelihood of bank loan denial of 13% points in 2008 which was about equally shared between the effects of tighter lending criteria and increased default risk uncertainty. This suggests that both increased default risk uncertainty and lender risk aversion have reduced credit availability following the credit crisis. 2008 Pre-2008 H1 Probability of Default
Loan demands Overdrafts Term loans 80.0% 70.0% 68.9% 62.0% 60.0% 57.0% 50.0% 41.5% 40.0% 34.3% 30.0% 26.8% 20.0% 10.0% 0.0% 2004 2008 2009
Credit ratings 45.0% 2004 2008 2009 42.1% 40.0% 37.3% 35.0% 31.7% 30.0% 26.2% 28.4% 27.4% 25.0% 19.1% 20.0% 16.4% 15.0% 11.0% 10.0% 9.0% 8.8% 5.0% 0.0% Minimal risk Low risk Average risk High risk 11.9%
Loan rejection rates Overdrafts Term loans 18.0% 16.1% 16.0% 15.3% 14.1% 14.0% 12.0% 10.9% 10.0% 9.0% 8.0% 6.0% 5.4% 4.0% 2.0% 0.0% 2004 2008 2009
Loan discouragement rates Overdrafts Term loans 3.5% 3.2% 3.0% 2.5% 2.0% 1.7% 1.4% 1.5% 1.2% 1.0% 0.9% 0.9% 0.5% 0.0% 2004 2008 2009
Business closure rates Closure rate Insolvency rate 3.00% 2.50% 2.40% 2.40% 2.00% 1.50% 1.20% 1.00% 0.70% 0.50% 0.00% 2008 2009
Sales growth Sales growth 12.0% 10.7% 10.0% 8.0% 7.2% 6.0% 4.0% 2.0% 0.0% 2004 2008 2009-2.0% -4.0% -6.0% -8.0% -6.0%
Policy implications These findings point to the importance of: Business mentoring to help with issues such as financial delinquency and discouragement. Enterprise Finance Guarantees: helping, in particular, with access to working capital. Improving financial relationships: Taskforce banks have committed to improving their customer service: e.g., making the loan application/decision process clearer; and establishing an appeals process for businesses refused loans. Business Finance Round Table may promote better understanding between banks and businesses. Proposed quarterly survey also important by providing reliable independent data on demand/supply of finance.