Preliminary and Incomplete-Please Do Not Cite or Circulate




Financing Entrepreneurship: How do Credit Card Interest Rat...
Financing Entrepreneurship with Credit Cards


I. Introduction

        The explosion of research on entrepreneurship over...
Financing Entrepreneurship with Credit Cards


the decision, rates began to converge, allowing credit card companies in st...
Financing Entrepreneurship with Credit Cards


cover the start up capital needs, but lack the necessary finances to cover ...
Financing Entrepreneurship with Credit Cards


Zimmerman (2003) find that black-owned small businesses are more than twice...
Financing Entrepreneurship with Credit Cards


governing credit card marketing. Prior to the Marquette decision, lenders b...
Financing Entrepreneurship with Credit Cards


sample period, we have hand collected the data from annual volumes of the C...
Financing Entrepreneurship with Credit Cards


interaction terms between race and interest rate cap and gender and interes...
Financing Entrepreneurship with Credit Cards


References

Barth, James, Glenn Yago and Betsy Zeidman. 2005. “Stumbling Bl...
Financing Entrepreneurship with Credit Cards



                      Table 1: Summary Statistics
Variable                ...
Financing Entrepreneurship with Credit Cards




                                                                         ...
Financing Entrepreneurship with Credit Cards

   Table 3: Interest Rates for Sample Years
State              1971        1...
Financing Entrepreneurship with Credit Cards




                              Table 4: Regression Results (Dependent Vari...
Financing Entrepreneurship with Credit Cards


Figure 1: Interest Rate Cap Changes across Time


                         ...
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Preliminary and Incomplete-Please Do Not Cite or Circulate

  1. 1. Preliminary and Incomplete-Please Do Not Cite or Circulate Financing Entrepreneurship: How do Credit Card Interest Rates Impact Entrepreneurship? AARON K. CHATTERJI Fuqua School of Business Duke University 1 Towerview Drive Durham, NC 27708 Tel: (919) 660-7903 Fax: (919) 681-6244 ronnie@duke.edu ROBERT SEAMANS Haas School of Business University of California Berkeley, California Tel: (510) 847-1026 seamans@haas.berkeley.edu November 6, 2007
  2. 2. Financing Entrepreneurship with Credit Cards I. Introduction The explosion of research on entrepreneurship over the last decade has uncovered several new avenues for research inquiry, but also left many of the most basic questions unanswered. For example, while numerous academic studies have focused on venture capital financing of new ventures, almost none have examined the role of credit cards as a financing tool for entrepreneurs. Since a very small percentage of entrepreneurs receive venture capital, most entrepreneurs finance their businesses through other means. Often before tapping “friends, families, and fools” or after being turned down for a bank loan, incipient entrepreneurs utilize credit cards to “bootstrap” their new venture. In fact, a recent study by MasterCard reported that 57% of small business owners used credit cards to finance their companies (Cole, Lahm Jr., Little Jr., and Seipel, 2005; de Paula, 2003). The entrepreneurship practitioner community is well aware of this phenomenon as well, as explained by Eric Rosenfeld, President of Adaptive Consulting Partners, who said “…I just discovered that credit cards have become today’s start-up business financing tool.”1 However, academics have yet to rigorously investigate the role of credit cards in the entrepreneurial process. We suggest that this inattention to short term methods of financing may also contribute to mixed results in an important body of work on liquidity constraints and entrepreneurship. In this paper, we evaluate the impact of variation in credit card interest rates on entrepreneurship in the United States. In particular, we use the Marquette decision, a 1978 Supreme Court ruling that eliminated state caps on credit card interest rates. Prior to Marquette, there was significant variation in credit card interest rates across states. After 1 http://www.eventuring.org/eShip/appmanager/eVenturing/eVenturingDesktop? _nfpb=true&_pageLabel=eShip_articleDetail&_nfls=false&id=Entrepreneurship/Resource/Resource_879.h tm 2
  3. 3. Financing Entrepreneurship with Credit Cards the decision, rates began to converge, allowing credit card companies in states with previously low interest rates to charge much higher rates and extend credit to different populations. Using this decision as a natural experiment, we are able to test the impact of rate changes on levels of entrepreneurship. Our preliminary results are mixed. Credit card interest rate caps do not seem to affect self employment rates. Female and African- American workers appear to be more likely to enter self-employment as interest rate caps rise, but this finding is not robust to state level clustered standard errors. In the next section, we review the extant literature and discuss the Marquette decision. We then proceed to discuss our methods and data, results, and the implications from our analysis. II. Literature Review and Theoretical Development The role of liquidity constraints in entrepreneurship has presented a persistent puzzle for scholars. While many prior academic studies have focused on the importance of liquidity constraints to entrepreneurs, they yield contradictory results. For example, Blanchflower and Oswald (1998), Fairlie (1999) and Lindh and Ohlsson (1996) all demonstrate that lack of a wealth constraint correlates with higher levels of entrepreneurship. Alternatively, Hurst and Lusardi (2004) and Petersen and Rajan (2002) find that there does not exist a wealth constraint to entrepreneurship. It is possible that these contradictory results may be related to inattention to the relative importance of different types of financing. Entrepreneurs need start up capital to invest in their projects as well as operating capital to finance everyday expenses. Presumably, individuals with very high levels of wealth should have enough finances to cover both these capital needs. However, individuals with moderate amounts of wealth may have enough finances to 3
  4. 4. Financing Entrepreneurship with Credit Cards cover the start up capital needs, but lack the necessary finances to cover operating capital needs. The ability to access capital for short term needs is important for small businesses and entrepreneurs. Entrepreneurs face regular cash outflows such as rent and utility bills, vendor bills, and salaries for employees. Yet entrepreneurs often face uncertain cash inflows - for example they typically have few customers and if one or more of these customers delays payment the entrepreneur can face a funding shortfall. In order to cover these funding shortfalls, entrepreneurs are willing to pay higher rates on borrowed capital. The importance of the availability of this type of capital to meet immediate needs is addressed in a recent study by the National Federation of Independent Business (NFIB). The NFIB asks small business owners to rank their most severe problems. Cash flow is ranked number seven, whereas obtaining long (5 years) and short ( 12 months) term business loans is ranked 68 and 70 respectively (see Barth, Yago and Zeidman (2005) for additional information). Despite the apparent importance of access to short term credit, no previous study to our knowledge has empirically analyzed the role of credit cards on entrepreneurship. If entrepreneurs rely on credit cards for short term financing, the availability of such credit should impact entrepreneurship rates. This is because when credit card interest rates are allowed to increase, credit card companies will extend credit to more people. Hence, we expect increases in state interest rate caps to be positively correlated with increases in self employment. The effect should be more pronounced on individuals that have difficulty obtaining credit through banks or other lending channels, and on individuals who are perceived as being higher risk than other individuals. Blanchflower, Levine and 4
  5. 5. Financing Entrepreneurship with Credit Cards Zimmerman (2003) find that black-owned small businesses are more than twice as likely to be denied bank credit. They also show that 21% of black owned small businesses in 1993 expected to face credit availability problems, compared to 6% of white owned small businesses. Building on this and similar studies, we expect that increases in credit card interest rates should have a positive effect on entrepreneurship for black individuals. Similarly, research has found that female entrepreneurs face more challenging capital raising requirements than male entrepreneurs. Coleman (2000) finds evidence that suggests female entrepreneurs are charged higher interest rates and are required to have higher levels of collateral. Thus, we expect that increases in credit card interest rates should have a positive effect on entrepreneurship for females. In the next section we describe our data and empirical approach, utilizing an exogenous shock to credit card interest rates. III. Data and Empirical Strategy Drawing from the insights of the NFIB study mentioned above, we hypothesize that access to high cost of capital financing is an important determinant of entrepreneurial activity. Our prediction is that higher credit card interest rates will lead to increased entrepreneurship. We are able to test this idea with a quasi-natural experiment. In the 1970s and early 1980s, maximum allowable credit card rates varied across states and across time (see Figure 1). For example, the maximum rate in Alabama in 1977 was 18% whereas in Minnesota it was 12%. In 1982, these rates were 21% and 18% respectively. Rate changes occurred because of growing concern about consumer credit protection and in response to the1978 US Supreme Court Marquette decision which changed rules 5
  6. 6. Financing Entrepreneurship with Credit Cards governing credit card marketing. Prior to the Marquette decision, lenders based in a high rate state such as Alabama were allowed to market credit cards to consumers in a low rate state such as Minnesota, but were prohibited from offering cards at rates higher than the prevailing maximum in the low rate state. Following the Marquette decision, lenders could use the maximum rate in their home state or the state to which they marketed their products. For example, in 1982, lenders based in South Dakota were able to market credit cards in Minnesota with rates up to 21% (the maximum allowable rate in South Dakota). Two effects of the Marquette decision were that a number of states changed their maximum allowable interest rate and credit card companies moved to high rate states with low costs (such as South Dakota). We treat these changes in credit card interest rates as exogenous and use them to proxy for changes in the availability of high cost of capital financing. We then check to see if these changes in credit card interest rates are statistically correlated with changes in levels and rates of entrepreneurial activity. In future analyses we aim to use the shock of the Marquette decision more directly to model the decision of states to raise interest rate caps. In our current study, we proxy for entrepreneurial activity using self employment data from the US Census Bureau’s Census Population Survey (CPS). Self employment has been used as a proxy for entrepreneurship in many other studies including Blanchflower and Oswald (1998) and Fairlie (1999). In addition, the CPS data provides demographic information such as age, gender, race and education that we use to control for demographic factors that may influence entrepreneurship. We have collected information on maximum allowable interest rates at the state level back to 1971, and have collected the relevant CPS data. To obtain the interest rate levels for each state during our 6
  7. 7. Financing Entrepreneurship with Credit Cards sample period, we have hand collected the data from annual volumes of the Cost of Personal Borrowing in the United States. To give an idea of the change in rates over time and across states, we report the state rates for 1971, 1977, and 1982 in Table 3. Unfortunately, from 1971-1976, the CPS data from many states is assigned to a region (e.g.: “New England” as opposed to “Maine”), so we are unable to use data prior to 1976 since we cannot assign a state level interest rate cap to the individual observation. In addition, there is evidence that credit card companies acted collusively in the 1980s (Knittel and Stango, 2003), so we do not want to include too many observations from the 1980s. As a result, for our analyses we focus on years 1977 to 1983. The effect of credit card company collusion on rates of entrepreneurship is something that we hope to more fully investigate and control in future work. Future work may also utilize a different data set that will allow us to use more of the 1970s interest rate data. IV. Results and Conclusion We present summary statistics in Table 1 and a correlation matrix in Table 2. State interest rate caps for selected years are presented in Table 3. In Table 4, we report the results of fixed effects regressions with a dummy variable for self employment as our dependent variable. The explanatory variables are basic demographic information, interest rate, and interactions between these variables. Columns 1 – 3 explore the relationship between state interest rate caps and self employment. In the first two columns, the coefficient on interest rate cap is negative and significant, which is the opposite of our expectation. However, this result disappears in Column 3 when we include fixed effects at the metropolitan-state level. Of particular interest are the 7
  8. 8. Financing Entrepreneurship with Credit Cards interaction terms between race and interest rate cap and gender and interest rate cap which we explore in Columns 4 – 5. As discussed above, when interest rates rise, we expect that women and African-Americans are more likely to be extended credit and thus more likely to become entrepreneurs. We find this interaction to be positive and significant in Column 4, controlling for metropolitan-state and year fixed effects. That is, the coefficients on female*rate and black*rate are positive and significant in Column 4, implying that higher interest rates are associated with higher levels of entrepreneurship for women and African-Americans. However, when we cluster our standard errors at the state level in Column 5, the coefficients are no longer statistically significant at conventional levels2. Taken together, the results presented in Table 4 suggest that in general, credit cards are not an important determinant of self employment, but may be important for women and African-American workers. However, other institutional variables at the state level may be more important (which would explain the drop in significance resulting from clustering standard errors at the state level). Indeed, Fan and White (2003) show that state variation in personal bankruptcy protection laws are significantly correlated with levels of entrepreneurial activity. As we refine this analysis, we expect to update these results and assess whether the effects we are observing are indeed robust. Future steps include filling in gaps in state level interest rate caps, obtaining information on state level bankruptcy exemption laws, and investigating the availability of other data sets that go further back in time. 2 The coefficient on black*rate is significant at the 15% level. 8
  9. 9. Financing Entrepreneurship with Credit Cards References Barth, James, Glenn Yago and Betsy Zeidman. 2005. “Stumbling Blocks to Entrepreneurship in Low- and Moderate- Income Communities,” working paper, Federal Reserve Board of Kansas City and The Ewing Marion Kauffman Foundation. Blanchflower, David, Andrew Oswald. 1998. “What Makes an Entrepreneur?” Journal of Labor Economics 16(1): 26-60. Blanchflower, David, Phillip Levine and David Zimmerman. 2003. “Discrimination in the Small Business Credit Market,” Review of Economics and Statistics 85(4): 930-943. Cole J. Dee, Robert Lahm Jr., Harold Little Jr., and Scott Seipel. 2005. “Credit Cards as a Source of Start-Up Capital and Ongoing Capital Management,” working paper, Small Business Advancement Center. Coleman, Susan. 2000. “Access to Capital and Terms of Credit: A Comparison of Men- and Women- Owned Small Businesses,” Journal of Small Busines Management 38(3): 37-52. De Paula, Matthew. 2003. “Business Owners are Putting Out with Plastic. US Banker 113 (8): 54. Fairlie, Robert. 1999. “The Absence of the African American Owned Business: An Analysis of the Dynamics of Self Employment,” Journal of Labor Economics, 17(1): 80-108. Fan, Wei and Michelle White. 2003. “Personal Bankruptcy and the Level of Entrepreneurial Activity,” Journal of Law and Economics 46: 543-567. Hurst, Erik and Annamaria Lusardi. 2004. “Liquidity Constraints, Household Wealth, and Entrepreneurship,” Journal of Political Economy 112(21): 319-347. Knittel, Christopher and Victor Stango. 2003. “Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards,” American Economic Review 93(5): 1703-1729. Lindh, Thomas and Henry Ohlsson. 1996. “Self Employment and Windfall Gains: Evidence from the Swedish Lottery,” The Economic Journal 106(439): 1515-1526. Petersen, Mitchell and Raghuram Rajan. 2002. “Does Distance Still Matter? The Information Revolution in Small Business Lending,” Journal of Finance 57(6): 2533-2570. 9
  10. 10. Financing Entrepreneurship with Credit Cards Table 1: Summary Statistics Variable Mean St. Dev Min Max Self Employed 0.0739 0.2616 0 1 Interest Rate Cap 0.1806 0.0267 0.10 0.25 Age 37 14 14 98 Age*Age 1532 1142 196 9604 Female 0.4321 0.4954 0 1 Black 0.0869 0.2817 0 1 Graduated High School 0.7574 0.4286 0 1 Some College 0.3609 0.4803 0 1 Graduated College 0.1827 0.3864 0 1 Metro Center 0.2436 0.4292 0 1 1977 0.1512 0.3583 0 1 1978 0.1527 0.3597 0 1 1979 0.1818 0.3857 0 1 1980 0.1838 0.3873 0 1 1981 0.1652 0.3713 0 1 Observations: 451,354 Sources: Census Population Survey, Cost of Personal Borrowing in the US 10
  11. 11. Financing Entrepreneurship with Credit Cards Table 2: Correlation Matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 Self Employed 1.0000 2 Interest Rate Cap -0.0209 1.0000 3 Age 0.1406 0.0021 1.0000 4 Age*Age 0.1405 0.0021 0.9831 1.0000 5 Female -0.0749 -0.0025 -0.0342 -0.0312 1.0000 6 Black -0.0507 0.0384 -0.0095 -0.0114 0.0442 1.0000 7 Graduated High School 0.0041 -0.0010 -0.0203 -0.0647 0.0412 -0.0783 1.0000 8 Some College 0.0225 0.0039 -0.0043 -0.0343 -0.0378 -0.0618 0.4253 1.0000 9 Graduated College 0.0298 -0.0005 0.0553 0.0260 -0.0595 -0.0658 0.2675 0.6291 1.0000 10 Metro Center -0.0349 0.0388 -0.0005 0.0011 0.0225 0.2039 -0.0265 0.0186 0.0153 1.0000 11 1977 -0.0048 -0.0195 0.0006 0.0038 -0.0105 0.0047 -0.0336 -0.0218 -0.0170 0.0055 1.0000 12 1978 -0.0062 -0.0180 -0.0027 0.0003 -0.0031 0.0024 -0.0191 -0.0119 -0.0101 0.0074 -0.1792 1.0000 13 1979 0.0016 -0.0351 -0.0036 -0.0021 -0.0021 -0.0042 -0.0056 -0.0032 -0.0032 -0.0054 -0.1990 -0.2001 1.0000 14 1980 0.0000 -0.0295 -0.0024 -0.0025 0.0013 -0.0026 0.0049 -0.0003 -0.0010 -0.0077 -0.2003 -0.2014 -0.2237 1.0000 15 1981 0.0029 -0.0226 0.0035 0.0015 0.0039 -0.0013 0.0136 0.0062 0.0062 0.0010 -0.1878 -0.1888 -0.2097 -0.2111 1.0000 11
  12. 12. Financing Entrepreneurship with Credit Cards Table 3: Interest Rates for Sample Years State 1971 1977 1982 AL - 18 21 AK 18 18 18 AZ 15.96 18 - AR 10 10 - CA 18 18 18 CO 24 18 21 CT 12 15 18 DE 18 18 17 DC 8 18 18 FL 18 18 18 GA 18 18 18 HI 12 12 12 ID - 18 21 IL 21.6 21.6 21.6 IN - 18 18 IA - 18 18 KS 21.36 21 21 KY - 18 18 LA 18 18 18 ME 16 18 18 MD 18 18 18 MA 18 18 18 MI 20.4 20.4 20.4 MN - 12 18 MS - 18 21 MO 18 18 18 MT 10 18 16 NE 18 18 21 NV 21.6 21.6 - NH - - - NJ 12 18 16 NM - 18 - NY 18 18 25 NC 18 18 18 ND 18 18 18 OH 24 24 - OK 18 18 21 OR - - - PA 24 15 15 RI 18 18 18 SC 18 18 24 SD 24 12 20.04 TN 18 18 18 TX 18 18 18 UT 18 18 - VT 18 18 18 VA 12 18 18 WA 12 12 18 WV - 18 18 WI 12 18 18 WY - 18 21 Source: Cost of Personal Borrowing in the United States, various years 12
  13. 13. Financing Entrepreneurship with Credit Cards Table 4: Regression Results (Dependent Variable: Self Employed) (1) (2) (3) (4) (5) Interest Rate Cap -0.1821*** -0.1941*** -0.0016 -0.0450 -0.0450 (0.0153) (0.0154) (0.0437) (0.0474) (0.0697) Female*Rate 0.0720** 0.0720 (0.0298) (0.0547) Black*Rate 0.1199*** 0.1199 (0.0452) (0.0852) Age 0.0009*** 0.0009*** 0.0009*** 0.0009*** 0.0009** (0.0002) (0.0002) (0.0002) (0.0002) (0.0004) Age*Age 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Female -0.0363*** -0.0364*** -0.0365*** -0.0496*** -0.0496*** (0.0008) (0.0008) (0.0008) (0.0055) (0.0100) Black -0.0348*** -0.0348*** -0.0331*** -0.0552*** -0.0552*** (0.0011) (0.0011) (0.0012) (0.0085) (0.0164) Graduated High School -0.0013 -0.0016 0.0002 0.0002 0.0002 (0.0011) (0.0011) (0.0011) (0.0011) (0.0022) Some College 0.0088*** 0.0088*** 0.0075*** 0.0075*** 0.0075*** (0.0012) (0.0012) (0.0012) (0.0012) (0.0019) Graduated College 0.0072*** 0.0071*** 0.0093*** 0.0093*** 0.0093*** (0.0015) (0.0015) (0.0015) (0.0015) (0.0029) Metro Center -0.0117*** -0.0116*** -0.0054*** -0.0055*** -0.0055*** (0.0009) (0.0009) (0.0011) (0.0011) (0.0013) Year Effects N Y Y Y Y Metroarea-State Fixed Effects N N Y Y Y State Level Cluster N N N N Y Observations 429,862 429,862 429,862 429,862 429,862 2 R 0.0278 0.0279 0.0335 0.0335 0.0335 All standard errors are robust. * Significant at 10%, **Significant at 5%, ***Significant at 1%. 13
  14. 14. Financing Entrepreneurship with Credit Cards Figure 1: Interest Rate Cap Changes across Time Interest Rate Cap Changes 25 20 Interest Rate Caps Average 15 AvgTop5 10 AvgBot5 5 0 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 14

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