Business failure rates: a look at sex and location.

Robinson, Sherry

ABSTRACT

Business failure rates can be difficult to analyze due to the variety of reasons a small business owner may terminate his or her business (retirement, sold business, bankruptcy, etc.).

This study provides further insight into business failure rates by examining data from the US Census Bureau, which investigated survey participants' business ownership over time. In particular, men's and women's rates for bankrupty/business failure are compared. Chi-square analyses performed on the data show that men were more likely to have remained in their businesses. However, among those who had separated from their businesses, women were less likely to name bankruptcy or business failure as the reason for termination.

INTRODUCTION

Business failures are an important aspect of the economy to study, but they are difficult to analyze due to varying definitions of business failure, varying causes business termination, and the lack of comprehensive data. These problems are likely one reason that some studies (Boden & Nucci, 2000; Du Rietz & Henrekson, 2000, Watson, 2003) have determined that women-owned businesses are more likely to be discontinued, while others (Cooper, Gimeno-Gascon, & Woo, 1994; Kalleberg & Leicht, 1991) have not found significant sex-based differences in failure rates.

This study attempts to provide additional insight into business failures by using the U.S. Census Bureau's Survey of Income and Program Participation (SIPP) to compare the rates at which women and men discontinued their businesses during a series of four-month periods and the proportion of business owners whose businesses were terminations due to bankruptcy. The data are then further examined to determine if these rates vary based on location (metropolitan versus non-metropolitan). The following section briefly reviews the literature on rural business issues and business failures, especially in regard to women-owned businesses. The methodology, results and analysis are then presented.

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BUSINESS FAILURE AND BANKRUPTCY

Business failure rates are difficult to study because of the variety of factors that influence business owners to discontinue their operations, such as retirement, sale of the business, bankruptcy, etc. Further complicating the issue is the question of how to define business failure (see Watson & Everett, 1993, 1996). While a business that ends in bankruptcy is no doubt a business failure, other unprofitable businesses may be terminated before bankruptcy, but would probably be best categorized as a failure. In contrast, a highly profitable business that is sold may be counted among business failures as would business that stopped because the owner sold the business, retired, started school full time, etc., if all businesses that do not continue with the same owner are counted among business failures.

Some studies (Boden & Nucci, 2000; Du Rietz & Henrekson, 2000, Watson, 2003) have determined that women-owned business have higher discontinuance rates. One suggested reason for this is that women tend to have a higher proportion of the businesses in industries with lower return rates, such as services and retailing (Watson, 2003). Another reason is that women tend to have younger businesses, while older, more established businesses are more likely to have lower termination rates (Rosa, Carter, & Hamilton, 1996). Multiple demands on many women's time reduce the time they can devote to business (Fasci & Valdez, 1998, Birley, 1989). Women may also, on average, be more risk averse (Anna, Chandler, Jansen & Mero, 1999; Cooper, 1993) and less concerned with financial gain (Rosa, Daphne, & Helen, 1994; Brush, 1992).

Another factor that could be related to business failures is location. A variety of studies (Beggs, Haines & Hurlbert, 1996; Frazier & Niehm, 2004; Fendley & Christenson, 1989; Kale, 1989; MacKenzie, 1992; Mueller, 1988; Small Business Administration [SBA], 2001; Tigges & Green, 1994; Trucker & Lockhart, 1989) have found that rural areas are economically disadvantaged due factors such as low levels of business development and limited work opportunities. The scarcity of affordable professional services combined with smaller, poorer markets make non-metropolitan areas especially challenging to entrepreneurs (Chrisman, Gatewood, & Donlevy, 2002; Fendley & Christenson, 1989; Kale, 1989; Lin, Buss, & Popovich, 1990; SBA, 2001; Tigges & Green, 1994; Trucker & Lockhart, 1989). Such difficulties could lead to higher business discontinuance rates.

Another issue is financing. The mergers of small banks with larger ones, a common phenomenon now, can make it more difficult for small rural businesses to gain financing (Chrisman et al., 2002; Green & McNamara, 1987; SBA, 2001). As with women who experience difficulty in obtaining financing, rural business owners may have lower bankruptcy rates when the business is terminated if the business owner did not have a high level of debt, although this lack of financing may have contributed to the discontinuance.

Other factors associated with rural areas such as the strong social networks, low costs, and a unique way of life, could, in contrast, translate into fewer business terminations. In studies (Robinson, 2002; Robinson & Janoski, 2005) restricted to individual states, non-metropolitan counties were found to have business separation rates that were equal to or lower than metropolitan counties. Studying business owners in South Dakota, Tosterud and Habbershon (1992) found that many of those people were born in the vicinity and had started businesses in order to remain there. It is possible that such business owners would have lower business termination rates as they might be willing to endure greater hardships to stay in business. However, if the economic challenges of starting and succeeding in a rural business outweigh the benefits, business separations rates could be higher.

This study further examines business failure rates by comparing the rates at which men and women stay in business during a given period, and the proportion of business terminations that are due to bankruptcy. For the purposes of this study, the fact that a person previously had, but no longer has, a given business shall be referred to as a business separation, termination or discontinuance. Only those businesses that ended in bankruptcy will be referred to as business failures.

METHODOLOGY, RESULTS AND ANALYSIS

This study used data from the US Census Bureau's 2001 Supplemental Income and Program Participation (SIPP) survey, in which participants were interviewed by phone or personal visit every four months from February 2001 to June 2003. Approximately 36,000 households were included in the study, with everyone over age 15 being interviewed each time. Over 360,000 people were included in the first wave (round of interviews).

During each wave respondents were asked a variety of questions pertaining only to the previous four month period. Questions included, "Do you still own your business?" This question was asked of those who initially indicated they were business owners and only people who owned a business sometime during the course of the survey were included in this study. In the first wave, this included 21,432 people, 412 of whom had discontinued their businesses in the previous four months. The total number of respondents decreased in each wave as respondents could not be interviewed or had become ineligible for the survey (had joined the service, had become institutionalized, or no longer lived with a core respondent). In wave nine, 705 out of 17,161 who had had a business during that wave's time period had terminated their businesses. However, this is not to say that 16,456 people kept their businesses for the duration of the entire study because each wave asked only about the last four months.

Those who stated that they no longer owned their businesses were asked the reason for the separation from their businesses. An advantage of the SIPP is this ability to distinguish bankruptcies from businesses separations that were attributable to other causes. A limitation of this current study is that it does not link the nine waves and therefore cannot present data regarding the number of businesses that survived during the entire nine waves of the SIPP survey. Future research will address this issue. In addition, business size was not determined. However, given that 99% of all businesses are small, the study will refer to the respondents as small business owners. Because the unit of analysis is the individual, a family business could count more than one time as each person who was involved in a business would be included in the sample.

In Table 1, the percentages of businesses that were discontinued during the four months of each wave are presented. Table 2 shows the percentage of those with discontinued businesses who experienced bankruptcy (bankruptcies divided by discontinued businesses). Chi-square analyses were conducted to determine if there was an association between sex and business separation or bankruptcy, and means tests (Mann-Whitney U) were performed to compare the averages.

Analysis of the data clearly shows a difference in the rates at which men and women remain in their businesses. In 8 of the 9 waves, men had significantly lower rates of business separation, which also resulted in a lower average discontinuance rate. However, the men who terminated their businesses were significantly more likely to do so due to bankruptcy. Women's rates of bankruptcy where significantly lower in one-third of the waves, with the overall average also being significantly lower. Taken together, these results suggest that although women were less likely to continue on with their businesses, their businesses were less likely to end in bankruptcy.

One explanation for this phenomenon is that women, in general, tend to be more risk averse (Anna, Chandler, Jansen & Mero, 1999; Cooper, 1993). People who want to minimize risk are less likely to take on debt, which could logically lead to a reduced problem in repaying loans (i.e. bankruptcy). On the other hand, women may find it more difficult to obtain desired financing. In addition, if women start smaller businesses in industries that require little capital, they may be more likely to discontinue their businesses (Brush & Chaganti, 1999) given the fact that businesses requiring less capital have higher termination rates (Bruderl, Preisendorfer, & Ziegler, 1992; Hutchinson, Hutchinson, & Newcomer, 1938; Watson & Everett, 1996).

Anther potential explanation comes from researchers (Rosa, Daphne, & Helen, 1994; Brush, 1992) who have found that many women are less concerned with financial gain than are their male counterparts. If women started their businesses for reasons that were not primarily financial, they may also terminate them for non-financial reasons. For example, a business could be profitable without fulfilling the owner's primary goals, thus influencing the owner to discontinue the business. To further investigate this issue, the data were broken down into two categories by location--metropolitan or residual (non-metropolitan or rural). Analysis of the data by location shows that the sex differences in the overall sample not only exist in each location, but also seem to be somewhat greater in the rural areas (Tables 3 and 4). While the non-metro men had the lowest average discontinuance rate of all groups, the non-metro women had the highest rates. While the smallest difference between men and women in both groups was 0.6 of a percentage point, the largest difference in the non-metro group was 5.3 whereas it was only 2.6 for the metro group.

In comparing same-sex respondents by location, the overall averages were not significantly different, although there were location-based differences among men in Waves 7 and 9, and among women in Waves 3, 7 and 9. During Waves 7 and 9, rural men had lower rates of business discontinuance than metro men, while rural women had higher rates than metro women. Similarly, fewer differences were evident among the bankruptcy rates when women were compared against women and men against men (Table 4). However, the differences were both greater and more frequent among men, with metro men showing generally lower rates of bankruptcy. Women's bankruptcy rates were significantly different in only two waves, with metro women higher in one wave and lower in the other. What is most unusual among women's rates is the number of times that the bankruptcy rate was 0%, especially among non-metro women. This could indicate truly low bankruptcy rates, a reluctance to admit bankruptcy, or the fairly small sample size once the data were broken down into such detailed categories. A limitation to this study was that despite the large overall sample size, the number of women who went bankrupt ranged from 0 to 36, while the number that discontinued their businesses for other reasons ranged from 176 to 357. Clearly, further research should be done with larger pools of people in these small detailed categories.

CONCLUSION

The results of this study confirm those of researchers (Boden & Nucci, 2000; Du Rietz & Henrekson, 2000, Watson, 2003) who determined that women were more likely to discontinue their businesses. However, the finding that women were less likely to discontinue their business because of bankruptcy or business failure is even more significant. Financial backers could be exposed to less risk when providing funds to women-owned businesses if they are more likely to pay off outstanding loans, although equity investing may be riskier due to a higher level of business termination. Organizations that provide assistance to business owners may find this information useful if they can tailor their services more to the market.

The reason a business is discontinued is vitally important not only to the business owner, but also to society. The overall proportion of businesses that ended in bankruptcy is relatively small, given that approximately 9 out of 10 businesses were discontinued for a reason other than business failure. Brush (1992) has suggested that women evaluate the performance of their businesses not only in financial terms, but also in social terms such as employee satisfaction, and social contribution. Future research should continue to investigate this issue by examining the reasons women terminate their business and seek to find ways to help them achieve their overall goals, which may not be strictly financial in nature.

Future research should also continue to examine any differences between rural and metropolitan business failure rates as the sex-based differences in this study were found to be emphasized in non-metro areas. In addition, the lack of significant differences between same-sex groups in meto and non-metro locations suggests that rural business are not more likely to end in failure, despite the generally perceived economic disadvantages of less-populated and developed areas.

REFERENCES

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Sherry Robinson, Penn State University

Table 1: Proportions of Discontinued Businesses

 All Respondents

Wave Total Men Women Chi-sq/M-WU Sig.

1 1.9% 1.7% 2.3% 8.37 .004 **
2 3.3% 2.3% 5.1% 100.63 .000 ***
3 4.6% 3.8% 5.9% 44.18 .000 ***
4 3.9% 3.2% 5.1% 39.51 .000 ***
5 4.4% 3.5% 5.9% 54.82 .000 ***
6 4.4% 3.3% 5.5% 54.72 .000 ***
7 3.8% 3.6% 4.0% 1.32 .250
8 3.2% 2.6% 4.2% 8.34 .004 **
9 4.1% 3.4% 5.4% 42.25 .000 ***
Ave. 3.7% 3.0% 5.0% 36.5 .000 ***

Metropolitan

Wave Total Men Women Chi-sq/M-WU Sig.

1 2.0% 1.8% 2.4% 5.92 .015 *
2 3.2% 2.3% 4.9% 10.20 .001 ***
3 4.8% 3.9% 6.4% 67.58 .000 ***
4 3.8% 3.1% 5.1% 46.52 .000 ***
5 4.2% 3.4% 5.6% 33.18 .000 ***
6 4.0% 3.1% 5.6% 38.33 .000 ***
7 4.0% 4.2% 3.6% 47.95 .000 ***
8 3.1% 2.7% 3.9% 23.91 .122
9 4.5% 4.0% 5.3% 3.54 .060
Ave. 3.7% 3.2% 4.8% 12.50 .013 *

Non-metropolitan

Wave Total Men Women Chi-sq/M-WU Sig.

1 1.8% 1.6% 2.2% 2.36 .124
2 3.6% 2.5% 5.8% 34.30 .000 ***
3 3.9% 3.7% 4.4% 1.38 .240
4 4.1% 3.6% 5.3% 7.23 .007 **
5 4.8% 3.9% 6.6% 17.39 .000 ***
6 4.3% 3.7% 5.5% 8.47 .004 **
7 3.2% 2.2% 5.1% 27.49 .000 ***
8 3.4% 2.4% 5.2% 1.14 .012 *
9 3.3% 1.7% 7.0% 74.02 .000 ***
Ave. 3.6% 2.8% 5.2% 6.50 .003 **

* sig. p<.05; ** sig. p<.01; *** sig. p<.001

Table 2: Proportion of (Former) Business Owners Whose Businesses
Failed (Bankruptcy)

All Respondents

Wave Total Men Women Chi-sq/M-WU Sig.

1 6.2% 10.2% 0% 19.01 .000 ***
2 6.0% 5.7% 6.2% 0.08 .779
3 8.5% 7.9% 9.2% 0.40 .528
4 10.3% 15.2% 4.8% 20.34 .000 ***
5 11.2% 12.9% 9.5% 2.27 .132
6 7.2% 8.6% 5.7% 2.31 .128
7 9.7% 9.9% 9.5% 1.32 .250
8 9.4% 9.6% 9.1% 0.01 .920
9 8.5% 10.9% 5.9% 5.61 .018 **
Ave. 8.5% 10.6% 6.3% 89.0 .021 *

Metropolitan

Wave Total Men Women Chi-sq/M-WU Sig.

1 5.1% 8.9% 0% 10.2 .001 ***
2 5.4% 4.0% 6.6% 1.49 .222
3 9.1% 8.4% 9.9% 0.50 .482
4 10.4% 14.1% 6.5% 7.64 .006 **
5 10.8% 12.7% 8.8% 2.27 .132
6 7.5% 10.5% 4.5% 6.94 .008 **
7 11.0% 10.7% 11.6% 0.10 .755
8 11.9% 10.9% 13.0% 0.11 .741
9 5.9% 6.3% 8.9% 7.64 .006 **
Ave. 8.6% 9.9% 7.1% 24.0 .145

Non-metropolitan

Wave Total Men Women Chi-sq/M-WU Sig.

1 8.3% 14.3% 0% 6.23 .013 *
2 6.9% 10.1% 4.3% 2.29 .130
3 6.3% 6.8% 5.6% 0.10 .754
4 10.0% 17.7% 0.0% 17.11 .000 ***
5 12.3% 13.2% 11.3% 0.19 .664
6 6.5% 4.3% 9.4% 2.11 .146
7 5.4% 5.9% 5.1% 0.05 .827
8 2.6% 5.6% 0.0% 1.14 .285
9 17.5% 23.5% 14.0% 2.03 .154
Ave. 8.4% 11.3% 5.5% 18.0 .046 *

* sig. p<.05; ** sig. p<.01; *** sig. p<.001

Table 3: Same-Sex Comparisons by Location: Discontinued Businesses

Men

Wave Metro Non-metro Chi-sq/M-WU Sig.

1 1.8% 1.6% 0.72 .396
2 2.3% 2.5% 0.43 .513
3 3.9% 3.7% 0.19 .586
4 3.1% 3.6% 2.19 .369
5 3.4% 3.9% 1.82 .177
6 3.1% 3.7% 2.36 .047 *
7 4.2% 2.2% 23.81 .000 ***
8 2.7% 2.4% 0.19 .503
9 4.0% 1.7% 31.90 .002 **
Ave. 3.2% 2.8% 29.5 .331

Women

Wave Metro Non-metro Chi-sq/M-WU Sig.

1 0.0% 2.2% 0.24 .623
2 6.6% 5.8% 2.46 .117
3 9.9% 4.4% 9.34 .002 **
4 6.5% 5.3% 0.10 .757
5 8.8% 6.6% 2.37 .124
6 4.5% 5.5% 0.01 .987
7 11.6% 5.1% 2.12 .145
8 13.0% 5.2% 6.77 .009 **
9 8.9% 7.0% 4.81 .028 *
Ave. 7.1% 5.2% 31.0 .401

* sig. p<.05; ** sig. p<.01; *** sig. p<.001

Table 4: Same-Sex Comparisons by Location: Business Failure
(Bankruptcy)

Men

Wave Metro Non-metro Chi-sq/M-WU Sig.

1 8.9% 14.3% 1.36 .243
2 4.0% 10.1% 4.02 .045 *
3 8.4% 6.8% 0.30 .586
4 14.1% 17.7% 0.81 .369
5 12.7% 13.2% 1.82 .890
6 10.5% 4.3% 3.95 .047 *
7 10.7% 5.9% 1.45 .229
8 10.9% 5.6% 0.45 .503
9 6.3% 23.5% 9.73 .002 **
Ave. 9.9% 11.3% 38.0 .825

Women

Wave Metro Non-metro Chi-sq/M-WU Sig.

1 0% 0%
2 6.6% 4.3% 0.66 .417
3 9.9% 5.6% 1.30 .255
4 6.5% 0.0% 5.97 .015 *
5 8.8% 11.3% 0.59 .444
6 4.5% 9.4% 2.91 .008 **
7 11.6% 5.1% 2.66 .103
8 13.0% 0.0% 2.87 .090
9 8.9% 14.0% 13.38 .000 ***
Ave. 7.1% 5.5% 31.5 .424

* sig. p<.05; ** sig. p<.01; *** sig. p<.001