5+7*8+12/4-2=5 5+7*8+12/4-2=102等号前面加括号 得到后面的数5+7*8+12/4-2=5 5+7*8+12/4-2=102 还有一题啊。5+7*8+12/4-2=5
(5+7)×8+12÷(4-2)=1025+7×(8+12)÷(4-2)=75[(5+7)×8+12]÷4-2=255+(7×8+12)÷4-2=205+7×(8+12)÷4-2=38(5+7)×(8+12)÷(4-2)=120你确定题目不是75或25么
为您推荐：
其他类似问题
（5+7）*8+12/（4-2）=102 我最喜欢做24点游戏 这个我比较会
（5+7）*8+12/(4-2)=102;
(1)(5+7)*8+12/(4-2)=102 (2)5+(7*8+12)/4-2=20(3)[(5+7)*8+12]/4-2=25
扫描下载二维码12.6×7.6×2.32÷1.9÷1.4÷2.9的简便算法
原式=（12.6÷1.4）×（7.6÷1.9）×（2.32÷2.9）=9×4×0.8=36×0.8=28.8
为您推荐：
其他类似问题
=(12.6÷1.4)×(7.6÷1.9)×(2.32÷2.9)=9×4××0.8=28.8
扫描下载二维码FRB: Finance and Economics Discussion Series: Screen Reader Version - Examining the Impact of Credit Access on Small Firm Survivability
Finance and Economics Discussion Series: 2012-10 Screen Reader version
Examining the Impact of Credit Access on Small Firm Survivability
Traci L. Mach
Board of Governors of the Federal Reserve System
John D. Wolken
Keywords: Small business finance, small firm survivability
This paper examines the effects of credit availability on small firm survivability over the period 2004 to 2008 for non-publicly traded small enterprises. Using data from the 2003 Survey of Small Business Finances, we develop failure prediction models for a sample of small firms that were confirmed
to have been in business as of December 2003, with particular attention to the impact of credit constraints. We find that credit constrained firms were significantly more likely to go out of business than non constrained firms. Moreover, credit constraint and credit access variables appear to be
among the most important factors predicting which small U.S. firms went out of business during the
period even though an extensive set of firm, owner, and market characteristics were also included as explanatory factors.
JEL Classification: G30, L20
The recent economic turmoil beginning in late 2007 has challenged businesses of all sizes. Firms have been faced with a great deal of uncertainty regarding sales and the economic outlook. At the same time, the recent downturn has dramatically impacted the availability and terms of credit. Over
period, financial institutions have reported tightening their credit standards for approving loans (SLOOS). Many small business owners rely on
personal assets to guarantee or collateralize loans for their firms. As their equity in real estate holdings has generally declined in value during the recent turmoil, owners' ability to tap into personal balance sheets to secure their business credit needs has also shrunk (NFIB 2010).
This paper examines the effects of credit availability on small firm survivability over the period 2004 to 2008 for non-publicly traded small enterprises. We develop failure prediction models for a sample of small firms that were confirmed to have been in business as of December 2003, with
particular attention to the impact of credit constraints. We find that credit constrained firms were significantly more likely to go out of business than non
constrained firms. Moreover, credit constraint and credit access variables appear to be among the most important factors predicting which small U.S. firms went out of business during the
period even though an extensive set of firm, owner, and market characteristics were also included as
explanatory factors.
Baseline data are taken from the 2003 Survey of Small Business Finances (SSBF) and provide information on the balance sheets, credit use, and credit constraints faced by a representative sample of more than 4,000 firms that were in operation at the end of 2003. The SSBF allows construction of
standard financial balance sheet and income ratios that are used in many failure prediction models (Altman 1968). In addition to financial ratios, the SSBF
data are rich in firm (e.g., employment, organizational form, location, age and industry) as well as owner characteristics (e.g., race, education, experience, home ownership and value of home). Importantly for this study, the data also contain many measures of credit constraint, including a firm
credit score, self-reported credit history variables such as late payments and bankruptcy for firm and principal owner, recent credit application experience of the firm, and whether firms borrow using trade credit and credit cards.
Our measure of survivability is constructed from the National Establishment Time Series (NETS) database from Walls & Associates. The NETS database takes
cross-sectional data from the Dun & Bradstreet Market Identifier File and constructs a panel of sales and employment over time. These data allow us to track which firms closed or went out of business, were absorbed by another firm, or continued operations in each year subsequent to 2004. We
estimate a reduced-form logistic model of a firm being in business in 2008. The results indicate that credit access and credit quality are significant indicators of the firm still being in business. We then estimate a proportional hazard model with yearly indicators of the firms' operational
status. The hazard model further underscores the importance of credit access and quality in the ability of the firms to continue operations.
The literature on default prediction studies is voluminous and involves several approaches. The earliest, most well-known and widely applied technique to
firm failure prediction models uses financial ratio analysis. Following the introduction of such models in the 1960s, researchers provided theoretical arguments for failure that tended to argue one or both of the following hypotheses. The first is that firms will fail because the present value of
their costs exceeds their revenues. The second is that inefficiencies in capital markets can lead to failure among firms with positive net present values. While financial statement data are required for ratio analysis studies, the theoretical literature on firm failure has suggested other
variables, including firm and owner characteristics other than financial statement variables, local and macro economic conditions, as well as indicators of credit access and credit quality. The various approaches will be discussed below.
Studies predicting failure of businesses began with work in the 1930s that established that accounting ratio measures exhibited by discontinuing firms were different from measures exhibited by surviving firms (Shailer (1989). Following up on this finding, Beaver (1966) and Altman (1968) explored
the differences on small samples of failed and non-failed businesses. Beaver examined some fourteen financial ratios individually on a matched sample of 79 failed and 79 non-failed institutions. Altman (1968) is often credited as the first to apply multivariate techniques to failure analysis. He
applied mutiple discriminant analysis to a sample of 33 failed and 33 non-failed institutions that filed bankruptcy petitions during the period 1946-65. From an initial 22 ratios, Altman settled on five ratios as providing in combination the best overall prediction of corporate bankruptcy. The
ratios included measures of liquidity, profitability, leverage, solvency and activity ratios.
Many studies since Altman (1968) have added further support to the potential usefulness of such models for failure prediction. The literature is quite large and studies that provide reviews of earlier works include Ohlson (1980), Taffler (1982), and Altman and Sabato (2007). Such studies have
used various statistical techniques (e.g., multiple discriminate analysis, logistic and probit analysis, and factor analysis) and have been applied to several countries including the United States and Great Britain. It is fair to say that most such studies have been concerned with large publicly
traded (listed or quoted) firms - in part because financial statements for such companies are generally available.
Because the focus has been on larger concerns, samples of failed enterprises in these studies tend to be quite small. The early work generally drew matching samples of non-failed companies, whereas more recent studies have included far larger samples of non-failed institutions. For example,
Altman and Sabato (2007) employ a sample of about 2000 firms between 1994 and 2002. However, only 120 observations were observed failures.
Little attention was initially given to small or private enterprises. Publicly available datasets are heavily biased towards large firms and usually contain little or no information on small firms. However, some efforts to study small firm failure have produced encouraging results, although
often the variables and models used for small enterprise studies differ from the ratio analysis described above. This is in large part because financial statement data for smaller enterprises or for non-publicly traded enterprises is often unavailable. Studies of private companies using financial ratio analysis are reviewed by Shailer (1989). With the exception of Edminster (1972) - who used a sample of 21 firms reporting losses and 21
non-failed businesses drawn from the US Small Business Administration borrowers for 1954-69 - these studies have generally analyzed private United Kingdom (Briggs and MacLennan (1983), Peel and Peel (1987)) or Australian companies (Shailer (1986)). Samples were typically on the order of 100 or less
observations. In- sample predictions averaged around 75 percent accurate. But when applied to a hold-out sample, Peel and Peel's models exhibited high classification error rates. Differences in the variables used between small (private) company models and large (public) company models as reported
in these studies suggest that small business failure models may be a separate avenue of research.
Employing the hypothesis that survival is dependent upon a positive net present value (of revenues and costs), several studies have identified firm characteristics other than financial ratios that are likely associated with increasing the difficulty firms have in maintaining solvency. Models
formulated by Argenti (1976), Jovanovic (1982) and Dunne, Roberts and Samuelson (1988) indicate that firms fail because of their management structure or because they are unlucky or inefficient. Argenti (1976) viewed financial ratios as symptoms of failure, not the cause. Rather, the process of failure is based on a number of defects in the organization and financial structure of the company. Based on case studies, Argenti concludes
that factors reflecting the management structure and the adequacy of the accounting information system such as one-man rule, lack of management depth and experience, and concentration of resources into a single project are factors that are associated with failure. Although not specifically
examining small firms, these factors are usually characteristics of small firms. Studies that included some of these factors include Storey et al (1987) and Keasey and Watson (1986). Both studies included financial ratios as well as some variables reflecting management structure. They found that
the nonfinancial ratio variables increased the predictive accuracy somewhat. The results are generally supportive of the Argenti model of business failure. Such variables also have some practical advantages in that some of the information required is available for small firms, whereas financial
statement variables are difficult to obtain for non-traded enterprises.
Jovanovic provides a two period model of firms' costs and Dunne et al test some of the implications to further explore why failures are more likely among small and young enterprises. In these models, small and young firms are likely to be more vulnerable because it takes time to acquire the
information and knowledge to better predict their costs and revenue functions than it does for larger and older firms.
Hall (1992, p. 240) further argues that small firms have limited portfolios - first with respect to the opportunity-sets of products or services they offer or markets in which they operate, and second with respect to the human capital embodied in management, which is likely less vis-&-vis
larger firms. Both the degree of diversification and level of human capital will likely increase with the size of the company. Consequently, small firms are more prone to failure. Limited portfolios will lower the expected earnings and increase the variance of small vis-&-vis larger firms.
And a result of a lower volume of human capital is that the income streams that it generates may be both lower and more sensitive to the situation being faced. These observations reinforce the importance of accounting for shocks to the firms' operation. But they also suggest that the
characteristics of management - including factors such as experience, education, and breadth of management - may be important to identifying firms likely to fail.
Another line of research focuses on capital market imperfections (Wadhwani (1986), Hudson (1986), Simmons (1989), and by implication credit constraints and credit quality. Inefficiencies in capital markets can lead to failure among firms with positive net present values. In particular banks (and
other lenders) have been accused of charging small firms too much, demanding too high a level of collateralization, being inefficient in their procedures for credit assessment (or economies of scale with firm size), and in some cases unwilling to lend to smaller firms. As a result of these
imperfections, small firms facing such constraints will be less able to adjust to shocks to their operations than larger firms.
Early tests of failure models based on capital market imperfections use variables that were intended to be surrogates for shocks to cost or revenue flows, as was true of the studies by Dunne et al (1988), Storey et al (1987) and Keasey and Watson (1986). Hudson includes a variable of company
profits to company GDP which might be considered a surrogate fo Wadhwani does not include inflation but does find that interest rates are positively correlated with the numbers of liquidations.
The literature also suggests several indicators of credit access and credit quality that are likely related to the probability of discontinuance. Applying and being denied credit signals that a firm is unable to secure the desired level of credit. Credit constrained firms have less access to
credit which may also force them to use relatively more expensive shorter term debt to finance their operations. Such restrictions and costs may leave them vulnerable to shocks in their cash flow. This in turn puts them at a higher risk of failure (Keasey and Watson (1991)). It may also constrain
the firm from initiating new projects, expanding operations, investing in research and development, or even making its payroll, and ultimately affect the firm's probability of survival. For example, Musso and Schiavo (2007), using an index of financial constraint, report a significant relationship
between survival and financial constraint.
Such constraints may cause firms to turn to alternative but somewhat more expensive types of credit. For example, borrowing using credit cards or using trade credit are often more expensive than traditional bank loans. Blanchflower and Evans (2004) found that firms that had their credit access
constrained-i.e., denied credit or feared applying because they thought they would be denied-were significantly more likely to borrow using credit cards. In a recent study of start-up firms, Scott (2009) reports a negative correlation between the level of credit card debt and subsequent survival.
And Peterson and Rajan (1997) report that firms that had loans requests denied were more likely to borrow using trade credit.
In addition to credit constraint indicators, the credit quality of firms may also be related to subsequent survival. Several studies have found that firms with poor credit histories (e.g., bankruptcy, delinquency on current debt, and judgments), or low credit scores, are less likely to have loan
requests approved (e.g., Blanchflower, et al (2004), Cavalluzzo and Wolken (2002), Ayytinen and Pajarinen (2007). Robb and Robinson (2010) examine differences between surviving and failing "new" firms and report that firm credit scores are positively associated with indicators of success -
revenues, assets, profits, and employees larger than the sample median levels.
In sum, the literature regarding predicting business failure has used a variety of approaches, starting with financial ratio analysis in the 1960s. Since then, researchers have identified a number of other factors likely to affect the probability of failure, in part due to the development of
theoretical models to help understand what leads to failure and in part due to necessity owing the lack of publicly available financial data for small firms. The literature indicates that factors that are associated with (small) firm insolvency include financial ratios as well as other income and
balance sheet variables such as size. Moreover, other firm characteristics (e.g., age) and owner/manager characteristics (experience, education) are likely important, as are variables reflecting credit constraints and credit quality of the firm. Finally, given that firm performance is likely to be
affected to some degree by the general health (or change in the health) of the local and national economy, it may be important to include such variables as well.
The majority of data for this paper come from the 2003 Survey of Small Business Finances (SSBF). The 2003 SSBF was conducted to collect information from the owners of a nationally representative sample of more than 4,000 U.S. small business enterprises. Owners were asked about firm income
fin lending
the number of branches and firm h the types of, and locations of financial institu and about various other firm characteristics.
The target population of the survey was defined as for-profit, non-governmental, non-depository and non-agricultural enterprises with fewer than 500 employees. Firms in the sample had to be either single establishments, or the headquarters of multiple establishment enterprises that were not
majority owned subsidiaries of other firms. Additionally, in order to be eligible, firms had to have been in business during December of 2003 and at the time of the interview. The majority of interviews occurred between June and December of 2004.
The Dun and Bradstreet (D&B) Market Identifier file (DMI) was used to construct the sampling frame for the 2003 SSBF. The DMI contains minimal basic company data on U.S. businesses. It is a meant to be a snapshot of active businesses at a particular point in
Many small business owners rely on personal assets to guarantee or collateralize loans for their firms. As their equity in real estate holdings has generally declined in value during the recent turmoil, owners' ability to tap into personal balance sheets to secure their business credit needs has
also shrunk (National Federation of Independent Businesses 2010). In order to account for this shrinking source of credit, we incorporate the change in the house price index into the model. The most prominent of such indices are from the Federal Housing Finance Agency (FHFA) and LoanPerformance (LP).& Both indices have the same basic foundation of identifying and utilizing sales pair data, there are a few major differences to point out and remember when analyzing the index results.& The most important distinction between the indices is that the FHFA uses
only loans purchased by Fannie Mae or Freddie Mac.& The LP data is obtained from their real estate database.& In addition, the weighting scheme with respect to the price of the property differs. FHFA's index weights changes in house prices equally for all properties and the LP HPI creates
sub-indices to breakout different home price levels about the area average.
These indices have diverged fairly substantially over the last several years. The FHFA index peaked at a much lower level later than the LP index. For example, using the 2000 index as a baseline, the FHFA home prices appreciated around 65 percent, peaking mid-2006. In 2008, the FHFA index was 45
percent higher than it had been in 2000. In contrast, the LP index appreciated 95 percent, peaking mid-2005; by fourth quarter of 2008 it was 60 percent above its 2000 level. Because it is unclear which one of the indices is best, we run the model separately for each index. The percentage change in
the index between 2004, the time of the SSBF interview, and 2008, the last year for which NETS data are available, is merged with the SSBF by the state or MSA in which the firm's headquarter office is located.
Because the location where the firm operates is likely to have an impact on the survivability of the firms, we also include a limited number of measures of the economic environment. Using the MSA or rural county where the firm's headquarters is located, we merge in measures of population and
establishment density from Census data, the average wage per job and per capita income from BEA, and the unemployment rate from BLS. As an area becomes
more densely populated, we would expect the firm to have more demand for its products, increasingly the likelihood that the firm will stay in business. The relationship with the number of establishments in the area is theoretically less clear. While higher densities of businesses would likely be
associated with better infrastructure decreasing the cost of doing business, more businesses could also mean more competition. Because small businesses are also potential employers, rising wages in the area where the firm operates is likely to increase th thus, we would expect
to see an inverse relationship between average wages and firm survivability. Per capita income, on the other hand, should have a positive relationship with survivability as more wealthy residents would be more capable of buying the firm's output. We would expect unemployment rates to be negatively
associated with survivability.
To capture the changing nature of the economic environment, we use the percentage change in these measures between 2004 and 2008 when we estimate the logistic models and the one-year lagged value (time-varying) when we estimate the proportional hazard model.
Of the 4,240 firms that completed the 2003 SSBF, 4,230 were successfully matched to firms on the NETS database. Of those firms, 332, just under 8
percent, were no longer in business as of January 2008 (Table 1). This represents slightly less than 10 percent of the population. Statistics on new employer firms indicate that 69 percent survive at least the first two years and 51 percent survive at least five years (Small Business
Administration, 2009). Because the 2004 SSBF firms are not all new firms--the median firm was 12 years old--and they are not all employer firms, one would not expect the survival rates to match perfectly, but the 8 percent does not seem extraordinarily high or low.
In order not to bias the results, we did not want to include firms in the analysis that already looked defunct when they were interviewed in 2004. For the analysis, we eliminated firms that reported assets of less than $0 or sales of less than $1,000. Because the SSBF were multiply imputed, this
restriction led to slightly different numbers of firms being used for each implicate. Table 2 provides the breakdown of the sample by implicate. Between 158 and 162 firms were dropped from analysis. About double the fraction of these marginal firms were no longer in business in 2008 than the other
firms. The analysis that follows was conducted using the remaining firms in the 2003 SSBF.
Table 3 provides some descriptive statistics of the sample by whether or not the firm was still in business by 2008. Definitions of variables are
summarized in Appendix A. A quick overview of the data shows that the firms that were still in business by 2008 looked different than those that were not when they were interviewed in 2004. In terms of credit history, the firms that were no longer in business in 2008 had credit scores that were 10
percentage points lower than their counterparts. In 2003, the majority of small firms were not rated by D&B--an indicator of the opacity of small firms. However, firms that were no longer in business by 2008 were much more apt to have not been credit rated by D&B, likely making it more
difficult for potential lenders to evaluate their credit worthiness. The discontinued firms were also more likely to report that the owner or the firm had been 60 or more days delinquent in paying 3 or more bills in the past three years or had a judgment rendered against them.
Discontinued firms were generally smaller than continuing firms, and reported smaller amounts for both balance sheet and income items: Discontinued firms had small they had lower liabilities and accounts receivable as well. As a baseline comparison to the early failure
prediction literature, we construct the financial ratios suggested by Altman. These ratios include multiple measures of leverage, liquidity, activity (e.g., sales to assets), profitability, and coverage (e.g., pre-tax profit to loans). There were large differences in the average ratios across the
two groups, although few differences are statistically significant. For instance, discontinued firms reported average short-term debt to equity ratios of around 10 while continued firms reported an average near 75. However, their medians were quite similar. This gives some idea of how heterogeneous
the finances of small businesses are and possibly some insights into the potential (or lack thereof) of financial ratios to predict the discontinuance of very small businesses.
In terms of firm demographics, the discontinued firms tended to be younger and smaller, although the differences in size measures (assets, sales, profits, and employment) other than profits are not statistically different. There is virtually no difference in forms of organization. There are only
small differences in the characteristics of the owners of the firms th they were slightly more likely to be minority owned and had less formal education and less business experience.
The data contain five completely imputed datasets. We utilize Rubin's formulas to use the imputed datasets to account for imputation error. In addition, all estimates are weighted to reflect unequal selection probabilities and response rates. All models are estimated in Stata. We estimate two
types of models. The logistic model, which reports estimates of log odds ratios, estimates the probability that a firm that was in business during 2004 went out of business sometime between 2004 and 2008. We then estimate a proportional hazards model including information on credit ratings and
geographic controls that vary over the
period. For each, several model variants are estimated: The baseline model (model 1) includes most variables. Models 2-6 adds owne model 3 adds the FHFA real estate prices and model 4 uses the alternative LP real estate prices.
Models 5 and 6 add geographic controls to models 3 and 4, respectively.
The variables described above are used to predict the probability of discontinuance using reduced form logistic models (Table 4). The dependent variable is equal to one if the firm in the 2004 sample discontinued sometime between 2004 and 2008.
The baseline model includes (column 1) credit quality variables, credit access measures, basic firm characteristics, financial ratios, and dummies for
Census divisions and major SIC industries. Results from this model are provided in column 1 of Table 4. Results for joint hypotheses tests of groups of variables (e.g., credit quality variables) are reported at the bottom of table 4. By groups, we find that the credit quality and access measures
are jointly significant at the one percent level, as are the Census division dummy variables, and financial ratios and credit constraint variables were significant at the five percent level. By group, firm size measures - employment, sales, assets, profit and firm age-- and industry dummies were
not significant.
On an individual basis, several of the credit quality and credit access variables are significant. Table 4 provides the log odds ratios for each of the controls. For each percentage point the firm's credit score (which ranges from 1 to 100) is above the average score, the odds of no longer being
in business decreases 0.6 percent. Being rated by D&B, regardless of the rating, decreases the likelihood of the firm being out of business by 2008 significantly. Having judgments in 2004 against the firm or its owner increase the odds of the firm no longer being in business 1.5 times. And
borrowing on credit cards and trade credit in 2004--an indicator of restricted access to credit--increases the odds that a given firm will no longer be in business by 2008 by 41 and 53 percent, respectively.
The NETS data provide no indication of why a firm is no longer in business. It could be that the firm became unprofitable and had to shut down. However, it could also be the case that the owner simply decided to close the firm, perhaps to retire. In column 2, we add in measures to control for
the age and experience to try to control for this uncertainty. Neither of these variables is marginally significant and the rest of the results are largely unchanged.
The next two models incorporate information on the changes in real estate prices that occurred over this period. In general, we would expect increasing prices to be associated with increasing access to credit and a negative correlation with firms no longer being in business in 2008. Column 3
uses the FHFA index and column 4 uses the LP index. As with the addition of the owner characteristics, the effects of other coefficients are largely unchanged. Both indices predict an inverse relationship between firm discontinuance and real estate prices, but neither index is significant at
traditional levels.
The final two models (columns 5 and 6) include additional measures specific to the geographic area where the firm is located. These measures include percentage changes between 2004 and 2008 in population density, average wages, unemployment rate, establishment density, and per capita income.
Jointly, these measures are not significant and, again, the other coefficients are robust to their inclusion. The age of the firm becomes significant as the stan the actual coefficient is unchanged. Individually, increases in average wages are significantly associated with an
increase in the odds of failure by 2008 while increases in per capita income as associated with a decrease in the odds.
The NETS data provide a yearly measure of whether the firm was in operation which allows us to incorporate a sense of timing into the estimates. We estimate a proportional hazard model of firm survival: given that the firm has survived until time t, what is the likelihood that it will fail at
time t+1. We use the same controls that we used in the logistic models, but examine failure in each year. The resultant hazard ratios are presented in Table 5. These models are also estimated using 5 implicates and weights, as discussed above.
The results are similar to variables that are significant in the logistic models are generally significant in the hazard models and vice versa. As with logistic model, the hazard ratios are very robust across specifications.
Results from the baseline model are provided in column 1 of Table 5. By groups, we find that only the credit quality and Census division dummy variables are jointly significant at the one percent level. None of the other groups of variables are jointly significant at traditional levels, although
the credit constraint variables are significant at the fifteen percent level.
Individually, the credit score is once again significant. However the estimated impact is near zero. A judgment rendered against the firm or owner increases the likelihood of going out of business by 10 percent. Not being rated by D&B in the previous year increases the likelihood of the firm
going out of business by 11 percent in each period. And borrowing trade credit in 2004--an indicator of restricted access to credit--increases the yearly likelihood that a given firm will go out of business by 4 percent. Borrowing on credit cards is marginally significant at the 15 percent
Column 2 adds in measures to control for the age and experience to try to control for this uncertainty about why the firm is no longer in business. As with the logistic model, neither of these variables is marginally significant and the rest of the results are largely unchanged.
The next two models incorporate information on the changes in real estate prices that occurred over this period. The LP index is significant at the 10 percent level, but the model predicts a positive relationship between increasing home prices and the likelihood of a firm going out of business.
This is counter to what theory would predict. As with the addition of the owner characteristics, the effects of other coefficients are largely unchanged.
The final two models include additional measures specific to the geographic area where the firm is located. These measures include one year lagged values from 2003 to 2007 in population density, average wages, unemployment rate, establishment density, and per capita income. Jointly and
individually, these measures are not significant and, again, the other coefficients are robust to their inclusion.
In sum, the results suggest that there are large differences between the continuing and discontinuing firms. Generally, credit access and credit quality measures in 2004 are important indicators of whether the firm will be in business in by 2008. Other significant characteristics include being
headquartered in the West North Central, East North Central or South Atlantic Census divisions or being a construction or retail trade business.
It is important to keep in mind that the recent economic turmoil is not representative of all periods. Despite the extraordinary times, the credit access measures do seem to be predictive of firm performance. In future research we would like to address the question of whether some of the credit
constrained firms may have fared better had the economy performed better by comparing firm failure during this period to earlier.
Table 1: Firms out of business, by last year in business
Last year in business
Unweighted Frequency
Unweighted Percent
Weighted Percent
Table 2: Sample restrictions by implicate
$0 or Sales $1,000
Dropped Sample Still in business in 2008
Not in business by 2008
Sample Used in Estimation Still in business in 2008
Not in business by 2008
Table 3: Means and Medians by Out of Business Status in 2008
Out of business by 2008 Mean
Out of business by 2008 Median
Still in business in 2008 Mean
Still in business in 2008 Median
Credit Quality Variables: Firm credit score
Credit Quality Variables: Firm or owner declared bankruptcy
Credit Quality Variables: Firm or owner delinquent 3+ times
Credit Quality Variables: Judgment against firm or owner
Credit Quality Variables: Zero or negative equity indicator
Credit Quality Variables: High or good D&B credit rating in 2003
Credit Quality Variables: Fair D&B credit rating in 2003
Credit Quality Variables: Limited D&B credit rating in 2003
Credit Quality Variables: No D&B credit rating in 2003
Credit Access Variables: Firm applied for credit
Credit Access Variables: Denied credit
Credit Access Variables: Did not apply for credit fearing denial
Credit Access Variables: Borrowed on credit cards
Credit Access Variables: Used trade credit
Credit Access Variables: Borrowed on trade credit
Credit Access Variables: Denied trade credit
Credit Access Variables: Used real estate as collateral
Credit Access Variables: LP home prices (%
Credit Access Variables: FHFA home prices (%
Balance Sheet Variables: Accounts payable
Balance Sheet Variables: Total loans
304,160.85
219,344.76
Balance Sheet Variables: Current liabilities
27,667.18*
Balance Sheet Variables: Other liabilities
Balance Sheet Variables: Total liabilities
396,447.06
327,185.69
Balance Sheet Variables: Cash on hand
Balance Sheet Variables: Accounts receivable
76,883.76*
106,460.77
Balance Sheet Variables: Inventory merchandise
122,571.96
Balance Sheet Variables: Current assets
Balance Sheet Variables: Total investments
Balance Sheet Variables: Book value of land
128,469.92
Balance Sheet Variables: Book value of depreciable assets
234,575.46
171,255.53
Balance Sheet Variables: Other assets
Balance Sheet Variables: Total assets
729,968.33
555,363.76
Balance Sheet Variables: Log of total assets
Balance Sheet Variables: Equity
328,800.69
227,797.29
Balance Sheet Variables: Short term debt
105,787.54
Balance Sheet Variables: Working capital
264,224.79
252,867.64
Income Statement Variables: Profits
131,919.01*
191,021.60
Income Statement Variables: Pretax profit
132,358.87*
193,997.48
Income Statement Variables: Sales
983,413.95
198,000.00
1,155,161.30
212,000.00
Income Statement Variables: Log sales
Income Statement Variables: Other income
8,192.62***
Income Statement Variables: Total cost
858,898.33
165,697.00
982,148.22
150,000.00
Income Statement Variables: Corporation tax
Financial Ratios: Leverage Short term debt/ (equity) ratio
Financial Ratios: Leverage Liabilities/(equity) ratio
Financial Ratios: Leverage Short term debt/assets ratio
Financial Ratios: Leverage Liabilities/assets ratio
Financial Ratios: Liquidity Cash/assets ratio
Financial Ratios: Liquidity Working capital/assets ratio
Financial Ratios: Liquidity Cash/sales ratio
Financial Ratios: Activity Sales/assets ratio
Financial Ratios: Activity Accounts payable/sales ratio
Financial Ratios: Activity Accounts receivable/sales ratio
Financial Ratios: Profitability Profits/assets ratio
Financial Ratios: Profitability Pre-tax profit/assets ratio
Financial Ratios: Profitability Profits/sales ratio
Financial Ratios: Profitability Pre-tax profit/sales ratio
Financial Ratios: Coverage Pretax profit/(loans) ratio
Financial Ratios: Coverage Pretax profit/(liabilities) ratio
Firm Information Variables: Firm age
Firm Information Variables: Log of firm age
Firm Information Variables: Total employment
Firm Information Variables: Number of owners
Firm Information Variables: Proprietorship
Firm Information Variables: Partnerships
Firm Information Variables: S-corporations
Firm Information Variables: C-corporations
Owner Information Variables: White
Owner Information Variables: Black
Owner Information Variables: Hispanic
Owner Information Variables: Asian
Owner Information Variables: Native American
Owner Information Variables: Male
Owner Information Variables: Equal
Owner Information Variables: Female
Owner Information Variables: Years of owner experience
Owner Information Variables: Owner managed
Owner Information Variables: Primary owner share
Owner Information Variables: High school
Owner Information Variables: Some college
Owner Information Variables: Completed college
Owner Information Variables: Own home
Owner Information Variables: Home equity
267,726.66
150,000.00
259,254.53
150,000.00
Owner Information Variables: Net worth
710,933.71
160,000.00
848,937.90
200,000.00
Owner Information Variables: Primary owner age
Owner Information Variables: Older than 60 indicator
Owner Information Variables: More
median experience
Industry Variables: Construction and mining
Industry Variables: Manufacturing
Industry Variables: Transportation
Industry Variables: Wholesale trade
Industry Variables: Retail trade
Industry Variables: Insurance agents and real estate
Industry Variables: Business services
Industry Variables: Professional services
Industry Variables: Business or professional services
Division Variables: New England
Division Variables: Mid Atlantic
Division Variables: East N Central
Division Variables: West N Central
Division Variables: South Atlantic
Division Variables: East S Central
Division Variables: West S Central
Division Variables: Mountain
Division Variables: Pacific
Market Condition Variables: Population density (%
Market Condition Variables: Unemployment rate (% 2004-08)
Market Condition Variables: Establishment density (%
Market Condition Variables: Per capita income (%
Market Condition Variables: Average wage (%
Notes: Means and medians are based on the firms that were matched to the NETS database and had assets greater than zero and sales of more than $1,000 when interviewed in the 2003 SSBF and have been weighted to account for sampling. Standard errors have been adjusted to account for the multiple
imputation * significant at 10%; ** significant at 5%; *** significant at 1%.
indicates $1 was added to the value to avoid $0 in the denominator.
Table 4: Log odds ratios from logistic models of going out of business
Baseline model (1)
(1) + owner experience & age (2)
(2) + real estate prices (FHFA) (3)
(2) + real estate prices (LP) (4)
(3) + geographic controls (5)
(4) + geographic controls (6)
Credit Quality Variables: Firm credit score
Credit Quality Variables: Firm credit score (t-statistic)
Credit Quality Variables: Firm or owner declared bankruptcy
Credit Quality Variables: Firm or owner declared bankruptcy (t-statistic)
Credit Quality Variables: Firm or owner delinquent 3+ times
Credit Quality Variables: Firm or owner delinquent 3+ times (t-statistic)
Credit Quality Variables: Judgment against the firm or owner
Credit Quality Variables: Judgment against the firm or owner (t-statistic)
Credit Quality Variables: Had no D&B rating in 2003
Credit Quality Variables: Had no D&B rating in 2003 (t-statistic)
Credit Access Variables: Firms fearing denial
Credit Access Variables: Firms fearing denial (t-statistic)
Credit Access Variables: Borrowing on credit cards
Credit Access Variables: Borrowing on credit cards (t-statistic)
Credit Access Variables: Borrowing on trade credit
Credit Access Variables: Borrowing on trade credit (t-statistic)
Credit Access Variables: Been denied trade credit
Credit Access Variables: Been denied trade credit (t-statistic)
Credit Access Variables: Firms using real estate as collateral
Credit Access Variables: Firms using real estate as collateral (t-statistic)
Credit Access Variables: LP home prices (%
Credit Access Variables: LP home prices (%
2004-08) (t-statistic)
Credit Access Variables: FHFA home prices (%
Credit Access Variables: FHFA home prices (%
2004-08) (t-statistic)
Firm/Owner Variables: Firm age
Firm/Owner Variables: Firm age (t-statistic)
Firm/Owner Variables: Firms employment
Firm/Owner Variables: Firms employment (t-statistic)
Firm/Owner Variables: Log of firm assets
Firm/Owner Variables: Log of firm assets (t-statistic)
Firm/Owner Variables: Log of firm sales
Firm/Owner Variables: Log of firm sales (t-statistic)
Firm/Owner Variables: Firm has zero or negative equity
Firm/Owner Variables: Firm has zero or negative equity (t-statistic)
Firm/Owner Variables: Owner over 60 years old
Firm/Owner Variables: Owner over 60 years old (t-statistic)
Firm/Owner Variables: Owner
median experience
Firm/Owner Variables: Owner
median experience (t-statistic)
Financial Ratio Variables: Liabilities/assets ratio
Financial Ratio Variables: Liabilities/assets ratio (t-statistic)
Financial Ratio Variables: Cash/sales ratio
Financial Ratio Variables: Cash/sales ratio (t-statistic)
Financial Ratio Variables: Accounts payable/sales ratio
Financial Ratio Variables: Accounts payable/sales ratio (t-statistic)
Financial Ratio Variables: Pretax profit/asset ratio
Financial Ratio Variables: Pretax profit/asset ratio (t-statistic)
Financial Ratio Variables: Pretax profit/liabilities ratio
Financial Ratio Variables: Pretax profit/liabilities ratio (t-statistic)
Division Variables: New England
Division Variables: New England (t-statistic)
Division Variables: Mid Atlantic
Division Variables: Mid Atlantic (t-statistic)
Division Variables: East N Central
Division Variables: East N Central (t-statistic)
Division Variables: West N Central
Division Variables: West N Central (t-statistic)
Division Variables: South Atlantic
Division Variables: South Atlantic (t-statistic)
Division Variables: East S Central
Division Variables: East S Central (t-statistic)
Division Variables: Mountain
Division Variables: Mountain (t-statistic)
Division Variables: Pacific
Division Variables: Pacific (t-statistic)
Industry Variables: Construction and mining
Industry Variables: Construction and mining (t-statistic)
Industry Variables: Manufacturing
Industry Variables: Manufacturing (t-statistic)
Industry Variables: Transportation
Industry Variables: Transportation (t-statistic)
Industry Variables: Wholesale trade
Industry Variables: Wholesale trade (t-statistic)
Industry Variables: Retail trade
Industry Variables: Retail trade (t-statistic)
Industry Variables: Insurance agents and real estate
Industry Variables: Insurance agents and real estate (t-statistic)
Industry Variables: Professional services
Industry Variables: Professional services (t-statistic)
Geographic Variables: Population density (%
Geographic Variables: Population density (%
2004-08) (t-statistic)
Geographic Variables: Average wage (%
Geographic Variables: Average wage (%
2004-08) (t-statistic)
Geographic Variables: Unemployment rate (%
Geographic Variables: Unemployment rate (%
2004-08) (t-statistic)
Geographic Variables: Establishment density (%
Geographic Variables: Establishment density (%
2004-08) (t-statistic)
Geographic Variables: Per capita income (%
Geographic Variables: Per capita income (%
2004-08) (t-statistic)
Observations
F Test: Division Variables = 0
P-Val: Division Variables = 0
F Test: Quality of Credit Variables = 0
P-Val: Quality of Credit Variables = 0
F Test: Constraint Variables = 0
P-Val: Constraint Variables = 0
F Test: Size Variables = 0
P-Val: Size Variables = 0
F Test: Ratio Variables = 0
P-Val: Ratio Variables = 0
F Test: Industry Variables = 0
P-Val: Industry Variables = 0
F Test: Geographic Variables = 0
P-Val: Geographic Variables = 0
Notes: t stati * significant at 10%; ** significant at 5%; *** significant at 1%. Estimates and standard errors have been adjusted for multiple imputations.
Table 5: Hazard ratios from proportional hazard model of time until out of business
Baseline model (1)
(1) + owner experience & age (2)
(2) + real estate prices (FHFA) (3)
(2) + real estate prices (LP) (4)
(3) + geographic controls (5)
(4) + geographic controls (6)
Credit Quality Variables: Firm credit score
Credit Quality Variables: Firm credit score (Standard Error)
Credit Quality Variables: Firm or owner declared bankruptcy
Credit Quality Variables: Firm or owner declared bankruptcy (Standard Error)
Credit Quality Variables: Firm or owner delinquent 3+ times
Credit Quality Variables: Firm or owner delinquent 3+ times (Standard Error)
Credit Quality Variables: Judgment against the firm or owner
Credit Quality Variables: Judgment against the firm or owner (Standard Error)
Credit Quality Variables: Has a no D&B rating ( year )
Credit Quality Variables: Has a no D&B rating ( year ) (Standard Error)
Credit Access Variables: Firms fearing denial
Credit Access Variables: Firms fearing denial (Standard Error)
Credit Access Variables: Borrowing on credit cards
Credit Access Variables: Borrowing on credit cards (Standard Error)
Credit Access Variables: Borrowing on trade credit
Credit Access Variables: Borrowing on trade credit (Standard Error)
Credit Access Variables: Been denied trade credit
Credit Access Variables: Been denied trade credit (Standard Error)
Credit Access Variables: Firms using real estate as collateral
Credit Access Variables: Firms using real estate as collateral (Standard Error)
Credit Access Variables: LP home prices (%
year-year)
Credit Access Variables: LP home prices (%
year-year) (Standard Error)
Credit Access Variables: FHFA home prices (%
year-year)
Credit Access Variables: FHFA home prices (%
year-year) (Standard Error)
Firm/Owner Variables: Firm age
Firm/Owner Variables: Firm age (Standard Error)
Firm/Owner Variables: Firms employment
Firm/Owner Variables: Firms employment (Standard Error)
Firm/Owner Variables: Log of firm assets
Firm/Owner Variables: Log of firm assets (Standard Error)
Firm/Owner Variables: Log of firm sales
Firm/Owner Variables: Log of firm sales (Standard Error)
Firm/Owner Variables: Firm has zero or negative equity
Firm/Owner Variables: Firm has zero or negative equity (Standard Error)
Firm/Owner Variables: Owner over 60 years old
Firm/Owner Variables: Owner over 60 years old (Standard Error)
Firm/Owner Variables: Owner
median experience
Firm/Owner Variables: Owner
median experience (Standard Error)
Financial Ratio Variables: Liabilities/assets ratio
Financial Ratio Variables: Liabilities/assets ratio (Standard Error)
Financial Ratio Variables: Cash/sales ratio
Financial Ratio Variables: Cash/sales ratio (Standard Error)
Financial Ratio Variables: Accounts payable/sales ratio
Financial Ratio Variables: Accounts payable/sales ratio (Standard Error)
Financial Ratio Variables: Pretax profit/asset ratio
Financial Ratio Variables: Pretax profit/asset ratio (Standard Error)
Financial Ratio Variables: Pretax profit/liabilities ratio
Financial Ratio Variables: Pretax profit/liabilities ratio (Standard Error)
Division Variables: New England
Division Variables: New England (Standard Error)
Division Variables: Mid Atlantic
Division Variables: Mid Atlantic (Standard Error)
Division Variables: East N Central
Division Variables: East N Central (Standard Error)
Division Variables: West N Central
Division Variables: West N Central (Standard Error)
Division Variables: South Atlantic
Division Variables: South Atlantic (Standard Error)
Division Variables: East S Central
Division Variables: East S Central (Standard Error)
Division Variables: Mountain
Division Variables: Mountain (Standard Error)
Division Variables: Pacific
Division Variables: Pacific (Standard Error)
Industry Variables: Construction and mining
Industry Variables: Construction and mining (Standard Error)
Industry Variables: Manufacturing
Industry Variables: Manufacturing (Standard Error)
Industry Variables: Transportation
Industry Variables: Transportation (Standard Error)
Industry Variables: Wholesale trade
Industry Variables: Wholesale trade (Standard Error)
Industry Variables: Retail trade
Industry Variables: Retail trade (Standard Error)
Industry Variables: Insurance agents and real estate
Industry Variables: Insurance agents and real estate (Standard Error)
Industry Variables: Professional services
Industry Variables: Professional services (Standard Error)
Geographic Variables: Population density ( year )
Geographic Variables: Population density ( year ) (Standard Error)
Geographic Variables: Average wage ( year )
Geographic Variables: Average wage ( year ) (Standard Error)
Geographic Variables: Unemployment rate ( year )
Geographic Variables: Unemployment rate ( year ) (Standard Error)
Geographic Variables: Establishment density ( year )
Geographic Variables: Establishment density ( year ) (Standard Error)
Geographic Variables: Per capita income ( year )
Geographic Variables: Per capita income ( year ) (Standard Error)
Observations
F Test: Division Variables = 0
P-Val: Division Variables = 0
F Test: Quality of Credit Variables = 0
P-Val: Quality of Credit Variables = 0
F Test: Constraint Variables = 0
P-Val: Constraint Variables = 0
F Test: Size Variables = 0
P-Val: Size Variables = 0
F Test: Ratio Variables = 0
P-Val: Ratio Variables = 0
F Test: Industry Variables = 0
P-Val: Industry Variables = 0
F Test: Geographic Variables = 0
P-Val: Geographic Variables = 0
Notes: t stati * significant at 10%; ** significant at 5%; *** significant at 1%. Estimates and standard errors have been adjusted for multiple imputations.
Appendix A: Variable Descriptions
Definition
Credit Quality Variables: Firm credit score
Firm D&B credit scor 0-100, with 0 being the worst ranking
Credit Quality Variables: Firm or owner declared bankruptcy
Indicator if either firm or owner declared bankruptcy in past 7 yes=1, no=0
Credit Quality Variables: Firm or owner delinquent 3+ times
Indicator if either firm or owner was 60+ days delinquent on payments 3+ times in the past 3 ; yes=1, no=0
Credit Quality Variables: Judgment against the firm or owner
Indicator if either firm or owner had a judgment rendered against them in the past 3 yes=1, no=0
Credit Quality Variables: Had high or good D&B rating in 2003
D&B creditworthiness assessment based on both payments and financial stability information of good or high=1, else=0
Credit Quality Variables: Had fair D&B rating in 2003
D&B creditworthiness assessment based on both payments and financial stability information of fair=1, else=0
Credit Quality Variables: Had limited D&B rating in 2003
D&B creditworthiness assessment based on both payments and financial stability information of limited=1, else=0
Credit Quality Variables: Had no D&B rating in 2003
Creditworthiness not assessed by D&B due to no score=1, else=0
Credit Access Variables: Firms fearing denial
Indicator that the firm did not apply for credit because it
yes=1, no=0
Credit Access Variables: Borrowing on credit cards
indicator that firm carried a bala yes=1, no=0
Credit Access Variables: Borrowing on trade credit
Indicator that the firm paid trade credit af yes=1, no=0
Credit Access Variables: Been denied trade credit
Indicator that the firm applied for and was yes=1, no=0
Credit Access Variables: Firms using real estate as collateral
firm has at least one loan collatera 1=yes, 0=no
Credit Access Variables: LP home prices (%
Percentage change in house price index within the state where the firm's headquarters was located, using the CoreLogic LoanPerformance price index. Note in Hazard models, one year changes are used.
Credit Access Variables: FHFA home prices (%
Percentage change in house price index within the MSA/rural county where the firm's headquarters was located, using the FHFA price index. Note in Hazard models, one year changes are used.
Firm/Owner Variables: Firm age
Firm age in years
Firm/Owner Variables: Firms employment
Total firm employment
Firm/Owner Variables: Log of firm assets
Firm/Owner Variables: Log of firm sales
Firm/Owner Variables: Firm has zero or negative equity
Indicator that the firm has zer yes=1, no=0
Financial Ratio Variables: Liabilities/assets ratio
Leverage ratio: liabilities/assets
Financial Ratio Variables: Cash/sales ratio
Liquidity ratio: cash/sales
Financial Ratio Variables: Accounts payable/sales ratio
Activity ratio: accounts payable/sales
Financial Ratio Variables: Pretax profit/asset ratio
Profit ratio: pretax profit/assets
Financial Ratio Variables: Pretax profit/liabilities ratio
Coverage ratio: pretax profit/liabilities
Division Variables: New England
Indicator that firms headquarters are located in the New England Census D yes=1, no=0
Division Variables: Mid Atlantic
Indicator that firms headquarters are located in the Mid Atlantic D yes=1, no=0
Division Variables: East N Central
Indicator that firms headquarters are located in the East North Central Census D yes=1, no=0
Division Variables: West N Central
Indicator that firms headquarters are located in the West North Central Census D yes=1, no=0
Division Variables: South Atlantic
Indicator that firms headquarters are located in the South Atlantic Census D yes=1, no=0
Division Variables: East S Central
Indicator that firms headquarters are located in the East South Central Census D yes=1, no=0
Division Variables: West S Central
Indicator that firms headquarters are located in the West South Central Census D yes=1, no=0
Division Variables: Mountain
Indicator that firms headquarters are located in the Mountain Census D yes=1, no=0
Division Variables: Pacific
Indicator that firms headquarters are located in the Pacific Census D yes=1, no=0
Industry Variables: Construction and mining
Indicator that the firm's primary industry is co yes=1, no=0
Industry Variables: Manufacturing
Indicator that the firm's primary indus yes=1, no=0
Industry Variables: Transportation
Indicator that the firm's primary indust yes=1, no=0
Industry Variables: Wholesale trade
Indicator that the firm's primary industr yes=1, no=0
Industry Variables: Retail trade
Indicator that the firm's primary indu yes=1, no=0
Industry Variables: Insurance agents and real estate
Indicator that the firm's primary industry is insurance a yes=1, no=0
Industry Variables: Business services
Indicator that the firm's primary industry
yes=1, no=0
Industry Variables: Professional services
Indicator that the firm's primary industry is p yes=1, no=0
Additional Owner Variables: Owner over 60 years old
Indicator that the owner with largest share of business was more than 60 in 2004; yes=1, no=1
Additional Owner Variables: Owner
median experience
Indicator that the owner with largest share of business had more than 20 years of experience running the business in 2004; yes=1, no=1
Geographic Variables: Population density
Persons per square mile within the MSA/rural county where the firm's headquarters were located
Geographic Variables: Average wage
Average wage earnings per job within the MSA/rural county where the firm's headquarters were located
Geographic Variables: Unemployment rate
Unemployment rate within the MSA/rural county where the firm's headquarters were located
Geographic Variables: Establishment density
Establishments per square mile within the MSA/rural county where the firm's headquarters were located
Geographic Variables: Per capita income
Per capita income within the MSA/rural county where the firm's headquarters were located
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