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The Capital Structure Decisions of New Firms

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lNBER WORKING PAPER SERIES THE CAPITAL STRUCTURE DECISIONS OF NEW FIRMS Alicia M. Robb David T. Robinson Working Paper 16272 http://www. nber. org/papers/w16272 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 August 2010 The authors are grateful to the Kauffman Foundation for generous financial support.

Malcolm Baker, Thomas Hellmann, Antoinette Schoar, Ivo Welch, and seminar participants at the Kauffman/Cleveland Federal Reserve Bank Entrepreneurial Finance Conference, the University of Michigan, the Stockholm School of Economics, the Atlanta Fed, and the NBER Summer Institute Entrepreneurship Meetings and the Kauffman/RFS conference on entrepreneurial finance provided helpful comments on previous drafts.

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The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

© 2010 by Alicia M. Robb and David T. Robinson. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

The Capital Structure Decisions of New Firms Alicia M. Robb and David T. Robinson NBER Working Paper No. 16272 August 2010 JEL No. G21,G24,L26 ABSTRACT This paper investigates the capital structure choices that firms make in their initial year of operation, using restricted-access data from the Kauffman Firm Survey. Contrary to many accounts of startup activity, the firms in our data rely heavily on external debt sources such as bank financing, and less extensively on friends and family-based funding sources. This fact is robust to numerous controls for credit quality, industry, and business owner characteristics.

The heavy reliance on external debt underscores the importance of well functioning credit markets for the success of nascent business activity. Alicia M. Robb UC, Santa Cruz [email protected] edu David T. Robinson Fuqua School of Business Duke University One Towerview Drive Durham, NC 27708 and NBER [email protected] edu 1 Introduction Understanding how capital markets a? ect the growth and survival of newly created ? rms is perhaps the de? ning question of entrepreneurial ? nance. Yet, much of what we know about entrepreneurial ? nance comes from ? ms that are already established, have already received venture capital funding, or are on the verge of going public—the dearth of data on very early stage ? rms makes it di? cult for researchers to look further back in ? rms’ life histories. 1 Even data sets that are oriented towards small businesses do not allow us to measure systematically the decisions that ? rms make at their founding. This paper uses a novel data set, the Kau? man Firm Survey (KFS), to study the behavior and decision-making of newly founded ? rms. As such, it provides a ? st-time glimpse into the capital structure decisions of nascent ? rms. In this paper we use the con? dential, restricted-access version of the KFS, which tracks nearly 5,000 ? rms from their birth in 2004 through their early years of operation. 2 Because the survey identi? es ? rms at their founding and follows the cohort over time, recording growth, death, and any later funding events, it provides a rich picture of ? rms’ early fundraising decisions. Rather than attempt to test speci? c theories of capital structure, our main goal is a more modest, descriptive one: to examine the ? nancing choices that ? ms make when they launch, and ask whether any patterns emerge from the data. This is motivated in part by the widely held view that frictions in capital markets prevent startups from achieving their optimal size, or indeed, from starting up at all. In the presence of such acute frictions, startups are assumed to pursue ? nancing from informal channels, or Some noteworthy recent exceptions are Kaplan, Sensoy and Str? mberg, 2009, which follows a small o sample of ? rms beginning at business plan stage, and Reynolds (2008) which uses data from individuals who are contemplating starting businesses. To be eligible for inclusion in the KFS, at least one of the following activities had to have been performed in 2004 and none performed in a prior year: Payment of state unemployment (UI) taxes; Payment of Federal Insurance Contributions Act (FICA) taxes; Presence of a legal status for the business; Use of an Employer Identi? cation Number (EIN); Use of Schedule C to report business income on a personal tax return. 1 2 through the heavy reliance on trade credit (see, for example, Peterson and Rajan, 1994, 2000. The richness of the KFS data allows us to explore the extent to which startups rely on friends and family versus more formal ? nancing arrangements, such as bank loans, credit cards, and venture capital. A working null hypothesis for this descriptive exercise is that no clear patterns in capital structure are present, because idiosyncracies in ? rm and owner characteristics, market conditions and access to ? nancial and human capital are associated with a high degree of variability in the capital structure choices that nascent ? rms make. The alternative o? red by conventional wisdom is that informal capital dominates the capital structure. Our main result is that newly founded ? rms rely heavily on formal debt ? nancing: owner-backed bank loans, business bank loans and business credit lines. Indeed, funding from formal debt dwarfs funding from friends and family. The average amount of bank ? nancing is seven times greater than the average amount of insider-? nanced debt; three times as many ? rms rely on outside debt as do inside debt. Even among ? rms that rely on inside debt, the average amount of outside debt is nearly twice that of inside debt.

The reliance on formal credit channels over personal credit cards and informal lending holds true even for the smallest ? rms at the earliest stages of founding. The average prerevenue ? rm in our sample has twice as much capital from bank loans than from insider sources. And when we look at only those ? rms who access outside equity sources, such as venture capital or angel ? nancing, we still see a heavy reliance on debt: the average ? rm that accesses external private equity markets still has around 25% of its capital structure in the form of outside debt.

We also examine trade credit as a potential source of capital, especially since it may be especially important in scenarios where trade creditors possess information (or stand to forge relationships through supply channels) that banks might not be able to obtain (Peterson and Rajan, 1997). While our data show that trade credit is undoubtedly 3 important, the average ? rm uses less than half as much trade credit as it does outside debt, and almost twice as many ? rms rely on outside debt than do trade credit. Indeed, if trade credit were counted as a source of ? ancial capital (instead of operating capital), it would rank third, behind outside debt and owner equity, but ahead of outside equity and inside debt/equity. Of course, these statements only speak to the equilibrium amount of borrowing from inside and outside sources; the quantities are determined by both the supply and the demand of di? erent types of capital. Ultimately, it is challenging to separate supply and demand in the absence of some quasi-experiment. We nevertheless take some small steps in this direction. First, to control for the fact that di? erences in ? m quality or creditworthiness may be driving the patterns we see in the data, we make use of commercial credit scores of the ? rms. This gives us two avenues to control for demand-side variation. The ? rst is simply to include the credit score directly in our analysis as a proxy for ? rm quality. (Alternatively, we can partition the data into high credit and low credit samples and compare capital structures in the two sub-samples. ) Surprisingly, this partitioning has little e? ect on the observed capital structure choices ? rms make. Firms with high credit scores simply have more ? nancial capital.

The level of ? nancing of these ? rms is nearly three times larger on average than low-credit ? rms. But the relative amount of outside debt to total capital is about the same for both types of ? rms. Second, we identify plausibly exogenous variation in access to capital by using housing price elasticity data calculated by Saiz (2010). Using sophisticated GIS techniques to measure geographical constraints on local land supply, as well as factors that account for endogenous restrictions on land use through zoning, he estimates housing supply elasticities at the MSA level which, in turn, allow us to capture the e? ct of the housing boom on access to capital. Roughly speaking, high elasticity areas saw housing inventories increase as the housing bubble expanded, while low elasticity areas saw home prices spike 4 instead. In areas with high elasticity of supply, homes provide better loan collateral, because the underlying home equity is less sensitive to local pricing conditions. This is exactly what we ? nd. Entrepreneurs in areas with high supply elasticity were more reliant on bank loans as a source of capital. Because our data do not map the entrepreneurs’ actual home prices onto bank ? nancing choices, we must remain cautious; nevertheless, we ? d evidence that high price stability acts as a catalyst for bank loans. This of course raises the concern that credit conditions at the time of our survey were so unique that they do not necessarily re? ect broader patterns from other time periods. While ultimately we are limited to the data that are available, we speak to this possibility by considering the impact of capital structure decisions on outcome variables like ? rm survival, employment growth, and pro? tability growth. We ? nd that having a capital structure that is more heavily tilted towards formal credit channels results in a greater likelihood of success.

This fact holds even when we include the credit score as a measure of ? rm quality to guard against the possibility that unobserved factors drive both success and credit access. Our ? ndings indicate that even if credit conditions in 2004 were unique, credit market access had an important impact on ? rm success. This paper is related to a number of papers in the banking, capital structure, and entrepreneurship literature. Given the emphasis in the current work on the role of formal banking channels and trade credit, our paper is also related to the literature on the role of banks and other sources of ? ancing for small ? rms (Peterson and Rajan, 1994, 1997, 2000). Cosh, Cumming and Hughes (2008) ? nd a similarly important role for bank capital using British data, but they observe ? rms at a later point in their life cycle. The remainder of the paper is as follows. We begin in Section 2 by describing the KFS in greater detail. Section 3 examines initial capital structure choices. We incorporate credit scores and other ? rm characteristics in Section 4. Section 5 explores multivariate regressions of capital structure on a range of business and owner characteristics to explain capital structure decisions.

Section 6 explores the link between home supply 5 elasticity and bank debt. In Section 7 we examine how initial capital structure a? ects ? rm outcomes. Section 8 concludes. 2 The Kau? man Firm Survey The KFS is a longitudinal survey of new businesses in the United States. This survey collected information on 4,928 ? rms that started in 2004 and surveys them annually. These data contain detailed information on both the ? rm and up to ten business owners per ? rm. In addition to the 2004 baseline year data there are four years of follow up data (2005 through 2007) now available.

Additional years are planned. Detailed information on the ? rm includes industry, physical location, employment, pro? ts, intellectual property, and ? nancial capital (equity and debt) used at start-up and over time. Information on up to ten owners includes age, gender, race, ethnicity, education, previous industry experience, and previous startup experience. For more information about the KFS survey design and methodology, please see Robb et. al (2009). A publicuse dataset is available for download from the Kau? man Foundation’s website and a more detailed con? ential dataset is available to researchers through a secure, remote access data enclave provided by the National Opinion Research Center (NORC). For more details about how to access these data, please see www. kau? man. org/kfs. A subset of the con? dential dataset is used in this research—those ? rms that either have data for all three survey years or have been veri? ed as going out of business in 2005, 2006 or 2007. This reduces the sample size to 3,972 businesses. The method we used for assigning owner demographics at the ? rm level was to de? ne a primary owner. For ? ms with multiple owners (35 percent of the sample), the primary owner was designated by the largest equity share. In cases where two or more owners owned equal shares, hours worked and a series of other variables were used to create a rank ordering of owners in order to de? ne a primary owner. (For more information on this methodology, see 6 Robb et. al, 2009). For this research, multi-race/ethnic owners are classi? ed into one race/ethnicity category based on the following hierarchy: black, Asian, other, Hispanic, and white. As a result of the ordering, the white category includes only non-Hispanic white.

Tables 1 and 2 provide details on business characteristics. In Table 1, we report key features of the business—its legal form, location, and other features of operations. Roughly 36% of all businesses in the data are sole proprietorships, and about 58% are structured to provide some form of limited liability to owners. About 28% are organized as S or C corporations. Half of the businesses in the survey operate out of the respondents home or garage; the vast majority (86%) market a service, and only a quarter of the ? rms in the survey have any form of intellectual property (patents, copyrights, and/or trademarks). Re? cting the fact that they are being measured at their inception, the ? rms are also tiny by almost any conceivable measure. Nearly 60% of the ? rms have no employees other than the founder, and less than 8% of ? rms in the sample have more than ? ve employees in their ? rst year of operations. Table 2 considers the cash ? ow characteristics of these nascent businesses. Even though these ? rms are small, nearly twenty percent of ? rms (16. 8%) have over $100,000 in revenue in their ? rst year. Indeed, 45% of the ? rms in the sample have more than $10,000 in annual revenue in their ? rst year. Of course, over 57% of ? ms have more than $10,000 in expenses, and almost one ? rm in four reports zero pro? t or loss. Table 3 examines owner characteristics in more detail. The entrepreneurs in our data are overwhelmingly male and white: less than one-third of respondents are female and over three-quarters are non-Hispanic white. In spite of the fact that most of the businesses in our data begin at home, in people’s garages, with fewer than ? ve employees, the overwhelming majority of business owners have at least some industry experience. Less than ten percent of owners have no previous industry experience, while more than 7 half have more than ? e years of industry experience. Likewise, more than forty percent of business owners have started a business before. More than 80% of respondents are over the age of 35 when they start their business, and roughly half the sample is aged 45 or older. The entrepreneurs in our sample are relatively well educated. Less than 20% of respondents have less than a high school degree, while well over half of respondents have completed some form of a college degree. Finally, nearly a quarter of all respondents have received some form of advanced, post-graduate education. In broad terms, these demographics match those reported in other data sources.

For example, these demographics are similar to those reported in Puri and Robinson (2008), using the Survey of Consumer Finances, and Fairlie and Robb (2007), using the Characteristics of Business Owners Survey. 3 Where do startups go for capital? This section explores descriptive statistics about the capital structure decisions that startup ? rms make. To impose some structure on the details of startup fundraising, we ? rst put forward a scheme for classifying the di? erent types of capital available to startups. We distinguish capital sources on two main dimensions. The ? rst is debt vs. equity.

Because we do not delve into the contractual details of VC funding agreements, simply distinguishing debt and equity serves our purposes: loans, credit cards, lines of credit and the like are classi? ed as debt. Next, we distinguish capital according to its source. Capital can be provided either by owners, by insiders, or by outsiders. The KFS is careful to distinguish owner equity from cash that a business owner obtained through, say, a home equity line, which in our 8 classi? cation scheme, would be a source of outside debt, since it was provided through a formal contract with a lending institution. Informal ? ancing channels include debt or equity from family members and personal a? liates of the ? rm, while formal ? nancing channels include debt accessed through formal credit markets (banks, credit cards, lines of credit) as well as venture capital and angel ? nancing. The most notable implication of our classi? cation scheme is that it groups together personal debt on the business owner’s household balance sheet with business bank loans, and places these under the “outside debt” category. We do this for several reasons. First, if the business is structured as a sole proprietorship, then there is no legal di? rence between the assets of the ? rm and those of the owner. Thus, for around 40% of our sample, the distinction is meaningless in the ? rst place. But more importantly, research has shown that personal guarantees and personal collateral must often be posted to secure ? nancing for startups (Moon, 2009; Avery, Bostic and Samalyk, 1998; Mann, 1998). This means that in the remaining 60% of the ? rms, the limited liability o? ered by incorporation would often be contractually circumvented in the borrower/lender agreement with the bank.

As such, our primary distinction is not whether the debt is claim on the business owner’s household assets or her business assets, but rather, whether this debt was issued by an institution, or by friends and family. 3. 1 A detailed look at capital structure In Table 4, we use this classi? cation scheme to provide a detailed look at the capital structure choices that nascent ? rms make. The thirty di? erent sources of capital for startup businesses are grouped into the six categories described above (owner/insider/outsider ? debt/equity). Over 75% of ? rms have at least some owner equity; of these, the mean amount is just over $40,500.

If we include the quarter of ? rms with no reported owner’s equity, the average owner equity amount drops to $31,734. 9 Owner debt plays a much smaller role. Only about 1/4 of ? rms have some form of owner personal debt, and the vast majority of this is mostly in the form of debt carried on an owner’s personal credit card. The overall average amount of credit card debt used to ? nance startups is a modest $5,000, but this includes the roughly 75% of owners who do not use personal credit cards to start their businesses. Among those who do, the balance is considerably larger—$15,700, or about 1/3 of the size of owner equity.

But in general, personal credit card balances make up a relatively small fraction of the startup’s overall capital structure at inception—only about 4 to 5% of the ? rm’s total capitalization is in the form personal credit card balances held by ? rm owners. While owner-provided capital is heavily tilted towards equity, the capital from other sources is heavily tilted towards debt. If we include the ? rms with zero values, ? rms use about ? ve times as much debt as they do equity. This holds for both inside debt ($6,362) to equity ($2,102), as well as outside debt ($47,847) to equity ($15,935). But seven times as many ? ms report outside debt as report outside equity. Yet, among those who do receive outside equity, there is no question that it is important. The average amount of outside equity among the 205 ? rms who access this source of ? nancing is over $350,000, roughly twice as large as the total ? nancial capital for the average ? rm in the survey. Turning ? rst to insiders, we see that equity is uncommon. Only about ? ve percent of the sample relies on equity from a spouse or other family members, and the overall average amount (including the 95% with no family equity) is only about two percent of the average funding.

Yet, among the group who uses family equity, the source is important: the magnitude of insider equity is roughly the same as that of owner equity, and many times larger than the magnitude of owner debt. Insider debt is more common, but still a small source of funding relative to outside debt and equity. The mean value of inside debt for all ? rms is $6,362, and this primarily comes from personal loans received by the respondent from family and other owners. Loans directly to the business from owners or other family members are also important, 10 but the fact that less than ten percent of surveyed ? ms rely on any one type of inside debt suggests that this funding source is not commonly relied upon by new ? rms. When we turn to outsider debt, we see that on average it is the largest single ? nancing category for startups during their ? rst year of operation. While this no doubt re? ects the relative supply of outside debt to other funding sources, it is noteworthy that only a relatively small fraction of this comes from credit card balances issued to the business. Of the $47,847 average debt level, less than $2,500 on average comes from business credit cards.

One widely held view about entrepreneurial ? nance is that startups lack access to formal capital markets, and thus are forced to rely on an informal network of family, friends, and other ? nancing sources like credit cards to bootstrap their initial ? nancing. Table 4 speaks against this idea. First, outside capital is extremely important, even at the earliest stages of a ? rm’s life. The average new ? rm has approximately $109,000 of ? nancial capital. Of that, roughly half comes from outside sources. To be clear, however, informal investors do play an important role for those ? ms who obtain external equity funding. Looking solely at the external equity funding, of the 205 ? rms who received some form of external equity funding, over half received funding from outside informal investors. The average amount, around $245,000, is roughly one-fourth the average for the handful of ? rms that report obtaining venture capital. 3 Second, the vast majority of this outside capital comes in the form of credit, either through personal loans made directly to the owner or through business credit cards. Moreover, credit cards play a relatively small role for the average startup.

If we total the average credit card holdings on all personal and business accounts associated with the business, the amount sums to less than half the average personal bank loan. If we tally the average personal bank loan and the average business bank loan, this amount is Some ? rms may indeed misclassify angel investors as venture capital, as the average amounts are quite low. 3 11 roughly four times the size of the average total credit card balances outstanding. 3. 2 Capital Structure and Firm Type Perhaps the most surprising ? ding in Table 4 is that formal credit channels—business and personal bank loans—are the most important sources of funding for startups. To push this observation further, we segment the data in Table 5 to report capital structure patterns for di? erent types of startup ? rms. The idea behind Table 5 is to isolate those ? rms that are in their very earliest stages of starting up, to see if the overall capital structure patterns hold there as well. This can be done according to a number of criteria. In the ? rst column of Table 5, we examine the 2,425 ? rms who have no employees other than the founder.

These ? rms are small relative to the average reported for all ? rms in Table 4—there total capital is only around $45,000 as compared to the roughly $110,000 in Table 4. But proportionately, outside debt plays a quite similar role: the average non-employer ? rm has $19,500 in outside debt, or about 43% of its total capital, compared to approximately $48,000, or about 44% of total capital on average for ? rms overall. Of the outside debt, we again see that business bank loans and personal bank loans make up the bulk of the $19,500. Only about $2,500 comes from other sources on average.

The second column examines the 2,168 businesses which are home-based, meaning that they do not operate any o? ce or warehouse space outside the home. These too are small, presumably including the proverbial “garage business” as well as businesses of a professional nature that operate out of a home o? ce. The capital structure patterns for these businesses are remarkably similar to the non-employer businesses: about forty percent of their total capital is ? nanced through outside debt, and the lion’s share of that comes from personal and business bank loans, rather than credit card balances. 12

Another way to pinpoint ? rms at their earliest stages is to focus only on pre-revenue or pre-pro? t ? rms. We examine these ? rms in columns (3) and (4), respectively. These ? rms are considerably larger than the previous two categories, presumably because these include many ? rms that have secured inventories in advance of sales, or require external building space to operate. Indeed, these columns look quite similar to the averages reported in Table 4 for the whole sample. Because the ? rst four columns of Table 5 monotonically expand the size and scope of ? rms under consideration, they o? r an alternative way to examine capital choice, albeit descriptively. Moving from the ? rst column of data to the fourth column of data more than doubles the ? rm’s size by adding an additional $80,000 of total capital to the ? rm. By far the bulk of this comes from outside debt and equity, which together make up about half the increase in ? rm capital. Since columns (3) and (4) also contain some non-employer and home-based ? rms, this comparison understates the magnitude of the shift in capital structure. Thus, the comparisons across the columns of Table 5 indicate that friends and family is probably an earlier source of ? ancing than outside debt, as previous accounts have indicated. It is just not terribly important in terms of total size. The ? nal two columns of Table 5 split the data according to whether the ? rm continued to operated throughout the ? rst four waves of the KFS, or whether the ? rm ceased operations. Firms that survive look very much like the overall average reported in Table 4. On the other hand, ? rms that ceased operations sometime before 2007 not only began smaller, but also had considerably smaller proportions of outside debt to total capital. Rather than focus on the ? ms least likely to access debt markets from a size perspective, in Table 6 we focus on ? rms that demonstrated an ability to access outside equity. Since here we are conditioning the sample on the presence of outside equity, we would naturally expect outside equity to play an important role for these ? rms. It does. For example, angel-backed ? rms are about 50% outside equity, and they are considerably larger than the average ? rm on the KFS. The ratio of outside equity to total capital 13 is even higher for VC-backed and corporate-equity backed ? rms. Notwithstanding the reliance on outside equity, these ? ms have large amounts of outside debt. Outside debt is the second largest source of capital for these ? rms, behind outside equity, for all types except corporate-backed ? rms. Outside debt dwarfs trade credit for these ? rm types, again, with the exception of corporate equity backed ? rms. 4 4. 1 Firm Quality and Capital Structure Decisions Credit worthiness, technology and the ? nancing pyramid Table 7 takes the detail of the preceding tables and boils it down to six categories: owner debt, owner equity, inside debt, inside equity, outside debt, and outside equity.

These classi? cations are as described in the left-most column of Table 4. Reducing the amount of detail not only makes the ? rms’ capital structure choice more apparent, it also facilitates more comparisons across di? erent types of ? rms. The rows of Table 7 are arranged from highest to lowest in terms of the overall weighted average level of 2004 funding. If we interpret the magnitudes as an indication of relative importance, then if not a pecking order, we at least see we see a clear “? nancing pyramid” emerge: ? rst outside debt, then owner equity, then debt from insiders.

Fourth in the pyramid is outside equity, followed by owner debt; the least used source is inside equity. An alternative way to characterize the ? nancing pyramid of nascent ? rms is to combine owner debt and equity into a single category, internal funding. Looking at capital structure this way, the average ? rm is roughly equal parts internal funding and outside debt. These two sources of funding are each roughly four times larger than the next largest source of ? nancing. Regardless of how the ? nancial pieces are assembled, outside debt plays a paramount role in funding newly founded ? rms. 4 One reason for this may simply be that outside debt is more plentiful than other sources of funding. To explore this possibility, we obtained commercial credit scores for each ? rm to identify high credit worthiness and low credit worthiness ? rms. Table 7 shows that while high credit worthiness ? rms have access to much more ? nancial capital, they access capital in roughly the same proportions as low credit worthiness ? rms. Thus, a ? rm’s credit score induces a ? rst-order shift in the level of ? nancing it obtains, but only a second-order shift in capital structure choice it makes.

Outside equity plays a substantially more important role in the capital structure of high tech ? rms. Across all high tech ? rms, outside equity is the third largest funding source behind outside debt and owner equity. Among high tech ? rms with high credit scores, outside equity is the largest form of ? nancing. It is only the low credit score ? rms in the high tech sector that display a capital structure that resembles the average ? rm in the data—but for those ? rms, owner equity is a more important source of ? nancing than outside debt. 4. 2 Separating credit quality from owner wealth

Of course, one reason why the capital structures of high and low credit quality ? rms may be so similar is that the credit scores of the business are highly correlated with those of the founders. All else equal, wealthier individuals may have higher credit scores, and simultaneously ? nd it easier to post their own equity to start the business, leaving the overall capital structure unchanged as underlying credit quality changes. One way to account for this possibility is to regress the ? rm’s credit score on variables that proxy for owner characteristics that would in? ence credit ratings. We consider two models. First, we run the following regression: scoreij = ? + ? j + (1) i 15 where scoreij is the credit score of ? rm i in industry j, ? j are industry ? xed e? ects. Thus, the ? rst estimation simply includes a set of 60 industry dummies. If some industries faced systematically lower entry costs, this would control for the fact that individuals with systematically lower wealth levels could enter this industry to start their business. For the second speci? cation, we run the following regression: scoreij = ? + ? j + ? 1 Fij + ? 2 Kij + (2) i here scoreij is the credit score of ? rm i in industry j, ? j are industry ? xed e? ects, and F is a vector of owner characteristics, and K is a vector of ? rm characteristics, both of which likely vary with demand for credit. For this speci? cation, we include a full set of industry dummies, a set of education dummies corresponding to the breakdown presented in Table 3, and we also include factors such as race, ethnicity, industry experience, intellectual property, legal structure of the enterprise, whether the business is home-based, and whether the business sells a product or provides a service.

While these coe? cient estimates are interesting in their own right, a full discussion is beyond the scope of this paper. Indeed, in Robb, Fairlie and Robinson (2009) we explore the issue of race and access to credit in greater detail. The idea behind both speci? cations is that by purging the credit score of variation that is linked to factors a? ecting personal credit scores, the remaining variation in credit score would re? ect supply-side credit restrictions. Firms with high unexplained credit scores should have easier access to capital, while ? ms with low unexplained credit scores should have relatively more di? cult access to capital. Moreover, the di? erences in their access to capital could re? ect suppliers willingness to lend, rather than di? erences in capital needs. Recovering the regression errors from these two models gives us a mechanism for classifying ? rms as credit constrained or unconstrained. Of course, a ? rm with a low 16 unconditional credit score is constrained, but this low score may arise endogenously because the ? rm has little need for external capital, low growth prospects, etc.

By relying on the conditional credit score as opposed to the raw credit score, we circumvent these problems. Table 8 reports ? nancing choices for ? rms in the lowest and highest quintiles of the unexplained credit score distribution. Firms in the lowest quintile face the most severe unexplained restrictions to credit access, since their credit scores are much lower than would be predicted based on their demand characteristics. In contrast, the top quintile have the easiest access to credit, since they have high unexplained credit scores, given their access to capital.

In general, the results of Table 8 mimic the results from the previous table, in that they show a ? rst order a? ect on the amount of capital raised, but only a second order e? ect on capital structure choice. Credit constrained ? rms have capital structures that look very similar to those of unconstrained ? rms. The primary di? erence is that unconstrained ? rms have much higher levels of capital investment. 5 Explaining Funding Decisions Having described initial capital structure choices in detail, we now turn to the task of decomposing capital structure choice in a multivariate framework.

We do this in Table 9, where we regress capital structure ratios on owner and ? rm characteristics. In general, Table 9 reports OLS regressions of the following form: Financing Category = ? + ? j + ? 1 Fij + ? 2 Kij + Total Capital (3) i where ? j are industry ? xed e? ects, F is a vector of owner characteristics, and K is a vector of ? rm characteristics. The dependent variable in each column is a ? nancial 17 ratio—either outside debt, outside equity, outside loans, or inside ? nance—each scaled by the ? rm’s total capital. (The unmeasured category is the ratio of owner ? ancing to total capital. ) Outside loans are a subset of outside debt that include only personal bank loans and business loans. The ? rm characteristics include not only the survey characteristics described in Tables 1-3, but also the ? rm’s credit score, a measure of quality that might well be unobserveable to the econometrician in other circumstances, but would be readily observable to credit market participants. Are gender and race correlated with initial capital structure choices? Table 9 suggests that this is de? nitely the case. First, gender: women receive signi? antly less outside capital than other groups. The results for women indicate that the average femaleowned business holds about 5% less outside debt than the same male-owned business. Although these results may re? ect the fact that women face more restricted access to capital in the credit market, the data do not allow us to rule out the possibility that, notwithstanding the industry ? xed e? ects, female-owned businesses simply may demand less outside capital, perhaps because they are more likely to be second-income businesses. Next, the question of race.

Table 9 shows that black-owned businesses hold much less outside debt in their initial capital structure than other businesses. The magnitudes are similar to those found for gender: the ratio of outside debt to total capital is about 13% lower for black-owned businesses than for otherwise equal white-owned businesses. Whether this attributable to supply-side or demand-side considerations, it is important to note that these regressions hold constant the industry of the business, the ? rm’s credit quality, the owner’s education, and their prior industry and startup experience.

Thus, unobserved heterogeneity in underlying business quality seems unlikely to be a ? rst-order explanation for the di? erence. We also observe other racial di? erences in capital structure choice. Hispanics and Asians, but not Blacks, rely heavily on inside ? nance. 4 While Hispanic or Asian ethnicity 4 This is measured as the sum of inside equity and debt. 18 explains little variation in access to external capital, these groups average about 25% more inside capital in their total capital structure. Given that the average ? rm in Table 4 has an inside-to-total capital ratio of around 12%, this e? ct is enormous in economic magnitude, representing a 75% increase in the average amount. Across the board, increasing hours worked in the business is associated with greater outside and inside capital, and consequently, lower owner ? nancing. Similarly, owner age has an increasing but concave relationship with access to external capital, for both debt and equity, while it has the opposite relationship for inside ?nancing. Prior experience plays an interesting role in determining initial capital structure. Owners with prior startup experience tend to rely on external equity more than others.

In contrast, Table 9 indicates that owners with more industry experience rely signi? cantly more on their own ? nancing, since the association between industry experience and capital type is negative across all types reported in the table. The regressions also include, but do not report, owner education. Di? erent categories of education have similar experiences accessing external debt equity, but there is a pronounced e? ect associated with inside ? nancing. Namely, those who do not ? nish high school are signi? cantly more likely to rely on inside ? nancing than other groups.

Since the regressions include industry ? xed e? ects, it is not the case that this is driven by sorting of low education respondents into industries with low capital requirements. Rather, this is probably an indication that lower quality businesses are more likely to rely on inside ? nancing instead of accessing external capital markets. The business characteristics reported in the bottom of the table demonstrate that ? rms with lower asymmetric information problems enjoy more ready access to external capital sources, and in particular, external credit funding. Home-based businesses rely more heavily on owner ? ancing, while ? rms with multiple owners have larger fractions of outside-to-total capital. Comparing the point estimates in Table 9 to the averages in Table 4 suggests that multiple-owner ? rms receive about a ten percent increase in 19 the baseline amount of outside debt, and about a 25% increase in the baseline level of external equity (from around 8% to around 10%). Firms that have intellectual property are not more likely to access outside debt, but are more likely to access external equity, than those that do not. 6 Housing Markets, Bankruptcy Exemptions, and Access to Debt

In this section we explore two potential strategies for decoupling supply and demand for capital. The ? rst is to examine housing price appreciation as a potentially exogenous source of variation in collateral that drives the availability of credit. Since housing prices are likely to be endogenous to the expected future pro? tability of the business ventures, we instead link housing supply elasticity to bank credit. This variable is obtained from Saiz (2010) and is based on exogenous geographical factors that a? ect the amount of developable land, as well as factors like zoning restrictions.

Because the housing price elasticity is largely predetermined prior to 2004, it provides an exogenous source of variation in collateral values. The data provided by Saiz (2010) contain housing supply elasticity estimates for 269 metropolitan statistical areas (MSAs) in the United States. While this includes all the major metropolitan areas in the United States, it also includes a great many smaller regions. For example, the 1st percentile of the population distribution (using the population in 2000) is less than 80,000 residents. The size of the 25th percentile is around 163,000 residents.

Nevertheless, this variable does not measure the actual home price appreciation (or home ownership status) of the respondents of the KFS; it contains only a regional measure of land developability. 20 If housing supply were perfectly inelastic, then demand shocks would translate directly into price shocks, and home equity values would be highly sensitive to underlying changes in housing demand. In such a world, home equity would provide poor collateral for business loans, because the value of the collateral would be sensitive to factors that were outside the borrower’s control.

In contrast, a region with a perfectly elastic supply of housing would experience no price change whatsoever as housing demand changed. In such a world, home equity would be una? ected by ? uctuations in housing demand. Thus, in regions where housing supply is elastic, we should expect to see a greater reliance on outside debt, since the underlying home equity is more pledgeable. 5 Table 10 tests this prediction by regressing the fraction of bank capital on the housing supply elasticity, controlling for a variety of owner and business characteristics.

Across the various speci? cations reported, increasing supply elasticity raises the fraction of bank debt by about 2%. To translate this into economic magnitudes, moving from the 25th to 75th percentile, which is approximately moving from Reno, Nevada to Peoria, Illinois, is associated with a 3% increase in bank debt. Since the average startup is about 40% bank ? nanced, this e? ect seems economically large. The second potential channel for decoupling supply and demand is to examine statelevel bankruptcy exemptions.

All else equal, borrowers in states with higher bankruptcy exemption levels should expect to receive less total outside capital in the form of bank debt, since increased bankruptcy protection impairs the collateral value of the assets they own. 6 Since state-level bankruptcy laws are unlikely to be determined by local variation in entrepreneurial opportunity, including an exemption measure gives us another opportunity to separate credit supply from credit demand. The KFS survey instrument explicitly instructs respondents to exclude from owner’s equity any cash they put into the business from home equity loans or lines of credit.

The survey instrument allocates these funding sources to personal bank debt. 6 This argument is consistent with Berkowitz and White (2004), who show that higher personal bankruptcy exemption levels are associated with more credit denials among small businesses. 5 21 Column (5) of Table 10 includes a bankruptcy exemption variable, which is the bankruptcy homestead exemption in the respondent’s state of residence, in tens of thousands of dollars. Taken by itself, the variable has the expected sign, but is statistically insigni? cant. But Column (5) does not include credit score dummies.

When we include credit score information, as in Column (7), we see that the loading on the bankruptcy exemption is both negative and statistically signi? cant. This indicates that borrowers in states with higher bankruptcy exemptions indeed obtained a lower ratio of outside bank debt to total capital (see also Cerquiero and Fabiana Penas, 2010). 7 Does Financial Access A? ect Survival? One possible explanation for our ? ndings that certainly merits consideration is that the fact that startups rely extensively on external credit markets to fund their early life is being driven by peculiarities in the credit market in 2004.

We address this possibility in two ways. First, in Table 11 we examine the importance of debt for later-stage fundraising decisions. Is the reliance on debt a feature of the starting conditions of the business only? Do businesses wean themselves o? of outside debt as they grow? Table 11 suggests not. It suggests that they continue to rely on debt in the years after the ? rm’s founding. Table 11 shows that, for the average ? rm, the fraction of new capital coming into the ? rm that is made up of outside debt is actually increasing as the ? rm matures. If anything, the fraction of owner equity falls as the ? m ages. This supports life-cycle theories such as Berger and Udell (1998) in favor of the idea that startups used personal loans to kick start the business and then moved away from debt as the ? rm matured. The columns of Table 11 consider di? erent types of ? rms to see if the increased reliance on outside debt is particularly important for certain kinds of ? rms. Column (2) 22 reports ? rms that have some form of outside equity at startup. These ? rms typically receive a large equity injection in the ? rst year after founding, but in the following years, they rely much more heavily on outside debt.

This is consistent with outside equity being staged to coincide with milestones, but at the same time, the reliance on outside debt in 2006 and 2007 suggest that these ? rms too continue to rely on outside debt. The ? nal two columns of Table 11 look at opposite ends of the spectrum. Column (3) only considers the set of ? rms that are incorporated, have employees, and have assets such as inventories in the year of their founding. These ? rms typically have about 40% of their initial capital coming from outside debt, and this ratio grows over time. By the time of the third year (2007), the total capital coming into the ? m is over 55% outside debt. And while the absolute levels of ? nancing are considerably smaller for home-based ? rms (column 4), the story is very much the same: these ? rms rely on outside debt to an increasing degree as they age. If our ? ndings simply re? ect the fact that credit was readily available in 2004, then there is no reason to believe that access to external credit should a? ect ? rm success. To test this, we report Probit analysis of three key measures of growth from 20042007. First, we create a dummy for whether a ? rm has above median revenues in 2007. Then we repeat this calculation for pro? s and for employees. Our key explanatory variable is the ratio of outside debt to total capital. The hypothesis that we are testing is that ? rms with greater levels of external capital had better growth prospects. Table 12 presents the ? ndings. It includes the same basic set of owner and ? rm characteristics, plus the ratio of outside debt to total capital and the level of 2004 sales. The outside debt ratio has a positive and highly signi? cant e? ect on revenue growth and employee growth, but a statistically insigni? cant positive e? ect on pro? t growth. Before it is possible to attach a causal interpretation to these ? dings, it is important to control for unobserved characteristics that might a? ect access to debt and success. 23 In that regard, including the credit score and other ? rm characteristics are essential for interpreting our ? ndings. Including the credit score allows us to conclude that controlling for ? rm creditworthiness, ? rms that accessed more external debt were nearly ten percent more likely to be in the top revenue group, and nearly six percent more likely to have hired employees. Note too that this also controls for the initial revenues the ? rm experienced in 2004, therefore the e? ct is not attributable to initial size. Table 12 indicates that, indeed, initial capital structure decisions are important for ? rm success. The owner and ? rm characteristics, which are included as controls in Table 12, are interesting in their own right and raise many questions for future research. First, they show that female-owned businesses are signi? cantly less likely to grow than male-owned businesses. Black-owned businesses are signi? cantly less likely to have grown in terms of pro? ts or sales, but they are more likely to have added employees than white-owned businesses.

Asian-owned businesses are also more likely to have added employees, although Asian ownership is unrelated to revenue or pro? t growth. And ? nally, the vector of ? rm characteristics that might describe a ? rm, a priori, as a lifestyle business or not indeed predicts whether a ? rm has grown. 8 Conclusions This paper uses a novel data set to explore the capital structure decisions that ? rms make in their initial year of operation. In the vast majority of cases, this is when the ? rms in question are still being incubated in their founders’ homes or garages, before outside employees have joined the ? m in any signi? cant number, and certainly well before the ? rms in question would be attractive to the types of funding sources that are the focus of most discussions of early stage ? nancing. In spite of the fact that these ? rms are at their very beginning of life, they rely to a surprising degree on bank debt. Partly this is a function of the availability of bank 24 debt: in regions that experienced an increase in the supply of home loans, startups relied to a larger extent on bank debt. Higher quality ? rms operate at a larger scale in part because they can access larger amounts of bank ? ancing. The notion that startups commonly rely on the bene? cence of a loose coalition of family and friends seems misleading given our ? ndings. While the data suggest that informal investors are important for the handful of ? rms that rely on outside equity at their startup, the data also indicate that most ? rms turn elsewhere for their initial capital. Indeed, roughly 80-90% of most ? rms’ startup capital is made up in equal parts of owner equity and bank debt. To be sure, our ? ndings underscore the importance of liquid credit markets for the formation and success of young ? ms. Because startups rely so extensively on outside debt as a source of startup capital, they are especially sensitive to changes in bank lending conditions, perhaps more sensitive than would be suggested based on accounts of entrepreneurial ? nance that focus on the importance of informal capital. 25 References [1] Avery, Robert B. and Raphael W. Bostic and Katherine A. Samolyk, 1998. “The role of personal wealth in small business ? nance,” Journal of Banking and Finance, vol. 22, pp. 1019 – 1061. [2] Berkowitz, Jeremy and Michelle J. White, 2004. “Bankruptcy and small ? ms’ access to credit,” RAND Journal of Economics, vol. 35, no. 1, Spring. pp. 69-84. [3] Cerqueiros, Geraldo and Maria Fabiana Penas, 2010. “Debtor protection and startup ? nancing sources: evidence from the US” Working Paper, Tilburg University. [4] Cosh, Andrew, Douglas Cumming and Alan Hughes, 2008. “Outside Entrepreneurial Capital,” forthcoming, Economic Journal. [5] Fairlie, Robert and Harry Krashinsky, 2007. ”Liquidity Constraints, Household Wealth, and Entrepreneurship Revisited,” working paper, University of California, Santa Cruz. [6] Fairlie, Robert and Alicia Robb, 2008. Why are Black-Owned Businesses Less Successful than White-Owned Businesses: The Role of Families, Inheritances, and Business Human Capital,” Journal of Labor Economics, 25(2): 289-323. [7] Faulkender, Michael and Mitchell Peterson, 2006. “Does the source of capital a? ect capital structure? ” Review of Financial Studies, vol. 19, pp. 45-79. [8] Gompers, Paul and Josh Lerner, 2001. “The venture capital revolution. ” Journal of Economic Perspectives. [9] Helwege, Jean and N. Liang, 1996. “Is there a pecking order? Evidence from a panel of IPO ? rms. ” Journal of Financial Economics, vol 40, no. 429-458. 10] Hurst, Erik and Annamaria Lusardi, 2004. Liquidity Constraints, Household Wealth, and Entrepreneurship, Journal of Political Economy, vol. 112. 26 [11] Kaplan, Steven N. , Berk Sensoy, and Per J. Str? mberg, 2009. “Should investors o bet on the jockey or the horse? Evidence from the evolution of ? rms from early business plans to public companies. ” Forthcoming, Journal of Finance. [12] Lemman, Michael L. , Michael R. Roberts and Jaime F. Zender, 2008. “Back to the beginning: persistence and the cross-section of corporate capital structure,” Journal of Finance, vol 63, no 4, pp. 1575-1603. [13] Mann, Ronald J. , 1998. Comment on Avery, Bostic and Samolyk” Journal of Banking and Finance, vol. 22, pp. 1062-1066. [14] Moon, John, 2009. “Small business ? nance and personal assets,”Community Investments, vol. 21, issue 3, Winter 2009/2010. [15] Myers, Steward and Nicholas Majluf, 1984. Financing and investment decisions when ? rms have information markets do not have, Journal of Financial Economics, vol. 13, no. 2, pp. 187-221. [16] Peterson, Mitchell and Raghuram Rajan, 2000. “Does distance still matter? The information revolution in small business lending,” NBER Working Paper #7685. [17] Peterson, Mitchell and Raghuram Rajan, 1994. The bene? ts of lending relationships: evidence from small business lending,” Journal of Finance, vol. 49, no. 1, pp 3-37. [18] Peterson, Mitchell and Raghuram Rajan, 1997. “Trade credit: theories and evidence,” Review of Financial Studies, vol. 10, no. 3, pp 661-691. [19] Puri, Manju and David T. Robinson, 2008. “Optimism and Economic Choice,” Journal of Financial Economics, vol. 86, no. 1, 71-99. [20] Rajan, Raghuram and Luigi Zingales, 1995. “What do we know about capital structure? Some evidence from international data,” Journal of Finance, vol. 50, no. 5, pp. 1421-1460. 27 Robb, A. , Ballou, J. , Barton, T. DesRoches, D. , Potter, F. , Reedy, E. J. , & Zhao, Z. (2009). An Overview of the Kau? man Firm Survey: Results from the 2004-2007 Data. Kau? man Foundation. [21] Robb, Alicia M. , Robert Fairlie and David T. Robinson, 2009. “Capital Injections among New Black and White Business Ventures: Evidence from the Kau? man Firm Survey,” Working paper. [22] Saiz, Albert (forthcoming) ”The Geographic Determinants of Housing Supply” Quarterly Journal of Economics. [23] Shyam Sunder and Stewart Myers, 1999. ”Testing static tradeo? against pecking order models of capital structure, Journal of Financial Economics, vol 51. 24] Wong, Andrew, 2003. “Angel ? nance: the ‘other’ venture capital. ” PhD dissertation, University of Chicago. 28 Table 1: Business Characteristics Sample includes 3,972 ? rms that either survived over the 2004-2007 period or were veri? ed as going out of business over the same period. Corporation includes C- or S-corporations. Limited liability corporation includes LLC or LLP designations. Home based business means that the primary business location was the same as the owner’s home. Credit score is a quintile score of the credit quality of the business. Weighted Percentage Business Legal Status Sole Proprietorship Partnership

Corporation Limited Liability Corporation Business Location Home Based Leased Space Other Business Product/Service O? erings Service O? ered Product O? ered Business O? ers Both Service(s)/Product(s) Intellectual Property Patents Copyrights Trademarks Employment Size Zero 1 2 3 4-5 6-10 11+ Credit Score High Credit Score Medium Credit Score Low Credit Score 0. 115 0. 553 0. 332 59. 2 14. 0 9. 1 4. 6 5. 8 3. 9 3. 6 0. 022 0. 086 0. 137 0. 858 0. 516 0. 378 0. 500 0. 396 0. 104 0. 360 0. 057 0. 277 0. 306 29 Table 2: Cash ? ow characteristics of startups in the KFS Sample includes 3,972 ? ms that either survived over the 2004-2007 period or were veri? ed as going out of business over the same period. Panel A refers to the distribution of revenues and expenses, while Panel B refers to the distribution of pro? ts and losses. In Panel B, 44. 5% of the sample reported earning pro? ts, of whom 19. 4% indicated approximately zero pro? ts; likewise, 55. 5% reported losses, of whom around 3. 4% reported zero loss. Panel A: Percent of Businesses by Revenues and Expenses Weighted Weighted Revenues Percentage Expenses Percentage Zero 35. 3% Zero 6. 7% $1,000 or less 5. 1% $1,000 or less 8. % $1,001- $5,000 7. 7% $1,001- $5,000 16. 0% $5,001- $10,000 6. 1% $5,001- $10,000 11. 3% $10,001- $25,000 10. 5% $10,001- $25,000 16. 2% $25,001- $100,000 18. 6% $25,001- $100,000 25. 3% $100,001 or more 16. 8% $100,001 or more 15. 8% Panel B: Percent of Businesses by Amount of Pro? ts or Losses Weighted Weighted Pro? t (44. 5 %) Percentage Loss (55. 5%) Percentage Zero 19. 4% Zero 3. 4% $1,000 or less 10. 2% $1,000 or less 13. 2% $1,001- $5,000 16. 4% $1,001- $5,000 27. 3% $5,001- $10,000 12. 5% $5,001- $10,000 17. 0% $10,001- $25,000 17. 4% $10,001- $25,000 17. 9% $25,001- $100,000 20. % $25,001- $100,000 16. 9% $100,001 or more 4. 1% $100,001 or more 4. 2% 30 Table 3: Business owner demographics Sample includes 3,972 ? rms that either survived over the 2004-2007 period or were veri? ed as going out of business over the same period. Characteristics Male Female White Black Asian Others Non-Hispanic Hispanic Owner Age 24 or younger 25-34 35-44 45-54 55 or older Owner Education HS Grad or Less Tech/Trade/Voc. Deg. Some Coll. , no deg. Associate’s Bachelor’s Some Grad, No Deg. Master’s Degree Professional/Doctorate Weighted Weighted Percentage Characteristics: Percentage 69. 30. 8 Industry Exp. (Yrs. ) Zero 9. 8 79. 3 1-2 13. 9 8. 6 3-5 15. 6 4. 2 6-9 9. 9 2. 3 10-14 13. 6 15-19 11. 3 94. 5 20-24 9. 3 5. 5 25-29 7. 5 30+ 9. 3 1. 3 16. 5 33. 6 29. 0 19. 6 Previous Start-ups 0 1 2 3 4 or more 57. 5 21. 5 10. 2 5. 0 5. 8 13. 9 6. 4 21. 8 8. 6 25. 3 5. 9 13. 4 4. 7 Hours Worked Less than 20 20-35 36-45 46-55 56 or more 18. 5 19. 5 14. 3 15. 2 32. 5 31 Table 4: Sources of Financing for 2004 Startups Sample includes 3,972 ? rms that either survived over the 2004-2007 period or were veri? ed as going out of business over the same period. The mean, in dollars, for all ? ms is reported in the ? rst column. The second column reports the mean, in dollars, for only ? rms with positive amounts of that source of funding. The sample size for that source of funding is reported in the third column. Category Owner Equity Owner Debt Funding Source Personal CC balance, resp. Personal loan, other owners Personal CC balance, others Insider Equity Parent Equity Spouse Equity Insider Debt Personal Loan from family Business loan from family Family loan to other owners Personal loan to other owners Other personal loans Business loan by owner Business loan by emp.

Outsider Equity Angel investors Venture Capital Business equity Govt. equity Other equity Outsider Debt Business bank loan Personal bank loan Credit line balance Non-bank bus. Loan Personal bank loan by other owners Govt. bus. Loan Owner bus. CC balance Bus. CC balance Other Bus. CC balance Other bus. Loan Other individual loan Other debt Total Financial Capital Trade credit Grand Mean 31,734 5,037 2,811 1,989 238 2,102 1,456 646 6,362 2,749 1,760 284 550 924 15 79 15,935 6,350 4,804 3,645 798 337 47,847 17,075 15,859 5,057 3,627 1,859 1,331 1,009 812 135 231 226 626 109,016 21,793

Mean if > 0 40,536 15,765 9,375 124,124 7,415 44,956 42,509 40,436 47,873 29,232 57,207 34,509 28,988 81,452 9,411 22,198 354,540 244,707 1,162,898 321,351 146,624 187,046 128,706 261,358 92,433 95,058 214,920 80,650 154,743 7,107 6,976 7,852 78,281 43,202 119,493 121,981 93,536 Count 3,093 1,241 1,158 67 132 177 126 62 480 327 115 29 73 45 5 9 205 110 26 56 27 8 1,439 243 641 210 72 92 34 543 452 62 19 22 22 3,972 838 32 Table 5: Sources of Financing for 2004 Startups by Firm Type This sample includes the 3,972 ? rms that either survived over the 2004-2007 period or were veri? d as going out of business over the same period. Non-employer means the ? rm had no employees apart from the owner. Home-based means that the ? rm did not have a place of business outside the owner’s home. Funding Source Owner Equity Owner Debt Personal Credit Card -Owner Personal Credit Card-Other Owners Insider Equity Spouse Equity Parent Equity Insider Debt Personal Family Loan Business Loan from family Other Personal Loan Outsider Equity Other Informal Investors Other Business Equity Government Equity Venture Capital Equity Outsider Debt Personal Bank Loan Bank Business

Loan Credit Line Total Financial Capital Trade Credit Observations NonEmployer $17,269 $2,318 $1,896 $159 $698 $270 $428 $2,381 $1,051 $350 $475 $2,774 $785 $1,529 $10 $441 $19,353 $11,453 $5,231 $341 $44,793 $6,883 2,425 HomeBased $20,035 $2,624 $2,093 $218 $1,024 $215 $809 $3,074 $1,683 $580 $302 $4,731 $2,489 $1,568 $226 $443 $26,960 $12,898 $9,180 $656 $58,448 $5,537 2,168 PreRevenue $31,201 $3,720 $1,937 $133 $2,271 $612 $1,659 $6,456 $2,451 $2,114 $1,233 $16,268 $7,006 $4,539 $550 $4,164 $44,839 $12,962 $18,474 $2,986 $104,755 $4,825 1,615

PrePro? ts $35,433 $5,445 $3,499 $305 $2,553 $638 $1,915 $7,852 $3,342 $2,335 $1,177 $21,530 $9,704 $4,727 $945 $5,618 $54,536 $17,738 $21,160 $4,823 $127,349 $14,640 2,144 Survived thru 2006 $31,784 $4,896 $2,634 $217 $1,705 $468 $1,237 $5,856 $2,437 $1,481 $1,191 $18,753 $7,992 $3,840 $1,083 $5,373 $50,087 $17,416 $18,653 $5,061 $113,080 $22,684 3,390 Closed by 2006 $31,609 $5,392 $3,256 $291 $3,101 $1,094 $2,007 $7,635 $3,535 $2,464 $252 $8,841 $2,218 $3,155 $81 $3,373 $42,208 $11,941 $13,103 $5,047 $98,787 $16,642 773 3 Table 6: Do Equity-backed Firms Embrace or Eschew Debt? Each column in this table reports capital structure decisions for ? rms with di? erent types of outside equity. Thus, the sample size of each column is reported in the third row of Table 4, in the “Outside Equity” section. Amounts are averages over all ? rms that had the type of funding in the column header in 2004. Some subcategories are suppressed for brevity, but they are included in the totals reported in each category.

Source Owner Equity Insider Equity Spouse Equity Parent Equity Outsider Equity Other Informal Investors Other Business Equity Government Equity Venture Capital Equity Other Equity Owner Debt Insider Debt Personal Family Loan Personal Family Loan-other owners Other Personal Loan Other Personal Funding Other Personal Owner Loan Outsider Debt Personal Bank Loan Bank Business Loan Credit Line Other Non-Bank Loan Other Bank Loan Government Business Loan Other Individual Loan Other Business Debt Total Financial Capital Trade Credit

Angel $116,792 $12,948 $1,080 $11,868 $328,999 $244,707 $60,568 $6,488 $17,084 $151 $19,558 $15,997 $8,196 $651 $1,033 $4,567 $14,139 $164,891 $21,629 $67,728 $25,590 $17,359 $10,416 $352 $3,402 $14,491 $659,184 $73,272 VC $119,459 $4,278 $0 $4,278 $1,499,644 $126,811 $209,130 $804 $1,162,898 $0 $9,949 $32,365 $4,051 $0 $15,862 $12,452 $6,176 $628,398 $286,853 $299,169 $1,216 $19,005 $128 $402 $12,170 $0 $2,294,093 $161,417 Corporate $105,062 $5,346 $3,507 $1,839 $515,051 $183,110 $321,351 $443 $10,148 $0 $13,041 $9,033 $4,008 $2,098 $878 $860 $4,668 $75,156 $23,295 $28,882 $5,855 $2,752 $6,513 $0 $73 $0 $722,690 $129,815

Govt-Other $47,062 $5,521 $58 $5,463 $171,145 $9,901 $4,335 $110,147 $229 $46,533 $5,450 $3,109 $190 $257 $0 $0 $0 $96,030 $8,046 $56,094 $1,918 $0 $2,080 $22,219 $420 $4,049 $328,316 $168,277 34 Table 7: Credit quality and capital structure Source: Kau? man Firm Survey Microdata. Sample includes only surviving ? rms over the 2004-2007 period and ? rms that have been veri? ed as going out of business over the same period. Sample size 3,972. This table reports mean levels of 2004 startup funding by type of funding. The ? rst column matches the category-level data reported in the previous table.

The remaining columns report breakdowns for various types of ? rms. Columns 2 and 3 focus on ? rms with high and low Dun and Bradstreet credit scores. The ? nal three columns repeat the ? rst three, but only examine high-tech ? rms. Owner Equity Owner Debt $2,102 $6,362 $15,935 $47,847 $41,527 $112,803 $6,225 $26,492 $53,736 $29,478 $6,190 $13,738 $1,316 $5,088 $4,503 $3,412 All $31,734 $5,037 All ? rms: High credit $53,994 $8,926 Low credit $21,199 $3,245 All Tech $27,875 $7,000 Only High Technology High credit $57,655 $28,760 $16,508 $6,860 $136,945 $115,590 ?rms: Low credit $17,122 $3,774 $252 $1,963 $215 $6,013 5 $237,179 472 $63,565 1264 $126,005 532 Insider Equity Insider Debt Outsider Equity Outsider Debt Total Financial Capital $109,016 N 3972 $362,317 85 $29,339 109 Table 8: Capital structure di? erences between High Residual Credit and Low Residual Credit ? rms Panel A reports capital structure based on quintiles from the residuals of regression of credit scores on industry ? xed e? ects. A total of 60 industry dummies are included. Panel B reports capital structure averages according to quintiles from the residuals of regressions of the following form: scoreij = ? + ? j + ? Fij + ? 2 Kij + i (4) where scoreij is the credit score of ? rm i in industry j, ? j are industry ? xed e? ects, and F is a vector of owner characteristics, and K is a vector of ? rm characteristics. For this speci? cation, we include a full set of industry dummies, a set of education dummies corresponding to the breakdown presented in Table 3, and we also include factors such as race, ethnicity, industry experience, intellectual property, legal structure of the enterprise, whether the business is home-based, and whether the business sells a product or provides a service.

Panel A: Regression based on INDUSTRY CONTROLS Overall Bottom Quintile Top Quintile Mean Percent Mean Percent Mean Percent Owner Equity 27,365 35% 39,503 32% 18,672 35% Insider Equity 1,695 2% 3,012 2% 1,497 3% Outsider Equity 6,979 9% 14,194 11% 4,117 8% Owner Debt 3,506 4% 3,649 3% 3,351 6% Insider Debt 7,605 10% 13,023 10% 6,574 12% Outsider Debt 31,255 40% 51,621 41% 18,758 35% Total Capital 78,406 100% 125,002 100% 52,969 100% % Zero Capital 10% 9% 12% Di? rence Mean Percent 20,831 -4% 1,515 0% 10,078 4% 298 -3% 6,449 -2% 32,863 6% 72,033 0% 3% Owner Equity Insider Equity Outsider Equity Owner Debt Insider Debt Outsider Debt Total Capital Zero Capital Panel B: Regression based on FULL MODEL Overall Bottom Quintile Top Quintile Di? erence Mean Percent Mean Percent Mean Percent Mean Percent 27,365 34. 9% 33,970 32. 5% 24,085 33. 9% 9,885 -1% 1,695 2. 2% 2,102 2. 0% 1,660 2. 3% 442 0% 6,979 8. 9% 8,392 8. 0% 6,622 9. 3% 1,770 -1% 3,506 4. % 3,431 3. 3% 3,848 5. 4% (417) -2% 7,605 9. 7% 11,738 11. 2% 7,181 10. 1% 4,558 1% 31,255 39. 9% 45,014 43. 0% 27,648 38. 9% 17,366 4% 78,406 100. 0% 104,648 100. 0% 71,043 100. 0% 33,604 0% 10% 9% 9% 0% 36 Table 9: Explaining Capital Structure Ratios for Startups Sample includes 3,972 ? rms that either survived over the 2004-2007 period or were veri? ed as going out of business over the same period. The dependent variable in each column is the ratio of that form of capital to total ? ancial capital (excluding trade credit). Bank debt includes personal loans for business as well as business bank loans, but excludes the other sources of outside debt. Inside ? nance is the sum of inside debt and equity. Outside debt encompasses column (1) but also includes the other sources of outside debt. Outside equity includes VC, angel, gov’t, business equity, and other outside equity. Robust standard errors in parentheses. 2-digit industry dummies and owner education dummies included. *** p

Cite this The Capital Structure Decisions of New Firms

The Capital Structure Decisions of New Firms. (2016, Sep 09). Retrieved from https://graduateway.com/the-capital-structure-decisions-of-new-firms/

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