Money Matters: An Examination of the Impact of Financial Structure on Nascent Venture Performance; Evidence from the Kauffman Firm Survey

This study was carried out to empirically test the impact of financial structure on nascent enterprise performance. The study used a centralistic nomothetic longitudinal methodology to examine a panel data derived from the first four years of the Kauffman firm Survey (KFS). The result revealed that financial structure (equity financing, debt financing, and trade-financing) influenced nascent enterprise performance, but inconsistently over the first four years of business existence. The average capital structure of the sample was supported by the literature and followed the pecking order of equity, debt, and trade financing. Results suggested that capital structure has an important ramification for nascent enterprise performance, but the capital mix of successful nascent enterprises do not necessarily follow an orthodox format.


INTRODUCTION
Approximately 45% of respondents in the European Union and 27% of those in the United States who participated in the Flash Euro-barometer (2000) survey acknowledged that difficulty in putting together initial financial capital is the most critical hindrance to starting a new enterprise (Psaltopoulos, Stathopoulou & Skuras,2005). Other scholars have also attributed business failure mostly to financial constraints (Chandler & Hank,1998). More importantly, Cooper et al. (1994) and O'Neill and Duker(1986) found a positive relationship between nascent enterprise performance and initial financial capital. Many great business ideas have failed to see the light of day because of a lack of finance. Therefore, financial capital is not only the lifeblood but the oxygen that most likely breathes life into a new business.
There are three broad sources of financing potentially available to a nascent entrepreneur. These sources are equity, debt, and trade-financing (Farhart et. al.,2018). The mix of these sources of financing that a particular firm adopts is called the firm's financial structure. However, there is little guidance in the literature to inform nascent entrepreneurs about how to determine the financial structure (Carter & Van Auken,1990) of a new business. This gap in the literature provides an opportunity to investigate the impact of financial structure on nascent enterprise performance. To bridge this gap in the literature, this study is conducted to empirically test the impact of financial structure on nascent enterprise performance; utilizing the first four years the Kauffman Frim Survey (KFS) panel dataset.

Theoretical Consideration
Debt and Equity Financing of Nascent Enterprises Nascent enterprises, particularly those founded by novice entrepreneurs are likely to have a smaller debt-toequity ratio since their cost of borrowing is likely to be higher than those of successful serial or portfolio entrepreneurs. This is because the serial or portfolio entrepreneur may have built reputational capital such as credit history and performance track records (Cressy,1996;Hanley & Girma,2006;Harris & Ravin,1991) that can help them attract external financing at a lower cost.
The nascent entrepreneur, on the other hand, neither has sufficient credit nor performance history to court outside stockholders (Deakins&Whitam,2000;Evers,2003) nor to raise debt capital at minimal borrowing cost. Generally, the financing hierarchy of a nascent enterprise would follow the trajectory of the entrepreneur's personal funds, short-term loan, long-term debt, and lastly, equity investments (Cosh & Hughes,1994;Hussain & Matlay,2007;Psaltopoulos et al.,2005). Since nascent businesses often do not have retained earnings to turn to for internal financing, their financing hierarchy sometimes typically follow the order of personal (savings) wealth, personal credit cards, short-term debt, long-term debt and outside equity investment.
Traditional pecking order theory of financing suggests that typical business finance preference hierarchy follows internal financing, debt, and finally equity (Myers,1984;Sjorgren & Zackrisson,2005). Firms may first look inward for internal resources to finance their investments because this does not incur borrowing costs. The next order is to seek financing from low-cost riskless debt, before settling for external equity. This hierarchy applies to some extent to start-ups as well, but the rationale may be a little different compared to those of established businesses. Most nascent businesses start small (Aldrich & Auster, 1986, Audretsch & Mahmood 1994Bruderl & Schussler, 1990;Mahmood, 2000) and small enterprises depend more on internal resources for financing than on outside resources (Sogorb-Mira, 2005) such as bank loans and venture-capital equity. Bhide (2000) studied 500 companies incorporated in 1996, and found that on average, personal savings constituted 55% of start-up capital, friends and family contributed 13%, bank loans constituted 7%, personal charge cards were a source for 6%, while venture capitalists contributed only 4% and angel investors pumped in about 3% of initial capital (Evers,2003). These findings support the typical pecking order of internal sources (personal savings, charge cards), debt (family, friends, and bank loans) and equity (family, friends, venture capital, and angels). Paul, Whittam, and Wyper (2007) contrarily found contradictory empirical evidence to the traditional pecking order of start-up financing in their Scottish sample. The hierarchy they discovered was internal resources, equity, and debt. Paul et al. (2007) explained this observation by arguing that nascent entrepreneurs see debt as a personal liability as loans have to be guaranteed with personal assets as collateral. Based on this observation, Paul et al. (2007) concluded that entrepreneurs may turn to outside equity more than debt. Moreover, outside investors may add value to the business by bringing on board managerial competencies and social capital.
Based on the information signaling theory, Poitevin (1989) argued that there is a potential competition between nascent enterprises and established firms in all industries. Information about the marginal costs of a new business is only fully known to the firm in question. This leads to information asymmetry in the financial market. As a result, firms that use more debt financing are signaled in financial markets to have a higher value. Therefore, low-cost nascent enterprises that want their stock value to be favored by the market tend to adopt more debt financing to signal higher value for their stock. Firms with high stock value however tend to become vulnerable to acquisition by more powerful competitors. To avoid predation by incumbent firms, high-cost nascent businesses would tend to rely more on equity financing than debt (Harris & Ravin, 1991).
In effect, low-cost public listed nascent enterprises will adopt a higher debt-to-equity ratio, while high-cost public listed new businesses will utilize a higher equityto-debt capital structure, ceteris paribus. Since this theory assumes equilibrium and open financial markets, it will more likely apply to big nascent businesses such as Ccorporations that can raise funds on capital markets (such as stock exchanges) than S-corporations, partnerships, and sole proprietorships in the United States and in other developed market economies.
According to the product or input market model (a derivative of industrial organization theory), the capital structure of a nascent business can be largely determined ©Center for Promoting Education and Research (CPER) USA www.cpernet.org International Journal of Business and Applied Social Science (IJBASS) E-ISSN: 2469-6501 VOL: 6, ISSUE: 7 July/2020 DOI: 10.33642/ijbass.v6n7p1 https://ijbassnet.com/ by a firm's strategy or nature of its product. A new business in an oligopolistic market is more likely to have more debt in its financial structure than a nascent enterprise which is a monopoly (Brander & Lewis,1986). On the other hand, an infant business offering a differentiated product or one whose product requires after-sales services or a firm in an industry that requires a high-quality reputation to leverage performance will tend to use less debt in its capital structure (Titman, 1984). Carter and Van Auken (1990) contended that the degree of equity investment a nascent entrepreneur sinks into a new business signal the entrepreneur's conviction about the perceived profitability of the new venture. Thus, in the face of financial constraints, the foundingowner equity component of initial capital tends to be higher where there is a perceived greater chance of high performance (Chandler&Hanks,1998),while debt proportion tends to be lower (Psaltopoulos et al.,2005).

Trade-Financing of Nascent ventures
Trade-credit may be used as a last resort bootstrapping strategy to finance a nascent venture. However, accessibility to trade-credit is influenced by industry-specific characteristics (Fisman & Love,2003). In theory, accessibility to trade-credit by business startups is largely influenced by factors such as resealability and value of inputs or products required by the buying firm (Frank & Maksimovic,1998;Mian & Smith,1992) and suppliers' ability to price-discriminate among cash and credit customers (Brennan, Maksimovic & Zezhner,1988;Fisman & Love,2003). In industries where materials neither easily deteriorate nor become quickly obsolete (Emery & Nayar,1998;Lee & Stowe, 1993;Long, Malitz & Ravid,1993), trading on credit-basis is relatively common (Cunat, 2000). Peterson and Rajan (1997) found that small businesses in the United States of America that do not have well-established banking relationships use tradecredit more. Burkart and Ellingsen (2004) and Fisman and Love (2003) on the other hand established that nascent businesses use trade-credit in growing industries where financial markets are weak. Besides industry practices, nascent businesses use trade-finance as a last resort bootstrapping strategy to finance their businesses where they find it difficult to raise credit from financial institutions. For a supplier to adopt credit-financing as a marketing technique in a dyadic relationship with a business customer, the supplier must be able to cheaply establish the creditworthiness of the buying firm and should be able to easily liquidate the goods (Peterson & Rajan,1997) when the buyer defaults. Thus, financial capital and its architecture have been found to influence business performance (Cooper, et al.,1994;Ebben & Johnson,2006;Lussier,1995;Mahmood,2000;Menefee & Parnell,2007;Song et al.,2008;Pratt & Morris,1988). Based on the synthesized literature above, we hypothesized that:

Main Hypothesis
Ha: The financial structure (equity financing, debt financing, and trade financing) of nascent enterprise will impact nascent enterprise performance.

Sub-Hypothesis
Ha1:The equity component of a nascent enterprise's financial structure will impact the nascent enterprise's performance.
Ha2:The debt component of a nascent enterprise's financial structure will impact the nascent enterprise's performance.
Ha3: The trade-financing component of a nascent enterprise's financial structure will impact the nascent enterprise's performance.

MATERIALS AND METHODS
The study used a centralistic nomothetic methodology to examine a panel data derived from the first four years of the Kauffman firm Survey (KFS). Chromy's (1979Chromy's ( , 1981 sequential random sampling technique was used to select the original sample of 4928. This was further pruned to an analysis sample of 754 after cleaning the data for this study.

Financial Structure Variables and their Measurements
Financial structure variables used in this study are equity financing, debt financing, and trade financing. The percentage that each constituted of total capital employed by each sample unit was calculated. The use of percentages instead of actual amounts enabled comparison between bigger and smaller firms. How financial structure variables were measured is presented as follows.

Equity Financing
The equity investment was strewn over several variables in the original KFS data set and had to be summated. The equity investment was captured in the KFS as follows: the amount of individual equity contribution of owners of the business up to not more than ten founders, equity investment by spouses of owners up to not more than ten founders, equity investment by parents of owners up to not more than ten founders, equity investment by companies, equity investment by Government, equity investment by angels, equity ©Center for Promoting Education and Research (CPER) USA www.cpernet.org International Journal of Business and Applied Social Science (IJBASS) E-ISSN: 2469-6501 VOL: 6, ISSUE: 7 July/2020 DOI: 10.33642/ijbass.v6n7p1 https://ijbassnet.com/ investment by venture capitalists and equity investment by others. These various sources of equity were captured either as a specific amount in dollars or as a code representing the range of value within which the amount fell. See Table 1 for the ranges, codes, and class-mark computation for all variables measured in money value.
Each code was used to identify the range it represents in the metadata of the KFS and classmidpoints were computed for each range and substituted for the code in question for the relevant sample units. Class-marks for both monetary values derived from codes and those stated specific monetary value ranges by respondents were consolidated in a single column for each sub-variable of equity. The aggregate amount of all the various classes of equity for each firm was then divided by the figure of total financing (including total equity, total debt, and total trade financing) for each respondent firm. This was subsequently multiplied by one hundred percent to arrive at the equity component of financial structure.

Debt Financing
Debt financing was also fragmented over several debt-related variables in the KFS and had to be aggregated as was in the case of equity financing. Debt financing was captured in the original KFS in two broad categories namely; amounts of personal debt and amounts of business debt. The personal debt included personal loan amounts by owners to the business up to a maximum of ten owners, amount of balance on the personal credit card used for business up to a maximum of ten founders, amount of personal loan from families of owners up to a maximum of ten founders, amount of personal loans owners secured from a bank for the business up to a maximum of ten owners and personal loan from any other sources. Business debts in the original KFS included business loan(s) from banks, business loan(s) from family members of owners, business loan(s) from owners, business loan(s) from other individuals, a business loan from the government, business loan(s) from non-bank financial institutions, business loan(s) from other individuals, business loan(s) from any other source and business credit line balance. Debts from all these varied sources were captured in the KFS either as a specific amount or as codes representing ranges of amounts. See Table 1 above.
Class mid-points were computed for the various codes and were combined with specific amounts provided by other sample units to form a single column of data for each debt source as was done in arriving at the summated value of equity financing. The amount of debt from all sources was cast for each sample unit to arrive at total debt financing. The total debt financing amount for each sample unit was then divided by its overall capital employed and multiplied by one hundred percent to arrive at the debt component of capital structure. 1Computation of Upper limit for the Range greater than $1,000,000: Difference between upper and lower class limits for each close-ended class was computed. The cumulative differences were then added to the lower limit of the open ended class to form an assumed upper limit. The class mark was then computed based on this value for the hitherto open ended class.

Trade Financing
Trade financing was captured in the original KFS data set either as a specific amount or a code representing the range of amount for each sample unit.
For the present study, class marks were computed for ranges of amounts just as was done for equity and debt financing respectively (see Table 1). The class midpoints were then combined with specific-amount responses to form one trade financing variable. Trade financing amount for each respondent firm was divided by overall financing and multiplied by one hundred percent to arrive at the percentage that trade financing constituted of each firm's entire capital structure.

Nascent Enterprise Performance Index Measurement
The variable of nascent enterprise performance is a composite variable (Fletcher & Neubaum, 2008) made up of profit or loss margin, return on assets, assets turnover ratio, and sales-to-expenses ratio. These four components of the nascent venture performance variable did not directly exist in the KFS data set, but variables that enabled them to be computed existed in the data set. The Elements and subsequent computation of the nascent venture performance index are as follows.

Profit or Loss Margin
The KFS contains data respectively on sales and profit or loss for each sample unit in either specific amounts or as codes representing ranges of amounts (see Table 1). To prepare the data for this study, where either sales, profit or loss were represented by codes, the codes were used to identify the amount ranges in the metadata document of the KFS and class midpoints of the amounts were calculated and combined to form a single variable for sales, profit or loss respectively. Subsequently, profits or losses were further consolidated to form a single variable on the bases that loss is a negative profit. The profit or loss margin was calculated by dividing the profit or loss figure by the sales figure of each firm and the resultant figure was multiplied by one hundred percent.

Return on Assets
Return on assets did not exist directly as a variable in the KFS and had to be computed. Assets value was fragmented over several classes of assets in the KFS. These classes were accounts receivable, cash on hand, and (or) at bank, equipment, land, and building, vehicles, inventory, other business properties, and other unclassified assets. The values of all these assets for each sample unit were either captured in the data set by specific monetary values or by codes representing the range of amount. Class-marks were computed for a range of values represented by codes and these were used to replace the codes to form a single-spine variable for each asset class in monetary measurement (see Table 1 above). The values of all classes of assets were subsequently aggregated for each sample unit to arrive at the value of its total assets. The profit or loss amount as discussed earlier under the profit margin was then divided over the total assets figure for each respondent firm and multiplied by one hundred percent to arrive at the return on assets figure for each sample unit.

Assets turnover
The total sales figure for each sample unit (as discussed under Profit Margin above) was divided by the total assets value of each firm (as discussed under Return on Assets) to compute the assets turnover for each sample unit.

Sales-to-Expenses Ratio
The Sales-to-expenses ratio was used as a measure of the efficiency of expenses in generating sales in the business. This ratio is logically sounder than the often-used expenses-to-sales ratio because expenses are made to generate revenue and not the other way around. Total expenses for each firm were captured in the KFS like many other monetary value variables as either a specific amount in the actual monetary unit or as a code representing the appropriate range within which the number of expenses fell. Class mid-points were computed for ranges of expenses represented by respective codes and these amounts were combined with other responses containing specific amounts of expenses to form a single expenses variable (see Table 1). The total sales figure for each firm was then divided by its expenses to derive the sales-to-expenses ratio.

Forming the Nascent Enterprise Performance Index
To use multiple regression techniques, the dependent variable has to be a single metric variable. To achieve this with richer data than just using one of the indicator variables out of the four performance indicators computed above, an index was formed with all four of them. This was done by first standardizing the values of all four variables and then summing them up to form a composite variable. The index arrived at provides a more rigorous measure of firm performance than just using one of the component variables as the dependent variable (Fletcher & Nusbaum, 2008 Financial structure variables were then tested to see if they do influence nascent venture performance in a regression model. The results are as follows:

Results of Test of Main Hypothesis: Ha
Hypothesis Ha was supported in year-2 and year-4, but not in years 1 and 3. Thus, financial structure (mix of equity financing, debt financing, and trade financing) influenced nascent enterprise performance in years 2 and 4, but not in years 1 and 3. The F-statistic for year-2 was 4.731 (p = .003,) while it was 4.222 (p = .006) for year-4 (see Table 2).

Results of Sub Hypotheses Test
In year-1, there was collinearity between Equity financing (Eqty1) and Debt (Dbt1) financing. Debt financing was therefore dropped from the analysis, but trade financing (TrFin1) which did not have any collinear relationship with any other variable in the model was retained as an archetype of debt financing in the analysis. Neither equity nor trade financing had a significant impact on Nascent Enterprise Performance in year-1. Therefore, none of the sub-hypotheses was supported in year-1. See Table 3. In Year-2, all the sub-hypotheses (Ha1, Ha2, and Ha3) where was supported. That means all three components of capital structure (equity, debt, and tradefinancing) had significant individual effects on nascent venture performance. There was no collinearity between any of the financial structure variables in year-2. Contrary to expectation, all three financial structure elements tended to have the negative in individual impacts on nascent venture performance in year-2. The t-statistics for the three variables were: equity financing -.3427 (p = .001), debt financing -2.410 (p= .016) and trade financing -3.496 (p = .001). Their standardized coefficients in Table  4 suggest that equity financing had the most negative impact on nascent venture performance in year-2. It was following by trade financing, and debt financing had the least significant effect on nascent venture performance in year-2.  None of the sub-hypotheses of the financial structure was supported in Year-3 Thus none of the individual components of financial structure tested significantly in year-3. See Table 5 below for the detailed results. Sub-Hypothesis Ha3 was supported in year-4. Thus, debt financing was the only component of capital structure that had a significant albeit negative effect on nascent venture performance in year-4 (see Table 6).

Financial structure and nascent venture performance
The capital structure did not plausibly have a significant effect on nascent venture performance in the first year of firm existence because of the overwhelming impact and importance of the presence of owner characteristics (Dzathor, et. al. 2013); particularly industry experience (Audia & Rider,2005) and level of education (Romijn&Albaladejo,2002). Other owner characteristics, particularly conscientiousness (Peiris & Koggalage,2020) has also been known to impact initial firm performance in other studies. However, the impact of financial structure on nascent enterprise performance became significant in the second year. This could probably be so because the financial structure has possibly started playing a more important role in nascent venture performance as the new firm begins to experience growth and expand from the second year onwards. The possible explanation for financial structure becoming insignificant in year-3 is because Year-3 marked the beginning of the economic recession of 2007-2011 in the United States and around the globe (Kashyap,2010). No variable in the study tested significantly in year-3, as firms struggled to adjust to the turbulent economic environment. As the recession continued in year-4 of the panel data, the impact of financial structure became significant again as many nascent ventures might have started to adjust to the economic recession and have tweaked their financial strategies to survive.
Equity and nascent venture performance Equity financing was not significant in any of the four years under study, except in year-2 when it had a significant but negative effect. This agrees with the pecking order theory that postulates that equity plays the least significant role in nascent enterprise financing (Myers,1984;Sjorgren & Zackrisson,2005). Besides, nascent enterprises depend more on internally generated sources of financing than on outside resources (Sogorb-Mira, 2005) Debt and nascent venture performance Debt financing was eliminated from the analysis in year-1 because of its collinearity with trade financing. Debt financing was negatively significant in years 2 and 4, but insignificant in year 3; with year-3 signifying the beginning of the global recession of 2007-2011. The undesirable impact of debt financing is in tandem with the observation of Paul et. al. (2007) that contrary to the pecking order theory in finance, debt could be at the bottom of nascent business financing pecking order. This is because nascent entrepreneurs see debt as personal liability that may require risking their assets as collateral.

Trade-financing and nascent venture performance
Like Equity financing, trade financing was insignificant in years 1, 3, and 4 and negatively significant in only year-2. The general lack of significance of trade financing underlies the fact that trade financing is usually used by nascent entrepreneurs as a last resort bootstrapping financing option. Nascent enterprise accessibility to trade-credit is also difficult and influenced by industry-specific characteristics (Fisman & Love,2003). Most suppliers may only supply goods to nascent enterprises on credit where the inventory neither deteriorate easily nor become easily obsolete (Emery & Nayar,1998;Lee & Stowe,1993;Long, Malitz & Ravid,1993) and where the inventory can be easily repossessed and resold close to their original value (Peterson & Rajan,1997).

Conclusion
Theoretical Implication The study suggests that even though the pecking order theory of financing holds to some extent for established as well as nascent enterprises, it does not always follow a linear path when it comes to nascent enterprises. It is also difficult to determine the appropriate mix of capital structure that will impact nascent enterprise performance successfully.

Managerial implication
The financial structure seems to have important ramifications for nascent enterprise performance from the second year onwards. Entrepreneurs must be aware that the individual components of financing (equity, debt, and trade-financing) may start impacting nascent venture performance from the second year of existence, and this may not necessarily be positive. Debt component seems to play a more significant role in nascent venture performance as a leveraging factor and managers of lowcost public listed nascent enterprises may use more debt to signal higher value for their stock. By year-4, business owners may cash in on the reputation they have carved for themselves in their industry, the goodwill they have created with banks and other financial institutions to access debt financing at a relatively cheaper cost to leverage the performance of their businesses. Trade financing may be a cheap source of financing in certain industries.

LIMITATIONS
As it is often the case in social science research, this study was impacted, but marginally by sampling and non-sampling errors. The sample-unit firms were in their infantile stage of existence at the time of data collection and many were unable to prove financial data in the survey. Thus, many firms with missing financial data were deleted from the dataset and this could have affected the distribution of the data. However, the pruned sample size of 754 used in the final analysis was big enough to assume a normal distribution.

DIRECTION FOR FUTURE RESEARCH
Another data set may be used to replicate the study to see if the result will be more robust. A comparative study can be undertaken using the same dataset, but different statistical methods such as discriminant analysis to look at the impact of the various elements of financial structure and their impact on nascent venture performance. Another study can also be conducted to look at the moderating or mediating effect of other variables such as owner characteristics and organizational characteristics in the relationship between financial structure and nascent enterprise performance.