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A. Introduction
While the relative importance of the small business sector to the economic growth of the US is still debated (Harrison, 14), it is clear that the small business sector is important enough to warrant research and discussion. There are many reasons for the increase in importance of the small business sector. One of the most apparent reasons is the increase in information based resources which has lead to technological change favoring small firms. One of the more dramatic examples is steel making where large integrated fabricating facilities are being replaced by small firms (Starr, 188). Other sources of small firm growth relative to large firm growth in the U.S. are 1) the increased rate of outsourcing, ) increase in deregulation, ) computerization, and 4) changes in financial markets.
Although the emphasis has been on the job generating ability of the entrepreneur in the creation of these small businesses, the importance of small business goes beyond the potential for job creation. These new business starts have been shown to lead to periods of more robust economic activity overall (Highfield and Smiley, 187) and contribute to economic wealth creation. On the other hand, business discontinuance is damaging to the efficient operation of a market when it results in bankruptcy and financial loss to creditors. The fundamental importance of small business to a nations economy has led public policy makers and researchers to look for factors that predict a firms chances for success and survival.
The economics literature has devoted attention to analyzing the firm in terms of market structure, industrial concentration and market efficiency. Relatively little work has been done on why firms come into existence and why some decide to cease operation (Hudson, 18). Lane and Schary (11) conclude that business failure/survival is certainly under-analyzed. More research on this significant issue can only improve our knowledge and enhance the probability of business survival.
Small business longevity is investigated in this study, building on the work by Holmes and Schmitz (15). Based on their model, there are two reasons why a business might fail. One, the match between the owner and the business is weak. This might occur where nontechnical individuals get involved in ventures that require detailed technical knowledge or when a single parent with six children under ten gets involved with a business that requires 60-80 hours a week out of their home. Two, the characteristics of the business are fatally flawed. The new firm might be located where it is inaccessible to targeted customers, or the main product suffers an unexpected defect.
We contribute to this research in two ways. First, we analyze the three dimensions in terms of problems that evidence themselves at the start-up of the venture. The severity of start-up problems or the ability of the entrepreneur to cope with the start-up problems appear to be important concepts that have yet to be incorporated into the models that attempt to explain survival. In this research we hypothesize that start-up problems that are difficult to overcome create a genetic impairment for the new firm. This genetic impairment then causes the new firm to discontinue. Firms will discontinue because the genetic impairment, was impossible to overcome. This may be due to conditions where entrepreneurs didnt posses the individual characteristics necessary for self-employment, because the match was bad, or because the business dimensions were not conducive to survival. Thus a fatal combination of factors produces a genetic impairment, precluding the possibility of survival. It would be substantially beneficial to entrepreneurs, their advisors, and to those who commit resources to their ventures to identify factors at start-up that hinder the survival of the firm.
Second, the dimension of the individual is added to the first two dimensions. Some entrepreneurs are not suited for self-employment or entrepreneurship regardless of the business match or business quality, and the business fails due to their lack of necessary skills. For example, a certain degree of literacy is required to file the forms necessary for any business venture, and a certain amount of financial skill is required to accrue funds for payments. Entrepreneurs are likely to differ in the stock of resources they can bring to the creation of new ventures. To the extent that these factors render firms vulnerable, firms begun by these sub-populations would seem more likely to die while emerging, or exhibit the liability of newness.
The article proceeds as follows. In the next section we discuss the model and related literature. In the third section we introduce the data. The fourth section contains the analysis, and is followed by conclusions.
THE MODEL
Survival models have been utilized by Freeman, Carrol, and Hannan, 18; Bruderl and Schussler, 10; Preisendorfer and Voss, 10; Bruderl, Preisendorfer, and Zeigler, 1; and Audretsch and Manhood, 14; Mata and Portugal, 14; and Mata, Portugal, and Guimaraes, 15. The hazard model is more popular than the survival model as a way of describing the probability distribution, and is defined as
h(t) = lim Pr{t£ Tt+Dt| T ³ t} = f(t)
Dt®0 Dt S(t)
where T is the firms life duration in months, f(t) is the probability density function, and S(t) is the survival function.
Taking the log of h(t) is a convenient way to ensure that h(t) is nonnegative. A useful specification of the hazard model is the proportional hazard model [Cox (17)]. Thus our model of business discontinuance can be represented as
ln h(t) = ln h0(t) + Xb
where h(t) is the instantaneous transition rate to discontinuance, t represents time since the beginning of the study period, h0(t) represents the baseline hazard function, the X represents a set of independent variables, and the Bs are the coefficients indicating the effects of the independent variables on h(t). Since we are analyzing the effects of the start-up problems faced by the firms i.e. their degree of genetic impairment, there are no time dependent covariates, and time does not enter the right side of the equation. We assume that the independent variables are continuous and random. Of interest here is the influence of the covariates on the probability of failure, specifically the effect of start-up problems encountered and the potential genetic impairment of the new firm. The b vector can be estimated without specifing the form for the baseline hazard model, avoiding the risk of imposing the wrong probability function for that behavior.
Of importance here is what to include in the X vector. The concern of this research is to determine the effects of the genetic impairment of the firm on the duration of survival. To that end, we analyze problems discernible at start-up that fall within the dimensions of individual, match, and business qualities. A two satge process is utilized for the inclusion of some of the variables in the X vector. Starting too small has been determined to affect survival, as has undercapitalization (Bates, 18; Bates, 10; Brito and Mello, 15; Cooper, Gimeno-Gascon, and Woo, ). At the same time, it has been shown that human capital affects the initial size and capitalization of the new venture (Cooper, Gimeno-Gascon, and Woo, ). In the first stage, we predict the size of the firm (number of employees) and personal capital levels by the amount of human capital. The predicted values are then used in the X vector. This method allows for inclusion of the human capital variables while avoiding the problem of multicollinearity if they were included in the equation with size and financial start-up capital.
Size -----®
= f(Age, Ed, Ex)
Start-Up
Problems Exit
Financial Start-Up Capital
= f(Age, Ed, Ex) -----®
In addition to the start-up problems, other variables that affect survival should be included to avoid misspecification error. Researchers studying survival have classified the independent varaibles into many different sets. Variable classifications have included internal and external (Acar, 1); owner characteristics and organizational characteristics (Auster, 188); firm and industry (Amit and Schoemaker, 1; Audretsch, 15; Mata, 14); firm, industry and macroenvironment (Mata, Portugal, and Guimaraes, 15); founding team, strategy, and environment (Eisenhardt and Schoonhoven, 10); organization and environment (Chandler and Hanks, 14); structural and cyclical (Boeri and Bellmann, 15); individual (Bates, 18, Bates, 10); individual and environment (Davidsson, 11); individual, business and environment (Bruderl, Preisendorfer, and Zeigler, 1); the match (between individuals and the firm) and firm (Holmes and Schmidtz, (15). Most of the classifications can be seen as to conform to the three sets of individual characteristics, business characteristics, environmental characteristics, and interactions among these sets. In order to isolate the variables that belong in each set, look at the firm in its orientation towards a traditional goal of maximizing profits
max(PtQt-c(Qt))
Building upon Holmes and Schmitz (15) we look at Qt, the output of the venture as the result of the match component, the business component, and the individual component
Qt = Qm + Qb + Qi
where Qt measures output, Qm measures the match quality, Qb the business quality, and Qi the individual qualities of the entrepreneur. Each quality is composed of a permanent component, a stock component, and a temporary component.
Where Qm = W + A + h
Qb = X + B + m
Qi = Y + C + n
So that Qt = (W + A + h) + (X + B + m) + (Y + C + n)
Begin by looking at individuals as they start a new business. They either start their new venture by adding resources to the industry or by buying an existing business. When they initiate the process, they enter the realm of the self-employed. They begin with individual characteristics like education levels, prior entrepreneurial experience, locus of control, risk perception, and access to social networks. At that time, the location and product or service is chosen, determining the business characteristic dimension, the environmental conditions they will face, and the match quality. For cases where the new entrepreneur purchases an existing business, the business characteristics have previously been determined, i.e. location, age, type of product. Table 1 summarizes the dimensions.
Table 1
THREE DIMENSIONS OF THE NEW VENTURE
FOUR COMPONENETS OF EACH DIMENSION PERMANENT, STOCK, FLOWS THAT CHANGE THE STOCK, AND TEMPORARY1
INDIVIDUAL DIMENSION Permanent - Gender, Race, Risk Aversion, Locus of Control Stock - Age, Experience, Education, Financial Compentence, Business Skills (marketing, pricing) Flow (time) (time) (schooling) (time, schooling) (time, schooling.) Temporary - illness, childbirth
MATCH QUALITY DIMENSION- Permanent - # of Partners ??? Stock - Understand Industry Trends, Find Qualified Employees, Expectations of Performance Flow (time, exp.) (time, exp.) (time) Temporary - Strike
BUSINESS QUALITY DIMENSION Permanent - Location, Industry Stock - # of Product Lines, Population, Income/cap, Age, Infrastructure, Government Regulations, Size Flow (new products) (D Pop) (D Income) (time) (new airport, etc.) (D in Regs) (time)Temporary - Road closing, Flood, Olympic site
1. Flow variables that affect the stock components are included in parentheses under the appropriate stock
variable.
The result of the genetic impairment can cause a business discontinuance at any point in time after the start-up. The stronger the impairment, the sooner the firm succumbs to hte hazard, and discontinues. After controlling for the match, individual, and business qualities, the new firms are in the same risk pool as other firms started during the same time period. In order to understand the timing of the event of a buiness discontinuance, we apply the methods of survival analysis - sometimes called duration analysis in the economics literature. One of the aims of the study is to establish a causal model of business discontinuance in which the effect of the different start-up problems can be estimated. To this end, a logistic regression model could be estimated, and would be an acceptable first attempt to understand the causal relationships. The logit model could explain the causal relationship between start-up problems and the continuation or discontinuance of an individual firm. An improvement on the logit model is the survival model which includes the length of time until discontinuance. Firms that discontinue sooner have a higher propensity to discontinue and ignoring that reduces the usefulness of the causal model.
Econometric Issues
a)
Potential data problems encountered in using longitudinal data include left censoring, right censoring, and left truncation. These terms are used in different ways by researchers in different fields. We use them in the following way
Let B = birth, D = death, A = beginning of the observation period, and E = end of observation period
A E
Firm #1 B----------------------D------
Firm # B-----------------------------D----
Firm # B-------------------------------
We begin to follow firms at point A with both a retrospective and prospective look. We know the survival times of firms one and two. Firm number three is still surviving and is considered right censored. Firms that are included in the sample at point of observation A and discontinue at some time less than E are considered left censored. Because most of the firms were alive prior to point A, but there is not enough retrospective data on each firm, this contributes left truncation. Both left and right censoring can be handled by survival models. Using age prior to point A is one method for handling the problem of left truncation. Taking right-censored cases into account, the survivor function shows which percentage of firms survived at each time t after birth. The hazard function, on the other hand, displays the risk of dying, the failure rate, or simply the rate at each time t i.e. the instantaneous probability of failing at time t given that failure had not occurred before time t.
b) Heterogeneity. To control for heterogeneity we present a fully specified model in addition to the model which includes measurements of start-up problems as covariates. Comparison of the models not only allows a test for the robustness of the genetic impairment coefficients, but reduces the concern of biased coefficients due to heterogeneity.
c) Sample selection. This problem arises because data sets that utilize large questionnaires are usually respective as well as perspective. Firms included in the original dataset have already survived for a certain period of time. Those most prone to survival might have already succumbed to the hazard of discontinuance. However previous results have shown
Sources of Data
Data for assessing start-up characteristics of new ventures were collected in 186 via a survey of new firms in Minnesota and Pennsylvania. For a description of initial database see (Reynolds and Freeman, 187; Reynolds and Miller, 188). All regions of both states were represented. The sample was based on firms listed by Duns Marketing Identifier (DMI) files as between one and six years-old just prior to the survey. Phone call verification excluded all listings that were not new, autonomous, and active, with about one-half of the listings qualifying. Each eligible new firm was mailed a questionnaire-three times if necessary-with a reminder post-card sent between the first and second mailings. Phone interviews were completed with about half of those firms that did not return the mail questionnaire. The final response rate was 6%.
Subsequent phone interviews to verify the status of the respondents to the initial survey were completed in 1. Five categories of responses were noted (1) firms that were still in operation at the time of 1 survey, () firms that were confirmed to be out of business, () firms that were dormant at the time of the 1 survey, (4) firms that had been sold or merged, and (5) firms we were unable to contact despite multiple follow-up strategies. The follow-up strategies included calling information operators in the area code of the original contact, accessing current Dun and Bradstreet Credit information and visiting the original site of the business. Since it is difficult to judge whether firms that were dormant and/or those that were sold or merged can be classified as surviving for the present analysis, firms in these two categories were disregarded in one model and included in the other. Comparison of the results allows one to test for the robustness of the estimators. Furthermore, persistence in contacting the firms in the sample provided confidence in categorizing those that were unable to be located as discontinued firms.
VARIABLE MEASURES
Genetic impairment of the Firm
The genetic impairment of the firm can be operationalized by measuring the start-up problems perceived by the founders along the individual dimensions, the match dimensions, and the business quality dimensions. These include labor and land accessibility and quality, conditions of the infrastructure, rent levels, local government regulations and level of business support, motivating employees, understanding industry trends and customer needs, selecting/developing a site, and finding qualified managers or professional staff, accountants, attorneys.
OTHER COVARIATES
Initial Size and Age of the Start-Up Firm
Much of the literature on firm survival also includes analysis of firm growth. Size of the firm at birth is included in the majority of studies in an attempt to test the validity of Gibrarts Law of Proportionate Growth, i.e. is the firms actual growth determined by random processes. Most of the studies reject Gibrarts Law (Chesher, 17; Kumar, 185; Evans, 187a, Evans, 187b, Hall, 187; Contini and Revelli, 18; Dunne, Roberts, and Samuelson, 18; Acs and Audretsch, 10; Acar, 1; Dunne and Hughes, 14; Mata and portugal, 14; Geroski, 15; Doms, Dunne, and Roberts, 15. Many of the studies also analyze the effect of initial size of the firm on the probability of survival and find the effect of size is inversely related to the probability of failure, and the effect is significant (Aldrich and Auster, 186; Evans, 187a; Auster, 188; Dunne, Roberts, and Samuelson, 18; Phillips, 1; Mata and Portugal, 14; Mata, Portugal, and Guimaraes, 15; Audretsch, 15; Doms, Dunne, and Roberts, 15).
Age of the firm is also included as a covariate, younger firms have drawbacks, barriers to overcome, and must travel down the learning curve. All firms are affected by changes in the external environment, but new firms tend to have a small number of product lines, few key resources, and the absence of deep pockets, making them more vulnerable. Several studies have documented a positive and significant relationship between age and survival rates for small businesses (Aldrich and Auster, 186; Evans, 187b; Hall, 187; Auster, 188; Dunne, Roberts, and Samuelson, 18; Bates, 10; Lane and Schary, 11; Dunne and Hughes, 14; Audrestch and Mahmood, 15; Doms, Dunne, and Roberts, 15).
Location is another factor cited as an external influence. It is a common belief that rural locations are hostile to firm survival and growth. Skilled labor is lacking, and the infrastructure is immature. Preisendorfer and Voss (10), in their study of 78,441 small firms in West Germany use location (Munich area, other towns in Upper Bavaria, rural areas) as a covariate and find it to be highly significant, although in the unexpected direction. In that study, town relative to rural location had a positive effect on the mortality rate of new businesses. Buss and Lin (10) find that urban and rural survival rates are comparable, although some industries fare better than others. Reynolds (187) found that survival rates were nearly identical for urban and rural areas. Comparing larger geographic regions, Lane and Schary (11) find that certain regions have higher failure rates, with the failure rate in the Pacific region nearly twice that of the next highest region, the Mid-Atlantic.
Income
Income in the area surrounding the business was found to be significant in explaining business profitability and survival (Auster, 188). On the national level, increases in income, strength in the economy and the increase in new firms may result in an increase in the overall failure rates due to the high failure rate among new firms (Lane and Schary, 11).
Race and/or Gender
Auster (188) finds that black business owners had less education and less experience in business, that black businesses were less profitable and smaller, located in poorer neighborhoods, more likely to rent, less likely to insure, and handled fewer customers. But, this study found that black businesses survived at the same rate as other businesses in the sample.
Entrepreneurial Human Capital (Age, Experience, Eductaion, etc.)
The other way age is incorporated into survival models is to analyze the age of the founder as a proxy for one measurement of human capital that the entrepreneur brings to the firm. The supposition that survival rates differ between new firms founded by entrepreneurs according to the degree of human capital inputs has received support. Research findings indicate that entrepreneurs are more likely to survive if they enter with prior management experience, high levels of education, or experience from owning prior businesses. Those aged 55 and over were more likely to discontinue, with those in the 45-55 group most likely to survive (Bates, 10).
In addition we used the average age of the start-up team (AVERAGE), the average number of previous businesses started by this team, and the average number of previous managerial positions held by members of the team. On the questionnaire the respondents were asked to indicate their level of education where
The number for each team was added and then averaged. The analysis was calculated for the nine sectors Agriculture, Mining, Construction, Durable Manufacturing, Nondurable Manufacturing, Wholesale Trade, Resale Trade, and Services. The results appear in the following tables.
Bibliography
Aldrich, H. and Auster, 186 Even Dwarfs started small Liabilities of age and size and their strategic implications. In L. Cumming and B. Staw (eds.) Research in Organizational Behavior, 8 165-18. San Francisco JAI Press.
Auster, E.R. Owner and Organizational Characteristics of Black-and White-Owned Businesses American Journal of Economics and Sociology Vol 47, No. (July, 188).
Cooper, A.C. and Dunkelberg, W.C. A new look at business entry experiences of 1805 Entrepreneurs.
Journal of Small Business Management
Freeman, J., Carroll G.R., and Hannan, M.T. (18) The liability of newness Age dependence in organizational death rates. American Sociological Review, 48 6-710.
Lane, S.J. and Schary, M., (11) Understanding the Business Failure Rate. Contemporary Policy Issues, vol IX, oct 11.
Reynolds, P. and Brenda Miller (18) New Firm Survival Analysis of a Panels Fourth Year Discussion Paper 115 Strategic Management Research Center may 18 Univer of minnesota
Singh, J. V., Tucker, D. J., and House, R. j (186) Organizational legitimacy and the liability of newness. ASQ 1 171-1.
Smallbone, d. Success and Failure in New Business Startups International Small Business Journal 8,
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