SUPPORT POLICIES
Explanatory power is astonishingly high for the survival analysis, even after consideration of the fact that statistics for measuring it are plagued by difficulties. This result defies strong opinions that failure is most elusive and difficult to explain. Statistical significance is extremely high for all covariates. This result also defies strong opinions that such studies are doomed to face inconsistency of key explanatory variables. All operationalizations of the important studied concepts seem very much to have played very well the roles expected from them.
Analyses produce evidence in favor of many postulated determinants of small business survival. Most of these determinants are, individually, combined or interacted, elements of the day-to-day operations of the enterprise, and, also in most cases, correspond to quantities that deserve to be kept under judicious control, once the corresponding level, amount or degree that benefits the enterprise the most is an optimal. These are the cases of the determinants concerning the financing strategy of the company, namely, Liquidity, operationalized by Net Working Capital, and Financial Leverage, operationalized by Total Financial Leverage and Medium- and Long-term Financial Leverage. Those characteristics are also the cases of the concepts of Working Capital, operationalized by Operational cycle, and Capital Intensity, operationalized by Automation Degree and the Machinery/Fixed Assets Ratio. Both characteristics are also the case of one of the operationalizations of Strategy, namely, Market Concentration. Sales Concentration in Big Clients, one of the operationalizations of Strategy, is an element of the day-to-day operations of the enterprise and indirectly involves an optimal in its interaction with The Machinery/Fixed Assets Ratio. Corporate Diversification, Client Concentration and Profitability, which are also operationalizations, do not involve an optimal, but are elements of the day-to-day operations of the enterprise. They are associated in a monotonic fashion with the probability of failure, being that Profitability and Corporate Diversification are negatively signed, and Client Concentration and Sales Concentration in Big Clients are positively signed.
Last, the interactions, one between Market Concentration and Operational Cycle, and the other between Sales Concentration in Big Clients and The Machinery/Fixed Assets Ratio, are elements of the day-to-day operations of the enterprise and involve an optimal, once they have to be taken into account in conjunction with the single variables and at least one of them in each interaction involves an optimal on its own. In all these cases the owner-manager may exert some control over the determinants of his/her enterprise’s survival prospects, at least in theory.
Other postulated determinants of small business survival receive strong support from evidence produced by the analyses, but they do not have the above nature. Business Risk, operationalized by Sales Unpredictability, and Economic Conditions, operationalized by The 3-year-lagged GDP Growth Rate and The 1998 Year Dummy are not elements of the day-to-day operations of the enterprise, nor involve optimals. Sales Unpredictability and The 1998 Year Dummy are positively signed, whereas The 3-year-lagged GDP Growth Rate is negatively signed, meaning that good environmental conditions diminish failure probability and bad
23
environmental conditions raise it. In none of these cases the owner-manager may exert any control over the determinants of his/her enterprise’s survival prospects, for sure.
The last concept receiving strong support from the evidence brought about by the analyses in this study, namely, Characteristics of the Entrepreneur, operationalized by Entrepreneur’s Risk Tolerance, has nothing to do with the two kinds of nature depicted above. Entrepreneur’s Risk Tolerance is positively signed, meaning that owner/managers with riskier profiles have higher probability of failure. The extent to which the owner/manager can exert a control over this determinant of his/her firm’s failure probability is open to question and so it seems to be the extent to which he/she can exert control over himself/herself.
Overall, the conclusion is that the main findings of this research effort suggest that the survival chances of the small manufacturing enterprises lie much within their own control.
However, it was not possible to conclude in the specific case of this small firm sample whether the successful choices were conscious or the corresponding enterprises were selected in the way predicted by the organizational ecology approach.
Some selected specific conclusions are as follows. The curve representing the effect of working capital perfectly mirrors the classical curves of total costs of the models of economic order quantity and economic production quantity. This is suggestive that the same decisions that impact the costs of investing in working capital have an effect upon the probability of exit. The curve representing the effect of capital intensity also has a minimum, but the U shape is perfectly symmetrical. A second important identified effect of fixed assets concerns their either higher or lower capacity of playing the role of collateral. Empirical findings concerning the market and competitive strategies indicate that, even for small owners, who do not have much to invest, the old rule of thumb that the eggs should be carried in many baskets, to lose the least in case of a fall, is still a very pertinent finance principle.
Of course, there are limitations to this study. Overcoming them is then the first suggestion for future enquiries. Increasing the size of the sample, adding more industry sectors and expanding geographical coverage would all benefit future attempts to replicate this research. This research has strengths that may as well serve as guidance for future investigations. It has explored apparently successfully some new avenues that may yield good results in research on the same areas of enquiry or related ones. For example, the use of many non-linear specifications and following modeling for the time to event data typically encountered in health related studies have proved to be invaluable in this work. Being specific, it may pay future studies to use binomials with powers either higher or lower than the conventional unity and square ones to obtain a better fit in the regression equations. In this research, this choice has enhanced in the survival analyses not only the fit for the factor being dealt with but also the fits for all the others in the same specification. Such drastic changes in methodology may even ease theory building in the field.
As to recommendations for policy making for small business development, small business support initiatives and small enterprises’ decision-making, it is very fortunate that the research found that the majority of the main survival determinants are elements of the small firms’ strategic choices, because, it is believed, if they were mostly either industry- or economy-level factors, little could be done to mitigate these concerns’ weaknesses. Official support to small business development should, then, be focused on the provision of financial and managerial assistance.
However, as many determinants of survival are related to the strategic choice in a complex manner, this certainly calls for human capital development before an adviser may be able to give orientation on this matter to small business owners. Small business owners also should seek first such a qualification before trying to make use of the results from this research by themselves.
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27 APPENDICES:
Appendix I Survival Variables Summary Statistics
Variables Mean Standard
Deviation Minimum Maximum
Duration (1) 2,455.62 1,749.86 21.00 4,748.00
Exit(2) 0.75 0.43 0.00 1.00
Net working capital (3) 0.49 0.27 -0.24 1.00
Total financial leverage (4) 0.31 0.20 0.00 0.90
Medium- and long-term financial leverage(5) 0.06 0.07 0.00 0.35
Profitability (6) 3.33 1.59 0.00 5.00
Operational cycle (7) 98.70 49.27 24.00 270.00
Machinery/fixed assets ratio (8) 0.58 0.30 0.00 1.00
Automation degree (9) 3.16 0.99 1.00 5.00
Corporate diversification(10) 8.30 19.70 0.00 80.00
Market concentration (11) 49.55 29.39 3.86 100.00
Client concentration (12) 59.34 27.96 6.67 100.00
Sales concentration in big clients (13) 40.98 38.37 0.00 100.00
Sales unpredictability (14) 2.77 2.74 0.00 6.00
Entrepreneur’s risk tolerance (15) 2.51 1.26 1.00 5.00
3-year-lagged GDP growth rate(16) 2.24 2.75 -4.30 5.90
1998 year dummy (17) 0.11 0.32 0.00 1.00
Variables Fractiles
Kurtosis* Skewness
0.10 0.50 0.90
Duration (1) 82.40 1,980.00 4,748.00 -1.55 0.16
Exit(2) 0.00 1.00 1.00 -0.56 -1.21
Net working capital (3) 0.14 0.56 0.81 0.75 -0.80
Total financial leverage (4) 0.10 0.28 0.58 0.76 0.84
Medium- and long-term financial leverage(5) 0.00 0.03 0.16 2.87 1.61
Profitability (6) 0.20 3.00 5.00 -0.40 -0.69
Operational cycle (7) 51.20 85.00 179.60 2.39 1.51
Machinery/fixed assets ratio (8) 0.11 0.60 0.92 -1.05 -0.39
Automation degree (9) 2.00 3.00 4.80 -0.06 -0.02
Corporate diversification(10) 0.00 0.00 35.00 6.04 2.60
Market concentration (11) 8.87 46.86 97.29 -1.07 0.21
Client concentration (12) 24.94 51.00 100.00 -1.19 0.32
Sales concentration in big clients (13) 0.00 33.00 100.00 -1.48 0.31
Sales unpredictability (14) 0.00 1.00 6.00 -1.85 0.24
Entrepreneur’s risk tolerance (15) 1.00 3.00 4.80 -0.63 0.39
3-year-lagged GDP growth rate(16) -3.54 2.70 5.90 0.97 -1.09
1998 year dummy (17) 0.00 0.00 1.00 4.28 2.48
Obs.: 1) Number of cases: 61; 2) Values in currency are in thousands and in 1992 prices, and the mean and year-end exchange rates for that year were Cr$4,516.74 and Cr$11,213.12 per US$ Dollar, respectively; 3) *According to Norušis (1992, p.167), in the SPSS the value of kurtosis for the normal distribution is, differently from many textbooks in statistics, 0 and not 3.
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Appendix II Survival Variables Intercorrelation Matrix
(1) (2) (3) (4) (5) (6) (7) (8)
Duration (1) -
Exit (2) -0.74 -
Net working capital (3) 0.21 -0.02 -
Total financial leverage (4) -0.06 -0.02 -0.62 - Medium- and long-term financial
leverage(5) -0.23 0.16 -0.37 0.38 -
Profitability (6) 0.21 -0.20 0.05 0.04 -0.15 -
Operational cycle (7) 0.03 0.20 0.01 0.04 0.17 0.09 -
Machinery/fixed assets ratio (8) -0.08 -0.00 -0.05 0.09 0.16 0.11 -0.07 - Automation degree (9) -0.04 -0.06 -0.15 0.33 0.22 0.28 -0.14 0.01 Corporate diversification (10) 0.11 0.06 -0.08 0.09 0.08 0.02 0.12 0.13 Market concentration (11) -0.01 -0.02 -0.05 -0.15 -0.05 0.20 0.00 -0.13 Client concentration (12) 0.07 0.02 0.14 -0.08 -0.07 0.08 -0.01 -0.01 Sales concentration in big clients (13) -0.23 0.13 0.23 -0.02 0.03 -0.06 0.00 -0.06 Sales unpredictability (14) -0.23 0.08 0.02 -0.18 -0.09 -0.19 -0.12 -0.01 Entrepreneur’s risk tolerance (15) -0.26 0.17 -0.18 0.25 0.12 0.00 -0.15 0.18 3-year-lagged GDP growth rate(16) 0.15 -0.10 -0.18 0.18 -0.00 0.01 0.06 -0.25 1998 year dummy (17) -0.13 0.21 -0.17 0.31 -0.02 0.25 0.02 -0.20
(9) (10) (11) (12) (13) (14) (15) (16)
Duration (1) Exit(2) Net working capital (3) Total financial leverage (4) Medium- and long-term financial
leverage (5) Profitability (6) Operational cycle (7) Machinery/fixed assets ratio (8)
Automation degree (9) -
Corporate diversification (10) -0.05 -
Market concentration (11) 0.01 -0.13 -
Client concentration (12) -0.10 -0.03 0.20 -
Sales concentration in big clients (13) 0.16 -0.13 -0.08 0.05 -
Sales unpredictability (14) -0.44 -0.16 0.20 0.02 -0.16 -
Entrepreneur’s risk tolerance (15) 0.05 -0.03 -0.09 -0.10 -0.15 -0.06 -
3-year-lagged GDP growth rate (16) -0.06 -0.06 -0.10 -0.17 0.02 0.04 -0.02 -
1998 year dummy (17) 0.10 0.10 0.00 -0.34 -0.07 -0.01 0.14 0.48
Obs.: 1) Number of cases: 61; 2) Coefficients in absolute values higher than 0.20 are statistically significant at the 5%
level, higher than 0.30 at the 1%, and higher than 0.40 at the 0.1%, in one-tail test.