SUPPORT POLICIES
The present study has successfully demonstrated that its posited framework for research dealing with the relationships between, on the one side, small firm size, growth and age of the firm and, on the other side, small business survival is highly invaluable in face of strong support lent by three robust sources. Firstly, by an in-depth piece of research that investigated a very wide range of postulated determinants of small business survival. Secondly, by sound results from a large number of previous empirical works that has been thoroughly revised. Thirdly and last, but not least, by the power of the postulated framework of reference to explain a set of unexpected, embarrassing and unacceptable findings in the extant literature on small business survival.
Of course, it has to be acknowledged that the main limitation of this work is that it is partly based upon a piece of research that has dealt with a very small sample. This, in fact, has been duly done to the extent that recourse has been taken to the extant related literature to search for external support to its postulations. In general, research has been characterized by the use of samples of huge sizes. However, future research shall, on the other hand, make a remarkable effort to extend
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investigation to many more firm-specific posited determinants than has until now been the case.
After all, the reference study has as one of its main strengths the inclusion of many more variables measured at the enterprise level than at both the industry and economy levels, and this has made it possible achieving an amazingly very high level in the explaining of variations in the probability of small business survival.
Empirical research interested in understanding why size, growth and age of the firm are so strongly related to small business survival should give high priority to the study of firm-specific strategy-related variables, for they have been shown to be the most important determinants of small business survival and also of firm size and growth, and the source of strength behind the small firms that live longer. Also, this kind of research and research that seeks either to study the impact of firm size, growth and age on small business survival or use firm size, growth and age only as control variables should pursue more vigorously the use of non-linear specifications.
Moreover, binomial specifications with pairs of powers different from the traditionally used unit and square combination should be preferred, since most strategy-related factors are associated with small business survival in asymmetrically U-shaped and inverted U-shaped relationships.
Firm size, growth and age of the firm also behave in this same way in their relationships with small business survival.
Some suggestions for future research come from the revision work mainly because of technical difficulties faced in carrying out such a task. A first suggestion is that all studies dealing with either new small firms or incumbent ones, or even a mix of them, should present separate results for micro-, small-, medium- and large-sized enterprises. It is becoming clear that each of these categories of size has its own set of survivorship determinants, which in turn have individually a particular way of behaving, there being cases that the determinants behave differently depending on which category of size is under consideration. Treating all categories of size alike hinders the development of meaningful theories of small business survivorship. A second suggestion is that authors should report more fully summary statistics and correlations between the variables of their studies. This eases understanding and evaluation of their results.
The value of this orientation is highlighted when it is recalled that science is an endless process of accumulation of knowledge carried out bit by bit through the contribution of new researchers that add on the achievements of previous ones.
The foregoing guides the focus of the analysis to the theories of the post-entry performance of small firms. Such theories draw upon both the learning process and productivity enhancement, both of which, as time passes by, pave the way to growth and survivorship, for the new small businesses that manage to master them. Empirical studies see the widely verified monotonic negative relationships between, on the one hand, size, growth and age and, on the other side, the new small firms’ hazard of exit as a confirmation of these theories. Unless productivity enhancement takes up a very inclusive meaning, these theories are about to be challenged as to their capacity to hold in face of many findings of U-shaped and even inverted U-shaped relationships between, on the one hand, size, growth and age of the firm and, on the other hand, small business survival. The corresponding works seem to be neglecting interpretation of these divergent results, stating that they are equally in line with the theories and carelessly labeling them
“liability of adolescence” or “the honeymoon effect”, only because firm size, growth and age are not central to the studies but only controls. The present study has replicated these findings and state that they may be even wave-shaped. They are the result of the influence of a myriad of actual determinants associated with the hazard of exit, many linearly and many others non-linearly, but,
most importantly, in asymmetrical U-shaped and inverted U-shaped relationships, what makes the fitting of the “artificial” linear specification always produce an estimated negative coefficient with the hazard of exit. Many of the actual determinants of small business survival are also determinants of size, and as a consequence, of growth too and some exert their influence as time passes by, being then associated with the age of the small businesses.
Whether the post-entry performance of small businesses is a result of learning is not disputed here. What does need rethinking is the other part of the interpretation of the relationships between, on the one hand, size, growth and age of the firm and, on the other hand, small business survival. It is reasonable to believe that learning is the mainspring behind the post-entry performance of new small businesses, although they are many times started by former employees of other small-, medium- and large-sized enterprises. The emphasis on production productivity enhancement seems exaggerated, for, if labor productivity has been found to be a small business determinant, the reference study has shown elsewhere that it is by far not among the most important ones. The factors behind the small firms’ size, growth and age are some components of the small firms’ financing, production and market strategies and some components of their risk and return matrix.
Of course, the foregoing does not tell the whole story. It is necessary to state why, how, when and under what conditions these components of the small firms’ financing, production and market strategies and of their risk and return matrix exert a net influence that almost always culminates in a negative linear relationship between, on the one side, the small firms’ size, growth and age and, on the other side, the small business hazard of exit. The best conjecture is that such a result is due to a combination of fortunate wise strategy management and the workings of organizational population ecology, but going into details here is a task that is beyond the scope of the present study and so will be left for the future, as the last step of the commitment stated in the first paragraph of the introduction to this article.
If most of the heterogeneity across firms is attributable to factors specific to the industry, to its performance, to the aggregate economy, to economic cycles, business climate, and so on, there is little that public policy can do in reducing small firm failure rates. On the other hand, if firm-specific factors result in heterogeneity with respect to survival rates, an important implication is that public policy can have a positive impact in reducing the likelihood of failure. This study has shown by way of studying the relationships between, on the one hand, size, growth and age and, on the other hand, small business survival, that none of these three factors are the real determinants of the small business exit rates, but instead a range of firm-specific factors that are proxied by size, growth and age. The majority of these firm-specific real determinants are elements of the day-to-day operations of the small enterprises, many of which are under the control of the small business owner, or are affected by his/her decision making, or are dependent on the interests of outsiders, such as bank managers, suppliers and the government. All this means that public policy can do much in the field of small business development by giving support in the area of financing, production and market strategy devising.
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REFERENCES
Barbosa, Evaldo Guimarães (2009), Common Determinants of the Firm’s Capital Structure and Business Survival – The Case of Very Small Enterprises of Traditional Manufacturing Sectors in Brazil, Createspace – An Amazon company, Charleston, SC, USA.
Barbosa, Evaldo Guimarães (2016), Determinants of Small Business Survival: The Case of Very Small Enterprises of the Traditional Manufacturing Sectors in Brazil, Working Paper, Forthcoming.
Becchetti, Leonardo and Jaime Sierra (2003), Bankruptcy risk and productive efficiency in manufacturing firms, Journal of Banking & Finance 27:2099–2120.
Bhattacharjee, Arnab, Chris Higson, Sean Holly and Paul Kattuman (2009), Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms, Economica 76(301):108–31.
Bottazzi, Giulio, Marco Grazzi, Angelo Secchi, and Federico Tamagni (2011), Financial and economic determinants of firm default, Journal of Evolutionary Economics 21(3): 373-406.
Bridges, Sarah and Alessandra Guariglia (2008), Financial Constraints, Global Engagement, and Firm survival in United Kingdom: Evidence from Micro Data, Scottish Journal of Political Economy 55(4):444–64.
Brüderl, Josef and Rudolf Schüssler (1990), Organizational Mortality: The Liabilities of Newness and Adolescence, Administrative Science Quarterly 35:530-47.
Buehler, Stefan, Christian Kaiser and Franz Jaeger (2006), Merge or Fail? The Determinants of Mergers and Bankruptcies in Switzerland, 1995-2000, Economics Letters 90(1):88–95.
Buehler, Stefan, Christian Kaiser and, Franz Jaeger (2012), The geographic determinants of bankruptcy: evidence from Switzerland, Small Business Economics 39:231–251.
Callejón, María and Vicente Ortún (2009), The black box of business dynamics, Investigaciones Regionales 15:167-89.
Carreira, Carlos and Paulino Teixeira (2011), The shadow of death: analysing the pre-exit productivity of Portuguese manufacturing firms, Small Business Economics 36:337–51.
Cefis, Elena and Orietta Marsili (2005), A matter of life and death: innovation and firm survival, Industrial and Corporate Change 14(6): 1167-92.
Disney, Richard, Jonathan Haskel and Ylva Heden (2003), Entry, Exit and Establishment Survival in UK Manufacturing, The Journal of Industrial Economics 51, Issue 1 (March): 91–112.
Dunne, Timothy, Mark J. Roberts and Larry Samuelson (1989), The Growth and Failure of U.S.
Manufacturing Plants, The Quarterly Journal of Economics 104(4):671–98.
Ericson, R. and A. Pakes (1992), Markov-Perfect Industry Dynamics: A Framework for Empirical Work, Review of Economic Studies 62(1):53-82.
Fernandes, Ana M. and Caroline Paunov (2015), The Risks of Innovation: Are Innovating Firms Less Likely to Die?, The Review of Economics and Statistics 97(3):638-53.
Fichman, Mark and Daniel A. Levinthal (1991), Honeymoons and the Liability of Adolescence: A New Perspective on Duration Dependence in Social and Organizational Relationships, Academy of Management Review 16(2): 442-68.
Fotopoulos, Giorgios and Helen Louri (2000), Determinants of Hazard Confronting New Entry:
Does Financial Structure Matter?, Review of Industrial Organization 17:285-300.
Frazer, Garth (2005), Which Firms Die? A Look at Manufacturing Firm Exit in Ghana, Economic development and cultural change, Chicago, Ill : Univ. of Chicago Press, ISSN 0013-0079, ZDB-ID 16883. - Vol. 53, 3, p. 585-618.
Geroski, Paul A. (1995), What do we know about entry?, International Journal of Industrial Organization - Special issue: the Post Entry Performance of Firms 13:421-40.
Geroski, Paul A., José Mata and Pedro Portugal (2010), Founding Conditions and the Survival of New Firms, Strategic Management Journal 31(5):510–29.
Grossi, Luigi and Giorgio Gozzi (2006), Firm turnover and duration of new firms in Italian mechanical sector: evidence in the period 1997-2002, in Mutamenti nella geografia dell'economia italiana (Ed. C. Filippucci), pp. 331-352, Milano, FrancoAngeli.
Ha, Nguyen Minh (2013), The Effect of Firm’s Growth on Firm Survival in Vietnam, International Business Research 6(5):142-56.
Hannan, Michael T. (1998), Rethinking Age Dependence in Organizational Mortality: Logical Formalizations, American Journal of Sociology 104(1): 126-64.
Heshmati A. (2001), On the Growth of Micro and Small Firms: Evidence from Sweden, Small Business Economics 17(3): 213-228.
Holmes, Phil, Paul Braidford and Ian Stone (2010), An analysis of new firm survival using a hazard function, Applied Economics 42(2): 185-95.
Hopehayn, H. (1992), Entry, exit and firm dynamics in long run equilibrium, Econometrica 60(3):1127-50.
Jensen, Paul H., Elizabeth Webster and Hielke Buddelmeyer (2008), Innovation, technological conditions and new firm survival, The Economic Record 84:434-48.
25
Jovanovic, B. (1982), Selection and evolution of industry, Econometrica 50(7):649-70. (May) Kelly, Robert, Eoin O Brien and Rebecca Stuart (2015), A long-run survival analysis of corporate liquidations in Ireland, Small Business Economics 44(3): 671-683.
Kimura, Fukunari and Takamune Fujii (2003), Globalizing activities and the rate of survival:
Panel data analysis on Japanese firms, Journal of the Japanese and International Economies (Elsevier) 17(4):538-60.
Klepper, Steven and Peter Thompson (2006), Submarkets and the Evolution of Market Structure, RAND Journal of Economics 37:861–86.
Kosová, Renáta (2010), Do Foreign Firms Crowd Out Domestic Firms? Evidence from the Czech Republic, The Review of Economics and Statistics 92(4):861-81.
Kosová, Renáta and Francine Lafontaine (2010), Survival and Growth in Retail and Service Industries: Evidence from Franchised Chains, The Journal of Industrial Economics LVIII(3): 542-78.
Low, Murray B. and Ian C. MacMillan (1988), Entrepreneurship: Past Research and Future Challenges, Journal of Management 14(2):139-61.
Mengistae, Taye (2006), Competition and Entrepreneurs’ Human Capital in Small Business Longevity and Growth, Journal of Development Studies 42(5):812–36.
Oh, Inha, Almas Heshmati, Chulwoo Baek and Jeong-Dong Lee (2009), Comparative Analysis of Plant Dynamics by Size: Korean Manufacturing, The Japanese Economic Review 60(4): 512-38.
Pakes, Ariel and Richard Ericson (1998), Empirical Implications of Alternative Models of Firm Dynamics, Journal of Economic Theory 79(1):1-46.
Saridakis, George, Kevin Mole and David Storey (2008), New Small Firm Survival in England, Empirica 35:25–39.
Shiferaw, Admasu (2009), Survival of private sector manufacturing establishments in Africa: The role of productivity and ownership, World Development, Elsevier 37(3):572-84.
Strotmann, Harald (2007), Entrepreneurial Survival, Small Business Economics 28:87–104.
Taymaz, Erol (2005), Are Small Firms Really Less Productive?, Small Business Economics 25(5):
429-45.
Taymaz, Erol and Miyase Y. Köksal (2006), El espíritu emprendedor, el tamaño de lanzamiento y la supervivencia de los pequeños empresarios, Ekonomiaz Basque Economic Review 62 (Entrepreneurism as a Driver for Economic Development):70-99.
Varum, Celeste, Vera Catarina Rocha and Hélder Valente da Silva (2014), Economic slowdowns, hazard rates and foreign ownership, International Business Review 23:761–73.
APPENDICES:
Appendix I Size, Growth, Age and Sales Variability Summary Statistics
(18)Total assets: Total assets; (19) Sales: Sales; (20) Number of employees: average number of employees; (21) Firm Age: number of years since establishment; (22) Growth: growth in employment level; (23) Sales variability: standard deviation of the first differences in annual sales, scaled by average sales over the period.
Variables Mean Standard
Deviation Minimum Maximum
Total assets (Cr$) (18) 1,913,983.03 1,965,088.20 1,286.20 9,919,551.32 Sales (19) 3,616,509.39 3,498,883.93 85,203.97 19,796,611.87
Number of employees (20) 52.62 34.40 4.83 156.67
Firm age (21) 15.52 11.26 2.00 59.00
Growth (22) 3.74 17.60 -38.41 54.92
Sales variability (23) 0.45 0.22 0.07 0.95
Variables Fractiles
Kurtosis* Skewness
0.10 0.50 0.90
Total assets (Cr$) (18) 329,377.54 1,310,938.16 3,843,979.09 6.52 2.35 Sales (19) 652,450.30 2,592,671.51 7,372,064.18 7.10 2.42
Number of employees (20) 18.93 42.67 102.50 1.00 1.13
Firm age (21) 5.20 13.00 29.60 3.76 1.75
Growth (22) -16.08 -0.08 25.92 0.92 0.59
Sales variability (23) 0.14 0.45 0.76 -0.40 0.20
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 Size, Growth, Age and Sales Variability Correlation Matrix
(18)Total assets: Total assets; (19) Sales: Sales; (20) Number of employees: average number of employees; (21) Firm Age: number of years since establishment; (22) Growth: growth in employment level; (23) Sales variability: standard deviation of the first differences in annual sales, scaled by average sales over the period.
(18) (19) (20) (21) (22) (23)
Duration (1) -0.02 0.06 0.11 -0.03 0.06 0.01
Exit (2) 0.16 0.08 0.01 0.02 -0.20 -0.09
Net working capital (3) -0.01 -0.09 -0.00 0.14 -0.24 -0.10
Total financial leverage (4) 0.07 0.32 0.12 -0.16 0.34 0.09
Medium- and long-term financial leverage(5) 0.34 0.26 0.09 0.27 0.04 0.16
Profitability (6) -0.05 -0.03 -0.02 -0.03 0.22 -0.20
Operational cycle (7) 0.11 0.09 -0.03 0.27 0.00 -0.03
Machinery/fixed assets ratio (8) -0.03 0.09 0.20 -0.18 -0.05 0.08
Automation degree (9) 0.02 0.07 0.25 0.11 0.09 0.09
Corporate diversification (10) 0.07 0.14 -0.10 -0.04 -0.07 0.04
Market concentration (11) -0.34 -0.41 -0.30 0.09 -0.03 -0.09
Client concentration (12) -0.03 -0.11 -0.08 -0.15 0.19 0.22
Sales concentration in big clients (13) -0.10 -0.04 -0.14 0.18 0.05 -0.11 Sales unpredictability (14) -0.16 -0.30 -0.19 0.03 -0.20 -0.15 Entrepreneur’s risk tolerance (15) 0.01 0.06 0.07 -0.09 0.18 0.34 3-year-lagged GDP growth rate(16) 0.01 -0.00 -0.04 0.20 0.06 -0.04
1998 year dummy (17) 0.22 0.25 0.04 0.01 0.08 -0.21
Total assets (18) 1.00 0.83 0.59 0.28 -0.08 -0.08
Sales (19) 0.83 1.00 0.64 0.08 0.01 -0.14
Number of employees (20) 0.59 0.64 1.00 0.12 -0.27 -0.18
Firm age (21) 0.28 0.08 0.12 1.00 -0.28 -0.11
Growth (22) -0.08 0.01 -0.27 -0.28 1.00 0.21
Sales variability (23) -0.08 -0.14 -0.18 -0.11 0.21 1.00
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.