«André van Stel David Storey Roy Thurik Zoetermeer, September, 2006 address: Italiëlaan 33 mail address: P.O. Box 7001 2701 AA Zoetermeer telephone: ...»
The effect of business regulations
on nascent and young business
André van Stel
Zoetermeer, September, 2006
address: Italiëlaan 33
mail address: P.O. Box 7001
2701 AA Zoetermeer
telephone: + 31 79 343 02 00
telefax: + 31 79 343 02 01
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The effect of business regulations on nascent and young business entrepreneurship André van Stel A, B, C, D David J. Storey E, A A. Roy Thurik C, A, D A EIM Business and Policy Research, Zoetermeer, the Netherlands B Bettany Centre for Entrepreneurial Performance and Economics, Cranfield School of Management, Cranfield, UK C Centre for Advanced Small Business Economics (CASBEC), Erasmus University Rotterdam, the Netherlands D Max Planck Institute of Economics, Jena, Germany E Centre for Small and Medium Sized Enterprises, University of Warwick, UK
We examine the relationship, across 39 countries, between regulation and entrepreneurship using a new two-equation model. We find the minimum capital requirement required to start a business lowers entrepreneurship rates across countries, as do labour market regulations. However the administrative considerations of starting a business – such as the time, the cost, or the number of procedures required – are unrelated to the formation rate of either nascent or young businesses.
Given the explicit link made by Djankov et al. (2002) between the speed and ease with which businesses may be established in a country and its economic performance – and the enthusiasm with which this link has been grasped by European Union policy makers – our findings imply this link needs reconsidering.
Version: October 2006 Prepared for: Small Business Economics (2007), Special issue GEM conference 2005 Keywords: nascent entrepreneurship, young businesses, business regulations, Global Entrepreneurship Monitor, World Bank Doing Business JEL-classification: K20, L51, M13, O57 Document: Van Stel et al GEM special issue_10b EIM.doc Save date: 9/25/2006 11:55 AM Correspondence: André van Stel, email@example.com Acknowledgement: We are grateful to Zoltan Acs, Sander Wennekers and two anonymous referees for their comments on earlier drafts of the paper. The paper has been written in the framework of the research program SCALES carried out by EIM and financed by the Dutch Ministry of Economic Affairs.
1. Introduction SME and Entrepreneurship policy makers seeking to increase rates of new firm formation and subsequent wealth creation are faced with choices. The central choice is to either follow a low regulation route or to follow a high “support” route. The low regulation route focuses policy upon two areas. The first is to enable the starting of a business to take place as quickly and cheaply as possible. The second is to minimise the number and severity of regulations upon that business whilst it is trading. The US is seen as the exemplar low regulation country.
The alternative policy is for government to provide “support” to new and small firms, funded by the taxpayer. It can be in the form of information, advice, training, or finance to new firms or existing small firms. EU countries have traditionally favoured “support” policies.
However, Djankov, La Porta, Lopez-de-Silanes and Shleifer (2002) claimed to show that countries where business regulation was most burdensome are more likely to be undemocratic, characterised by official corruption, have larger unofficial economies and lower levels of wealth.
This finding was highly influential, triggering the introduction of legislation in countries to lower the “barriers” to new business creation. EU countries, where such barriers were high, responded.
Between 1999 and 2006 France reduced the number of days taken to start a business from 53 to
8. Other examples are Spain where the number of days fell from 82 to 47 and Italy where they fell from 62 to 13.
This paper investigates the link between business regulation and new firm formation in thirty nine countries. It suggests that the association between the time and costs of starting a business and several measures of entrepreneurship is by no means as clear as implied by Djankov et al. (2002).
However it does find that labour market regulations depress measures of entrepreneurship. Our conclusion has to be that there is a need for a serious review of this policy area, with better data being a key requirement.
Our results are obtained by estimating a new two-equation model while 112 averaged country data points covering both developed and developing countries are used. The first equation explains the nascent entrepreneurship rate using policy regulations and various controls. The second equation explains the young business entrepreneurship rate using policy regulations, various controls and the nascent entrepreneurship rate. This enables us to discriminate between direct effects on the young business entrepreneurship rate and indirect effects through the nascent entrepreneurship rate because the nascent phase precedes the young business phase.
The paper begins by setting out some hypotheses on the relation between entrepreneurship and policy intervention. It then moves on to describe the data available and presents some simple tables. The modelling framework is then presented, followed by our key results. We conclude by reviewing the results, identifying the limitations of the study, but pointing to some provisional conclusions.
2. Entrepreneurship and policy options
Governments have a range of policies to enable Small and Medium-sized Enterprises (SMEs) to come into existence and to grow. The simple justification for such policies is that SMEs are major sources of job creation, innovation and competitiveness in a modern economy and that it is governments’ task to promote these characteristics in order to enhance the welfare of its citizens.1 According to Lundström and Stevenson (2002) “The general goal of SME Policy is to strengthen the existing base of small enterprises by ensuring they can compete in the marketplace and they are not prejudiced because of their small size, relative to large firms”.
To deliver such policies governments are faced with clear choices, with these being set out in Table 1, developed originally from Dennis (2004). The key choices are shown in the columns.
The first is to focus attention upon lowering the entry “barriers” to new firm formation. Examples of such “barriers” include the length of time taken to start a business, the number and cost of any permits or licenses required, or the minimum capital requirements of a new firm.
A second policy option is to reduce the “burdens” on those individuals already operating SMEs.
Such “burdens”2 might include the difficulties over the hiring and firing of labour, obtaining access to credit, the severity of the tax regime or the difficulties of closing a business. These barriers are referred to in the Table as “barriers to expansion and growth”.
A third policy option is to use public funds to provide finance directly and indirectly, or to provide information, training and advice -soft support- to both individuals considering starting a firm and to existing established SMEs.
As Dennis points out, governments in different countries make different choices- the US broadly favouring the first two policy options over the third whereas, until recently, EU countries have favoured the third. Our purpose in Table 1 is not to review the practicalities of these choices but to theorise about their implications for new and small firms.
The rows in Table 1 show that policy choices influence three groups of new and small firms. The first two are nascent entrepreneurs – defined as individuals taking active steps to start a business – with a distinction being made between necessity and opportunity entrepreneurs. The third group are the actual entrepreneurs – defined as individuals actually running a business. This third group consists of newly established – young – businesses, as well as established SMEs or small firms.
We now take each of these groups in turn and theorise about the expected impact of the policies on each group, beginning with the two groups of nascent entrepreneurs. It would certainly be argued by Djankov et al. that the number of nascents would be increased if barriers to start up were lowered. However, it is less clear whether it is the necessity or the opportunity nascent entrepreneurs that will be most influenced by the lowering of entry barriers. On the one hand necessity entrepreneurs may be particularly strongly influenced by, for example, the costs of start up being lowered since these individuals are likely to have lower wealth than opportunity entrepreneurs. On the other hand, opportunity nascents are assumed to have a wider range of employment options than necessity nascents and so lowering entry costs may have a strong marginal effect.
It seems likely that nascent entrepreneurs will be less influenced by barriers to growth than by barriers to entry on the grounds that nascents are less likely to have business experience. They will be less likely therefore to have actually encountered such barriers. A possible distinction is that opportunity nascents may have higher growth expectations than necessity nascents and so may be more likely to be deterred if they think they are likely to be prevented from their business reaching optimal size. Finally we might expect, all else equal3, for nascent rates to be higher in countries that provide advice, support and funds.
Turning now to young and established businesses we assume that they would be more strongly influenced by barriers to expansion and by the provision of advice and support, than by start-up barriers. A priori it is not clear whether the advice or the barriers would be more influential.
The hypothesised effects described above are summarised in Table 1.
Table 1: Linking Entrepreneurial Groups with Policy Options
Actual entrepreneurs Weak impact Strong impact Strong impact (young businesses and established SMEs) The table reports the hypothesised impact (strong or weak) of the policy option in the columns on the size of the entrepreneurial groups in the rows.
3. Data on entrepreneurship rates and regulations Ideally, we would like to quantify all the relationships in Table 1 but, in practice, we are constrained by data limitations. In particular, cross-country data on the provision of advice and support are not available. Whilst Lundstrom and Stevenson (2005) provide a comprehensive description of such policies, this is restricted to only thirteen countries and there is no data on aggregate policy expenditure. In terms of Table 1 therefore, the relationships in the final column cannot be estimated. We will now provide an overview of our data on entrepreneurship rates and on business regulations. The variables are set out in full in the next section of this paper.
Data on rates of entrepreneurship are derived from the Global Entrepreneurship Monitor (GEM).
A distinction is made between the young business entrepreneurship rate, defined as the percent of the adult population that is the owner/manager of a business that is less than 42 months old, and the nascent entrepreneurship rate, defined as the percent of adult population that is actively involved in starting a new venture (Reynolds et al., 2002; Reynolds et al., 2005).
These rates are the dependent variables in our model (see also the next section) but, following Table 1, we distinguish between the opportunity and necessity nascent entrepreneurship rates and the young business entrepreneurship rate. Our final entrepreneurship measure is the established business rate, defined as the percent of the adult population that is the owner/manager of a business that is older than 42 months. This is not a dependent variable in our model because we expect the impact of business regulations to be particularly important in the early stages of the business. However the established business rate does play a role in our empirical exercises ‘on the right hand side of the equations. Full details of the various GEM measures are shown in Table 3c.
Data on business regulations are taken from the World Bank Doing Business (WBDB) data base.
According to the WBDB website “The Doing Business database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across 155 economies. They indicate the regulatory costs of business and can be used to analyse specific regulations that enhance or constrain investment, productivity and growth. The indicators are placed in categories such as ‘Starting a business’, ‘Hiring and firing of workers’, ‘Getting credit’, etc. The precise definition of these indicators is provided in the next section.
As an illustration, Table 2 shows the number of days required to start a business for those countries participating in the Global Entrepreneurship Monitor in 2005. This variable is taken from the WBDB category ‘Starting a business’, which is associated with the policy option ‘lowering barriers to start up’ in Table 1.4 While in this paper we use several variables to reflect regulation we focus on this particular indicator because it plays such an important role in the influential Djankov et al. study. Table 2 also includes data on the dependent variables of this study, i.e. young business and nascent entrepreneurship rates, classified by opportunity and necessity entrepreneurship.
4. Modelling and data considerations Model To examine the determinants of nascent entrepreneurship and young business entrepreneurship we will estimate a two-equation model explaining these entrepreneurship rates separately, while taking into account the interrelationship between the two variables. Our model takes the
N = f (X1, G) (1) Y = f (N, X1, X2, G) (2)