Abstract
We investigate developments in private sector activity as proxied by business ownership (BO) rates in four Central and East European (CEE) transition economies (the Czech Republic, Hungary, Poland and the Slovak Republic) and compare them with similar developments in other OECD countries in the period 1989–2008. Our analysis reveals that BO rates in the four CEE countries have been converging rapidly towards the levels of other OECD countries, and these CEE countries were able to rebuild their private sectors in a relatively short period of transition. We also find sizable differences among the four CEE countries under study in the level and dynamics of change of BO since 1989.
Appendix: re-estimating Carree et al. (2002, 2007)
The model8
The main equation in the model by Carree et al. (2002, 2007) explains changes in the rate of business ownership (self-employment) from an error-correction process towards “equilibrium” or “natural” BO rates. A second equation acts as a definition and describes the “equilibrium” rate of BO as a function of economic development. Eq. [1] relates the change in the rate of BO
Where
E: number of business owners per labour force,
E*: “equilibrium” number of business owners per labour force,
YCAP: per capita GDP in thousands of purchasing power parities per US $ in 2000 prices,
U,
LIQ,
DITA: dummy variable with value 1 for Italy, and 0 for other countries,
i, t: indices for country and year, respectively.
The rationale of the model
The variable to be explained in eq. [1] is the growth in the number of business owners per labour force in a 4-year period. The parameter b1 reflects the speed of an error-correction mechanism between the “equilibrium” and the actual rate of self-employment at the start of the period and is expected to have a positive sign. As control variables, unemployment and the labour income share are included. Unemployment is expected to act as a push factor for self-employment and its expected sign is positive. The labour income share is an (inverse) proxy for business profitability and its expected sign is negative. Finally, we follow Carree et al. (2002) incorporating a dummy for Italy. Italy, and Northern Italy in particular, is exceptional in the sense that a relatively high value of GDP per capita is combined with a high and rising self-employment rate. The model is estimated by substituting the definition [2a] or [2b] into eq. [1]:
For this paper, we are mainly interested in the parameter estimates of
Using these parameter estimates, variable E* can be computed (see eq. [2]).
Variables and data sources
E: business ownership or self-employment is defined as the number of business owners (in all sectors excluding the agricultural sector), expressed as a fraction of the labour force. Business owners include unincorporated and incorporated self-employed individuals but exclude unpaid family workers. Data on BO are taken from EIM’s COMPENDIA data base (available through www.eim.net). In COMPENDIA numbers of self-employed reported in OECD Labour Force Statistics are harmonized across countries and over time. For the model estimations in the present paper, version 2008.1 of the COMPENDIA data base is used. See van Stel (2005) for an account of how an earlier version of this data set is put together. Data on total labour force are from OECD Labour Force Statistics.
YCAP: Gross domestic product per capita. The variables gross domestic product and total population are taken from OECD National Accounts and OECD Labour Force Statistics, respectively. GDP (in thousands of US $) is measured in constant prices. Furthermore, purchasing power parities of 2000 are used to make the monetary units comparable between countries.
U: Unemployment rate. It is measured as the number of unemployed as a fraction of the total labour force. The labour force consists of employees, self-employed persons, unpaid family workers, people employed by the armed forces and unemployed persons. The main source for this variable is OECD Main Economic Indicators.
LIQ: Labour income share. It is defined as the share of labour income (including the “calculated” compensation of the self-employed for their labour contribution) in the gross national income. Total compensation of employees is multiplied by total employment/number of employees to correct for the imputed wage income for the self-employed persons. Next, the number obtained is divided by total income (compensation of employees plus gross operating surplus and gross mixed income). The data of these variables are from OECD National Accounts.
Estimation results
Following Carree et al. (2002, 2007), we estimate the model using weighted least squares (with population as the weight factor). Instead of the 23 OECD countries originally used by Carree et al., we now use data of 27 OECD countries to estimate the model, where the four CEE countries have been newly added to the data base.9 For the 23 original countries we use data for the years 1996, 2000, 2004 and 2008.10 However, since the early years after the collapse of communism cannot be considered representative for the relation between economic development and self-employment in the four CEE countries (as self-employment was artificially low), we only use the last 2 years of data (i.e. 2004 and 2008) for these four countries. Our sample then consists of 100 observations. The results are presented in Table 4.
Estimation results
| Quadratic “equilibrium rate”: eq. [2a] | Inverse “equilibrium rate”: eq. [2b] | ||
| Eqs [3a] and [3b], dependent variable: 4-year growth of BO rate | |||
| a0 | Autonomous effect | 0.057*** | 0.186** |
| (3.6) | (2.6) | ||
| b1 | Error correction | 0.118*** | 0.111*** |
| (4.1) | (3.9) | ||
| b2 | Unemployment | 0.023 | 0.027 |
| (1.1) | (1.3) | ||
| b3 | Labour income share | −0.060*** | −0.052*** |
| (3.6) | (3.3) | ||
| a4 | Per capita GDP | −0.000189 | −0.148** |
| (0.3) | (2.2) | ||
| a5 | Per capita GDP | −2.54E–06 | |
| (0.2) | |||
| bITA | Italy | 0.013*** | 0.013*** |
| (3.4) | (3.3) | ||
| α | 2(a) and 2(b) | 0.158** | 1.381*** |
| (2.3) | (9.3) | ||
| β | 2(a) and 2(b) | −0.0016 | 1.333** |
| (0.3) | (2.0) | ||
| γ | 2(a) and 2(b) | −2.15E–5 | |
| (0.2) | |||
| Minimum | – | ||
| Asymptote | 0.048 | ||
| 0.232 | 0.221 | ||
Since the inverse model seems to perform somewhat better, we will use this model in the paper. According to our estimation results, the “equilibrium” rate of self-employment can then be written as
Acknowledgement
The research has been supported by the framework of the research program SCALES, carried out by Panteia/EIM and financed by the Dutch Ministry of Economic Affairs.
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- 1
BO is a rather crude measure of entrepreneurship. In particular, not every business owner is an innovative entrepreneur in the Schumpeterian sense (Wennekers and Thurik 1999). In the present paper, we view the BO rate as a useful indicator of private sector development and individual initiation (as opposed to state-oriented solutions) in a transitional context.
- 2
COMPENDIA is an acronym for COMParative ENtrepreneurship Data for International Analysis.
- 3
See http://www.entrepreneurship-sme.eu/ for the data and Van Stel (2005) for a justification of the harmonization methods. The EIM COMPENDIA data base has been used and acknowledged widely (Marcotte 2013).
- 4
In turn, GDP per capita data in EIM’s COMPENDIA data base are based on various OECD statistics (in particular, OECD National Accounts and OECD Labour Force Statistics).
- 5
The COMPENDIA-23 countries are those originally included in EIM’s COMPENDIA data base. The group includes the first 20 countries listed in Table 1 of this paper (i.e. excluding the CEE countries), and Luxembourg, Iceland and Japan (Van Stel, Cieślik, and Hartog 2010).
- 6
Note that, whereas the relation between economic development and business ownership (a measure of incumbent entrepreneurship) is mostly found to be L-shaped, the relation between economic development and measures of new-firm entrepreneurship, such as GEM’s TEA rate, is often found to be U-shaped (see, e.g. Kelley, Bosma, and Amorós 2011). The high exit rates in modern economies form the most likely explanation for these different patterns.
- 7
Since their education implies a bigger span of control, higher-educated entrepreneurs optimize their profits by running larger firms – implying a lower number of business owners at the macro-level.
- 8
Apart from some minor adjustments, the description of the model, variables and data sources is taken over from Carree et al. (2007).
- 9
The 23 OECD countries originally used by Carree et al. comprise the former EU-15, together with Iceland, Norway, Switzerland, the United States, Japan, Canada, Australia and New Zealand.
- 10
Due to the 4-year lag in the model, the “equilibrium” relation between self-employment and economic development is actually estimated using data for the years 1992, 1996, 2000 and 2004.
©2014 by Walter de Gruyter Berlin / Boston
