Relationship Between Unemployment and Entrepreneurship Dynamics in the Czech Regions: a Panel VAR Approach

Abstract

Investigation of the relationship between unemployment and entrepreneurship still does not provide conclusive results and scholars argue that the relationship needs to be further investigated. In the Czech context, the knowledge about entrepreneurship is still underdeveloped. The purpose of this paper is to investigate the dynamics of the relationship between unemployment and entrepreneurship, applying the methodology used by Koellinger and Thurik (2012) with usage of the quarterly data for the Czech NUTS 3 regions for the period of years 2003 -2014. Collected sample of 672 region-quarter observations was obtained from the Czech Statistical Office. Estimated panel vector autoregressive (VAR) models with impulse response function supported hypothesis assuming a positive relationship between unemployment and entrepreneurship, operationalized as annual growth in registered business activity. Obtained results also showed that after the shock in unemployment, dynamics of entrepreneurship increased above its initial level after two years, concluding that it may take up to two years before positive effects on entrepreneurship reveal. This finding provides value for entrepreneurship policy makers. Based on the obtained results author suggests to support entrepreneurial activity, especially during the times of higher unemployment rate.

Can policy makers count with positive impact of entrepreneurship on economic development of the Czech regions?

“…Increase in self-employment hence does not always have to lead to an economic boost and creation of new jobs (Shane, 2009). However self-employment can result in a reduction in unemployment, as some unemployed individuals perceive entrepreneurship as a means to earn income in the meantime till the conditions in the labour market improve (Dvouletý, 2017b;Dvouletý and Lukeš, 2016;Dvouletý and Mareš, 2016b). A boost in the regional economic growth would be expected more from dynamic and innovative small-and medium-sized enterprises (SMEs), business companies and partnerships, and therefore, one needs to distinguish among several forms of entrepreneurship and to empirically test their impact on regional economic development (Audretsch et al, 2015;Brekke, 2015;Shaffer et al, 2015).…”

“…Unfortunately, these surveys are not that extensive to cover detailed time series at the country level or on regional levels. Therefore researchers often substitute entrepreneurship by registered business activity (Llopis et al, 2015;Koellinger and Thurik, 2012;Carree and Thurik, 2007) and argue that this quantification does not substantially bias the results (Dvouletý, 2017a(Dvouletý, , 2017bFreytag and Thurik, 2007). Fritsch and Wyrwich (2016), among others, show that it is important to calculate the number of the registered enterprises/ self-employed, as regions differ in total number of (newly) registered businesses, size and JEEE 9,3 population.…”

“…On studying interregional differences and disparities in the Czech Republic, scholars emphasized the role of entrepreneurship (Dvouletý, 2017b;Šebestová et al, 2015;Baštová et al, 2011;Kutscherauer et al, 2010;Martin cík, 2008); however, the more rigorous approach testing the impact of entrepreneurship on regional economic development is still missing. By positive economic development, I mean, in this paper, an increase in regional gross domestic product (GDP) per capita and decrease in regional unemployment rate.…”

Effects of Soft Loans and Credit Guarantees on Performance of Supported Firms: Evidence from the Czech Public Programme START

“…However, the empirical evidence investigating the outcomes of the soft loans and credit guarantees and generally questioning the outcomes of entrepreneurship policies in the Central and especially Eastern European countries have not deserved much research attention so far, e.g., [27,[49][50][51][52][53][54][55][56][57][58][59]. This increasing research gap attracts researchers, who strive to form policy recommendations that are based on the empirical evidence, and those who are willing to be trained as evaluators since a lot of programmes need to be assessed.…”

Evaluating Performance and Elucidating the Mechanisms of Collaborative Development within the Beijing–Tianjin–Hebei Region, China

“…The unit root tests are performed and the results are displayed in Table 3. The results show that all variables are level stationary at the 0.05 level or higher [49]. In addition, to determine the lag order (p) of the VAR model is of great importance, results for six criteria are presented in Table 4, including the LR, FPE, and SC criteria, which all have p = 1 as their optimal lag order.…”