The entrepreneurship-unemployment nexus in Croatia

Abstract

Purpose The purpose of this paper is to extend the literature on the entrepreneurship-unemployment nexus to the case of Croatia. Design/methodology/approach The study uses the Toda-Yamamoto causality test within a vector autoregressive model to determine the causal dynamics between the self-employment rate (SER), unemployment rate (UR), industrial production, and credit in the case of Croatia from March 1998 to December 2016. Findings The results reveal support for the recession-push hypothesis. Specifically, the authors find that an increase in the UR Granger causes an increase in the SER. Research limitations/implications Due to data availability, a more detailed analysis of self-employment by industry was prohibitive. Practical implications The results emphasize the importance of recognizing business cycle dynamics and the availability of credit when evaluating the causal relationship between entrepreneurship and unemployment. Social implications As policy makers view entrepreneurship as a potential remedy for unemployment, particular attention needs to be given to both the phases of the business cycle and credit availability to support entrepreneurial ventures in the design of policy. Originality/value Previous studies on the causal dynamics between entrepreneurship and unemployment pertain to OECD countries. This is the first study to examine a transition economy recently admitted to the European Union.

The level of entrepreneurship and the business cycle: A case study of Poland at the national and regional level

“…The recession push entrepreneurship theory assumes a more intensive development of companies during an economic slowdown. The causes are seen in the deteriorating situation of people, who are forced to start own businesses due to the unfavourable changes occurring on the labour market (Gaweł, 2007;Payne & Mervar, 2017). During an economic recession, the unemployment rate rises and the opportunities for finding work are limited, which inclines people to set up own businesses.…”

“…Divergent results may have been due to differences in the methods applied in these studies, including a different choice of economies covered by a study or time periods submitted to analysis. Noteworthy, the vast majority of the empirical studies that verified the presence of a correlation focused on the variable connected with unemployment as the one that determines the level of entrepreneurship (Baptista et al, 2006;Dilanchiev, 2014;Cueto et al, 2015;Payne & Mervar, 2017). However, as Aubry et al (2015) indicated, it is necessary to apply time series methods, also to a business cycle, to identify the true character of this problem.…”

“…An analysis of the level of entrepreneurship in this approach included the verification of the motivation for starting a business activity (Aubry et al, 2015). Furthermore, as underlined by Payne and Mervar, special attention should be drawn to the phases of a business cycle because this could be a significant implication for policy-makers in terms of designing an economic policy and supporting entrepreneurial activities (Payne & Mervar, 2017).…”

“…This is consistent with the assumptions of the theory of entrepreneurship, where at least two types of justification for the relations between a business cycle and the activity of entrepreneurs are implicated. One is the prosperity pull entrepreneurship theory, associated with the theory developed by Schumpeter, while the other one is the recession push entrepreneurship theory (Schumpeter, 1939;Payne & Mervar, 2017).…”

Management and entrepreneurship management mechanism of college students based on support vector machine algorithm

“…In the specific post, the classification and filtering of the graduates' samples by this classification method has obtained ideal results, and it is expected that the massive graduates' data will be well distributed 6 . Payne and Mervar 7 proposed that scientifically sorting and evaluating all kinds of talent information can greatly improve the management efficiency of recruiting units on graduate databases. Debus et al 8 proposed that the algorithm is expected to have a strong application in the employment of college graduates, which can predict the employment situation of college graduates.…”