Do labour productivity, capital productivity and education influence the technical efficiency of entrepreneurial outcomes? Evidence from europe

Abstract The differences in Labour and Capital Productivity and Education in Europe were used to provide a comprehensive evaluation of the performance of technical efficiency of entrepreneurship activities and new firm creation. For this purpose, two distinct methodologies were used: a non-parametric Data Envelopment Analysis (DEA) and a parametric Stochastic Frontier Analysis (SFA). Firstly, to obtain the ranking for assessing entrepreneurship efficiency, two outputs (birth rate and total early-stage entrepreneurial activity) were combined, and four inputs (long-term unemployment rate, household disposable income ratio S80/S20; young people neither in employment nor in education or training and employment rate of recent graduates) were applied. In the second step, two estimators were used to examine the effect of capital productivity, labour productivity, non-qualified labour, and population share of education on the technical efficiency score of entrepreneurial outcomes. The estimators were the Tobit regression, including random effects and mixed effects models, and the quantile regression model. The results for technical efficiency in the first step reveal that during 2008–2014 and after this period, 2015–2019, the European countries of Lithuania, Estonia and the Netherlands present the highest efficiency scores according to the DEA-CRS model. Applying the SFA technique, Belgium, Germany, and Malta show the highest levels of inefficiency during both periods of financial crisis. The second stage results demonstrate that there was a negative and significant effect of capital productivity on the efficiency scores of entrepreneurial outcomes in the periods of financial crises. This statistical evidence mirrors the observed decrease in average EU investments in fixed capital, structural changes in the labour market, and structural changes in education level in the active and inactive population, particularly in countries with economic growth, during the sub-periods between 2008 and 2019 under consideration.