We address the issue of Western European regional productivity growth and convergence by means of Data Envelopment Analysis (DEA), decomposing labor productivity into efficiency change, technical change and capital accumulation. The decomposition shows that most regions have fallen behind the production frontier in efficiency and that capital accumulation has had a diverging effect on the labor productivity distribution. We also account for the inherent bias and the stochastic elements in the efficiency estimation using bootstrapping methods. We find that the relative ranking of the bias-corrected efficiency scores remains stable after the bias correction and that the DEA successfully identifies the regions on the production frontier as significantly more efficient than other regions.JEL Classification: C14, C15, O47, R1

Abstract:Though there is a very large literature examining whether energy use Granger causes economic output or vice versa this literature is fairly inconclusive. Almost all existing studies use relatively short time series or panels with a relatively small time dimension. Additionally, many recent papers continue to use what seem to be misspecified models. We apply Granger causality and cointegration techniques to a Swedish time series data set on energy and economic growth spanning 150 years to test whether increases in energy use and energy quality have driven economic growth. We show that these techniques are very sensitive to variable definition, choice of additional variables in the model, and sample periods. All of the following appear to make a finding that energy causes growth more likely: using multivariate models, defining variables to better reflect their theoretical definition, using larger samples, and including appropriate structural breaks. However, it is also possible that the relationship between energy and growth has changed over time and that results from recent smaller samples reflect this. Energy prices have a significant causal impact on both energy use and output.