The Europe 2020 Regional Index

We develop a composite index to measure regional progress in meeting objectives set forth by the Europe 2020 strategy. Performance along the thematic areas of the Europe 2020 strategy was computed via an appropriately normalized shortfall measure. The final composite scores were calculated by assigning equal weight to each dimension of the index and taking their arithmetic average. While we should be mindful not to overstate the reach of this analysis, a few general patterns are worth noting. First, we see that southern and central European countries such as Spain, Bulgaria, Greece, Portugal, Poland, Hungary and Romania fall behind Scandinavian and other northern European countries, despite the latters’ more ambitious targets. Second, our analysis makes plain the significant inter-regional heterogeneity of Europe 2020 performance for many countries. The cases of Spain and Italy are particularly suggestive in this regard. We investigated the robustness of index ranks via a rigorous uncertainty and sensitivity analysis. While the ranks of a handful of regions were sensitive to changes in weights and aggregation, index ranks as a whole were quite robustJRC.DDG.01-Econometrics and applied statistic

Consumer Markets Scoreboard: refinement, further development and analysis of micro-data

The present report addresses the refinement and further development of the Consumer Markets Scoreboard (CMS). The main objectives of the report are: i) to provide a comprehensive review of the theoretical framework and methodology behind the CMS, and to assess the statistical soundness and robustness of the existing Market Performance Indicator (MPI); ii) to review the empirical tools that can be used to analyse micro-level data on market performance, as perceived and reported by the experienced consumers responding to the Market Monitoring Survey (MMS).JRC.DDG.01-Econometrics and applied statistic

A Simple Framework for Climate-Change Policy under Model Uncertainty

We propose a novel framework for the economic assessment of climate-change policy. Our main point of departure from existing work is the adoption of a "satisficing", as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-à-vis some intertemporal objective function. Consistent to the nature of climate-change policy making, our model takes explicit account of model uncertainty. To this end, the value function we propose is an analogue of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply this decision criterion to probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. The main result that emerges is the superiority of "medium" carbon budgets in line with a 3°C target (i.e., 2000-3000 GtCO2) in preventing large future consumption losses with high probability. Insights from computational geometry facilitate computations considerably, and allow for the efficient application of the model in high-dimensional settings

A satisficing framework for environmental policy under model uncertainty

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty