Indicators for the internal market? An unfinished business

At the recent event ‘Scientific support to Internal Market’ organized by JRC in Brussels on 21 February 2013 and attended inter alia by Commissioner Michel Barnier, the Deputy Secretary-General of the OECD Yves Leterme, and the Chairman of the STOA panel António Correia de Campos, a discussion has taken place on the role of statistical indicators in the measuring of the Internal Market, for both analytic and communicational purposes. Some speakers argued that between the ‘hard’ transposition numbers offered by the Internal Market Scoreboard and the ‘soft’ surveys offered by the two yearly editions of the Consumer Market Scoreboard there is perhaps a space which could be populated if a harmonized measurement system could be designed. Such a system would ideally cover all functions which may be needed to put the internal market to the center stage of a policy discourse. This could range from the quantification of the quality of transpositions to the generation of meaningful data driven narratives on the benefits of the internal market. In the present short note we recall the main points of this argument.JRC.G.3-Econometrics and applied statistic

Screening Active Inputs in Factor-Rich Models by Factors' Main Effect and Interaction Properties

We introduce a new method for screening factors in mathematical or computational models with large numbers of factors. The method proposed here represents an improvement over the best available practice for this setting, the method of elementary effects. This approach is particularly efficient for factors screening of non-linear and non-additive models.JRC.G.9-Econometrics and statistical support to antifrau

Ethics of quantification or quantification of ethics?

Something can be gained by looking at common ethical features of different instances of quantification. While ethics of algorithms is perceived at present as an urgent issue, similar concerns can easily be associated to the use of metrics, of statistical inference, and of mathematical modelling. By reviewing these common features, strategies of resistance can be imagined to cope with computational dystopias, metrics fixation and numerical abuse.publishedVersio

A sensitivity analysis of the PAWN sensitivity index

The PAWN index is gaining traction among the modelling community as a sensitivity measure. However, the robustness to its design parameters has not yet been scrutinized: the size (N) and sampling (ε) of the model output, the number of conditioning intervals (n) or the summary statistic (θ). Here we fill this gap by running a sensitivity analysis of a PAWN-based sensitivity analysis. We compare the results with the design uncertainties of the Sobol’ total-order index (S*Ti). Unlike in S*Ti, the design uncertainties in PAWN create non-negligible chances of producing biased results when ranking or screening inputs. The dependence of PAWN upon (N, n, ε, θ) is difficult to tame, as these parameters interact with one another. Even in an ideal setting in which the optimum choice for (N, n, ε, θ) is known in advance, PAWN might not allow to distinguish an influential, non-additive model input from a truly non-influential model input

A short comment on statistical versus mathematical modelling

publishedVersio

Quantitative storytelling in the making of a composite indicator

The reasons for and against composite indicators are briefly reviewed, as well as the available theories for their construction. After noting the strong normative dimension of these measures—which ultimately aim to ‘tell a story’, e.g. to promote the social discovery of a particular phenomenon, we inquire whether a less partisan use of a composite indicator can be proposed by allowing more latitude in the framing of its construction. We thus explore whether a composite indicator can be built to tell ‘more than one story’ and test this in practical contexts. These include measures used in convergence analysis in the field of cohesion policies and a recent case involving the World Bank’s Doing Business Index. Our experiments are built to imagine different constituencies and stakeholders who agree on the use of evidence and of statistical information while differing on the interpretation of what is relevant and vital