A framework for the analysis of firm risk communication

In this paper, we propose a framework for the analysis of risk communication and an index to measure the quality of risk disclosure. Mainstream literature on voluntary disclosure has emphasized that quantity can be used as a sound proxy for quality. We contend that, in the analysis of the disclosure of risks made by public companies, attention has to be paid not only to how much is disclosed but also to what is disclosed and how.We apply the framework to a sample of nonfinancial companies listed in the ordinary market on the Italian Stock Exchange. To verify that the framework and synthetic index are not influenced by the two factors recognized in the literature as the most powerful drivers of disclosure behavior for listed companies, we use an OLS model. The regression shows that the index of disclosure quantity is not influenced either by size or industry. Thus, the synthetic measure can be used to rank the quality of the disclosure of risks.     

What is science’s crisis really about?

Present day reasoning about difficulties in science reproducibility, science governance, and the use of science for policy could benefit from a philosophical and historical perspective. This would show that the present crisis was anticipated by some scholars of these disciplines, and that diagnoses were offered which are not yet mainstream among crisis-aware disciplines, from statistics to medicine, from bibliometrics to biology. Diagnoses in turn open the path to possible solutions. This discussion is urgent given the impact of the crises on public trust in institutions. We ask whether the present crisis may be seminal in terms of drawing attention to alternative visions for the role of Science in society, and its relevant institutional arrangements. We finish by offering a number of suggestions in this direction.     

WHAT'S TO KNOW ABOUT THE CREDIBILITY OF EMPIRICAL ECONOMICS?

The scientific credibility of economics is itself a scientific question that can be addressed with both theoretical speculations and empirical data. In this review, we examine the major parameters that are expected to affect the credibility of empirical economics: sample size, magnitude of pursued effects, number and pre‐selection of tested relationships, flexibility and lack of standardization in designs, definitions, outcomes and analyses, financial and other interests and prejudices, and the multiplicity and fragmentation of efforts. We summarize and discuss the empirical evidence on the lack of a robust reproducibility culture in economics and business research, the prevalence of potential publication and other selective reporting biases, and other failures and biases in the market of scientific information. Overall, the credibility of the economics literature is likely to be modest or even low.     

THE (NON‐)SIGNIFICANCE OF REPORTING ERRORS IN ECONOMICS: EVIDENCE FROM THREE TOP JOURNALS

We investigate the prevalence and sources of reporting errors in 30,993 hypothesis tests from 370 articles in three top economics journals. We define reporting errors as inconsistencies between reported significance levels by means of eye‐catchers and calculated ‐values based on reported statistical values, such as coefficients and standard errors. While 35.8% of the articles contain at least one reporting error, only 1.3% of the investigated hypothesis tests are afflicted by reporting errors. For strong reporting errors for which either the eye‐catcher or the calculated ‐value signals statistical significance but the respective other one does not, the error rate is 0.5% for the investigated hypothesis tests corresponding to 21.6% of the articles having at least one strong reporting error. Our analysis suggests a bias in favor of errors for which eye‐catchers signal statistical significance but calculated ‐values do not. Survey responses from the respective authors, replications, and exploratory regression analyses indicate some solutions to mitigate the prevalence of reporting errors in future research.     

Methods for determining route distances in active commuting - Their validity and reproducibility

Distance is a variable of pivotal importance in transport studies. Therefore, after checking the validity of a potential criterion method for measuring active commuting route distances, this method was used to assess the validity and reproducibility of four methods of approximating the commuting route distances covered by pedestrians and bicyclists. The methods assessed were: self-estimated distance, straight-line distance, GIS shortest-route distance, and GPS-measured distance. For this purpose, participants were recruited when walking or bicycling in Stockholm, Sweden. Questionnaires and individually-adjusted maps were sent twice to 133 participants. The distances of map-drawn commuting routes functioned as criterion distances. The participants were also asked to estimate their distances. The straight-line distance between origin and destination was measured using map-drawn routes. The shortest route between home addresses and workplace addresses was calculated with three GIS algorithms. Eighty-six trips were measured with GPS. The main results were that test-retest intraclass correlation coefficients (ICC) exceeded 0.99 for all methods, except for self-estimated distance (ICC = 0.76). No order effects existed between test and retest. Significant differences were, however, noted between criterion distance and self-estimated distance (114 ± 63%), straight-line distance (79.1 ± 10.5%), GIS shortest route (112 ± 18% to 121 ± 22%) and GPS distance (105 ± 4%). We conclude that commonly-used distance estimation methods produce systematic errors of differing magnitudes when used in a context of active commuting in suburban and urban environments. These errors can at average level be corrected for, whereas individual relative errors will remain.