Economic Uncertainty: A Geometric Indicator of Discrepancy Among Experts’ Expectations

Clavería, Oscar; Monte, Enric; Porras, Salvador Torra

doi:10.1007/s11205-018-1984-2

Cited by 19 publications

(30 citation statements)

References 46 publications

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“…We also compute the level of consensus among respondents using a geometry-based disagreement measure following on from the work of Saari (2008); Claveria et al (2019). Here, the authors define a framework to proxy economic uncertainty or to determine the likelihood of discrepancy among respondents.…”

Section: Appendix B Mapping Abilities To Tasksmentioning

confidence: 99%

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

Tolan

Pesole

Martínez-Plumed

et al. 2021

jair

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In this paper we develop a framework for analysing the impact of Artificial Intelligence (AI) on occupations. This framework maps 59 generic tasks from worker surveys and an occupational database to 14 cognitive abilities (that we extract from the cognitive science literature) and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity across a broad range of different AI areas. The use of cognitive abilities as an intermediate layer, instead of mapping work tasks to AI benchmarks directly, allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly created. An application of our framework to occupational databases gives insights into the abilities through which AI is most likely to affect jobs and allows for a ranking of occupations with respect to AI exposure. Moreover, we show that some jobs that were not known to be affected by previous waves of automation may now be subject to higher AI exposure. Finally, we find that some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies (e.g., visual and auditory processing using deep learning, and sensorimotor interaction through (deep) reinforcement learning). This article appears in the special track on AI and Society.

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Section: Appendix B Mapping Abilities To Tasksmentioning

confidence: 99%

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

Tolan

Pesole

Martínez-Plumed

et al. 2021

jair

View full text Add to dashboard Cite

show abstract

“…The fact that expression (2) does not include the share of neutral responses ( t E ) causes the level of disagreement to be overestimated, as shown by means of a simulation experiment in Claveria et al (2019). Therefore in this study, we use the disagreement metric proposed by Claveria (2021), which incorporates the information coming from all the reply options.…”

Section: Datamentioning

confidence: 99%

“…Second, we compute a disagreement measure of consumer unemployment expectations. With this aim we apply the geometric approach proposed by Claveria et al (2019). This method allows to compute a dimensionless metric that gives the proportion of discrepancy among survey respondents, where zero corresponds to the point of minimum disagreement, and one indicates that the answers are equidistributed among the different response categories.…”

Section: Introductionmentioning

confidence: 99%

“…On the one hand, following Dibiasi and Iselin ( 2019), we use the share of consumers unable to formalize expectations about unemployment (Knightian-type uncertainty). On the other hand, we use the geometric discrepancy indicator proposed by Claveria et al (2019) to quantify the proportion of disagreement in business and consumer expectations. We have used information from 22 European countries.…”

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confidence: 99%

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Employment Uncertainty a Year after the Irruption of the COVID-19 Pandemic

Sorić

Clavería

2021

SSRN Journal

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This paper examines the evolution of consumer uncertainty about unemployment one year after the irruption of the covid-19 pandemic in European countries. Since uncertainty is not directly observable, we use two alternative methods to directly approximate it. Both approaches are based on qualitative expectations elicited form the consumer survey conducted by the European Commission. On the one hand, following Dibiasi and Iselin ( 2019), we use the share of consumers unable to formalize expectations about unemployment (Knightian-type uncertainty). On the other hand, we use the geometric discrepancy indicator proposed by Claveria et al. (2019) to quantify the proportion of disagreement in business and consumer expectations. We have used information from 22 European countries. We find that both uncertainty measures covary. Although we observe marked differences across countries, in most cases the perception of employment uncertainty peaked before the outbreak of the crisis, plummeted during the first months of the lockdown, and started rising again since the past few months. When testing for cointegration with the unemployment rate, we find that the discrepancy indicator exhibits a long-term relationship with unemployment in most countries, while the Knightian uncertainty indicator shows a purely short-run relationship. The impact of both indicators on unemployment is characterised by considerable asymmetries, showing a more intense reaction to decreases in the level of uncertainty. While this finding may seem counterintuitive at first sight, it somehow reflects the fact that during recessive periods, the level of disagreement in the employment expectations of consumers drops considerably.

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“…We use all the forward-looking information coming from these surveys to proxy economic uncertainty in 32 European countries and the European Union (EU). To this end, we use Claveria et al's (2019) geometric indicator of discrepancy to compute the proportion of disagreement among firms and households.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Business and Consumer Uncertainty in the Midst of the COVID-19 Pandemic. a Sector Analysis in 32 European Countries

Clavería

2020

SSRN Journal

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This paper examines the evolution of business and consumer uncertainty amid the coronavirus pandemic in 32 European countries and the European Union (EU). Since uncertainty is not directly observable, we approximate it using the geometric discrepancy indicator of Claveria et al. (2019). This approach allows us quantifying the proportion of disagreement in business and consumer expectations of 32 countries. We have used information from all monthly forward-looking questions contained in Joint Harmonised Programme of Business and Consumer Surveys conducted by the European Commission (EC): the industry survey, the service survey, the retail trade survey, the building survey and the consumer survey. First, we have calculated a discrepancy indicator for each of the 17 survey questions analysed, which allows us to approximate the proportion of uncertainty about different aspects of economic activity, both form the demand and the supply sides of the economy. We then use these indicators to calculate disagreement indices at the sector level. We graphic the evolution of the degree of uncertainty in the main economic sectors of the analysed economies up to June 2020. We observe marked differences, both across variables, sectors and countries since the inception of the COVID-19 crisis. Finally, by adding the sectoral indicators, an indicator of business uncertainty is calculated and compared with that of consumers. Again, we find substantial differences in the evolution of uncertainty between managers and consumers. This analysis seeks to offer a global overview of the degree of economic uncertainty in the midst of the coronavirus crisis at the sectoral level.

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Economic Uncertainty: A Geometric Indicator of Discrepancy Among Experts’ Expectations

Cited by 19 publications

References 46 publications

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

Employment Uncertainty a Year after the Irruption of the COVID-19 Pandemic

Evolution of Business and Consumer Uncertainty in the Midst of the COVID-19 Pandemic. a Sector Analysis in 32 European Countries

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