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on Neuroeconomics |
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Issue of 2018‒11‒26
three papers chosen by |
| By: | Péter Hudomiet; Michael D. Hurd; Susann Rohwedder; Robert J. Willis |
| Abstract: | Physical and cognitive abilities of older workers decline with age, which can cause a mismatch between abilities and job demands, potentially leading to early retirement. We link longitudinal Health and Retirement Study data to O*NET occupational characteristics to estimate to what extent changes in workers’ physical and cognitive resources change their work-limiting health problems, mental health, subjective probabilities of retirement, and labor market status. While we find that physical and cognitive decline strongly predict all outcomes, only the interaction between large-muscle resources and job demands is statistically significant, implying a strong mismatch at older ages in jobs requiring large-muscle strength. The effects of declines in fine motor skills and cognition are not statistically different across differing occupational job demands. |
| JEL: | J26 J81 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25229&r=neu |
| By: | Isamaël Rafaï; Sébastien Duchêne; Eric Guerci; Guerci; Ariane Lambert-Mogiliansky; Fabien Mathy |
| Abstract: | In this paper, we will put forward an original experiment to reveal empirical “anomalies” in the process of acquisition, elaboration and retrieval of information in the context of reading economic related content. Our results support the existence of the memory dual process suggested in the Fuzzy Trace Theory: acquisition of information leads to the formation of a gist representation which may be incompatible with the exact verbatim information stored in memory. We give to subjects complex and complete information and evaluate their cognitive ability. To answer some specific questions, individuals used this gist representation rather than processing verbatim information appropriately. |
| Keywords: | Fuzzy Trace Theory, memory, dual process, cognitive reflection test, bounded rationality |
| JEL: | C91 D83 D89 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:lam:wpceem:18-25&r=neu |
| By: | Jon Kleinberg (Department of Computer Science, Cornell University); Annie Liang (Department of Economics, University of Pennsylvania); Sendhil Mullainathan (Department of Economics, Harvard University) |
| Abstract: | When testing a theory, we should ask not just whether its predictions match what we see in the data, but also about its \completeness": how much of the predictable variation in the data does the theory capture? Defining completeness is conceptually challenging, but we show how methods based on machine learning can provide tractable measures of completeness. We also identify a model domain - the human perception and generation of randomness - where measures of completeness can be feasibly analyzed; from these measures we discover there is significant structure in the problem that existing theories have yet to capture. |
| Date: | 2017–08–09 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:17-025&r=neu |