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on Neuroeconomics |
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Issue of 2020‒11‒09
five papers chosen by |
| By: | Adrián Caballero; R. López-Pérez |
| Abstract: | A recent experimental literature has documented that people are (sometimes) asymmetric updaters: Good news are over-weighted, relative to bad news. We contribute to this literature with a novel experimental test of a potential mechanism of asymmetric updating, that is, that people recall better the positive than the negative evidence. In our design, this account predicts inflated posteriors regarding some future financial prize. Contrary to that, the average subject (slightly) underestimates the mode of the posterior beliefs about that payoff prospect. Although subjects tend to exhibit self-serving recall (SSR) in that they remember better the positive realizations of a signal in a memory task, this has little effect on their estimates and the extent and direction of the bias. A difficulty to recall accurately, we conclude, is not a sufficient cause for a positivity bias. |
| Keywords: | Belief Updating; Biases; Motivated Beliefs; Optimism; Self-Serving Recall |
| JEL: | C91 D03 D80 D83 D84 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:ipp:wpaper:2005&r=all |
| By: | Adrián Caballero; Raúl López-Pérez |
| Abstract: | Economic theories of optimism provide different rationales for the phenomenon of motivated reasoning, and a recent empirical literature has tested some of them, with mixed results. We contribute to this literature with a novel experimental test of two mechanisms, according to which optimism is respectively predicted when (1) the potential material losses due to the bias are relatively small or (2) the cognitive costs of the bias are small enough. In our design, these two accounts predict inflated expectations regarding some future payoff. Contrary to that, the average subject tends to (slightly) underestimate that financial prospect. Although a minority of the subjects overestimate systematically, the size of their errors is rather reduced, and they hardly differ in their personal characteristics from the rest of the subjects. In fact, optimism in our experiment is correlated with the sample observed, in that it is more likely when a subject observes relatively few good signals. This is again at odds with (1) and (2). These mechanisms, we conclude, do not appear to fully capture under which circumstances people fail into a positivity bias. Yet (1) seems to be empirically less relevant, in that we observe a similarly limited level of bias irrespectively of its monetary cost. |
| Keywords: | BBelief Updating; Biases; Motivated Beliefs; Optimism; Wishful Thinking |
| JEL: | D03 D80 D83 D84 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:ipp:wpaper:2006&r=all |
| By: | Elizabeth Caucutt (Western University Canada); Lance Lochner (University of Western Ontario); Joseph Mullins (University of Western Ontario); Youngmin Park (Bank of Canada) |
| Abstract: | This paper studies the multidimensional nature of investments in children within a dynamic framework. In particular, we examine the roles of parental time investments, purchased home goods/services inputs, and market-based child care services. We first document strong increases in total investment expenditures by maternal education; yet, expenditure shares, which skew heavily towards parental time, vary little with parental schooling. Second, we develop an intergenerational lifecycle model with multiple child investment inputs to study these patterns and the impacts of policies that alter the prices of different inputs. We analytically characterize investment behavior, focusing on the substitutability of different investment inputs and the way parental skills affect the productivity of family-based inputs. Third, we develop an estimation strategy that exploits intratemporal optimality conditions based on relative demand to estimate substitutability between inputs, the relative productivity of different inputs, and the role played by parental education. This approach requires no assumptions about the dynamics of skill investment, preferences, or credit markets. We also account for mis-measured inputs and wages, as well as unobserved heterogeneity in parenting skills. We further show how noisy measures of child achievement (measured several years apart) can also be incorporated in a GMM approach to additionally identify the dynamics of skill accumulation. Fourth, we use data from the Child Development Supplement of the Panel Study of Income Dynamics to estimate the skill production technology for children ages 12 and younger. Our estimates suggest complementarity between parental time and home goods/services inputs as well as between these family-based inputs and market-based child care, with elasticities of substitution ranging from 0.2 to 0.5. We find no systematic effects of parental education on the relative productivity of parental time and other home inputs. Finally, we use counterfactual simulations to explore the extent and sources of variation in investments across families, as well as investment responses to changes in input prices. We find that variation in prices explains 48% of the overall variance in investment expenditures, and differences in wages explain more than half of the investment expenditure gap between college and non-college educated parents. We further show that accounting for the degree of input complementarity implied by our estimates has important implications for the responses of individual inputs to any price change and for the responses in total investments and skill accumulation to large (but not small) price changes. |
| Keywords: | parental investments, parental inputs, PSID, Child Development |
| JEL: | J24 J13 C33 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:hka:wpaper:2020-076&r=all |
| By: | Hassan Afrouzi; Spencer Yongwook Kwon; Augustin Landier; Yueran Ma; David Thesmar |
| Abstract: | We study how biases in expectations vary across different settings, through a large-scale randomized experiment where participants forecast stable random processes. The experiment allows us to control the data generating process and the participants’ relevant information sets, so we can cleanly measure forecast biases. We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for the variation in overreaction across settings. We provide a theory of expectations formation with imperfect utilization of past information. Our model closely fits the empirical findings. |
| JEL: | C91 D03 D83 D84 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27947&r=all |
| By: | Dobromir Rahnev; Kobe Desender; Alan Lee; William Adler; David Aguilar-Lleyda; Başak Akdoğan; Polina Arbuzova; Lauren Atlas; Fuat Balcı; Ji Won Bang; Indrit Bègue; Damian Birney; Timothy Brady; Joshua Calder-Travis; Andrey Chetverikov; Torin Clark; Karen Davranche; Rachel Denison; Troy Dildine; Kit Double; Yalçın Duyan; Nathan Faivre; Kaitlyn Fallow; Elisa Filevich; Thibault Gajdos; Regan Gallagher; Vincent de Gardelle (CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Sabina Gherman; Nadia Haddara; Marine Hainguerlot; Tzu-Yu Hsu; Xiao Hu; Iñaki Iturrate; Matt Jaquiery; Justin Kantner; Marcin Koculak; Mahiko Konishi; Christina Koß; Peter Kvam; Sze Chai Kwok; Maël Lebreton; Karolina Lempert; Chien Ming Lo; Liang Luo; Brian Maniscalco; Antonio Martin; Sébastien Massoni; Julian Matthews; Audrey Mazancieux; Daniel Merfeld; Denis O’hora; Eleanor Palser; Borysław Paulewicz; Michael Pereira; Caroline Peters; Marios Philiastides; Gerit Pfuhl; Fernanda Prieto; Manuel Rausch; Samuel Recht; Gabriel Reyes; Marion Rouault; Jérôme Sackur; Saeedeh Sadeghi; Jason Samaha; Tricia Seow; Medha Shekhar; Maxine Sherman; Marta Siedlecka; Zuzanna Skóra; Chen Song; David Soto; Sai Sun; Jeroen van Boxtel; Shuo Wang; Christoph Weidemann; Gabriel Weindel; Michał Wierzchoń; Xinming Xu; Qun Ye; Jiwon Yeon; Futing Zou; Ariel Zylberberg |
| Abstract: | Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-02958766&r=all |