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on Neuroeconomics |
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Issue of 2024‒07‒29
three papers chosen by |
| By: | Khudri, Md Mohsan (Austin Community College); Hussey, Andrew (University of Memphis) |
| Abstract: | Using data from the Panel Study of Income Dynamics, we estimate the impact of breastfeeding initiation and duration on multiple cognitive, health, and behavioral outcomes spanning early childhood through adolescence. To mitigate the potential bias from misspecification, we employ a doubly robust (DR) estimation method, addressing misspecification in either the treatment or outcome models while adjusting for selection effects. Our novel approach is to use and evaluate a battery of supervised machine learning (ML) algorithms to improve propensity score (PS) estimates. We demonstrate that the gradient boosting machine (GBM) algorithm removes bias more effectively and minimizes other prediction errors compared to logit and probit models as well as alternative ML algorithms. Across all outcomes, our DR-GBM estimation generally yields lower estimates than OLS, DR, and PS matching using standard and alternative ML algorithms and even sibling fixed effects estimates. We find that having been breastfed is significantly linked to multiple improved early cognitive outcomes, though the impact reduces somewhat with age. In contrast, we find mixed evidence regarding the impact of breastfeeding on non-cognitive (health and behavioral) outcomes, with effects being most pronounced in adolescence. Our results also suggest relatively higher cognitive benefits for children of minority mothers and children of mothers with at least some post-high school education, and minimal marginal benefits of breastfeeding duration beyond 12 months for cognitive outcomes and 6 months for non-cognitive outcomes. |
| Keywords: | breastfeeding, human capital, cognitive and non-cognitive outcomes, doubly robust estimation, machine learning |
| JEL: | I12 I18 J13 J24 C21 C63 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17080&r= |
| By: | Lee Elliot Major; Andrew Eyles; Esme Lillywhite; Stephen Machin |
| Abstract: | The impact of the pandemic on education means that pupils in England are on course for lower GCSE grades well into the 2030s. Lee Elliot Major, Andrew Eyles, Esme Lillywhite and Stephen Machin say that the school system needs rebalancing to support pupils' cognitive and socio-emotional skills and thereby improve the education and life outcomes for the Covid generation. |
| Keywords: | Covid-19, Social mobility, education, equality |
| Date: | 2024–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepcnp:678&r= |
| By: | Joel P. Flynn; Karthik Sastry |
| Abstract: | We study the macroeconomic implications of narratives, defined as beliefs about the economy that spread contagiously. In an otherwise standard business-cycle model, narratives generate persistent and belief-driven fluctuations. Sufficiently contagious narratives can "go viral, " generating hysteresis in the model's unique equilibrium. Empirically, we use natural-language-processing methods to measure firms' narratives. Consistent with the theory, narratives spread contagiously and firms expand after adopting optimistic narratives, even though these narratives have no predictive power for future firm fundamentals. Quantitatively, narratives explain 32% and 18% of the output reductions over the early 2000s recession and Great Recession, respectively, and 19% of output variance. |
| JEL: | D84 E32 E70 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32602&r= |