nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2019‒04‒01
six papers chosen by
Maximo Rossi
Universidad de la República

  1. Childhood Circumstances and Young Adulthood Outcomes: The Role of Mothers' Financial Problems By Marta Barazzetta; Andrew E. Clark; Conchita D'Ambrosio
  2. Risk Preferences of Children and Adolescents in Relation to Gender, Cognitive Skills, Soft Skills, and Executive Functions By James Andreoni; Amalia Di Girolamo; John List; Claire Mackevicius; Anya Samek
  3. The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand By Daron Acemoglu; Pascual Restrepo
  4. Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior By Curtis Atkisson; Piotr J. G\'orski; Matthew O. Jackson; Janusz A. Ho{\l}yst; Raissa M. D'Souza
  5. Presidential Elections, Divided Politics, and Happiness in the U.S. By Sergio Pinto; Panka Bencsik; Tuugi Chuluun; Carol Graham
  6. Automation and New Tasks: How Technology Displaces and Reinstates Labor By Daron Acemoglu; Pascual Restrepo

  1. By: Marta Barazzetta; Andrew E. Clark; Conchita D'Ambrosio
    Abstract: We here consider the cognitive and non-cognitive consequences on young adults of growing up with a mother who reported experiencing major financial problems. We use UK data from the Avon Longitudinal Study of Parents and Children to show that early childhood financial problems are associated with worse adolescent cognitive and non-cognitive outcomes, controlling for both income and a set of standard variables, and in value-added models controlling for children's earlier age-5 outcomes. The estimated effect of financial problems is almost always larger in size than that of income. Around one-quarter to one-half of the effect of financial problems on the non-cognitive outcomes seems to transit through mother's mental health.
    Keywords: income, financial problems, child outcomes, subjective well-being, behaviour, education, ALSPAC
    JEL: I31 I32 D60
    Date: 2019–03
  2. By: James Andreoni; Amalia Di Girolamo; John List; Claire Mackevicius; Anya Samek
    Abstract: We conduct experiments eliciting risk preferences with over 1,400 children and adolescents aged 3-15 years old. We complement our data with an assessment of cognitive and executive function skills. First, we find that adolescent girls display significantly greater risk aversion than adolescent boys. This pattern is not observed among young children, suggesting that the risk gap in risk preferences emerges in early adolescence. Second, we find that at all ages in our study, cognitive skills (specifically math ability) are positively associated with risk taking. Executive functions among children, and soft skills among adolescents, are negatively associated with risk taking. Third, we find that greater risk-tolerance is associated with higher likelihood of disciplinary referrals, which provides evidence that our task is equipped to measure a relevant behavioral outcome. For academics, our research provides a deeper understanding of the developmental origins of risk preferences and highlights the important role of cognitive and executive function skills to better understand the association between risk preferences and cognitive abilities over the studied age range.
    Date: 2019
  3. By: Daron Acemoglu; Pascual Restrepo
    Abstract: Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes.
    JEL: J23 J24
    Date: 2019–03
  4. By: Curtis Atkisson; Piotr J. G\'orski; Matthew O. Jackson; Janusz A. Ho{\l}yst; Raissa M. D'Souza
    Abstract: Anthropologists have long appreciated that single-layer networks are insufficient descriptions of human interactions---individuals are embedded in complex networks with dependencies. One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains. The analysis of multi-dimensional social networks has recently garnered the attention of the mathematics and physics communities. Multilayer networks are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions. Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich datasets, which give better, richer, and more accurate pictures of social interactions.
    Date: 2019–03
  5. By: Sergio Pinto (University of Maryland); Panka Bencsik (University of Sussex); Tuugi Chuluun (Loyola University Maryland); Carol Graham (The Brookings Institution)
    Abstract: We examine the effects of the 2016 and 2012 U.S. presidential election outcomes on the subjective well-being of Democrats and Republicans using large-scale Gallup survey data and a regression discontinuity approach. We use metrics that capture two dimensions of well-being – evaluative (life satisfaction) and hedonic (positive and negative affect) – and document a significant negative impact on both dimensions of well-being for Democrats immediately following the 2016 election and a negative but much smaller impact for Republicans following the 2012 election. However, we found no equivalent positive effect for those identifying with the winning party following either election. The results also vary across gender and income groups, especially in 2016, with the negative well-being effects more prevalent among women and middle-income households. In addition, in 2016 the votes of others living in the respondent’s county did not have a large impact on individual well-being, although there is some suggestive evidence that Democrats in more pro-Trump counties suffered a less negative effect, while Republicans in less pro-Trump and more typically urban counties were actually negatively impacted by the election outcome. We also find evidence that being on the losing side of the election had negative effects on perceptions about the economy, financial well-being, and the community of residence. Lastly, the evaluative well-being gaps between the different party affiliations tend to persist longer, with those in expected life satisfaction lasting until at least the end of 2016, while the hedonic well-being gaps typically dissipate within the two weeks following the election.
    Keywords: Elections, political parties, subjective well-being, life satisfaction, emotions
    JEL: D72 I31
    Date: 2019–03
  6. By: Daron Acemoglu; Pascual Restrepo
    Abstract: We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.
    JEL: J23 J24
    Date: 2019–03

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