nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2023‒02‒13
three papers chosen by
Maximo Rossi
Universidad de la República

  1. Human wellbeing and machine learning By Oparina, Ekaterina; Kaiser, Caspar; Gentile, Niccoló; Tkatchenko, Alexandre; Clark, Andrew E.; De Neve, Jan-Emmanuel; D'Ambrosio, Conchita
  2. What makes a satisfying life? Prediction and interpretation with machine-learning algorithms By Clark, Andrew E.; D'Ambrosio, Conchita; Gentile, Niccoló; Tkatchenko, Alexandre
  3. Inequality and Risk Preference By Pickard, Harry; Dohmen, Thomas; van Landeghem, Bert

  1. By: Oparina, Ekaterina; Kaiser, Caspar; Gentile, Niccoló; Tkatchenko, Alexandre; Clark, Andrew E.; De Neve, Jan-Emmanuel; D'Ambrosio, Conchita
    Abstract: There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches perform better than traditional models. Although the size of the improvement is small in absolute terms, it is substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - i.e. material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.
    Keywords: subjective wellbeing; prediction methods; machine learning
    JEL: C63 C53 I31
    Date: 2022–07–20
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:117955&r=ltv
  2. By: Clark, Andrew E.; D'Ambrosio, Conchita; Gentile, Niccoló; Tkatchenko, Alexandre
    Abstract: Machine Learning (ML) methods are increasingly being used across a variety of fields and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non-linear method, Random Forests. We present two key model-agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non-penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non-negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non-linear analysis.
    Keywords: life satisfaction; well-being; machine learning; British cohort study
    JEL: I31 C63
    Date: 2022–06–07
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:117887&r=ltv
  3. By: Pickard, Harry (Newcastle University); Dohmen, Thomas (University of Bonn and IZA); van Landeghem, Bert (University of Sheffield)
    Abstract: This paper studies the relationship between income inequality and risk taking. Increased income inequality is likely to enlarge the scope for upward comparisons and, in the presence of reference-dependent preferences, to increase willingness to take risks. Using a globally representative dataset on risk preference in 76 countries, we empirically document that the distribution of income in a country has a positive and significant link with the preference for risk. This relationship is remarkably precise and holds across countries and individuals, as well as alternate measures of inequality. We find evidence that individuals who are more able to understand inequality and individuals who fall behind their inherent point of reference increase their preference for risk. Two complementary instrumental variable approaches support a causal interpretation of our results.
    Keywords: income inequality, risk preference, risk sensitivity
    JEL: D91 O15 D81 D01
    Date: 2023–01
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp15854&r=ltv

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