nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2022‒11‒07
nine papers chosen by
Maximo Rossi
Universidad de la República

  1. Human wellbeing and machine learning By Ekaterina Oparina; Caspar Kaiser; Niccolo Gentile; Alexandre Tkatchenko; Andrew E. Clark; Jan-Emmanuel De Neve; Conchita D'Ambrosio
  2. Gender, Loneliness and Happiness during COVID-19 By Lepinteur, Anthony; Clark, Andrew E.; Ferrer-i-Carbonell, Ada; Piper, Alan; Schröder, Carsten; D’Ambrosio, Conchita
  3. What makes a satisfying life? Prediction and interpretation with machine-learning algorithms By Andrew E. Clark; Conchita D'Ambrosio; Niccolo Gentile; Alexandre Tkatchenko
  4. The midlife crisis By Giuntella, Osea; McManus, Sally; Mujcic, Redzo; Oswald, Andrew J; Powthavee, Nattavudh; Tohamy, Ahmed
  5. Inequality and Social Distancing during the Pandemic By Caitlin S. Brown; Martin Ravallion
  6. Women's Careers and Family Formation By Bhalotra, Sonia R.; Clarke, Damian; Walther, Selma
  7. Health Shocks and Housing Downsizing: How Persistent Is 'Ageing in Place'? By Costa-Font, Joan; Vilaplana-Prieto, Cristina
  8. The Timing of Parental Job Displacement, Child Development and Family Adjustment By Carneiro, Pedro; Salvanes, Kjell G.; Willage, Barton; Willén, Alexander
  9. The Syrian Refugee Life Study : First Glance By Miguel,Edward A.; Palmer,Bailey; Rozo Villarraga,Sandra Viviana; Stillman,Sarah Virginia; Smith,Emma; Tamim,Abdulrazzak

  1. By: Ekaterina Oparina; Caspar Kaiser; Niccolo Gentile; Alexandre Tkatchenko; Andrew E. Clark; Jan-Emmanuel De Neve; Conchita D'Ambrosio
    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
    Date: 2022–07–20
    URL: http://d.repec.org/n?u=RePEc:cep:cepdps:dp1863&r=
  2. By: Lepinteur, Anthony; Clark, Andrew E.; Ferrer-i-Carbonell, Ada; Piper, Alan; Schröder, Carsten; D’Ambrosio, Conchita
    Abstract: We analyse a measure of loneliness from a representative sample of German individuals interviewed in both 2017 and at the beginning of the COVID-19 pandemic in 2020. Both men and women felt lonelier during the COVID-19 pandemic than they did in 2017. The pandemic more than doubled the gender loneliness gap: women were lonelier than men in 2017, and the 2017-2020 rise in loneliness was far larger for women. This rise is mirrored in life-satisfaction scores. Men’s life satisfaction changed only little between 2017 and 2020; yet that of women fell dramatically, and sufficiently so to produce a female penalty in life satisfaction. We estimate that almost all of this female penalty is explained by the disproportionate rise in loneliness for women during the COVID-19 pandemic.
    Keywords: Loneliness, Life Satisfaction, Gender, COVID-19, SOEP
    Date: 2022–10
    URL: http://d.repec.org/n?u=RePEc:cpm:docweb:2210&r=
  3. By: Andrew E. Clark; Conchita D'Ambrosio; Niccolo Gentile; Alexandre Tkatchenko
    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
    Date: 2022–06–07
    URL: http://d.repec.org/n?u=RePEc:cep:cepdps:dp1853&r=
  4. By: Giuntella, Osea (Department of Economics, University of Pittsburgh); McManus, Sally (National Centre for Social Research, London); Mujcic, Redzo (Warwick Business School, University of Warwick); Oswald, Andrew J (Department of Economics, University of Warwick, and CAGE Centre, IZA Institute, Bonn,); Powthavee, Nattavudh (Department of Economics, Nanyang Technological University, Singapore & IZA Institute, Bonn); Tohamy, Ahmed (Nuffield College, Oxford University)
    Abstract: This paper documents a longitudinal crisis of midlife among the inhabitants of rich nations. Yet middle-aged citizens in our data sets are close to their peak earnings, have typically experienced little or no illness, reside in some of the safest countries in the world, and live in the most prosperous era in human history. This is paradoxical and troubling. The finding is consistent, however, with the prediction -- one little-known to economists -- of Elliott Jaques (1965). Our analysis does not rest on elementary cross-sectional analysis. Instead the paper uses panel and through-time data on, in total, approximately 500,000 individuals. It checks that the key results are not due to cohort effects. Nor do we rely on simple life-satisfaction measures. The paper shows that there are approximately quadratic hill-shaped patterns in data on midlife suicide, sleeping problems, alcohol dependence, concentration difficulties, memory problems, intense job strain, disabling headaches, suicidal feelings, and extreme depression. We believe the seriousness of this societal problem has not been grasped by the affluent world’s policy-makers. JEL Codes: I31 ; I14 ; I12
    Keywords: Mental health ; affluence ; suicide ; depression ; aging ; midlife crisis ; happiness.
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:wrk:warwec:1430&r=
  5. By: Caitlin S. Brown; Martin Ravallion
    Abstract: We study how pre-pandemic inequalities in America influenced social distancing over the course of the COVID-19 pandemic. Richer counties tended to see more protective mobility responses in the initial (pre-pharmaceutical) phase, but less protective responses later. Near linearity of this income effect implies that inequality between counties contributed very little to overall mobility reductions. By contrast, higher within-county inequality and/or poverty measures came with substantially larger attenuations to non-residential mobility at given average incomes. There were also significant effects of the county’s racial and age composition. Standard epidemiological covariates of contact rates were also relevant, controlling for the socioeconomic factors.
    JEL: I14 I15
    Date: 2022–10
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:30540&r=
  6. By: Bhalotra, Sonia R. (University of Warwick); Clarke, Damian (University of Chile); Walther, Selma
    Abstract: This paper discusses research on the relationship between fertility and women's labour force participation. It surveys methods used to obtain causal identification, and provides an overview of the evidence of causal effects in both directions. We highlight a few themes that we regard as important in guiding research and in reading the evidence. These include the importance of distinguishing between extensive and intensive margin changes in both variables; consideration not only of women's participation but also of occupational and sectoral choice and of relative earnings; the relevance of studying dynamic effects and of analysing changes across the lifecycle and across successive cohorts; and of recognizing that women's choices over both fertility and labour force participation are subject to multiple constraints. We observe that, while technological innovations in reproductive health technologies have muted the familycareer tradeoff primarily by allowing women to time their fertility, policy has not achieved as much as it might.
    Keywords: fertility, birth spacing, abortion, ART, IVF, contraception, female labour force participation, gender wage gap, job loss, recession
    JEL: J01 J13 O15
    Date: 2022–10
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp15639&r=
  7. By: Costa-Font, Joan (London School of Economics); Vilaplana-Prieto, Cristina (Universidad de Murcia)
    Abstract: Individual preferences for 'ageing in place' (AIP) in old age are not well understood. One way to test the strength of AIP preference is to investigate the effect of health shocks on residential mobility to smaller size or value dwellings, which we refer to as 'housing downsizing'. This paper exploits more than a decade worth of longitudinal data to study older people's housing decisions across a wide range of European countries. We estimate the effect of health shocks on the probability of different proxies for housing downsizing (residential mobility, differences in home value, home value to wealth ratio), considering the potential endogeneity of the health shock to examine the persistence of AIP preferences. Our findings suggest that consistently with the AIP hypothesis, every decade of life, the likelihood of downsizing decreases by two percentage points (pp). However, the experience of a health shock partially reverts such culturally embedded preference for AIP by a non- negligible magnitude on residential mobility (9pp increase after the onset of a degenerative illness, 9.3pp for other mental disorders and 6.5pp for ADL), home value to wealth ratio and the new dwelling's size (0.6 and 1.2 fewer rooms after the onset of a degenerative illness or a mental disorder). Such estimates are larger in northern and central European countries.
    Keywords: ageing in place, housing downsizing, health shocks at old age, Europe, residential mobility, mental degenerative mental illness, mental disorder
    JEL: I18 G51 J61 R31
    Date: 2022–10
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp15636&r=
  8. By: Carneiro, Pedro (Department of Economics, University College London); Salvanes, Kjell G. (Dept. of Economics, Norwegian School of Economics and Business Administration); Willage, Barton (Department of Economics, University of Colorado); Willén, Alexander (Dept. of Economics, Norwegian School of Economics and Business Administration)
    Abstract: This paper examines if the effect of parental labor market shocks on child development depends on the age of the child at the time of the shock. To address this question, we leverage rich Norwegian population-wide register data and exploit mass layoffs and establishment closures as a source of exogenous variation in parental labor market shocks. We find that, even though displacement episodes early in children’s lives have the largest impacts on household income (because they persist for many years), displacement episodes occurring in the children’s teenage years have the largest effects on human capital accumulation. We show that most of the effects operate through the intensive margin of schooling, and that children – across childhood – are significantly more influenced by maternal labor shocks compared to paternal labor shocks. In terms of mechanisms, we show that the heterogeneous effects across child age likely are driven by short-term increases in maternal stress rather than by differences in how the parents respond to the shocks.
    Keywords: Job Displacement; Labor Market Shocks; Intergenerational Transmission; Human Capital
    JEL: D10 I20 J12 J13 J63
    Date: 2022–09–30
    URL: http://d.repec.org/n?u=RePEc:hhs:nhheco:2022_012&r=
  9. By: Miguel,Edward A.; Palmer,Bailey; Rozo Villarraga,Sandra Viviana; Stillman,Sarah Virginia; Smith,Emma; Tamim,Abdulrazzak
    Abstract: This paper presents descriptive statistics from the first wave of the Syrian Refugee LifeStudy (S-RLS), which was launched in 2020. S-RLS is a longitudinal study that tracks a representative sample of2,500 registered Syrian refugee households in Jordan. It collects comprehensive data on socio-demographic variablesas well as information on health and well-being, preferences, social capital, attitudes, and safety and crimeperceptions. This study uses these novel data to document the socio-demographic characteristics of Syrian refugees inJordan, and compare them to those of the representative Jordanian and non-Jordanian populations interviewed in the2016 Jordan Labor Market Panel Survey. The findings point to lags in basic service access, housing quality, andeducational attainment for the Syrian refugee population, relative to the non-refugee population. The impacts of thepandemic may serve to partially explain these documented disparities. The data also illustrate that most Syrianrefugees have not recovered economically from the shock of COVID-19 and that this population has larger genderdisparities in terms of income, employment, prevalence of child marriage, and gender attitudes than their non-refugeecounterparts. Finally, mental health problems are common for Syrian refugees in 2020, with depression indicated amongover 61 percent of the population.
    Keywords: Health Care Services Industry,Gender and Development,Economics and Gender,Gender and Economics,Gender and Economic Policy,Gender and Poverty,Mental Health
    Date: 2022–02–16
    URL: http://d.repec.org/n?u=RePEc:wbk:wbrwps:9940&r=

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