Family Health Behaviors

Fadlon, Itzik; Nielsen, Torben Heien

doi:10.3386/w24042

Cited by 35 publications

(55 citation statements)

References 31 publications

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“…Our use of timing differences across firms is in the spirit of a recent literature exploiting event timing differences in other contexts, e.g. see Duggan et al (2016); Fadlon and Nielsen (2017);Miller (2017); Lafortune et al (2018). In the context of automation, our identification relies in part on the nature of major automation events.…”

Section: An Event Study Differences-in-differences Designmentioning

confidence: 99%

Automatic Reaction - What Happens to Workers at Firms that Automate?

Bessen¹,

Salomons

Berge

2019

SSRN Journal

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We provide the first estimate of the impacts of automation on individual workers by combining Dutch micro-data with a direct measure of automation expenditures covering firms in all private non-financial industries over 2000-2016. Using an event study differences-indifferences design, we find that automation at the firm increases the probability of workers separating from their employers and decreases days worked, leading to a 5-year cumulative wage income loss of about 8% of one year's earnings for incumbent workers. We find little change in wage rates. Further, lost wage earnings are only partially offset by various benefits systems and are disproportionately borne by older workers and workers with longer firm tenure. Compared to findings from a literature on mass layoffs, the effects of automation are more gradual and automation displaces far fewer workers, both at the individual firms and in the workforce overall.A broader empirical literature indeed makes clear that large-scale automation need not bring about labor displacement in aggregate, but rather, leads to labor reallocation. For one, even within the affected industry, automation can increase employment if industry demand is sufficiently elastic (Acemoglu and Restrepo (2018a,d); Bessen (2018)). Moreover, there is evidence that productivity gains generate employment increases in other industries through input-output linkages as well as final demand effects, offsetting any employment losses in automating industries (Autor and Salomons (2018); Gregory et al. (2018)). It should be noted that our analyses do not consider these countervailing forces: this implies our findings do not inform on the macroeconomic impacts of automation. However, to understand how automation affects work, it is critical to also study its effects on individual workers. After all, the absence of displacement in aggregate need not imply the absence of losses for individual workers directly affected by automation. These micro-level impacts are also of first-order importance for policymakers aiming to assuage adverse impacts out of distributional concerns. This paper is structured as follows. We first introduce our data source, Dutch matched employer-employee data which we link to a firm survey containing a direct measure of automation expenditures. Section 3 contains our empirical approach, outlining a definition of automation events and the resulting estimation framework using a combination of event study and differences-in-differences. Our results are divided into total impacts on workers' wage income (section 4), which we decompose into firm separation and employment impacts (section 5), and daily wage impacts conditional on employment (section 6). We next consider to what extent wage income losses are compensated by various benefit schemes (section 7), and how these losses differ across worker types (section 8). Lastly, in section 9 we consider the worker costs of automation conditional on displacement and compare these to income losses arising from mass lay-offs and firm closur...

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Section: An Event Study Differences-in-differences Designmentioning

confidence: 99%

Automatic Reaction - What Happens to Workers at Firms that Automate?

Bessen¹,

Salomons

Berge

2019

SSRN Journal

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“…This literature has focused on the effects of sibling health status on educational outcomes (Black et al, 2017;Fletcher et al, 2012), on the effect of early health shocks on child human capital formation (Yi et al, 2015), on the effects of teen motherhood on their siblings' short and medium term human capital development (Heissel, 2017(Heissel, , 2019, on the effect of siblings on educational choices and early career earnings (Dustan, 2018;Joensen & Nielsen, 2018;Nicoletti & Rabe, 2019;Qureshi, 2018), and on the effects of health shocks to individuals on their family members consumption of preventive care (Fadlon & Nielsen, 2019).…”

Section: Child Obesity and Peer Effectsmentioning

confidence: 99%

Family ties and child obesity in Italy

Crudu

Neri

Tiezzi

2021

Economics & Human Biology

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This paper examines the impact of overweight family members on weight outcomes of Italian children aged 6 to 14 years. We use an original dataset matching the 2012 cross sections of the Italian Multipurpose Household Survey and the Household Budget Survey. Since the identification of within-family peer effects is known to be challenging, we implement our analysis on a partially identified model using inferential procedures recently introduced in the literature and based on standard Bayesian computation methods. We find evidence of a strong, positive effect of both overweight peer children in the family and of overweight adults on children weight outcomes. The impact of overweight peer children in the household is larger than the impact of adults. In particular, the estimated confidence sets associated to the peer children variable is positive with upper bound around one or larger, while the confidence sets for the parameter associated to obese adults often include zero and have upper bound that rarely is larger than one.

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“…It is, however, likely that the exact month of pregnancy is random within a certain window, given that it is not possible to choose precisely when one becomes pregnant (but households may start to change their behavior prior to pregnancy if they intend to become pregnant). Following Fadlon and Nielsen (2019), we use the potential randomness of the timing of a pregnancy to create a quasi‐experimental research design. We construct counterfactuals for pregnant households using households that experienced pregnancy 2 years in the future.…”

Section: Empirical Strategymentioning

confidence: 99%

“…We construct counterfactuals for pregnant households using households that experienced pregnancy 2 years in the future. Choosing a 2‐year period involves a trade‐off between comparability and analysis horizon, as described further in Fadlon and Nielsen (2019). The identifying assumption is that within 2 years, the particular month in which households become pregnant is random.…”

Section: Empirical Strategymentioning

confidence: 99%

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Pregnancy persistently reduces alcohol purchases: Causal evidence from scanner data

Janssen

Parslow

2020

Health Economics

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We analyze household‐level changes in alcohol consumption in response to pregnancy. Using scanner data, we identify households with a pregnant household member. Within an event study and a dynamic difference‐in‐differences estimation, we find that during a first pregnancy, households reduce their alcohol purchases by 36%. After pregnancy, purchases of alcohol are 34% lower than before pregnancy. We do not find any effect during the second pregnancy. One possible explanation for our result is that lower consumption during pregnancy changes habits and reduces consumption in the long term. We discuss other explanations and comment on policy implications.

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Family Health Behaviors

Cited by 35 publications

References 31 publications

Automatic Reaction - What Happens to Workers at Firms that Automate?

Automatic Reaction - What Happens to Workers at Firms that Automate?

Family ties and child obesity in Italy

Pregnancy persistently reduces alcohol purchases: Causal evidence from scanner data

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