Changes Over Time in the Cost of Job Loss for Young Men and Women

Barnette, Justin; Odongo, Kennedy; Reynolds, C. Lockwood

doi:10.1515/bejeap-2020-0005

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Importantly, though, our above arguments require identifying the entire conditional distribution of X t * (0) for the treated group (not just its mean). 7 That said, difference in differences approaches that recover the distribution of untreated potential outcomes, such as Callaway and Li (2019) and Callaway, Li, and Oka (2018), could be applied here (though note that these approaches require additional assumptions). Likewise, the change-in-changes approach in Athey and Imbens (2006) and Melly and Santangelo (2015), which can recover distributions of untreated potential outcomes, could be applied to the time-varying covariates in this context.…”

Section: Identification Notation and Setupmentioning

confidence: 99%

“…That is, the conditions in part (1) of Corollary 1 and part (2) of Corollary 2 hold when one wants to control for the number of untreated potential Covid-19 cases. 7 In Section 4, we propose some alternative approaches where standard parallel trends assumptions for time-varying covariates can be used though these approaches require imposing a linear model for the path of untreated potential outcomes in Assumption 2 that are not used in this section. 8 This particular application uses state-level data, so, in practice, it may be difficult to use machine learning approaches with only 50 or so observations.…”

Section: Identification Notation and Setupmentioning

confidence: 99%

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Difference in Differences with Time-Varying Covariates

Caetano¹,

Callaway²,

Payne³

et al. 2022

Preprint

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This paper considers identification and estimation of causal effect parameters from participating in a binary treatment in a difference in differences (DID) setup when the parallel trends assumption holds after conditioning on observed covariates. Relative to existing work in the econometrics literature, we consider the case where the value of covariates can change over time and, potentially, where participating in the treatment can affect the covariates themselves. We propose new empirical strategies in both cases. We also consider two-way fixed effects (TWFE) regressions that include time-varying regressors, which is the most common way that DID identification strategies are implemented under conditional parallel trends. We show that, even in the case with only two time periods, these TWFE regressions are not generally robust to (i) time-varying covariates being affected by the treatment, (ii) treatment effects and/or paths of untreated potential outcomes depending on the level of time-varying covariates in addition to only the change in the covariates over time, (iii) treatment effects and/or paths of untreated potential outcomes depending on time-invariant covariates, (iv) treatment effect heterogeneity with respect to observed covariates, and (v) violations of strong functional form assumptions, both for outcomes over time and the propensity score, that are unlikely to be plausible in most DID applications. Thus, TWFE regressions can deliver misleading estimates of causal effect parameters in a number of empirically relevant cases. We propose both doubly robust estimands and regression adjustment/imputation strategies that are robust to these issues while not being substantially more challenging to implement.

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Section: Identification Notation and Setupmentioning

confidence: 99%

Section: Identification Notation and Setupmentioning

confidence: 99%

Difference in Differences with Time-Varying Covariates

Caetano¹,

Callaway²,

Payne³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Young adult substance use following involuntary job loss

Jolly,

Davis

2023

IZA Journal of Labor Economics

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This paper uses data from the Panel Study of Income Dynamics to study the relationship between job displacement and substance use among young adults. Results show that displacement is associated with increases in the probability of smoking cigarettes and drinking and the intensity of consuming alcohol and marijuana. Men, whites, and those who live with family/friends at the time of displacement are more likely to use hard drugs after job loss. Findings suggest that government policy designed to aid displaced workers should contain provisions to anticipate and respond to substance use disorders that may arise, particularly among younger job losers.

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Changes Over Time in the Cost of Job Loss for Young Men and Women

Cited by 2 publications

References 35 publications

Difference in Differences with Time-Varying Covariates

Difference in Differences with Time-Varying Covariates

Young adult substance use following involuntary job loss

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