The Evolution of Rotation Group Bias: Will the Real Unemployment Rate Please Stand Up?

Krueger, Alan B.; Mas, Alexandre; Niu, Xiaotong

doi:10.1162/rest_a_00630

Cited by 33 publications

(13 citation statements)

References 7 publications

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“…We switched to all rotation groups once we determined that rotation group bias existed, with the highest rates of multiple job holding reported in rotation groups 1 and 5 and lower rates reported the longer one is in the survey (see the note by Hirsch and Winters ). This pattern is identical to that reported by Krueger et al () for unemployment rates, but the bias for multiple job holding is larger than for unemployment. That said, the basic conclusions of our analysis have changed little.…”

supporting

confidence: 90%

The Puzzling Pattern of Multiple Job Holding across U.S. Labor Markets

Hirsch

Husain²,

Winters³

2017

Southern Economic Journal

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Multiple job holding (MJH) rates differ substantially across U.S. regions, states, and metropolitan areas. Rates decrease markedly with respect to labor market size. These patterns have been largely overlooked, despite being relatively fixed over (at least) the past 20 years. This article explores explanations for these persistent differences. We account for roughly two‐thirds of the mean absolute deviation in MJH across local labor markets (MSAs). The results suggest that variation in MJH across labor markets is driven by labor market differences in job opportunities and worker preferences. Most important in explaining variation in MJH are MSA industry and occupation structure, ancestry shares, commute times, and, to a lesser extent, labor market churn.

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supporting

confidence: 90%

The Puzzling Pattern of Multiple Job Holding across U.S. Labor Markets

Hirsch

Husain²,

Winters³

2017

Southern Economic Journal

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“…The Fama-MacBeth approach is a special case of the t-statistic based approach to inference, with observations of the same year collected in a group. See [36][37][38][39][40][41] for empirical applications of the robust t−statistic inference approaches [11,12]. Importantly, the t−statistic based approaches to robust inference may also be used under convergence of group estimators of a parameter interest to scale mixtures of normal distributions as in the case of models under heavy-tailedness with infinite variances and in regressions with non-stationary exogenous regressors.…”

Section: Plos Onementioning

confidence: 99%

COVID-19: Tail risk and predictive regressions

et al. 2022

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The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

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“…Similar to Bernhardt (2018), they note that there has been a large recent increase in non-response driven primarily by survey refusals. 27 Numerous studies have found that non-response biases some of the most important survey based statistics. A 2015 study from the University of Kentucky Poverty Research Center found that the official poverty rate, which is derived from the March supplement of the CPS, was underestimated by a percentage point due to the relationship between low socio-economic status and non-response.…”

Section: ) Quantifying Non-response and Its Effectsmentioning

confidence: 99%