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Oil price shocks and US unemployment: evidence from disentangling the duration of unemployment spells in the labor market

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Abstract

This paper examines how structural oil price shocks affect the US unemployment at the aggregate level and across the duration of unemployment spells. I find that adverse oil supply shocks produce a recessionary effect by significantly increasing the aggregate unemployment measures and the number of persons unemployed for 5 weeks or more. Aggregate demand shocks increase unemployment with a lag of a year and show a short-run fall in the number of unemployed between 5 and 26 weeks. While precautionary demand shocks have a muted effect on the unemployment rate, they temporarily raise short-term spells of unemployment but drop the number of unemployed between 5 and 26 weeks. Additionally, aggregate demand shocks are essential in explaining the duration of unemployment during the oil price surge of 2003–2008, the Great Recession, and the 2010–2014 period of stable oil prices. The findings also reflect that supply-driven shocks played a more important role in long-term unemployment during the 1970s–1980s period, whereas demand-driven shocks dominated the labor market during later periods of the 2000s.

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Notes

  1. Kilian (2009) denotes those shifts in the demand for crude oil associated with unexpected fluctuations in global real economic activity as “aggregate demand shocks.”

  2. The monthly index is now published by the FRED at: https://fred.stlouisfed.org/series/IGREA

  3. For a detailed analysis of the Kilian index advantages over other measures and some possible limitations, see Kilian and Zhou (2018), Kilian (2019), Funashima (2020), Nonejad (2020), and Kilian (2022a). Funashima (2020), for instance, shows that the Kilian index is a leading indicator for the world industrial production index.

  4. Throughout the text and figures “aggregate unemployment” refers to “total number of unemployed”

  5. I de-trend the \(U_{<5}\) and \(U_{>26}\) measures.

  6. Finding opposite responses for different unemployment spells is not surprising. In times of economic crisis, unemployed take longer to find a new job and thus move along the spell ladder from shorter to longer spells.

  7. As a robustness check, I reconsider the transformation of the real price of crude oil from log difference to log deviation from the sample mean as in Kilian (2009). I also experiment expressing the duration variables as a fraction of the total number of unemployed persons rather than the log difference of the number of persons unemployed for a certain duration. I find that all the results are robust to the transformation choices.

  8. Following the literature in the global oil market, I include 24 monthly lags which is appropriate to remove serial correlation by giving a sufficient delay in the response of the economy to structural shocks in the crude oil market (see, among others, Kilian 2009; Kilian and Lütkepohl 2017). I also estimate the model using 12 monthly lags and find that the results are robust to this change in lag length.

  9. I specify the block-recursive VAR model with the global oil market block ordered first and the domestic variable on unemployment ordered last.

  10. See Kilian (2022b), and Kilian and Zhou (2018, 2020) for a detailed analysis on the assumption of a vertical short-run supply curve.

  11. Oil-specific demand shocks, also referred to as precautionary demand shocks, represent changes in oil demand, as a precaution, due to concerns about future oil supply availability.

  12. Note that instead of the recursive identifying approach, different types of identifications would work in modeling oil price fluctuations. More recent alternative approaches include, among others, the work by Kilian and Murphy (2014), and Baumeister and Hamilton (2019).

  13. In the Sensitivity Analysis section, I examine the responses of the unemployment measures in this study to alternative structural shocks in the crude oil market including the oil market shocks of Baumeister and Hamilton (2019) , and the oil supply news shock of Känzig (2021)).

  14. To preserve space, I only include the responses of the real price of oil and the unemployment measures. The responses of world oil production and real economic activity are available upon request.

  15. Note the methodological differences in both studies in terms of the choice of data and the questions tackled (global versus the USA).

  16. To preserve space, I only include the responses of \(U_{<5}\) and \(U_{>26}\). The responses of the unemployment rate, aggregate unemployment duration, and unemployment spells of \(U_{5-14}\) and \(U_{15-26}\) are available upon request.

  17. See Borenstein and Kellogg (2012) and Kilian (2014) for a detailed discussion of the domestic sweet crude oil glut during that period of time.

  18. In this paper, I use “OECD index” and “World Industrial Production Index (WIP)” interchangeably.

  19. Note that the identifying restrictions in the Kilian (2009) model can be rigorously derived for the original specification, but need not hold when using world industrial production (see, Kilian and Zhou 2018).

  20. The index is available at: https://sites.google.com/site/cjsbaumeister/datasets.

  21. Funashima (2020) and Herrera and Rangaraju (2020) use log-linearly detrended series of WIP when comparing with the Kilian’s index. I use growth rate given that all the variables in the model are included in growth rates. I also run the model using the log-linearly detrended WIP, the results are unaffected by this change.

  22. Alternatively, one can estimate the oil market shocks using the global oil market model first developed by Kilian and Murphy (2014). Since both models generate close results after conditioning on a range of prior distribution supported by micro econometric verification (see, Herrera and Rangaraju 2020, I use the off-the-shelf structural oil market shocks from Baumeister and Hamilton 2019.

  23. The time series for the oil market structural shocks are available from Baumeister’s website starting from 1975.2 and updated regularly at: https://sites.google.com/site/cjsbaumeister/research.

  24. See Känzig (2021) for a detailed construction of the oil supply surprise series and identification of oil supply news shock.

  25. OSNS is available at: https://github.com/dkaenzig/oilsupplynews.

  26. The series is available at Güntner’s website from 1973.1 onward at: https://sites.google.com/site/econjochen/research.

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  1. Department of Economics, 338B Elliott Hall, School of Business Administration, Oakland University, Rochester, MI, 48309, USA

    Zeina Alsalman

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I am thankful to Ana Maria Herrera, Jochen Güntner, and Lutz Kilian for helpful comments and suggestions.

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Alsalman, Z. Oil price shocks and US unemployment: evidence from disentangling the duration of unemployment spells in the labor market. Empir Econ 65, 479–511 (2023). https://doi.org/10.1007/s00181-022-02351-0

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