Abstract
This paper investigates whether Covid-19 increased the demand for digital occupations in the United States. Using O*NET data to capture the occupations’ digital content, we find that the digital share of employment and vacancies increased more in regions that were hit harder during the pandemic. However, this was driven by a smaller decline in employment and vacancies for digital occupations relative to non-digital ones, and not by an absolute increase in demand for digital workers. While digital occupations were more insulated, particularly in urban areas and cognitive occupations, there is little evidence of a permanent shift.
This is a preview of subscription content, log in via an institution to check access.
Sources: Indeed, JOLTS, and authors’ calculations.
Sources: CPS, Indeed, ACS, QWI, BEA, JOLTS, and authors’ calculations.
Sources: CPS, Indeed, ACS, QWI, BEA, JOLTS, and authors’ calculations.
Sources: Indeed, ACS, QWI, BEA, JOLTS, and authors’ calculations.
Sources: Indeed, ACS, QWI, BEA, JOLTS.
Sources: Indeed, ACS, QWI, BEA, JOLTS, and authors’ calculations
Similar content being viewed by others
Notes
Using the 2019 CPS, we find that 70% of digital occupations are teleworkable, while the remaining 30% are non-teleworkable. Some examples of digital teleworkable occupations are software developers, computer programmers, and computer system managers. Examples of digital non-teleworkable occupations are chemical engineers, avionics technicians, and manufacturing managers. Please see Online Appendix 2.2 for further details.
This restriction arises because O*NET updates come with lags and are staggered as explained in detail later in Sect. 3.
Our baseline result that Covid-19 did not induce a structural reallocation of labor is consistent with related literature (e.g., see Pizzinelli and Shibata 2022).
For instance, Barrero et al. (2021) argue that Covid-19 shifted relative employment growth toward more teleworkable industries, while looking at job adverts, Adrjan et al. (2021) find that most of the increase in advertised ability to work remotely comes from a rise within industries that can accommodate telework rather than a permanent shift toward more teleworkable industries. See also other papers that studied the labor market impact of the Covid-19 recession (e.g., Cajner et al. 2020; Crossley et al. 2021; Larrimore et al. 2022; Shibata 2021)
We use the SOC 2010 Occupation classification.
For more information on the distinction between importance and level, see the O*NET classification here.
We standardize following the O*NET’s suggested formula: \(S = ( (O - L) / (H - L) ) \times 100\), where O is the original rating score, and L and H are the lowest and higher raw scores across all occupations, respectively.
O*NET updates the raw scores for a subset of occupations yearly. With regards to the scores on knowledge and activities related to computers, O*NET has currently updated 200 occupations out of roughly 900 occupations over the past two years. However, since occupations are updated sequentially, the previous update of these 200 occupations occurred more than six years ago, making it impossible to detect whether upskilling took place during or prior to Covid-19.
Flood et al. (2021).
Indeed data on vacancies are only available since January 2019. We thus also restrict our employment data from 2019Q1 onward.
For employment, the data reports location of residence; for vacancies, Indeed reports location of the job. While this discrepancy could be an issue in light of the rise in work-from-home arrangements since Covid-19, we do not view it is a major issue in our specific context. For vacancies the core-based statistical areas (CBSA) are constructed based on urban centers and adjacent counties that are economically tied to them. This implies that by construction the majority of people residing in a CBSA also work there and commute within its boundaries. For employment, the share of people living near state boundaries, and therefore more likely to cross state lines for work, is small relative to the full labor force. Importantly, there is recent work that suggests that despite the significant rise in work-from-home arrangements, the median distance of workers from their workplace rose by less than one mile between 2020 and 2023 (Davis 2023).
All controls to measure pre-Covid-19 state-level heterogeneity are 2017-18 averages.
For the employment regression, we construct the national growth rate of employment in industry j and the average employment share \(\phi _{m, j, 2017-2018}\) using the state-level data from the Current Population Survey. For the vacancies regression, the data source for the same measures is calculated at the CBSA level using the Quarterly Workforce Indicators.
We perform sensitivity analysis using the Google mobility measure as an alternative measure of the Covid-19 shock. Our results, which are discussed in detail in the Online Appendix Sect. 2, remain robust to the choice of the Covid-19 shock used.
There is a potential bias in our Covid-19 shock estimates due to the concentration of digital workers in regions with high shares of national employment, as these regions may be more affected by the shock and also have more digital workers. To address this concern, we control for the regions’ pre-Covid-19 shares of digital employment and vacancies in the baseline results to isolate the effect of the Covid-19 shock.
While the authors mostly focus on instrumental-variable (IV) issues, they note that their argument and the diagnostics proposed also apply to OLS approaches if exogeneity can be achieved without the need for an instrument.
At most, one may expect that travel-related industries in tourism-intensive locations may have temporarily paused hiring in early 2020. But this would have a very minor impact on total employment shares in an area.
As previously mentioned, the drivers of employment and vacancies are different. Employment reflects shifts in both labor demand and labor supply, which materialize through changes in both hiring and separations.
We focus only on showing the vacancies results here, given the more granular regional information (CBSA level instead of state level) for vacancies allows us to explore the heterogeneity in more detail. We also conduct the same analysis for employment as robustness-check in Online Appendix Sect. 2 for readers’ reference. Overall, our results remain similar for the employment data despite more uncertainty in the estimates due to the much smaller sample size.
Region classification is based on the definition provided by the US Census Bureau for the CBSAs. The graphs are constructed by estimating Eq. 2 and Eq. 6 separately for the urban/metropolitan and rural/micropolitan CBSAs’ vacancy data. The dependent variable of focus is vacancies since vacancy data are available at a more granular regional level. On the other hand, employment data are at the state level which creates ambiguity in terms of the urban/rural categorization.
For example, digital cognitive occupations include software developers, computer programmers, and computer system managers. Digital routine occupations include statistical assistants, sales engineers, and industrial equipment repairers, which tend to be more repetitive. Digital manual occupations include dental assistants, gaming surveillance officers, and pharmacy aides. Based on 2019 CPS and Indeed data, digital routine occupations comprised 36% of total digital employment and 28% of total digital vacancies, respectively, while digital manual occupations comprised 5% and 3% of total digital employment and vacancies, respectively.
The estimation on manual occupations is not as informative given that only 3% of digital vacancies fall under the manual occupational group.
For comparison, the share of teleworkable occupations among non-digital employment (vacancies) in 2019 is 11.6 (11.4)%.
The full sensitivity analysis is discussed in the Online Appendix Sect. 2.
Higher thresholds imply that the definition of digital occupations includes a smaller subset of jobs that involve a greater intensity of digital skills compared to the baseline definition.
References
Adrjan, P., G. Ciminelli, A. Judes, M. Koelle, C. Schwellnus, and T. Sinclair 2021. Will it stay or will it go? analysing developments in telework during COVID-19 using online job postings data. OECD Productivity Working Papers 30, Organization for Economic Cooperation and Development.
Alcedo, J., A. Cavallo, B. Dwyer, P. Mishra, and A. Spilimbergo. 2022. E-commerce during COVID: Stylized facts from 47 economies. National Bureau of Economic Research: Technical report.
Alfaro, L., O. Becerra, and M. Eslava. 2020. EMEs and COVID-19: Shutting down in a world of informal and tiny firms. National Bureau of Economic Research: Technical report.
Autor, D.H., F. Levy, and R.J. Murnane. 2003. The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics 118: 1279–1333.
Barrero, J.M., N. Bloom, S.J. Davis, and B.H. Meyer. 2021. Covid-19 is a persistent reallocation shock. AEA Papers and Proceedings 111: 287–91.
Bartik, T.J. 1991. Who Benefits from State and Local Economic Development Policies? Upjohn Institute: W.E.
Bellatin, A. and G. Galassi 2022. What Covid-19 may leave behind: Technology related job postings in Canada. Bank of Canada Staff Working Paper 2022-17.
Bloom, N., R. Han, and J. Liang 2022. How hybrid working from home works out. Working Paper 30292, National Bureau of Economic Research.
Cajner, T., L. D. Crane, R. A. Deker, J. Grigsby, A. Hamins-Puertolas, E. Hurst, C. Kurz, and A. Yildrmaz 2020. The U.S. labor market during the beginning of the pandemic recession. Brookings Papers on Economic Activity, 3–34.
Chetty, R., J. N. Friedman, N. Hendren, M. Stepner, and T. O. I. Team 2020. The economic impacts of COVID-19: Evidence from a new public database built using private sector data. NBER Working Papers 27431, National Bureau of Economic Research.
Coibion, O., Y. Gorodnichenko, and M. Weber 2020. Labor Markets During the COVID-19 Crisis: A Preliminary View. Nber working papers, National Bureau of Economic Research, Inc.
Cortes, G. M., C. J. Nekarda, N. Jaimovich, and H. E. Siu 2020. The dynamics of disappearing routine jobs: A flows approach. Labour Economics 65.
Crossley, T.F., P. Fisher, and H. Low. 2021. The heterogeneous and regressive consequences of Covid-19: Evidence from high quality panel data. Journal of Public Economics 193: 104334.
Davis, S. J. (2023). The big shift to remote work. Presentation Slides. Institute for Capacity Development, International Monetary Fund, 29 March 2023. Available at: https://stevenjdavis.com/s/Big-Shift-to-Remote-Work-IMF-29-March-2023-Part-1.pdf.
Dingel, J.I., and B. Neiman. 2020. How many jobs can be done at home? Journal of Public Economics 189: 104235.
Dingel, J. I. and B. Neiman 2022. How many jobs can be done at home? Becker Friedman Institute White Paper.
Flood, S., M. King, R. Rodgers, S. Ruggles, J. R. Warren, and M. Westberry 2021. Integrated public use microdata series, current population survey: Version 9.0 [dataset]. minneapolis, mn: Ipums https://doi.org/10.18128/d030.v9.0.
Forsythe, E., L.B. Kahn, F. Lange, and D. Wiczer. 2020. Labor demand in the time of Covid-19: Evidence from vacancy postings and UI claims. Journal of Public Economics 189: 104238.
Forsythe, E., L.B. Kahn, F. Lange, and D. Wiczer. 2022. Where have all the workers gone? Recalls, retirements, and reallocation in the Covid recovery. Labour Economics 78: 104349.
Goldsmith-Pinkham, P., I. Sorkin, and H. Swift. 2020. Bartik instruments: What, when, why, and how. American Economic Review 110 (8): 2586–2624.
Hallas, L., A. Hatibie, R. Koch, S. Majumdar, M. Pyarali, A. Wood, and T. Hale 2021. Variation in us states’ covid-19 policy responses. Blavatnik School of Government.
Hansen, N.-J.H., and R.C. Mano. 2023. Mask mandates save lives. Journal of Health Economics 88: 102721.
Hensvik, L., T. Le Barbanchon, and R. Rathelot. 2021. Job search during the Covid-19 crisis. Journal of Public Economics 194: 104349.
Hershbein, B., and L.B. Kahn. 2018. Do recessions accelerate routine-biased technological change? Evidence from vacancy postings. American Economic Review 108 (7): 1737–1772.
Hershbein, B., and L.B. Kahn. 2018. Do recessions accelerate routine-biased technological change? Evidence from vacancy postings. American Economic Review 108: 1737–1772.
Jaimovich, N., and H.E. Siu. 2020. Job polarization and jobless recoveries. Review of Economics and Statistics 102 (1): 129–147.
Larrimore, J., J. Mortenson, and D. Splinter. 2022. Earnings shocks and stabilization during Covid-19. Journal of Public Economics 206: 104597.
Malkov, E. 2020. Nature of work and distribution of risk: Evidence from occupational sorting, skills, and tasks. Covid Economics 34 (3): 15–49.
Muro, M., S. Liu, J. Whiton, and S. Kulkarni 2017. Digitalization and the american workforce. Brookings Paper.
Oikonomou, M., N. Pierri, and Y. Timmer. 2023. It shields: Technology adoption and economic resilience during the Covid-19 pandemic. Labour Economics 81: 102330.
Pizzinelli, C. and I. Shibata 2022. Has Covid-19 induced labor market mismatch? evidence from the US and the UK. Labour Economics.
Shibata, I. 2021. The distributional impact of recessions: The global financial crisis and the Covid-19 pandemic recession. Journal of Economics and Business 115: 105971.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Florence Jaumotte for the guidance and invaluable suggestions. A version of this paper was presented at the 2023 EALE conference, the research seminar at the University of Tokyo and the IMF’s Jobs, Growth and Structural Reforms Seminar. We thank the participants at the seminar, Romain Duval, Niels-Jakob Hansen and Matthew Shapiro, and Shintaro Yamaguchi for their suggestions and comments. We also thank Yi Ji and Longji Li for their excellent research assistance. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Soh, J., Oikonomou, M., Pizzinelli, C. et al. Did the Covid-19 Recession Increase the Demand for Digital Occupations in the USA? Evidence from Employment and Vacancies Data. IMF Econ Rev (2024). https://doi.org/10.1057/s41308-024-00246-x
Published:
DOI: https://doi.org/10.1057/s41308-024-00246-x