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Job Transitions during the Covid-19 Pandemic

For Better of for Worse?

Melanie Arntz, Sarra Ben Yahmed, Eduard Brüll and Michael Stops

NGE Spring Workshop in Hohenheim and Mannheim - 20.04.2023

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Motivation

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Introduction

Differential impact of COVID-19 pandemic across regions and occupations

  • Demand for various occupations changed drastically
    • Some sectors recovered quickly while others faced high rates of short-time work
    • Similar differences between regions (i.e. tourist regions vs. others)
  • Long-term inequalities could arise due to the severity of impact on certain occupations
    • Workers in severely affected occupations might need to adapt to the crisis by finding work in other occupations
    • Workers in hard hit occupations were less likely to find new jobs in the same occupation due to the fall in demand
    • Lack of social protection in part-time and temporary help jobs in hospitality/retail increased the incentive for affected workers to change occupations

Opportunities to move to jobs that require a similar skill set vary greatly between workers in different types of affected occupations and between local labour markets.

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This paper

Objectives

  • Analyse how the COVID-19 crisis affected occupational mobility in Germany
  • Consider how job transitions depended on the occupational structure of the local labour market prior to the crisis
  • Examine how workers' long-term labour market outcomes responded to the occupation-specific shocks in different local labour markets.

Approach

  • Develop a new measure of occupational switching opportunities
    • Combine measures of occupational distances with vacancy information
  • Compute forecast-based counterfactuals and a generalised difference-in-difference setting to get at causal effects of changes in occupational switching opportunities during the crisis
  • Examine changes in switching rates and job quality using individual social security data
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Contribution

Our paper contributes to three strands of economic literature :

  1. Workers' responses to local labor market shocks

    • Redondo (2022): Job transitions of workers in the construction sector during the Great Recession in Spain
    • Arntz et al (2022): Effect of job loss depends on the evolution of demand for tasks in a region
    • Our contribution: Long-term effects due to local occupation structure and occupation-specific shocks
  2. Job transitions after the COVID-19 shock

    • Hensvik et al.(2021): Job seekers in Sweden redirected their search effort towards less badly hit sectors
    • Costa Dias et al. (2021): Pandemic was detrimental to workers' careers in the UK
    • Our contribution: Occupational mobility due to the COVID-19 crisis in Germany
  3. Long-term effects of occupational mobility on labor market trajectories

    • Our contribution: Examine crisis-induced shifts in occupations and how they differ from normal mobility.
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Measuring the COVID-19 schock on occupations


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Measuring the COVID-19 schock on occupations

Novel measure of occupational switching opportunities due to labor demand changes

  • Individuals in the same occupation in different areas may have different incentives to switch due to:
    • Differences in shock to labor demand across space
    • Ability to switch depends on availability and similarity of tasks in other occupations
  • Use changes in vacancy postings to identify shifts in labor demand
    Use vacancy data from Federal Employment Agency
  • Use BIBB survey to measure overlapping task-content between occupations
    Compute Gathmann and Schönberg (2010) occupational distance from BIBB task information
  • Compare available vacancies vtro inoccupation o, region r and month t to distance-weighted average of vacancies in all other occupations in the same region vtro
    Compute vacancy ratio: vrtro=vtrovtro
    Measure of local opportunity to switch for each occupation
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Forecast-based counterfactuals for the vacancy ratio

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National deviations from prediction

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Exposure measure

Using both forecast-based counterfactual and actual data, we can compute measures for exposure to the Corona-induced changes in the vacancy ratio:

Average Exposurero=t>Feb.x2020(vrtrovrtro^)t>Feb.x2020vrtro^

This measure captures the shift in the availability of vacancies outside an individual's occupation relative to vacancies within their occupation during the Covid-19 pandemic. It depends on an individual's pre-crisis occupation o and region of residence r.

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Average exposures by region for different occupations

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Exposures and switching rates

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Data


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Individual social-security data

Monthly panel of individual social security records

  • Detailed information on employment status and occupation
  • 80% of the German labor force (excluding civil servants)
  • Data from 2016 to 2021
  • Balance panel (carryforward leavers source occupation info)
  • Detailed information on job switches
  • Detailed wages (censored, but censoring irrelevant for most affected occupations)
  • 2% sample at the moment but could expand to universe of data

Main outcomes

  • Switching rates by occupation
  • Pre-computed AKM-firm-effects to identify switches from low-paying to high-paying firms
  • Add further occupational info from the BIBB data to get at changes in occupational quality
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Switching rates during the pandemic

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Empirical Strategy


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Empirical strategy

Directly using the prediction-based counterfactual

Simplest possible estimation using the prediction-based counterfactual: Yitro=βExposuretro+αr+γo+λt+εitro, for outcomes Y like regional occupational switching or occupational quality measures of switches

Disadvantages:

  • Unlikely that reactions to the shock are immediate
  • Timing unknown

Alternative:
Keep only average exposure by occupation and region and get at timing using an event-study approach

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Empirical strategy

Difference-in-differences with continuous treatment

Get timing from an event-study specification: Yitro=t0βt(Average Exposurero×Dt)+αr+γo+λt+εitro

The reference period (t=0) is February 2020.

Advantages:

  • Check differential pre-trends
  • Can estimate timing and use in direct specification

Disadvantages:

  • Stronger parallel trends assumption than classical DiD
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Results


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Occupational switching by avg. exposure terzile

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Occupational switching by avg. exposure event study

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Occupational switching by avg. exposure event study

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Occupational switching by avg. exposure event study

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Occupational switching by avg. exposure event study

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Occupational switching by avg. exposure event study

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Conslusion


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What's next?

  1. Change estimation strategy to use time-varying exposure given that we see immediate reactions during high-demand slump periods
  2. Use an additional measure that use past occupational switching instead of a task distance
  3. Analyse destination and source jobs together:
    • More permanent or the same?
    • Use BIBB survey data on job quality measures
    • Use AKM-Effects to compare previous current firm of workers
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Preliminary conclusion

  • New insights into the effect of the Covid-crisis on occupational switching
    • Novel measure combining labour demand and task-based switching possibilities was used
    • Hospitality sector suffered the most strongest increase in switching,
    • Almost imediate reaction during biggest demand changes
    • During lockdowns, less-educated, low-wage workers in marginal employment relations were affected the most
  • First step to study where excess switchers end up
    • For better or for worse?
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Motivation

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