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.

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

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.

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
  $\to$ Use vacancy data from Federal Employment Agency
• Use BIBB survey to measure overlapping task-content between occupations
  $\to$ Compute Gathmann and Schönberg (2010) occupational distance from BIBB task information
• Compare available vacancies $v_{tro}$ inoccupation $o$, region $r$ and month $t$ to distance-weighted average of vacancies in all other occupations in the same region $v_{tro}^{\prime }$
  $\to$ Compute vacancy ratio: ${\text{vr}}_{tro}=\frac{v_{tro}^{\prime }}{v_{tro}}$
  $\to$ Measure of local opportunity to switch for each occupation

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

$${\text{Average Exposure}}_{ro}=\frac{\sum _{t>Feb.\phantom{x}2020}({\text{vr}}_{tro}-\widehat{{\text{vr}}_{tro}})}{\sum _{t>Feb.\phantom{x}2020}\widehat{{\text{vr}}_{tro}}}$$

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 $r$.

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

Directly using the prediction-based counterfactual

Simplest possible estimation using the prediction-based counterfactual: $$Y_{itro}=\beta {\text{Exposure}}_{tro}+{\alpha }_r+{\gamma }_o+{\lambda }_t+{\epsilon }_{itro},$$ 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

Difference-in-differences with continuous treatment

Get timing from an event-study specification: $$Y_{itro}=\sum _{t\ne 0}{\beta }_t({\text{Average Exposure}}_{ro}\times D_t)+{\alpha }_r+{\gamma }_o+{\lambda }_t+{\epsilon }_{itro}$$

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