The Moderating Effect of Firm‐Specificity on the Impact of Unemployment on the Demand for Apprentices: Evidence From Switzerland During <scp>COVID19</scp>

ABSTRACT This paper evaluates how firm‐specificity of apprenticeship training, equivalently firm‐specificity, moderates the response of the demand for apprentices to the unemployment rate. We first present a simplified conceptual framework to illustrate mechanisms through which this moderating effect could occur. We then empirically analyse the impact of unemployment on the demand for apprentices, and the abovementioned moderating effect. Using Poisson pseudo maximum likelihood methodology, we exploit monthly variation in the unemployment rate within sectors and cantons. We find that the unemployment rate is insignificantly associated with the demand for apprentices overall during the COVID19 period in Switzerland. We subsequently use a firm‐level survey conducted before COVID19 to construct three measures of firm‐specificity. Using each measure, firm‐specificity significantly moderates the effect of the unemployment rate on the demand for apprentices. A rise in the unemployment rate statistically and economically significantly reduces the demand for apprentices for firms imparting less firm‐specific training. On the other hand, a rise in the unemployment rate statistically and economically insignificantly affects the demand for apprentices within firms imparting more firm‐specific training. Our results are robust to the use of multiple measures of firm‐specificity and to the inclusion of controls capturing exposure to the pandemic and its economic support measures.

Authors

Institutions

Publication Details

Journal
Labour
Published
2025-06-17
DOI
https://doi.org/10.1111/labr.70000
Primary Topic
Labor market dynamics and wage inequality
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Moderating Effect of Firm‐Specificity on the Impact of Unemployment on the Demand for Apprentices: Evidence From Switzerland During COVID19

Thomas Bolli, Guillaume Maxence Morlet
Labour
Labor market dynamics and wage inequality
article

The Moderating Effect of Firm‐Specificity on the Impact of Unemployment on the Demand for Apprentices: Evidence From Switzerland During COVID19

Thomas Bolli, Guillaume Maxence Morlet
article en

Abstract

ABSTRACT This paper evaluates how firm‐specificity of apprenticeship training, equivalently firm‐specificity, moderates the response of the demand for apprentices to the unemployment rate. We first present a simplified conceptual framework to illustrate mechanisms through which this moderating effect could occur. We then empirically analyse the impact of unemployment on the demand for apprentices, and the abovementioned moderating effect. Using Poisson pseudo maximum likelihood methodology, we exploit monthly variation in the unemployment rate within sectors and cantons. We find that the unemployment rate is insignificantly associated with the demand for apprentices overall during the COVID19 period in Switzerland. We subsequently use a firm‐level survey conducted before COVID19 to construct three measures of firm‐specificity. Using each measure, firm‐specificity significantly moderates the effect of the unemployment rate on the demand for apprentices. A rise in the unemployment rate statistically and economically significantly reduces the demand for apprentices for firms imparting less firm‐specific training. On the other hand, a rise in the unemployment rate statistically and economically insignificantly affects the demand for apprentices within firms imparting more firm‐specific training. Our results are robust to the use of multiple measures of firm‐specificity and to the inclusion of controls capturing exposure to the pandemic and its economic support measures.

LabourVol. 39(4)
ZHAW Zurich University of Applied Sciences (CH)
H2020 European Research Council
Reduced inequalities
Openalex Percentile: Top 80%
Labor market dynamics and wage inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.