Testing for wage-specific search intensity
Abstract Most job search intensity models assume uniform search effort across all potential wage offers. I depart from this conventional assumption by proposing that agents allocate wage-specific search intensity, strategically avoiding effort on low-paying, unacceptable jobs or high-paying, improba...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Journal for Labour Market Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12651-024-00389-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Most job search intensity models assume uniform search effort across all potential wage offers. I depart from this conventional assumption by proposing that agents allocate wage-specific search intensity, strategically avoiding effort on low-paying, unacceptable jobs or high-paying, improbable ones. This alternative model generates wage distributions at acceptance that differ markedly from the truncated distributions typical of models with constant arrival rates for wage offers. I leverage these distinct empirical predictions to develop two new nonparametric tests, applied to NLSY97 data, both of which reject the hypothesis of constant search intensity across wages. Furthermore, I estimate the structural parameters identifiable in each model, revealing that wage-specific search leads to greater total search effort, faster transitions into the upper tail of the wage distribution, and ultimately higher accepted wages—more than a 25% increase following unemployment. For low wages, the classic random search model delivers a fair replication of the actual data, but for higher wages targeted search is better. Wage-specific search suggests that job seekers not only need to search more, but also search better. This insight has important implications for employment policy, particularly in promoting job search literacy among the unemployed. |
|---|---|
| ISSN: | 2510-5027 |