Frictionless Firm and Occupational Retention Stability

In Simple Words

Imagine an employee who gets a financial windfall and decides to work fewer hours on Fridays, but keeps working at the exact same desk, for the same boss, in the same city, without switching careers or moving to a new company.

Research question

Identifying whether unexpected financial wealth induces workers to change careers, switch employers, or relocate geographic regions tests whether wealth functions primarily as a match-improvement search subsidy or as a simple hours-reduction mechanism.

Participants

N = 244,826 Swedish lottery winners tracked over a 10-year follow-up period.

Study Design

Panel regression tracking administrative registry records on workplace identifiers, firm IDs, occupational classifications, industry codes, and geographic work locations up to 10 years post-win.

Results

Wealth shocks produce no statistically significant effect on employer switching, workplace changes, occupation transitions, industry moves, or geographic relocation across 10 post-win years, proving that intensive-margin labor supply adjustments occur almost entirely within existing employment arrangements.

Takeaway

Model unearned wealth shocks as adjustments to working hours within existing employment matches rather than drivers of career re-allocation or inter-firm labor mobility.

Read alongside the research

Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Follow-up

Collecting more information about participants later in a study to see what has happened to them. The term can also mean the length of time researchers keep track of them.

ExampleResearchers interview the same workers three months after a programme ends. That later interview is a follow-up.

GET-IT glossary · Follow-up ↗Read more about follow-up →

Statistically significant

A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.

ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.

American Statistical Association · Statement on p-values ↗Read more about statistically significant →

Related findings

• Inter-Firm Job Mobility for Wage Growth →Labour & employment

  Imagine climbing a ladder where each rung is in a different building—to reach higher floors, a climber cannot stay on the same ladder forever; they must use their proven track record to jump to higher-paying rungs at new companies.

• Ability-Dependent Peer Screening →Labour & employment

  Imagine trying to pick the best chess player out of a crowd. Only a master chess player has the expertise to accurately assess who among their acquaintances truly plays at an elite level, whereas a beginner cannot distinguish between a decent amateur and a grandmaster.

• Ability-Heterogeneous Wage Returns →Labour & employment

  Imagine a nightclub with a strict dress code based on exam scores where high scorers enter easily without extra effort. For high-scoring individuals, additional work experience adds little value because employers already hire them based on test scores.

• Activation Program Threat Effect →Labour & employment

  Think of a mandatory weekend study hall for students behind on homework. Many students work much harder during the week to finish their assignments on time simply to avoid having to show up for the study hall in the first place.