Probabilistic Job Offer Instrument

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In Simple Words

Imagine a raffle where job seekers are handed lottery tickets that offer different winning probabilities for a temporary paid job. Instead of picking workers based on talent or enthusiasm, the random probability lottery decides who gets work experience. This acts like a roll of the dice that forces work experience onto randomly selected individuals, allowing researchers to measure wage growth without confounding worker ability.

Young male job applicants in Malawi

No chance of a fallback job
16.3% obtained a study job
50% chance of a fallback job
40.2 points more obtained a job
Guaranteed fallback job
83.7 points more obtained a job

Randomized offer probabilities; unadjusted first-stage estimates. Percentage-point changes versus zero chance. Selected arms; 1%, 5% and 75% omitted.

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Research question

Measuring the true economic return of work experience is confounded because workers who acquire jobs often possess unobservable noncognitive skills or motivation. Randomly assigning probabilities of temporary employment isolates exogenous variation in short-term work experience to establish causal impacts on future labor market outcomes.

Participants

N = 268 male youth job seekers (aged 18+, secondary school educated) in Lilongwe, Malawi, with N = 227 successfully re-interviewed at 9-month follow-up.

Study Design

Randomized Controlled Trial (RCT) utilizing Instrumental Variables (IV), assigning random job offer probabilities (0%, 1%, 5%, 50%, 75%, and 100%) stratified by baseline ability quintiles and recruiter history.

Results

The probabilistic job offer assignment strongly predicts short-term employment acquisition with a first-stage F-statistic of 76.36. Assigning 50%, 75%, or 100% probabilities increases job acquisition by 40.2, 56.8, and 83.7 percentage points respectively (p < 0.01).

Takeaway

Structure pilot active labor market evaluations with probabilistic selection lotteries to construct valid instrumental variables for wage effect identification.

Source and citation

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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 →
Baseline

The starting measurements in a study. Researchers use these to compare what participants were like at the start with what happens later.

ExampleBefore a training programme starts, researchers measure workers’ earnings. That first measurement is the baseline.

GET-IT glossary · Baseline characteristics ↗Read more about baseline →
Percentage points

The difference between two percentages.

ExampleA rise from 40% to 50% is 10 percentage points. Relative to the starting value, that is a 25% increase.

Office for National Statistics · Percentages ↗Read more about percentage points →
Randomized controlled trial

A study that uses chance, like drawing lots, to decide who receives each option being compared. Researchers compare the groups’ outcomes to estimate the effect of those options.

ExampleA lottery assigns volunteers to a training programme or a comparison group. Researchers then compare their outcomes.

GET-IT glossary · Randomized study ↗Read more about randomized controlled trial →

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