Mixed Proportional Hazard (MPH) Model

In Simple Words

The model breaks down an individual's probability of leaving unemployment into three distinct multiplying parts: a baseline time clock, a score based on known background factors (like age and past work), and a hidden personal factor that accounts for unobserved drive or luck.

Research question

Evaluating dynamic exit rates from unemployment requires separating true policy treatment effects over time from compositional changes caused by individual baseline traits or unobserved participant differences.

Participants

N = 4,513 unemployed individuals in two Danish counties (2,393 treatment, 2,284 control).

Study Design

Parametric hazard estimation with discrete unobserved heterogeneity distributions (two-type mixture model) and duration-varying treatment indicators.

Results

The MPH framework isolated a 20% to 40% time-varying increase in unemployment escape rates attributable to the policy regime, preventing bias from a small sub-population (1-2%) of unobserved 'immune' individuals with zero exit probability.

Takeaway

Estimate duration models using a mixed proportional hazard specification with unobserved heterogeneity controls when analyzing dynamic panel data from social experiments.

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Plain-English glossary

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

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 →

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