Among the applications of event history analysis, in the last 10 years the lion's share has been played by proportional transition rate model. This type of models suffers from a major draw-back: it does not allow us to distinguish whether a covariate affects the event timing (the event occurs sooner/later) or the overall probability of the ultimate event occurrence (the chances of occurrence are constantly higher/lower). Thus, a positive/negative effect of a covariate found using a proportional transition rate model might reflect an acceleration/deceleration in the timing of the event and/or a high/low probability of the ultimate event occurrence (Yamaguchi, 1992). This paper shows how this problem can be reformulated in terms of the proportionality/non proportionality of the covariate effects. A twofold solution to disentangle the timing/probability problem is presented: this solution consists of a test of the proportionality of the covariate effects and a computation of the survival function at the end of the time interval studied. Two applications are discussed. The first one is based on four simulated processes. The second is based on an analysis of unemployment exit in Italy, with particular attention being paid to the effects of unemployment benefits. In the conclusion, implications for future applications of event history analysis are discussed.
Quality & Quantity – Springer Journals
Published: Oct 3, 2004
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