Discussion Papers,
Statistics Norway, Research Department

Abstract: Dynamic discrete-choice models have been an important tool in studies of state dependence in benefit receipt. An assumption of such models is that benefit receipt sequences follow a conditional Markov process. This property has implications for how estimated period-to-period benefit transition probabilities should relate when receipt processes are aggregated over time. This paper assesses whether the conditional Markov property holds in welfare benefit receipt dynamics using high-quality monthly data from Norwegian administrative records. We find that the standard conditional Markov model is seriously misspecified. Estimated state dependence is affected substantially by the chosen time unit of analysis, with the average treatment effect of past benefit receipt increasing with the level of aggregation. The model can be improved considerably by permitting richer types of benefit dynamics: Allowing for differences between the processes for entries and persistence we find important disparities especially in terms of the effects of permanent unobserved characteristics. Extending the model further, we obtain strong evidence for duration and occurrence dependence in benefit receipt. Based on our preferred model, the month-to-month persistence probability in benefit receipt for a first-time entrant is 37 percentage points higher than the entry rate of an individual without previous benefit receipt. Over a 12-month period, the average treatment effect is about 5 percentage points.

Keywords: Time aggregation; Markov property; State dependence; Welfare dynamics

JEL-codes: I38; J60; J64; C23; C41 March 2014

Full text files

166110  

Download statistics

• Statistics Norway, Research Department
• Home page for this series

Questions (including download problems) about the papers in this series should be directed to L Maasø ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:ssb:dispap:771This page generated on 2024-10-30 04:36:29.