Time Aggregation and State Dependence in Welfare Receipt

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Dynamic discrete-choice models are an important tool in studies of state dependence in benefit receipt. A common assumption of such models is that benefit receipt sequences follow a conditional Markov process. This property has implications for how estimated periodto-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 in Norway using high-quality monthly data from 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: We find strong evidence for both duration and occurrence dependence in benefit receipt. Allowing for heterogeneity in the entry and persistence processes, we find important disparities in the effects of observed and persistent unobserved characteristics. 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, this corresponds to an average treatment effect of 5 percentage points. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SJEL Classification: I38, J60, J64, C23, C41

The Specification of Dynamic Discrete-Time Two-State Panel Data Models

“…Consequently, the MSD models provide better within-sample predictions than do the DBR models. Consistent with recent literature (e.g., Bhuller et al 2017), we conclude that the standard dynamic binary response model is unacceptably restrictive in this context.…”

“…To the best of our knowledge, the relationship between the DBR and MSD models has not been formally recognized, and very few studies have discussed the implications of the DBR models for transition probabilities and spell durations. Cappellari et al (2007) compared a duration and a Markov model for employment transitions, and Bhuller et al (2017) analyzed the adequacy of first-order dynamic binary response models against more general models that allow for duration and occurrence dependence. Gørgens and Hyslop (2018) showed that the DBR and MSD approaches are equivalent in a nonparametric context, and that nonparametric DBR models of order r ≤ 2 are nested within a simple MSD model.…”

In or out? Poverty dynamics among older individuals in the UK

“…In order to address this issue Section 6 describes the components of income, which are attributable to the changes in income and poverty status between t − 1 and t. 16 An exception here is Biewen (2009). 17 Bhuller et al (2016) using Norwegian administrative using monthly micro-level data show that the conditional Markov property has implications for the aggregation of a dynamic processes over time. Their findings show the estimated state dependence is affected time aggregation bias if the conditional Markov assumption is violated, nonetheless they find even more complex higher-order structures are unable to satisfactorily account for the true data-generating process.…”

Do Job Creation Schemes Improve the Social Integration and Well-Being of the Long-Term Unemployed?

“…One reason is that employers often interpret long unemployment durations as a significant negative signal (see e.g. Kroft et al, 2013;Bhuller et al, 2017). If this is the case, such re-integration policies would be ineffective on average.…”