Instrumental variable estimation for duration data

In this article we focus on time-to-event studies with a randomised treatment assignment that may be compromised by selective compliance. Contrary to most of the extensive literature on evaluation studies we do not consider the effect of the treatment on some average outcome but on the hazard rate. In time-to-event studies the treatment may vary over time. Another complication of duration data is that they are usually heavy censored. Censoring limits the observation period, but is not a feature of the treatment program. Therefore, a natural choice is to relate the treatment to the hazard rate. We show that even if the compliance is selective, we can still use the randomisation to estimate the impact of the program corrected for selective compliance on the hazard. The only requirement is that participation in the program is affected by a variable that is not correlated with the baseline duration. We develop an Instrumental Variable estimation procedure for the Generalized Accelerated Failure Time (GAFT) model. The GAFT model is a duration data model that encompasses two competing approaches to such data; the (Mixed) Proportional Hazard (MPH) model and the Accelerated Failure Time (AFT) model. We discuss the large sample properties of this Instrumental Linear Rank Estimation and show how we can improve its efficiency. The estimator is used to re-analyze the data from the Illinois unemployment bonus experiment

Modelling the time on unemployment insurance benefits

A duration model based on the time on Unemployment Insurance (UI) benefits instead of a model based on the time till re-employment is more relevant from a cost-benefit perspective. The contribution of this paper is to extend the standard (mixed) Proportional Hazard model to account for an upper bound on the duration. We use a modified mover-stayer model to this end and discuss the interpretation of the parameters. In an empirical application we compare the method with the standard analysis of unemployment duration. We also derive the expected UI-benefit costs implied by the model for some typical unemployed individuals

Instrumental variable estimation of treatment effects for duration outcomes

In this article we propose and implement an instrumental variable estimation procedure to obtain treatment effects on duration outcomes. The method can handle the typical complications that arise with duration data of time-varying treatment and censoring. The treatment effect we define is in terms of shifting the quantiles of the outcome distribution based on the Generalized Accelerated Failure Time (GAFT) model. The GAFT model encompasses two competing approaches to duration data; the (Mixed) Proportional Hazard (MPH) model and the Accelerated Failure Time (AFT) model. We discuss the large sample properties of the proposed Instrumental Variable Linear Rank (IVLR), and show how we can, with one additional step, improve upon its efficiency. We discuss the empirical implementation of the estimator and apply it to the Illinois re-employment bonus experiment

A note on stock sampling and maximum duration

An issue hardly ever mentioned in the analysis of labour market transitions is that for some individuals labour market transitions occur at a very low rate. Therefore, these individuals might stay on disability benefits or in domestic care till they reach the retirement age of 65. This implies that the duration on disability and of non-participating women has a upper bound of the time till retirement. Despite the growing availability of panel data on labour market transitions many household surveys are still based on stock based sampling. In this paper estimation of a duration model in which a positive fraction of individuals reaches a maximum duration is derived for stock sampled data. A mixed proportional hazard model with a piecewise constant baseline hazard leads to a relatively simple closed-form expression in the log likelihood. Discrete unobserved heterogeneity is assumed. Non-constant entry rates into the labour market state are allowed for by assuming a yearly fluctuating rate

Regularity in individual shopping trips: Implications for duration models in marketing

Most models for purchase timing behavior of households do not take into account that many households have regular and non-shopping days. I propose a statistical model for purchase timing that exploits information on the shopping days of households. It delivers forecasts for the number of purchases in the next period and for the timing of the first and consecutive purchases. Purchase occasions are modeled in terms of a counting process, which counts the recurrent purchases for each household as they evolve over time. I illustrate the model for yogurt and detergent purchases and highlight its useful managerial implications

Migration dynamics of immigrants: who leaves, who returns and how quick?

In this paper we analyze the demographic factors that influence the return and repeated migration of immigrants. Using longitudinal data from Statistics Netherlands we track migration histories of recent immigrants to The Netherlands and analyze which migrants will stay in the country, which migrants are more prone to leave and how quick they leave. In order to identify these migrants we apply a mover-stayer duration model on the time spent in the country. We also analyze the return from abroad to The Netherlands of these migrants. Results disclose differences among migrants by migration motive and by country of origin and lend support to our analytical framework. Combining the model for departure from the country and the model for returning to the country provides the long-run stay probability of a specific migrant. It also yields a framework for simulating the life-cycle migration dynamics. The major findings are: (1) labor migrants and students are more prone to leave and migrants who come for family reasons remain in the country more often, (2) migrants from the `guestworker' countries, Turkey and Morocco, will stay in the country more often than migrants from Western countries

Instrumental variable estimation for duration data

In this article we focus on duration data with an endogenous variable for which an instrument is available. In duration analysis the covariates and/or the effect of the covariates may vary over time. Another complication of duration data is that they are usually heavy censored. The hazard rate is invariant to censoring. Therefore, a natural choice is to model the hazard rate instead of the mean. We develop an Instrumental Variable estimation procedure for the Generalized Accelerated Failure Time (GAFT) model. The GAFT model is a duration data model that encompasses two competing approaches to such data; the (Mixed) Proportional Hazard (MPH) model and the Accelerated Failure Time (AFT) model. We discuss the large sample properties of this Instrumental Variable Linear Rank (IVLR) estimation based on counting process theory. We show that choosing the right weight function in the IVLR can improve its efficiency. We discuss the implementation of the estimator and apply it to the Illinois re-employment bonus experiment

Does work-related training reduce the discrepancy between function requirements and competencies?

The issue of lifelong learning is high on the political agenda. However, despite this political interest and the large economic literature on human capital, the impact of work-related training on the discrepancy between function requirements and the skills of the employee has been ignored. In this paper we use an ordered probit model to analyze the perceived change in discrepancy. Based on the bi-annual OSA panel from 1998 till 2002 for The Netherlands, we show that taking a work-related course decreases the discrepancy significantly. We correct for the endogeneity between the decision to take a course and the change in discrepancy and we argue that ignoring the selective decision to take a course leads to misleading conclusions about the effect of these courses on the change in discrepancy. Some respondents of the OSA-panel drop out between two waves. To correct for the possibility of selective attrition we develop an Inverse Probability Weight (IPW) estimation method for the ordered probit with an endogenous binary regressor. From the implied marginal effects of the IPW estimation we conclude that taking a course increases the probability to change the fit between skills and function requirements from Bad to Good with 16~percent-point

Modeling Migration Dynamics of Immigrants

In this paper we analyze the demographic factors that influence the migration dynamics of recent immigrants to The Netherlands. We show how we can allow for both permanent and temporary migrants. Based on data from Statistics Netherlands we analyze both the departure and the return from abroad for recent non-Dutch immigrants to The Netherlands. Results disclose differences among migrants by migration motive and by country of origin and lend support to our analytical framework. Combining both models, for departure and returning, provides the probability that a specific migrant ends-up in The Netherlands. It also yields a framework for predicting the migration dynamics over the life-cycle. From the obtained insight in the dynamic composition of migrants in the country important policy implications can be derived, including admission procedures for different countries and/or migration motives

Econometric analysis of ship life cycles - are safety inspections effective?

Due to the shipping industry’s international legal framework and the existence of loopholes in the system, an estimated 5-10 percent of substandard ships exist which are more likely to have incidents with high economic cost. This article uses ship life cycles to provide insight into the effectiveness of inspections on prolonging ship lives. We account for fluctuations in the relevant economic environment and the (possible time-varying) ship particulars. We use a unique dataset containing information on the timing of accidents, inspections, ship particular changes of more than fifty thousand ships over a 29 year time period (1978-2007). The results of our duration analysis reveal that the shipping industry is a relative safe industry but there is a possible over-inspection of vessels. It also reveals the need to improve transparency related to class withdrawals and changes of classification of the vessel. Another interesting finding is that for the majority of ship types an increase in earnings decreases the incident rate. This is in contrast to the industry perception of the impact of earnings. The effect of inspections vary across ship types and the prevention of incidents with high economic costs can be improved by better coordination of inspections, data sharing and a decrease in the number of inspections . Further, more emphasis should be placed on the rectification and follow up of deficiencies