Counting on count data models

Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples. DOI: https://doi.org/10.15185/izawol.148 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-110642 Published Version Originally published at: Winkelmann, Rainer (2015). Counting on count data models : Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data. IZA world of labor, 148:online. DOI: https://doi.org/10.15185/izawol.148 World of Labor Evidence-based policy making RaineR Winkelmann University of Zurich, Switzerland, and IZA, Germany Counting on count data models. IZA World of Labor 2015: 148 doi: 10.15185/izawol.148 | Rainer Winkelmann © | May 2015 | wol.iza.org 1 eleVaTOR PiTCH Often, economic policies are directed toward outcomes that are measured as counts. Examples of economic variables that use a basic counting scale are number of children as an indicator of fertility, number of doctor visits as an indicator of health care demand, and number of days absent from work as an indicator of employee shirking. Several econometric methods are available for analyzing such data, including the Poisson and negative binomial models. They can provide useful insights that cannot be obtained from standard linear regression models. Estimation and interpretation are illustrated in two empirical examples. aUTHOR’S main meSSaGe Empirical analyses often encounter variables on a 0, 1, 2, etc., scale, such as hours of work or the annual number of doctor visits made by a person. Policymakers may be interested in the distributional effects of a reform on such outcomes, not just the mean effects. For example, does a policy affect heavy users of a service more than occasional users? Poisson and negative binomial models and their extensions can answer such a question, and they are no more complicated than a linear regression model. Hurdle models are useful for predicting the effect of a policy on the probability of a zero count as opposed to a count of one or more. Counting on count data models Quantitative policy evaluation can benefit from a rich set of econometric methods for analyzing count data