Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams (gologit2) with the random effects estimation command regoprob by Stefan Boes.generalized ordered probit; panel data; autofit, self-assessed health

Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams (gologit2) with the random effects estimation command regoprob by Stefan Boes.generalized ordered probit; panel data; autofit, self-assessed health

Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams (gologit2) with the random effects estimation command regoprob by Stefan Boes.Generalized ordered probit; panel data; autofit, self-assessed health.

Reporting Heterogeneity in Self-Assessed Health among Elderly Europeans: The Impact of Mental and Physical Health Status

Self-assessed health (SAH) is a frequently used measure of individuals’ health status. It is also prone to reporting heterogeneity. To control for reporting heterogeneity valid measures of the objective health status are needed. The topic becomes even more complex for cross-country comparisons, as many key variables tend to vary strongly across countries, influenced by cultural and institutional differences. This study aims at exploring the key drivers for reporting heterogeneity in SAH in an international context. To this end, country specific effects are accounted for and the objective health measure is concretized, separating out effects of mental and physical health conditions. We use panel data from the Survey of Health, Ageing and Retirement in Europe (SHARE) which provides a rich dataset on the elderly European population. To obtain distinct indicators for physical and mental health conditions two indices were constructed. Finally, to identify potential reporting heterogeneity in SAH a generalized ordered probit model is estimated. We find evidence that health behaviour as well as health care utilization, mental and physical health condition as well as country characteristics affect reporting behaviour. We conclude that observed and unobserved heterogeneity play an important role when analysing SAH and have to be taken into account.reporting heterogeneity, SHARE, generalized ordered probit

Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories (parallel-lines assumption). This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams (gologit2) with the random effects estimation command regoprob by Stefan Boes.Generalized ordered probit; panel data; autofit, self-assessed health.

Angebotsinduzierung und Mitnahmeeffekt im Rahmen der Riester-Rente: eine empirische Analyse

In 2001, the voluntary additional Riester pension scheme was implemented in Germany. Financial subsidies should incentivize people to increase their private pension savings. In this paper, we hypothesize that these publicly subsidized savings mainly replace existing not subsidized savings and that supplier induced demand is an important factor. Using data from the Socio-economic Panel we analyze the key determinants in the choice of a Riesterpension. We find greater participation of those who already have life insurance or other public subsidized savings. Furthermore, we show that a contact with an insurance agent in the previous year is a major factor for the possession of a Riester-pension.Riester-Rente, Demografie, angebotsinduzierte Nachfrage, Mitnahmeeffekt

Demand inducement, crowding in and the German Riester pension scheme. (Angebotsinduzierung und Mitnahmeeffekt im Rahmen der Riester-Rente)

In 2001, the voluntary additional Riester pension scheme was implemented in Germany. Financial subsidies should incentivize people to increase their private pension savings. In this paper, we hypothesize that these publicly subsidized savings mainly replace existing not subsidized savings and that supplier induced demand is an important factor. Using data from the Socio-economic Panel we analyze the key determinants in the choice of a Riester-pension. We find greater participation of those who already have life insurance or other public subsidied savings. Furthermore, we show that a contact with an insurance agent in the previous year is a major factor for the possession of a Riester-pension.Riester-Rente, Demografie, angebotsinduzierte Nachfrage, Mitnahmeeffekt.

I feel good! Gender differences and reporting heterogeneity in self-assessed health

For empirical analysis and policy-oriented recommendation, the precise measurement of individual health or well-being is essential. The problem with variables based on questionnaires such as self-assessed health is that the answer may depend on individual reporting behaviour. Moreover, if individual‟s health perception varies with certain attitudes of the respondent reporting heterogenei-ty may lead to index or cut-point shifts of the health distribution, causing estimation problems. We analyse the reporting behaviour of individuals on their self-assessed health status, a five-point categorical variable. We explore observed heterogeneity in categorical variables and include unob-served individual heterogeneity using German panel data. Estimation results show different im-pacts of socioeconomic and health related variables on the five subscales of self-assessed health. Moreover, the answering behaviour varies between female and male respondents, pointing to gen-der specific perception and assessment of diseases. Reporting behaviour on self-assessed health questions in surveys is problematic due to a possible heterogeneity. Hence, in case of reporting heterogeneity, using self-assessed measures in empirical studies may be misleading or at least ambiguous.reporting heterogeneity, generalized ordered probit, self-assessed health

Meltzer-Richard and social mobility hypothesis: revisiting the income-redistribution nexus using German choice data

Whereas the supply of redistribution is relatively easy to measure, the determinants of the demand for redistribution are controversially discussed in international literature. Economic theory typically models redistribution as the result of a voting mechanism; this is only inadequately reflected by the existing empirical studies. In general, these studies use survey data and are therefore not able to predict individuals’ decision making under the restriction of a budget constraint. This study aims at eliciting preferences for redistribution in Germany with the help of a Discrete Choice Experiment (DCE), based on a representative sample of 1,538 individuals. A DCE solves the aforementioned problems by forcing individuals to overcome trade-offs. The results show a strong preference for redistribution that overshoots the current level. Considering socio-demographic characteristics, the results contradict the Meltzer-Richard-Model and the POUM hypothesis, while Piketty’s learning model is strongly supported by the data