Handling the measurement error problem by means of panel data: Moment methods applied on firm data

The estimation of a linear equation from panel data with measurement errors is considered. The equation is estimated (I) by methods operating on the equation in differenced period means, and (II) by Generalized Method of Moments (GMM) procedures using (a) the equation in differences with instruments in levels and (b) the equation in levels with instruments in differences. Both difference transformations eliminate unobserved individual heterogeneity. Examples illustrating the input response to output changes for materials and capital inputs from an eight year panel of Norwegian manufacturing firms, are given.Panel Data. Errors-in-Variables. GMM Estimation. Factor Demand. Returns to scale

Does quality influence choice of general practitioner? An analysis of matched doctor-patient panel data

The impact of quality on the demand facing health care providers has important implications for the industrial organization of health care markets. In this paper we study the consumers' choice of general practitioner (GP) assuming they are unable to observe the true quality of GP services. A panel data set for 484 Norwegian GPs, with summary information on their patient stocks, renders the opportunity to identify and measure the impact of GP quality on the demand, accounting for patient health heterogeneity in several ways. We apply modeling and estimation procedures involving latent structural variables, inter alia, a LISREL type of model,is used. The patient excess mortality rate at the GP level is one indicator of the quality. We estimate the effect of this quality variable on the demand for each GP's services. Our results, obtained from two different econometric model versions, indicate that GP quality has a clear positive effect on demand.GP services; Health care quality; Health care demand; Latent variables; LISREL; Panel data; Norway

Prevalence and substitution effects in tobacco consumption: A discrete choice analysis of panel data

This paper analyzes tobacco demand within a discrete choice framework. Using binomial and multinomial logit models with random effects, and an unbalanced panel data set of Norwegian households over a twenty year period, we first consider the decisions a) whether to smoke or not, and b) given the choice is to smoke, whether to smoke hand rolled or manufactured cigarettes. Next, we consider a multinomial logit framework, in which the households choose between no tobacco, only manufactured cigarettes, only hand rolled cigarettes, and a combination of manufactured and hand rolled cigarettes. In this process, we utilize the potential offered by panel data to investigate unobserved heterogeneity, which is crucial for commodities where consumers have different tastes and where users tend to become addicted. Using Maximum Likelihood in combination with bootstrap estimation of standard errors, we find that income and prices influence the 'type of tobacco choice probabilities' at least as strongly as the 'smoking/non-smoking probabilities'. Cet.par., an increase in the price of manufactured cigarettes could lead consumers to switch to hand rolled cigarettes, rather than quit smoking. Socio-demographic variables seem to be at least as important in explaining the discrete aspects of tobacco consumption as income and prices. Finally, we find significant unobserved household specific effects in the smoking pattern.Tobacco. Discrete choice. Panel data. Logit analysis. Heterogeneity. Bootstrapping

The effect of activity-based financing on hospital efficiency: A panel data analysis of DEA efficiency scores 1992-2000

Activity-based financing (ABF) was implemented in the Norwegian hospital sector from 1 July 1997. A fraction (30 to 50 per cent) of the block grant from the state to the county councils has been replaced by a matching grant depending upon the number and composition of hospital treatments. As a result of the reform, the majority of county councils have introduced activity-based contracts with their hospitals. This paper studies the effect of activity-based funding on hospital efficiency. We predict that hospital efficiency will increase because the benefit from cost-reducing efforts in terms of number of treated patients is increased under ABF compared with global budgets. The prediction is tested using a panel data set from the period 1992-2000. Efficiency indicators are estimated by means of data envelopment analysis (DEA) with multiple inputs and outputs. Using a variety of econometric methods, we find that the introduction of ABF has improved efficiency when measured as technical efficiency according to DEA analysis. Contrary to our prediction, the result is less uniform with respect to the effect on cost-efficiency. We suggest several reasons why this prediction fails. Keywords are poor information of costs, production-oriented drive, tight factor markets and soft budget constraints.Public hospitals; financing; efficiency; DEA-scores; panel data; Norway

New Taxonomies for Limited Dependent Variables Models

We establish a `map' for describing a wide class of Limited Dependent Variables models much used in the econometric literature. The classification system, or language, is an extension of Amemiya's typology for tobit models and is intended to facilitate communication among researchers. The class is defined in relation to distributions of latent variables of an arbitrarily high dimension; the region of support can be divided into an arbitrary number of subsets, and the observation rules in each subset can be any combination of the observed, censored, and missing status. Consistent labeling is suggested at different levels of detail

Estimating Seemingly Unrelated Regression Models from Incomplete Cross-Section / Time-Series Data

Most of the theoretical contributions to models for handling combined cross-section/time-series data are based on the assumption that complete time-series of equal length exist for all observation units. This paper is concerned with the estimation of multi-equation models in situations where the observation units "rotate" over time. The data then become incomplete cross-section/time-series data. The situation with complete cross-section/time-series data emerges as a special case of this specification. Econometric work with the Norwegian Surveys of Consumer Expenditure, which are based on rotating panels, has been a main motivation for exploring these problems. The conclusions, however, have for wider applicability. A substantial part of the work reported in this paper was carried out during the author's visit s i to Institut National de la Statistique et des Etudes Economiques (INSEE), Paris, in the autumn 1980