Xiaohui Zhang

Chronic diseases and labour force participation in Australia

We examine the impact of several chronic diseases on the probability of labour force participation using data from the Australian National Health Surveys. An endogenous multivariate probit model is used to account for the potential endogeneity of the incidence of chronic conditions such as diabetes, cardiovascular diseases and mental illnesses. The cross-equation correlations are significant, rejecting the exogeneity of the chronic illnesses. Marginal effects of exogenous socio-demographic and lifestyle variables are estimated through their direct effects on labour market participation and indirect effects via the chronic diseases. The treatment effects of chronic diseases on labour force participation are estimated via conditional probabilities using five-dimensional normal distributions. The estimated effects differ by gender and age groups. Although computationally more demanding, these treatment effects are compared with results from a univariate model treating the chronic conditions exogenous and the structural effects from the multivariate probit model; both significantly overestimate the effects.

Heterogeneity in the effect of common shocks on healthcare expenditure growth

Healthcare expenditure growth is affected by important unobserved common shocks such as technological innovation, changes in sociological factors, shifts in preferences, and the epidemiology of diseases. While common factors impact in principle all countries, their effect is likely to differ across countries. To allow for unobserved heterogeneity in the effects of common shocks, we estimate a panel data model of healthcare expenditure growth in 34 OECD countries over the years 1980 to 2012, where the usual fixed or random effects are replaced by a multifactor error structure. We address model uncertainty with Bayesian model averaging, to identify a small set of robust expenditure drivers from 43 potential candidates. We establish 16 significant drivers of healthcare expenditure growth, including growth in GDP per capita and in insurance premiums, changes in financing arrangements and some institutional characteristics, expenditures on pharmaceuticals, population ageing, costs of health administration, and inpatient care. Our approach allows us to provide robust evidence to policy makers on the drivers that were most strongly associated with the growth in healthcare expenditures over the past 32xa0years. Copyright

Patient safety in hospitals - a Bayesian analysis of unobservable hospital and specialty level risk factors.

This paper demonstrates how Bayesian hierarchical modelling can be used to evaluate the performance of hospitals. We estimate a three-level random intercept probit model to attribute unexplained variation in hospital-acquired complications to hospital effects, hospital-specialty effects and remaining random variations, controlling for observable patient complexities. The combined information provided by the posterior means and densities for latent hospital and specialty effects can be used to assess the need and scope for improvements in patient safety at different organizational levels. Posterior densities are not conventionally presented in performance assessment but provides valuable additional information to policy makers on what poorly performing hospitals and specialties may be prioritized for policy action. We use surgical patient administrative data for 2005/2006 for 16 specialties in 35 public hospitals in Victoria, Australia. We use posterior means for latent hospital and specialty effects to compare hospital performance in patient safety. Posterior densities and variances are also compared for different specialties to identify clinical areas with greatest scope for improvement. We also show that the same hospital may rank markedly differently for different specialties.

Estimation of Technical Change and Price Elasticities: A Categorical Time-varying Coefficient Approach

In this paper we propose a categorical time-varying coefficient translog cost function to estimate technical change and productivity. The primary feature of this model is that each of its coefficients is expressed as a nonparametric function of a categorical time variable, thereby allowing each time period to have its own set of coefficients and thus its own cost function. Our application of this model to a panel of 80 electricity firms in the U.S. over the period 1986-1998 reveals that this model offers two major advantages over the traditional time trend representation of technical change: (1) it is capable of producing estimates of productivity growth that closely track those obtained using the Tornqvist approximation to the Divisia index; and (2) it can solve a well-known problem commonly referred to as the problem of trending elasticities.

Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index

In this paper, we propose a variable selection procedure based on the shrinkage estimation technique for a categorical varying-coefficient model. We apply the method to identify the relevant determinants for body mass index (BMI) from a large amount of potential factors proposed in the multidisciplinary literature, using data from the 2013 National Health Interview Survey in the United States. We quantify the varying impacts of the relevant determinants of BMI across demographic groups.

Productivity and Efficiency at Bank Holding Companies in the U.S.: A Time-Varying Heterogeneity Approach

This paper investigates the productivity and efficiency of large bank holding companies (BHCs) in the United States over the period 2004–2013, by estimating a translog stochastic distance frontier (SDF) model with time-varying heterogeneity. The main feature of this model is that a multi-factor structure is used to disentangle time-varying unobserved heterogeneity from inefficiency. Our empirical results strongly suggest that unobserved heterogeneity is not only present in the U.S. banking industry, but also varies over time. Our results from the translog SDF model with time- varying heterogeneity show that the majority of large BHCs in the U.S. exhibit increasing returns to scale, a small percentage exhibit constant returns to scale, and an even smaller percentage exhibit decreasing returns to scale. Our results also show that on average the BHCs have experienced small positive or even negative technical change and productivity growth.