Jongsay Yong

Bio: Jongsay Yong is an academic researcher from University of Melbourne. The author has contributed to research in topics: Productivity & Health care. The author has an hindex of 19, co-authored 67 publications receiving 849 citations. Previous affiliations of Jongsay Yong include Melbourne Institute of Applied Economic and Social Research & National University of Singapore.

Population ageing and its implications on aggregate health care demand: empirical evidence from 22 OECD countries.

Abstract: Recent evidence indicates that the relationship between age and health care expenditure is not as straightforward as it appears. In fact, micro-level studies find that time to death, rather than ageing, is possibly the main driver of the escalating health care costs in developed countries. Unfortunately, the evidence at the macro level is less clear and often depends on the specification of the empirical model used. We use an aggregate demand framework to assess whether health expenditure is more likely to be driven by ageing per se or proximity to death. Using panel data from 22 OECD countries from the first half of the 1990s, we find population ageing to be negatively correlated with health expenditure once proximity to death is accounted for. This suggests that the effects of ageing on health expenditure growth might be overstated while the effects of the high costs of medical care at the end of life are potentially underestimated. With respect to the latter, our finding highlights the importance of long-term and hospice care management. An expanded long-term care program may not only improve patient welfare, but also reduce costs of care by reducing the duration of hospital care for terminally ill patients. If expensive medical treatment for patients near the end of life can be controlled for, health expenditure growth resulting from population ageing is unlikely to present a most serious problem.

Effects of Recent Carrot-and-Stick Policy Initiatives on Private Health Insurance Coverage in Australia

Abstract: The Australian government implemented a sequence of new policies during 1997-2000 and raised the take-up rate of private health insurance (PHI) by 35 per cent. Because they were implemented sequentially, their individual effectiveness is not quite clear. We isolate the effects of Lifetime Health Cover (LHC) introduced at the last stage of the sequence using a counterfactual analysis of PHI demand with and without the new policies. Unlike earlier studies which attributed the bulk of the increase in PHI coverage to LHC, we find LHC may only account for as low as 42 per cent and no more than 75 per cent of the increase.

Effects of recent carrot-and-stick policy initiatives on private health insurance coverage in Australia

Abstract: The Australian government implemented a sequence of new policies during 1997–2000 and raised the take-up rate of private health insurance (PHI) by 35 per cent. Because they were implemented sequentially, their individual effectiveness is not quite clear. We isolate the effects of Lifetime Health Cover (LHC) introduced at the last stage of the sequence using a counterfactual analysis of PHI demand with and without the new policies. Unlike earlier studies which attributed the bulk of the increase in PHI coverage to LHC, we find LHC may only account for as low as 42 per cent and no more than 75 per cent of the increase.

Airline code-share alliances and costs: Imposing concavity on translog cost function estimation

Abstract: This paper provides an assessment of how airline code-share alliances affect the costs of the airline industry. It makes two contributions to the literature. First, it measures the effects of airline alliances by estimating a translog cost function using a panel dataset of 10 major U.S.-based airlines over 29 quarters. Secondly, it ensures concavity of the estimated cost function by using the procedure suggested by Ryan and Wales (2000, Economics Letters 67, 253–260). A conventional translog cost function is first estimated and scale estimates are computed. Unfortunately, the estimated function fails the curvature requirement, which makes interpreting the estimated effects of alliances somewhat dubious. Hence, we re-estimate the cost function by imposing local concavity restrictions. We find that large alliance partners have a small negative effect on airlines’ costs, but small alliance partners’ effect on costs appear to be positive, although the magnitude is negligible. We also find material differences in the estimates of scale economies after imposing local concavity.

Financial Incentives to Encourage Value-Based Health Care.

Abstract: This article reviews the literature on the use of financial incentives to improve the provision of value-based health care. Eighty studies of 44 schemes from 10 countries were reviewed. The proportion of positive and statistically significant outcomes was close to .5. Stronger study designs were associated with a lower proportion of positive effects. There were no differences between studies conducted in the United States compared with other countries; between schemes that targeted hospitals or primary care; or between schemes combining pay for performance with rewards for reducing costs, relative to pay for performance schemes alone. Paying for performance improvement is less likely to be effective. Allowing payments to be used for specific purposes, such as quality improvement, had a higher likelihood of a positive effect, compared with using funding for physician income. Finally, the size of incentive payments relative to revenue was not associated with the proportion of positive outcomes.

Treating tobacco use and dependence.

Abstract: This month, the Clinical Practice Guideline (CPG) column reviews the recently published guideline, Treating Tobacco Use and Dependence. This set of recommendations is available in a quick reference format and a more comprehensive clinician's guide that includes detailed information on the methodology and references used to develop the guidelines. Both are available in electronic and hard copy versions through a variety of sources, including the National Guidelines Clearinghouse, the Centers for Disease Control and Prevention, and the National Cancer Institute.

Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data

Abstract: This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within- and between-effects is crucial when choosing modelling strategies. The downside of Random Effects (RE) modelling – correlated lower-level covariates and higher-level residuals – is omitted-variable bias, solvable with Mundlak’s (1978a) formulation. Consequently, RE can provide everything FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plumper and Troeger’s FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions, and complex variance functions. We argue not simply for technical solutions to endogeneity, but the substantive importance of context/heterogeneity, modelled using RE. The implications extend beyond political science, to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.