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Dr Olena Stavrunova

Biography

Olena joined the School in July 2007 after completing her Ph.D. at the University of Iowa.
Her research interests include Bayesian Econometrics, Health Economics and Labour Economics.
Olena's current research projects address (i) econometric modelling of health care expenditure;
(ii) the impact of public hospital waiting times on patient utilization of public and private health care services in a mixed public-private health care system;
(iii) econometric modelling of the demand for private health insurance in USA and Australia.

Senior Lecturer, Economics Discipline Group
Associate Member, Centre for the Study of Choice
BUSINESS ADMINISTRATION, MArts in Economics, PhD
Download CV  (PDF 334 Kb, 3 pages)
Phone
+61 2 9514 3597
Room
CB08.09.96

Research Interests

Labor Economics, Applied Econometrics, Bayesian Econometrics

Conferences

Keane, M. & Stavrunova, O. 2011, 'Adverse Selection, Moral Hazard and the Demand for Medigap Insurance'.
The size of adverse selection and moral hazard effects in health insurance markets has important policy implications. For example, if adverse selection effects are small while moral hazard effects are large, conventional remedies for inefficiencies created by adverse selection (e.g., mandatory insurance enrolment) may lead to substantial increases in health care spending. Unfortunately, there is no consensus on the magnitudes of adverse selection vs. moral hazard. This paper sheds new light on this While both adverse selection and moral hazard effects of Medigap have been studied separately, this is the first paper to estimate both in an unified econometric framework. We develop an econometric model of insurance demand and health care expenditure, where adverse selection is measured by sensitivity of insurance demand to expected expenditure. The model allows for correlation between unobserved determinants of expenditure and insurance demand, and for heterogeneity in the size of moral hazard effects. Inference relies on an MCMC algorithm with data augmentation. Our results suggest there is adverse selection into Medigap, but the effect is small. A one standard deviation increase in expenditure risk raises the probability of insurance purchase by 0.037. In contrast, our estimate of the moral hazard effect is much larger. On average, Medigap coverage increases health care expenditure by 32%.
Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2010, 'Expected waiting times and the decision to buy private health insurance'.
Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2010, 'The demand for private health insurance: Do waiting lists or waiting times matter?'.
Stavrunova, O. 2010, 'Adverse selection, moral hazard and the demand for medigap insurance'.
Stavrunova, O. 2009, 'Equilibrium model of waiting times for non-emergency procedures in the NSW public hospitals'.
Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2009, 'The influence of waiting times on the decision to purchase private health insurance'.

Journal articles

Stavrunova, O. & Yerokhin, O. 2014, 'Tax incentives and the demand for private health insurance', Journal Of Health Economics, vol. 34, pp. 121-130.
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We analyze the effect of an individual insurance mandate (Medicare Levy Surcharge) on the demand for private health insurance (PHI) in Australia. With administrative income tax return data, we show that the mandate has several distinct effects on taxpayers' behavior. First, despite the large tax penalty for not having PHI coverage relative to the cost of the cheapest eligible insurance policy, compliance with mandate is relatively low: the proportion of the population with PHI coverage increases by 6.5 percentage points (15.6%) at the income threshold where the tax penalty starts to apply. This effect is most pronounced for young taxpayers, while the middle aged seem to be least responsive to this specific tax incentive. Second, the discontinuous increase in the average tax rate at the income threshold created by the policy generates a strong incentive for tax avoidance which manifests itself through bunching in the taxable income distribution below the threshold. Finally, after imposing some plausible assumptions, we extrapolate the effect of the policy to other income levels and show that this policy has not had a significant impact on the overall demand for private health insurance in Australia.
Johar, M., Jones, G., Keane, M.P., Savage, E. & Stavrunova, O. 2013, 'Discrimination in a universal health system: Explaining socioeconomic waiting time gaps', Journal of Health Economics, vol. 32, no. 1, pp. 181-194.
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One of the core goals of a universal health care system is to eliminate discrimination on the basis of socioeconomic status. We test for discrimination using patient waiting times for non-emergency treatment in public hospitals. Waiting time should reflect patients' clinical need with priority given to more urgent cases. Using data from Australia, we find evidence of prioritisation of the most socioeconomically advantaged patients at all quantiles of the waiting time distribution. These patients also benefit from variation in supply endowments. These results challenge the universal health system's core principle of equitable treatment. 2012 Elsevier B.V.
Stavrunova, O. & Yerokhin, O. 2012, 'Two-part fractional regression model for the demand for risky assets', Applied Economics, vol. 44, no. 1, pp. 21-26.
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Empirical studies of household portfolio choices are often interested in quantifying the effects of various covariates on the fraction of a household's wealth invested in risky assets such as common stocks. The preferred econometric specification in these studies is the two-limit Tobit model, which can accommodate the fractional nature of the dependent variable. However, it is restrictive, because it assumes that the same data generating process determines both whether households participate in the stock market and the fraction of wealth invested in stocks. This article demonstrates that, in this setting, a two-part version of the fractional response model of Papke and Wooldridge (1996) constitutes an attractive alternative to Tobit by comparing the performance of the two models using data on portfolio choices of Australian households. We find that (1) the Tobit model is rejected by our data in favour of a two-part specification; and (2) marginal effects of covariates on the share of risky assets conditional on participation estimated from Tobit are confounded by the effects of these covariates on the participation decision. 2012 Taylor & Francis.
Johar, M., Savage, E., Stavrunova, O., Jones, G. & Keane, M. 2012, 'Geographic Differences in Hospital Waiting Times', Economic Record, vol. 88, no. 281, pp. 165-181.
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Access to elective surgery in Australian public hospitals is rationed using waiting lists. In this article, we undertake a DiNardo-Fortin-Lemieux reweighting approach to attribute variation in waiting time to clinical need or to discrimination. Using data from NSW public patients in 2004-2005, we find the discrimination effect dominates clinical need especially in the upper tail of the waiting time distribution. We find evidence of favourable treatment of patients who reside in remote areas and discrimination in favour of patients residing in particular Area Health Services. These findings have policy implications for the design of equitable quality targets for public hospitals. 2012 The Economic Society of Australia.
Johar, M., Jones, G., Keane, M., Savage, E. & Stavrunova, O. 2011, 'Waiting times for elective surgery and the decision to buy private health insurance', Health Economics, vol. 20, no. SUPPL. 1, pp. 68-86.
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More than 45% of Australians buy health insurance for private treatment in hospital. This is despite having access to universal and free public hospital treatment. Anecdotal evidence suggests that avoidance of long waits for public treatment is one possible explanation for the high rate of insurance coverage. In this study, we investigate the effect of waiting on individual decisions to buy private health insurance. Individuals are assumed to form an expectation of their own waiting time as a function of their demographics and health status. We model waiting times using administrative data on the population hospitalised for elective procedures in public hospitals and use the parameter estimates to impute the expected waiting time and the probability of a long wait for a representative sample of the population. We find that expected waiting time does not increase the probability of buying insurance but a high probability of experiencing a long wait does. On average, waiting time has no significant impact on insurance. In addition, we find that favourable selection into private insurance, measured by self-assessed health, is no longer significant once waiting time variables are included. This result suggests that a source of favourable selection may be aversion to waiting among healthier people. Copyright 2011 John Wiley & Sons, Ltd.
Stavrunova, O. & Yerokhin, O. 2011, 'An equilibrium model of waiting times for elective surgery in NSW public hospitals', Economic Record, vol. 87, no. 278, pp. 384-398.
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This article studies the effects of waiting times on the demand and supply of elective surgery in NSW public hospitals. The demand and supply equations are estimated at the level of postal code areas using data on public hospital elective surgery admissions in 2004-2005, postal code area characteristics and area-level provisions of public and private hospital capacities. Empirical results imply that demand for elective surgery is affected negatively, and supply positively, by waiting time. The estimated elasticity of demand with respect to waiting time is higher in NSW than estimates reported in studies based on data from the UK National Health Service. 2011 The Economic Society of Australia.
Keane, M. & Stavrunova, O. 2011, 'A smooth mixture of Tobits model for healthcare expenditure', Health Economics, vol. 20, no. 9, pp. 1126-1153.
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This paper develops a smooth mixture of Tobits (SMTobit) model for healthcare expenditure. The model is a generalization of the smoothly mixing regressions framework of Geweke and Keane (J Econometrics 2007; 138: 257-290) to the case of a Tobit-type limited dependent variable. A Markov chain Monte Carlo algorithm with data augmentation is developed to obtain the posterior distribution of model parameters. The model is applied to the US Medicare Current Beneficiary Survey data on total medical expenditure. The results suggest that the model can capture the overall shape of the expenditure distribution very well, and also provide a good fit to a number of characteristics of the conditional (on covariates) distribution of expenditure, such as the conditional mean, variance and probability of extreme outcomes, as well as the 50th, 90th, and 95th, percentiles. We find that healthier individuals face an expenditure distribution with lower mean, variance and probability of extreme outcomes, compared with their counterparts in a worse state of health. Males have an expenditure distribution with higher mean, variance and probability of an extreme outcome, compared with their female counterparts. The results also suggest that heart and cardiovascular diseases affect the expenditure of males more than that of females. Copyright 2011 John Wiley & Sons, Ltd.

Other

Keane, M., Savage, E.J., Stavrunova, O. & Jones, G. 2010, 'Hospital waiting times and the demand for private health insurance'.
Johar, M., Jones, G., Keane, M., Savage, E. & Stavrunova, O., 'Waiting times and the decision to buy private health insurance. CHERE Working Paper 2010/9'.
Over 45% of Australians buy health insurance for private treatment in hospital. This is despite having access to universal and free public hospital treatment. Anecdotal evidence suggests that one possible explanation for the high rate of insurance coverage is to avoid long waiting times for public hospital treatment. In this study, we investigate the effect of expected waiting time on individual decisions to buy private health insurance. Individuals are assumed to form an expectation of their own waiting time as a function of their demographics and health status. We estimate models of expected waiting time using administrative data on the population hospitalised for elective procedures in public hospitals in 2004-05 and use the parameter estimates to impute expected waiting times for individuals in a representative sample of the population. We model the impact of expected waiting time on the decision to purchase private health insurance. In the insurance demand model, cross-sample predictions are adjusted by the individuals? probability of hospital admission. We find that expected waiting time does not increase the probability of buying insurance but a high probability of experiencing a long wait does. Overall we find there is no significant impact of waiting time on insurance purchase. In addition, we find that the inclusion of individual waiting time variables removes the evidence for favourable selection into private insurance, as measured by self-assessed health. This result suggests that a source of the favourable selection by reported health status may be aversion to long waits among healthier people.
Johar, M., Jones, G., Keane, M., Savage, E. & Stavrunova, O., 'Differences in waiting times for elective admissions in NSW public hospitals: A decomposition analysis by non-clinical factors. CHERE Working Paper 2010/7'.
In the Australian public health system, access to elective surgery is rationed through provision of health care services, it is generally assumed that a patient?s waiting time and locations. In this paper we undertake Oaxaca-Blinder and DiNardo-Fortin-Lemieux decompostition analyses to attribute variation in waiting time to a component explained by clinical need and to differential treatment effects. The latter have an interpretation as discrimination, since treatments vary by non-clinical factors such as socioeconomic status. Using data from public patients in NSW public hospitals in 2004-2005, we find socioeconomically advantaged patients, patients in remote areas, and patients in several Area Health Services have shorter waiting times than their clinical comparable counterparts. Furthermore, the discrimination effect dominates clinical admission if their treatments are delayed. This finding has policy implications for the current operation of waiting lists and order of admission and for the design of equitable quality targets for public hospitals.
Johar, M., Jones, G., Keane, M., Savage, E. & Stavrunova, O., 'The demand for private health insurance: do waiting lists or waiting times matter? CHERE Working Paper 2010/8'.
Besley, Hall, and Preston (1999) estimated a model of the demand for private health insurance in Britain as a function of regional waiting lists and found that increases in the number of people waiting for more than 12 months (the long-term waiting list) increased the probability of insurance purchase. In the absence of waiting time data, the length of regional long-term waiting lists was used to capture the price-quality trade-off of public treatment. We revisit Besley et al.?s analysis using Australian data and test the use of waiting lists as a proxy for waiting time in models of insurance demand. Unlike Besley et al., we find that the long-term waiting list is not a significant determinant of the demand for insurance. However we find that long waiting times do significantly increase insurance. This suggests that the relationship between waiting times and waiting lists is not as straightforward as is commonly assumed.