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.

Lecturer, Economics Discipline Group
Associate Member, Centre for the Study of Choice
MArts in Economics, PhD
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Phone
+61 2 9514 3597
Fax
+61 2 9514 7711
Room
CM05D.02.25A

Research Interests

Labor Economics, Applied Econometrics, Bayesian Econometrics

Conference Papers

Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2010, 'Expected waiting times and the decision to buy private health insurance', 1st Australasian Workshop on Econometrics and Health Economics, Melbourne, March 2010.
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?', American Society of Health Economists Conference, Cornell University, USA, June 2010.
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?', European Conference of Health Economics, Helsinki, Finland, July 2010.
Stavrunova, O. 2010, 'Adverse selection, moral hazard and the demand for medigap insurance', 13th Australian Labour Econometrics Workshop, Melbourne, Australia, August 2010.
Stavrunova, O. 2010, 'Adverse selection, moral hazard and the demand for medigap insurance', 19th European Workshop on Econometrics and Health Economics, Lausanne, Switzerland, September 2010.
Stavrunova, O. 2010, 'Adverse selection, moral hazard and the demand for medigap insurance', Australian Health Economics Society Conference, Sydney, Australia, September 2010.
Stavrunova, O. 2009, 'Equilibrium model of waiting times for non-emergency procedures in the NSW public hospitals', 31st Australian Conference for Health Economists, Hobart, Australia, October 2009.
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', Labour Econometrics Workshop, Brisbane, August 2009.
Stavrunova, O. 2009, 'Equilibrium model of waiting times for non-emergency procedures in the NSW public hospitals', III World Conference of Spatial Econometrics, Barcelona, Spain, July 2009.

Journal Articles

Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2013, 'Discrimination in a universal health system: Explaining socioeconomic waiting times 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.
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.
Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 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.
Johar, M., Jones, G., Keane, M., Savage, E.J. & Stavrunova, O. 2011, 'Waiting times for elective surgery and the decision to buy private health insurance', Health Economics, vol. 20, no. S1, 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.
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.
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.