Economics; econometrics; policy evaluations; individual and household behaviour
Xiaodong joined NATSEM in March 2011. Xiaodong has published in International and Australian journals such as Journal of Human Resources, Journal of Econometrics, Journal of Applied Econometrics, Economic Development and Cultural Change, The Economic Record and many other reputable journals. His work includes modelling labour supply, tax and transfer policy, childcare, labour mobility, child development and education, and household consumption.
Prior to joining NATSEM, Xiaodong has worked at the Institute for the Study of Labour (IZA) in Germany, The Australian National University, and the Australian Treasury. He is adjunct to the Crawford School of Public Policy, Australian National University and a Fellow of the IZA.
You can download a copy of Xiaodong's CV here.
- Freyens, B. and Gong, X. (2017), “Judicial Decision-Making under Changing Legal Standards”, Journal of Economic Behaviour and Organisation, 133, 1, pp. 108-126
- Gong, X. and Rao, M. (2016), “The economic impact of prolonged political instability: a case study of Fiji”, Policy Studies, 37, 4, pp. 370-386
- Gong, X., Kang, Y., Gao, J. and Qiu, P. (2015), “Jump Detection in Generalized Error-in-Variables Regression with an Application to Australian Health Tax Policies”, Annals of Applied Statistics, 9, 2, pp. 883-900
- Breunig, R., Gong, X., and Declan, T. (2014), “The new National Quality Framework: quantifying some of the effects on labour supply, child care demand and household finances for Two-Parent Households”, The Economic Record, 90, 288, pp. 1-16
- Gong, C., Kendig, H., Harding A., Miranti, R. and McNamara, J. (2014), “Economic Advantage and Disadvantage among Older Australians: Producing National and Small Area Profiles”, Australasian Journal of Regional Studies, 20, 3, pp. 512-539
Research Projects & Grants
Areas of Supervision:
- Policy evaluations
- Individual and household behaviour
Involvement in PhD Supervisory Panels:
- Wei Si, "To study the impact of the latest trends in digital and social networking technology using machine learning techniques. To apply the insights from the study to enhance the user experience on social networking sites - enabling political discourse and an empowering user experience in a digital economy" (Secondary Supervisor)