arXiv 23 Apr 2026 · Econometrics
arXiv:2604.21258 · PDF · DOI · OpenAlex · Extracted main text
Survey data are widely used to study how income inequality, poverty, and welfare evolve over time. A common practice is to estimate the income distribution separately for each year, treating annual observations as independent cross-sections. For population subgroups with relatively small sample sizes, however, this approach can produce unstable parameter estimates, imprecise inference for inequality and poverty measures, and potentially misleading posterior probabilities of Lorenz and stochastic dominance. This paper develops flexible Bayesian models for time-varying income distributions that borrow strength across adjacent years by allowing the parameters of income distributions to evolve dynamically. We consider a random walk specification and an extended model with shrinkage priors. The proposed framework yields coherent inference for the full income distributions over time, as well as for associated inequality measures, poverty indices, and dominance probabilities. Simulation studies show that, relative to independent year-by-year models, the proposed approach produces substantially more precise and stable inference, while avoiding spurious variation in welfare comparisons. An application to the Aboriginal and residents of the Australian Capital Territory (ACT) population subgroups in the Household, Income and Labour Dynamics in Australia survey shows that the dynamic models deliver improved inference for income distributions and related welfare measures, and can change conclusions about distributional dominance over time.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dagum, C (1977) A new model of personal income distribution: Specification and estimation | 0.737 | 3 | 3 | 67% |
| 2 | Gunawan, D., Griffiths, W. E., and Chotikapanich, D (2021) Posterior probabilities for Lorenz and stochastic dominance of Australian income distributions self | 0.737 | 3 | 3 | 67% |
| 3 | Cowell, F. A (2011) Measuring Inequality | 0.644 | 2 | 2 | 100% |
| 4 | Foster, J., Greer, J., and Thorbecke, E (1984) A class of decomposable poverty measures | 0.644 | 2 | 2 | 100% |
| 5 | Barrett, G. F. and Donald, S. G (2003) Consistent tests for stochastic dominance | 0.644 | 2 | 2 | 100% |
| 6 | Barrett, G. F., Donald, S. G., and Bhattacharya, D (2014) Consistent nonparametric tests for Lorenz dominance | 0.644 | 2 | 2 | 100% |
| 7 | Carvalho, C. M., Polson, N. G., and Scott, J. G (2010) The horseshoe estimator for sparse signals | 0.644 | 2 | 2 | 100% |
| 8 | Singh, S. K. and Maddala, G. S (1976) A function for the size distribution of incomes | 0.511 | 2 | 2 | 50% |
| 9 | Lander, D., Gunawan, D., Griffiths, W., and Chotikapanich, D (2020) Bayesian assessment of Lorenz and stochastic dominance self | 0.511 | 2 | 2 | 50% |
| 10 | Makalic, E. and Schmidt, D. F (2015) A simple sampler for the horseshoe estimator | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 25 scored citations.