Statistical Inference in Practice
Assessing statistically the extent of poverty and equity in a distribution, or checking for distributive differences, usually involves three steps. First, one formulates hypotheses of interest, such as that the poverty headcount is less than 20%, or that tax equity has increased over time, or that inequality is greater in one country than in another. Second, one computes distributive statistics, weighting observations by their sampling weights and (when appropriate) by a size variable. Third, one uses these statistics to test the hypotheses of interest. This last step can involve testing the hypotheses directly, or building confidence intervals of where we can confidently locate the true population values of interest. This third step may allow for the effects of survey design on the sampling distributions of distributive indices and test statistics, and may also involve performing numerical simulations of such sampling distributions, if the circumstances make it desirable to do so.
KeywordsStatistical Inference Sampling Distribution Stochastic Dominance Lorenz Curve Poverty Index
Unable to display preview. Download preview PDF.
- Aaberge, R. (2001b): “Sampling Errors and Cross-Country Comparisons of Income Inequality,” Journal of Income Distribution, 10, 69–76.Google Scholar
- Bahadur, R. (1966): “A Note on Quantiles in Large Samples,” Annals of Mathematical Statistics, 37.Google Scholar
- Bishop, J. and J. Formby (1999): “Tests of Significance for Lorenz Partial Orders,” in Handbook of income inequality measurement. With a foreword by Amartya Sen, ed. by J. Silber, Boston; Dordrecht and London: Kluwer Academic, Recent Economic Thought Series, 315–36.Google Scholar
- Bishop, J., J. Formby, and P. Thistle (1992): “Convergence of the South and Non-South Income Distributions, 1969–1979,” American Economic Review, 82, 262–72.Google Scholar
- Cowell, F. (1999): “Estimation of Inequality indices,” in Handbook of income inequality measurement. With a foreword by Amartya Sen, ed. by J. Silber, Boston; Dordrecht and London: Kluwer Academic, Recent Economic Thought, 269–86.Google Scholar
- Davies, J., D. Green, and H. Paarsch (1998): “Economic Statistics and Social Welfare Comparisons; A Review,” in Handbook of applied economic statistics, ed. by A. Ullah and D. E. A. Giles, Statistics: Textbooks and Monographs, vol. 155. New York; Base and Hong Kong: Dekker, 1–38.Google Scholar
- Hentschel, J., J. Lanjouw, P. Lanjouw, and J. Poggiet (2000); “Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty; A Case Study of Ecuador,” World Bank Economic Review, 14, 147–65.Google Scholar
- Horrace, W., P. Schmidt, and A. Witte (1995): “Sampling Errors and Confidence Intervals for Order Statistics: Implementing the Family Support Act,” National Bureau of Economic Research, 32.Google Scholar
- Ryu, H. and D. Slottje (1999): “Parametric Approximations of the Lorenz Curve,” in Handbook of income inequality measurement. With a foreword by Amartya Sen. Recent Economic Thought Series, ed. by J. Silber, Boston; Dordrecht and London: Kluwer Academic, 291–312.Google Scholar
- Xu, K. and L. Osberg (1998): A Distribution-Free Test for Deprivation Dominance, Halifax: Department of Economics, Dalhousie University.Google Scholar