In this session the joint winners of the 2023 Cochran Hansen Prize will present their work. 𝗔𝗹𝗲𝗷𝗮𝗻𝗱𝗿𝗮 𝗔𝗿𝗶𝗮𝘀-𝗦𝗮𝗹𝗮𝘇𝗮𝗿 will present a paper on small area estimation of poverty incidence in Costa Rica under a Structure Preserving Estimation (SPREE) approach and 𝗭𝗶𝗾𝗶𝗻𝗴 𝗗𝗼𝗻𝗴 a paper on linearization and variance estimation of the Bonferroni inequality index.

Alejandra Arias Salazar: Small area estimation of poverty incidence in Costa Rica under a Structure Preserving Estimation (SPREE) approach. 
 
Obtaining reliable estimates in small areas is a challenge because of the coverage and periodicity of data collection. Several techniques of small area estimation have been proposed to produce quality measures in small areas, but few of them are focused on updating these estimates. By combining the attributes of the most recent versions of the structure-preserving estimation methods, this paper proposes a new alternative to estimate and update cross-classified counts for small domains, when the variable of interest is not available in the census. The proposed methodology is used to obtain and update
estimates of the incidence of poverty in 81 Costa Rican cantons for six
postcensal years (2012 - 2017). As uncertainty measures, mean squared errors are estimated via parametric bootstrap, and the adequacy of the proposed method is assessed with a design-based simulation.

Ziqing Dong: Linearization and variance estimation of the Bonferroni inequality index.

The study of income inequality is important for predicting the wealth of a country. There is an increasing number of publications where the authors call for the use of several indices simultaneously to better account for the wealth distribution. Due to the fact that income data are usually collected through sample surveys, the sampling properties
of income inequality measures should not be overlooked. The most widely used inequality measure is the Gini index, and its inferential aspects have been deeply investigated.  An alternative inequality index could be the Bonferroni inequality index, although less attention on its inference has been paid in the literature. The aim of this paper is to address the inference of the Bonferroni index in a finite population
framework. The Bonferroni index is linearized by differentiation with respect to the sample indicators which allows for conducting a valid inference. Furthermore, the linearized variables are used to evaluate the effects of the different observations on the Bonferroni and Gini indices.
The result demonstrates once for all that the former is more sensitive to the lowest incomes in the distribution than the latter.