Suppose that a population, composed of a minority and a majority group, is allocated into units, which can be neighborhoods, firms, classrooms, etc. Qualitatively, there is some segregation whenever allocation leads to the concentration of minority individuals in some units more than in others. Quantitative measures of segregation have struggled with the small-unit bias. When units contain few individuals, indices based on the minority shares in units are upward biased. For instance, they would point to a positive amount of segregation even when allocation is strictly random. The command segregsmall implements three recent methods correcting for such bias: the nonparametric, partial identification approach of D’Haultfœuille and Rathelot (2017, Quantitative Economics 8: 39–73); the parametric model of Rathelot (2012, Journal of Business & Economic Statistics 30: 546–553); and the linear correction of Carrington and Troske (1997, Journal of Business & Economic Statistics 15: 402–409). The package also allows for conditional analyses, namely, measures of segregation accounting for characteristics of the individuals or the units.

假设将由少数群体和多数群体组成的人口分配到各个单元中，这些单元可以是邻里，公司，教室等。从质上讲，每当分配导致少数族裔个体更多地集中在某些单元中时，就会存在一定的隔离。在其他人。隔离的量化措施一直在与小单位偏见作斗争。当单位中只有很少的个人时，基于单位中少数股份的指数就会出现偏向。例如，即使分配是严格随机的，它们也会指出正的隔离量。segregsmall命令实施了三种纠正这种偏差的最新方法：D'Haultf？uille和Rathelot的非参数，部分识别方法（2017，定量经济学8：39-73）；Rathelot的参数模型（2012，商业与经济统计杂志30：546-553）；以及Carrington和Troske的线性校正（1997，《商业与经济统计杂志》 15：402–409）。一揽子计划还允许进行条件分析，即考虑个人或单位特征的隔离措施。