Harris School of Public Policy, University of Chicago, Chicago, IL 60637, USA.
Eval Rev. 2012 Dec;36(6):449-74. doi: 10.1177/0193841X13482125.
Social experiments frequently exploit data from administrative records. However, most administrative data systems are designed to track earnings or benefit payments among residents within a single state. When an experimental participant moves across state lines, his entries in the data system of his state of origin consist entirely of zeros. Such attrition may bias the estimated effect of the experiment.
To estimate the attrition arising from interstate mobility and provide bounds on the effect of the experiment.
Attrition is estimated from runs of zeros at the end of the sample period. Bounds are constructed from these estimates. These estimates can be refined by imposing a stationarity assumption.
The width of the estimated bounds depends importantly on the nature of the data being analyzed. Negatively correlated outcomes provide tighter bounds than positively correlated outcomes.
Attrition can introduce considerable ambiguity into the estimated effects of experimental programs. To reduce ambiguity, one should collect as much data as possible. Even data on outcomes of no direct interest to the objectives of the experiment may be valuable for reducing the ambiguity that arises due to attrition.
社会实验经常利用行政记录中的数据。然而,大多数行政数据系统是为了跟踪一个州内居民的收入或福利支付而设计的。当实验参与者跨越州界迁移时,他在原籍州数据系统中的记录完全为零。这种流失可能会影响实验效果的估计。
估计因州际流动造成的流失,并提供实验效果的界限。
从样本期末的零记录中估计流失情况。从这些估计中构建界限。通过施加平稳性假设,可以对这些估计进行细化。
估计界限的宽度取决于正在分析的数据的性质。负相关的结果比正相关的结果提供更紧的界限。
流失可能会给实验计划的估计效果带来相当大的模糊性。为了减少模糊性,应该尽可能多地收集数据。即使是对实验目标没有直接兴趣的结果数据,也可能对减少因流失而产生的模糊性有价值。