Trouble in the Tails? What We Know about Earnings Nonresponse 30 Years after Lillard, Smith, and Welch
利用当前人口调查与行政收入数据的链接记录，发现收入无应答呈U形分布，导致收入差距和不平等测量出现偏差，尤其在尾部。
Earnings nonresponse in household surveys is widespread, yet there is limited knowledge of how nonresponse biases earnings measures. We examine the consequences of nonresponse on earnings gaps and inequality using Current Population Survey individual records linked to administrative earnings data. The common assumption that earnings are missing at random is rejected. Nonresponse across the earnings distribution is U-shaped, highest in the left and right tails. Inequality measures differ between household and administrative data due in part to nonresponse. Nonresponse biases earnings differentials by race, gender, and education, particularly in the tails. Flexible copula-based models can account for nonrandom nonresponse.