Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch - Christopher R. Bollinger; Barry Hirsch; Charles M. Hokayem; James P. Ziliak (Institute of Labor Economics (IZA), 2018)
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.
Keywords: J31; C8; D31; 330; CPS ASEC; nonresponse bias; copula; measurement error; hot deck imputation; proxy reports; earnings inequality