Does working in a low-wage job lead to increased opportunities for upward mobility, or is it a dead-end that traps workers? In this article, we examine whether low-wage jobs are “stepping-stones” that enable workers to move to higher-paid jobs that are linked by institutional mobility ladders and skill transferability. To identify occupational linkages, we create two measures of occupational similarity using data on occupational mobility from matched samples of the Current Population Survey (CPS) and data on multiple dimensions of job skills from the O*NET. We test whether work experience in low-wage occupations increases mobility between linked occupations that results in upward wage mobility. Our analysis uses longitudinal data on low-wage workers from the 1979 National Longitudinal Study of Youth (NLSY) and the 1996 to 2008 panels of the Survey of Income and Program Participation (SIPP). We test the stepping-stone perspective using multinomial conditional logit (MCL) models, which allow us to analyze the joint effects of work experience and occupational linkages on achieving upward wage mobility. We find evidence for stepping-stone mobility in certain areas of the low-wage occupational structure. In these occupations, low-wage workers can acquire skills through work experience that facilitate upward mobility through occupational changes to skill and institutionally linked occupations.

Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate use of pretreatment covariates can further improve the estimation efficiency. Missingness in covariates is nevertheless common in practice, and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The unadjusted difference in means is always unbiased. The complete-covariate analysis adjusts for all completely observed covariates, and is asymptotically more efficient than the difference in means if at least one completely observed covariate is predictive of the outcome. Then what is the additional gain of adjusting for covariates subject to missingness? To reconcile the conflicting recommendations in the literature, we analyze and compare five strategies for handling missing covariates in randomized experiments under the design-based framework, and recommend the missingness-indicator method, as a known but not so popular strategy in the literature, due to its multiple advantages. First, it removes the dependence of the regression-adjusted estimators on the imputed values for the missing covariates. Second, it does not require modeling the missingness mechanism, and yields consistent estimators even when the missingness mechanism is related to the missing covariates and unobservable potential outcomes. Third, it ensures large-sample efficiency over the complete-covariate analysis and the analysis based on only the imputed covariates. Lastly, it is easy to implement via least squares. We also propose modifications to it based on asymptotic and finite sample considerations. Importantly, our theory views randomization as the basis for inference, and does not impose any modeling assumptions on the data-generating process or missingness mechanism. Supplementary materials for this article are available online.