Revisiting U.S. Wage Inequality at the Bottom 50%

While inequality at the top half of the wage distribution has been rising steadily since 1980, inequality at the bottom of the distribution has been unstable: it increased in the early 1980s, decreased in the 1990s, and then moderately increased since the 2000s. Several papers have argued that these trends are the result of a routine-biased-technological-change (RBTC). Models of RBTC predict a decline in middle-wages(“Wage Polarization”) which generates a decline in bottom-half inequality as occurred during the 1990s. However, these models cannot explain why inequality at the bottom 50% resumed growing. They also do not explain why specifically middle-wages declined when routine workers are dispersed across the entire bottom half of the wage distribution. Earlier decomposition exercises argued that RBTC cannot explain these wage trends in full. I show that a small-yet-important refinement to the RBTC model can resolve all these puzzles. Instead of assuming technology replaces workers, I assume it replaces their skill. At first, skill-replacing RBTC (SR-RBTC) lowers wages for middle-wage workers since they have the highest skill among routine workers. Middle-wage workers then leave routine occupations. When SR-RBTC continues it reduces wages for the remaining routine-workers who are mostly low-wage, and in-equality at the bottom 50% resumes growing. I test the model predictions using an interactive-fixed-effect-model. I find that the return to skill sharply declined in routine occupations and the composition of routine workers became less skilled. Finally, I develop a new decomposition method, “Skewness Decomposition”, to show that the drop in inequality at routine occupations is the main driver of wage polarization. This was not captured with other decomposition methods as it violates the ignorability assumption that underlies them

Productivity and Selection of Human Capital with Machine Learning

Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired—which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications—police hiring and teacher tenure decisions

In Vivo Expansion of Cancer Stemness Affords Novel Cancer Stem Cell Targets: Malignant Rhabdoid Tumor as an Example

Summary: Cancer stem cell (CSC) identification relies on transplantation assays of cell subpopulations sorted from fresh tumor samples. Here, we attempt to bypass limitations of abundant tumor source and predetermined immune selection by in vivo propagating patient-derived xenografts (PDX) from human malignant rhabdoid tumor (MRT), a rare and lethal pediatric neoplasm, to an advanced state in which most cells behave as CSCs. Stemness is then probed by comparative transcriptomics of serial PDXs generating a gene signature of epithelial to mesenchymal transition, invasion/motility, metastasis, and self-renewal, pinpointing putative MRT CSC markers. The relevance of these putative CSC molecules is analyzed by sorting tumorigenic fractions from early-passaged PDX according to one such molecule, deciphering expression in archived primary tumors, and testing the effects of CSC molecule inhibition on MRT growth. Using this platform, we identify ALDH1 and lysyl oxidase (LOX) as relevant targets and provide a larger framework for target and drug discovery in rare pediatric cancers. : Golan et al. demonstrate that long-term propagation of human MRT xenografts robustly enriches for cancer stem cell frequency. This was exploited in turn for the identification of potential therapeutic targets in MRT such as lysyl oxidase and disclosed a platform to identify CSC targets in other rare pediatric tumors for which novel therapeutics are sought. Keywords: stem cells, cancer stem cells, PDX, MRT, targeted therapy, ALDH1, LOX inhibitio