Mary E. Laski

Researchers have posited various theories to explain supposed declines in teaching quality: the expansion of labor market opportunities for women, low relative wages, compressed compensation structures, and substituting quantity for quality. We synthesize these previous theories and expand on the current literature by incorporating a useful comparison group: the nursing workforce. We document historical trends in skill level, average and relative wages, wage dispersion, unionization rates, and quantity, and find important divergences in the teaching and nursing professions that cannot be explained by previous theories. We posit two new theories that align with our documented trends: technological innovation and occupational differentiation in nursing. We argue that trends in the nursing profession indicate that declines in teaching quality were (and are) not inevitable.

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Teacher shortages are a persistent challenge in the United States. I evaluate the effectiveness of an innovative pilot program that allowed principals to hand-select experienced staff members and paraeducators already working in schools to lead classrooms. Pilot educators are predominantly Black or African American. Districts reported randomly assigning students to teachers, and my analysis cannot reject randomization. Controlling for demographics and baseline scores, I find that students assigned to these pilot teachers perform just as well as those assigned to traditionally licensed teachers on average and outperform their peers in math. My results point to an untapped resource of potential teachers and underscore the value of principals’ local knowledge to identify capable candidates for teaching positions.

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We propose a new method for estimating school-level characteristics from publicly available census data. We use a school’s location to impute its catchment area by aggregating the nearest n census block groups such that the number of school-aged children in those n block groups is just over the number of students enrolled in that school. We then weight census data by the number of school-aged children in the block-group to estimate school-level measures. We conduct several robustness checks to assess the quality of our estimates and find that our method is broadly successful in replicating known school-level characteristics and producing unbiased estimates for school-level income. This method expands the available set of school-level variables to the broader and richer set of characteristics measured in the census, which can then be used to conduct descriptive and observational research across a long time horizon.

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