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Goldhaber, Dan; Theobald, Roddy – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2020
One of the unfortunate consequences of the COVID-19 pandemic is that the sharp downturn in tax revenues, in the absence of a federal bailout, likely foretells unprecedented cuts in state and local budgets. This will in turn mean large cuts in teaching positions across the country; indeed, some projections suggest that the number of teacher layoffs…
Descriptors: Crisis Management, Disease Incidence, Public Health, Job Layoff
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Goldhaber, Dan; Theobald, Roddy – Education Finance and Policy, 2013
Over 2,000 teachers in the state of Washington received reduction in force (RIF) notices across the 2008-09 and 2009-10 school years. We link data on these RIF notices to an administrative data set that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF…
Descriptors: Job Layoff, Teachers, Prediction, Employment Level
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Goldhaber, Dan; Theobald, Roddy – Education Next, 2011
Tough economic times mean tight school district budgets, possibly for years to come. Education is a labor-intensive industry, and because most districts devote well over half of all spending to teacher compensation, budget cuts have already led to the most substantial teacher layoffs in recent memory. Although the 2010 federal Education Jobs and…
Descriptors: School District Spending, Teacher Effectiveness, Teacher Dismissal, Academic Achievement
Goldhaber, Dan; Theobald, Roddy – Center for Education Data & Research, 2011
Over 2,000 teachers in the state of Washington received reduction-in-force (RIF) notices across the 2008-09 and 2009-10 school years. We link data on these RIF notices to an administrative dataset that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF notice.…
Descriptors: Teaching (Occupation), Economic Climate, Structural Unemployment, Job Layoff
Goldhaber, Dan; Theobald, Roddy – National Center for Analysis of Longitudinal Data in Education Research, 2010
Over 2000 teachers in the state of Washington received reduction-in-force (RIF) notices in the past two years. The authors link data on these RIF notices to a unique dataset that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF notice. They find a teacher's…
Descriptors: Teacher Effectiveness, Job Layoff, Teacher Competencies, Predictor Variables