Geographic space is an important friction preventing the instantaneous matching of unemployed workers to job vacancies. Cities reduce spatial frictions by decreasing the average distance between potential match partners. Owing to these search efficiencies, theories of agglomeration predict that unemployed workers in larger labor markets find employment faster than observationally similar workers in smaller markets. Existing studies rely on cross-sectional variation in aggregate unemployment rates across spatially distinct labor markets to test for scale effects in job search. A major difficulty with these studies is that the unemployment rate is, at any given time, simultaneously the incidence and duration of unemployment. Therefore, conclusions about unemployment exits using the unemployment rate are confounded by transitions into unemployment. This dissertation examines the relationship between market scale unemployment duration for permanently laid off workers in the U.S. Using a large sample of individual unemployment spells in 259 MSAs, proportional hazard model estimates predict a negative relationship between market scale and the hazard of exiting unemployment. This effect is strengthened when space is explicitly controlled for and measured with greater precision. These results are consistent with the hypothesis that search efficiencies lead workers to increase their reservation wages. 2SLS estimates show that re-employment earnings for permanently laid off workers increase with market scale after controlling for endogenous search duration. These effects are robust to standard controls, as well as controls for local labor market conditions. These results challenge the view that search efficiencies lead to lower unemployment rates through faster job-finding rates.