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Many argue that minimum wages can prevent efficiency losses from monopsony power. We assess this argument in a general equilibrium model of oligopsonistic labor markets with heterogeneous workers and firms. We decompose welfare gains into anefficiencycomponent that captures reductions in monopsony power and aredistributivecomponent that captures the way minimum wages shift resources across people. The minimum wage that maximizes the efficiency component of welfare lies below $8.00 and yields gains worth less than 0.2% of lifetime consumption. When we add back in Utilitarian redistributive motives, the optimal minimum wage is $11 and redistribution accounts for 102.5% of the resulting welfare gains, implying offsetting efficiency losses of −2.5%. The reason a minimum wage struggles to deliver efficiency gains is that with realistic firm productivity dispersion, a minimum wage that eliminates monopsony power at one firm causes severe rationing at another. These results hold under an EITC and progressive labor income taxes calibrated to the U.S. economy. 

Progressive income taxes distort hiring and wages when firms have labor market power. We characterize this novel monopsony cost of progressivity in a simple monopsony economy and derive efficiency wedges that depend on progressivity. A simple quantification of these wedges points to the possibility that the monopsony cost may be of similar magnitudes to redistribution and insurance benefits. 

We develop, estimate, and test a tractable general equilibrium model of oligopsony with differentiated jobs and concentrated labor markets. We estimate key model parameters by matching new evidence on the relationship between firms’ local labor market share and their employment and wage responses to state corporate tax changes. The model quantitatively replicates quasi-experimental evidence on imperfect productivity-wage pass-through and strategic wage setting of dominant employers. Relative to the efficient allocation, welfare losses from labor market power are 7.6 percent, while output is 20.9 percent lower. Lastly, declining local concentration added 4 percentage points to labor’s share of income between 1977 and 2013. (JEL E25, H71, J24, J31, J42, R23) 

Abstract Predicting if, when, and how populations can adapt to climate change constitutes one of the greatest challenges in science today. Here, we build from contributions to the special issue on evolutionary adaptation to climate change, a survey of its authors, and recent literature to explore the limits and opportunities for predicting adaptive responses to climate change. We outline what might be predictable now, in the future, and perhaps never even with our best efforts. More accurate predictions are expected for traits characterized by a well-understood mapping between genotypes and phenotypes and traits experiencing strong, direct selection due to climate change. A meta-analysis revealed an overall moderate trait heritability and evolvability in studies performed under future climate conditions but indicated no significant change between current and future climate conditions, suggesting neither more nor less genetic variation for adapting to future climates. Predicting population persistence and evolutionary rescue remains uncertain, especially for the many species without sufficient ecological data. Still, when polled, authors contributing to this special issue were relatively optimistic about our ability to predict future evolutionary responses to climate change. Predictions will improve as we expand efforts to understand diverse organisms, their ecology, and their adaptive potential. Advancements in functional genomic resources, especially their extension to non-model species and the union of evolutionary experiments and “omics,” should also enhance predictions. Although predicting evolutionary responses to climate change remains challenging, even small advances will reduce the substantial uncertainties surrounding future evolutionary responses to climate change. 

Abstract Dispersal is a central life history trait that affects the ecological and evolutionary dynamics of populations and communities. The recent use of experimental evolution for the study of dispersal is a promising avenue for demonstrating valuable proofs of concept, bringing insight into alternative dispersal strategies and trade‐offs, and testing the repeatability of evolutionary outcomes.Practical constraints restrict experimental evolution studies of dispersal to a set of typically small, short‐lived organisms reared in artificial laboratory conditions. Here, we argue that despite these restrictions, inferences from these studies can reinforce links between theoretical predictions and empirical observations and advance our understanding of the eco‐evolutionary consequences of dispersal.We illustrate how applying an integrative framework of theory, experimental evolution and natural systems can improve our understanding of dispersal evolution under more complex and realistic biological scenarios, such as the role of biotic interactions and complex dispersal syndromes.