Labor Market Power

Berger, David; Herkenhoff, Kyle; Mongey, Simon

doi:10.3386/w25719

Cited by 112 publications

(72 citation statements)

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“…An emerging literature studies the effects of firms' power in setting wages. Firms can hire and retain workers at wages lower than the competitive fringe if they are large in a market (Berger et al 2019;Jarosch et al 2019) or if they do not face much competition from other employers, either due to contractual restrictions on job mobility (Johnson et al 2019) or other frictions. One interpretation of the estimated differences in outside contacts is about employers' ability to restrict poaching.…”

Section: Resultsmentioning

confidence: 99%

Anatomy of Lifetime Earnings Inequality: Heterogeneity in Job Ladder Risk vs. Human Capital

2019

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractWe study the determinants of lifetime earnings (LE) inequality in the United States, for which differences in lifetime earnings growth are key. Using administrative data and focusing on the roles of job ladder dynamics and on-the-job learning, we document that 1) lower LE workers change jobs more often, mainly driven by higher nonemployment; 2) earnings growth for job stayers is similar at around 2 percent in the bottom two-thirds of the LE distribution, whereas for job switchers it rises with LE; and 3) top LE workers enjoy high earnings growth regardless of job switching. We estimate a job ladder model with on-the-job learning featuring ex ante heterogeneity in learning ability and job ladder risk-job loss, job finding, and contact rates. We find that learning ability differences explain almost all earnings growth heterogeneity above the median, whereas ex ante heterogeneity in job ladder risk accounts for 80 percent of LE growth differences below the median.1 Next, we investigate average earnings growth for job stayers and switchers across the LE distribution. We find that average growth for job stayers is surprisingly similar at around 2% in the bottom two thirds of the LE distribution, and increases steeply in the top tercile, reaching around 10% at the top quantile of the LE distribution. As for the average earnings growth of job switchers, we find much larger heterogeneity across the LE quantiles: It rises almost linearly from zero for the bottom LE quantile to around 4% for the 90th percentile, after which it accelerates to 10% for the top LE individuals. This large heterogeneity indicates that the nature of job switches is very different throughout the LE distribution. In fact, we argue that more than 35% of job switches are a result of a significant unemployment spell for bottom LE individuals, compared to only around 15% for the 90th percentile workers. These facts imply that differences in lifetime earnings growth in the bottom half of the LE distribution are coming from growth differences of job switchers, whereas stayer growth differences should be the main culprit in the upper half, as high LE workers rarely switch employers. Building on this intuition, we develop a structural model to disentangle the economic forces that shape the distributions of the earnings growth of job stayers and switchers across the LE distribution. Specifically, we estimate a job ladder model with two-sided heterogeneity in the spirit of Cahuc et al. (2006) ...

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Section: Resultsmentioning

confidence: 99%

Anatomy of Lifetime Earnings Inequality: Heterogeneity in Job Ladder Risk vs. Human Capital

2019

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“…Our paper builds on a large literature. The recent evolution of the capital share has commanded much attention (e.g., Autor et al, 2017;Barkai, 2017;Berger et al, 2019;De Loecker and Eeckhout, 2017;Elsby et al, 2013;Karabarbounis and Neiman, 2014;Koh et al, 2015;Oberfield and Raval, 2014). Some of the proposed explanations highlight technological change, the fall in the relative price of capital, increases in firm concentration, globalization, or the role of intellectual property products.…”

Section: Introductionmentioning

confidence: 99%

Bargaining Shocks and Aggregate Fluctuations

Drautzburg¹,

Fernández-Villaverde²,

Guerrón‐Quintana³

2020

Working Paper (Federal Reserve Bank of Philadelphia)

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We argue that social and political risk causes significant aggregate fluctuations by changing bargaining power. To that end, we document significant changes in the capital share after large political events, such as political realignments, modifications in collective bargaining rules, or the end of dictatorships, in a sample of developed and emerging economies. These policy changes are associated with significant fluctuations in output. Using a Bayesian proxy-VAR estimated with U.S. data, we show how distribution shocks cause movements in output and unemployment. To quantify the importance of these political shocks for the U.S. as a whole, we extend an otherwise standard neoclassical growth model. We model political shocks as exogenous changes in the bargaining power of workers in a labor market with search and matching. We calibrate the model to the U.S. corporate non-financial business sector and we back out the evolution of the bargaining power of workers over time using a new methodological approach, the partial filter. We show how the estimated shocks agree with the historical narrative evidence. We document that bargaining shocks account for 28% of aggregate fluctuations and have a welfare cost of 2.4% in consumption units.

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“…8 The National Establishment Time Series (NETS) dataset covers the universe of U.S. firms and their plants. 9 The dataset includes sales and employment numbers of all plants at different levels of geographic and industrial disaggregation down to the SIC 8 product code. Neumark, Zhang and Wall (2006), and Barnatchez, Crane and Decker (2017), provide thorough discussions of the advantages and disadvantages of this data source relative to U.S. Census data.…”

Section: Introductionmentioning

confidence: 99%

Diverging Trends in National and Local Concentration

Rossi‐Hansberg¹,

Sarte²,

Trachter³

2018

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Using U.S. NETS data, we present evidence that the positive trend observed in national productmarket concentration between 1990 and 2014 becomes a negative trend when we focus on measures of local concentration. We document diverging trends for several geographic definitions of local markets. SIC 8 industries with diverging trends are pervasive across sectors. In these industries, top firms have contributed to the amplification of both trends. When a top firm opens a plant, local concentration declines and remains lower for at least 7 years. Our findings, therefore, reconcile the increasing national role of large firms with falling local concentration, and a likely more competitive local environment.

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Labor Market Power

Cited by 112 publications

References 0 publications

Anatomy of Lifetime Earnings Inequality: Heterogeneity in Job Ladder Risk vs. Human Capital

Anatomy of Lifetime Earnings Inequality: Heterogeneity in Job Ladder Risk vs. Human Capital

Bargaining Shocks and Aggregate Fluctuations

Diverging Trends in National and Local Concentration

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