A Distributional Framework for Matched Employer Employee Data

Lamadon, Thibaut; Manresa, Elena

doi:10.3982/ecta15722

Cited by 188 publications

(200 citation statements)

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“…Bagger and Lentz (2014) use poaching patterns between firms to rank firms with respect to their productivity. More recently, Bonhomme et al (2017) develop a new approach to classify firms into discrete groups using a k-means algorithm. We follow a different approach based on the differences between average earnings growth for job stayers and switchers over the LE distribution.…”

Section: Identificationmentioning

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: Identificationmentioning

confidence: 99%

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

2019

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“…The result shows the presence of two types of bias for θ: the approximation bias B α (K) that vanishes as K increases, and a contribution akin to a form of incidental parameter bias that decreases at the rate 1/T . 16 In conditional models such as Example 2 the relevant approximation bias is B (α,µ) (K).…”

Section: Assumption 3 Requires the Individual Momentmentioning

confidence: 99%

“…be a conditional expectation across individuals (see the proof for details). Suppose in addition: 16 Although Theorem 1 is formulated in a likelihood setup, it holds more generally for M-estimators, inter-…”

Section: Assumption 3 Requires the Individual Momentmentioning

confidence: 99%

“…In Supplementary Appendix S3 we show the results of a small simulation exercise that illustrates the convergence rate in (16).…”

Section: Time-varying Unobserved Heterogeneitymentioning

confidence: 99%

“…The results align closely with Theorem 3. Indeed, according to (16) the rate of convergence consists of three terms: a term is fitted to the solid line. We see that the MSE of grouped fixed-effects and the theoretical fit align closely.…”

Section: S3 Additional Simulation Exercises S31 Time-varying Unobsermentioning

confidence: 99%

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Discretizing Unobserved Heterogeneity

2017

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We study panel data estimators based on a discretization of unobserved heterogeneity when individual heterogeneity is not necessarily discrete in the population. We focus on two-step grouped-fixed effects estimators, where individuals are classified into groups in a first step using kmeans clustering, and the model is estimated in a second step allowing for group-specific heterogeneity. We analyze the asymptotic properties of these discrete estimators as the number of groups grows with the sample size, and we show that bias reduction techniques can improve their performance. In addition to reducing the num-ber of parameters, grouped fixed-effects methods provide effective regularization. When allowing for the presence of time-varying unobserved heterogeneity, we show they enjoy fast rates of convergence depending of the underlying dimension of heterogeneity. Finally, we document the finite sample properties of two-step grouped fixedeffects estimators in two applications: a structural dynamic discrete choice model of migration, and a model of wages with worker and firm heterogeneity.JEL codes: C23, C38.

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Workplace heterogeneity and wage inequality in Denmark

Morin

2022

J of Applied Econometrics

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Summary Wage inequality is on the rise in most developed economies, and this phenomenon has fostered a growing body of research on its potential drivers. Using German data over the period 1985–2009, Card et al. (The Quarterly Journal of Economics 2013, 128(3), 967‐1015) argue that rising workplace heterogeneity has contributed substantially to the rise in wage inequality. I revisit their findings in two ways. First, because the generalization of their findings remains an open question, I apply their methodological approach to Danish register data and test whether rising workplace heterogeneity explains a significant share of the rise in wage inequality in Denmark. I find that, contrary to Germany, workplace heterogeneity remained practically stable over time, and this pattern contributed slightly negatively to the rise in wage inequality. Second, I complement Card et al.'s (2013) methods with the variance decomposition exercise proposed by Song et al. (2019) to identify more precisely the sources of the rise in wage inequality in Denmark. Although the rise in wage inequality is partly a between‐establishment phenomenon, I show that the strengthening of assortative matching patterns and the rising heterogeneity of workers within establishments are the main drivers of growing inequality.

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A Distributional Framework for Matched Employer Employee Data

Cited by 188 publications

References 76 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

Discretizing Unobserved Heterogeneity

Workplace heterogeneity and wage inequality in Denmark

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