Pith. sign in

REVIEW 1 cited by

Teams: Heterogeneity, Sorting, and Complementarity

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2102.01802 v1 pith:ACOPBFDL submitted 2021-02-02 econ.EM

classification econ.EM
keywords contributionsoutputproposeteamscomplementarityestimateheterogeneityindividual
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

How much do individuals contribute to team output? I propose an econometric framework to quantify individual contributions when only the output of their teams is observed. The identification strategy relies on following individuals who work in different teams over time. I consider two production technologies. For a production function that is additive in worker inputs, I propose a regression estimator and show how to obtain unbiased estimates of variance components that measure the contributions of heterogeneity and sorting. To estimate nonlinear models with complementarity, I propose a mixture approach under the assumption that individual types are discrete, and rely on a mean-field variational approximation for estimation. To illustrate the methods, I estimate the impact of economists on their research output, and the contributions of inventors to the quality of their patents.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Indirect Variational Inference: Applications to Earnings Dynamics

    econ.GN 2026-07 conditional novelty 7.0 of 10

    Indirect variational inference treats a variational approximation as an auxiliary model and inverts its binding function, correcting the bias of variational estimates in nonlinear earnings dynamics.

Pith tools