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arxiv: 2604.13224 · v1 · submitted 2026-04-14 · 💰 econ.GN · q-fin.EC

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Micro and Macro Perspectives on Production-Based Markups

John Fernald , Amit Gandhi , Dimitrije Ruzic , James Traina
Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:21 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC
keywords markupsproduction approachmarginal costmarket powermeasurement errorSolow residualfirm-level dataestimation methods
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The pith

The production-based markup is a residual whose estimates vary sharply with small implementation choices and potential mismeasurement.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews the production approach to estimating markups, which calculates the ratio of price to marginal cost without needing models of demand or market structure. The key insight is that this markup acts like a residual in production functions, making it theoretically clean but vulnerable to errors in specification and data. Small variations in how researchers implement the method can lead to very different conclusions about whether markups have increased over time. The authors explain these disagreements conceptually and offer practical advice on data use and estimation while urging more transparency about what part of the variation comes from technology rather than market power.

Core claim

The production approach treats the markup as the residual after accounting for inputs in a production function, similar to the Solow residual. This makes it scalable across firms and time but susceptible to contamination from misspecification or mismeasurement. Consequently, different studies using the same data arrive at opposing conclusions on markup trends, with some finding sharp rises and others none. The paper provides rationales for these differences and guidance to improve reliability.

What carries the argument

The production-based markup as a residual measure derived from production function estimation to recover the price over marginal cost ratio.

If this is right

  • Researchers should report sensitivity analyses for data and model choices to separate markup changes from technological factors.
  • Greater transparency about estimation details would help reconcile conflicting findings on market power.
  • If mismeasurement dominates, refined production methods could yield consistent markup estimates across micro and macro scales.
  • The importance of tracking market power persists, but requires isolating it from residual contamination.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standardizing estimation protocols across studies could reduce apparent conflicts between micro and macro markup views.
  • Linking this residual perspective to joint errors in productivity measurement might reveal shared biases in both quantities.
  • Simulated data experiments with known true markups could quantify how much contamination arises from common misspecifications.

Load-bearing premise

That the observed disagreements across studies are driven primarily by implementation choices and mismeasurement rather than deeper conceptual problems with the production approach itself.

What would settle it

Replicating markup estimates on the same dataset with fully standardized data cleaning, functional form assumptions, and estimation procedures that still produce opposing trend conclusions would show that implementation differences do not explain the disagreements.

read the original abstract

We review the "production approach" to estimating markups, the ratio of price to marginal cost. The approach is uniquely scalable: it requires no model of consumer demand or market structure and applies broadly across firms, industries, and time. Our organizing insight is that the production-based markup is a residual. Like the Solow residual, it is clean in theory but potentially contaminated by misspecification and mismeasurement. This framing helps explain why small differences in implementation can produce starkly different results from the same data. In some cases, markups have risen sharply. In others, they have not. Despite the disagreements in the literature, the importance of understanding and measuring market power cannot be overstated. We provide conceptual rationales for this disagreement, offer practical guidance on data and estimation, and call for greater transparency about how much of the variation attributed to markups may instead reflect technology.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reviews the production approach to estimating markups (price over marginal cost), which is scalable as it requires no demand or market structure model. Its central organizing insight is that the production-based markup is a residual analogous to the Solow residual: theoretically clean but vulnerable to contamination by misspecification and mismeasurement. This framing is invoked to rationalize why small implementation differences yield starkly divergent results across studies on the same data, with some finding sharp markup increases and others not. The manuscript supplies conceptual rationales for these disagreements, practical guidance on data and estimation choices, and a call for greater transparency in separating markup variation from technological factors.

Significance. If the residual framing and attribution of disagreements hold, the review provides a useful synthetic lens for a fragmented literature, underscoring the method's broad applicability and the high stakes of accurate market-power measurement. Explicit credit is due for the emphasis on transparency and for synthesizing micro and macro perspectives without introducing new fitted equations or ad-hoc parameters. However, the absence of quantitative decomposition limits the paper's ability to move the field beyond qualitative reconciliation.

major comments (2)
  1. [Abstract and introduction] Abstract and introduction: The core claim that implementation choices (functional form, input measurement, estimation) primarily drive 'starkly different results from the same data' is load-bearing for the organizing insight yet is advanced without a variance decomposition, replication exercise, or controlled comparison that holds the underlying dataset fixed while varying only those choices. This leaves open whether conceptual issues (e.g., violations of cost minimization or returns-to-scale assumptions that invalidate the residual interpretation) contribute materially to the observed dispersion.
  2. [Macro perspectives section] Discussion of macro perspectives: The assertion that the residual framing 'helps explain' the literature disagreements is presented conceptually but without systematic evidence separating implementation effects from deeper problems with the production approach itself (such as simultaneity or returns-to-scale misspecification). A quantitative attribution exercise on replicated specifications would be required to substantiate that implementation dominates.
minor comments (2)
  1. [Practical guidance] The practical guidance section would benefit from concrete numerical illustrations showing how a specific change (e.g., in input aggregation or instrument choice) alters the implied markup on a common sample.
  2. [Introduction] Notation for the markup residual could be standardized more explicitly when contrasting it with the Solow residual to avoid any ambiguity for readers unfamiliar with the production-function literature.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive report and for recognizing the paper's value as a synthetic lens on the production approach to markups. The major comments correctly note that our organizing claim about implementation differences rests on conceptual and qualitative grounds rather than new quantitative decompositions. We address each point below, clarifying the scope of the review and indicating where we will revise for greater precision.

read point-by-point responses

  1. Referee: [Abstract and introduction] The core claim that implementation choices (functional form, input measurement, estimation) primarily drive 'starkly different results from the same data' is load-bearing for the organizing insight yet is advanced without a variance decomposition, replication exercise, or controlled comparison that holds the underlying dataset fixed while varying only those choices. This leaves open whether conceptual issues (e.g., violations of cost minimization or returns-to-scale assumptions that invalidate the residual interpretation) contribute materially to the observed dispersion.

    Authors: We agree that a formal variance decomposition would provide stronger evidence. However, the manuscript is a review paper whose contribution lies in synthesizing the literature and offering a residual-based conceptual framework, not in performing new empirical replications. The claim draws from documented cases in which studies using the same underlying datasets (e.g., Compustat or Census Bureau files) reach divergent markup conclusions after differing only in functional-form or input-measurement choices. We will revise the abstract and introduction to state explicitly that the assessment is qualitative, to acknowledge that conceptual violations of cost-minimization or returns-to-scale assumptions could also drive dispersion, and to suggest that future work conduct controlled decompositions. This constitutes a partial revision. revision: partial

  2. Referee: [Macro perspectives section] The assertion that the residual framing 'helps explain' the literature disagreements is presented conceptually but without systematic evidence separating implementation effects from deeper problems with the production approach itself (such as simultaneity or returns-to-scale misspecification). A quantitative attribution exercise on replicated specifications would be required to substantiate that implementation dominates.

    Authors: The residual framing is offered as an organizing device to highlight how misspecification and mismeasurement can contaminate the markup measure, analogous to the Solow residual; we do not assert that implementation effects dominate deeper econometric problems. We will expand the macro perspectives section to reference existing studies that have examined simultaneity and returns-to-scale issues, and we will add language clarifying that both implementation choices and fundamental identification problems matter. A full quantitative attribution exercise lies outside the scope of this review and would require a separate empirical project. We will note this limitation explicitly in the revised text. revision: partial

standing simulated objections not resolved
  • Request for a quantitative variance decomposition or replication exercise holding data fixed while varying only implementation choices, which cannot be performed without converting the paper from a conceptual review into an original empirical study.

Circularity Check

0 steps flagged

Review paper with no new derivations or fitted predictions

full rationale

This is a review paper that organizes existing literature on the production approach to markup estimation without introducing any original derivations, equations, or empirical models. The central framing—that the production-based markup is a residual akin to the Solow residual—is presented as an organizing insight drawn from prior work rather than a new result derived from the paper's own inputs. No self-definitional steps, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided abstract or framing. The discussion of literature disagreements attributes them to implementation choices conceptually, without any quantitative decomposition or new estimation that could reduce claims to construction from the paper's own data or assumptions. The paper is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identified because the provided text is only an abstract of a review paper.

pith-pipeline@v0.9.0 · 5451 in / 1103 out tokens · 40148 ms · 2026-05-10T13:21:54.216860+00:00 · methodology

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