Corporate Hierarchy

Michael Ewens and Xavier GiroudColumbia Business School & NBERSeptember 2026

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Corporate Hierarchy

Public firms are complicated

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…and they differ

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So are business schools

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Corporate Hierarchy

The public firm hierarchy

Rarely find org. chart in 10-Ks
- Voluntary disclosure → valuable proprietary information?
Few studies of public firms: unique regulatory and financial challenges to interact with hierarchy
Empirical literature focuses on the costs of public ownership

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Current approaches to measurement

Constrained number of layers (4 to 8 in most papers); no metric for U.S. firms
# of CEO reports: view of the top, <1% of employees
Homogeneous structures imposed across firms; titles mapped to one occupational code system

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Research questions

What are the determinants and implications of different organizational structures?

How do we measure public firm corporate hierarchies?
What do the hierarchies look like?
Guided by theoretical frameworks, how does corporate hierarchy relate to firm decision-making and outcomes?

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Hierarchies: measurable and important

Resumes of 18.1 million workers at 3,454 US public firms
Firm-specific algorithm estimates the number of hierarchical layers
Average firm has 6.4 layers (median 6), pyramidal shape
More layers: longer tenure, more internal promotion, higher operating performance, lower volatility
AI adoption predicts flattening within firm

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Theory

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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What is hierarchy?

“[D]etermine how much each employee knows, how many employees to hire, and how many layers of management to use in production”

Caliendo et al. (2020)

Exist to “solve coordination problems in the presence of specialization” (Garicano 2000)

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What is a layer?

“a group of employees, with similar characteristics summarized in their knowledge, who perform similar tasks within the organization. Conceptually, these layers are hierarchical in the sense that higher layers of management are smaller and include more knowledgeable employees who have as subordinates employees in lower layers.”

Caliendo et al. (2015)

Our challenge: “Dividing the employees in real firms into layers requires some mapping between these concepts and the data.”

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Three classes of models

Knowledge hierarchy: managers as problem solvers handling exceptions from below; layers add specialistsGaricano 2000; Garicano and Rossi-Hansberg 2006; Caliendo et al. 2015
Information hierarchy: CEO’s span limited by information processing → delegation via layersRadner 1993; Bolton and Dewatripont 1994; Stein 2002
Incentive hierarchy: promotions provide incentives; layers create the career path; tournamentsWilliamson 1967; Lazear and Rosen 1981; Malcomson 1984

The tradeoff: gains from specialization vs. costs of communication + fixed cost of training managers

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Testing theory? …no.

Other than knowledge hierarchy, the direct empirical content of these theories is limited

We focus on

Documenting facts
Correlating with outcomes of interest in corporate finance
Let the set of frameworks help us interpret

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Measurement

Using internal labor markets to reveal organizational form

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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Worker flows inside of firms

We implement the Huitfeldt et al. (2023, J of Econometrics) method

Steps:

Identify largest internal labor market
Rank jobs/roles within the largest ILM
Cluster roles using ranks and signals of lateral moves

Intuition: job transitions reveal the ranking of titles and cluster based on similar ranks

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Baker et al. (1994): what transitions reveal

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Job promotion network

Jobs connected through promotion reveal internal labor markets (ILM)
Estimator can be run on any ILM
Focus on the largest ILM as measured by % of roles
What sits outside: board members, lawyers, lobbyists, front-line workers

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Toy example: the observed promotions

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Rank the roles

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Why swap? Revealing the job ladder

Swaps test where a role sits: lateral roles can trade places without breaking any promotion; true ladder steps cannot
A violation: a promotion that points down the ordering
- The best ranking has the least violations
Repeatedly propose swaps and accept any that does not increase violations
In the set of minimum violations:
- Roles that swap freely land at similar average ranks
- Roles that cannot swap keep their separation

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Many rankings fit equally well

Swapping the Engineering and Sales branches rank by rank breaks no promotion
Every zero-violation ranking is equally consistent: the minimum-violation set

Candidate ranking	Violations
Ranking 1 (blue)	0
Ranking 2 (orange)	0
VP, Sales below Sales Manager	1
CEO at the bottom	2

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Average ranks, then cluster into layers

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Choosing the number of layers

Repeated search runs give each role a mean rank and an uncertainty
Roles the data cannot place at all (rank indistinguishable from random placement) are excluded from the layer count
Rank noise → the noise floor
- Average squared rank uncertainty of the surviving roles
Given a candidate K, 1-D k-means groups roles into contiguous bands of mean rank, minimizing within-layer variance
K is the smallest number of layers whose within-layer variance fits the budget

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Estimated ranks with uncertainty

Example: Regal Entertainment

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Group roles for one candidate K

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Repeat for every candidate K

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Stop at the noise floor

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Data and representativeness

Worker job histories and public firms

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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Goal: worker histories with public firms

Two vendors: Revelio (2013–2024, default) + CoreSignal (longer histories; used where Revelio inadequate)

Titles → Roles

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Sample construction

Universe: 8,128 matched firms
Gates: industry exclusions; LinkedIn/Compustat coverage ≥ 15%; largest-ILM worker share ≥ 25%; Compustat requirements; shells/employee floor; γ>2 cap
Final sample: 3,454 firms / 26,112 firm-years, fiscal years 2015–2024; panels 2013–2024

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Merge LinkedIn to Compustat (and more)

Compustat: baseline firms
Execucomp: fills missing CEOs and top management
CRSP (stock prices)
BoardEx (boards)
DISCERN/KPSS (patents)
SDC (mergers)
AI use (Babina et al. 2024)

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In and out of sample differences

In-sample firms are larger, more levered, higher-level profits (directions unchanged)

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Summary statistics

	Mean	SD	Min	Median	Max	N
Number of internal labor markets	254.96	263.82	1.00	183.00	2678.00	3,454
Size of largest ILM (roles)	190.53	316.58	1.00	91.00	3900.00	3,454
Number of unique roles	304.14	392.41	2.00	188.00	4623.00	3,454
% role in largest ILM	55.00	20.35	3.77	58.82	100.00	3,454
% worker-years in largest ILM	77.27	20.47	25.00	85.04	100.00	3,454
Employees (Compustat, 1000s)	7.99	37.41	0.00	0.98	1556.00	3,406
Number employees (LinkedIn, '000s)	3.23	11.37	0.00	0.64	325.56	3,454
Number hierarchy layers (selected)	6.35	2.14	2.00	6.00	18.00	3,454
Hierarchization (German)	0.60	0.10	0.00	0.62	1.00	1,091
Hierarchization (ONet)	0.51	0.12	0.00	0.52	0.76	3,454
Hierarchization (this paper)	0.77	0.10	0.14	0.79	0.91	3,326

Algorithm rules

Not all employees

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Layers over time: flat to weakly increasing

Mean 6.39 (2015) → 6.55 (2024); median 6 throughout
19% of firms ever change their layer count

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Is LinkedIn representative? BLS comparison

Covers 73% of reported occupations and 89% of BLS employment in them
Occupations absent industry-wide: mid-pay nationally
Occupations absent within a firm: lower-pay relative to that firm, sit mid-rank or off the main ILM
Undercounted workers: the bottom of the firm

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Does missingness bias the count? Simulation

Synthetic firm with known structure; degrade coverage the way LinkedIn is incomplete; re-run the estimator
At the measured gaps, the true layer count is recovered; breaks only at extreme truncation

https://hierarchy-coverage-sim.onrender.com/

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The simulator, live

https://hierarchy-coverage-sim.onrender.com/

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Validation

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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What should a measure look like?

intuitive titles and roles in hierarchical layers
higher layers → more experience
strongly correlates with firm size
firms should be (roughly) pyramidal in shape
where there are layers, there should be managers

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Titles by layer match expectations

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Worker experience in higher layers

The clustering algorithm sorts workers by experience level (since first job or BA)

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Firm size predicts layers

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Typical firm has a pyramidal shape

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Managers and executives by layer

Managers and executives are increasingly likely up the hierarchy
Consistent with knowledge hierarchy theory

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External gradients: wages, work styles, skill

BLS wages: top rank quintile pays about twice the bottom
O*NET work styles: leadership, innovation, initiative rise up the hierarchy
O*NET Job Zone: top quintile sits more than a full zone above the bottom

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Known job ladders: investment banks

Jefferies: 7 layers; analyst → associate → VP → MD land on rising layers (modal layers 2/3/5/6)
Evercore: 7 layers; same weakly increasing ladder (2/3/4/5)
Lazard not in sample

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Summary of validations

intuitive titles and roles in hierarchical layers
higher layers → more experience
strongly correlates with firm size
firms should be (roughly) pyramidal in shape
where there are layers, there should be managers

We are confident that this data-driven measure captures the basic feature of a quality hierarchy measure.

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Outcomes

Does hierarchy predict firm decisions and outcomes?

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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Questions we ask

Are more hierarchical firm’s human capital different?
Does hierarchy correlate with firm productivity and costs?
Can we explain firm investment in innovation?
Do stock volatility and stock returns depend on layers?

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What follows is not identified

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Human capital: education

The share of workers with a post-graduate degree rises with the layer
Consistent with theory: additional layers require costly-to-acquire managerial knowledge (Rosen 1982; Garicano 2000)

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Human capital: tenure and turnover

The question: does the internal labor market of high-layer firms differ?
High-layer firms hire internally at higher rates and their workers have longer tenure
Consistent with firm-specific human capital

	Employees		Tenure (log years)
	% new	% promote	Non-new	All
		layer
	(1)	(2)	(3)	(4)
log # layers (t−1)	-0.015***	0.0053***	0.067***	0.094***
	(0.0036)	(0.0016)	(0.022)	(0.026)
Observations	22,531	22,531	22,531	22,531
# firms	3,314	3,314	3,314	3,314
Mean dep. var.	0.18	0.04	1.72	1.51
R²	0.03	0.00	0.07	0.07
Size decile FE?	Y	Y	Y	Y
Year FE?	Y	Y	Y	Y
Industry FE?	Y	Y	Y	Y

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Employee equity and the CEO

High-layer firms are more likely to offer employee stock option plans
Significantly higher wage inequality (CEO pay ratio): consistent with tournament models and increasingly costly skills at the top

	ESOP?	Internal	CEO	CEO
		CEO	pay	pay ratio

	(5)	(6)	(7)	(8)
log # layers (t−1)	0.095***	0.026	0.065	35.3***
	(0.024)	(0.056)	(0.064)	(5.78)
Observations	13,578	1,215	11,198	7,278
# firms	2,073	863	1,470	1,865
Mean dep. var.	0.14	0.59	8.60	109.32
R²	0.10	0.00	0.31	0.29
Size decile FE?	Y	Y	Y	Y
Year FE?	Y	Y	Y	Y
Industry FE?	Y	Y	Y	Y

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Are high-layer firms more productive?

Higher ROA and net profit margin; gross margin flat
Lower revenue per employee: more managers not directly involved in production

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Innovation: less R&D, more patents, same quality

More layers does not correlate with worse patent outcomes
- More efficient with R&D?

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Hierarchy and firm risk

Theories of corporate hierarchy give no explicit predictions for firm risk
In knowledge hierarchies, additional layers add problem solvers: better ability to manage risks and adjust to unexpected shocks
This would reduce the volatility of the firm’s cash flows

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More layers, lower stock volatility

More layers predict lower stock volatility
About 3% lower volatility for a 1 s.d. increase in log layers
Problem solvers may enhance the firm’s ability to manage risks and adjust to shocks

	Stock σ
		Wins.
	(1)	(2)
log # layers (t−1)	-0.044***	-0.042***
	(0.011)	(0.010)
Observations	20,071	20,071
# firms	3,146	3,146
Mean dep. var.	0.54	0.53
R²	0.19	0.23
Size decile FE?	Y	Y
Industry FE?	Y	Y
Year FE?	Y	Y

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Business or financial risk?

Lower operating-asset volatility: business risk, not financial structure
No difference in debt; high-layer firms hold less cash

	Operations and financial
	σ(OA)	σ(CF)	Debt/Assets	Cash/Assets
	(3)	(4)	(5)	(6)
log # layers (t−1)	-0.033**	-0.0063	-0.0024	-0.025***
	(0.016)	(0.0047)	(0.011)	(0.0083)
Observations	20,025	15,273	19,284	21,916
# firms	3,145	2,572	2,966	3,302
Mean dep. var.	0.40	0.04	0.28	0.23
R²	0.10	0.14	0.05	0.10
Size decile FE?	Y	Y	Y	Y
Industry FE?	Y	Y	Y	Y
Year FE?	Y	Y	Y	Y

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Hierarchy-sorted portfolio returns

Each year sort firms into quartiles of lagged log layers; long top quartile, short bottom; value-weighted; 2016 to 2024
Negative alphas regardless of the asset pricing model

	Monthly value-weighted long-short return (Q4-Q1), 2016–2024
	(1)	(2)	(3)	(4)	(5)	(6)
α	-0.0035	-0.0047**	-0.0059***	-0.0048**	-0.0044**	-0.0057***
	(0.0024)	(0.0020)	(0.0019)	(0.0020)	(0.0020)	(0.0019)
R ^m - r ^f	-0.072	0.0072	-0.033	0.016	-0.026	-0.044
	(0.052)	(0.057)	(0.047)	(0.060)	(0.065)	(0.057)
SMB		-0.37***	-0.21*	-0.36***	-0.42***	-0.25**
		(0.10)	(0.11)	(0.10)	(0.093)	(0.10)
HML		0.17***	0.095	0.13**	0.14***	0.033
		(0.060)	(0.062)	(0.059)	(0.055)	(0.071)
RMW			0.41***			0.37***
			(0.11)			(0.11)
CMA				0.085		0.11
				(0.13)		(0.13)
Momentum					-0.13**	-0.11*
					(0.063)	(0.059)
Observations	108	108	108	108	108	108
R²	0.03	0.18	0.27	0.18	0.21	0.28

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Hierarchy-sorted portfolio returns

Each year sort firms into quartiles of lagged log layers; long top quartile, short bottom; value-weighted; 2016 to 2024
Negative alphas regardless of the asset pricing model

	Monthly value-weighted long-short return (Q4-Q1), 2016–2024
	(1)	(2)	(3)	(4)	(5)	(6)
α	-0.0035	-0.0047**	-0.0059***	-0.0048**	-0.0044**	-0.0057***
	(0.0024)	(0.0020)	(0.0019)	(0.0020)	(0.0020)	(0.0019)
R ^m - r ^f	-0.072	0.0072	-0.033	0.016	-0.026	-0.044
	(0.052)	(0.057)	(0.047)	(0.060)	(0.065)	(0.057)
SMB		-0.37***	-0.21*	-0.36***	-0.42***	-0.25**
		(0.10)	(0.11)	(0.10)	(0.093)	(0.10)
HML		0.17***	0.095	0.13**	0.14***	0.033
		(0.060)	(0.062)	(0.059)	(0.055)	(0.071)
RMW			0.41***			0.37***
			(0.11)			(0.11)
CMA				0.085		0.11
				(0.13)		(0.13)
Momentum					-0.13**	-0.11*
					(0.063)	(0.059)
Observations	108	108	108	108	108	108
R²	0.03	0.18	0.27	0.18	0.21	0.28

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Corporate Hierarchy

Hierarchy-sorted portfolio returns

Each year sort firms into quartiles of lagged log layers; long top quartile, short bottom; value-weighted; 2016 to 2024
Negative alphas regardless of the asset pricing model

	Monthly value-weighted long-short return (Q4-Q1), 2016–2024
	(1)	(2)	(3)	(4)	(5)	(6)
α	-0.0035	-0.0047**	-0.0059***	-0.0048**	-0.0044**	-0.0057***
	(0.0024)	(0.0020)	(0.0019)	(0.0020)	(0.0020)	(0.0019)
R ^m - r ^f	-0.072	0.0072	-0.033	0.016	-0.026	-0.044
	(0.052)	(0.057)	(0.047)	(0.060)	(0.065)	(0.057)
SMB		-0.37***	-0.21*	-0.36***	-0.42***	-0.25**
		(0.10)	(0.11)	(0.10)	(0.093)	(0.10)
HML		0.17***	0.095	0.13**	0.14***	0.033
		(0.060)	(0.062)	(0.059)	(0.055)	(0.071)
RMW			0.41***			0.37***
			(0.11)			(0.11)
CMA				0.085		0.11
				(0.13)		(0.13)
Momentum					-0.13**	-0.11*
					(0.063)	(0.059)
Observations	108	108	108	108	108	108
R²	0.03	0.18	0.27	0.18	0.21	0.28

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Interpreting the negative alphas

Factor loadings agree: profitability (RMW) loads positively, size (SMB) negatively; high-layer stocks look like large-cap quality firms
Explanation 1: investors overestimate high-layer firms’ future performance
- Little support: IBES analyst EPS forecasts show no systematic over- or underestimation of high- vs. low-layer firms
Explanation 2 (suggestive): layers may capture a priced risk factor the models miss
- High-layer firms are significantly less risky across several metrics; low-layer firms may carry an “organizational risk” premium

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Putting it altogether

Tradeoffs of a more hierarchical structure; theory only partial guidance
Prior measures impose a common 4 to 8 layer grid on every firm
Firm-specific measure: middle half of firms 5 to 8 layers in 2024; 18% outside the 4 to 8 band; up to 18 layers
O*NET Job Zone clustering: every public firm 5 layers, no variation

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What explains layers

Moving hierarchy to the left-hand side

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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Layers are an investment

We have thus far taken layers as given, correlating with firm observables

Theory has a lot to say about how firms choose their hierarchy

Consider two explanations:

Positive demand or “complexity” shocks → more layers
Improvements in knowledge acquisition → fewer layers

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Going public: more layers after the IPO

Bernstein (2012); Bias et al. (JF 2026) German hierarchization

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AI: cheaper knowledge acquisition

Key prediction from Garicano (2000): lower the cost of knowledge acquisition – incorporating information and solving problems – lowers the need for layers
Lower layer workers can solve more problems themselves
Babina et al. (2024): identify when firms first post jobs for AI positions = proxy for AI adoption

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Introduction of AI: flattening of the firm

All four AI adoption measures predict fewer layers within the firm
Consistent with AI lowering the cost of knowledge acquisition and information processing

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Conclusion

Motivation

Theory

Measurement

Data and representativeness

Validation

Outcomes

What explains layers

Conclusion

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Hierarchies: measurable and important

Public workforce information reveals the firm’s hierarchy
Novel, data-driven measure for 3,454 US public firms
Benefits: workforce tied to the firm, operating performance, patent output, lower volatility
Costs: revenue per employee falls, with more managers not directly involved in production

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What’s next

Connecting hierarchy to firm innovation and innovation teams
Investigating multiple labor markets in the firm
Estimating the model in rolling windows for firm-level variation
Public vs. private firm organizational structure

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Appendix

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The alternative rule: Greenhill

Large promotion-heavy firms with many incomparable roles: the search does not settle
Alternative rule: merge neighboring roles until gaps exceed the Bonferroni family-wise threshold
Supplies K for 1,492 firms (43%)