Regulatory Fragmentation: Kalmenovitz, Lowry & Volkova (2025)

What this is. The paper’s core results, the measurement framework (LDA-based regulatory fragmentation measure), and the regression specifications: enough to know what it found and how, without reading all 46 pages. To replicate or extend, read the full source at the original. Firm-year data are publicly available at evolkova.info.

TL;DR

The paper introduces the concept of regulatory fragmentation: the regulation of a single topic by multiple federal agencies. Using the full text of the Federal Register (783,950 documents, 1994-2019), it applies Latent Dirichlet Allocation (LDA) to identify 100 regulatory topics and measure which agencies regulate each. It then matches these topics to firm-level 10-K filings to build a firm-specific, time-varying exposure measure. Across 60,573 firm-year observations (CRSP/Compustat, 1995-2019), higher regulatory fragmentation is associated with significantly higher SG&A costs, lower total factor productivity, lower profitability, slower sales and asset growth, less industry entry, and greater exit of small firms. Inconsistency across agencies (not just redundancy) drives the harm. Agency incentives, proxied by unexplained promotion activity, predict more fragmentation.

Core results

Magnitudes and significance are as reported; \*\*\*/\*\*/\* = 1%/5%/10%. All variables are normalized so coefficients reflect a one-standard-deviation change. Independent variables are lagged one period. Standard errors clustered at the Fama-French 48-industry level.

#	Result	Locator	Magnitude
R1	Regulatory fragmentation raises SG&A expenses: higher fragmentation is associated with significantly higher overhead costs in both year+company and industry x year+company FE specifications	Table IV, cols (1)/(4), p. 1106	Coefficient on Regulatory Fragmentation: 0.043*** (SE 0.015) [year+co FE]; 0.055*** (SE 0.014) [ind x yr+co FE]
R2	Regulatory fragmentation reduces TFP: a one-SD increase in fragmentation is followed by a 3.2-3.6% SD decrease in total factor productivity	Table IV, cols (2)/(5), p. 1106	-0.036** (SE 0.016) [year+co FE]; -0.032* (SE 0.018) [ind x yr+co FE]
R3	Regulatory fragmentation lowers profitability (ROA): the effect ranges from -5.3% to -5.9% of a SD	Table IV, cols (3)/(6), p. 1106	-0.059*** (SE 0.013) [year+co FE]; -0.053*** (SE 0.015) [ind x yr+co FE]
R4	Regulatory fragmentation slows sales growth: a one-SD increase is followed by a 9.9-11.0% SD decrease in sales growth	Table V, cols (1)/(4), p. 1109	-0.099*** (SE 0.017) [year+co FE]; -0.110*** (SE 0.022) [ind x yr+co FE]
R5	Regulatory fragmentation slows asset growth: a one-SD increase is followed by a 13.9-14.3% SD decrease in asset growth	Table V, cols (2)/(5), p. 1109	-0.143*** (SE 0.024) [year+co FE]; -0.139*** (SE 0.027) [ind x yr+co FE]
R6	Regulatory fragmentation deters entry and increases small-firm exit: fragmentation reduces new IPOs, raises small-firm exit rates, and has a net negative effect on industry size; large firms benefit from the resulting barriers	Table VIII, p. 1115	IPOs: -0.050*** (SE 0.014); small-firm exit: +0.036** (SE 0.016); large-firm exit: -0.046** (SE 0.018); total peers: -0.035*** (SE 0.008); all industry x year+company FE
R7	Regulatory fragmentation reduces lobbying: firms reduce lobbying as fragmentation rises, consistent with lower returns to regulatory capture when oversight is dispersed across many agencies	Table X, cols (1)/(3), p. 1118	log(Lobbying $): -0.106*** (SE 0.032) [year+co FE]; -0.102** (SE 0.038) [ind x yr FE]; n = ~14,983

Overall (paper’s conclusion). Regulatory fragmentation is a costly but previously undocumented dimension of regulatory burden. The negative effects arise primarily from inconsistency, not mere duplication: effects are weaker when agencies co-author documents (coordinated regulation), and stronger when agencies independently regulate the same topic. Agency promotion incentives are positively linked to rulemaking activity outside core expertise, suggesting empire-building as one mechanism driving fragmentation. These findings extend the regulatory-intensity measurement of Kalmenovitz (2023) by adding the multi-agency dimension, are consistent with the theory of economic regulation of Stigler (1971), draw on the evidence of inconsistency across banking regulators in Agarwal et al. (2014), and apply the LDA methodology of Lowry, Michaely and Volkova (2020).

Theory / model

The paper has no formal model. The empirical strategy is built on two competing hypotheses about the sign of the fragmentation effect, drawn from the industrial organization literature on regulation.

Hypothesis 1 (fragmentation is beneficial). Multiple agencies create regulatory competition, let firms choose the least restrictive regulator, and may enable more efficient regulation via a race to the top. Firms can also focus lobbying on the agency most susceptible to capture. Under this view, higher fragmentation should lower costs and raise productivity and growth.

Hypothesis 2 (fragmentation is costly). Multiple agencies create duplicative compliance requirements, and more critically, inconsistent requirements that raise uncertainty. Firms cannot fully anticipate how to resolve discrepancies across agencies. Fragmentation also reduces firms’ ability to direct lobbying effort. Under this view, higher fragmentation should raise costs and reduce productivity and growth.

The empirical results uniformly support Hypothesis 2. The paper provides no general equilibrium or structural model; its contribution is the measurement framework and the empirical evidence.

The identification strategy relies on within-firm, within-industry variation in regulatory fragmentation over time, with company fixed effects absorbing time-invariant firm heterogeneity and industry x year fixed effects removing industry-wide trends. The main endogeneity concern (firm operational changes causing apparent fragmentation changes) is addressed by: (i) excluding firm-years with industry switches, segment count changes, or asset changes exceeding 20% (Table VII); (ii) 1,000-iteration placebo tests that randomly reassign topics to firms (Figure 7); and (iii) coefficient stability analysis following Oster (2019). The authors acknowledge they lack an exogenous shock that randomly shifts fragmentation across firms (p. 1085).

Method

Step 1: Topic identification in the Federal Register. LDA (Latent Dirichlet Allocation) is applied to the full text of 783,950 FR documents (1994-2019) from the Rules, Proposed Rules, and Notices sections. LDA generates 100 topics; each document receives a topic distribution. Topic labels are assigned using CFR subject classifications. For each topic $i$ and year $t$, the fraction of words written by agency $a$ is:

$$\omega^2_{i,a,t} = \frac{\text{words by agency } a \text{ on topic } i \text{ in year } t}{\text{total words on topic } i \text{ in year } t} \tag{1 (within AgencyHHI)}$$

The fragmentation of topic $i$ in year $t$ across agencies (equation (2), p. 1091) equals one minus the HHI:

$$\text{Regulatory Fragmentation}_{i,t} = 1 - \text{AgencyHHI}_{i,t} = 1 - \sum_{\text{Agency}} \omega^2_{i,a,t} \tag{2}$$

Values near zero indicate a single dominant agency; values near one (maximum 0.992 with 121 agencies) indicate dispersion across many agencies.

Step 2: Measuring firm-level topic exposure. The same LDA model (trained on FR documents) is applied to each firm’s annual 10-K. The fraction of the 10-K devoted to topic $i$ in year $t$ is $P_{f,i,t}$. The dispersion of topics within a firm (equation (3), p. 1091) is:

$$\text{Dispersion of Topics within Firm}_{f,t} = 1 - \sum_{\text{Topic}} P^2_{f,i,t} \tag{3}$$

Step 3: Firm-level regulatory fragmentation. The main measure (equation (4), p. 1093) is the weighted average of topic-level fragmentation, where weights are each topic’s share in the firm’s 10-K:

$$\text{Regulatory Fragmentation}_{f,t} = \sum_{i} P_{f,i,t} \cdot \text{Regulatory Fragmentation}_{i,t} \tag{4}$$

Step 4: Regulation quantity control. To separate fragmentation from sheer regulatory volume, the paper constructs a control for the quantity of regulation (equation (5), p. 1095):

$$\text{RegulationQuantity}_{f,t} = \sum_{\text{Topic}} P_{f,i,t} \cdot \log(\text{Words}_{i,t}) \tag{5}$$

This is the weighted average of log(FR words) across topics, weighted by firm relevance.

Validation. Two alternative dispersion measures are constructed from FR agency mentions of companies (equation (6), p. 1096) and from 10-K agency mentions by companies (equation (7), p. 1096). Panel D of Table II (p. 1098) shows that the primary measure is significantly positively correlated with both alternatives, with a 1-SD increase in fragmentation associated with a 5.0% increase in agency-mention dispersion in the FR.

Empirical specifications

The main regression specification (equation (8), p. 1104) is:

$$y_{f,t+1} = \alpha + \beta \cdot \text{Regulatory Fragmentation}_{f,t} + \vec{X}_{f,t}' \gamma + \tau_t + \theta_k + \mu_f + \varepsilon \tag{8}$$

where $y_{f,t+1}$ is a firm-level outcome (SG&A/AT, TFP, ROA, sales growth, asset growth, Emp/AT), $\tau_t$ are year fixed effects, $\theta_k$ are industry (Fama-French 48) fixed effects, and $\mu_f$ are company fixed effects. The tighter specifications replace $\tau_t + \theta_k$ with industry x year fixed effects. All continuous independent variables are winsorized at the 0.5% and 99.5% levels and normalized; standard errors are clustered at the Fama-French 48-industry level. Independent variables are lagged one period.

Firm-level controls $\vec{X}_{f,t}$ include: log(Words, 10-K), PPE/AT, EBITDA/AT, log(Sales), Tobin’s Q, Dispersion of Topics within Firm, and Regulation Quantity.

Core outcome regressions (Tables IV and V, pp. 1106, 1109): The main results are estimated for six outcomes: SG&A/AT (costs), TFP (productivity), ROA (profitability), sales growth, asset growth, and Emp/AT (employment). The baseline coefficient on Regulatory Fragmentation ranges from 0.043 (SG&A, positive) to -0.143 (asset growth, negative) in standardized units (R1-R5).

Industry composition regressions (Table VIII, p. 1115): For entry (R6), the dependent variable is the count of new peers (IPOs or industry joiners) in year t+1; for exit, it is the rate of peers in year t no longer present in year t+1, separately for small and large firms (defined by above/below-average assets within Fama-French 48 industry). Product-market peers are defined using Hoberg and Phillips (2016) TNIC-3 similarity scores.

Channel test: co-authorship (Table IX, p. 1117): The indicator $\text{CoAuthored}_{f,t}$ (above-median exposure to topics where FR documents are co-authored by multiple agencies) is interacted with Regulatory Fragmentation in a fully interacted model. The interaction coefficient is negative (e.g., SG&A: Regulatory Fragmentation x CoAuthored = -0.014**, SE 0.006), confirming that inconsistency (solo-authored, potentially conflicting rules) drives more harm than coordination.

Lobbying regressions (Table X, p. 1118): The dependent variable is log(1 + lobbying expenditures in USD millions) and raw lobbying expenditures, from the LobbyView database (Kim (2018)), for 14,983-14,986 company-year observations. Regressions include company and year fixed effects, or industry x year fixed effects.

Agency incentives (Table XI, p. 1121): The dependent variable is Unexpected Promotions at agency a in year t+1, constructed as residuals from an employee-level promotion model (controlling for tenure, agency x rank, and occupation fixed effects). The key independent variable is the number of FR words written by the agency in notices (or rules) in year t, and its interaction with the share of words in the agency’s top 10 topics. The positive coefficient on Words (e.g., Words in notices: 0.615**, SE 0.234) and the negative coefficient on the interaction with core topics support the view that agency employees are rewarded for expanding rulemaking, particularly outside core areas.

Datasets used

Dataset	Role in paper	Wiki page
Federal Register (full text, 1994-2019)	Main input; LDA topic model trained on 783,950 documents to measure regulatory fragmentation and quantity	Federal Register
SEC EDGAR (10-K filings)	Applied LDA model to firm 10-Ks to measure firm-topic exposure $P_{f,i,t}$; log(Words, 10-K) control	EDGAR
CRSP / Compustat (via WRDS)	Firm-year outcome variables (SG&A, TFP, ROA, sales, assets, Tobin’s Q), sample construction; 60,573 firm-years 1995-2019	WRDS (licensed)
LobbyView lobbying database	Lobbying expenditures for ~14,983 firm-year observations; from Kim (2018)	No page yet
Hoberg-Phillips TNIC-3	Product-market peer similarity scores for industry composition analysis	No page yet
OPM/FOIA employee compensation data	Individual-level compensation and rank for 75 federal agencies; used in agency incentive analysis	No page yet
Fragmentation firm-year data (evolkova.info)	Authors’ public release of the main regulatory fragmentation measure	No page yet

Sample: 60,573 company-year observations, 1995-2019 (CRSP/Compustat firms with nonmissing 10-Ks in SEC EDGAR). Frequency: annual.

When to read the full paper

Read the original if you are: building a measure of multi-agency regulatory exposure for a specific sector or firm set; extending the fragmentation analysis to enforcement actions or international regulation; studying the industrial organization of the federal government and agency incentives; or replicating the TFP or sales-growth results, which have the largest magnitudes and require careful construction of the LDA model and Imrohoroglu-Tuzel TFP measure. The Internet Appendix (at the publisher’s site) contains additional robustness tables and the LDA methodology details.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(2). This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The article is paywalled (Wiley VOR terms; no Creative Commons licence). Extract-only.

Kalmenovitz, Joseph, Michelle Lowry, and Ekaterina Volkova. “Regulatory Fragmentation.” The Journal of Finance 80, no. 2 (April 2025): 1081-1126. DOI: 10.1111/jofi.13423. © 2025 the American Finance Association.