Securing Technological Leadership? The Cost of Export Controls: Crosignani et al. (2026)

What this is. The paper’s core results, the identification strategy, and the empirical design: enough to know what it found and how, without reading all 16 pages. To replicate or extend it, read the full source at the original.

TL;DR

To safeguard its technological edge, the U.S. government has restricted domestic firms from selling advanced technology to selected Chinese companies by adding those companies to Bureau of Industry and Security (BIS) export control lists. Crosignani et al. (2026) use hand-collected BIS list data matched to FactSet Revere global supply-chain linkages to document how these restrictions affect U.S. domestic suppliers and their Chinese customers. The intended effect materializes: affected U.S. suppliers terminate relations with their targeted Chinese customers. However, the broader supply-chain reconfiguration that U.S. policymakers hope for (reshoring or friendshoring) does not happen in the three years following the controls. Instead, affected U.S. firms experience negative stock market reactions, declines in revenues, cash flow, and employment, and tighter bank credit conditions. Chinese firms targeted by export controls, by contrast, are more proactive: they terminate U.S. supplier relations and replace them with domestic Chinese alternatives, consistent with the prior work of Crosignani et al. (2023) on supply-chain propagation. Total U.S. supplier stock market losses ($158 billion across all affected suppliers) far exceed the $18-19 billion in losses on the Chinese side; even the conservative estimate for U.S. suppliers linked to publicly listed Chinese targets ($77 billion) is approximately four times larger (p. 15), raising doubts about the effectiveness of export controls as a tool for preserving U.S. technological leadership.

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Export controls lead to broad-based decoupling: affected U.S. suppliers terminate relations with targeted AND non-targeted Chinese customers	Table 4, cols 1-3 and 5-6, p. 8	Full: coefficient 0.572*** (SE 0.209); excluding targeted Chinese customers: 0.414* to 0.560**, equivalent to +51% to +75% more terminations
R2	No reshoring or friendshoring: affected U.S. suppliers form fewer new Chinese customer relations and cannot offset the loss with domestic or Asia-Pacific alternatives	Table 4, cols 7-9, p. 8; Table 5, col 1, p. 9-10; Table 6, p. 10	Total customers: -0.145** (SE 0.064); new Chinese relations: -0.473** to -0.523*** (Table 4, cols 7-9); domestic, Asia, Asia-ally, and EU shares: all coefficients insignificant
R3	Stock market reaction: affected U.S. suppliers lose -3.6% in cumulative abnormal return in the 30-day window around BIS list announcements	Fig. 3, p. 11-12	-3.6% CAR over [-10, 20] (3-factor FF model); $1 billion market cap loss per firm; $158 billion total across 156 affected suppliers
R4	Cash flow declines significantly: export controls reduce operating cash flow by an amount equal to 21% of average treated-firm value	Table 7, col 1 (Panel A), p. 12	Coefficient -0.018** (SE 0.007) in stacked panel regression
R5	Revenue declines: export controls reduce revenues by 8.9%	Table 7, col 2 (Panel A), p. 12	Coefficient -0.093** (SE 0.032)
R6	Employment falls 7.3%; capital expenditure not significantly affected	Table 7, cols 5 and 4 (Panel A), p. 12	Employees: -0.076** (SE 0.031); CapEx: 0.004 (SE 0.003, insignificant)
R7	Banks tighten lending to affected U.S. suppliers: term loans fall, spreads rise, maturities shorten	Table 8, cols 2, 5, 6, p. 13	Term loans: -0.630** (SE 0.251); spread: +0.179** bps (SE 0.088); maturity: -4.874*** months (SE 1.538)
R8	Chinese firms targeted by export controls successfully reshore by forming new domestic supplier relations, replacing U.S. suppliers faster than U.S. firms can friendshore	Table 9, cols 3-4, p. 13	New relations with Chinese suppliers: +0.469*** (SE 0.181) to +0.517*** (SE 0.172); U.S. supplier share of targeted Chinese firms falls significantly (Table 10, cols 5-6)
R9	Targeted Chinese firms also lose market capitalization, but U.S. supplier losses are considerably larger (approximately 4x by the conservative subsample estimate)	Fig. 5, p. 14-15	CAR [-10, 20]: -8.2% (3-factor) to -9.0% (4-factor China model); Chinese total $18-19 billion; U.S. full-sample $158 billion; U.S. subsample linked to listed Chinese targets $77 billion (approximately 4x the Chinese losses, p. 15)

Overall (paper’s conclusion). Export controls prompt immediate and broad-based decoupling of U.S. suppliers from their Chinese customers, but U.S. firms struggle to find new customers in the three years following the controls. These supply-chain rigidities translate into sizable declines in market capitalization, revenues, cash flow, employment, and bank credit access. Chinese firms targeted by export controls are more proactive in supply-chain reconfiguration, increasing reliance on domestic Chinese alternatives. The total capitalization losses incurred by U.S. suppliers ($158 billion full sample; $77 billion for the subsample linked to listed Chinese targets) are considerably larger than those experienced by their Chinese counterparts ($18-19 billion); the conservative ratio is approximately four times (p. 15), questioning whether export controls are an effective tool for preserving U.S. technological leadership at acceptable cost.

Theory / model

The paper has no formal theoretical model. It operates in the nascent theoretical literature on geoeconomics summarized by Clayton et al. (2025b) and the literature on targeted trade restrictions such as Liu et al. (2024), who develop a calibrated model in which comprehensive semiconductor restrictions could raise domestic welfare by facilitating technology transfer.

The paper tests three sets of hypotheses grounded in the policy context:

1. Decoupling hypothesis: Export controls cause affected U.S. suppliers to terminate supply relations with Chinese customers named in BIS lists, and plausibly also with non-targeted Chinese customers (“wake-up call” or re-export concern effect).
2. Reshoring / friendshoring hypothesis: Following decoupling, U.S. firms form new customer relations with domestic or politically aligned (Asia-ally, EU) customers to offset lost Chinese business. The paper finds this does NOT happen within three years.
3. Collateral damage hypothesis: The inability to substitute customers generates measurable declines in firm value (stock market), operating performance (revenues, cash flow, employment), and credit supply (bank lending).

Identification logic. The BIS Entity List, Military End User (MEU) List, and Unverified List (UVL) have been expanded continuously since the late 1990s. The staggered, firm-specific nature of these additions constitutes a quasi-natural experiment: some U.S. suppliers become “affected” earlier (their Chinese customer is added to a list), others later. By comparing affected firms with not-yet-affected firms exporting to China in the same industry-size cell around each BIS event, the paper identifies the causal effects of export controls on supply-chain configurations and firm outcomes.

Method

The paper uses two main estimators: a stacked difference-in-differences (DiD) panel regression for supply-chain and balance-sheet outcomes, and a standard event study for stock-market reactions.

Stacked DiD. Because BIS additions are staggered and could produce biased two-way fixed-effects (TWFE) estimates, the paper follows the stacked regression methodology developed by Gormley and Matsa (2011) and described in Baker, Larcker, and Wang (2022). Cohorts are defined around each BIS inclusion event. Within each cohort, treated firms are U.S. suppliers whose Chinese customer is included in that cohort’s event; control firms are U.S. suppliers exporting to China that are never treated or not yet treated at event time. A [-3, 3] year window centers each cohort. The estimating equation (p. 7, Eq. 1) is:

$$y_{ict} = \beta \, \text{Affected}_{ic} \times \text{Post}_{ict} + \mu_{ic} + \mu_{ckt} + \varepsilon_{ict} \tag{1}$$

where $i$ indexes a firm, $c$ a cohort (round of export controls), and $t$ a year. $\text{Affected}_{ic}$ is an indicator equal to one if export control $c$ is imposed on a Chinese customer of U.S. firm $i$. $\text{Post}_{ict}$ equals one after the imposition. $\mu_{ic}$ are cohort-firm fixed effects; $\mu_{ckt}$ are cohort-industry-size-year fixed effects (absorbing demand shocks that hit similar firms in the same year). Standard errors are double-clustered at the firm and year level.

For count-like outcomes (number of terminated or new relations), the paper estimates Poisson pseudo-maximum-likelihood (PPML) regressions following Cohn, Liu, and Wardlaw (2022), with the same fixed-effects structure. Coefficients in PPML regressions are interpreted as percentage effects via $\exp(\hat\beta) - 1$.

Event study (stock market reactions). Cumulative abnormal returns (CARs) are estimated over a [-10, 20] trading-day window around the BIS list announcement date (p. 11). Pre-event betas are estimated on the [-150, -50] day window using the Fama and French (1993) 3-factor model or the Fama and French (2015) 5-factor model. Affected suppliers are the U.S. firms that supply Chinese entities included in the BIS lists; the sample includes 250 events involving 156 unique affected suppliers (one firm can contribute multiple events if it exports to multiple Chinese targets added at different times).

Empirical specifications

Supply-chain reconfiguration (R1, R2, Table 4-6, p. 8-10). The outcome variables are: (a) the total number of terminated relations with Chinese customers (including/excluding directly targeted firms); (b) the number of new Chinese customer relations; (c) total customer count; (d) regional customer shares (domestic, China, Asia, Asia-ally, EU). Estimated by PPML with cohort-firm and cohort-SIC-size-year fixed effects, double-clustered SEs. Treatment requires all control firms to export to China in the pre-treatment period; within each cohort, controls are matched on industry (2-digit SIC) and firm-size quartile. A “Restrictive Sample” narrows to only Entity List and MEU List events, excluding the less restrictive UVL.

Balance sheet and real outcomes (R4-R6, Table 7, p. 12). Outcome variables are: cash flow (operating income before depreciation minus interest and taxes divided by lagged assets), revenues (log total revenues), EBIT (earnings before interest and taxes divided by lagged assets), CapEx (capital expenditures divided by lagged assets), and employees (log total employees). Specification: OLS stacked panel regression (Eq. 1) with cohort-firm and cohort-SIC-size-year fixed effects. Results are robust to NAICS fixed effects (Table C.1 online appendix).

Bank lending (R7, Table 8, p. 13). Outcome variables: committed total credit, committed term loans, committed credit lines, utilized credit lines, the interest rate spread, and loan maturity. Sample: 331 firms exporting to China that borrow from a total of 38 banks over 2012:Q3-2023:Q3; 71 are affected by export controls. Specification: stacked OLS panel with firm fixed effects (absorbing time-invariant firm characteristics), industry-size-quarter fixed effects (absorbing common demand conditions for similar firms), and bank-quarter fixed effects (capturing bank-specific credit-supply shocks):

$$y_{ibqt} = \beta \, \text{Affected}_{i} \times \text{Post}_{iqt} + \mu_{i} + \mu_{kqt} + \mu_{bqt} + \varepsilon_{ibqt}$$

The coefficient $\beta$ is identified from within-bank-quarter comparisons of affected vs control firms in the same industry-size cohort.

Chinese supply-chain reconfiguration (R8, R9, Table 9-11, Fig. 5, p. 13-15). Chinese targeted firms’ supply-chain adjustments are estimated symmetrically to the U.S. side, with Targeted replacing Affected and the control group being Chinese firms importing from U.S. suppliers not in the BIS lists. This documents whether Chinese firms actively reshore (R8) and how non-U.S. third-country firms benefit (Table 11: revenues of non-U.S., non-allied suppliers to targeted Chinese firms increase by 15.7%*** after controls). Stock market reactions for Chinese targets (R9) use the China-specific 3-factor and 4-factor models of Liu, Stambaugh, and Yuan (2019).

Datasets used

Dataset	Role in paper	Wiki page
FactSet Revere supply-chain linkages	Global firm-to-firm customer-supplier relations 2007-2023; identifies U.S. suppliers of BIS-targeted Chinese firms	FactSet Revere
BIS Entity List, MEU List, UVL (hand-collected)	Export control targets; hand-collected additions and removals of Chinese entities from federalregister.gov and ecfr.gov with dates and aliases	no page yet
CRSP daily stock file	U.S. equity prices and returns for event-study CAR estimation	WRDS (licensed)
Compustat North America fundamentals (annual)	Firm-level balance sheet characteristics (assets, revenues, employment, CapEx)	WRDS (licensed)
Ken French Data Library	Fama-French 3- and 5-factor daily returns for beta estimation	Ken French library
Federal Reserve Y-14Q (CCAR)	Confidential quarterly loan-level data for 331 U.S. firms borrowing from 38 large banks, 2012:Q3-2023:Q3	FR Y-14Q
Refinitiv (Chinese stock prices)	Daily stock price data for publicly listed Chinese firms targeted by export controls	no page yet
S&P Capital IQ	International firm balance sheet data (EBIT, revenues) for 6,068 suppliers of targeted Chinese firms	Capital IQ
Mingshi	Chinese stock market 3-factor and 4-factor model returns for Chinese-side CAR estimation	no page yet

Sample: U.S. supply-chain panel 2007-2023 (up to Q3); bank-lending sample 2012:Q3-2023:Q3; stock event study 250 events, 156 unique affected U.S. suppliers.

When to read the full paper

Use the original if you are: studying the real effects of geoeconomic policy on domestic supply chains; estimating the collateral costs of U.S. export controls on firms in the technology sector; applying stacked DiD methods with staggered treatment to firm-level data; or examining the asymmetric reconfiguration capacity of U.S. versus Chinese firms in a supply-chain disruption. Locators above point to the exact tables and figures in the source PDF.

Attribution and rights

Source: peer-reviewed, Journal of Financial Economics 175 (2026) 104192. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. The paper is paywalled; reproduction is extract-only.

Crosignani, Matteo, Lina Han, Marco Macchiavelli, and André F. Silva. “Securing technological leadership? The cost of export controls on firms.” Journal of Financial Economics 175 (2026): 104192. DOI: 10.1016/j.jfineco.2025.104192. © 2025 Elsevier B.V. All rights reserved. This page is an extract-only distillation by the Institute for Automated Research.