1.0.0.1 Related Literature

This paper relates to several strands of the literature. First, we contribute to research on the value of trade secrets. Our paper is closely connected to two recent papers studying the effects of trade secret theft and espionage on firms. Curti et al. (2026) examine how corporate innovation responds to the theft of trade secrets, while Michaelides et al. (2026) study the value of trade secrets as imputed from espionage incidents. Relative to these papers, we make several contributions. First, each paper’s analysis focuses on a single outcome in targeted firms (innovation and market capitalization, respectively), while we examine a much broader set of outcomes including revenue, R&D, assets, patenting behavior, employment discrimination, the geography of business, and corporate speech, with a particular focus on a firm’s openness to international knowledge flows and collaboration. Second, our inclusion of unsuccessful cases provides a novel strategy for identifying the causal effects of espionage. Third, we analyze the industry-level effects of espionage using international trade data, allowing us to characterize the net effects of espionage on US industry and show that the damages extend beyond redistribution between firms within the US. Fourth, our dataset of espionage incidents is more comprehensive in scope than the datasets of these two papers (covering 164 incidents, compared to 82 incidents and 72 incidents respectively in these two papers). This makes our analysis more generalizable, despite the difficulty of observing espionage empirically. Other papers on the subject of economic espionage study different questions than ours. Kim (2018) and Fang and Li (2021) examine whether cases of economic espionage pursued by the United States have discriminated towards ethnically Chinese defendants, while Glitz and Meyersson (2020; Glitz et al. 2026) showed that the beneficiaries of industrial espionage during the Cold War became meaningfully more productive.

Second, we contribute to the literature on how international knowledge diffusion affects firms. The (lack of) international diffusion of innovation and technology is often considered a cause of productivity differences between countries (Keller 2004), with trade and FDI identified as two major channels through which innovation may diffuse (Coe et al. 1997; Keller and Yeaple 2009). Recent work has emphasized the importance of human capital for the diffusion of innovation (Terry et al. 2026; Prato 2025), as well as how geopolitical competition has shaped science and innovation (Flynn et al. 2024; Jia et al. 2022). A key property of espionage is that it is simply the illicit counterpart to the diffusion of knowledge across countries—and in order to restrict knowledge flow outward, firms can choose to restrict knowledge flow inward (e.g. by deciding not to patent with Chinese inventors). As a result, our analysis of espionage provides a window into the value of inward knowledge flows, relative to the cost of outward knowledge flows. Our results suggest firms value inward knowledge flows highly, and even threats to their proprietary knowledge may not lead to decoupling.

Third, we speak to the literature on creative destruction and business dynamism (Klette and Kortum 2004; Aghion et al. 2015). Recent work highlights “knowledge diffusion”—the ease with which ideas spread from a leader firm to other firms in an industry—as the central factor behind the decline in US business dynamism (Akcigit and Ates 2023). Our finding that targeted firms fall behind untargeted firms—combined with the fact that targeted firms are disproportionately industry leaders—provides direct evidence for the theory that “knowledge diffusion” generates business dynamism. This provides causal evidence on the importance of knowledge diffusion for business dynamism, complementing the macroeconomic evidence in the literature. Our results also speak to the older Schumpeter-Arrow debate on the effects of competition on innovation; faced with an exogenous shock to competition, in our setting, leader firms do not double down on innovation, but instead cut R&D spending, consistent with the Schumpeterian channel in which competition decreases the rents available to innovators.

The rest of this paper is organized as follows. Section 2 describes the context of economic espionage and the US-China relationship. Section 3 describes the construction of our database of espionage incidents and other data sources used, and provides descriptive statistics on our data. Section 4 examines the economic damages from espionage on targeted firms and industries. Section 5 analyzes whether firms respond to espionage with restrictions on international knowledge flows. Section 6 concludes.

2.0.0.1 The Economic Espionage Act

The United States’ approach to economic espionage underwent a fundamental shift with the passage of the Economic Espionage Act (EEA) in 1996. Prior to the EEA, trade secret theft was primarily addressed through civil litigation, leaving prosecutors with limited tools to combat state-sponsored economic espionage. The Act established two key criminal offenses: theft of trade secrets to benefit foreign entities (Section 1831) and domestic trade secret theft (Section 1832). Penalties under Section 1831 are particularly severe, reflecting Congress’s concern about foreign economic espionage, with individual defendants facing up to 15 years imprisonment and fines up to $5 million.

The EEA’s focus has evolved significantly over time. Its initial motivation was based on concerns about industrial espionage by France, who had made it clear that they wanted to develop a computer industry to compete with the US’s computer industry, and were suspected of using industrial espionage to aid that process (Times 1991). However, in the 2000s, the focus shifted toward China, reflecting broader changes in geopolitical tensions and technological competition. From 1997-2008, defendants of Chinese descent represented 17% of EEA prosecutions. This proportion increased dramatically to 52% during 2009-2015 (Kim 2018). This shift coincided with China’s rapid technological advancement, and increasingly explicit policies aimed at acquiring foreign technology and expertise.

2.0.0.2 US-China Rivalry and Espionage

As China has grown, it has pursued ways to gain knowledge from the US. These efforts are exemplified by the Thousand Talents Program, launched by China in 2008. The Thousand Talents Program represents one of China’s most significant efforts to acquire foreign technology and expertise. It aimed to recruit leading international scientists, researchers, and entrepreneurs to work in China, with a particular focus on those with expertise in strategic technologies and access to intellectual property at major Western institutions. By 2018, over 7,000 individuals had participated, including both Chinese nationals working abroad and foreign scientists. Although research collaboration in most forms is legitimate, the program has also been used to facilitate unauthorized technology transfer, including several incidents in our sample. The Thousand Talents Program evolved significantly after receiving heightened scrutiny. China has stopped publicizing information about participants and rebranded various components of the program.

In the context of countering the Thousand Talents Program, the US Department of Justice launched the China Initiative in 2018. The China Initiative marked an aggressive shift in US law enforcement’s approach to Chinese economic espionage. The initiative prioritized investigating and prosecuting cases involving technology theft benefiting China, with a particular focus on academic and research institutions. It was the first DOJ counterintelligence initiative to explicitly name a single country as its prosecutorial focus, and made it clear that the US was going to invest in countering China’s talent program.

The China Initiative expanded traditional counterintelligence work in several ways. It increased scrutiny of research collaborations, requiring academics to disclose Chinese funding sources and affiliations. For example, the FBI maintains a page about China’s talent plans (FBI 2020). This page proactively warns companies and academic researchers about the risk of espionage from their students and employees (emphasis added):

Talent plans can sometimes foster legitimate sharing and collaboration as part of an appropriate business arrangement or research exchange, but this is not the norm. Instead, talent plans usually involve undisclosed and illegal transfers of information, technology, or intellectual property that are one-way and detrimental to U.S. institutions. Your students and/or employees could be talent plan participants.

The China Initiative also broadened prosecution strategies, using tools like grant fraud charges and failures to disclose foreign ties rather than relying solely on evidence of direct technology theft. This enabled prosecutions even without direct evidence of unauthorized technology transfer.

By 2021, the DOJ had brought over 70 cases under the initiative, charging both Chinese nationals and American citizens, particularly those of Chinese descent. However, the China Initiative faced criticism for disproportionately targeting Asian-American scientists—88% of defendants charged with grant fraud were of Chinese heritage (Guo et al. 2021)—and for creating a chilling effect on scientific collaboration. After the high-profile dismissal of charges against MIT professor Gang Chen in January 2022 (Times 2022), the DOJ ended the initiative in February 2022, replacing it with a broader “Strategy for Countering Nation-State Threats” that retains a focus on Chinese espionage.

2.0.0.3 Vignettes of Economic Espionage

It is useful to characterize what incidents of economic espionage actually look like. An emblematic case comes from American Superconductor Corp (AMSC), an American company that produces electronic control systems for wind farms. In 2011, the company’s largest customer, Sinovel, bribed an AMSC engineer for the control systems source code. Sinovel canceled their contract with AMSC upon receipt of the code and AMSC subsequently lost over 80% of their market value (Riley and Vance 2012). The case is typical in that it involved the illegal transfer of technology and the cooperation of a company insider.

A second case concerns Orbit Irrigation, an American company that manufactures sprinkling and irrigation systems. Janice Kuang Capener was in charge of operations at an Orbit plant in China from 2003 to 2009. She provided proprietary information about Orbit’s pricing strategy to Zhejiang Hongchen Irrigation Equipment, a Chinese competitor to Orbit, which they used to undercut Orbit’s position in the market (DOJ 2015). This case demonstrates that economic espionage is not always about the theft of technology, but is sometimes about economic information or market research that can also be important to businesses.

Another example is the case of Valspar Corp, an American paint company. David Lee—a technical director at Valspar at the time—illegally downloaded trade secrets from Valspar and Huarun (a Chinese subsidiary of Valspar) to an external thumb drive, with the intention of giving these secrets to the Shanghai office of Nippon Paint (a Japanese paint company) where he had accepted a vice-president position. However, he was arrested before he could actually deliver the information to Shanghai (FBI 2010). This mechanism of espionage—an individual downloading information illegally with the benefit of financial reward and employment in China—is representative of many cases. This case also exemplifies an “unsuccessful” case of espionage—where no information was actually leaked from Valspar. This kind of case will later be important for assessing the credibility of our results.

3.1 Espionage Data

Our primary data on economic espionage come from court filings, Department of Justice (DoJ) announcements, and reports from relevant think tanks. This dataset compiles all publicly documented cases brought under the Economic Espionage Act since its enactment in 1996, as well as other documented instances of foreign economic espionage against US firms since 1990. For the purposes of this study, we define economic espionage as theft of a firm’s proprietary economic information—these are most often technological trade secrets, but may also include confidential information such as business development or pricing strategies. We do not include cases of explicit forced technology transfer, such as through China’s quid pro quo policy (Holmes et al. 2015). We also omit cases where the only data stolen is customer information, such as data breaches of emails or passwords, since this kind of information is not central to a firm’s economic advantage over its competitors.⁵

3.2 Firm-Level Data

Out of 164 cases of economic espionage, we are able to identify a victim firm in 130 incidents. There are 142 unique firms that we identify as targeted by espionage (some incidents target more than one firm). Of these 142 firms, 108 are ever publicly listed (or are subsidiaries of listed firms), allowing us to collect detailed financial information on them. Of these public firms, 75% of cases are successful incidents, and 83% are attributed to China.

4.1 Baseline Specification

Our core empirical specification for understanding how espionage affects firm revenue and R&D is given by the following dynamic two-way fixed effects (TWFE) regression:

\[\begin{equation} y_{i,t} = \sum_{k = -4}^{8} \gamma_k D_{i,t+k} + \alpha_i + \alpha_{j(i),t} + \varepsilon_{i,t} \label{eq:twfe} \end{equation}\] where \(i\) denotes a firm, \(t\) a year, and \(j(i)\) the industry firm \(i\) belongs to. We interpret the timing of treatment to be when espionage started against a targeted firm. \(D_{i,t+k} = \mathbf{1}\{t - E_i = k\}\), where \(E_i\) denotes the year espionage began against firm \(i\), so that each firm-year observation is assigned to exactly one relative-time bin \(k\) (with one bin omitted for normalization). The coefficients \(\gamma_k\) trace the evolution of outcomes before and after espionage begins, allowing us to examine both the pre-trend dynamics and post-treatment effects on various firm outcomes. This specification treats untargeted firms as a valid comparison group. We test this assumption directly in the spillover analysis of Section 4.4 (Figure 11), finding no meaningful positive or negative spillovers to within-SIC-4 competitors.

An important feature of this specification compared to a standard event study is that our year fixed effects are actually industry-year fixed effects. In other words, we are comparing targeted firms to untargeted firms within the same industry. This feature is important because industries could be selected in whether they are targeted for espionage or not, based on the trends of firms in that industry. Thus, we want to control for industry-specific trajectories when determining whether espionage affects a firm.

To facilitate interpretation of our results and extend the time horizon over which we can measure effects, we interpret espionage as an absorbing treatment. That is, for any firm targeted multiple times by espionage, we use the first time it experiences an incident as the treatment date, and drop later espionage events. Standard errors are clustered at the firm level. The key identifying assumption is that in the absence of espionage, the outcomes of targeted firms would have evolved similarly to those of untargeted firms within the same industry.

We estimate effects on two main firm outcomes: revenue and R&D expenditures. We use revenue as a summary statistic for how much firms are affected by the loss of the competitive advantage they previously had from having proprietary knowledge/technology. However, the welfare effects of espionage may be especially large if they affect firm innovative activity, given that innovation has well-documented positive spillovers (Myers and Lanahan 2022; Jones and Summers 2020; Arque-Castells and Spulber 2022). As a result, we use R&D expenditures to capture this dimension of the effects of espionage.

Figure 4, Panel A, plots the dynamic treatment effects on firm revenue and R&D expenditures for firms hit by an espionage incident. We find that firm revenues decline by 40% within 5 years of espionage beginning, with effects persisting for the 8 years of our window. Espionage also appears to reduce firm R&D spending by a similar magnitude, but this effect is more gradual and noisier. These results show that firms substantially shrink (or fail to grow) as a result of economic espionage, and the decline in R&D expenditures suggests that espionage may have important consequences for a firm’s innovative capacity. We find no evidence of pre-trends in the event study.

4.2 Placebo Test: Unsuccessful Cases

In addition to the standard pre-trends test, our setting allows a unique approach to testing the validity of our identifying assumption. The key feature of our setting is that we not only observe successful espionage cases, we also observe cases of espionage that failed—i.e. no knowledge was transferred out of the targeted firm. This usually happens because the attempt to steal knowledge was detected and stopped before the theft could occur successfully. This is illustrated by the case of Valspar and David Yen Lee discussed in Section 3. There are 38 such cases in our dataset.

Under the assumption that successfully-targeted and unsuccessfully-targeted firms would have had parallel trends in the absence of espionage, the sample of unsuccessful cases actually allows us to test whether our parallel trends assumption holds in the post-period. Formally, let \(S_i = I[\text{espionage on $i$ is successful}]\). Then under the assumption that \[\begin{equation} \mathbb{E}[y_{i,t}(0) | D_{i,t} = 1, S_i = 1] = \mathbb{E}[y_{i,t}(0) | D_{i,t} = 1, S_i = 0] \label{eq:placebo} \end{equation}\] estimating Equation 1 in the set of unsuccessful cases (\(S_i = 0\)) provides a direct test of the parallel trends assumption. This set of cases is excluded from our main estimation sample. If our empirical design is valid, we expect to find no significant effects in this placebo sample (\(\gamma_k = 0\) for \(k > 0\)), as no actual knowledge theft occurred. Any significant effect detected in this placebo sample would be evidence of a violation in parallel trends. Conversely, a null effect in this placebo sample provides evidence for the parallel trends assumption—stronger support than is normally available in difference-in-difference research designs.

In Figure 4 Panel B, we plot results for the placebo test using unsuccessful cases of espionage, and find null results, also supporting a causal interpretation of our main estimate. Failed attempts to steal information do not harm the targeted firms, nor are targeted firms already on a downward trajectory.

To verify that this null effect in the placebo test is not simply noise caused by a small sample size, we estimate a triple-difference specification in Table 2 that allows us to specifically test whether the treatment effect differs between the two groups (successful firms and unsuccessful firms). We find that even with the small size of our placebo sample, we can reject that the placebo effect on revenue is the same as the effect on successful firms—providing evidence that our effects are not simply driven by pre-trends. However, R&D is a noisier outcome, and the effect we show in Figure 4 only becomes large many years after the espionage. This is why the triple difference coefficient, which includes the years just after espionage, is smaller and thus can’t be separated from the placebo.

The validity of this placebo test rests on Assumption 2: that successfully and unsuccessfully targeted firms would have had parallel trends absent espionage. We acknowledge this assumption is not trivially satisfied—in principle, firms with weaker internal security may be both more likely to have espionage succeed and more likely to decline for unrelated reasons. Nonetheless, this assumption is substantially weaker than the standard parallel trends assumption required for a simple difference-in-differences design, which requires every non-targeted firm to be a valid counterfactual for every targeted firm. Here, we only require comparability between two subgroups that are both targeted for espionage—with the only distinction being whether the attempt succeeded or failed, a determination that often hinges on idiosyncratic factors such as whether an employee was caught before transmitting stolen information. To further validate this assumption, we compare pre-espionage observables between successfully and unsuccessfully targeted firms. We present a balance table in Table 3, finding no statistically significant differences along any characteristic between firms successfully and unsuccessfully targeted by espionage. This triple-difference specification can be interpreted as a formal mediation analysis (Kwon and Roth 2024), with the success of the espionage attempt as the mediator—the coefficient on the post \(\times\) successful interaction isolates the causal effect mediated by actual knowledge transfer.

4.3 Mechanisms

To understand the mechanism by which espionage affects firms, we estimate heterogeneous effects of espionage on firms and plot the results in Figure 5.

First, we separate cases based on whether the damages claimed by the victim firm in court are above or below the median claimed damages, and estimate Equation 1 separately for these two groups. Panels A.1 and A.2 show that our estimated revenue effects are concentrated entirely in cases with above-median claimed damages. Claimed damages are a legal construct that firms calculate based on the self-assessed market view of their stolen technology. Thus, cases with high claimed damages are cases where the stolen technology was especially critical to the firm’s competitive edge, and espionage may represent the loss of this competitive edge.

Second, we separate cases based on whether the targeted industry’s baseline R&D spending is above or below the median for targeted industries. Panel B.1 shows that the revenue damages from espionage are most pronounced in industries with below-median R&D spending. Although potentially counterintuitive, our preferred interpretation is that espionage is most damaging when a firm is in an industry with relatively few key technologies, compared to in a more fast-moving sector where technologies become obsolete quickly and/or the marginal value of each technology is lower.¹⁴

Third, we separate cases based on how concentrated patent values are in the targeted industry (as measured by HHI), based on returns to patenting from stock market reactions (Kogan et al. 2017). Panel C.1 shows that the revenue effects of espionage are much larger in high-HHI industries than low-HHI industries, with most of the separation coming four years after espionage. Panel C.2 shows that R&D responses are also typically larger in high-HHI industries, but this is a much noisier measure and we are unable to statistically reject the null of an equal response. This heterogeneity suggests that espionage is most harmful when there are a few key valuable technologies at stake, as it may be difficult to innovate a suitable replacement for it. On the other hand, the null result for low HHI industries (especially after four years) suggests that even if the loss of a technology to a competitor may hurt in the short-run, the relative ease of innovating a substitute may mute the impact of espionage once the product has time to come to market.

Together, these heterogeneity analyses paint a picture whereby espionage makes a firm’s valuable technology obsolete. Thus, espionage is less harmful when these technologies are less valuable or when the industry’s high rate of R&D spending means the natural obsolescence rate of technology is higher.

4.4 Robustness Checks and Additional Analyses

We briefly report additional robustness checks, which are described in more detail in 8. First, while our main specification is a dynamic two-way fixed effects estimator, we show that our results are robust to a variety of alternative event-study estimators (Figure 9). Second, in addition to firm revenue and R&D expenditures, we show that espionage also reduces a firm’s total assets and intangible assets (Figure 10), and that both domestic sales and exports to China decline (Figure 12). Third, we relax the assumption that firms in the targeted industry are untreated, allowing for spillovers to those firms (Figure 11), and find no evidence of meaningful positive or negative spillovers to competitor firms within the same SIC-4 code. Fourth, we look at how firms’ stock returns are affected by both the onset and announcement of espionage, finding that both events lead to persistent declines in returns, consistent with losses from both real factors and the market’s perception of these incidents (Figure 13). Finally, we show that results are unlikely to be driven by mean-reversion of leader firms (Figure 14) or by selection of targeted industries (Table 4).

4.5 Aggregate Effects: Exports

From a policy standpoint, what matters more than effects on targeted firms is the aggregate effect of espionage. If targeted firms lose revenue and conduct less R&D, but non-targeted firms gain revenue and conduct more R&D, the net result could be zero, simply reflecting a reallocation between firms. Policymakers would be much less concerned about espionage in that case, especially if the reallocation happened within a country. In our prior analyses, the SIC-4 industry-year fixed effects in Equation 1 absorb within-industry reallocation, so the firm-level estimates presented already net out substitution between victim and competitor firms in the same SIC-4. The export analysis tests whether further reallocation exists at the country level.

To understand these aggregate effects, we test how espionage incidents affect exports in both the US and China. We use trade data to capture industry-level effects for three reasons. First, it covers all firms operating in each country, including non-publicly listed firms that would not appear in Compustat. Second, in Ricardian models of trade, exports reveal a country’s productivity in a sector, so changes in exports can be interpreted as the best proxy for changes in industry productivity (Costinot et al. 2012). Finally and most importantly, exports are the cleanest way by which we are able to estimate how espionage affects China. For the rest of the analysis, we cannot reliably link incidents to suspect firms, and even if we could, the benefits of espionage could be diffused across Chinese industry. Therefore, looking at how Chinese exports evolve in targeted sectors is key to understanding the other side of the equation.¹⁵

We map SIC codes to HS6 codes using the 2018 revision of the concordance from (Pierce and Schott 2012), and define treatment at the industry level as the first period in which an industry is ever targeted by an espionage incident. In other words, we keep only the first time any firm in the industry was targeted. This definition relies on publicly detected espionage; if detection is selective across industries, some nominally never-treated industries may have been targeted without public disclosure, which would bias our estimates toward null results. In this specification, our never-treated HS6 codes are “nearby” codes—those that share an HS4 code with a targeted HS6 code, but are never themselves targeted for espionage. Our baseline specification is, for the outcome log(exports by country \(i\) in sector \(s\) at year \(t\)):

\[\begin{equation} y_{i,s,t} = \sum_{k=-4}^{8} \left(\gamma_k D_{s,t+k} + \beta_k D_{s,t+k} \mathbb{I}[i \text{ is US}] + \delta_k D_{s,t+k} \mathbb{I}[i \text{ is CN}]\right) + \alpha_{i,s} + \alpha_{i,t} + \epsilon_{i,s,t} \label{eq:trade_ddd} \end{equation}\]

Regressions are at the exporter-HS6-year level, and include exporter-HS6 and exporter-year fixed effects.¹⁶ Apart from the direct treatment effect, we also conduct triple differences, interacting time to treatment with the exporter being the US or China. For interpretation of coefficients, in this section, we restrict cases to only the 129 where the suspect country is China.

We plot results in Panel A of Figure 6. We find that ten years after espionage occurs in a sector, exports by the US fall by 40%. Puzzlingly, exports from China also fall in the short term before recovering in the long term, suggesting that China does not appear to gain through international markets in a way that offsets US losses.

These effects are surprisingly large. Are they due to selection of which industries are targeted for espionage? We test this with the same placebo test as before: under Assumption 2, we can test whether these effects are due to espionage by estimating Equation 3 in the sample of unsuccessful cases. Panel B of Figure 6 reports the result of this placebo test. Here, the placebo test is not as clean as before. We see no decline in exports from the US or the rest of the world, suggesting that the effects on the US and the rest of the world from Figure 6 are truly causal. However, we do see Chinese exports rising in sectors even when espionage is unsuccessful. This implies that the appropriate counterfactual for China is a rising baseline, meaning the true negative causal effect of espionage on Chinese exports is likely larger than our raw estimate—our detected effects on China are therefore if anything understated.¹⁷ The clean US and rest-of-world placebos support a causal interpretation of the export declines for those exporters.

We note two additional reasons that effects may be large. First, many targeted firms are large, multiproduct firms, and so losses in affected HS6 codes may be larger than the average losses the firm faces. Second, on a ten year horizon, the loss in revenue for targeted firms is similar in magnitude to the decline in exports. Given the size distribution of firms follows a power law (Axtell 2001) and that many sectors are repeatedly targeted, this can reconcile the large estimated effects on exports.

5.1 Patent-Based Openness

One way to understand firm openness and knowledge-sharing with the world is through patenting behavior. Patenting inherently involves disclosure of inventions, providing knowledge spillovers to the rest of the world. Patenting data additionally includes information about how the technology was produced (e.g., the location of collaborators) that indicate how internally open the firm is. Thus, we can get a richer understanding of how espionage affects knowledge diffusion by measuring patenting outcomes.

We estimate Equation 1 with patent-based outcome measures, with two modifications. First, because we are especially interested in how firms respond to geopolitically sensitive incidents and US-China competition, we restrict our focus to the cases where China is the beneficiary of espionage. Second, we define “treatment” as when espionage against a firm ends, rather than when it begins. Previously, the relevant event was when a firm had its knowledge stolen. Here, we are interested in firms’ endogenous responses to espionage, which requires that the firm be aware it has been targeted. The date of espionage ending is our best proxy for when a firm discovers that it has been targeted for espionage.

As Figure 1 shows, there is substantial variation in how long espionage lasts. Sometimes there are years between when a company is first compromised and when espionage ends, other times the espionage is a one-time event and so there is no distinction. The date of espionage ending is our best proxy for when a firm discovers that it has been targeted for espionage.

A natural concern with adapting Equation 1 is that now our treatment is an information shock, not a technology shock—and untargeted firms can respond to this information shock. Put differently; Airbus cannot (directly) lose revenue because Boeing was targeted for espionage, but Airbus can restrict its knowledge sharing in response to Boeing being targeted for espionage. In 8.2, we show that competitor firms in the same targeted SIC-4 do not change their innovation management in response to espionage against their competitors, which supports treating them as a valid comparison group for estimating the effect of espionage on targeted firms.

We first estimate the effect of espionage on the propensity for firms to patent. In Figure 19, Panel A, we show that firms targeted by espionage are about 20-30% less likely to patent in the 5-10 years following an espionage incident. This decline in innovation is consistent with the overall declines in firm size, spending on R&D, and number of scientists employed that we document above, and may represent a substantial loss of knowledge given the frontier research that these firms tend to engage in.

However, it is important to distinguish between a reduction in patenting because of scale effects (less revenue and liquidity to fund R&D), and a reduction in patenting because firms are being more secretive about the technologies they are developing. Only the latter contributes to a reduction in knowledge openness. We proxy for this measure of “secrecy” by measuring patents filed per dollar of R&D expenditure—reflecting the fact that if the results of R&D were instead held as trade secrets, that would decrease the patents filed per R&D dollar. In Figure 7, Panel A, we estimate how espionage affects the patent-to-R&D ratio. The results show that if anything, espionage increases the patent-R&D ratio. For firms to have become more secretive, they would have had to become much more efficient in converting R&D spending into real innovation, which is unlikely given the other measures of firm decline. Thus, we do not see firms becoming less likely to share knowledge through patenting after being targeted for espionage.¹⁸

Consistent with this null result, we find no evidence of firms restricting their international collaborations across other dimensions. In Figure 7, Panel B, we show that firms are no less likely to patent with inventors living in China, indicating that firms may not adjust their international scientific collaboration practices in response to espionage. The innovation literature also uses patent citations as an indicator of knowledge diffusion. In Figure 7, Panel C, we find that Chinese firms are no less likely to cite patents from the targeted firm in response to espionage. In Figure 19, we examine two other outcomes of interest. Firms are no less likely to patent with inventors living outside the US, and citations to the targeted firm’s patents also do not change in response to espionage. This indicates that targeted firms do not seem to restrict other firms’ awareness of their technologies.

Collectively, these results indicate that targeted firms do not deliberately make their patented technologies less open to China or the rest of the world. This implies that firms view the benefits they get from patenting and having an international set of collaborators as sufficiently large to offset any costs associated with espionage.

5.2 Employment Responses

The key vector for espionage is employees of the targeted firm. Thus, an important margin of response for targeted firms is how they adjust their employment decisions. In particular, we ask: do firms become more discriminatory towards employees who they perceive as “high espionage risk”? In espionage cases involving China, the most likely category that firms would want to restrict is Chinese scientists—especially those doing deep technical work that they could take to Chinese firms. This kind of employment discrimination would be illegal, but is certainly a plausible response by firms.

5.3 Geographic Markets

We use segment data from Compustat that describes the geographical markets in which a firm operates, and their sales in each market.²¹ Firms may respond to espionage by reducing their global exposure, focusing efforts on certain markets or excluding other markets; they may also expand their coverage in an attempt to diversify.

In Figure 7 Panel E, we find no evidence that firms contract their geographical footprint in response to espionage; the number of markets that firms operate in does not fall after espionage. We also directly examine whether firms change whether they operate in China, the country most associated with espionage in our sample. If firms respond to Chinese espionage by pulling back from China as a market, we would expect a decline in the probability of having a Chinese business segment. In Figure 22, Panel A, we find no evidence that firms exit the Chinese market in response to espionage targeting. One may also think that discrete changes in geographic markets are too large of a response to detect: firms may reallocate operations across geographic markets, while maintaining all preexisting global ties. We examine whether these intensive margin allocative effects occur by testing whether the HHI of sales across geographic segments changes after espionage. In Figure 22, Panel B, we find no systematic evidence of this occurring. This indicates that firms are not strategically responding to espionage incidents by altering their geographic business strategy.

Firms may also respond to espionage across their business operations. We next examine whether firms change their operations across business segments (major product lines or divisions within the company).²² In Figure 22, Panel C, we track the number of business segments a firm operates in. A firm may strategically narrow its product lines in response to espionage, either to focus resources or to reduce the surface area of potential theft. We find no significant change in business scope by this measure. In Panel D, we test for intensive margin changes by looking at the HHI of sales across business segments. We also find no evidence of changes in their business strategy along this margin. In short, across both geographical markets and strategic business segments, we find no evidence of firms adapting to espionage.

5.4 Earnings Calls

We do not find that firms respond to espionage by adjusting their patenting activity, employment, or geographical coverage. However, the space of possible actions that a firm could take in response to espionage is immense—it would not be possible to directly test for them all. To get as comprehensive of a view as possible, we instead test for changes in the nature of corporate speech.

We test for changes across the high-dimensional space of corporate speech using the method of Carlson (2026), which uses LLM representations of concepts to summarize text data and test for differences in any concept contained in the text. This method allows us to (in principle) pick up on any dimension of a response to espionage represented in public speeches by corporate executives and the analysts questioning them.²³ This method is designed to be automated, minimizing researcher degrees of freedom, while allowing for a very large number of hypotheses to be tested with appropriate multiple hypothesis correction. More details on the method can be found in 7.3.

Figure 23 shows the results of this analysis. Of 16,000 tested hypotheses for differences in corporate speech between successfully targeted and unsuccessfully targeted firms, we find that only two differences are statistically significant. Both of these represent stylistic differences in speech and are not economically meaningful (“patterns of formatting changes or markup language” and “the word ‘while’ in various contexts”). Thus, earnings call data provides a rich null result, ruling out effects on a wide range of firm responses that would be reflected in earnings call transcripts.

This null result is corroborated by our own qualitative reading of earnings calls by these firms; when we read earnings call transcripts in the aftermath of espionage, neither firm representatives nor analysts questioning them tend to discuss the news of espionage and how it affects the firm, even after the espionage has become public knowledge. One prominent exception is the case of AMSC (introduced in Section 2). When discussing how they would adapt to the espionage incident, executives noted:

The actions of Sinovel are not a referendum on China wind or China in general. We have, and will continue to have, many valued relationships in China. We remain strongly committed to Chinese wind customers, like Dongfang, XJ Group, Shenyang Blower Works and JCNE. It is an economic reality that we must do business in China and I believe we can do it securely and profitably.

In 7.3, we excerpt more of the transcripts, noting a general throughline of a failure to systematically adapt to the espionage incident.

5.5 Aggregate Firm Openness Index

One possible concern with interpreting our null results is that given our small sample size, a null result could reflect either a true null effect or simply a lack of power in our sample. To address this concern, we aggregate the outcomes tested in the sections above into an index of “firm openness”, and test for effects on this index. We define a firm openness index by standardizing each of the five outcomes captures in Panels A-E of Figure 7. We then define the firm openness index as the inverse-covariance-weighted average of these standardized outcomes (Anderson 2008).

Panel F in Figure 7 shows the results of this analysis. Interpreting the index as a measure of firm openness, we can rule out espionage decreasing firm openness by more than 0.04 SDs in the eight years after espionage ends. This null is much more precise than its constituent outcomes, and provides clearer evidence that our results reflect true non-responsiveness by firms to espionage, rather than limitations of our data.

Another concern is that firms may not realize that they have been targeted for espionage until years after an espionage incident ends, mechanically biasing our early year coefficients towards 0. To address this, we replicate Figure 7 using the year a firm announces an espionage incident as the timing of treatment instead. This guarantees that firms are aware of the incident. We plot results in Figure 16. We continue to find a sharp null under this alternate specification.

5.6 Discussion

The preceding analysis establishes that across a variety of outcomes—patenting, employment of Chinese scientists, geographical market presence, and corporate speech—firms do not respond to espionage by becoming less open or more restrictive in their innovation management. Given the large economic damages we document, this is a striking and perhaps puzzling result.

Our preferred explanation for this finding is that firms navigate a difficult tradeoff when deciding on their openness to international knowledge flows. Most levers for international openness—e.g., collaboration with foreign scientists, opening branches abroad—simultaneously bring in new information to firms, while also creating risks of proprietary knowledge flowing outwards. We find that firms decide not to reduce their international openness even after a large shock to their stock of proprietary knowledge. This suggests that, on average, the benefits of inward knowledge flows are substantial enough to offset the risks of knowledge outflows.

There are alternative explanations compatible with this finding. One possibility for the non-response of firms could be that espionage does not constitute a genuine information shock. If firms already know that their industry is frequently targeted for espionage, the discovery that they themselves have been targeted need not trigger any response if their security decisions were already optimal. We investigate this possibility in 8.2, focusing on the first time a sector is targeted by a public espionage incident. We find no clear evidence of differential adjustment in these cases, either. We note that even if firms were fully informed about the costs and risks of espionage ex ante, the realization of espionage is still an unexpected negative shock. This makes a response from firms via international openness plausible. As an analogy, firms may understand both the costs and probability of tariffs being imposed; yet when a new tariff is announced, this new information is a shock that redirects international trade flows. Similarly, the timing of when espionage occurs is unlikely to be known by firms, making a response along international openness theoretically possible.

Another possibility is that the margins we study (patenting, international collaboration, hiring of foreign scientists, international business operations) are simply not important margins for firms to adjust on. It could be that firms see these margins as low-risk vectors for espionage. This is unlikely, given that a large literature documents knowledge flows through patenting, scientific collaborations and business operations as being central to innovation, as well as our examination of corporate speech, which may capture many other margins of adjustment.

An alternative explanation is that some firms become much more open and others much more closed following espionage, due to idiosyncratic conditions, in such a manner that the net effect is zero. Such heterogenous treatment effects should produce a noisily measured null, whereas ours is relatively precise. Such large variation in firm responses is also inconsistent with our reading of the transcripts around these events.

We note several other mechanisms that could explain why firms may not become less open following espionage. First, firms may deliberately minimize public acknowledgment of espionage to limit damage to shareholder confidence; in this view, the absence of any strategic response is itself strategic, aimed at projecting stability. Second, principal-agent problems within firms may prevent adjustment: the managers closest to the affected projects may have strong incentives to downplay the threat rather than flag a failure that could affect their own careers, making it organizationally difficult for firms to mount a coherent response. Third, firms may simply hold an overoptimistic belief in the durability of their technological lead—believing that even if a competitor has stolen their technology, their own accumulated capabilities and ongoing investment will prevent them from being overtaken. Each of these mechanisms features an additional friction or behavorial explanation (e.g., myopic shareholders) that our preferred interpretation does not require.²⁴ Although we cannot rule these types of mechanisms out, they are also not mutually exclusive with firms valuing knowledge inflows enough to offset the costs of knowledge outflows.

Finally, whatever the underlying mechanism is, the reduced form finding that firms do not become more closed following espionage incidents suggests that even following (geopolitically) adverse shocks, we may not see strong decoupling in international knowledge flows.

7.1 Scientist Job Classification

Revelio Labs categorizes all LinkedIn job titles into 1500 broad jobs. We manually code jobs as being “scientist” jobs based on their title. Any category whose title contains the word “research” or the word “scientist” is classified as a scientist job (so that, for example, “Senior Scientist” and “Principal Scientist” are captured by the latter rule). Additionally, the following specific categories are also classified as scientists: analytical chemist, automation engineer, CAD designer, CAD engineer, chemical engineer, chemist, clinical research, electrical design engineer, electrical engineering, environmental engineer, environmental scientist, geotechnical engineer, hardware design engineer, hardware engineer, industrial designer, industrial engineer, industrial engineering, innovation, lab, lab tech, lab technician, laboratory, laboratory analyst, laboratory technician, materials engineer, mechanical design engineer, mechanical designer, process developer, process development, process development engineer, process engineer, process engineering, product design, product design engineer, product designer, product developer, product development, product development engineer, product engineer, product engineering, R&D, R&D engineer, R&D project, and scientific.

7.2 Employee Reviews on Diversity and Inclusion

RevelioLabs provides individual review data for many of the companies in our sample. These reviews include both an individual’s overall rating of the company, and also the individual’s rating of the company on diversity and inclusion. Additionally, the full text of the employee’s review is also recorded.²⁵ While coverage of the overall ratings and the text of employee reviews range from 2008 to 2025, the diversity and inclusion reviews only begin in 2020. This poses a substantial sample restriction.

To overcome this restriction, we make use of the text of the reviews and natural language processing techniques to impute diversity and inclusion ratings. This allows us to extend coverage of these ratings back to 2008.²⁶ We conduct this imputation in two separate ways: TF-IDF and ridge regression, and a fine-tuned BERT model. Heuristically, the TF-IDF and ridge regression method identifies words that are highly predictive of the ratings and uses a penalized regression on these words to estimate the final rating, while the fine-tuned BERT model involves tuning a language transformer model to our particular text corpus to predict the final rating. We describe each method in more detail.

7.3 Automated Discoveries from Earnings Calls

Do firms communicate in systematically different ways following successful espionage incidents on earnings calls? We use the method in (Carlson 2026) to simultaneously test over 16,000 hypotheses. This method is designed to be automated, minimizing researcher degrees of freedom, while allowing for a very large number of hypotheses to be tested. It also appropriately statistically controls for false discovery rates. Beneath, we summarize the method (including a difference-in-difference extension we develop for our setting), and then discuss technical implementation details.

8.1 Effects on Targeted Firms

8.2 Knowledge Diffusion Spillovers to Other Firms

Targeted firms are not the only firms that could endogenously respond to espionage. For example, Boeing might reduce its willingness to patent with Chinese inventors in response to espionage against Airbus. This is not only an independently important margin of response, but also an identification threat to the previous sections—by taking non-targeted firms as never-treated, we risk underestimating the endogenous response to espionage that could be common across firms.

One way to estimate this aggregate margin of response would be to aggregate all of our outcome measures to the industry level. However, a direct industry-level regression has too few targeted industries (roughly 50 unique SIC-4 codes) to yield informative estimates. We therefore implement the aggregate test at the firm-year level using a triple-difference between targeted and nearby SIC-4 codes. Thus, in this analysis, our never-treated group is firms in nearby SIC 4-digit codes—i.e. firms in the same SIC 3-digit code \(k(i)\) but not in the same SIC 4-digit code \(j(i)\). Let \(D_{j(i),t+k} = 1\) if firm \(i\) is in an SIC \(j(i)\) that has been targeted for espionage, and let \(l(i)\) denote the SIC-3 code of firm \(i\). Then the estimating equation is

\[\begin{equation} y_{i,t} = \sum_{k = -4}^{8} \gamma_k D_{i,t+k} + \sum_{k = -4}^{8} \delta_k D_{j(i),t+k} + \alpha_i + \alpha_{l(i),t} + \varepsilon_{i,t} \label{eq:spillover_ddd} \end{equation}\]

Here \(\gamma_k\) captures the direct effect of espionage on victim firms, while \(\delta_k\) captures the spillover response of competitor firms in the same targeted industry. Note that this specification includes SIC-3 by year fixed effects \(\alpha_{l(i),t}\), in contrast to the SIC-4 by year fixed effects used in Equation 1. This reflects the fact that our never-treated comparison group here consists of firms in nearby SIC-4 codes within the same SIC-3 industry. The competitor spillover effects we report in Figures 21 and 18 come from the \(\delta_k\) coefficients; victim firm effects are captured by the separate \(\gamma_k\) term.

For the employment outcomes, since Revelio Labs only provides NAICS codes and not SIC codes for firms in the LinkedIn data, we assign treatment at the NAICS 6-digit level, and we use firms in “nearby” NAICS 4-digit codes as the never-treated group. We only keep the first incident targeting an industry in estimation.

In Figure 18, we estimate the spillover impacts of espionage to patenting by other firms in the industry. Panel A shows that firms in industries targeted by espionage are about 5% more likely to file a patent, consistent with the small increases in the hiring of scientists that we observe. However, we find no effects on the likelihood of patents Taken together with the results on employment, these results suggest that firms in industries targeted by espionage may try to increase their innovation activities—potentially to develop a lead over the stolen technology, or to outcompete the victim firm—but that they do not necessarily become less open to foreign collaboration and innovation.

In Figure 21, we estimate the spillover impacts of espionage to the employment of other firms in the targeted industry. Panels A and B reveal that firms in industries targeted by espionage also appear to hire slightly fewer employees, in the 5-10% range, among both Asians and non-Asians.³⁵ On the other hand, Panels C and D reveal that the hiring of scientists does not change at untargeted firms, both among Asian and non-Asian scientists—suggesting that that untargeted firms do not necessarily update their hiring practices from the occurrence of espionage in other firms in their industry.

One explanation for this non-response even at the industry level is that firms’ decisions about optimal openness depend on their perception of espionage risk. As such, even if espionage makes them less open to knowledge flows, they might not respond to individual events of espionage. Indeed, given that many sectors are targeted repeatedly (Table 1) and thus presumably face a high background espionage risk, this is the natural interpretation to our null findings on the endogenous response of firms. However, our estimation only includes the first time an industry is targeted for espionage. Thus, as long as the first espionage incident in a sector updates firms’ beliefs about espionage risk, our strategy should capture the responses of firms to espionage. The fact that we find no effects on other firms even from the first espionage incident in a sector implies that this is not simply an artifact of firms having generally well-calibrated beliefs about espionage.³⁶