O31 | Macro Paper Warehouse

Heterogeneous innovations and growth under imperfect technology spillovers

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question. Jo and Kim ask two related questions: (1) How do firms use different types of innovation when learning others’ technology takes time? (2) How does this process alter the aggregate implications of firm innovation, particularly in the context of increasing competition?

Model. The paper develops a discrete-time infinite-horizon endogenous growth model with multi-product firms pursuing two types of innovation — “own-innovation” (improving existing product quality) and “creative destruction” (entering new product markets by displacing incumbents) — subject to a novel friction called “imperfect technology spillovers.” The friction takes the specific form of lagged learning: creative destruction builds on the one-period-lagged technology of the target market’s incumbent, while only the incumbent can observe the current frontier technology level. This one-period lag creates a technology gap (Δ = q_t / q_{t−1}) between the incumbent’s frontier and the level available to rivals. Four possible technology gap values arise in equilibrium: Δ₁ = 1 (no gap), Δ₂ = λ (one successful own-innovation), Δ₃ = η (one successful creative destruction), and Δ₄ = η/λ. The step sizes satisfy λ² > η > λ, meaning a single creative destruction improves quality more than a single own-innovation, but two consecutive own-innovations dominate a single creative destruction.

Key Mechanisms. The learning friction generates two novel mechanisms. First, the “market-protection effect”: incumbents with a technology advantage (Δ > 1) intensify own-innovation to widen the gap and protect their product lines when competitive pressure rises. Formally, own-innovation probability is highest for Δ₂ products and declines monotonically (z₂ > z₃ > z₄ > z₁), and ∂z₂/∂x > ∂z₃/∂x > 0 while ∂z₁/∂x < 0, conditional on value coefficients. Second, the “technological barrier effect”: higher overall own-innovation and creative destruction intensity widens the average technology gap across products, reducing rivals’ conditional probability of successfully taking over a product market. This is distinct from the standard Schumpeterian effect (lower expected future profits) and from the escape-competition effect in step-by-step models (which apply only to neck-and-neck, single-product firms).

Data and Empirical Strategy. The empirical analysis combines the USPTO PatentsView database, the Longitudinal Business Database (LBD), the Longitudinal Firm Trade Transactions Database (LFTTD), the Census of Manufactures (CMF), Compustat, and NBER-CES data, covering the universe of U.S. patenting firms from 1976 to 2016, with main analyses from 1982 to 2007. Own-innovation is proxied by the self-citation ratio of patents (the ratio of self-citations to total backward citations); creative destruction by new products added and low-self-citation patents. Exogenous competitive pressure comes from China’s WTO accession in 2001, instrumented by the industry-level NTR tariff gap (the gap between non-NTR and NTR rates in 1999) following Pierce and Schott (2016).

Empirical Findings. Pre-shock (1982–1999): patents with lower self-citation ratios (closer to creative destruction) have significantly longer backward citation gaps (coefficient −2.29 to −2.59, p < 0.01 across specifications), confirming that learning others’ technology takes more time. Creative-destruction-type patents also have higher market value (Kogan et al. stock return measure) and scientific value (forward citations), with self-citation ratio negatively associated with both (e.g., coefficient on self-citation for market value: −0.289 without firm FE; −0.110 with firm FE, p < 0.01). Conditional on patenting, higher self-citation ratios are negatively associated with employment growth (coefficient −0.256, p < 0.05), number of industries added (−0.158, p < 0.05), and products added (−0.274, p < 0.01).

Post-shock (DID): foreign competition had no statistically significant effect on overall patent counts, but firms with above-average innovation intensity in industries with high NTR gaps significantly increased their self-citation ratio — indicating a shift toward own-innovation. The triple-interaction coefficient is 0.795 (p < 0.01) with baseline controls. For a firm with average lagged innovation intensity (0.18) in an industry with an average NTR gap (0.291), this corresponds to a 4.2 percentage point increase in the seven-year growth rate of the self-citation ratio, representing a 15.0% increase relative to the average growth rate of 28.2 percentage points. Consistent with the technological barrier effect, firm entry rates are lower in industries with higher TFPR-skewness-based technological barriers (coefficient −0.012 to −0.016, p < 0.05).

Quantitative Analysis. Calibrated to the U.S. manufacturing sector in 1992, the model matches six target moments including average number of products (2.3), products added (0.3), firm entry rate (7.6%), average productivity growth (1.9%), high-growth-firm employment growth (22.5%), and import penetration (15.3%). Creative destruction contributes approximately 1.88 times more to growth per unit than own-innovation (step size ratio 0.075/0.04). The aggregate R&D-to-sales ratio (untargeted) is 4.6% in the model vs. 4.1% in data.

A counterfactual increasing outside entrants by 83% (matching the rise in import penetration from 15.3% to 25.1% between 1992 and 2007) generates a 1.51% increase in aggregate creative destruction arrival rate x, but firm-level creative destruction probability falls 1.33% and startup creative destruction also falls 1.33%. The aggregate R&D-to-sales ratio falls 1.6% and creative destruction R&D intensity falls 1.2%. Average domestic productivity growth declines 11.0%, with growth from creative destruction falling 13.0% and growth from domestic startups falling 1.7%. The total mass of domestic firms falls 6.4%.

In economies with creative destruction costs 80 times higher than the U.S. baseline, the same competitive pressure shock raises rather than lowers total R&D (by 1.0%), but domestic growth still falls 9.7%, because the marginal decline in creative destruction impedes the growth contribution and firm entry even when aggregate innovation spending rises.

In depth

Q1. What is the key friction that distinguishes this model from the existing multi-product firm literature (e.g., Klette and Kortum 2004; Akcigit and Kerr 2018)?

A: The key friction is “imperfect technology spillovers,” modeled as lagged learning: creative destruction can only build on the one-period-lagged technology of the target product (q_{j,t−1}), while the product’s current owner observes the frontier technology (q_{j,t}). In models without this friction — such as Akcigit and Kerr (2018) — rivals can instantly learn and copy frontier technology, so firms have no technological advantage and cannot protect their markets. In the current model, own-innovation by the incumbent widens the gap between q_{j,t} and q_{j,t−1}, creating a barrier that a rival must overcome even after successful creative destruction. This makes own-innovation an endogenous function of the technology gap, a feature absent from existing multi-product firm frameworks.

Q2. Why does the model predict that own-innovation increases with the technology gap up to a point, then decreases?

A: From Corollary 1, the ordering z₂ > z₃ > z₄ > z₁ reflects competing forces. Products with gap Δ₂ = λ gain the most from additional own-innovation in terms of reducing the probability of losing the product line (equation 2), so own-innovation is highest there. Products with Δ₃ = η or Δ₄ = η/λ already have substantial technological advantages from prior creative destruction, so the marginal value of own-innovation in reducing market loss probability is lower. Products with Δ₁ = 1 have no advantage at all: if a rival succeeds in creative destruction, the incumbent loses the product regardless of own-innovation (equation 1), so z₁ is lowest. Beyond a certain gap level, the incumbent is sufficiently protected that additional own-innovation has diminishing returns in deterrence.

Q3. What is the market-protection effect formally, and for which products is it strongest?

A: The market-protection effect (Corollary 2) is the positive response of a firm’s own-innovation to an increase in the aggregate creative destruction arrival rate x, conditional on the value coefficients A₁ and A₂ being fixed. It is strongest for products with Δ₂ = λ (∂z₂/∂x is the largest and positive), positive but weaker for Δ₃ = η (∂z₃/∂x > 0), of ambiguous sign for Δ₄ = η/λ, and negative for Δ₁ = 1 (∂z₁/∂x < 0). The asymmetry reflects the asymmetric payoff to own-innovation across gap levels: for Δ₂ products, successful own-innovation can turn a losing situation into a winning one because it shifts the technology gap from Δ₁ to Δ₂ from the rival’s perspective, effectively defeating the rival’s creative destruction attempt. This mechanism provides a micro-foundation for why frontier firms (like Google or NVIDIA) keep innovating intensely despite their technological leads, a pattern the standard step-by-step model cannot explain.

Q4. What is the technological barrier effect and how does it differ from the Schumpeterian effect?

A: The technological barrier effect refers to the reduction in rivals’ incentive for creative destruction caused by an increase in the average technology gap across product lines. When incumbents do more own-innovation or when outside firms do more creative destruction, the distribution of technology gaps shifts rightward (density at Δ₁ falls; density at Δ₂, Δ₃, Δ₄ rises). This raises the average technology barrier rivals must overcome to successfully take over a product market, reducing the conditional takeover probability x^{takeover} and the expected value of creative destruction B. In the U.S. counterfactual, the technological barrier effect accounts for 17.0% of the total change in the aggregate creative destruction rate x and 15.0% of the change in startup creative destruction x_e. In contrast, the Schumpeterian effect refers to the reduction in expected future profits from owning a product due to increased displacement risk (through the value coefficient A₂), a mechanism present in standard quality-ladder models. Both operate simultaneously but the technological barrier effect is a novel feature of this framework.

Q5. How is own-innovation vs. creative destruction measured empirically, and what validates this measure?

A: The self-citation ratio (the share of a patent’s backward citations that cite the same assignee’s earlier patents) is used as the primary measure: a higher ratio indicates greater reliance on the firm’s own prior knowledge, hence a higher probability that the innovation improves an existing product line (own-innovation). This is validated empirically in three ways. First, patents with lower self-citation ratios have significantly larger backward citation gaps (coefficient −2.29 to −2.59 across fixed-effect specifications on 728,721 observations), consistent with creative destruction requiring more time to learn others’ technology. Second, lower self-citation patents have higher market value and scientific value (forward citations), consistent with η > λ (creative destruction contributes more per event to quality). Third, firm-level regressions show that lower self-citation ratios are associated with higher employment growth, more products added, and more industries entered, consistent with creative destruction contributing more to firm expansion.

Q6. How does the DID identification strategy work, and what are the main results?

A: The identification exploits the removal of trade policy uncertainty (TPU) after China’s WTO accession in 2001. The treatment variable is the industry-level NTR gap (the gap between non-NTR and NTR tariff rates in 1999): industries with larger gaps experienced a larger reduction in uncertainty and thus a greater increase in Chinese import competition. The DID compares patenting firms across periods (1992–1999 vs. 2000–2007) and across high- vs. low-NTR-gap industries, with a triple interaction for firm-level innovation intensity (lagged five-year average patents per employee, normalized within two-digit NAICS). The main finding (Table 4): the NTR gap × Post interaction has no significant effect on overall patent counts (coefficient 0.238 without controls, standard error 0.237), but the triple interaction (NTR gap × Post × innovation intensity) has a positive and significant effect on the growth rate of the self-citation ratio (0.732 without controls, p < 0.05; 0.795 with baseline controls, p < 0.01). This implies that innovation-intensive firms in high-competition industries shifted their composition toward own-innovation, while overall patenting was unchanged — consistent with an offsetting rise in own-innovation and fall in creative destruction.

Q7. What are the aggregate growth effects of increasing competitive pressure in the calibrated model?

A: Using an 83% increase in outside entrants (matching the 1992–2007 rise in import penetration from 15.3% to 25.1%), average domestic productivity growth falls 11.0%. Decomposing: growth from domestic own-innovation falls 11.4%, growth from domestic creative destruction falls 13.0%, and growth from domestic startups falls 1.7% (Table 9). The aggregate R&D-to-sales ratio falls 1.6% and the creative destruction R&D intensity falls 1.2%, indicating that the decline in creative destruction R&D outweighs the rise in own-innovation R&D. The total mass of domestic firms falls 6.4% and the average number of products per firm falls 5.5%.

Q8. How do results differ in economies with high creative destruction costs vs. the U.S.?

A: When creative destruction costs (χ̃) are set 80 times higher than the U.S. baseline, the initial equilibrium has much lower creative destruction: R&D-to-sales ratio is 1.39% (vs. 4.58% in U.S.), creative destruction R&D intensity is 8.6% (vs. 63.9%), average number of products is 1.0 (vs. 2.3), and average domestic productivity growth is 1.4% (vs. 1.9%). Under the same competition shock, total R&D actually rises by 1.0% in this high-CD-cost economy (because own-innovation increases more than creative destruction falls, given the already low baseline of creative destruction), in contrast to the −1.6% in the U.S. However, domestic growth still falls 9.7% even in this economy, driven by reductions in creative destruction by incumbents and startups combined with a decline in the mass of domestic incumbents. This result holds even with a fixed firm mass (Table E5), confirming the mechanism is not solely due to entry/exit dynamics.

Q9. What is the technological barrier effect’s quantitative contribution to the decline in creative destruction?

A: In the U.S. counterfactual (Table 8 and associated decomposition), 17.0% of the total change in the aggregate creative destruction arrival rate x and 15.0% of the total change in startup creative destruction x_e are attributable specifically to the technological barrier effect — that is, to the shift in the technology gap distribution µ(Δℓ) holding all else equal. The conditional takeover probability x^{takeover} declines from 73.2% to 73.0%. The density at Δ₁ (the easiest gap to overcome) falls 0.4%, while densities at Δ₃ and Δ₄ rise 1.1% and 1.4% respectively, driven by increased creative destruction by outside firms and intensified own-innovation by incumbents.

Q10. What are the policy implications the paper draws from its framework?

A: The paper argues that policies evaluating innovation should account for composition, not just aggregate R&D levels or patent counts. Increased overall innovation driven by defensive own-innovation contributes less to economic growth than creative destruction and restricts firm entry — so it is less beneficial than it appears. In low-creativity economies (e.g., European economies with high regulatory barriers to creative destruction), increased foreign competition may raise aggregate R&D while still lowering domestic growth, misleading policymakers who track only total innovation spending. The model also suggests that the mixed empirical findings in the competition-innovation literature (Aghion et al. 2005; Bloom et al. 2016; Autor et al. 2020) can be reconciled by accounting for compositional shifts: the net effect of competition on total innovation is ambiguous because it raises own-innovation for technologically advantaged firms while reducing creative destruction for all firms.

Key Concepts

Imperfect Technology Spillovers: The novel friction introduced in this paper, modeled as lagged learning: firms attempting creative destruction can only access the one-period-lagged technology of the target product market (q_{j,t−1}), while the incumbent product owner observes and can improve from the current frontier (q_{j,t}). This asymmetry creates a persistent technological advantage for incumbents and enables strategic defensive innovation.

Own-Innovation: R&D investment by a firm to improve the quality of its existing product lines. Successful own-innovation raises product quality by a step size λ > 1. Own-innovation does not require learning others’ technology and, in the model, constitutes the incumbents’ defensive margin against creative destruction. At the aggregate level, it contributes more to total growth than creative destruction because it succeeds more frequently, but per successful event it contributes less (λ < η).

Creative Destruction: R&D investment enabling a firm to enter a new product market by displacing the incumbent. Successful creative destruction improves the lagged quality of the target product by a step size η > λ, where λ² > η > λ. It requires learning the incumbent’s one-period-lagged technology, takes longer to develop (evidenced empirically by longer backward citation gaps), and contributes more to firm growth and product expansion per event than own-innovation.

Technology Gap (Δ): The ratio of a product’s current-period technology to its previous-period technology (Δ_{j,t} = q_{j,t}/q_{j,t−1}). This gap summarizes the technological advantage the incumbent holds in a product market under imperfect spillovers. Four values are possible in equilibrium: Δ₁ = 1, Δ₂ = λ, Δ₃ = η, Δ₄ = η/λ. The gap determines both the incumbent’s own-innovation incentive and the rival’s probability of successfully completing a product takeover conditional on creative destruction.

Market-Protection Effect: The mechanism by which incumbents with a technological advantage (Δ > 1) increase own-innovation in response to heightened competitive pressure (an increase in the aggregate creative destruction arrival rate x). This effect is maximized for products with Δ₂ = λ and positive but diminishing for Δ₃. It is absent for Δ₁ = 1 products (where own-innovation cannot prevent displacement) and is formally distinct from the escape-competition effect in step-by-step innovation models, which applies only to neck-and-neck single-product firms.

Technological Barrier Effect: The reduction in rivals’ incentive for creative destruction caused by an increase in the average technology gap across the economy’s product lines. When incumbents intensify own-innovation and/or when outside creative destruction increases, the distribution of technology gaps shifts toward higher Δ values, reducing the conditional probability that a rival successfully takes over any given product market. This feedback mechanism endogenously suppresses creative destruction and firm entry beyond what the Schumpeterian effect alone would predict.

Self-Citation Ratio: The share of a patent’s backward citations that cite patents previously owned by the same firm. Used in the paper as a continuous proxy for the likelihood that a patent represents own-innovation vs. creative destruction: a ratio of 1 (100% self-citations) implies 100% probability of own-innovation; a ratio of 0 implies 100% probability of creative destruction. This measure follows Akcigit and Kerr (2018) and is validated in the paper against learning time, quality, and firm growth outcomes.

NTR Gap (Trade Policy Uncertainty Shock): The industry-level difference between non-NTR (column 2) and NTR (column 1) U.S. tariff rates in 1999, used as an instrument for the exogenous increase in Chinese competitive pressure following China’s WTO accession and the U.S. granting of Permanent Normal Trade Relations (PNTR) in 2002. Industries with larger NTR gaps experienced a greater reduction in trade policy uncertainty and thus a larger increase in competitive pressure from foreign firms.

Patent Term, Innovation, and the Role of Technology Disclosure Externalities

Mon, 01 Jan 0001 00:00:00 +0000

This paper examines how anticipated changes in patent term affect R&D and innovation, using the U.S. ratification of the Trade-Related Aspects of Intellectual Property Rights (TRIPs) agreement in 1995 as a quasi-natural experiment. The central research question is whether and how policy anticipation shapes the short- and long-run dynamics of innovative activity, given ambiguous theoretical predictions: news of a patent term reduction could either deter innovation (by signaling lower future returns) or accelerate it (by inducing innovators to file under the more favorable existing regime before it expires).

The identification strategy exploits a difference-in-differences (DiD) design using two sources of variation across 621 4-digit International Patent Classification (IPC) technological fields. The first is cross-sectional variation in field-specific pending periods — the time between patent application and grant during which monopoly rights are not fully enforceable — which determines whether TRIPs increased or reduced each field’s effective patent term (from 17 years post-grant to 20 years post-application minus the pending period). Fields with average pending periods exceeding three years faced expected reductions; those below faced extensions. On average across fields, TRIPs extended patent term by approximately 473 days (about 15 months), but approximately 45% of fields faced greater than 5% probability that individual patents would receive a term reduction. The second source is time variation from two events: a news event at the end of 1992 (when the Blair House Accord substantially reduced uncertainty about TRIPs adoption) and implementation in June 1995. The empirical sample spans 1985Q1–2000Q4 using PATSTAT patent data, augmented by firm-level R&D data from NBER-Compustat for 2,410 listed U.S. firms.

Three main empirical facts emerge. First (Fact 1), innovation and R&D accelerate more during the anticipation phase (1992Q4–1995Q2) in fields with a higher probability of patent term reduction. A one-percentage-point higher reduction probability corresponds to a 1.4% larger increase in granted patent applications before implementation; a one-month shorter average patent term extension corresponds to a 2.9% larger increase. At the firm level, a one-percentage-point higher reduction probability is associated with a 1.9% increase in annual R&D expenditure (approximately $1.7 million), ruling out the interpretation that rising patent counts merely reflect strategic filing adjustments.

Second (Fact 2), this heightened innovative activity persists for at least five years after implementation. Two years post-implementation, a one-percentage-point higher reduction probability corresponds to 1.44 additional quarterly patents (+2.7% in Poisson estimates), and a one-month shorter term extension corresponds to 3.3 more patents (+5.9%). This persistence is driven by indirect effects: the anticipation-induced burst in patenting generates additional follow-on innovation through technology disclosure externalities linked to cumulative knowledge creation. The elasticity of post-implementation innovation to news-phase innovation is estimated at approximately 2.1.

Third (Fact 3), the direct effect of patent term on innovation — estimated by augmenting the DiD specification to control for field-specific innovation histories — is negative for shorter extensions and consistent with prior literature. A one-month shorter patent term extension reduces quarterly patents by 1.7%, and a one-year reduction reduces them by 20.9%. These estimates align with Budish, Roin, and Williams (2015, 2016), who find that a one-year extension of patent monopoly increases R&D by 7%–22% in pharmaceuticals. The identification is supported by the absence of pre-trends, by the finding that pre-news pending period distributions predict realized post-news variation with coefficients near one (0.957–1.104), and by extensive robustness checks.

Q: What was the effective change in U.S. patent term under TRIPs, and why did it differ across fields? A: TRIPs shifted patent expiry from 17 years after grant to 20 years after application date. Because monopoly rights are only fully enforceable after grant, the effective term became 20 years minus the pending period. Fields with average pending periods shorter than three years received net extensions; fields with longer average pending periods faced net reductions. Cross-field variation in pending periods arises because applications in different technical fields are reviewed by distinct USPTO technical units with different complexity and backlog levels.

Q: What was the news event, and how was anticipation established? A: The paper identifies November 1992 — when the Blair House Accord substantially reduced uncertainty about TRIPs adoption — as the news event, with formal ratification in December 1994 and implementation in June 1995. Documentary evidence confirms anticipation: U.S. business executives were involved in TRIPs negotiations from 1986; the patent term change appeared in a 1991 GATT draft; an Advisory Committee report co-signed by IBM, 3M, Motorola, and others referenced it in August 1992; and a New York Times article noted proposed changes in September 1992.

Q: How is the probability of patent term reduction (PL_j) constructed, and what is its distribution? A: PL_j is the fraction of patents in field j granted before the TRIPs news with a pending period exceeding three years, computed using PATSTAT data on U.S. patents granted between January 1990 and May 1992. Approximately 45% of fields faced a reduction probability exceeding 5%, and 15% faced a probability exceeding 10%. Even fields with an average term extension greater than one year had individual-patent reduction probabilities as high as 40%. A 10-percentage-point increase in PL_j corresponds to approximately a four-month shorter average term extension.

Q: What is Fact 1 and what are its quantitative magnitudes? A: Fact 1 states that during the news phase, innovation and R&D increase relatively more in fields with higher patent term reduction probability and shorter average term extension. One year after the news (two years before implementation), a one-percentage-point higher reduction probability generates 0.19 additional quarterly patents (+0.5% in Poisson estimates); a one-month shorter average extension generates 0.35 additional units (+0.8%). These effects approximately triple one year before implementation. At the firm level, a one-percentage-point higher probability is associated with a 1.9% increase in annual R&D (~$1.7 million) in 1993.

Q: Why does news of a potential patent term reduction accelerate rather than deter innovation? A: Innovators who anticipate a reduction in future patent protection under the new regime have strong incentives to file applications before implementation to secure the longer 17-years-from-grant term while it remains available. The acceleration is therefore consistent with innovators preferring longer protection: they rush to file under the more favorable old regime rather than curtailing innovation. Complementary analyses exploiting within-field dispersion in pending periods find that firms were particularly responsive to scenarios involving adverse policy changes, consistent with loss aversion. The dynamics of the news-phase acceleration are also consistent with an R&D gestation lag of approximately two years, as estimated by Pakes and Schankerman (1984).

Q: What is Fact 2 and what drives the post-implementation persistence? A: Fact 2 states that the heightened innovation in fields with higher reduction probability persists for at least five years after June 1995, even though the direct effect of a shorter patent term is innovation-reducing. Two years post-implementation, a one-percentage-point higher reduction probability corresponds to 1.44 additional quarterly patents (+2.7% Poisson) and a one-month shorter extension to 3.3 additional patents (+5.9% Poisson). The persistence is driven by technology disclosure externalities: the news-phase acceleration generates new patented knowledge that subsequent innovations build upon. Fields where new inventions rely more heavily on past innovations from the same field — proxied by backward citation intensity — display stronger post-implementation persistence.

Q: How does the paper separate direct from indirect (externality-driven) post-implementation effects? A: Following Angrist and Pischke (2009), the paper augments the baseline DiD specification to control for field-specific innovation histories via a lagged moving average of past outcomes and pre-determined field attributes interacted with quarterly fixed effects. The resulting coefficients capture the effect of patent term variation orthogonal to the news-induced innovation dynamics. The direct effect estimates are negative post-implementation (Fact 3), while the overall estimates are positive (Fact 2), confirming that the indirect externality channel outweighs the direct channel in the post-implementation period.

Q: What is Fact 3 and how does its magnitude compare to prior literature? A: Fact 3 states that, controlling for the news shock, a shorter patent term extension leads to a relative decline in innovation post-implementation. The estimated semi-elasticity is 1.7% per one-month increase in patent term and 20.9% per one-year increase. These estimates align with Budish, Roin, and Williams (2015, 2016), who find a 7%–22% increase in pharmaceutical R&D per one-year extension, and with Hemous et al. (2023), whose model implies a 1.2% innovation increase per one-month extension.

Q: What is the estimated elasticity of post-implementation innovation to news-phase innovation, and what does it imply? A: Point estimates imply that one additional patent during the news phase generates approximately 5.1 additional patents post-implementation. Given average patent counts of 408.5 during the news phase and 1,000.3 post-implementation, this corresponds to a percent-to-percent elasticity of approximately 2.1. This elasticity captures the technology disclosure externality channel by which transitory accelerations in patenting generate persistent follow-on innovation.

Q: Why is ignoring anticipation (as in Abrams 2009) a problem for DiD identification? A: Anticipation inflates patenting in fields with higher reduction probability during the pre-implementation period, violating the DiD assumption that pre-implementation outcomes provide an unaffected baseline. For example, between April 1994 and March 1995, average monthly patents in field C12P (high reduction probability) were 15.1 units above pre-news levels, versus only 2.4 in field E05D (low reduction probability). Using this inflated pre-implementation level as the DiD reference baseline reverses the sign of the estimated implementation effect relative to the specification that uses the unaffected pre-news baseline.

Q: What evidence supports the technology disclosure externality mechanism over alternative explanations? A: The paper proxies technological dependence by backward citation intensity at the field level and finds that the news-phase acceleration propagates more strongly into post-implementation innovation in fields where new inventions more heavily cite prior same-field patents. Time-varying measures of technological dependence identify this channel as the primary driver of indirect post-implementation effects. Two alternative mechanisms — changes in technological competition and adjustments in patenting strategies — lack comparable empirical support. The finding is consistent with Hegde, Herkenhoff, and Zhu (2023), who document that permanent increases in knowledge diffusion speed permanently raise follow-on innovation rates.

Q: What are the policy implications of jointly considering anticipation and knowledge spillovers? A: Standard patent term analyses that abstract from anticipation effects and knowledge spillovers may substantially mischaracterize full welfare implications. The paper shows that innovation-policy interventions shape both short- and long-run outcomes, and that near-term variation in innovative activity can itself drive medium- to long-term effects through technological externalities. The estimated semi-elasticities of news, direct, and indirect effects provide empirical calibration targets for normative endogenous growth models used to derive optimal patent term, complementing prior normative recommendations ranging from zero protection (Boldrin and Levine, 2013) to infinite protection (Gilbert and Shapiro, 1990).

Effective patent term: The duration of legally enforceable monopoly granted by a patent, equal to 17 years after grant under the pre-TRIPs U.S. regime and 20 years after application minus the pending period under the post-TRIPs regime. Because enforcement begins only at grant, the pending period directly erodes effective protection.

Patent term reduction probability (PL_j): The field-specific fraction of pre-TRIPs patents with a pending period exceeding three years, representing the probability that individual patent applications in that field obtain a net reduction in patent term under the new 20-years-from-filing rule.

News effect: The incremental change in innovation or R&D at the time of policy announcement, induced by future anticipated changes in patent term, before the new policy enters into force. In this paper’s setting, the news effect is positive: higher reduction probability accelerates patenting as innovators rush to file under the favorable existing regime.

Direct implementation effect: The component of the post-implementation change in innovation attributable to the patent term change itself, isolated by controlling for field-specific innovation histories (i.e., abstracting from the indirect effects of anticipation-induced knowledge accumulation). It is negative for shorter patent term extensions, with a semi-elasticity of 1.7% per one-month increase.

Technology disclosure externality: The mechanism by which newly patented knowledge, disclosed through the patent system, enables subsequent inventors to build on prior innovations, generating follow-on inventive activity. In this paper, the transitory news-phase burst in patenting generates a persistent externality, particularly in fields with high backward citation intensity.

Policy anticipation: The phenomenon whereby forward-looking agents adjust behavior in response to credible news about future policy changes before those changes take effect. In this paper, anticipation induces a pre-implementation acceleration in patenting that temporarily pushes innovation in the opposite direction from the direct long-run effect and generates persistent indirect post-implementation effects through knowledge spillovers.

Pending period: The time between patent application and grant during which USPTO examines the application and during which full monopoly rights are not enforceable. Field-level heterogeneity in pending periods — arising from differences in examination complexity and USPTO unit congestion — is the source of cross-sectional identification in the DiD design.

Trust and Innovation Within the Firm

Mon, 01 Jan 0001 00:00:00 +0000

This paper investigates whether and how a CEO’s inherited generalized trust enhances innovation within firms, offering a micro-foundation for the well-documented macro-level relationship between societal trust and economic growth. The author argues that trust — by inducing tolerance of failure — encourages researchers to undertake high-risk, explorative R&D rather than safe exploitation of known approaches.

The empirical foundation is a matched CEO-firm-patent dataset covering 5,753 CEOs at 3,598 US public firms during 2000–2011, encompassing 700,000 patents and over one million inventors. CEO trust is measured as an inherited trait: each CEO’s ethnic origin is inferred probabilistically from their last name using de-anonymized US censuses from 1910–1940, and ethnic-origin-specific trust levels are drawn from the US General Social Survey (GSS), restricted to respondents in highly prestigious occupations. The resulting trust measure is the weighted average of ethnic-specific trust scores across a CEO’s likely ethnic composition.

The main empirical strategy exploits within-firm variation across CEO transitions, using firm and year fixed effects to compare patenting before and after a CEO change. The identifying assumption — that the timing of CEO transitions and the new CEO’s trust level are not predicted by prior firm patenting trends — is supported by event-study tests showing flat pre-trends. A one-standard-deviation increase in CEO inherited generalized trust (equivalent to the difference between Greek and English averages) is associated with a 6.2–6.3% increase in patent filings, statistically significant at the 1% level. For the average firm, this equals approximately 1.1 additional patents annually, worth roughly $6.8 million. The effect is larger among exogenous transitions (CEO retirement or death): 8.5% in the restricted sample, and an IV estimate of 8.2%. The back-of-envelope calculation suggests this trust-innovation channel could account for approximately 37% (range: 16–58%) of the effect of trust on GDP per capita growth.

The paper’s central mechanism — risk taking — is tested by examining the distribution of patent quality rather than the mean. Under the risk-taking mechanism, trust should increase the variance of R&D project quality, raising high-quality patents without necessarily increasing low-quality ones. Consistent with this, CEO trust raises only above-median quality patents (measured by forward citation decile), with effects increasing monotonically toward the top decile and no statistically significant effect on below-median patents. Average patent quality as measured by citation-weighted counts or patent value rises by 4–6%. Trust also disproportionately raises the share of explorative patents (those with at least 90% of backward citations outside the firm’s existing knowledge stock) by 1 percentage point over a base of 17%.

The transmission channel is examined using BERT-based classification of nearly one million Glassdoor employee reviews. Under more trusting CEOs, firms exhibit stronger top-down trust sentiment (managers trusting workers), particularly among R&D workers and scientists. The effect materializes within the first two years of a CEO term. Director selection provides an additional transmission mechanism: under more trusting CEOs, newly appointed directors are more trusting and departing directors are less trusting.

A within-CEO design using bilateral trust (toward researchers in specific countries) with CEO fixed effects addresses omitted CEO characteristics. A one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab, controlling for firm-by-year, CEO, and inventor-country fixed effects.

The effect is strongest when CEO trust is matched to a high-quality researcher pool; in firms with mostly low-quality researchers, high trust may be counterproductive. Trust is also a substitute for R&D knowledge: the effect disappears when the CEO holds a non-MBA graduate degree or has prior R&D experience.

Q: What is the main research question? A: The paper asks whether a CEO’s generalized trust causes more and higher-quality innovation within the firm, and through what mechanism. It also asks how trust transmits from the CEO to researchers who rarely interact with the CEO directly.

Q: How is CEO trust measured? A: CEO trust is measured as an inherited trait using a two-step procedure. First, each CEO’s last name is probabilistically mapped to one or more ethnic origins using four de-anonymized US censuses (1910–1940). Second, ethnic-origin-specific trust is computed from GSS respondents in highly prestigious occupations. The CEO’s trust measure is the weighted average across ethnic compositions. This measure is shown to be more precise than an individual-level survey measure and approximately 80% as precise as a game-based measure, without introducing attenuation bias.

Q: What is the baseline patent effect and how large is it economically? A: A one-standard-deviation increase in CEO inherited trust is associated with a 6.2–6.3% increase in patent filings (statistically significant at 1%). For the average baseline firm, this is approximately 1.1 additional patents per year, valued at roughly $6.8 million. When patent quality is accounted for, the effect rises to 9.9% using citation-weighted patent count and 11.5% using patent value based on excess stock returns on grant dates.

Q: Is the effect causal? What identification strategy is used? A: The main strategy uses firm and year fixed effects, identifying the effect from within-firm variation around CEO transitions. Pre-trend tests confirm that neither the timing of CEO changes nor the new CEO’s trust level predicts prior firm patenting. Among exogenous transitions (CEO retirements and deaths), the effect is 8.5%, and an IV estimate using the predecessor’s trust as instrument yields 8.2% (significant at 10%), both comparable to the baseline.

Q: What is the macroeconomic significance of the trust-innovation channel? A: Combining the paper’s trust-to-patents estimate (0.042–0.062) with Akcigit et al.’s (2017) patents-to-GDP-growth estimate (0.026–0.066) and the cross-country trust-to-growth coefficient (0.007), the trust-innovation channel could explain approximately 37% of the effect of trust on growth, with a plausible range of 16–58%.

Q: What is the mechanism linking CEO trust to innovation? A: The conceptual mechanism is that a more trusting manager interprets researcher failure as bad luck rather than bad type, making her more likely to tolerate failure and continue employing the researcher. This increases the researcher’s incentive to pursue explorative, high-risk R&D over safe exploitation of known approaches. The mechanism implies a variance-increasing effect on the R&D quality distribution, rather than a mean shift.

Q: How is the risk-taking mechanism tested against alternative mechanisms? A: The paper examines the distribution of patent quality by citation decile. Under mean-shifting alternatives (delegation, cooperation, relational contracting), trust should raise all quality brackets. Under risk-taking, trust raises only high-quality patents. The results show CEO trust has monotonically increasing effects from low to high quality deciles, with no statistically significant effect on below-median patents, consistent only with the variance-increasing (risk-taking) mechanism.

Q: What patent quality measures are used and what do they show? A: Beyond forward citation deciles, the paper uses explorativeness (patents with at least 90% of backward citations outside the firm’s existing knowledge stock), disruptiveness (Funk and Owen-Smith, 2017), patent importance (Kelly et al., 2021), backward citations to scientific literature, and patent scope. Trust increases all these measures with statistically significant positive coefficients. The share of explorative patents rises by 1 percentage point over a base of 17%. Average citation count and patent value increase by 4–6%.

Q: Does CEO trust raise R&D expenditure? A: No. The coefficients from regressing R&D expenditure on CEO trust are neither statistically significant nor large enough to explain the innovation effect. The patent effect is also robust to controlling for R&D inputs, suggesting that trust affects the type of projects chosen (consistent with risk-taking) or their realized outcomes, rather than the scale of R&D.

Q: How does CEO trust transmit to corporate culture? A: Using BERT-based classification of nearly one million Glassdoor reviews covering 266 firms and 397 CEO terms between 2008 and 2017, the paper finds that CEO trust is associated with stronger top-down trust sentiment (managers trusting workers). The normalized effect of a one-standard-deviation increase in CEO trust on overall trust sentiment is 0.257, on top-down trust 0.531, and on bottom-up trust only 0.141 (statistically insignificant). The effect is strongest among reviewers who identify as scientists, researchers, or engineers, and materializes within the first two years of the CEO term.

Q: What evidence exists for transmission via director selection? A: Under more trusting CEOs, newly appointed directors — especially those who remain until the end of the CEO term — are more trusting, and departing directors are less trusting. The average director trust improves during the CEO’s term. Because 54% of director hirings and 46% of turnovers occur within the first two years, this change also materializes quickly, consistent with the dynamic pattern of trust culture change.

Q: What is the within-CEO bilateral trust result and what does it add? A: Using within-CEO variation in bilateral trust toward researchers from different countries (from Eurobarometer surveys), and controlling for CEO, inventor-country, and firm-by-year fixed effects, a one-standard-deviation increase in CEO bilateral trust toward a country is associated with a 5% increase in patents by inventors in that country’s R&D lab. This design allows CEO fixed effects, ruling out unobserved CEO-level confounders such as management style or R&D ability.

Q: When is CEO trust counterproductive? A: CEO trust is beneficial only when matched to a high-quality researcher environment. Using residual patent output (controlling for observable firm and CEO characteristics) as a proxy for researcher quality, the effect of CEO trust on patents, patent output per R&D dollar, and future sales/employment/TFP is significant only among firms in the top two quintiles of researcher quality. In firms with mostly low-quality researchers, high CEO trust may be counterproductive by failing to screen out bad researchers.

Q: How does the trust effect vary by industry and CEO background? A: The effect is ubiquitous across industries but especially pronounced in pharmaceutical and ICT firms. The timing varies: it manifests quickly in ICT (short R&D lag) and more slowly in pharma (long R&D horizon). The effect vanishes when the CEO holds a non-MBA graduate degree or has prior R&D experience, suggesting trust is a substitute for direct knowledge of R&D processes.

Q: Are the results robust? A: Yes. The paper reports 14 categories of robustness checks including alternative patent transformations, alternative trust measures (LASSO, World Value Survey, Global Preference Survey, alternative GSS questions), alternative standard error clustering, Poisson count models, restriction to granted patents, exogenous transition subsamples, modern difference-in-differences estimators (de Chaisemartin et al., 2024; Sun and Abraham, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2024), and leave-one-ethnicity-out. The baseline result is stable across all these checks.

Inherited generalized trust: The paper’s measure of a CEO’s trust disposition, defined as the probability-weighted average of ethnic-origin-specific trust levels (from the GSS) based on the CEO’s likely ethnic composition inferred from their last name and historical census records. It captures the culturally transmitted component of trust, distinct from individual-level noise.

Explorative R&D: In the paper’s framework (building on March, 1991), research activities that involve testing untested paths, carrying high risk of failure but high potential for innovation, as opposed to exploitation of well-known approaches with low failure risk. The paper argues CEO trust encourages researchers to shift toward exploration.

Tolerance of failure: A manager’s propensity to attribute a researcher’s failure to bad luck rather than bad type. Under the paper’s mechanism, a more trusting manager gives greater weight to bad luck, making her more likely to retain the researcher after failure, thereby incentivizing risk taking.

Top-down trust: In the paper’s BERT-based classification of Glassdoor reviews, the direction of trust from managers toward workers (as opposed to bottom-up trust from workers toward managers). The paper finds CEO trust primarily raises top-down trust sentiment, especially among R&D workers.

Patent explorativeness: A patent quality measure defined as the share of its backward citations that fall outside the firm’s existing knowledge stock; patents are classified as explorative if at least 90% of backward citations are outside that stock. The paper uses this as a direct measure of explorative R&D output.

Bilateral trust: CEO d’s directed trust toward individuals from country c, computed analogously to inherited generalized trust but using Eurobarometer survey data on country-pair trust attitudes among European-origin populations. Used in the within-CEO design to control for CEO fixed effects.

Variance-increasing mechanism: The paper’s characterization of the risk-taking channel, in which CEO trust raises the variance (not the mean) of the R&D project quality distribution by encouraging researchers to pursue high-risk, high-reward exploration. Empirically identified by the pattern that trust raises only above-median quality patents with monotonically increasing effects toward the top decile.