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The innovation of new financial products, processes, and services is a key driver of economic development and technological progress. Yet, the issue of how new technology itself affects financial innovation activity is not well understood. We argue that, although new technologies can spur financial innovation via knowledge spillovers, they can also lead to the "crowding out" of financial innovation by increasing the relative profitability of competing investment opportunities. To test our hypotheses, we use time-series data during 2005-2019 on the occurrence of major waves of non-financial innovation and their impact on firms' financial patenting and the hiring of financial inventors. We find evidence of aggregate-level crowding-out in the earlier part of the sample: firms tend to shift from financial to non-financial patenting following the onset of an innovation wave. A likely driver of this substitution effect is a relative labor demand shift away from financial inventors and toward nonfinancial inventors. This demand shift appears to be stronger among firms with fewer financial or real constraints and those with better access to local inventor human capital. In more recent years, substitution effects have diminished, likely due to the increasing breadth and attractiveness of financial patenting. Overall, our results shed light on the economic trade-offs that drive financial innovation and suggest that the growing integration of finance with non-financial technology has increasingly blurred the lines between different innovation types.

As artificial intelligence (AI) becomes increasingly embedded in educational ecosystems, its role in shaping how knowledge is acquired, interpreted, and applied raises new questions about human cognition, behavior, and ethics. Rather than reexamining AI’s efficiency or acceptance, this study explores how the public constructs meanings, responsibilities, and governance expectations around AI as a learning partner. Framed within the broader context of digital education policy and ethical AI governance, this study develops a Cognition–Behavior–Ethics (CBE) × Three-Stage (Acquisition, Comprehension, Application) analytical model to examine how public discussions on Reddit (2013–2025, N=10,503 posts) reflect collective reactions and reflections toward AI-assisted learning as a social phenomenon. Topic Modeling reveals three recurrent dilemmas: ensuring informational reliability during knowledge acquisition, maintaining cognitive agency amid growing AI reliance, and reconciling ethical responsibility with institutional and policy constraints in application contexts. These findings demonstrate AI is simultaneously constructed as an intellectual partner, behavioral facilitator, and ethical interlocutor—roles that transform learning from a pedagogical to a socio-technological process of reflection and negotiation. The study contributes theoretically by extending AI acceptance research into a discourse-driven framework of knowledge construction and practically by highlighting the need for transparent, participatory, and stage-specific governance in educational AI systems. Sustainable AI integration requires not only technological innovation also ethical oversight, public deliberation, and adaptive policy mechanisms fostering accountable, human-centered learning environments.

We analyze the problem of matching asylum seekers to member states, incorporating wait times, preferences of asylum seekers, and the priorities, capacities, and burden-sharing commitments of member states. We identify a unique choice rule that addresses feasibility while balancing priorities and capacities. We examine the effects of both homogeneous and heterogeneous burden-sizes among asylum seekers on the matching process. Our main result shows that when all asylum seekers are treated as having identical burden-sizes, the asylum-seeker-proposing cumulative offer mechanism guarantees both stability and strategy-proofness. In contrast, when burden-sizes vary, there are scenarios where achieving stability or strategy-proofness is no longer possible.

Direct experience of a peer's punishment might have a sobering effect above and beyond deterrence (information about punishments). We test this mechanism in China, studying the reactions to listed state-owned enterprises' (SOEs) punishments for fraudulent loan guarantees by firms in the same locations (peers) and nonpeer firms across SOEs and non-SOEs. After experiencing SOEs' punishments, peer SOEs cut their loan guarantees by more than nonpeer SOEs and peer non-SOEs even if information and local economic shocks are common to all firms. The reaction is stronger for peer SOEs whose CEOs have higher career concerns and/or face lower costs of reducing loan guarantees.

Born-digital firms increasingly pursue virtual involvement, a digital market-entry strategy that prioritizes iterative data collection and algorithmic adaptation over physical expansion. We measure virtual involvement by the number of unique digital advertisements and define it as the intensity of a firm's digital experimentation to collect user data, train algorithms, and enable continuous learning through a recursive feedback loop. Using a novel panel dataset covering 194 US and Chinese born-digital firms across 33 countries from 2016 to 2022, our empirical analysis finds that stringent host-country digital privacy regulation significantly reduces virtual involvement. This negative influence is weakened in larger digital markets, for firms from home countries with robust digital regulatory regimes, and when firms receive positive public sentiment. These results extend institutional theory and non-traditional entry mode research by introducing virtual involvement as a new dimension of international strategy under regulatory constraints. For policymakers and managers, our study offers actionable guidance: strict privacy rules protect user welfare, but supportive market conditions and regulatory experience are critical to sustaining data-driven innovation. Policymakers should balance user protection with market dynamism, while firms should leverage large markets and positive sentiment to maintain their virtual involvement.

This paper revisits the predictive power of asset prices for inflation, focusing on individual stock prices ratherthan aggregate indices. Using a large panel data of firm-level stock prices and applying machine learningtechniques, we demonstrate that individual stock prices significantly enhance the accuracy of inflationforecasts, particularly over medium- to long-term horizons and during periods of high inflationary anddeflationary pressure. Compared to composite and industry-level stock indices, other aggregate asset prices,and Fama-French factors, individual stock prices contain valuable heterogeneous information, offeringricher insights for inflation forecasting. These findings provide new empirical support for macro-financetheory, affirming the predictive value of asset prices from a micro-level perspective.

We challenge a recently popular view that a negative interest-growth rate gap (r < g) offers a "free lunch" for debt-financed government spending by formulating a model in which r and g are endogenous variables shaped by fiscal policy through its effects on equilibrium multiplicity and capital allocation. Observing r < g can signal that sustained government deficits have generated multiple steady states, and the economy has converged to a stable low-efficiency equilibrium. With its heterogeneous entrepreneurs, the model's real interest rate serves as a screening device for investment efficiency. Causation runs from the fiscal regime to equilibrium selection and outcomes: Fiscal surpluses eliminate equilibrium multiplicity and anchor expectations that sustain a unique, high-productivity equilibrium, thereby rationalizing Alexander Hamilton's characterization of "debt as a blessing." Persistent deficits can push the economy into a "misallocation trap" characterized by scarce safe assets, low interest rates, survival of inefficient firms, depressed aggregate productivity, and self-validating low growth. Thus, costs of debt-financed fiscal deficits consist not only of deferred taxes, but also of permanently lower national productive capacity.

Using county-level and zip-code-level data from the United States during the period 1998–2018, we document: (i) Increased flood risk has a large negative impact on firm entry, employment, and output in the long run; (ii) Flood events reduce output in the short run while impact on firm entry and employment is limited. We then develop a quantitative spatial model to characterize how flood risk shapes firms' locations choices and employment. We find flood risk reduced US aggregate output by 0.53 percent in 2018, 23 percent of which stemmed from direct damages and 77 percent from long-run adjustments of firms and workers.

This paper studies the estimation of characteristics-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics, and the idiosyncratic error terms are subject to conditional quantile restrictions. We propose a three-stage estimation procedure that is easy to implement and has nice properties. The convergence rates, the limiting distributions of the estimated factors and loading functions, plus a consistent selection criterion for the number of factors at each quantile are derived under general conditions. Our proposed estimators are shown to work satisfactorily when: (i) the idiosyncratic errors have heavy tails, (ii) the time dimension of the panel dataset is not large, and (iii) the number of factors differs from the number of characteristics. Further, a consistent estimation method based on quantile factor analysis and sieve regression is proposed when the factor loadings depend on additional unobserved characteristics. Monte Carlo simulations and an empirical application aimed at estimating the loading functions of the daily returns of a large panel of S&P 500 index securities help illustrate these properties.

This study moves beyond the traditional atomistic view of firms engaged in alliance agreements. By integrating current research on internal organizational design with research on interfirm collaboration, we investigate the implications of the organizational design of parent firms for the design and governance of technology partnerships. More specifically, we propose that in a research and development (R&D) alliance between a client firm and an R&D firm, the client firm's internal organizational structure in R&D plays an important role in alliance design and governance. Centralized firms are better able to integrate knowledge in the organization compared with decentralized firms, and hence, we theorize that they will design their technology partnerships for greater knowledge access. Empirical findings from alliances in the biopharmaceutical industry offer support for our predictions: firms with centralized R&D decision making are more likely to capture intellectual property rights. Further, these firms tend to engage in alliances with broader vertical and horizontal scope and greater technological interdependence in order to access, recombine, and distribute knowledge. This study extends research on alliance design and governance by underscoring the important influence of a partner firm's organizational structure.

This paper studies the design of monetary policy in small open economies with domestic and cross-border production networks and nominal rigidities. The monetary policy that closes the domestic output gap is nearly optimal and is implemented by stabilizing the aggregate inflation index those weights sectoral inflation according to the sector's roles as a supplier of inputs and a net exporter of products within the international production networks. To close the output gap, monetary policy should assign large weights to inflation in sectors with small direct or indirect (i.e., via the downstream sectors) import shares and failing to account for the cross-border production networks overemphasizes inflation in sectors that export intensively directly and indirectly (i.e., via the downstream sectors). We validate our theoretical results using the World Input-Output Database and show that the monetary policy that closes the output gap outperforms alternative policies that abstract from the openness of the economy or the input-output linkages.

Luxury brands have increasingly embraced meme content as an innovative way to engage with consumers. However, our understanding of consumer reactions to luxury-branded memes is limited. Four experiments were conducted to examine the influence of luxury-branded memes on perceived funniness and sharing intention. Study 1 (N = 374) revealed that consumers' perceived funniness increased when exposed to a branded meme, with perceived unexpectedness mediating the impact. Study 2 (N = 184) replicated the findings of Study 1 and demonstrated the influence of branded meme exposure on sharing intention through perceived unexpectedness and funniness. In Study 3, a field experiment using Facebook A/B testing demonstrated that a luxury brand post featuring a meme (vs. a non-meme) significantly increased social media users' clicks and post-engagement. Study 4 (N = 425) revealed that exposure to a branded meme increased sharing intention for luxury brands but not for non-luxury brands. Theoretical and practical implications for luxury brand advertisers are discussed.

This paper studies the impact of trade openness on welfare through alterations in workers' skill acquisition. Guided by empirical evidence, we integrate endogenous choices of learning investments into a multisector Eaton–Kortum model. Our model reveals that trade openness influences skill acquisition by two channels: (1) reallocating labor between sectors with varying skill intensities and on-the-job learning opportunities and (2) allowing producers in each country to source varieties from more cost-effective suppliers in other countries, thus reducing costs of material inputs for learning. Our quantification indicates that the gains in skill acquisition account for 5% of the total gains from trade.

In China, state owners make minority equity investments in private firms. We study the allocative implications of such government investments using a two-sector DSGE model with financial friction and idiosyncratic productivities. In our model, private owners are more productive than state owners but face tighter financial constraints. Equity investment by a state owner alleviates the private owner’s financial constraint but dampens its own productivity, consistent with Chinese firm-level data. Under this setup, only private owners with sufficiently high productivity accept such investment, while only state owners with sufficiently low productivity make such investment. As a result, expansion of such government investment improves capital allocation within each sector and across sectors. Our analysis shows that financial liberalizations, including liberalizing interest-rate controls and reducing the loan-to-value gap between sectors, stimulate private owners’ demand for such government investment but discourage state owners from making it, thus generating an ambiguous effect on aggregate productivity.

This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-“average of averages” estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Then, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. Finally, I discuss covariate adjustment, which incorporates additional baseline covariates not used for treatment assignment. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application.