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| const items = await Promise.all( | ||
| results.map((article) => { | ||
| const link = `${baseUrl}${article.url}`; | ||
| const listingAuthors = Array.isArray(article.authors) ? article.authors.map((a) => a.replace(/<[^>]+>/g, '').trim()).join(', ') : undefined; |
| @@ -0,0 +1,98 @@ | |||
| import { load } from 'cheerio'; | |||
| const items = await Promise.all( | ||
| results.map((article) => { | ||
| const link = `${baseUrl}${article.url}`; | ||
| const listingAuthors = Array.isArray(article.authors) ? article.authors.map((a) => a.replace(/<[^>]+>/g, '').trim()).join(', ') : undefined; |
| let doi; | ||
|
|
||
| try { | ||
| const $ = load(await ofetch(link)); |
| if (pdf) { | ||
| description += `<p><a href="${pdf}">Download PDF</a></p>`; | ||
| } | ||
| author = author || $('meta[name="dcterms.creator"]').attr('content'); |
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Successfully generated as following: http://localhost:1200/nber/topic/itm_topics_term_id/656 - Success ✔️<?xml version="1.0" encoding="UTF-8"?>
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<title>NBER - Topic 656</title>
<link>https://www.nber.org/taxonomy/term/656</link>
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<description>National Bureau of Economic Research outputs under topic 656 - Powered by RSSHub</description>
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<lastBuildDate>Sun, 21 Jun 2026 08:08:25 GMT</lastBuildDate>
<ttl>5</ttl>
<item>
<title>Real Effects of Academic Research Revisited</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-science-fall-2025">September 25-26, 2025</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead">
<span>The Economics of Science: Taking Stock and Looking Ahead</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/megan_macgarvie">Megan MacGarvie</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/reinhilde_veugelers">Reinhilde Veugelers</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
</div>
<p>
This Chapter surveys the findings of social science research on the contribution of universities to innovation and economic growth, both locally/regionally and globally. In the last several decades research has demonstrated universities’ causal effects through the mechanisms of knowledge creation, education and training of students, and technology transfer/entrepreneurship. The Chapter summarizes how the literature has studied each of these mechanisms, and how the findings have probed variation across disciplines and economic sectors. The depth and breadth of understanding have been advanced by new microdata and new methods of linking data across inventions, scientists and institutions, and by application of methods from network science. We emphasize that research has proven the importance of these effects on average, but to date has less to say about the determinants of success or failure in different contexts. These findings have implications for public policy to foster innovation both regionally and globally.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/real-effects-academic-research-revisited</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/real-effects-academic-research-revisited</guid>
<pubDate>Thu, 11 Jun 2026 16:00:00 GMT</pubDate>
<author>Adam B. Jaffe, Laura B. Shupp, Valentina Tartari</author>
<category>Chapter</category>
</item>
<item>
<title>The i3 BigQuery Workspace: Shared Infrastructure for Open Science</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-science-fall-2025">September 25-26, 2025</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead">
<span>The Economics of Science: Taking Stock and Looking Ahead</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/megan_macgarvie">Megan MacGarvie</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/reinhilde_veugelers">Reinhilde Veugelers</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
</div>
<p>
Large-scale open datasets have transformed empirical research on science and innovation, but working with these data presents persistent challenges: computational barriers, provider dependence, reproducibility difficulties, transparency gaps, and resource inequality across institutions. We describe the i3 BigQuery Workspace, a shared cloud platform that hosts curated datasets (including OpenAlex, PatentsView, and community-contributed resources like Reliance on Science) and enables researchers to query terabyte-scale data in seconds, integrates multiple sources, and implements advanced tools like machine learning at scale. We document examples of research enabled by this infrastructure and discuss how shared data resources can improve research efficiency and expand the scope of feasible research in the economics of science and innovation.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/i3-bigquery-workspace-shared-infrastructure-open-science</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/i3-bigquery-workspace-shared-infrastructure-open-science</guid>
<pubDate>Tue, 09 Jun 2026 16:00:00 GMT</pubDate>
<author>Matt Marx, Dror Shvadron</author>
<category>Chapter</category>
</item>
<item>
<title>The Political Economy of Artificial Intelligence</title>
<description>Ajay Agrawal, Joshua Gans, Avi Goldfarb, and Catherine Tucker, editors. As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with</description>
<link>https://www.nber.org/news/political-economy-artificial-intelligence</link>
<guid isPermaLink="false">https://www.nber.org/news/political-economy-artificial-intelligence</guid>
<pubDate>Mon, 01 Jun 2026 16:00:00 GMT</pubDate>
<category>Article</category>
</item>
<item>
<title>Automation and Repression</title>
<description><p>
We consider a model of automation embedded in a political environment where workers can undertake a revolt (modeled as a global game), and greater inequality between capital and labor increases the likelihood of a revolt. Decentralized automation decisions raise the share of capital in national income and increase the likelihood of a successful revolt. A capitalist state (representing capital-owners) prefers to regulate the level of automation to lessen the threat of a successful revolt. The capitalist state can also redistribute to workers via the tax system or repress political action, thus creating greater room for further automation. We characterize the trade-off between the regulation of automation, redistribution and repression.<br>
Our main result is a complementarity between automation and repression. Unless the threat of revolt is quite weak or the capital stock is very low, the capitalist state prefers repression. A higher capital stock in turn encourages more automation and thus more repression. In our full dynamic model with capital accumulation, in the long run the economy tends to repression (again unless the threat of revolt is very weak). We also prove that the same conclusions apply when firms can additionally invest in new labor-intensive tasks. Finally, we show that, starting in a democracy, capital accumulation and thus greater automation encourages the capitalists to support a coup against democracy and set up a repressive system.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35336/w35336.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35336</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35336</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Daron Acemoglu, A. Arda Gitmez, Mehdi Shadmehr</author>
<category>Working Paper</category>
</item>
<item>
<title>Data-Driven Automation</title>
<description><p>
We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35320/w35320.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35320</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35320</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Maryam Farboodi, Andrew J. Koh, Anchi Xia</author>
<category>Working Paper</category>
</item>
<item>
<title>Before the Exodus? Young Scientists and the Future of US Science</title>
<description><p>
Shortly after major policy changes to US science funding began in early 2025, we surveyed 916 young biomedical scientists – PhD students and postdoctoral researchers – about their career intentions and expectations. The results document a dramatic shift in sentiment. Barely half of respondents now say they are likely to remain in academia, down 22 percentage points from how they felt six months earlier. The fraction likely to stay in the United States fell by 21 percentage points. Even satisfaction with having pursued a PhD in science declined by 16 percentage points. These are not the complaints of established scientists defending their budgets, but rather the stated intentions of the next generation – the scientists who would, in ordinary times, become the principal investigators of the future.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35330/w35330.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35330</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35330</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Pierre Azoulay, Raffaella Sadun, Daniela Scur</author>
<category>Working Paper</category>
</item>
<item>
<title>GLP-1 Therapy and the Reshaping of Socioeconomic Gradients in Health</title>
<description><p>
GLP-1 therapies for obesity promise substantial health improvements, but little is known about how their benefits vary across socioeconomic and demographic groups. Using a nationally representative microsimulation model of US adults and Shapely-value decomposition, we estimate the lifetime health and economic benefits of GLP-1 treatment and examine how those gains vary across individuals. The largest differences emerge across education. Individuals with less than a high school education experience experience roughly 14% higher gains in lifetime net social value, 16-17% larger improvements in discounted generalized risk- and severity-adjusted life-years (GRASA-QALYs), and 20% greater increases in life expectancy relative to the cohort mean, whereas individuals with college degrees experience gains 15-27% below the mean across these outcomes. Black and Hispanic individuals also tend to experience larger improvements in health outcomes and social value than White individuals, including larger gains in GRASA-QALYs and life expectancy and larger reductions in diabetes risk and duration. Females likewise experience larger predicted treatment gains than men. These patterns are consistent with the idea that the largest gains arise among populations facing greater socioeconomic constraints in sustaining behavioral weight control. GLP-1 innovation may therefore mitigate inequality in obesity-related disease and survival, advancing equity in population health.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35296/w35296.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35296</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35296</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>J. Felipe Montano-Campos, Bryan Tysinger, Dana Goldman, Darius N. Lakdawalla</author>
<category>Working Paper</category>
</item>
<item>
<title>But I Like Doing This! Enjoyable Tasks, Contracting, and Automation</title>
<description><p>
Workers sometimes enjoy productive tasks and voluntarily devote unpaid time to them. We study O-ring jobs in which firms can either price a complete task bundle or specify paid task floors while workers remain free to add time. For any allocation supported by both hourly contracts, the wage bill is identical: voluntary top-up is not a discount. The contracts differ because paid floors cannot cap attractive tasks below the worker's voluntary supply. This implementability constraint adds a containment motive for automation alongside replacement and scale effects. It also makes payroll measures incomplete: conditional on a common automated set and a common AI technology, jobs with the same payroll footprint can differ in worked time and task mix. Rich salaried bundle pricing removes the hourly-contract distortion.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35309/w35309.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35309</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35309</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
</item>
<item>
<title>Is the iPhone Birth Control? Causal Evidence from AT&T’s 2007–2011 Carrier Monopoly</title>
<description><p>
The U.S. general fertility rate has fallen by 22% since 2007, a sustained decline not readily explained by economic conditions, contraceptive use, housing or childcare costs, or other commonly cited factors. We assess the potential role of a different shock: the diffusion of the smartphone. The U.S. rollout of the iPhone, the first modern smartphone, provides a natural experiment: from June 2007 through February 2011, the device was sold only on AT&amp;T, allowing us to identify its effect from variation in AT&amp;T’s mobile broadband coverage. Entropy-balanced Poisson and synthetic difference-in-differences event studies imply that access to the iPhone reduced births by 4.5–8.0% at ages 15–19 and 3.2–6.6% at ages 20–24, with statistically significant but smaller declines among older cohorts. Placebo analyses applied to Verizon and Sprint’s pre-2011 coverage footprint are null. Taken together, these cohort effects imply that the diffusion of the iPhone deepened the decline in births among women under 30 while suppressing the rise in births among older women. Overall, the diffusion of the iPhone explains 33–52% of the decline in the general fertility rate among women aged 15–44. National-survey evidence on time use and sexual behavior is consistent with the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35310/w35310.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35310</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35310</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Caitlin K. Myers, Ezekiel Hooper</author>
<category>Working Paper</category>
</item>
<item>
<title>What Investment Data Implies about the AI Transition</title>
<description><p>
The five largest U.S. technology firms spent $380 billion on capital expenditure in 2025 and are forecast to spend roughly double that in 2026. These firms risk bankruptcy unless expected profits grow commensurately. We embed this observation in a two-sector open-economy model with rare productivity booms. We calibrate the boom size to match the observed increase in investment projected through 2027, implying that a boom raises AI-sector productivity by a factor of roughly 2.7. We then calibrate a two-year window of a 50% annual probability of an increase of the same magnitude, generating a range of scenarios consistent with the wide variety of industry forecasts, along with an elevated permanent probability tied to the valuation of the aggregate market. The implied additional cumulative GDP growth ranges from 5 to 58 percentage points by 2030, with AI shares of the economy ranging from 8% to 39%. Long-term annual growth is in expectation approximately 7% but with substantial risk. With risk aversion of 3, and an elasticity of intertemporal substitution equal to 1, the risk-free rate increases by approximately half a percentage point, and the equity premium rises by approximately 3 percentage points.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35290/w35290.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35290</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35290</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Jessica Wachter, Jonathan Wachter</author>
<category>Working Paper</category>
</item>
<item>
<title>Innovation without Borders? The Geography of Technological Diffusion</title>
<description><p>
How well does innovation diffuse across geographic boundaries? To shed light on this question, we present a large-scale field experiment involving 3,300 firms across twelve European Union countries. We elicit firms' perceptions of the share of similar firms in their own country that had invested in artificial intelligence (AI), as well as the corresponding share among similar firms in Germany, France, and Italy. We randomly provide half of the sample with accurate information about both domestic and foreign AI investment. We show that firms substantially underestimate competitors' current AI investment, both domestically and abroad, and that they update their expectations about competitors' future AI investment in response to the information treatment. The treatment also causes a statistically significant increase in firms' own expected AI investment rate (p-value &lt; 0.001). We find strong strategic complementarities within borders: a 1 pp increase in the expected share of domestic peers investing in AI raises a firm's own expected AI investment rate by 0.570 pp. These complementarities are absent across borders: the effect of an increase in the expected share of foreign peers investing in AI on a firm's own expected AI investment rate is statistically insignificant. Overall, our evidence shows that innovation diffusion and strategic complementarities in AI investment are much stronger domestically than internationally.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35314/w35314.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35314</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35314</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ursel Baumann, Zoë B. Cullen, Ester Faia, Annalisa Ferrando, Ricardo Perez-Truglia, Judit Rariga</author>
<category>Working Paper</category>
</item>
<item>
<title>Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools</title>
<description><p>
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity (“commits”), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35275/w35275.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35275</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35275</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Mert Demirer, Leon Musolff, Liyuan Yang</author>
<category>Working Paper</category>
</item>
<item>
<title>Defining Innovatisation: The Case of NewSpace and the Changing Space Sector</title>
<description><p>
The space sector has become far more dynamic and innovative, with new actors (e.g., start-ups, venture capital) entering and the ever-growing importance of private firms. In this paper we introduce a novel concept, innovatisation, to understand this phenomenon. Innovatisation describes the transformation of a sector between two modes. In a mode of technological achievements (TA), only technological (not economic) performance matters, primarily for prestige purposes; in innovation, customer preferences, commercial opportunities, and costs become essential. Studying the economics of Apollo and the commercialization attempts of the 1980s, we show how the space sector has long featured a logic of TA. Then, analyzing recent trends, we provide quantitative empirical evidence (e.g., costs) that innovation now shapes the sector, thanks to various driving forces. The driving forces behind the innovatisation process are identified building on Jones (2022) and the disruptive innovation theory.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35254/w35254.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35254</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35254</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Benoit Cornet, Marc-André Chavy-Macdonald, Dominique Foray</author>
<category>Working Paper</category>
</item>
<item>
<title>Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda</title>
<description><p>
The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes that describe its characteristic exposure profile. The remaining six attributes capture recurring implications at the venture level (staged financing strategies, dual scientific and commercial maturation, and the multidisciplinary broadening of teams) and the ecosystem level (multi-actor interactions, specialized incubation support, and industrial de-risking and scaling partnerships). We use this framework to delineate the boundaries of deep-tech innovation, distinguish it from adjacent concepts, and propose an agenda for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35255/w35255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35255</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35255</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray</author>
<category>Working Paper</category>
</item>
<item>
<title>The AGI Race and Existential Risk</title>
<description><p>
Concerns about the race to artificial general intelligence often assume that competition and resources increase risk by accelerating development. We study a model in which firms allocate scarce resources between speed and safety. Speed increases a firm's chance of reaching AGI first but leaves fewer resources for safety; safety lowers doom risk but slows arrival. Fragmentation increases total speed and conditional doom risk by shifting a fixed industry resource pool toward speed. The model also identifies a critical market size: below it, firms have positive expected payoff from achieving AGI, while above it, firms race even though achieving AGI has negative expected value. More per-firm resources always accelerate expected arrival, but their effect on conditional doom risk changes sign at this cutoff. Policy affects risk by changing equilibrium incentives: consolidation, resource regulation, commitment devices, and cautious public entry can improve welfare in some environments. The results show that AGI risk depends not only on technical considerations, but also on market structure, resource constraints, and institutions that shape the equilibrium allocation between speed and safety.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35276/w35276.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35276</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35276</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ethan Bueno de Mesquita, Wioletta Dziuda, Mattias Polborn</author>
<category>Working Paper</category>
</item>
<item>
<title>Does the Import Invasion Explain the Mysterious Disappearance of Productivity Growth in U.S. Manufacturing?</title>
<description><p>
Why did U.S. manufacturing productivity stop growing after 2010? Productivity growth disappeared, evaporating from an annual rate of +3.3 percent during 1987-2010 to -0.3 percent from 2010 to 2023. This paper shifts attention from 2010 as the start of the puzzle to a decade earlier when output stopped growing. This cessation of output growth in 2000 is attributed to the invasion of imports that closed domestic plants, destroyed jobs, and squeezed profits. Then followed a chain of causation that ultimately undermined productivity growth – from falling capacity utilization, to lower investment in fixed capital and R&amp;D, and to an erosion of innovation. Beyond the import invasion, the paper identifies a set of handicaps ranging from self-inflicted wounds by private manufacturing firms to a marked reduction in government-funded R&amp;D spending. Corporate funds were diverted from productive investment to share buybacks. Investment was distorted by environmental, health, safety, and fuel economy regulations. Innovation slowed not only because of diminishing returns to R&amp;D, but also because of a decline in public R&amp;D, and a diversion of private R&amp;D from basic science and process improvements to product refinements and brand extensions. Skilled worker shortages have plagued manufacturing for decades in the absence of sufficient public and private investment in vocational training.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35285/w35285.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35285</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35285</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Robert J. Gordon, Kenneth Ryu</author>
<category>Working Paper</category>
</item>
<item>
<title>The Missing Value of Data</title>
<description><p>
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5% of GDP.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35266/w35266.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35266</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ankit Bhutani, Guillermo Ordoñez, Laura Veldkamp</author>
<category>Working Paper</category>
</item>
<item>
<title>Frontier Knowledge in College and Student Success</title>
<description><p>
We study whether exposure to frontier knowledge in college affects student outcomes. Combining 459,415 syllabi from seven Texas public universities with 107 million publications and linked student records, we measure each course’s proximity to recent versus older research in its field. Exploiting syllabus updates unobserved at enrollment, we find that frontier exposure increases completion, GPA, graduate-school attendance, and earnings, and reduces time-to-degree. Completion, GPA, and progression gains are broad, while graduate-school and earnings returns are larger for students with stronger preparation and family resources. The evidence suggests two mechanisms: frontier content keeps students engaged, and sustained exposure builds labor-market skills.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35269/w35269.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35269</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Barbara Biasi, Song Ma</author>
<category>Working Paper</category>
</item>
<item>
<title>The Missing Value of Data</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/41st-annual-conference-macroeconomics-2026">April 16-17, 2026</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41">
<span>NBER Macroeconomics Annual 2026, volume 41</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/john_leahy">John Leahy</a>, </span>
<span class="page-header__author-item">
<a href="https://www.nber.org/people/valerie_ramey">Valerie A. Ramey</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/giovanni_violante">Giovanni L. Violante</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
<div class="label-link">
<span class="label-link__label">Series</span>: Macroeconomics Annual
</div>
</div>
<p>
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5 percent of GDP.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41/missing-value-data</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41/missing-value-data</guid>
<pubDate>Thu, 21 May 2026 16:00:00 GMT</pubDate>
<author>Ankit Bhutani, Guillermo Ordoñez, Laura Veldkamp</author>
<category>Chapter</category>
</item>
<item>
<title>The Political Economy of Artificial Intelligence</title>
<description><img loading="lazy" src="https://www.nber.org/sites/default/files/styles/book_cover/public/2026-03/Political%20Economy%20AI.jpg?itok=xthIL1MS" width="294" height="440" alt="cover of book, The Political Economy of Artificial Intelligence, fine art flowers in vase" class="lazyload page-header__cover" data-src="/sites/default/files/styles/book_cover/public/2026-03/Political%20Economy%20AI.jpg?itok=xthIL1MS" typeof="foaf:Image" referrerpolicy="no-referrer">
<div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-artificial-intelligence-fall-2024">September 19-20, 2024</a></span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: <a href="https://press.uchicago.edu/ucp/books/book/chicago/P/bo270631423.html">University of Chicago Press</a> </div>
</div>
<p>As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with regulation, military power, and political identity—offering analytical frameworks and identifying key open questions for future research.<br>
The contributions address topics such as the allocation of property rights for AI inputs, trade-offs among alternative regulatory regimes, and the role of interest groups in shaping the technology’s trajectory. They explore how AI-related capabilities influence military effectiveness, resource allocation, and bargaining power among nations, and consider AI’s effects on political preferences, from the influence of AI-curated information on polarization to the implications of targeted political advertising and personalized education for national identity formation.<br>
The volume highlights key trade-offs that arise in AI’s political economy, and points toward empirical strategies and theoretical models that can advance understanding in this emerging field. Drawing on diverse disciplinary perspectives, the collection provides a foundation for rigorous inquiry into how AI both shapes and is shaped by political and economic forces.</p>
<div class="page-header__intro-links">
<a class="link link--arrow ml-2" href="https://www.nber.org/forms/permissionrequestform.pdf">Get permission to reprint part of this book</a>
<a href="https://press.uchicago.edu/ucp/books/book/chicago/P/bo270631423.html" class="btn btn--primary">Purchase Book</a>
</div>
</description>
<link>https://www.nber.org/books-and-chapters/political-economy-artificial-intelligence</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/political-economy-artificial-intelligence</guid>
<pubDate>Wed, 20 May 2026 16:00:00 GMT</pubDate>
<author>Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker</author>
<category>Book - Conference Volume</category>
</item>
<item>
<title>Designing More Informative Tests: Separating Execution from Recognition</title>
<description><p>
Tests are widely used to measure ability, yet performance on a test often reflects more than the ability to execute assigned tasks. It also reflects the ability to recognize which tasks are worth attempting, how they should be prioritized, and how effort should be allocated under uncertainty. This paper studies how tests can be designed to separate these capabilities.<br>
We model a test as a sequential decision problem. Tasks differ in difficulty, their ordering is uncertain, and examinees may acquire costly information about that ordering before choosing how to proceed. The testing environment is the informational structure surrounding the realized test: in particular, the examinee's beliefs about how task difficulty has been arranged. Performance is therefore generated by an optimal recognition–execution policy, not by execution skill alone.<br>
The analysis delivers two negative results. First, even in the simplest two-task environment, a single score exhibits dimensional collapse: distinct combinations of execution skill and recognition capability generate identical expected scores. Second, with three tasks, the relationship between capabilities and scores becomes environment-dependent: changing beliefs about task ordering can change which actions are considered and how capabilities translate into performance.<br>
These results imply that standard scores are not generally informative enough to separate the capabilities that generate performance. This matters because scores are used to summarize what individuals can do and to guide downstream decisions about placement, training, and instruction. If a test does not separately reveal execution and recognition, it provides limited guidance about which capability is strong, which is weak, and where improvement should be directed.<br>
We then show how more informative tests can be designed. Under a simple communicability constraint, two canonical environments—ordered and randomized tests—induce distinct relationships between capabilities and scores. In an ordered test, recognition is suppressed and performance isolates execution. In a randomized test, recognition is activated and performance reflects both execution and recognition. Observing performance across these environments separates capabilities that are confounded in any single score.<br>
The paper reframes testing as a problem of informational design: tests should be designed not only to record performance, but to reveal the distinct capabilities that generate it.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35232/w35232.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35232</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Andrew Caplin, Leo Zhu</author>
<category>Working Paper</category>
</item>
<item>
<title>Old Space, New Space: A Commercial Revolution in Innovation?</title>
<description><p>
The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&amp;D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government's most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35212/w35212.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35212</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35212</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ruben Gaetani, Alexander T. Whalley</author>
<category>Working Paper</category>
</item>
<item>
<title>More Paths or More Contrast? A Theory of Experimentation Breadth</title>
<description><p>
How should an organisation choose the breadth of its experimentation portfolio? Breadth has two distinct margins—the number of paths kept alive, and the degree of contrast among them—and prior research has largely studied them in isolation. We bring them into a single framework and show that they need not move together. Under a fixed experimentation budget, adding paths creates more chances to find a strong direction, but it also dilutes learning across paths and weakens the strongest feasible contrast. When the task is primarily ranking among already-viable alternatives, broader portfolios become more attractive as the budget rises. When paths share common viability uncertainty, and experimental signals track payoff relatedness, however, additional paths partly repeat the same viability test rather than provide independent information. We identify conditions under which testing exactly two sharply contrasting paths is optimal, dominating both a single deep test and broader portfolios. The framework reconciles competing prescriptions—many parallel shots versus a few sharp comparisons—by clarifying when each applies, and shows why empirical measures of breadth should not treat the number of options and their relatedness as separable margins.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35207/w35207.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35207</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35207</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans, Luca Gius</author>
<category>Working Paper</category>
</item>
<item>
<title>Endogenous Task Bundling, Skills and Automation</title>
<description><p>
Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape. We argue that this assumption hides much of the action. When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle them into one broad role or split them into specialist roles changes which surviving skills the labour market actually rewards. A skill that played no role in the pre-AI wage can become the dominant component of the post-AI wage, while a skill that anchored the pre-AI wage can disappear from the schedule. We develop an assignment model in which the priced human bundle is endogenous, and we use it to show that a fixed-bundle wage regression can mis-sign the effect of AI exposure. In general, the omitted-redesign bias has no unconditional sign: it is the residual covariance between exposure and role-specific redesign terms. Under explicit sufficient conditions, exposure-correlated unbundling loads specialist comparative-advantage premia onto the exposure coefficient, while exposure-correlated rebundling loads a different, often opposite, omitted term. The sign must therefore be measured from local post-AI partition changes rather than assumed from exposure alone.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35211/w35211.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35211</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35211</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
</item>
<item>
<title>Estimating the Present Value of R&D Tax Benefits in the United States</title>
<description><p>
Using a panel of confidential corporate tax returns, we provide the first direct estimates of the realized present value of corporate tax benefits from R&amp;D credits and deductions in the United States. Realized tax benefits can deviate from statutory tax benefits because firms in loss status are typically unable to fully utilize credits and deductions to offset current-year taxes and instead must carry these attributes forward. We develop a novel procedure to track the intertemporal firm-level utilization of tax attributes generated by corporate R&amp;D spending, and find that the present value of R&amp;D tax benefits varies substantially with firms’ loss status, age, and size. Old and large firms typically use R&amp;D tax benefits quickly, while young firms – especially those that are small – frequently operate in loss status and use tax attributes more slowly. From 2012–2016, the average firm generated $0.41 in statutory tax benefits per dollar of R&amp;D investment, with a realized present value of $0.36. Young and small firms in a loss position realized only $0.23 per dollar, a 44% decrease relative to the statutory benchmark.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35208/w35208.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35208</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Brandon Pecoraro, Nicholas C. Hoffman, Martin Lopez-Daneri, Elena C. Derby, Rachel Moore, Shannon E. Sledz</author>
<category>Working Paper</category>
</item>
<item>
<title>Competitive Exposure and Entrepreneurial Experimentation</title>
<description><p>
Entrepreneurs learn by experimenting, but experiment choices are often public. A closed beta, private pilot, or public launch not only generates evidence; it also reveals what kind of entrepreneur would choose that action. We develop a dynamic model in which a founder chooses between stealthy and public experiments while potential entrants infer from both actions and outcomes. Public outcomes are modelled as garblings of the founder's private experimental evidence, so public leakage informs outsiders without giving the founder information beyond the private signal already observed. The key state variable is competitive exposure: the public runway before entry becomes attractive. Exposure is depleted by two forces, leakage burn from public outcomes, and action burn from public inference about experiment choice. This distinction implies that competition can distort experiment design without forcing earlier scale: lower-confidence founders choose stealthier tests, while higher-confidence founders spend exposure to obtain faster private learning through more public tests. Scale is accelerated only when exposure reduces the value of waiting more than it reduces the value of scaling. Finally, exogenous funding gates make observable scale more selective and, therefore, more informative to entrants. The analysis shows that entrepreneurial experimentation is not merely private learning under uncertainty; it is public action under competitive inference.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35172/w35172.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35172</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35172</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
</item>
<item>
<title>Data Centers and Local Economies in the Age of AI: A Shift--Share Approach</title>
<description><p>
Data centers are the physical infrastructure behind cloud computing, artificial intelligence, and enterprise software. The rapid diffusion of artificial intelligence (AI) is intensifying demand for compute, accelerating investment in data centers, and raising concerns about the local economic and environmental footprint of these facilities. Their expansion creates a local policy tradeoff. A data center can bring capital investment, construction activity, and specialized employment, but it can also increase demand for electricity, land, and grid capacity. This paper studies these effects at the U.S. county level. We assemble a facility-level panel of global data centers with precise coordinates, scale metrics, and annualized revenue. We map facilities to U.S. counties and combine them with County Business Patterns, county-level IRS income, county-level house prices, and electricity prices. To address endogenous siting, we instrument for data center growth using two shift-share instruments, which leverage pre-existing proximity to InterTubes long-haul fiber nodes and the 1980 county share of U.S. urban college population as shares, and both Chinese and rest-of-the-world data center revenue growth as shifts. The IV estimates show positive effects on total employment, data-processing employment, construction employment, establishments, house prices, and electricity prices at different horizons after data center growth. We also find positive effects on tax returns, adjusted gross income, and wages, while annual payroll responds less robustly. The results suggest that data centers create measurable local activity, increase house prices, and affect local electricity markets through higher prices.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35194/w35194.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35194</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Fernando E. Alvarez, David Argente, Joyce Chow, Diana Van Patten</author>
<category>Working Paper</category>
</item>
<item>
<title>On the Negative Consequences of Low-Wage Offshoring for Innovation</title>
<description><p>
Conventional wisdom holds that offshoring intermediates to China stimulates innovation. This is not entirely compelling. On the one hand, (a) offshoring lowers marginal costs and expands sales, thereby increasing the returns to innovation, especially for large firms. On the other hand, (b) offshoring low-quality intermediates reduces the costs of older-generation products, thereby reducing the returns to innovating into newer generations. We examine these two opposing forces over 2002-2011 for 6,024 Canadian firms. Our empirical strategy regresses measures of innovation, such as R&amp;D, on imports of intermediate inputs. To address endogeneity, we construct a model-consistent shift-share instrument whose shocks are the often-dramatic improvements in the quality of HS6 Chinese intermediate inputs. We find that greater offshoring reduced R&amp;D spending over 2002-2011 by 15% as (1) firms engaged in R&amp;D in 2002 reduced their expenditures, and (2) firms not initially engaged in R&amp;D were discouraged from starting up new R&amp;D projects. Our model explains these findings: Rising quality of Chinese intermediates is a positive supply shock (rather than a negative China shock) that raises profits for all offshorers, raises innovation for the largest offshorers (channel a above), and lowers innovation for all other offshorers (channel b). These predictions are confirmed in the data.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35167/w35167.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35167</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Wulong Gu, Alla Lileeva, Daniel Trefler</author>
<category>Working Paper</category>
</item>
<item>
<title>The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks</title>
<description><p>
Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three layers: firm-wide adoption, business-function deployment, and worker-task use. During Nov 2025–Jan 2026, 18% of firms used AI in at least one function (32%, employment-weighted), with adoption expected to reach 22% within six months. Use is concentrated in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70%, employment-weighted) among very large firms in Information, Professional Services, and Finance. Among adopters, scope remains limited: 57% use AI in three or fewer functions, most often Sales and Marketing (52%), Strategy (45%), and IT (41%). Worker-level use appears in 23% (41%, employment-weighted) of firms, primarily for writing, document analysis, and information search; 65% restrict use to three or fewer tasks. Evidence suggests both top-down and bottom-up diffusion: worker use can occur without firm adoption, and vice versa. Most firms (66%) use AI for task augmentation, while employment reductions are rare (2%). Regression results show a positive relationship between firm performance and AI integration breadth. However, functional deployment and operational investment are associated with employment declines, while worker-task use is not once these factors are controlled for.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35141/w35141.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35141</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Kathryn Bonney, Cory L. Breaux, Emin Dinlersoz, Lucia S. Foster, John C. Haltiwanger, Aditya A. Pande</author>
<category>Working Paper</category>
</item>
<item>
<title>How Artificial Intelligence Shapes Science: Evidence from AlphaFold</title>
<description><p>
We study how a frontier AI model affects scientific discovery by examining the release of the AlphaFold2 algorithm and its impact on structural biology and related fields of science. Structural biology is the field of science concerned with understanding the structure and function of proteins. Researchers in this field historically devoted substantial time and resources to experimentally solving three-dimensional protein structures. AlphaFold can predict these structures without running experiments. In July 2021, researchers gained access to hundreds of thousands of these AI-predicted structures virtually overnight. Yet, to date, we find that the rate of experimental structure determination has remained almost unchanged. Instead, researchers appear to use predicted structures to facilitate and complement experimental structure determination. Looking at downstream science that builds on protein structures, we find that basic research on proteins that had no structure information prior to AlphaFold increases by 15 to 40% relative to proteins that already had a structure, shifting the direction of research toward less-studied proteins. However, we find no evidence so far that more applied, early-stage drug development is targeting these proteins, though such activity may emerge in the future.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35143/w35143.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35143</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ryan R. Hill, Carolyn Stein</author>
<category>Working Paper</category>
</item>
<item>
<title>When Does Automating AI Research Produce Explosive Growth? Feedback Loops in Innovation Networks</title>
<description><p>
AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. We develop a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (``explosive''). We find that automating research can offset diminishing returns to ideas by activating two reinforcing channels: a technological feedback loop across research sectors, and an economic feedback loop in which higher output finances further research. Growth becomes explosive if the combined strength of technological and economic feedback loops overcomes diminishing returns. In a simple simulation calibrated to trends in AI progress, fully automating software research and modest (5%) automation in other sectors generates a singularity within six years. Bottlenecks do not overturn the result if task automation advances sufficiently fast.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35155/w35155.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35155</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Tom Davidson, Basil Halperin, Thomas Houlden, Anton Korinek</author>
<category>Working Paper</category>
</item>
<item>
<title>China's Global Ownership</title>
<description><p>
We study the global footprint and real effects of Chinese overseas corporate ownership. By assembling a comprehensive micro-level dataset of 161,773 firms across 159 countries (2012–2021), we independently reconstruct multi-layered ownership chains to trace capital through offshore tax havens to its ultimate origin. This approach reveals a global footprint substantially broader than official FDI statistics. Chinese-controlled foreign assets expanded at 20% annually, reaching $2.1 trillion or roughly 3% of global corporate assets by 2021. Chinese investors—particularly state-owned enterprises (SOEs)—strategically target R&amp;D-intensive and supply-chain-linked firms. Following acquisition, target firms increase capital stock and R&amp;D expenditures, yet these inputs fail to generate higher patent output and are accompanied by a significant decline in profitability. We document a novel 'innovation spillback' mechanism: while target innov... |
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<title>Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda</title>
<description><p>
The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes that describe its characteristic exposure profile. The remaining six attributes capture recurring implications at the venture level (staged financing strategies, dual scientific and commercial maturation, and the multidisciplinary broadening of teams) and the ecosystem level (multi-actor interactions, specialized incubation support, and industrial de-risking and scaling partnerships). We use this framework to delineate the boundaries of deep-tech innovation, distinguish it from adjacent concepts, and propose an agenda for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35255/w35255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35255</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray</author>
<category>Working Paper</category>
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<item>
<title>Startups in Africa</title>
<description><p>
We build new data on startups in Africa to study which types of financing these firms demand, how financing is allocated in practice, and the implications for startup creation and the composition of the sector. We combine a continent-wide founder survey, an incentive-compatible experiment estimating financing preferences, and venture capital (VC) deal records matched to founders’ education and work histories. We find that startups strongly prefer equity over debt, but equity is supplied mainly by foreign investors and flows disproportionately to foreign-connected founders. About 80 percent of VC deals involve a foreign investor, and more than 60 percent of funded founders have studied or worked outside Africa. A simple accounting framework shows that this foreignness reflects three main forces: scarce local equity capital, a thin pool of local entrepreneurs able to access startup finance, and frictions limiting local entrepreneurs’ access to foreign investors. Together, these forces reduce startup creation and tilt the sector toward foreign investors and foreign-connected founders.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35261/w35261.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35261</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Emanuele Colonnelli, Marcio Cruz, Mariana Pereira-Lopez, Tommaso Porzio, Chun Zhao</author>
<category>Working Paper</category>
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<title>Old Space, New Space: A Commercial Revolution in Innovation?</title>
<description><p>
The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&amp;D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government's most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35212/w35212.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35212</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35212</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ruben Gaetani, Alexander T. Whalley</author>
<category>Working Paper</category>
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<title>Growth is Getting Harder to Find, Not Ideas</title>
<description><p>
Relatively flat US productivity growth versus rising R&amp;D expenditures is often interpreted as evidence that ideas are getting harder to find. We build a new 45-year panel tracking the universe of US firms' patenting to investigate the micro underpinnings of this conclusion, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. We find that average patents per R&amp;D input are increasing, the elasticity of patents to R&amp;D inputs is flat or rising, and there is not systematic evidence of a secular decline in patenting after controlling for research inputs. We then document a positive, significant, and fairly steady relationship between firms' patent and labor productivity growth rates. Average firm growth after controlling for patent growth, however, declines. Together, these results suggest that firms' innovative efforts play a key role in sustaining growth that has not diminished over the last four decades.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35182/w35182.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35182</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Teresa C. Fort, Nathan Goldschlag, Jack Liang, Peter K. Schott, Nikolas Zolas</author>
<category>Working Paper</category>
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<title>Anti-Harassment Policy and the Startup Labor Market</title>
<description><p>
This paper examines how anti-harassment legal reforms that weaken non-disclosure agreements (NDAs) in cases of workplace sexual harassment affect startups' hiring and organizational decisions. Using a staggered difference-in-differences design and LinkedIn data on over 50,000 U.S. venture-capital-backed startups from 2014–2022, we find that NDA reforms, although intended for employee protection, reduce female hiring by about 8%, with effects concentrated among junior women, who are statistically more prone to sexual harassment, and in small or male-dominated startups. The results apply to both the intensive and extensive margins of female hiring. Treated entrepreneurial firms also witness more departures of male managers, promote more women, and receive less VC funding. These results suggest that while NDA-weakening laws increase firms’ perceived legal risk and reduce female hiring, they also trigger internal restructuring that promotes women's advancement into leadership and may, over time, foster more accountable and inclusive organizational cultures.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35187/w35187.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35187</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Jun Chen, Song Ma, Feng Zhang</author>
<category>Working Paper</category>
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<title>Global Evidence on Business Use of AI</title>
<description>Despite the rapid rise of artificial intelligence (AI), internationally comparable data on how businesses use this new tool are scarce. In Firm Data on AI (NBER Working Paper 34836), Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H.</description>
<link>https://www.nber.org/digest/202605/global-evidence-business-use-ai</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Pawel Smietanka, Gregory Thwaites, Ben Zhe. Wang</author>
<category>Article</category>
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<title>Understanding Firms' AI Efforts and Their Economic Impact</title>
<description><p>
This paper reviews firm-level data on artificial intelligence and the emerging evidence on AI's economic effects. It argues that measurement is central: different AI datasets capture different objects (including invention versus use, internal capability building versus outsourcing, and realized activity versus investor perceptions) and can therefore lead to different conclusions. The paper develops a framework for choosing among these measures and surveys available data sources on firm AI efforts. It synthesizes evidence on AI's effects on firm growth, valuation, productivity, risk, labor, competition, financial markets and applications. The paper concludes by suggesting some ideas for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35123/w35123.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35123</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Tania Babina</author>
<category>Working Paper</category>
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<title>The Effect of Choice Screens on Mobile Browser Usage: Evidence from the EU Digital Markets Act</title>
<description><p>
Can active choice mitigate the effects of preset defaults? We study this question using a difference-in-differences design around the rollout of the EU’s Digital Markets Act, which required iOS and Android to display browser choice screens under certain conditions. We find large effects, with notable differences across platforms: from 15 months after the mandate onward, Firefox usage was 113 percent higher on iOS and 12 percent higher on Android relative to a no-mandate counterfactual. This gap is consistent with rollout differences, as Android showed choice screens primarily on new devices, whereas iOS also showed them on existing devices.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35112/w35112.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35112</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Jesper Akesson, Kush Amlani, Raul Cepeda Suarez, Emily Chissell, Stefan Hunt, Michael Luca, Gemma Petrie</author>
<category>Working Paper</category>
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<title>Belief Dispersion and Entrepreneurial Entry</title>
<description><p>
When should a founder act on a strong belief about an opportunity, knowing that rivals assessing the same opportunity may hold very different views? This paper studies entry decisions when entrepreneurs hold heterogeneous beliefs about an opportunity's value and each founder knows only the range of views rivals might hold. In equilibrium, a founder enters only when their conviction exceeds a threshold set by anticipated rival optimism. The relationship between belief dispersion and entry is surprisingly rich: depending on the founder's conviction and the cost of entry, there may be no level of dispersion that supports entry, all levels may support it, or only a middle range may, so that an outside observer may see the most entry at intermediate levels of belief dispersion. When founders can delay, high dispersion that deters immediate entry need not prevent it altogether: the absence of rival action gradually reveals that competitors are less bullish than feared. Finally, not all conviction-building is equal. Validation that only the founder sees strengthens entry incentives fully, whereas validation visible to the whole market partly backfires by encouraging rivals. The paper formalises the intuition that entrepreneurial value comes not from optimism alone but from optimism that the founder anticipates rivals will not share, and derives predictions linking belief dispersion to entry patterns.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35091/w35091.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35091</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
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<title>The Declining Local Bias of Entrepreneurship in the United States</title>
<description><p>
Multiple studies document a local bias of entrepreneurship (LBE) in recent decades, where self-employed entrepreneurs are systematically more likely than wage workers to operate in their region of birth. This paper documents an important new fact: the LBE has been declining in the United States since 1970. The LBE is still present for white men engaged in self-employment, but it no longer exists for the overall U.S.-born workforce. We connect that decline to the transformation of self-employment away from high startup-capital sectors and the reduced opportunity for local self-employed entrepreneurs to achieve high incomes compared to wage work.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35088/w35088.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35088</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Innessa Colaiacovo, Margaret G. Dalton, Sari Pekkala Kerr, William R. Kerr</author>
<category>Working Paper</category>
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<title>Beyond Demo Day: Sorting and Value Added in Startup Accelerators</title>
<description><p>
We study who joins startup accelerators, how founders sort across programs, and which accelerators improve startup outcomes. Using a comprehensive sample of about 750,000 U.S. startups linked to 329 accelerators, we adapt the teacher value-added framework from education economics to estimate accelerator value added (AVA) while accounting for sorting. Selection is systematic: observably better ventures are more likely to enter accelerators and to sort into higher-AVA programs. Yet accelerator performance is highly dispersed. Most accelerators have negative value added relative to a no-accelerator benchmark, while a small right tail generates large gains. High-AVA accelerators predict better long-term outcomes, including acquisition, employment, revenue, and valuation, and are also more likely to accelerate the shutdown of weaker ventures. We validate AVA using internal applicant data from a large U.S. non-equity accelerator.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35063/w35063.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35063</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Youn Baek, Deepak Hegde</author>
<category>Working Paper</category>
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<title>Capital Gains Taxation and Startup Founders</title>
<description>The US capital gains tax is realization based, which means that taxes are due when appreciated assets are sold. Critics of this approach argue that it allows asset holders, such as corporate founders, to defer their tax obligations, sometimes indefinitely. An alternative approach, taxing gains on</description>
<link>https://www.nber.org/be/20261/capital-gains-taxation-and-startup-founders</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Eduardo M. Azevedo, Florian Scheuer, Kent Smetters, Min Yang</author>
<category>Article</category>
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<title>Featured Researcher: Jorge Guzman</title>
<description>Jorge Guzman is the Gantcher Associate Professor of Business at the Columbia Business School, where he has been a faculty member since 2018, and a faculty research fellow in the NBERs Productivity, Innovation, and Entrepreneurship program. His research focuses on entrepreneurship policy, regional</description>
<link>https://www.nber.org/be/20261/featured-researcher-jorge-guzman</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<category>Article</category>
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<title>The Geographic Expansion of Innovative Firms</title>
<description>Most US innovation stems from firms that operate R&amp;D facilities in many local markets. IBM and Google are two prominent examples, with R&amp;D activitiesmeasured by patentingin approximately 70 and 20 distinct locations, respectively. When a technology company opens an R&amp;D facility in a new location, it</description>
<link>https://www.nber.org/be/20261/geographic-expansion-innovative-firms</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Craig A. Chikis, Benny Kleinman, Marta Prato</author>
<category>Article</category>
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<title>Mixed Immigrant-Native Founding Teams Excel</title>
<description>Roughly one-quarter of new employer businesses in the United States are started by immigrants. Immigrant inventors have been responsible for approximately 23 percent of US patents produced since 1976 despite making up only 16 percent of the total US-based inventor population. Yet immigrant</description>
<link>https://www.nber.org/be/20261/mixed-immigrant-native-founding-teams-excel</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Zhao Jin, Amir Kermani, Timothy McQuade</author>
<category>Article</category>
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<title>Contract Enforcement and Young Firm Capital Structure: A Global Perspective</title>
<description><p>
We develop a framework to measure the severity of financial constraints for young firms across countries. Using ORBIS balance-sheet data for 23 economies, we show that short-term leverage rises while long-term leverage falls early in firms’ life cycles, with this pattern persisting longer where contract enforcement is weaker. We build a model of optimal financing under limited enforcement with endogenous debt maturity and blueprint capacity that matches these patterns and enables structural measurement of financial constraints. The framework decomposes the funding gap into within-firm borrowing constraints that ease with repayment history and a scale distortion identifiable through cross-country comparisons.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34985/w34985.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34985</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Gonzalo E. Basante Pereira, Ina Simonovska</author>
<category>Working Paper</category>
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<title>Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent</title>
<description><p>
We construct a novel dataset linking academic publication records to U.S. Census employer–employee data to track 42,000 AI researchers over two decades. We document systematic changes in the allocation of AI talent. Industry increasingly attracts younger and foreign-born researchers, while gender representation improves more in academia. The top 1% of publishing industry scientists now earn $1.5 million more annually than comparable academics, a fivefold increase since 2001. Rising wage premia coincide with greater sorting into large incumbent firms. Researchers who move to industry publish less but patent more, consistent with a shift from open science toward proprietary innovation.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34964/w34964.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34964</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Ufuk Akcigit, Craig A. Chikis, Emin Dinlersoz, Nathan Goldschlag</author>
<category>Working Paper</category>
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<title>Impact of Minimum Pay Rules on Gig Delivery Drivers</title>
<description>Platform-based delivery work has expanded dramatically over the past decade, creating millions of work opportunities for independent contractors who operate outside traditional employment protections. Because gig workers are not covered by standard minimum wage laws, several jurisdictions have</description>
<link>https://www.nber.org/digest/202603/impact-minimum-pay-rules-gig-delivery-drivers</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Yuan An, Andrew Garin, Brian K. Kovak</author>
<category>Article</category>
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<title>Intangible Intensity</title>
<description><p>
We develop a text-based measure of intangible investment intensity derived from firms’ 10-K filings, and offer a general methodology for semantic theme scoring (STS). Our approach further classifies disclosure text into knowledge, customer, and organization capital. Firms with high intangible intensity are smaller, younger, and invest heavily in R&amp;D and human capital. The three subcomponents map cleanly to distinct economic firm types: knowledge-intensive firms are R&amp;D-driven with high valuations and skilled labor; customer-intensive firms are mature, profitable, and commercially oriented; and organization-intensive firms are large, asset-heavy incumbents. Managerial expenditure descriptions thus provide informative signals about intangible investment, complementing financial statements in capturing corporate capital stocks.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34882/w34882.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34882</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Andrea L. Eisfeldt, Barney Hartman-Glaser, Edward T. Kim, Ki Beom Lee</author>
<category>Working Paper</category>
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<title>Venture Fraud</title>
<description><p>
We assemble the first comprehensive sample of venture fraud cases involving 614 U.S. venture capital (VC)-backed startups founded since 2000. We find that VC-backed firms are 54% more likely to face fraud charges than comparable non-VC-backed firms within a subsample of newly public firms where detection likelihood is high and homogeneous. We then examine the role of governance in explaining venture fraud, focusing on two features that have risen in recent years—founder-friendly structures and cap table complexity. In a panel prediction model examining all venture fraud cases, we find that fraud is more likely in startups with stronger founder control rights, more convex founder cash flow rights, more investors, and greater participation of non-traditional investors. Founder-controlled boards are 88% more likely to commit fraud than VC-controlled or shared-control boards, even within the same firm. Governance variables matter much more than founder characteristics in predicting fraud. Hot funding conditions at the initial round, which weaken governance incentives, predict future fraud. Fraudulent entrepreneurs continue to found new VC-backed startups unharmed relative to non-fraudulent entrepreneurs, suggesting a lack of market discipline. Overall, our results highlight rising agency costs in VC-backed firms that could lead to misallocation and broader social costs.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34868/w34868.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34868</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Alexander Dyck, Freda Fang, Camille Hebert, Ting Xu</author>
<category>Working Paper</category>
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<title>Firm Data on AI</title>
<description><p>
We survey nearly 6,000 senior business executives at US, UK, German, and Australian firms to develop new evidence on AI adoption and its effects on jobs, productivity, and output. Specifically, we ask executives about AI usage, its effects at their own firms over the past three years and, looking ahead, what they anticipate over the next three years. We find four main results. First, 69% of firms actively use AI, with higher usage rates at younger and more productive firms. Second, more than two thirds of executives regularly use AI, but their usage rate averages only 1.5 hours a week. Third, executives report little own-firm impact of AI over the last 3 years, with nine-in-ten reporting no impact on employment or productivity. Fourth, these same executives predict sizable effects over the next 3 years, predicting that AI will boost productivity at their firms by an average of 1.4%, raise output 0.8%, and cut employment 0.7%. In contrast, employees anticipate that AI will raise employment 0.5% at their firms in the next 3 years, highlighting an expectations gap between employers and employees.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34836/w34836.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34836</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Pawel Smietanka, Gregory Thwaites, Ben Zhe Wang</author>
<category>Working Paper</category>
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<title>How Do Workers Think About The Costs and Benefits of Freelance Work? New Evidence From a Survey Experiment</title>
<description><p>
We examine how workers perceive the trade-offs of freelancing using a novel survey design that explores the nature of workers' perceptions of their own jobs and the implications of work arrangements for their take-home pay. We find that, across several alternative classifications of freelance work, workers in such arrangements make less per hour than traditional employees, but report having greater control of when, where, and how they work. We find that on average, self-employed workers spend an additional 5 to 8 percentage points of gross pay covering unreimbursed expenses relative to traditional employees. However, when asked about expectations of net pay in freelance and traditional employment jobs with the same gross pay, respondents who received no quantitative information expected net pay to be higher in freelance arrangements than in employment arrangements, on average. This pattern reversed among respondents who were randomly assigned to receive customized estimates of their expected total expense and tax burdens in each arrangement, who estimated that freelance arrangements would generate lower net lower earnings than employment arrangements (consistent with the estimates we provided to them). This suggests that workers may not be fully aware of the tax and expense burdens freelance workers are responsible for. Interestingly, we find similar results both for workers who are currently employees in their main job and those who are currently self-employed, suggesting that the low salience of the tax and expense burdens associated with freelance work are not merely driven by those with no self-employment experience.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34843/w34843.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34843</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Edward Freeland, Andrew Garin, Dmitri K. Koustas</author>
<category>Working Paper</category>
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<title>Younger Firms and CEOs Allow More Work from Home</title>
<description><p>
We establish three facts about work from home (WFH) in the United States. First, employees WFH more often at younger firms – almost twice as often at firms founded after 2015 than at firms founded before 1990. Second, employees working under younger CEOs have higher levels of WFH. The average WFH rate is 1.4 days per week when the CEO is under 30, compared to 1.1 days when the CEO is 60 or older. Third, the self-employed WFH more than twice as often as wage-and-salary employees. These facts highlight the importance of organizational and managerial attributes for the prevalence of WFH.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34795/w34795.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34795</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Cevat Giray Aksoy, Jose Maria Barrero, Nicholas Bloom, Katelyn Cranney, Steven J. Davis, Mathias Dolls, Pablo Zarate</author>
<category>Working Paper</category>
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<title>Do Rideshare Users Comparison Shop?</title>
<description>The widespread adoption of mobile web and smartphone apps was expected to dramatically reduce consumer search costs and intensify price competition, particularly in markets where comparing prices requires little more than opening a second application. The US rideshare market, dominated by Uber and</description>
<link>https://www.nber.org/digest/202602/do-rideshare-users-comparison-shop</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Jeffrey Fossett, Michael Luca, Yejia Xu</author>
<category>Article</category>
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<title>Technology and Economic Development</title>
<description><p>
This chapter presents a tractable framework for the study of technology adoption and diffusion in the context of economic development. Firms in countries behind the world technology frontier can rapidly adopt new techniques from the world frontier. Lower absorptive capacity (because of weak education systems, poor management practices, or barriers to technology adoption), institutional distortions, mismatch between frontier technologies and the needs of firms in the country (i.e., “inappropriate technology”), and credit market frictions slow down technology adoption and cause the economy in question to have a greater distance to the frontier and thus lower income per capita—although the long-run growth rate of the country still remains equal to that of the frontier. This framework is extended to study the choice between innovation and imitation, as well as the role of selection for higher-productivity and higher-absorptive capacity firms during the process of economic development. We illustrate the main comparative statics of our framework with a number of correlations based on cross-country and firm-level data. The tractability of the framework makes it amenable to a range of additional extensions.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34730/w34730.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34730</link>
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<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>Daron Acemoglu, Ufuk Akcigit, Simon Johnson</author>
<category>Working Paper</category>
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<title>Making Entrepreneurs: Long Term Returns to Training Youth in Business Skills</title>
<description><p>
We study the medium and long term impacts of Skills for Effective Entrepreneurship Development (SEED), a 3-week entrepreneurship training program for secondary school students in Uganda. The mini-MBA, modeled after business school curricula, was implemented as a randomized field experiment with a nationally representative sample of 4,402 youth. After four years, the training improved both hard and soft skills. SEED graduates became more effective negotiators and communicators and exhibited improved self-efficacy, stability, plasticity, and stress management. In the medium run, treated youth were more likely to start enterprises and more successful in ensuring their survival, thereby gaining greater entrepreneurial experience. Their ventures were also of higher quality: more likely to be formal, have employees, be in collaboration with other entrepreneurs, and use effective business management practices. With 52% of the sample still enrolled in post-secondary education, we find suggestive evidence that businesses led by the treatment groups performed better. After nine years, business ownership converged between treatment and control groups as control ownership rates doubled. However, SEED graduates maintained their edge in terms of business quality and operated firms with 20% higher revenues and 16% higher profits, without corresponding increases in capital or labor inputs, consistent with higher total factor productivity. Entrepreneurial success was achieved through the adoption of better business practices and experimentation, with soft skills related to entrepreneurial mindset playing a complementary role. SEED generated high returns on investment: the present discounted values of SEED-induced business and total earnings equal 20 and 27 times program costs, respectively.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34637/w34637.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34637</link>
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<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>Laura Chioda, Paul Gertler, David Contreras-Loya, Dana R. Carney</author>
<category>Working Paper</category>
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<item>
<title>The Growth of Private Lending and Retail Access to Alternative Investments</title>
<description><p>
Private lending has exploded recently, owing not only to the retreat of banks from corporate lending, but also to the expansion of private equity (PE). Given the growing interest in retail access to alternative assets, we explore fees, performance, and investment behavior for publicly traded Business Development Companies (BDCs). Their compensation structures include fees and provisions common in PE, and they collectively provide debt for PE-sponsored deals and make PE-like investments themselves, especially for higher-spread investments. In-sample risk-adjusted abnormal returns are high, but fees and performance are inversely related. Moreover, BDCs with larger non-institutional investor bases charge higher fees.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34617/w34617.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34617</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34617</guid>
<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>David T. Robinson, Melanie Wallskog</author>
<category>Working Paper</category>
</item>
<item>
<title>Place-Based Policies and Entrepreneurship, Fall 2025</title>
<description><div class="page-header__intro-links page-header__intro-notice">
<a class="page-header__intro_links_icon" href="https://conference.nber.org/agenda/simple_printable?conf_id=ENTf25" target="_blank">
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<a href="https://conference.nber.org/agenda/simple_printable?conf_id=ENTf25" target="_blank">Print agenda</a>
</div>
<div class="page-subtitle page-subtitle--centered page-subtitle--blue page-subtitle__sponsor-info">
<div class="page-subtitle__summary">
<h2 class="page-subtitle__sponsored-by">Supported by
the Alfred P. Sloan Foundation grant <a href="https://www.nber.org/programs-projects/projects-and-centers/8691-place-based-entrepreneurship-and-innovation">#G-2024-22521</a> </h2>
</div>
</div>
</description>
<link>https://www.nber.org/conferences/place-based-policies-and-entrepreneurship-fall-2025</link>
<guid isPermaLink="false">https://www.nber.org/conferences/place-based-policies-and-entrepreneurship-fall-2025</guid>
<pubDate>Thu, 04 Dec 2025 16:00:00 GMT</pubDate>
<category>Conference</category>
</item>
<item>
<title>The Economics of Climate Innovation: Technology, Climate Policy, and the Clean Energy Transition</title>
<description><p>
This chapter examines the economics of climate innovation and its role in the clean technology transition. It outlines the incentives, market failures, and policy levers that shape the development and diffusion of clean technologies; traces global patterns in technology development and deployment; and highlights frontier challenges and open questions related to climate adaptation, critical mineral supply chains, artificial intelligence, and geopolitics. The analysis explores the role of effective climate policy, stressing the relevance of coordinated approaches that match instruments to technology maturity and local context.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34601/w34601.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34601</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34601</guid>
<pubDate>Sun, 30 Nov 2025 16:00:00 GMT</pubDate>
<author>Eugenie Dugoua, Jacob Moscona</author>
<category>Working Paper</category>
</item>
<item>
<title>Delivering Higher Pay? The Impacts of a Task-Level Pay Standard in the Gig Economy</title>
<description><p>
How does a task-level minimum pay requirement for gig workers affect their earnings and employment? We study this question in the context of a January 2024 law in Seattle that establishes a per-task minimum pay standard for app-based delivery workers. Drawing on novel cross-platform, trip-level gig activity data, we compare earnings and employment trajectories around the implementation of the law for workers who were doing delivery work in Seattle before the reform against workers who had been active in other regions of Washington State. We find that the minimum pay law raised delivery pay per task, though the increases in base pay per task were partially offset by a substantial reduction in average tips, a major component of delivery pay. At the same time, the policy led to a reduction in the number of tasks completed by highly attached incumbent drivers (but not an increase in exit from delivery work), completely offsetting increased pay per task and leading to zero effect on monthly earnings. We find evidence that drivers experienced more unpaid idle time and longer distances driven between tasks, but find no evidence that drivers reduced their total time working on delivery apps and only limited evidence of switching from delivery to ride-hailing work. Using a simple model of the labor market for platform delivery drivers, we show that our evidence is consistent with free entry of drivers into the delivery market driving down the task-finding rate until expected earnings return to their pre-reform level. These findings highlight the challenges of raising pay in spot markets for tasks where there is free entry of workers.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34545/w34545.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34545</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34545</guid>
<pubDate>Sun, 30 Nov 2025 16:00:00 GMT</pubDate>
<author>Yuan An, Andrew Garin, Brian K. Kovak</author>
<category>Working Paper</category>
</item>
<item>
<title>Dilution vs. Risk Taking: Capital Gains Taxes and Entrepreneurship</title>
<description><p>
Recent proposals to tax unrealized capital gains or wealth have sparked a debate about their impact on entrepreneurship. We show that accrual-based taxation creates two opposing effects: successful founders face greater dilution from advance tax payments, whereas unsuccessful founders receive tax credits that effectively provide insurance. Using comprehensive new data on U.S. venture capital deals, we find that founder returns remain extremely skewed, with 84% receiving zero exit value while the top 2% capture 80% of total value. Moving from current realization-based to accrual-based taxation would reduce founder ownership at exit by 25% on average but would also increase the fraction receiving positive payoffs from 16% to 47% when tax credits are refunded. Embedding these distributions in a dynamic career choice model, we find that founders with no or moderate risk aversion prefer the current realization-based tax system, while more risk-averse founders prefer accrual-based taxation. We estimate that a 2% annual wealth tax has a similar impact on dilution as taxing unrealized capital gains but produces no risk-sharing benefits due to the absence of tax credits in case of down rounds.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34512/w34512.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34512</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34512</guid>
<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Eduardo M. Azevedo, Florian Scheuer, Kent Smetters, Min Yang</author>
<category>Working Paper</category>
</item>
<item>
<title>Selling to Yourself: Continuation Funds in Private Equity</title>
<description><p>
Continuation funds (CFs) are private equity structures in which a manager raises a new fund to purchase assets from their existing fund. This structure has surged in popularity, from five funds in 2018 to 130 in 2024. We use a hand-collected sample of 472 CFs to test a model in which heterogeneous preferences drive CFs. Consistent with the model’s predictions, CFs emerge when LPs are more heterogeneous and managers have earned carried interest that they can roll. LPs typically choose to exit rather than invest, with this decision driven by both LP-level frictions and time varying LP liquidity demands.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34471/w34471.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34471</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34471</guid>
<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Rustam Abuzov, Will Gornall, Sophie Shive, Ilya A. Strebulaev, Michael S. Weisbach</author>
<category>Working Paper</category>
</item>
<item>
<title>Positioned at Extremes: Future Job Placements of Immigrant Students at U.S. Colleges</title>
<description><p>
Immigrant students who attend U.S. colleges are disproportionately employed in either large firms—especially multinationals—or small firms and self-employment. Using linked Census and longitudinal employment data, we trace the jobs taken by college students in 2000 during the 2001-20 period and evaluate four mechanisms shaping sector and firm size placement: geographic clustering, degree specialization, firm capabilities/visas, and ethnic self-employment specialization. Degree fields predict large firm and MNE placement, while ethnic specialization explains small firm sorting. Immigrant students who remain in the U.S. earn more than their native peers, suggesting the segmentation reflects productive sorting rather than blocked opportunity.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34440/w34440.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34440</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34440</guid>
<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Francis M. Dillon, Sari Pekkala Kerr, William R. Kerr, Andrew J. Wang</author>
<category>Working Paper</category>
</item>
<item>
<title>Leaving Money on the Dashboard: Price Dispersion and Search Frictions on Uber and Lyft</title>
<description><p>
We document price differences for identical trips on Uber and Lyft, based on an audit of the two platforms. While price dispersion exists in the market, device-level data show that only 16.1 percent of consumers opening one app also open the other. Our estimates suggest that the modest frictions involved in comparison shopping increase platforms’ gross booking volume by over $300 million annually in New York City alone. While price-comparison engines could in principle reduce frictions, Uber’s API terms of use limit such services, reducing riders’ ability to price compare.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34441/w34441.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34441</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34441</guid>
<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Jeffrey Fossett, Michael Luca, Yejia Xu</author>
<category>Working Paper</category>
</item>
<item>
<title>Working it out: Randomized Modification and Entrepreneurial Effort in a Collateralized Debt Market</title>
<description><p>
We enrich a standard debt overhang model with liquidity constraints to guide the design and interpretation of a collateralized debt modification experiment on a publicly traded lender’s delinquent vehicle loans to minibus entrepreneurs. Liquidity constraints add another borrower incentive compatibility constraint that interacts with debt overhang to shape repayment and effort. Consistent with model predictions, we find: debt reduction does not affect liquidity constrained borrowers; payment reduction improves both repayment and effort for borrowers with sufficient vehicle equity; payment reduction induces repayment without effort increases for low-equity borrowers. These results suggest a pecking order strategy for modification practice and policy.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34398/w34398.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34398</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34398</guid>
<pubDate>Tue, 30 Sep 2025 16:00:00 GMT</pubDate>
<author>Christopher Eaglin, Apoorv Gupta, Filippo Mezzanotti, Jonathan Zinman</author>
<category>Working Paper</category>
</item>
<item>
<title>Fractional Ownership and Copyright Licensing: Evidence from the Music Industry</title>
<description><p>
Creative content is often the product of collaboration, which may lead to fractional ownership of intellectual property. We study the effect of fractional ownership on the licensing of copyrighted material and its reuse. To do so, we compile new data on the copyright ownership structure of songs and their licensing for use in movies. We document that fractional song ownership has increased substantially: the mean number of songwriters and publishers per song has tripled between 1958 and 2021. We show that, conditional on a rich set of controls, greater fractionalization is associated with lower likelihood of licensing. We leverage the Sony-led acquisition of EMI Music Publishing in 2012 to obtain within-song variation in ownership and find that consolidating ownership rights significantly increases licensing, beyond any standalone effects of the merger.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34336/w34336.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34336</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34336</guid>
<pubDate>Tue, 30 Sep 2025 16:00:00 GMT</pubDate>
<author>Alberto Galasso, El Hadi Caoui</author>
<category>Working Paper</category>
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<item>
<title>Underwriting Based on Cash Flow Helps Younger Entrepreneurs Access Credit</title>
<description>Younger entrepreneurs are disadvantaged in small business loan markets because lenders rely heavily on personal credit scores, which favor long histories of repaying debt. In Modernizing Access to Credit for Younger Entrepreneurs: From FICO to Cash Flow (NBER Working Paper 33367), researchers</description>
<link>https://www.nber.org/be/20252/underwriting-based-cash-flow-helps-younger-entrepreneurs-access-credit</link>
<guid isPermaLink="false">https://www.nber.org/be/20252/underwriting-based-cash-flow-helps-younger-entrepreneurs-access-credit</guid>
<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Christopher M. Hair, Sabrina T. Howell, Mark J. Johnson, Siena Matsumoto</author>
<category>Article</category>
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<title>The Gig Economy and Entrepreneurship</title>
<description>The rise of platform-based work has transformed labor markets. Nearly 10 million Americans have participated in the gig economy over the past decade. This transformation may have important effects on entrepreneurship by allowing individuals to gain industry experience, encouraging experimentation,</description>
<link>https://www.nber.org/be/20252/gig-economy-and-entrepreneurship</link>
<guid isPermaLink="false">https://www.nber.org/be/20252/gig-economy-and-entrepreneurship</guid>
<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Matthew R. Denes, Spyridon Lagaras, Margarita Tsoutsoura</author>
<category>Article</category>
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<title>Remote Work and Employee Transitions to Entrepreneurship</title>
<description>The widespread transition to remote work during the COVID-19 pandemic fundamentally altered workplace arrangements. Full work-from-home days accounted for 28 percent of paid workdays in the US by 2023, four times higher than the 2019 level. This shift may affect entrepreneurial activity since most</description>
<link>https://www.nber.org/be/20252/remote-work-and-employee-transitions-entrepreneurship</link>
<guid isPermaLink="false">https://www.nber.org/be/20252/remote-work-and-employee-transitions-entrepreneurship</guid>
<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Alan Kwan, Ben Matthies, Richard R. Townsend, Ting Xu</author>
<category>Article</category>
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<item>
<title>Featured Researcher: Ufuk Akcigit</title>
<description>Ufuk Akcigit is the Arnold C. Harberger Professor of Economics and director of the Global Center for Economic Growth at the University of Chicago. He is a research associate in the NBER's Productivity, Innovation, and Entrepreneurship and Economic Fluctuations and Growth programs. Additionally, he</description>
<link>https://www.nber.org/be/20252/featured-researcher-ufuk-akcigit</link>
<guid isPermaLink="false">https://www.nber.org/be/20252/featured-researcher-ufuk-akcigit</guid>
<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Ufuk Akcigit</author>
<category>Article</category>
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<item>
<title>Assessing Career Attainment via a Non-Wage Measure</title>
<description><p>
This paper proposes a non-pecuniary measure of career achievement, seniority. Based on a database of over 150 million resumes, this metric exploits the variation in how long it takes workers to attain job titles. A person’s seniority is defined as the number of years it takes the median individual—within the same industry and firm size category—to achieve that person’s job title. Seniority aligns with standard markers of success—it is positively correlated with both wages and educational attainment. To demonstrate its value as a measure of career progression, we show that individuals with higher seniority levels in the public sector are more likely to transition to higher-paying positions in the private sector. When non-monetary factors influence career choice, evaluating labor market outcomes using non-wage measures, such as seniority, offers significant advantages.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34292/w34292.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34292</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34292</guid>
<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Natee Amornsiripanitch, Paul Gompers, George Hu, Will Levinson, Vladimir Mukharlyamov, Sachin Srivastava</author>
<category>Working Paper</category>
</item>
<item>
<title>How People Use ChatGPT</title>
<description><p>
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We document the growth of ChatGPT’s consumer product from its launch in November 2022 through July 2025, when it had been adopted by around 10% of the world’s adult population. Early adopters were disproportionately male but the gender gap has narrowed dramatically, and we find higher growth rates in lower-income countries. Using a privacy-preserving automated pipeline, we classify usage patterns within a representative sample of ChatGPT conversations. We find steady growth in work-related messages but even faster growth in non-work-related messages, which have grown from 53% to more than 70% of all usage. Work usage is more common for educated users in highly-paid professional occupations. We classify messages by conversation topic and find that “Practical Guidance,” “Seeking Information,” and “Writing” are the three most common topics and collectively account for nearly 80% of all conversations. Writing dominates work-related tasks, highlighting chatbots’ unique ability to generate digital outputs compared to traditional search engines. Computer programming and self-expression both represent relatively small shares of use. Overall, we find that ChatGPT provides economic value through decision support, which is especially important in knowledge-intensive jobs.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34255/w34255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34255</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34255</guid>
<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Aaron Chatterji, Thomas Cunningham, David J. Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, Kevin Wadman</author>
<category>Working Paper</category>
</item>
<item>
<title>Understanding Patenting Disparities via Causal Human+Machine Learning</title>
<description><p>
We develop an empirical approach for analyzing multi-dimensional discrimination using multimodal data, combining human perception measures with language-embedding-based, nonlinear controls for latent quality to relax restrictive assumptions in causal machine learning. Applying it to the U.S. patent examination process, we find that, ceteris paribus, applications from female inventors are 1.8 percentage points less likely to be approved, and those from Black inventors are 3 percentage points less likely—inconsistent with legally prescribed criteria. Jointly studying multiple bias dimensions and their intersections for the first time, we uncover new biases, including an affiliation bias—individual inventors are disadvantaged by 6.6 percentage points relative to employees of large, public firms, a disparity larger than any demographic gap. Moreover, innovation quality, location, and other factors can mitigate or compound discrimination, and the disparities interact: for example, racial gaps vanish among public-firm employees, masking more severe discrimination against individuals. Existing theories such as homophily cannot fully explain the results, but a simple model of correlation neglect does.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34197/w34197.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34197</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34197</guid>
<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Lin William Cong, Stephen Q. Yang</author>
<category>Working Paper</category>
</item>
<item>
<title>Which Individuals Create Jobs? Managerial Talent and Occupational Skills</title>
<description><p>
We consider founders of limited liability firms who previously held jobs in the formal sector of Brazil. Managers are five percent of former job holders but their startups account for 27 percent of new firm employment. Relatively little of their overrepresentation as founders or the larger size of their startups is explained by their previous wages or other standard human capital variables. Among non-managerial former occupations we examined those clearly connected to demand (sales) and to supply (technology, purchasing). Only purchasing was comparable to managerial occupations in entrepreneurship and new firm size. Further examination suggests that a key to greater entrepreneurship and larger initial firm size is that workers’ former jobs entailed building relationships with other businesses: in demand-side occupations, they sold to other businesses; in supply-side occupations, they bought from other businesses.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34158/w34158.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34158</link>
<guid isPermaLink="false">https://www.nber.org/papers/w34158</guid>
<pubDate>Thu, 31 Jul 2025 16:00:00 GMT</pubDate>
<author>Marc-Andreas Muendler, James E. Rauch, Sergio Mikio Koyama</author>
<category>Working Paper</category>
</item>
<item>
<title>Engineering Ukraine's Wirtschaftswunder</title>
<description><p>
As Ukraine emerges from the devastation of war, it faces a historic opportunity to engineer its own Wirtschaftswunder—a productivity-driven economic transformation akin to post-war West Germany. While investment-led growth may offer quick wins, it is efficiency, innovation, and institutional reform that will determine Ukraine’s long-term economic trajectory. Drawing on rich micro-level firm data spanning 25 years, this paper uncovers deep structural distortions that have suppressed creative destruction and productivity in Ukraine. It finds that business dynamism is on the decline, alongside rising market concentration among incumbent businesses, including low productivity state owned enterprises. To inform priorities for reviving business dynamism, this study develops a model of creative destruction drawing on Acemoglu et al. (2018) and Akcigit et al. ( |
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<title>Real Effects of Academic Research Revisited</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-science-fall-2025">September 25-26, 2025</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead">
<span>The Economics of Science: Taking Stock and Looking Ahead</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/megan_macgarvie">Megan MacGarvie</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/reinhilde_veugelers">Reinhilde Veugelers</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
</div>
<p>
This Chapter surveys the findings of social science research on the contribution of universities to innovation and economic growth, both locally/regionally and globally. In the last several decades research has demonstrated universities’ causal effects through the mechanisms of knowledge creation, education and training of students, and technology transfer/entrepreneurship. The Chapter summarizes how the literature has studied each of these mechanisms, and how the findings have probed variation across disciplines and economic sectors. The depth and breadth of understanding have been advanced by new microdata and new methods of linking data across inventions, scientists and institutions, and by application of methods from network science. We emphasize that research has proven the importance of these effects on average, but to date has less to say about the determinants of success or failure in different contexts. These findings have implications for public policy to foster innovation both regionally and globally.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/real-effects-academic-research-revisited</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/real-effects-academic-research-revisited</guid>
<pubDate>Thu, 11 Jun 2026 16:00:00 GMT</pubDate>
<author>Adam B. Jaffe, Laura B. Shupp, Valentina Tartari</author>
<category>Chapter</category>
</item>
<item>
<title>The i3 BigQuery Workspace: Shared Infrastructure for Open Science</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-science-fall-2025">September 25-26, 2025</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead">
<span>The Economics of Science: Taking Stock and Looking Ahead</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/megan_macgarvie">Megan MacGarvie</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/reinhilde_veugelers">Reinhilde Veugelers</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
</div>
<p>
Large-scale open datasets have transformed empirical research on science and innovation, but working with these data presents persistent challenges: computational barriers, provider dependence, reproducibility difficulties, transparency gaps, and resource inequality across institutions. We describe the i3 BigQuery Workspace, a shared cloud platform that hosts curated datasets (including OpenAlex, PatentsView, and community-contributed resources like Reliance on Science) and enables researchers to query terabyte-scale data in seconds, integrates multiple sources, and implements advanced tools like machine learning at scale. We document examples of research enabled by this infrastructure and discuss how shared data resources can improve research efficiency and expand the scope of feasible research in the economics of science and innovation.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/economics-science-taking-stock-and-looking-ahead/i3-bigquery-workspace-shared-infrastructure-open-science</link>
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<pubDate>Tue, 09 Jun 2026 16:00:00 GMT</pubDate>
<author>Matt Marx, Dror Shvadron</author>
<category>Chapter</category>
</item>
<item>
<title>The Political Economy of Artificial Intelligence</title>
<description>Ajay Agrawal, Joshua Gans, Avi Goldfarb, and Catherine Tucker, editors. As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with</description>
<link>https://www.nber.org/news/political-economy-artificial-intelligence</link>
<guid isPermaLink="false">https://www.nber.org/news/political-economy-artificial-intelligence</guid>
<pubDate>Mon, 01 Jun 2026 16:00:00 GMT</pubDate>
<category>Article</category>
</item>
<item>
<title>Automation and Repression</title>
<description><p>
We consider a model of automation embedded in a political environment where workers can undertake a revolt (modeled as a global game), and greater inequality between capital and labor increases the likelihood of a revolt. Decentralized automation decisions raise the share of capital in national income and increase the likelihood of a successful revolt. A capitalist state (representing capital-owners) prefers to regulate the level of automation to lessen the threat of a successful revolt. The capitalist state can also redistribute to workers via the tax system or repress political action, thus creating greater room for further automation. We characterize the trade-off between the regulation of automation, redistribution and repression.<br>
Our main result is a complementarity between automation and repression. Unless the threat of revolt is quite weak or the capital stock is very low, the capitalist state prefers repression. A higher capital stock in turn encourages more automation and thus more repression. In our full dynamic model with capital accumulation, in the long run the economy tends to repression (again unless the threat of revolt is very weak). We also prove that the same conclusions apply when firms can additionally invest in new labor-intensive tasks. Finally, we show that, starting in a democracy, capital accumulation and thus greater automation encourages the capitalists to support a coup against democracy and set up a repressive system.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35336/w35336.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35336</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35336</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Daron Acemoglu, A. Arda Gitmez, Mehdi Shadmehr</author>
<category>Working Paper</category>
</item>
<item>
<title>Data-Driven Automation</title>
<description><p>
We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35320/w35320.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35320</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35320</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Maryam Farboodi, Andrew J. Koh, Anchi Xia</author>
<category>Working Paper</category>
</item>
<item>
<title>Before the Exodus? Young Scientists and the Future of US Science</title>
<description><p>
Shortly after major policy changes to US science funding began in early 2025, we surveyed 916 young biomedical scientists – PhD students and postdoctoral researchers – about their career intentions and expectations. The results document a dramatic shift in sentiment. Barely half of respondents now say they are likely to remain in academia, down 22 percentage points from how they felt six months earlier. The fraction likely to stay in the United States fell by 21 percentage points. Even satisfaction with having pursued a PhD in science declined by 16 percentage points. These are not the complaints of established scientists defending their budgets, but rather the stated intentions of the next generation – the scientists who would, in ordinary times, become the principal investigators of the future.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35330/w35330.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35330</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35330</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Pierre Azoulay, Raffaella Sadun, Daniela Scur</author>
<category>Working Paper</category>
</item>
<item>
<title>GLP-1 Therapy and the Reshaping of Socioeconomic Gradients in Health</title>
<description><p>
GLP-1 therapies for obesity promise substantial health improvements, but little is known about how their benefits vary across socioeconomic and demographic groups. Using a nationally representative microsimulation model of US adults and Shapely-value decomposition, we estimate the lifetime health and economic benefits of GLP-1 treatment and examine how those gains vary across individuals. The largest differences emerge across education. Individuals with less than a high school education experience experience roughly 14% higher gains in lifetime net social value, 16-17% larger improvements in discounted generalized risk- and severity-adjusted life-years (GRASA-QALYs), and 20% greater increases in life expectancy relative to the cohort mean, whereas individuals with college degrees experience gains 15-27% below the mean across these outcomes. Black and Hispanic individuals also tend to experience larger improvements in health outcomes and social value than White individuals, including larger gains in GRASA-QALYs and life expectancy and larger reductions in diabetes risk and duration. Females likewise experience larger predicted treatment gains than men. These patterns are consistent with the idea that the largest gains arise among populations facing greater socioeconomic constraints in sustaining behavioral weight control. GLP-1 innovation may therefore mitigate inequality in obesity-related disease and survival, advancing equity in population health.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35296/w35296.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35296</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>J. Felipe Montano-Campos, Bryan Tysinger, Dana Goldman, Darius N. Lakdawalla</author>
<category>Working Paper</category>
</item>
<item>
<title>But I Like Doing This! Enjoyable Tasks, Contracting, and Automation</title>
<description><p>
Workers sometimes enjoy productive tasks and voluntarily devote unpaid time to them. We study O-ring jobs in which firms can either price a complete task bundle or specify paid task floors while workers remain free to add time. For any allocation supported by both hourly contracts, the wage bill is identical: voluntary top-up is not a discount. The contracts differ because paid floors cannot cap attractive tasks below the worker's voluntary supply. This implementability constraint adds a containment motive for automation alongside replacement and scale effects. It also makes payroll measures incomplete: conditional on a common automated set and a common AI technology, jobs with the same payroll footprint can differ in worked time and task mix. Rich salaried bundle pricing removes the hourly-contract distortion.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35309/w35309.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35309</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35309</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
</item>
<item>
<title>Is the iPhone Birth Control? Causal Evidence from AT&T’s 2007–2011 Carrier Monopoly</title>
<description><p>
The U.S. general fertility rate has fallen by 22% since 2007, a sustained decline not readily explained by economic conditions, contraceptive use, housing or childcare costs, or other commonly cited factors. We assess the potential role of a different shock: the diffusion of the smartphone. The U.S. rollout of the iPhone, the first modern smartphone, provides a natural experiment: from June 2007 through February 2011, the device was sold only on AT&amp;T, allowing us to identify its effect from variation in AT&amp;T’s mobile broadband coverage. Entropy-balanced Poisson and synthetic difference-in-differences event studies imply that access to the iPhone reduced births by 4.5–8.0% at ages 15–19 and 3.2–6.6% at ages 20–24, with statistically significant but smaller declines among older cohorts. Placebo analyses applied to Verizon and Sprint’s pre-2011 coverage footprint are null. Taken together, these cohort effects imply that the diffusion of the iPhone deepened the decline in births among women under 30 while suppressing the rise in births among older women. Overall, the diffusion of the iPhone explains 33–52% of the decline in the general fertility rate among women aged 15–44. National-survey evidence on time use and sexual behavior is consistent with the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35310/w35310.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35310</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35310</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Caitlin K. Myers, Ezekiel Hooper</author>
<category>Working Paper</category>
</item>
<item>
<title>What Investment Data Implies about the AI Transition</title>
<description><p>
The five largest U.S. technology firms spent $380 billion on capital expenditure in 2025 and are forecast to spend roughly double that in 2026. These firms risk bankruptcy unless expected profits grow commensurately. We embed this observation in a two-sector open-economy model with rare productivity booms. We calibrate the boom size to match the observed increase in investment projected through 2027, implying that a boom raises AI-sector productivity by a factor of roughly 2.7. We then calibrate a two-year window of a 50% annual probability of an increase of the same magnitude, generating a range of scenarios consistent with the wide variety of industry forecasts, along with an elevated permanent probability tied to the valuation of the aggregate market. The implied additional cumulative GDP growth ranges from 5 to 58 percentage points by 2030, with AI shares of the economy ranging from 8% to 39%. Long-term annual growth is in expectation approximately 7% but with substantial risk. With risk aversion of 3, and an elasticity of intertemporal substitution equal to 1, the risk-free rate increases by approximately half a percentage point, and the equity premium rises by approximately 3 percentage points.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35290/w35290.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35290</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35290</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Jessica Wachter, Jonathan Wachter</author>
<category>Working Paper</category>
</item>
<item>
<title>Innovation without Borders? The Geography of Technological Diffusion</title>
<description><p>
How well does innovation diffuse across geographic boundaries? To shed light on this question, we present a large-scale field experiment involving 3,300 firms across twelve European Union countries. We elicit firms' perceptions of the share of similar firms in their own country that had invested in artificial intelligence (AI), as well as the corresponding share among similar firms in Germany, France, and Italy. We randomly provide half of the sample with accurate information about both domestic and foreign AI investment. We show that firms substantially underestimate competitors' current AI investment, both domestically and abroad, and that they update their expectations about competitors' future AI investment in response to the information treatment. The treatment also causes a statistically significant increase in firms' own expected AI investment rate (p-value &lt; 0.001). We find strong strategic complementarities within borders: a 1 pp increase in the expected share of domestic peers investing in AI raises a firm's own expected AI investment rate by 0.570 pp. These complementarities are absent across borders: the effect of an increase in the expected share of foreign peers investing in AI on a firm's own expected AI investment rate is statistically insignificant. Overall, our evidence shows that innovation diffusion and strategic complementarities in AI investment are much stronger domestically than internationally.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35314/w35314.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35314</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35314</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ursel Baumann, Zoë B. Cullen, Ester Faia, Annalisa Ferrando, Ricardo Perez-Truglia, Judit Rariga</author>
<category>Working Paper</category>
</item>
<item>
<title>Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools</title>
<description><p>
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity (“commits”), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35275/w35275.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35275</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35275</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Mert Demirer, Leon Musolff, Liyuan Yang</author>
<category>Working Paper</category>
</item>
<item>
<title>Defining Innovatisation: The Case of NewSpace and the Changing Space Sector</title>
<description><p>
The space sector has become far more dynamic and innovative, with new actors (e.g., start-ups, venture capital) entering and the ever-growing importance of private firms. In this paper we introduce a novel concept, innovatisation, to understand this phenomenon. Innovatisation describes the transformation of a sector between two modes. In a mode of technological achievements (TA), only technological (not economic) performance matters, primarily for prestige purposes; in innovation, customer preferences, commercial opportunities, and costs become essential. Studying the economics of Apollo and the commercialization attempts of the 1980s, we show how the space sector has long featured a logic of TA. Then, analyzing recent trends, we provide quantitative empirical evidence (e.g., costs) that innovation now shapes the sector, thanks to various driving forces. The driving forces behind the innovatisation process are identified building on Jones (2022) and the disruptive innovation theory.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35254/w35254.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35254</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35254</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Benoit Cornet, Marc-André Chavy-Macdonald, Dominique Foray</author>
<category>Working Paper</category>
</item>
<item>
<title>Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda</title>
<description><p>
The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes that describe its characteristic exposure profile. The remaining six attributes capture recurring implications at the venture level (staged financing strategies, dual scientific and commercial maturation, and the multidisciplinary broadening of teams) and the ecosystem level (multi-actor interactions, specialized incubation support, and industrial de-risking and scaling partnerships). We use this framework to delineate the boundaries of deep-tech innovation, distinguish it from adjacent concepts, and propose an agenda for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35255/w35255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35255</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray</author>
<category>Working Paper</category>
</item>
<item>
<title>The AGI Race and Existential Risk</title>
<description><p>
Concerns about the race to artificial general intelligence often assume that competition and resources increase risk by accelerating development. We study a model in which firms allocate scarce resources between speed and safety. Speed increases a firm's chance of reaching AGI first but leaves fewer resources for safety; safety lowers doom risk but slows arrival. Fragmentation increases total speed and conditional doom risk by shifting a fixed industry resource pool toward speed. The model also identifies a critical market size: below it, firms have positive expected payoff from achieving AGI, while above it, firms race even though achieving AGI has negative expected value. More per-firm resources always accelerate expected arrival, but their effect on conditional doom risk changes sign at this cutoff. Policy affects risk by changing equilibrium incentives: consolidation, resource regulation, commitment devices, and cautious public entry can improve welfare in some environments. The results show that AGI risk depends not only on technical considerations, but also on market structure, resource constraints, and institutions that shape the equilibrium allocation between speed and safety.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35276/w35276.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35276</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ethan Bueno de Mesquita, Wioletta Dziuda, Mattias Polborn</author>
<category>Working Paper</category>
</item>
<item>
<title>Does the Import Invasion Explain the Mysterious Disappearance of Productivity Growth in U.S. Manufacturing?</title>
<description><p>
Why did U.S. manufacturing productivity stop growing after 2010? Productivity growth disappeared, evaporating from an annual rate of +3.3 percent during 1987-2010 to -0.3 percent from 2010 to 2023. This paper shifts attention from 2010 as the start of the puzzle to a decade earlier when output stopped growing. This cessation of output growth in 2000 is attributed to the invasion of imports that closed domestic plants, destroyed jobs, and squeezed profits. Then followed a chain of causation that ultimately undermined productivity growth – from falling capacity utilization, to lower investment in fixed capital and R&amp;D, and to an erosion of innovation. Beyond the import invasion, the paper identifies a set of handicaps ranging from self-inflicted wounds by private manufacturing firms to a marked reduction in government-funded R&amp;D spending. Corporate funds were diverted from productive investment to share buybacks. Investment was distorted by environmental, health, safety, and fuel economy regulations. Innovation slowed not only because of diminishing returns to R&amp;D, but also because of a decline in public R&amp;D, and a diversion of private R&amp;D from basic science and process improvements to product refinements and brand extensions. Skilled worker shortages have plagued manufacturing for decades in the absence of sufficient public and private investment in vocational training.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35285/w35285.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35285</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Robert J. Gordon, Kenneth Ryu</author>
<category>Working Paper</category>
</item>
<item>
<title>The Missing Value of Data</title>
<description><p>
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5% of GDP.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35266/w35266.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35266</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Ankit Bhutani, Guillermo Ordoñez, Laura Veldkamp</author>
<category>Working Paper</category>
</item>
<item>
<title>Frontier Knowledge in College and Student Success</title>
<description><p>
We study whether exposure to frontier knowledge in college affects student outcomes. Combining 459,415 syllabi from seven Texas public universities with 107 million publications and linked student records, we measure each course’s proximity to recent versus older research in its field. Exploiting syllabus updates unobserved at enrollment, we find that frontier exposure increases completion, GPA, graduate-school attendance, and earnings, and reduces time-to-degree. Completion, GPA, and progression gains are broad, while graduate-school and earnings returns are larger for students with stronger preparation and family resources. The evidence suggests two mechanisms: frontier content keeps students engaged, and sustained exposure builds labor-market skills.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35269/w35269.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35269</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35269</guid>
<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Barbara Biasi, Song Ma</author>
<category>Working Paper</category>
</item>
<item>
<title>The Missing Value of Data</title>
<description><div class="page-header__intro-meta">
<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/41st-annual-conference-macroeconomics-2026">April 16-17, 2026</a></span>
</div>
<div class="label-link">
<span class="label-link__label">Book</span>: <a href="https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41">
<span>NBER Macroeconomics Annual 2026, volume 41</span>
</a>
</div>
<div class="label-link">
<span class="label-link__label">Book editors</span>:
<span class="page-header__author-item">
<a href="https://www.nber.org/people/john_leahy">John Leahy</a>, </span>
<span class="page-header__author-item">
<a href="https://www.nber.org/people/valerie_ramey">Valerie A. Ramey</a> </span>
<span class="page-header__author-item">
&amp; <a href="https://www.nber.org/people/giovanni_violante">Giovanni L. Violante</a> </span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: University of Chicago Press </div>
<div class="label-link">
<span class="label-link__label">Series</span>: Macroeconomics Annual
</div>
</div>
<p>
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5 percent of GDP.
</p>
</description>
<link>https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41/missing-value-data</link>
<guid isPermaLink="false">https://www.nber.org/books-and-chapters/nber-macroeconomics-annual-2026-volume-41/missing-value-data</guid>
<pubDate>Thu, 21 May 2026 16:00:00 GMT</pubDate>
<author>Ankit Bhutani, Guillermo Ordoñez, Laura Veldkamp</author>
<category>Chapter</category>
</item>
<item>
<title>The Political Economy of Artificial Intelligence</title>
<description><img loading="lazy" src="https://www.nber.org/sites/default/files/styles/book_cover/public/2026-03/Political%20Economy%20AI.jpg?itok=xthIL1MS" width="294" height="440" alt="cover of book, The Political Economy of Artificial Intelligence, fine art flowers in vase" class="lazyload page-header__cover" data-src="/sites/default/files/styles/book_cover/public/2026-03/Political%20Economy%20AI.jpg?itok=xthIL1MS" typeof="foaf:Image" referrerpolicy="no-referrer">
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<div class="label-link">
<span class="label-link__label">CONFERENCE HELD <a href="https://www.nber.org/conferences/economics-artificial-intelligence-fall-2024">September 19-20, 2024</a></span>
</div>
<div class="label-link">
<span class="label-link__label">PUBLISHER</span>: <a href="https://press.uchicago.edu/ucp/books/book/chicago/P/bo270631423.html">University of Chicago Press</a> </div>
</div>
<p>As the effects of artificial intelligence are felt across economies and societies, many of its ramifications are still emerging. This volume brings together economists and political scientists to examine how AI intersects with regulation, military power, and political identity—offering analytical frameworks and identifying key open questions for future research.<br>
The contributions address topics such as the allocation of property rights for AI inputs, trade-offs among alternative regulatory regimes, and the role of interest groups in shaping the technology’s trajectory. They explore how AI-related capabilities influence military effectiveness, resource allocation, and bargaining power among nations, and consider AI’s effects on political preferences, from the influence of AI-curated information on polarization to the implications of targeted political advertising and personalized education for national identity formation.<br>
The volume highlights key trade-offs that arise in AI’s political economy, and points toward empirical strategies and theoretical models that can advance understanding in this emerging field. Drawing on diverse disciplinary perspectives, the collection provides a foundation for rigorous inquiry into how AI both shapes and is shaped by political and economic forces.</p>
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</description>
<link>https://www.nber.org/books-and-chapters/political-economy-artificial-intelligence</link>
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<pubDate>Wed, 20 May 2026 16:00:00 GMT</pubDate>
<author>Ajay Agrawal, Joshua Gans, Avi Goldfarb, Catherine Tucker</author>
<category>Book - Conference Volume</category>
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<title>Designing More Informative Tests: Separating Execution from Recognition</title>
<description><p>
Tests are widely used to measure ability, yet performance on a test often reflects more than the ability to execute assigned tasks. It also reflects the ability to recognize which tasks are worth attempting, how they should be prioritized, and how effort should be allocated under uncertainty. This paper studies how tests can be designed to separate these capabilities.<br>
We model a test as a sequential decision problem. Tasks differ in difficulty, their ordering is uncertain, and examinees may acquire costly information about that ordering before choosing how to proceed. The testing environment is the informational structure surrounding the realized test: in particular, the examinee's beliefs about how task difficulty has been arranged. Performance is therefore generated by an optimal recognition–execution policy, not by execution skill alone.<br>
The analysis delivers two negative results. First, even in the simplest two-task environment, a single score exhibits dimensional collapse: distinct combinations of execution skill and recognition capability generate identical expected scores. Second, with three tasks, the relationship between capabilities and scores becomes environment-dependent: changing beliefs about task ordering can change which actions are considered and how capabilities translate into performance.<br>
These results imply that standard scores are not generally informative enough to separate the capabilities that generate performance. This matters because scores are used to summarize what individuals can do and to guide downstream decisions about placement, training, and instruction. If a test does not separately reveal execution and recognition, it provides limited guidance about which capability is strong, which is weak, and where improvement should be directed.<br>
We then show how more informative tests can be designed. Under a simple communicability constraint, two canonical environments—ordered and randomized tests—induce distinct relationships between capabilities and scores. In an ordered test, recognition is suppressed and performance isolates execution. In a randomized test, recognition is activated and performance reflects both execution and recognition. Observing performance across these environments separates capabilities that are confounded in any single score.<br>
The paper reframes testing as a problem of informational design: tests should be designed not only to record performance, but to reveal the distinct capabilities that generate it.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35232/w35232.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35232</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Andrew Caplin, Leo Zhu</author>
<category>Working Paper</category>
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<title>Old Space, New Space: A Commercial Revolution in Innovation?</title>
<description><p>
The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&amp;D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government's most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35212/w35212.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35212</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ruben Gaetani, Alexander T. Whalley</author>
<category>Working Paper</category>
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<item>
<title>More Paths or More Contrast? A Theory of Experimentation Breadth</title>
<description><p>
How should an organisation choose the breadth of its experimentation portfolio? Breadth has two distinct margins—the number of paths kept alive, and the degree of contrast among them—and prior research has largely studied them in isolation. We bring them into a single framework and show that they need not move together. Under a fixed experimentation budget, adding paths creates more chances to find a strong direction, but it also dilutes learning across paths and weakens the strongest feasible contrast. When the task is primarily ranking among already-viable alternatives, broader portfolios become more attractive as the budget rises. When paths share common viability uncertainty, and experimental signals track payoff relatedness, however, additional paths partly repeat the same viability test rather than provide independent information. We identify conditions under which testing exactly two sharply contrasting paths is optimal, dominating both a single deep test and broader portfolios. The framework reconciles competing prescriptions—many parallel shots versus a few sharp comparisons—by clarifying when each applies, and shows why empirical measures of breadth should not treat the number of options and their relatedness as separable margins.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35207/w35207.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35207</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35207</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans, Luca Gius</author>
<category>Working Paper</category>
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<title>Endogenous Task Bundling, Skills and Automation</title>
<description><p>
Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape. We argue that this assumption hides much of the action. When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle them into one broad role or split them into specialist roles changes which surviving skills the labour market actually rewards. A skill that played no role in the pre-AI wage can become the dominant component of the post-AI wage, while a skill that anchored the pre-AI wage can disappear from the schedule. We develop an assignment model in which the priced human bundle is endogenous, and we use it to show that a fixed-bundle wage regression can mis-sign the effect of AI exposure. In general, the omitted-redesign bias has no unconditional sign: it is the residual covariance between exposure and role-specific redesign terms. Under explicit sufficient conditions, exposure-correlated unbundling loads specialist comparative-advantage premia onto the exposure coefficient, while exposure-correlated rebundling loads a different, often opposite, omitted term. The sign must therefore be measured from local post-AI partition changes rather than assumed from exposure alone.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35211/w35211.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35211</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
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<title>Estimating the Present Value of R&D Tax Benefits in the United States</title>
<description><p>
Using a panel of confidential corporate tax returns, we provide the first direct estimates of the realized present value of corporate tax benefits from R&amp;D credits and deductions in the United States. Realized tax benefits can deviate from statutory tax benefits because firms in loss status are typically unable to fully utilize credits and deductions to offset current-year taxes and instead must carry these attributes forward. We develop a novel procedure to track the intertemporal firm-level utilization of tax attributes generated by corporate R&amp;D spending, and find that the present value of R&amp;D tax benefits varies substantially with firms’ loss status, age, and size. Old and large firms typically use R&amp;D tax benefits quickly, while young firms – especially those that are small – frequently operate in loss status and use tax attributes more slowly. From 2012–2016, the average firm generated $0.41 in statutory tax benefits per dollar of R&amp;D investment, with a realized present value of $0.36. Young and small firms in a loss position realized only $0.23 per dollar, a 44% decrease relative to the statutory benchmark.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35208/w35208.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35208</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Brandon Pecoraro, Nicholas C. Hoffman, Martin Lopez-Daneri, Elena C. Derby, Rachel Moore, Shannon E. Sledz</author>
<category>Working Paper</category>
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<title>Competitive Exposure and Entrepreneurial Experimentation</title>
<description><p>
Entrepreneurs learn by experimenting, but experiment choices are often public. A closed beta, private pilot, or public launch not only generates evidence; it also reveals what kind of entrepreneur would choose that action. We develop a dynamic model in which a founder chooses between stealthy and public experiments while potential entrants infer from both actions and outcomes. Public outcomes are modelled as garblings of the founder's private experimental evidence, so public leakage informs outsiders without giving the founder information beyond the private signal already observed. The key state variable is competitive exposure: the public runway before entry becomes attractive. Exposure is depleted by two forces, leakage burn from public outcomes, and action burn from public inference about experiment choice. This distinction implies that competition can distort experiment design without forcing earlier scale: lower-confidence founders choose stealthier tests, while higher-confidence founders spend exposure to obtain faster private learning through more public tests. Scale is accelerated only when exposure reduces the value of waiting more than it reduces the value of scaling. Finally, exogenous funding gates make observable scale more selective and, therefore, more informative to entrants. The analysis shows that entrepreneurial experimentation is not merely private learning under uncertainty; it is public action under competitive inference.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35172/w35172.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35172</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
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<title>Data Centers and Local Economies in the Age of AI: A Shift--Share Approach</title>
<description><p>
Data centers are the physical infrastructure behind cloud computing, artificial intelligence, and enterprise software. The rapid diffusion of artificial intelligence (AI) is intensifying demand for compute, accelerating investment in data centers, and raising concerns about the local economic and environmental footprint of these facilities. Their expansion creates a local policy tradeoff. A data center can bring capital investment, construction activity, and specialized employment, but it can also increase demand for electricity, land, and grid capacity. This paper studies these effects at the U.S. county level. We assemble a facility-level panel of global data centers with precise coordinates, scale metrics, and annualized revenue. We map facilities to U.S. counties and combine them with County Business Patterns, county-level IRS income, county-level house prices, and electricity prices. To address endogenous siting, we instrument for data center growth using two shift-share instruments, which leverage pre-existing proximity to InterTubes long-haul fiber nodes and the 1980 county share of U.S. urban college population as shares, and both Chinese and rest-of-the-world data center revenue growth as shifts. The IV estimates show positive effects on total employment, data-processing employment, construction employment, establishments, house prices, and electricity prices at different horizons after data center growth. We also find positive effects on tax returns, adjusted gross income, and wages, while annual payroll responds less robustly. The results suggest that data centers create measurable local activity, increase house prices, and affect local electricity markets through higher prices.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35194/w35194.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35194</link>
<guid isPermaLink="false">https://www.nber.org/papers/w35194</guid>
<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Fernando E. Alvarez, David Argente, Joyce Chow, Diana Van Patten</author>
<category>Working Paper</category>
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<title>On the Negative Consequences of Low-Wage Offshoring for Innovation</title>
<description><p>
Conventional wisdom holds that offshoring intermediates to China stimulates innovation. This is not entirely compelling. On the one hand, (a) offshoring lowers marginal costs and expands sales, thereby increasing the returns to innovation, especially for large firms. On the other hand, (b) offshoring low-quality intermediates reduces the costs of older-generation products, thereby reducing the returns to innovating into newer generations. We examine these two opposing forces over 2002-2011 for 6,024 Canadian firms. Our empirical strategy regresses measures of innovation, such as R&amp;D, on imports of intermediate inputs. To address endogeneity, we construct a model-consistent shift-share instrument whose shocks are the often-dramatic improvements in the quality of HS6 Chinese intermediate inputs. We find that greater offshoring reduced R&amp;D spending over 2002-2011 by 15% as (1) firms engaged in R&amp;D in 2002 reduced their expenditures, and (2) firms not initially engaged in R&amp;D were discouraged from starting up new R&amp;D projects. Our model explains these findings: Rising quality of Chinese intermediates is a positive supply shock (rather than a negative China shock) that raises profits for all offshorers, raises innovation for the largest offshorers (channel a above), and lowers innovation for all other offshorers (channel b). These predictions are confirmed in the data.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35167/w35167.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35167</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Wulong Gu, Alla Lileeva, Daniel Trefler</author>
<category>Working Paper</category>
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<title>The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks</title>
<description><p>
Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three layers: firm-wide adoption, business-function deployment, and worker-task use. During Nov 2025–Jan 2026, 18% of firms used AI in at least one function (32%, employment-weighted), with adoption expected to reach 22% within six months. Use is concentrated in large firms and knowledge-intensive sectors, reaching 50%–60% (60%–70%, employment-weighted) among very large firms in Information, Professional Services, and Finance. Among adopters, scope remains limited: 57% use AI in three or fewer functions, most often Sales and Marketing (52%), Strategy (45%), and IT (41%). Worker-level use appears in 23% (41%, employment-weighted) of firms, primarily for writing, document analysis, and information search; 65% restrict use to three or fewer tasks. Evidence suggests both top-down and bottom-up diffusion: worker use can occur without firm adoption, and vice versa. Most firms (66%) use AI for task augmentation, while employment reductions are rare (2%). Regression results show a positive relationship between firm performance and AI integration breadth. However, functional deployment and operational investment are associated with employment declines, while worker-task use is not once these factors are controlled for.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35141/w35141.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35141</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Kathryn Bonney, Cory L. Breaux, Emin Dinlersoz, Lucia S. Foster, John C. Haltiwanger, Aditya A. Pande</author>
<category>Working Paper</category>
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<title>How Artificial Intelligence Shapes Science: Evidence from AlphaFold</title>
<description><p>
We study how a frontier AI model affects scientific discovery by examining the release of the AlphaFold2 algorithm and its impact on structural biology and related fields of science. Structural biology is the field of science concerned with understanding the structure and function of proteins. Researchers in this field historically devoted substantial time and resources to experimentally solving three-dimensional protein structures. AlphaFold can predict these structures without running experiments. In July 2021, researchers gained access to hundreds of thousands of these AI-predicted structures virtually overnight. Yet, to date, we find that the rate of experimental structure determination has remained almost unchanged. Instead, researchers appear to use predicted structures to facilitate and complement experimental structure determination. Looking at downstream science that builds on protein structures, we find that basic research on proteins that had no structure information prior to AlphaFold increases by 15 to 40% relative to proteins that already had a structure, shifting the direction of research toward less-studied proteins. However, we find no evidence so far that more applied, early-stage drug development is targeting these proteins, though such activity may emerge in the future.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35143/w35143.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35143</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ryan R. Hill, Carolyn Stein</author>
<category>Working Paper</category>
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<item>
<title>When Does Automating AI Research Produce Explosive Growth? Feedback Loops in Innovation Networks</title>
<description><p>
AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. We develop a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (``explosive''). We find that automating research can offset diminishing returns to ideas by activating two reinforcing channels: a technological feedback loop across research sectors, and an economic feedback loop in which higher output finances further research. Growth becomes explosive if the combined strength of technological and economic feedback loops overcomes diminishing returns. In a simple simulation calibrated to trends in AI progress, fully automating software research and modest (5%) automation in other sectors generates a singularity within six years. Bottlenecks do not overturn the result if task automation advances sufficiently fast.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35155/w35155.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35155</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Tom Davidson, Basil Halperin, Thomas Houlden, Anton Korinek</author>
<category>Working Paper</category>
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<title>China's Global Ownership</title>
<description><p>
We study the global footprint and real effects of Chinese overseas corporate ownership. By assembling a comprehensive micro-level dataset of 161,773 firms across 159 countries (2012–2021), we independently reconstruct multi-layered ownership chains to trace capital through offshore tax havens to its ultimate origin. This approach reveals a global footprint substantially broader than official FDI statistics. Chinese-controlled foreign assets expanded at 20% annually, reaching $2.1 trillion or roughly 3% of global corporate assets by 2021. Chinese investors—particularly state-owned enterprises (SOEs)—strategically target R&amp;D-intensive and supply-chain-linked firms. Following acquisition, target firms increase capital stock and R&amp;D expenditures, yet these inputs fail to generate higher patent output and are accompanied by a significant decline in profitability. We document a novel 'innovation spillback' mechanism: while target innov... |
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<item>
<title>Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda</title>
<description><p>
The term “deep-tech innovation” has attracted growing attention in research, policy, and practice, but it is applied inconsistently and lacks an agreed-upon definition. This limits cumulative knowledge building and blurs how deep-tech innovation relates to adjacent concepts. We address this gap by developing a framework that treats deep-tech innovation as a distinct object of inquiry. Using a multi-method design that combines a systematic, integrative, concept-centric literature review and semi-structured interviews with deep-tech founders, we identify twelve defining attributes structured across three levels: invention, venture, and ecosystem. At the invention level (the conceptual core), we specify six attributes: three foundational attributes that capture the scientific and technological basis of the invention, and three attributes that describe its characteristic exposure profile. The remaining six attributes capture recurring implications at the venture level (staged financing strategies, dual scientific and commercial maturation, and the multidisciplinary broadening of teams) and the ecosystem level (multi-actor interactions, specialized incubation support, and industrial de-risking and scaling partnerships). We use this framework to delineate the boundaries of deep-tech innovation, distinguish it from adjacent concepts, and propose an agenda for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35255/w35255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35255</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray</author>
<category>Working Paper</category>
</item>
<item>
<title>Startups in Africa</title>
<description><p>
We build new data on startups in Africa to study which types of financing these firms demand, how financing is allocated in practice, and the implications for startup creation and the composition of the sector. We combine a continent-wide founder survey, an incentive-compatible experiment estimating financing preferences, and venture capital (VC) deal records matched to founders’ education and work histories. We find that startups strongly prefer equity over debt, but equity is supplied mainly by foreign investors and flows disproportionately to foreign-connected founders. About 80 percent of VC deals involve a foreign investor, and more than 60 percent of funded founders have studied or worked outside Africa. A simple accounting framework shows that this foreignness reflects three main forces: scarce local equity capital, a thin pool of local entrepreneurs able to access startup finance, and frictions limiting local entrepreneurs’ access to foreign investors. Together, these forces reduce startup creation and tilt the sector toward foreign investors and foreign-connected founders.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35261/w35261.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35261</link>
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<pubDate>Sun, 31 May 2026 16:00:00 GMT</pubDate>
<author>Emanuele Colonnelli, Marcio Cruz, Mariana Pereira-Lopez, Tommaso Porzio, Chun Zhao</author>
<category>Working Paper</category>
</item>
<item>
<title>Old Space, New Space: A Commercial Revolution in Innovation?</title>
<description><p>
The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&amp;D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government's most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35212/w35212.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35212</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ruben Gaetani, Alexander T. Whalley</author>
<category>Working Paper</category>
</item>
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<title>Anti-Harassment Policy and the Startup Labor Market</title>
<description><p>
This paper examines how anti-harassment legal reforms that weaken non-disclosure agreements (NDAs) in cases of workplace sexual harassment affect startups' hiring and organizational decisions. Using a staggered difference-in-differences design and LinkedIn data on over 50,000 U.S. venture-capital-backed startups from 2014–2022, we find that NDA reforms, although intended for employee protection, reduce female hiring by about 8%, with effects concentrated among junior women, who are statistically more prone to sexual harassment, and in small or male-dominated startups. The results apply to both the intensive and extensive margins of female hiring. Treated entrepreneurial firms also witness more departures of male managers, promote more women, and receive less VC funding. These results suggest that while NDA-weakening laws increase firms’ perceived legal risk and reduce female hiring, they also trigger internal restructuring that promotes women's advancement into leadership and may, over time, foster more accountable and inclusive organizational cultures.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35187/w35187.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35187</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Jun Chen, Song Ma, Feng Zhang</author>
<category>Working Paper</category>
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<item>
<title>Growth is Getting Harder to Find, Not Ideas</title>
<description><p>
Relatively flat US productivity growth versus rising R&amp;D expenditures is often interpreted as evidence that ideas are getting harder to find. We build a new 45-year panel tracking the universe of US firms' patenting to investigate the micro underpinnings of this conclusion, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. We find that average patents per R&amp;D input are increasing, the elasticity of patents to R&amp;D inputs is flat or rising, and there is not systematic evidence of a secular decline in patenting after controlling for research inputs. We then document a positive, significant, and fairly steady relationship between firms' patent and labor productivity growth rates. Average firm growth after controlling for patent growth, however, declines. Together, these results suggest that firms' innovative efforts play a key role in sustaining growth that has not diminished over the last four decades.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35182/w35182.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35182</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Teresa C. Fort, Nathan Goldschlag, Jack Liang, Peter K. Schott, Nikolas Zolas</author>
<category>Working Paper</category>
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<title>Global Evidence on Business Use of AI</title>
<description>Despite the rapid rise of artificial intelligence (AI), internationally comparable data on how businesses use this new tool are scarce. In Firm Data on AI (NBER Working Paper 34836), Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H.</description>
<link>https://www.nber.org/digest/202605/global-evidence-business-use-ai</link>
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<pubDate>Thu, 30 Apr 2026 16:00:00 GMT</pubDate>
<author>Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Pawel Smietanka, Gregory Thwaites, Ben Zhe. Wang</author>
<category>Article</category>
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<title>Understanding Firms' AI Efforts and Their Economic Impact</title>
<description><p>
This paper reviews firm-level data on artificial intelligence and the emerging evidence on AI's economic effects. It argues that measurement is central: different AI datasets capture different objects (including invention versus use, internal capability building versus outsourcing, and realized activity versus investor perceptions) and can therefore lead to different conclusions. The paper develops a framework for choosing among these measures and surveys available data sources on firm AI efforts. It synthesizes evidence on AI's effects on firm growth, valuation, productivity, risk, labor, competition, financial markets and applications. The paper concludes by suggesting some ideas for future research.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35123/w35123.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35123</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Tania Babina</author>
<category>Working Paper</category>
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<title>The Effect of Choice Screens on Mobile Browser Usage: Evidence from the EU Digital Markets Act</title>
<description><p>
Can active choice mitigate the effects of preset defaults? We study this question using a difference-in-differences design around the rollout of the EU’s Digital Markets Act, which required iOS and Android to display browser choice screens under certain conditions. We find large effects, with notable differences across platforms: from 15 months after the mandate onward, Firefox usage was 113 percent higher on iOS and 12 percent higher on Android relative to a no-mandate counterfactual. This gap is consistent with rollout differences, as Android showed choice screens primarily on new devices, whereas iOS also showed them on existing devices.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35112/w35112.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35112</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Jesper Akesson, Kush Amlani, Raul Cepeda Suarez, Emily Chissell, Stefan Hunt, Michael Luca, Gemma Petrie</author>
<category>Working Paper</category>
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<title>Belief Dispersion and Entrepreneurial Entry</title>
<description><p>
When should a founder act on a strong belief about an opportunity, knowing that rivals assessing the same opportunity may hold very different views? This paper studies entry decisions when entrepreneurs hold heterogeneous beliefs about an opportunity's value and each founder knows only the range of views rivals might hold. In equilibrium, a founder enters only when their conviction exceeds a threshold set by anticipated rival optimism. The relationship between belief dispersion and entry is surprisingly rich: depending on the founder's conviction and the cost of entry, there may be no level of dispersion that supports entry, all levels may support it, or only a middle range may, so that an outside observer may see the most entry at intermediate levels of belief dispersion. When founders can delay, high dispersion that deters immediate entry need not prevent it altogether: the absence of rival action gradually reveals that competitors are less bullish than feared. Finally, not all conviction-building is equal. Validation that only the founder sees strengthens entry incentives fully, whereas validation visible to the whole market partly backfires by encouraging rivals. The paper formalises the intuition that entrepreneurial value comes not from optimism alone but from optimism that the founder anticipates rivals will not share, and derives predictions linking belief dispersion to entry patterns.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35091/w35091.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35091</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Joshua S. Gans</author>
<category>Working Paper</category>
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<title>The Declining Local Bias of Entrepreneurship in the United States</title>
<description><p>
Multiple studies document a local bias of entrepreneurship (LBE) in recent decades, where self-employed entrepreneurs are systematically more likely than wage workers to operate in their region of birth. This paper documents an important new fact: the LBE has been declining in the United States since 1970. The LBE is still present for white men engaged in self-employment, but it no longer exists for the overall U.S.-born workforce. We connect that decline to the transformation of self-employment away from high startup-capital sectors and the reduced opportunity for local self-employed entrepreneurs to achieve high incomes compared to wage work.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35088/w35088.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35088</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Innessa Colaiacovo, Margaret G. Dalton, Sari Pekkala Kerr, William R. Kerr</author>
<category>Working Paper</category>
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<title>Beyond Demo Day: Sorting and Value Added in Startup Accelerators</title>
<description><p>
We study who joins startup accelerators, how founders sort across programs, and which accelerators improve startup outcomes. Using a comprehensive sample of about 750,000 U.S. startups linked to 329 accelerators, we adapt the teacher value-added framework from education economics to estimate accelerator value added (AVA) while accounting for sorting. Selection is systematic: observably better ventures are more likely to enter accelerators and to sort into higher-AVA programs. Yet accelerator performance is highly dispersed. Most accelerators have negative value added relative to a no-accelerator benchmark, while a small right tail generates large gains. High-AVA accelerators predict better long-term outcomes, including acquisition, employment, revenue, and valuation, and are also more likely to accelerate the shutdown of weaker ventures. We validate AVA using internal applicant data from a large U.S. non-equity accelerator.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w35063/w35063.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w35063</link>
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<pubDate>Tue, 31 Mar 2026 16:00:00 GMT</pubDate>
<author>Youn Baek, Deepak Hegde</author>
<category>Working Paper</category>
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<title>Capital Gains Taxation and Startup Founders</title>
<description>The US capital gains tax is realization based, which means that taxes are due when appreciated assets are sold. Critics of this approach argue that it allows asset holders, such as corporate founders, to defer their tax obligations, sometimes indefinitely. An alternative approach, taxing gains on</description>
<link>https://www.nber.org/be/20261/capital-gains-taxation-and-startup-founders</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Eduardo M. Azevedo, Florian Scheuer, Kent Smetters, Min Yang</author>
<category>Article</category>
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<title>Featured Researcher: Jorge Guzman</title>
<description>Jorge Guzman is the Gantcher Associate Professor of Business at the Columbia Business School, where he has been a faculty member since 2018, and a faculty research fellow in the NBERs Productivity, Innovation, and Entrepreneurship program. His research focuses on entrepreneurship policy, regional</description>
<link>https://www.nber.org/be/20261/featured-researcher-jorge-guzman</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<category>Article</category>
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<title>The Geographic Expansion of Innovative Firms</title>
<description>Most US innovation stems from firms that operate R&amp;D facilities in many local markets. IBM and Google are two prominent examples, with R&amp;D activitiesmeasured by patentingin approximately 70 and 20 distinct locations, respectively. When a technology company opens an R&amp;D facility in a new location, it</description>
<link>https://www.nber.org/be/20261/geographic-expansion-innovative-firms</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Craig A. Chikis, Benny Kleinman, Marta Prato</author>
<category>Article</category>
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<title>Mixed Immigrant-Native Founding Teams Excel</title>
<description>Roughly one-quarter of new employer businesses in the United States are started by immigrants. Immigrant inventors have been responsible for approximately 23 percent of US patents produced since 1976 despite making up only 16 percent of the total US-based inventor population. Yet immigrant</description>
<link>https://www.nber.org/be/20261/mixed-immigrant-native-founding-teams-excel</link>
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<pubDate>Wed, 25 Mar 2026 16:00:00 GMT</pubDate>
<author>Zhao Jin, Amir Kermani, Timothy McQuade</author>
<category>Article</category>
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<title>Contract Enforcement and Young Firm Capital Structure: A Global Perspective</title>
<description><p>
We develop a framework to measure the severity of financial constraints for young firms across countries. Using ORBIS balance-sheet data for 23 economies, we show that short-term leverage rises while long-term leverage falls early in firms’ life cycles, with this pattern persisting longer where contract enforcement is weaker. We build a model of optimal financing under limited enforcement with endogenous debt maturity and blueprint capacity that matches these patterns and enables structural measurement of financial constraints. The framework decomposes the funding gap into within-firm borrowing constraints that ease with repayment history and a scale distortion identifiable through cross-country comparisons.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34985/w34985.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34985</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Gonzalo E. Basante Pereira, Ina Simonovska</author>
<category>Working Paper</category>
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<title>Attention (And Money) Is All You Need: Why Universities Are Struggling to Keep AI Talent</title>
<description><p>
We construct a novel dataset linking academic publication records to U.S. Census employer–employee data to track 42,000 AI researchers over two decades. We document systematic changes in the allocation of AI talent. Industry increasingly attracts younger and foreign-born researchers, while gender representation improves more in academia. The top 1% of publishing industry scientists now earn $1.5 million more annually than comparable academics, a fivefold increase since 2001. Rising wage premia coincide with greater sorting into large incumbent firms. Researchers who move to industry publish less but patent more, consistent with a shift from open science toward proprietary innovation.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34964/w34964.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34964</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Ufuk Akcigit, Craig A. Chikis, Emin Dinlersoz, Nathan Goldschlag</author>
<category>Working Paper</category>
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<title>Impact of Minimum Pay Rules on Gig Delivery Drivers</title>
<description>Platform-based delivery work has expanded dramatically over the past decade, creating millions of work opportunities for independent contractors who operate outside traditional employment protections. Because gig workers are not covered by standard minimum wage laws, several jurisdictions have</description>
<link>https://www.nber.org/digest/202603/impact-minimum-pay-rules-gig-delivery-drivers</link>
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<pubDate>Sat, 28 Feb 2026 16:00:00 GMT</pubDate>
<author>Yuan An, Andrew Garin, Brian K. Kovak</author>
<category>Article</category>
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<title>Intangible Intensity</title>
<description><p>
We develop a text-based measure of intangible investment intensity derived from firms’ 10-K filings, and offer a general methodology for semantic theme scoring (STS). Our approach further classifies disclosure text into knowledge, customer, and organization capital. Firms with high intangible intensity are smaller, younger, and invest heavily in R&amp;D and human capital. The three subcomponents map cleanly to distinct economic firm types: knowledge-intensive firms are R&amp;D-driven with high valuations and skilled labor; customer-intensive firms are mature, profitable, and commercially oriented; and organization-intensive firms are large, asset-heavy incumbents. Managerial expenditure descriptions thus provide informative signals about intangible investment, complementing financial statements in capturing corporate capital stocks.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34882/w34882.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34882</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Andrea L. Eisfeldt, Barney Hartman-Glaser, Edward T. Kim, Ki Beom Lee</author>
<category>Working Paper</category>
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<title>Venture Fraud</title>
<description><p>
We assemble the first comprehensive sample of venture fraud cases involving 614 U.S. venture capital (VC)-backed startups founded since 2000. We find that VC-backed firms are 54% more likely to face fraud charges than comparable non-VC-backed firms within a subsample of newly public firms where detection likelihood is high and homogeneous. We then examine the role of governance in explaining venture fraud, focusing on two features that have risen in recent years—founder-friendly structures and cap table complexity. In a panel prediction model examining all venture fraud cases, we find that fraud is more likely in startups with stronger founder control rights, more convex founder cash flow rights, more investors, and greater participation of non-traditional investors. Founder-controlled boards are 88% more likely to commit fraud than VC-controlled or shared-control boards, even within the same firm. Governance variables matter much more than founder characteristics in predicting fraud. Hot funding conditions at the initial round, which weaken governance incentives, predict future fraud. Fraudulent entrepreneurs continue to found new VC-backed startups unharmed relative to non-fraudulent entrepreneurs, suggesting a lack of market discipline. Overall, our results highlight rising agency costs in VC-backed firms that could lead to misallocation and broader social costs.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34868/w34868.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34868</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Alexander Dyck, Freda Fang, Camille Hebert, Ting Xu</author>
<category>Working Paper</category>
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<title>Firm Data on AI</title>
<description><p>
We survey nearly 6,000 senior business executives at US, UK, German, and Australian firms to develop new evidence on AI adoption and its effects on jobs, productivity, and output. Specifically, we ask executives about AI usage, its effects at their own firms over the past three years and, looking ahead, what they anticipate over the next three years. We find four main results. First, 69% of firms actively use AI, with higher usage rates at younger and more productive firms. Second, more than two thirds of executives regularly use AI, but their usage rate averages only 1.5 hours a week. Third, executives report little own-firm impact of AI over the last 3 years, with nine-in-ten reporting no impact on employment or productivity. Fourth, these same executives predict sizable effects over the next 3 years, predicting that AI will boost productivity at their firms by an average of 1.4%, raise output 0.8%, and cut employment 0.7%. In contrast, employees anticipate that AI will raise employment 0.5% at their firms in the next 3 years, highlighting an expectations gap between employers and employees.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34836/w34836.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34836</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Pawel Smietanka, Gregory Thwaites, Ben Zhe Wang</author>
<category>Working Paper</category>
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<title>How Do Workers Think About The Costs and Benefits of Freelance Work? New Evidence From a Survey Experiment</title>
<description><p>
We examine how workers perceive the trade-offs of freelancing using a novel survey design that explores the nature of workers' perceptions of their own jobs and the implications of work arrangements for their take-home pay. We find that, across several alternative classifications of freelance work, workers in such arrangements make less per hour than traditional employees, but report having greater control of when, where, and how they work. We find that on average, self-employed workers spend an additional 5 to 8 percentage points of gross pay covering unreimbursed expenses relative to traditional employees. However, when asked about expectations of net pay in freelance and traditional employment jobs with the same gross pay, respondents who received no quantitative information expected net pay to be higher in freelance arrangements than in employment arrangements, on average. This pattern reversed among respondents who were randomly assigned to receive customized estimates of their expected total expense and tax burdens in each arrangement, who estimated that freelance arrangements would generate lower net lower earnings than employment arrangements (consistent with the estimates we provided to them). This suggests that workers may not be fully aware of the tax and expense burdens freelance workers are responsible for. Interestingly, we find similar results both for workers who are currently employees in their main job and those who are currently self-employed, suggesting that the low salience of the tax and expense burdens associated with freelance work are not merely driven by those with no self-employment experience.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34843/w34843.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34843</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Edward Freeland, Andrew Garin, Dmitri K. Koustas</author>
<category>Working Paper</category>
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<title>Younger Firms and CEOs Allow More Work from Home</title>
<description><p>
We establish three facts about work from home (WFH) in the United States. First, employees WFH more often at younger firms – almost twice as often at firms founded after 2015 than at firms founded before 1990. Second, employees working under younger CEOs have higher levels of WFH. The average WFH rate is 1.4 days per week when the CEO is under 30, compared to 1.1 days when the CEO is 60 or older. Third, the self-employed WFH more than twice as often as wage-and-salary employees. These facts highlight the importance of organizational and managerial attributes for the prevalence of WFH.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34795/w34795.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34795</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Cevat Giray Aksoy, Jose Maria Barrero, Nicholas Bloom, Katelyn Cranney, Steven J. Davis, Mathias Dolls, Pablo Zarate</author>
<category>Working Paper</category>
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<title>Do Rideshare Users Comparison Shop?</title>
<description>The widespread adoption of mobile web and smartphone apps was expected to dramatically reduce consumer search costs and intensify price competition, particularly in markets where comparing prices requires little more than opening a second application. The US rideshare market, dominated by Uber and</description>
<link>https://www.nber.org/digest/202602/do-rideshare-users-comparison-shop</link>
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<pubDate>Sat, 31 Jan 2026 16:00:00 GMT</pubDate>
<author>Jeffrey Fossett, Michael Luca, Yejia Xu</author>
<category>Article</category>
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<title>Technology and Economic Development</title>
<description><p>
This chapter presents a tractable framework for the study of technology adoption and diffusion in the context of economic development. Firms in countries behind the world technology frontier can rapidly adopt new techniques from the world frontier. Lower absorptive capacity (because of weak education systems, poor management practices, or barriers to technology adoption), institutional distortions, mismatch between frontier technologies and the needs of firms in the country (i.e., “inappropriate technology”), and credit market frictions slow down technology adoption and cause the economy in question to have a greater distance to the frontier and thus lower income per capita—although the long-run growth rate of the country still remains equal to that of the frontier. This framework is extended to study the choice between innovation and imitation, as well as the role of selection for higher-productivity and higher-absorptive capacity firms during the process of economic development. We illustrate the main comparative statics of our framework with a number of correlations based on cross-country and firm-level data. The tractability of the framework makes it amenable to a range of additional extensions.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34730/w34730.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34730</link>
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<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>Daron Acemoglu, Ufuk Akcigit, Simon Johnson</author>
<category>Working Paper</category>
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<title>Making Entrepreneurs: Long Term Returns to Training Youth in Business Skills</title>
<description><p>
We study the medium and long term impacts of Skills for Effective Entrepreneurship Development (SEED), a 3-week entrepreneurship training program for secondary school students in Uganda. The mini-MBA, modeled after business school curricula, was implemented as a randomized field experiment with a nationally representative sample of 4,402 youth. After four years, the training improved both hard and soft skills. SEED graduates became more effective negotiators and communicators and exhibited improved self-efficacy, stability, plasticity, and stress management. In the medium run, treated youth were more likely to start enterprises and more successful in ensuring their survival, thereby gaining greater entrepreneurial experience. Their ventures were also of higher quality: more likely to be formal, have employees, be in collaboration with other entrepreneurs, and use effective business management practices. With 52% of the sample still enrolled in post-secondary education, we find suggestive evidence that businesses led by the treatment groups performed better. After nine years, business ownership converged between treatment and control groups as control ownership rates doubled. However, SEED graduates maintained their edge in terms of business quality and operated firms with 20% higher revenues and 16% higher profits, without corresponding increases in capital or labor inputs, consistent with higher total factor productivity. Entrepreneurial success was achieved through the adoption of better business practices and experimentation, with soft skills related to entrepreneurial mindset playing a complementary role. SEED generated high returns on investment: the present discounted values of SEED-induced business and total earnings equal 20 and 27 times program costs, respectively.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34637/w34637.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34637</link>
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<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>Laura Chioda, Paul Gertler, David Contreras-Loya, Dana R. Carney</author>
<category>Working Paper</category>
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<title>The Growth of Private Lending and Retail Access to Alternative Investments</title>
<description><p>
Private lending has exploded recently, owing not only to the retreat of banks from corporate lending, but also to the expansion of private equity (PE). Given the growing interest in retail access to alternative assets, we explore fees, performance, and investment behavior for publicly traded Business Development Companies (BDCs). Their compensation structures include fees and provisions common in PE, and they collectively provide debt for PE-sponsored deals and make PE-like investments themselves, especially for higher-spread investments. In-sample risk-adjusted abnormal returns are high, but fees and performance are inversely related. Moreover, BDCs with larger non-institutional investor bases charge higher fees.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34617/w34617.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34617</link>
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<pubDate>Wed, 31 Dec 2025 16:00:00 GMT</pubDate>
<author>David T. Robinson, Melanie Wallskog</author>
<category>Working Paper</category>
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<title>Place-Based Policies and Entrepreneurship, Fall 2025</title>
<description><div class="page-header__intro-links page-header__intro-notice">
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<h2 class="page-subtitle__sponsored-by">Supported by
the Alfred P. Sloan Foundation grant <a href="https://www.nber.org/programs-projects/projects-and-centers/8691-place-based-entrepreneurship-and-innovation">#G-2024-22521</a> </h2>
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<link>https://www.nber.org/conferences/place-based-policies-and-entrepreneurship-fall-2025</link>
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<pubDate>Thu, 04 Dec 2025 16:00:00 GMT</pubDate>
<category>Conference</category>
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<title>The Economics of Climate Innovation: Technology, Climate Policy, and the Clean Energy Transition</title>
<description><p>
This chapter examines the economics of climate innovation and its role in the clean technology transition. It outlines the incentives, market failures, and policy levers that shape the development and diffusion of clean technologies; traces global patterns in technology development and deployment; and highlights frontier challenges and open questions related to climate adaptation, critical mineral supply chains, artificial intelligence, and geopolitics. The analysis explores the role of effective climate policy, stressing the relevance of coordinated approaches that match instruments to technology maturity and local context.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34601/w34601.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34601</link>
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<pubDate>Sun, 30 Nov 2025 16:00:00 GMT</pubDate>
<author>Eugenie Dugoua, Jacob Moscona</author>
<category>Working Paper</category>
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<title>Delivering Higher Pay? The Impacts of a Task-Level Pay Standard in the Gig Economy</title>
<description><p>
How does a task-level minimum pay requirement for gig workers affect their earnings and employment? We study this question in the context of a January 2024 law in Seattle that establishes a per-task minimum pay standard for app-based delivery workers. Drawing on novel cross-platform, trip-level gig activity data, we compare earnings and employment trajectories around the implementation of the law for workers who were doing delivery work in Seattle before the reform against workers who had been active in other regions of Washington State. We find that the minimum pay law raised delivery pay per task, though the increases in base pay per task were partially offset by a substantial reduction in average tips, a major component of delivery pay. At the same time, the policy led to a reduction in the number of tasks completed by highly attached incumbent drivers (but not an increase in exit from delivery work), completely offsetting increased pay per task and leading to zero effect on monthly earnings. We find evidence that drivers experienced more unpaid idle time and longer distances driven between tasks, but find no evidence that drivers reduced their total time working on delivery apps and only limited evidence of switching from delivery to ride-hailing work. Using a simple model of the labor market for platform delivery drivers, we show that our evidence is consistent with free entry of drivers into the delivery market driving down the task-finding rate until expected earnings return to their pre-reform level. These findings highlight the challenges of raising pay in spot markets for tasks where there is free entry of workers.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34545/w34545.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34545</link>
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<pubDate>Sun, 30 Nov 2025 16:00:00 GMT</pubDate>
<author>Yuan An, Andrew Garin, Brian K. Kovak</author>
<category>Working Paper</category>
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<title>Dilution vs. Risk Taking: Capital Gains Taxes and Entrepreneurship</title>
<description><p>
Recent proposals to tax unrealized capital gains or wealth have sparked a debate about their impact on entrepreneurship. We show that accrual-based taxation creates two opposing effects: successful founders face greater dilution from advance tax payments, whereas unsuccessful founders receive tax credits that effectively provide insurance. Using comprehensive new data on U.S. venture capital deals, we find that founder returns remain extremely skewed, with 84% receiving zero exit value while the top 2% capture 80% of total value. Moving from current realization-based to accrual-based taxation would reduce founder ownership at exit by 25% on average but would also increase the fraction receiving positive payoffs from 16% to 47% when tax credits are refunded. Embedding these distributions in a dynamic career choice model, we find that founders with no or moderate risk aversion prefer the current realization-based tax system, while more risk-averse founders prefer accrual-based taxation. We estimate that a 2% annual wealth tax has a similar impact on dilution as taxing unrealized capital gains but produces no risk-sharing benefits due to the absence of tax credits in case of down rounds.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34512/w34512.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34512</link>
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<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Eduardo M. Azevedo, Florian Scheuer, Kent Smetters, Min Yang</author>
<category>Working Paper</category>
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<title>Selling to Yourself: Continuation Funds in Private Equity</title>
<description><p>
Continuation funds (CFs) are private equity structures in which a manager raises a new fund to purchase assets from their existing fund. This structure has surged in popularity, from five funds in 2018 to 130 in 2024. We use a hand-collected sample of 472 CFs to test a model in which heterogeneous preferences drive CFs. Consistent with the model’s predictions, CFs emerge when LPs are more heterogeneous and managers have earned carried interest that they can roll. LPs typically choose to exit rather than invest, with this decision driven by both LP-level frictions and time varying LP liquidity demands.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34471/w34471.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34471</link>
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<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Rustam Abuzov, Will Gornall, Sophie Shive, Ilya A. Strebulaev, Michael S. Weisbach</author>
<category>Working Paper</category>
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<title>Positioned at Extremes: Future Job Placements of Immigrant Students at U.S. Colleges</title>
<description><p>
Immigrant students who attend U.S. colleges are disproportionately employed in either large firms—especially multinationals—or small firms and self-employment. Using linked Census and longitudinal employment data, we trace the jobs taken by college students in 2000 during the 2001-20 period and evaluate four mechanisms shaping sector and firm size placement: geographic clustering, degree specialization, firm capabilities/visas, and ethnic self-employment specialization. Degree fields predict large firm and MNE placement, while ethnic specialization explains small firm sorting. Immigrant students who remain in the U.S. earn more than their native peers, suggesting the segmentation reflects productive sorting rather than blocked opportunity.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34440/w34440.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34440</link>
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<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Francis M. Dillon, Sari Pekkala Kerr, William R. Kerr, Andrew J. Wang</author>
<category>Working Paper</category>
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<title>Leaving Money on the Dashboard: Price Dispersion and Search Frictions on Uber and Lyft</title>
<description><p>
We document price differences for identical trips on Uber and Lyft, based on an audit of the two platforms. While price dispersion exists in the market, device-level data show that only 16.1 percent of consumers opening one app also open the other. Our estimates suggest that the modest frictions involved in comparison shopping increase platforms’ gross booking volume by over $300 million annually in New York City alone. While price-comparison engines could in principle reduce frictions, Uber’s API terms of use limit such services, reducing riders’ ability to price compare.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34441/w34441.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34441</link>
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<pubDate>Fri, 31 Oct 2025 16:00:00 GMT</pubDate>
<author>Jeffrey Fossett, Michael Luca, Yejia Xu</author>
<category>Working Paper</category>
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<title>Working it out: Randomized Modification and Entrepreneurial Effort in a Collateralized Debt Market</title>
<description><p>
We enrich a standard debt overhang model with liquidity constraints to guide the design and interpretation of a collateralized debt modification experiment on a publicly traded lender’s delinquent vehicle loans to minibus entrepreneurs. Liquidity constraints add another borrower incentive compatibility constraint that interacts with debt overhang to shape repayment and effort. Consistent with model predictions, we find: debt reduction does not affect liquidity constrained borrowers; payment reduction improves both repayment and effort for borrowers with sufficient vehicle equity; payment reduction induces repayment without effort increases for low-equity borrowers. These results suggest a pecking order strategy for modification practice and policy.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34398/w34398.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34398</link>
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<pubDate>Tue, 30 Sep 2025 16:00:00 GMT</pubDate>
<author>Christopher Eaglin, Apoorv Gupta, Filippo Mezzanotti, Jonathan Zinman</author>
<category>Working Paper</category>
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<title>Fractional Ownership and Copyright Licensing: Evidence from the Music Industry</title>
<description><p>
Creative content is often the product of collaboration, which may lead to fractional ownership of intellectual property. We study the effect of fractional ownership on the licensing of copyrighted material and its reuse. To do so, we compile new data on the copyright ownership structure of songs and their licensing for use in movies. We document that fractional song ownership has increased substantially: the mean number of songwriters and publishers per song has tripled between 1958 and 2021. We show that, conditional on a rich set of controls, greater fractionalization is associated with lower likelihood of licensing. We leverage the Sony-led acquisition of EMI Music Publishing in 2012 to obtain within-song variation in ownership and find that consolidating ownership rights significantly increases licensing, beyond any standalone effects of the merger.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34336/w34336.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34336</link>
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<pubDate>Tue, 30 Sep 2025 16:00:00 GMT</pubDate>
<author>Alberto Galasso, El Hadi Caoui</author>
<category>Working Paper</category>
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<title>Underwriting Based on Cash Flow Helps Younger Entrepreneurs Access Credit</title>
<description>Younger entrepreneurs are disadvantaged in small business loan markets because lenders rely heavily on personal credit scores, which favor long histories of repaying debt. In Modernizing Access to Credit for Younger Entrepreneurs: From FICO to Cash Flow (NBER Working Paper 33367), researchers</description>
<link>https://www.nber.org/be/20252/underwriting-based-cash-flow-helps-younger-entrepreneurs-access-credit</link>
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<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Christopher M. Hair, Sabrina T. Howell, Mark J. Johnson, Siena Matsumoto</author>
<category>Article</category>
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<title>The Gig Economy and Entrepreneurship</title>
<description>The rise of platform-based work has transformed labor markets. Nearly 10 million Americans have participated in the gig economy over the past decade. This transformation may have important effects on entrepreneurship by allowing individuals to gain industry experience, encouraging experimentation,</description>
<link>https://www.nber.org/be/20252/gig-economy-and-entrepreneurship</link>
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<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Matthew R. Denes, Spyridon Lagaras, Margarita Tsoutsoura</author>
<category>Article</category>
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<title>Remote Work and Employee Transitions to Entrepreneurship</title>
<description>The widespread transition to remote work during the COVID-19 pandemic fundamentally altered workplace arrangements. Full work-from-home days accounted for 28 percent of paid workdays in the US by 2023, four times higher than the 2019 level. This shift may affect entrepreneurial activity since most</description>
<link>https://www.nber.org/be/20252/remote-work-and-employee-transitions-entrepreneurship</link>
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<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Alan Kwan, Ben Matthies, Richard R. Townsend, Ting Xu</author>
<category>Article</category>
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<title>Featured Researcher: Ufuk Akcigit</title>
<description>Ufuk Akcigit is the Arnold C. Harberger Professor of Economics and director of the Global Center for Economic Growth at the University of Chicago. He is a research associate in the NBER's Productivity, Innovation, and Entrepreneurship and Economic Fluctuations and Growth programs. Additionally, he</description>
<link>https://www.nber.org/be/20252/featured-researcher-ufuk-akcigit</link>
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<pubDate>Sun, 28 Sep 2025 16:00:00 GMT</pubDate>
<author>Ufuk Akcigit</author>
<category>Article</category>
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<title>Assessing Career Attainment via a Non-Wage Measure</title>
<description><p>
This paper proposes a non-pecuniary measure of career achievement, seniority. Based on a database of over 150 million resumes, this metric exploits the variation in how long it takes workers to attain job titles. A person’s seniority is defined as the number of years it takes the median individual—within the same industry and firm size category—to achieve that person’s job title. Seniority aligns with standard markers of success—it is positively correlated with both wages and educational attainment. To demonstrate its value as a measure of career progression, we show that individuals with higher seniority levels in the public sector are more likely to transition to higher-paying positions in the private sector. When non-monetary factors influence career choice, evaluating labor market outcomes using non-wage measures, such as seniority, offers significant advantages.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34292/w34292.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34292</link>
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<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Natee Amornsiripanitch, Paul Gompers, George Hu, Will Levinson, Vladimir Mukharlyamov, Sachin Srivastava</author>
<category>Working Paper</category>
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<title>How People Use ChatGPT</title>
<description><p>
Despite the rapid adoption of LLM chatbots, little is known about how they are used. We document the growth of ChatGPT’s consumer product from its launch in November 2022 through July 2025, when it had been adopted by around 10% of the world’s adult population. Early adopters were disproportionately male but the gender gap has narrowed dramatically, and we find higher growth rates in lower-income countries. Using a privacy-preserving automated pipeline, we classify usage patterns within a representative sample of ChatGPT conversations. We find steady growth in work-related messages but even faster growth in non-work-related messages, which have grown from 53% to more than 70% of all usage. Work usage is more common for educated users in highly-paid professional occupations. We classify messages by conversation topic and find that “Practical Guidance,” “Seeking Information,” and “Writing” are the three most common topics and collectively account for nearly 80% of all conversations. Writing dominates work-related tasks, highlighting chatbots’ unique ability to generate digital outputs compared to traditional search engines. Computer programming and self-expression both represent relatively small shares of use. Overall, we find that ChatGPT provides economic value through decision support, which is especially important in knowledge-intensive jobs.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34255/w34255.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34255</link>
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<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Aaron Chatterji, Thomas Cunningham, David J. Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, Kevin Wadman</author>
<category>Working Paper</category>
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<title>Understanding Patenting Disparities via Causal Human+Machine Learning</title>
<description><p>
We develop an empirical approach for analyzing multi-dimensional discrimination using multimodal data, combining human perception measures with language-embedding-based, nonlinear controls for latent quality to relax restrictive assumptions in causal machine learning. Applying it to the U.S. patent examination process, we find that, ceteris paribus, applications from female inventors are 1.8 percentage points less likely to be approved, and those from Black inventors are 3 percentage points less likely—inconsistent with legally prescribed criteria. Jointly studying multiple bias dimensions and their intersections for the first time, we uncover new biases, including an affiliation bias—individual inventors are disadvantaged by 6.6 percentage points relative to employees of large, public firms, a disparity larger than any demographic gap. Moreover, innovation quality, location, and other factors can mitigate or compound discrimination, and the disparities interact: for example, racial gaps vanish among public-firm employees, masking more severe discrimination against individuals. Existing theories such as homophily cannot fully explain the results, but a simple model of correlation neglect does.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34197/w34197.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34197</link>
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<pubDate>Sun, 31 Aug 2025 16:00:00 GMT</pubDate>
<author>Lin William Cong, Stephen Q. Yang</author>
<category>Working Paper</category>
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<title>Which Individuals Create Jobs? Managerial Talent and Occupational Skills</title>
<description><p>
We consider founders of limited liability firms who previously held jobs in the formal sector of Brazil. Managers are five percent of former job holders but their startups account for 27 percent of new firm employment. Relatively little of their overrepresentation as founders or the larger size of their startups is explained by their previous wages or other standard human capital variables. Among non-managerial former occupations we examined those clearly connected to demand (sales) and to supply (technology, purchasing). Only purchasing was comparable to managerial occupations in entrepreneurship and new firm size. Further examination suggests that a key to greater entrepreneurship and larger initial firm size is that workers’ former jobs entailed building relationships with other businesses: in demand-side occupations, they sold to other businesses; in supply-side occupations, they bought from other businesses.
</p>
<p><a href="https://www.nber.org/system/files/working_papers/w34158/w34158.pdf">Download PDF</a></p></description>
<link>https://www.nber.org/papers/w34158</link>
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<pubDate>Thu, 31 Jul 2025 16:00:00 GMT</pubDate>
<author>Marc-Andreas Muendler, James E. Rauch, Sergio Mikio Koyama</author>
<category>Working Paper</category>
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<title>Engineering Ukraine's Wirtschaftswunder</title>
<description><p>
As Ukraine emerges from the devastation of war, it faces a historic opportunity to engineer its own Wirtschaftswunder—a productivity-driven economic transformation akin to post-war West Germany. While investment-led growth may offer quick wins, it is efficiency, innovation, and institutional reform that will determine Ukraine’s long-term economic trajectory. Drawing on rich micro-level firm data spanning 25 years, this paper uncovers deep structural distortions that have suppressed creative destruction and productivity in Ukraine. It finds that business dynamism is on the decline, alongside rising market concentration among incumbent businesses, including low productivity state owned enterprises. To inform priorities for reviving business dynamism, this study develops a model of creative destruction drawing on Acemoglu et al. (2018) and Akcigit et al. ( |
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