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VoxTalks Economics

VoxTalks Economics

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Learn about groundbreaking new research, commentary and policy ideas from the world's leading economists. Presented by Tim Phillips.

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  • S9 Ep20: What triggered January 6?

    S9 Ep20: What triggered January 6?
    Mar 20 2026
    Two explanations circulated immediately after the March to Save America on January 6, 2021 turned into a riot: a mob manipulated by a demagogue, or ordinary citizens defending democracy against a stolen election. Konstantin Sonin, David Van Dijcke, and Austin Wright have used anonymised location data from forty million mobile devices to investigate why the protests escalated so dramatically.No surprise: partisanship was the strongest predictor of attendance, proximity to Proud Boys chapters and use of the far-right social network Parler also increased participation. But political isolation amplified the movement: the communities most over-represented among those who traveled to Washington were small Republican enclaves surrounded by Democrat-leaning areas, politically and socially cut off from their neighbours. And participation also spiked in counties that experienced a "midnight swing," where the reported vote count favoured Trump on election night before shifting to Biden as mail-in ballots were counted. These were precisely the counties where the "Stop the Steal" narrative landed hardest. The research behind this episode:Sonin, Konstantin, David Van Dijcke, and Austin L. Wright. 2023. "Isolation and Insurrection: How Partisanship and Political Geography Fueled January 6, 2021." CEPR DP18209. To cite this episode:Phillips, Tim, and Konstantin Sonin. 2026. “What triggered January 6?” VoxTalks Economics (podcast). Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestKonstantin Sonin is the John Dewey Distinguished Service Professor at the Harris School of Public Policy at the University of Chicago. Born in the Soviet Union, he has spent his career studying how political institutions work under stress, with particular attention to how information and misinformation shape political behaviour, elections, and collective action. He is one of the leading economists working on the political economy of authoritarian and democratic governance, and his research on protest, polarisation, and political geography has made him a central figure in the study of democratic backsliding.Research cited in this episodeRegression discontinuity design is a statistical method used to identify causal effects by exploiting a threshold or cutoff. Sonin, Van Dijcke, and Wright use two regression discontinuity designs: one exploiting the narrow margins by which Trump lost certain states, and one exploiting the gap between the election-night vote tally and the final certified result in individual counties. In both cases, the design allows them to isolate the effect of a specific trigger on protest participation, separating it from the general background of partisan feeling.The "midnight swing" refers to the shift in reported vote tallies that occurred in many counties on election night 2020 as large batches of mail-in ballots were counted. Because mail-in voters skewed heavily Democratic, counties where in-person votes were reported first showed strong Trump leads that reversed overnight as the mail-in totals arrived. For professional observers and election administrators, this pattern was entirely expected; it followed directly from the different rules different states used to count mail-in ballots during the pandemic. For many voters, particularly those already primed to distrust the electoral process, it read as suspicious. The paper finds that communities exposed to larger swings sent disproportionately more participants to Washington on January 6.Network Exposure design is a methodological innovation introduced in this paper. It measures how much exposure a given community had to election-denial signals flowing through its social networks, and distinguishes this from exposure arising simply through geographic proximity to other communities. Isolated communities proved hypersensitive to information traveling through their social networks, but not to information spreading through neighbouring areas. This suggests the amplification mechanism was social, not spatial.Political isolation in this paper refers to being a minority political community within a larger, differently-leaning area. A small Republican-voting enclave inside a Democrat-leaning county or district is politically isolated in this sense. The paper finds that isolation of this kind was a strong amplifier of partisanship in predicting participation. Two other measures of isolation, one based on mobile device travel patterns ("locational isolation") and one based on Facebook connections ("social media isolation"), produce consistent results, suggesting the effect is not an artefact of how isolation is measured.The Proud Boys are a far-right extremist organisation active in the United States. The paper finds that communities with a local Proud Boys chapter were over-represented among those who traveled to Washington on January 6, making proximity to the organisation a robust correlate of ...
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    21 m
  • S9 Ep19: Can blockchain decentralise money, contracts, and finance?

    S9 Ep19: Can blockchain decentralise money, contracts, and finance?
    Mar 17 2026
    Every Bitcoin transaction needs to be verified on the blockchain. There is no central authority that does this, but Bitcoin's blockchain has run uninterrupted since 2009 and now carries a market capitalisation of $1.3 trillion, roughly 4% of US GDP. Its original promise was more radical: that we do not need a trusted intermediary to spend money, write contracts, or create finance. In the fifth LTI report, published today, Yackolley Amoussou-Guenou, Bruno Biais, and Sara Tucci-Piergiovanni ask how much of that promise has held. Bruno talks to Tim Phillips about blockchain’s potential, its flaws, and its future. It is a Nash equilibrium: if you believe others will follow the rules, it is in your interest to follow them too. On that foundation Bitcoin’s ledger has been running continuously for 16 years. Smart contracts, pioneered by Vitalik Buterin's Ethereum, extend the logic to financial agreements. Decentralised finance promised to cut out rent-seeking intermediaries. Cryptocurrencies can step in where banks are broken or currencies have collapsed; in Lebanon, when bank accounts were frozen and payments stopped, businesses switched to crypto and kept operating. But the technology's libertarian origins may need to be sacrificed: As Bruno says, without transparency there is no trust, and transparency in this market may require regulation.The research behind this episode:Amoussou-Guenou, Yackolley, Bruno Biais, and Sara Tucci-Piergiovanni. 2026. "Can Blockchain Decentralize Money, Contracts, and Finance?" LTI Report 5. CEPR and Long-Term Investors@UniTo. Freely available to download at cepr.org. To cite this episode:Phillips, Tim, and Bruno Biais. 2025. "Can Blockchain Decentralize Money, Contracts, and Finance?" VoxTalks Economics (podcast). Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestBruno Biais is Professor of Finance at HEC Paris and a Research Fellow at the Centre for Economic Policy Research (CEPR). His research spanning financial market microstructure, corporate finance, and the economics of blockchain has made him one of the leading economists working at the intersection of finance and decentralised technology. He has studied blockchain and cryptocurrency markets since their early years, and his theoretical models of consensus mechanisms and cryptocurrency valuation have shaped how economists understand the conditions under which decentralised systems can and cannot sustain themselves.Research cited in this episodeThe blockchain is a distributed ledger maintained by a network of nodes, each holding an identical copy of the record of ownership. When a transaction is submitted, all nodes verify it against the existing ledger and update their copies to reach consensus on the new state. No central authority manages this process; its stability rests entirely on the incentive structure built into the protocol.Nash equilibrium is a concept from game theory, named for the mathematician John Nash, describing a situation in which each participant's strategy is the best response to the strategies of all others; no individual has an incentive to deviate unilaterally. Biais and co-authors identify the Bitcoin protocol as a Nash equilibrium: if you believe others will follow the rules, it is in your own interest to follow them too. That self-reinforcing alignment of incentives, rather than goodwill or central enforcement, is why the blockchain has remained valid since 2009.Smart contracts are lines of code deposited on a blockchain that execute automatically when specified conditions are met: if X, then Y. Vitalik Buterin introduced them through the Ethereum platform, which offers a richer programming language than Bitcoin and allows users to hold collateral on-chain to guarantee the contract will pay out. Smart contracts underpin automated market makers, decentralised lending, and a wide range of financial applications that require no counterparty or intermediary to enforce the agreement.Oracles are third-party services that transmit data about real-world events to a blockchain, allowing smart contracts to respond to things that happen off-chain. A contract that pays out when a house burns, for example, requires an oracle to report that event to the network. Oracles introduce a point of fragility: the authenticity and accuracy of off-chain information must be established before the network accepts it, and that verification is more vulnerable to error and manipulation than the on-chain consensus mechanism itself.Front-running and miner extractable value (MEV) describe the practice by which technically sophisticated actors exploit the public visibility of pending transactions to extract profits at the expense of ordinary users. Because transactions on public blockchains are broadcast to all nodes before they are confirmed, an actor who sees a large pending purchase can execute the same trade first, drive the price up, and then sell ...
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    33 m
  • S9 Ep18: Will AI transform economic growth?

    S9 Ep18: Will AI transform economic growth?
    Mar 13 2026
    Could AI transform our economies to produce explosive growth? Most economists are sceptical at best. Anton Korinek of the University of Virginia, leader of the CEPR research policy network on AI, thinks the threshold is closer than those models suggest.In his latest work, Korinek, Tom Davidson, Basil Halperin, and Thomas Houlden, have built a growth model that captures what happens when AI starts automating AI research itself. Automation does two things simultaneously: it accelerates research, and it offsets the diminishing returns that have historically stopped self-improving processes from compounding. Three reinforcing feedback loops: software quality, hardware quality, and general technological progress, each amplify the others. Korinek's findings are more optimistic than even the AI labs' own roadmaps, which focus on software capability alone. The research behind this episode:Davidson, Tom, Basil Halperin, Thomas Houlden, and Anton Korinek. 2026. "When Does Automating AI Research Produce Explosive Growth? Feedback Loops in Innovation Networks." Working paper, January 2026.To cite this episode:Phillips, Tim, and Anton Korinek. 2026. "When Does Automating AI Research Produce Explosive Growth?" VoxTalks Economics (podcast). Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestsAnton Korinek is a professor of economics at the University of Virginia. He leads the CEPR Research Policy Network on AI, which is building a community of researchers to understand and anticipate the economic impact of artificial intelligence. He is a member of Anthropic's Economic Advisory Council and was named by Time magazine among the hundred most influential people in AI. His research spanning the economics of transformative AI, growth theory, and the implications of advanced automation for labor markets and inequality has made him one of the most widely cited economists working on these questions. He is also the founder of the Economics of Transformative AI initiative at the University of Virginia, which focuses on the long-run economic consequences of AI systems that approach or exceed human-level capabilities.Visit the CEPR Research Policy Network on AI.Research cited in this episodeDaron Acemoglu's estimate of AI's growth impact. Acemoglu calculated that AI would raise annual growth by approximately 0.07 percentage points, arriving at this figure by multiplying the share of jobs likely to be affected by AI, the fraction of tasks within those jobs that AI could perform, and the productivity gain per task. Korinek argues the estimate was a reasonable description of the AI that existed in 2024 but did not account for the trajectory of capabilities since, nor for the feedback loops between AI progress and further AI development that his own paper models.Recursive self-improvement. The idea that an AI system, once capable enough, could design improved versions of itself, triggering an accelerating cycle of capability gains. The concept was first articulated by John von Neumann in the 1950s and has since become central to debates about transformative AI. All major AI labs, Korinek notes, are working towards some version of this vision; the economic question is whether the resulting growth would be explosive or would be damped by diminishing returns.Semi-endogenous growth models. A class of economic growth models in which long-run growth depends on the scale of the research workforce and the returns to research effort. The canonical insight, associated most closely with Nicholas Bloom and co-authors, is that "ideas get harder to find"; maintaining a given rate of progress requires ever-increasing research investment. Korinek and co-authors use and extend this framework, showing that automation can counteract diminishing returns by replacing human labor with capital in the research process, creating a new feedback loop that was absent from earlier models.Kaldor's balanced growth facts. Nicholas Kaldor's observation, made in the mid-twentieth century, that the major macroeconomic aggregates, including the capital-output ratio, the labor share of income, and the rate of return to capital, remain roughly stable over long periods. Growth economists built their models, including the Solow and Ramsey models, to fit these regularities. Korinek notes that those models were appropriate precisely because they matched the historical data; the question his paper raises is whether the data of the next few decades will look different enough to require a different class of models.Moore's Law. The empirical regularity, observed in computing hardware since the 1960s, that the number of transistors on a chip approximately doubles every two years. Korinek uses chip progress as a calibration benchmark: maintaining that rate of doubling has historically required roughly an eight percent annual increase in the scientific workforce working on chips. This figure allows the model to be ...
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    31 m
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