VoxTalks Economics

John Vickers, Oxford economist and former Bank of England chief economist, and David Aikman, NIESR director and ex-Bank official, debate a controversial cut to UK bank equity requirements. They probe why the Financial Policy Committee trimmed the benchmark now. They discuss potential effects on capital, risks from weaker backstops, and incentives that could steer freed funds toward buybacks rather than more lending.

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 participation, independent of general partisan leanings.Parler was a social media platform popular among far-right users in the United States during the period leading up to January 6, 2021. Communities where Parler usage was relatively higher were also over-represented among participants in the March to Save America, suggesting that the platform played a role in amplifying mobilisation signals within the networks most susceptible to them.Collective action theory is the study of how individuals decide to participate in group action, particularly when the costs fall on participants individually but the benefits are shared. Sonin, Van Dijcke, and Wright contribute behavioural evidence on the specific role of political isolation and network-amplified grievance in driving participation.More VoxTalks EconomicsThe Grievance Doctrine What if trade policy wasn’t really about trade at all? What if it was about revenge, power, and punishment, tariffs as tantrums and diplomacy as drama? Richard Baldwin on what is driving the US policy agenda. How protests are born, and how they die Every year we see thousands of protest movements on our city streets. Benoît Schmutz-Bloch explains why do some protests persist, and some disappear, and some remain peaceful, but others become violent.

Bruno Biais, Professor of Finance at HEC Paris and CEPR Research Fellow, explains how blockchain replaces bank ledgers and why Bitcoin survives as a collective belief. He discusses smart contracts, their limits when real-world data is needed, and how DeFi can reproduce intermediaries and front-running. He explores regulation, stablecoins versus CBDCs, and crypto’s role in failing banking systems.

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 parameterised with a real-world measurement of how much additional research input is needed to sustain a given rate of technological progress.Consumer surplus from digital technologies. Korinek raises the problem that GDP statistics are designed to measure market transactions and therefore do not capture the value people derive from digital goods and services beyond what they pay for them. He references research from the Stanford Digital Economy Lab as an example of work attempting to quantify this surplus. The implication for the paper's argument is that explosive AI-driven growth could be underestimated even in the statistics used to monitor it.More VoxTalks Economics episodes"Our Workless Future", an earlier conversation with Anton Korinek from September 2022, in which he set out the case for taking AI's impact on labor markets seriously.Related reading on VoxEUFirms predict an AI productivity boom is coming, a survey of over 5,000 CFOs, CEOs, and executives shows that around 70% of firms actively use AI, particularly younger, more productive firms. They forecast AI will boost productivity by 1.4%, increase output by 0.8%, and cut employment by 0.7% over the next three years.How AI is affecting productivity and jobs in Europe, firm-level evidence on AI’s effects in Europe. The authors find that AI adoption increases labour productivity levels by 4% on average in the EU, with no evidence of reduced employment in the short run.From AI investment to GDP growth: An ecosystem view, how the current AI wave is contributing to US GDP, both directly through investment and indirectly through ongoing service flows. 

Oleg Itskhoki, Harvard economist known for work on exchange rates and capital flows, discusses how financial repression works and why governments use it. He recounts Russia’s 2022 measures like forced currency conversion and withdrawal limits. He examines when such tools can halt crises, the political difficulty of reversing them, and the risk they pose as a long-term alternative to fiscal reform.

Giacomo Anastasia, Assistant Professor studying labour markets in conflict zones, discusses how Ukraine's workforce adapted to massive displacement and mobilization. He covers firms’ surprising resilience, regional collapses near the frontline, hires of women and older workers, the rise of remote work, and policies for reintegrating soldiers, fixing education losses, and reviving devastated areas.

Andrii Parkhomenko, urban economist tracking Ukraine's housing and population shifts. Martina Kirchberger, economist on procurement, labour flexibility, and prep for big investment. Edward Glaeser, Harvard urban economist on city-scale rebuilding. They debate where to concentrate reconstruction, balancing planning with individual choice. They compare decentralised Tokyo-style rebuilding to centralised approaches and discuss procurement, labour supply, and sequencing challenges.

Yuriy Gorodnichenko, a UC Berkeley macroeconomist focused on Ukraine's growth, and Maurice Obstfeld, a former IMF chief now at Berkeley, debate rebuilding strategy. They outline a $40B a year investment plan, argue for deep debt restructuring or forgiveness, and discuss directing funds into productive FDI, institutional safeguards, and using frozen Russian assets as financing.

Mario Carrillo, researcher at Universitat Autònoma de Barcelona studying political economy and leadership psychology. He explores what makes a leader 'alpha' and why such traits attract voters. He discusses how alpha traits show up in business and politics, methods for measuring them from faces, global trends in strongman leaders, and how alpha pairs can heighten trade frictions and tensions.

Tom Schwantje, economist at Bocconi who studies management in low-income settings, and Simon Quinn, economist at Imperial College and CEPR focused on organizational behavior, discuss a studio experiment with Ethiopian young professionals. They explore how management traits were measured with recorded vignettes. They describe four distinct managerial styles and reveal which types employers and employees tend to prefer.