S9 Ep20: What triggered January 6?

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.

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 at a profit once the original transaction goes through. The cost falls on the smaller trader. Biais notes that the barriers to entry and economies of scale in this activity have concentrated power in the hands of a small, technically skilled group, recreating the kind of intermediary rents that decentralised finance was designed to eliminate.Automated market makers are smart contracts that provide continuous liquidity for trading between two assets by holding reserves of both in a pool and setting prices according to the ratio of the reserves. A large purchase of one asset depletes that side of the pool and raises its price; a large sale depresses it. Automated market makers have become a central mechanism of decentralised finance, replacing the order-book systems used in traditional exchanges.Stablecoins are cryptocurrency tokens designed to maintain a fixed value relative to a conventional currency, typically the US dollar. They are issued by private entities that hold reserves intended to back the peg. Tether, the largest stablecoin by market capitalisation, holds its reserves in a mix of Treasury bills, Bitcoin, and precious metals; in 2021, the US Commodity Futures Trading Commission fined Tether for misrepresenting those reserves and required it to disclose their composition, making this information publicly available for the first time. Dai is an algorithmically managed stablecoin that maintains its peg through over-collateralisation in cryptocurrency rather than conventional reserves.The Diamond-Dybvig model is a theoretical framework developed by Douglas Diamond and Philip Dybvig explaining why financial intermediaries that hold illiquid assets while issuing liquid claims are inherently vulnerable to runs. When enough depositors demand withdrawal simultaneously, the institution is forced to sell assets at a loss, making further withdrawals impossible and confirming the fears that triggered the run. Biais applies this logic to stablecoins: if enough holders attempt to redeem simultaneously, the issuer must sell its reserves in volume, driving down their price and potentially breaking the peg.Central bank digital currencies (CBDCs) are digital tokens issued and managed by central banks, distinct from both commercial bank deposits and private stablecoins. Biais distinguishes two potential use cases: retail CBDCs, which would allow individuals to hold central bank money directly, and wholesale CBDCs, which would facilitate settlement between large financial institutions. He regards the wholesale application as the more promising; a wholesale CBDC could enable fast, low-cost atomic settlement of cross-currency transactions between banks under central bank oversight, a significant improvement on current interbank settlement systems.MiCA (Markets in Crypto-Assets Regulation) is the European Union's regulatory framework for crypto-asset service providers, which came fully into force in December 2024. It requires licensing for issuers and service providers operating within the EU and imposes disclosure, reserve, and conduct requirements intended to align the sector more closely with the standards applied in traditional financial markets.Hayek's currency competition refers to the argument by Friedrich Hayek that competition between privately issued currencies would discipline monetary policy: users would switch away from currencies managed irresponsibly, and that threat would encourage better central bank behaviour. Biais applies this argument to cryptocurrencies and stablecoins in countries where the domestic currency has been mismanaged. He cites Nigeria, where sharp depreciation of the naira was accompanied by rising crypto adoption; over the following period, Nigeria's central bank raised interest rates and created a more transparent foreign exchange market. Biais suggests, tentatively, that the competitive pressure from crypto alternatives may have contributed to that improvement.More VoxTalks EconomicsDo stablecoins threaten financial stability? Stablecoins are digital tokens, pegged to a fiat currency. What could possibly go wrong? For one type of stablecoin the answer is: plenty, according to Richard Portes. In coin we trust Crypto investors make a lot of noise, but who are they, and do they behave differently to other retail investors?Do cryptocurrencies matter? Can cryptocurrencies be useful? Not just for crypto bro speculators, but as a shield against the depreciation of the official currency if a government is determined to pursue inflationary policies.

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. 

Financial repression forces banks and citizens to hold government debt on terms the market would never accept. Economists have called it distortionary for fifty years. It never went away.Oleg Itskhoki and Dmitry Mukhin study what happens when a government runs out of options. Their paper traces how Russia deployed financial repression in 2022 to survive the largest sanctions package in postwar history. The ruble was in freefall; banning cash withdrawals and forcing exporters to hand over foreign currency revenues stopped the crisis. The measures worked because Russia kept earning export income, and the sanctions never closed that tap. But with government debt in advanced economies now at historic highs, financial repression is no longer confined to authoritarian regimes under siege. It is a path of least resistance for a government that would rather suppress the symptoms of unsustainable debt than carry out the fiscal reforms needed to fix it.The research behind this episode:Itskhoki, Oleg, and Dmitry Mukhin. 2026. "Sanctions, Capital Outflows, and Financial Repression." Economic Policy: Papers on European and Global Issues.To cite this episode:Phillips, Tim. 2026. "Sanctions, Capital Outflows, and Financial Repression." Economic Policy: Papers on European and Global Issues (podcast).Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestsOleg Itskhoki is a professor of economics at Harvard University. His research spanning international macroeconomics, exchange rates, capital flows, and financial frictions has reshaped how economists think about currency crises and the limits of open-economy models. He received the John Bates Clark Medal from the American Economic Association in 2022.Research cited in this episodeThe Washington Consensus was the post-Cold War policy framework, closely associated with the International Monetary Fund and the World Bank, that advocated free capital markets and discouraged government intervention in exchange rates or cross-border capital flows. Under this framework, financial repression was considered illegitimate; the goal was a more market-oriented, liberal macroeconomic order. As Itskhoki notes, the consensus has frayed considerably since the 2008 financial crisis, and the IMF now endorses certain forms of capital flow management under specific circumstances, though the broader norm against persistent financial repression remains.Financial repression is any government intervention that distorts the private financial decisions of domestic agents. In its traditional form, it meant forcing the banking sector to hold government debt at below-market returns, crowding out private investment and reducing the fiscal cost of high debt levels. The term covers a wide range of tools: restrictions on cash withdrawals, requirements that exporters convert foreign currency revenues to the central bank, interest rate ceilings, and policies designed to prevent citizens from holding savings in foreign currencies. Itskhoki distinguishes between its use in normal times (which he regards as distortionary and unjustified except as a last resort) and its deployment in emergencies such as financial crises, bank runs, or external sanctions, where it may be the only available stabilising instrument.Capital controls are government restrictions on cross-border capital flows. They are related to but distinct from financial repression: capital controls concern what money can cross borders; financial repression concerns what domestic agents can do with money at home. The two are often deployed together under external pressure.Dollarization describes the tendency of households and businesses in economies with weak or unstable currencies to save and transact in foreign currency, typically US dollars, rather than the domestic currency. Governments often use financial repression to discourage dollarization, restricting access to foreign currency holdings domestically. Itskhoki notes this is one of the many forms the policy takes beyond its traditional debt-management role.Russia's use of financial repression after the 2022 sanctions. Following the invasion of Ukraine in February 2022, Western governments imposed an unprecedented package of financial sanctions, trade restrictions, and asset freezes. The ruble depreciated sharply. Russia's response included a tax on foreign currency purchases, mandatory conversion of exporters' foreign currency revenues to the central bank, and direct restrictions on cash withdrawals from bank accounts. The ruble stabilised and recovered within weeks. Itskhoki argues the measures succeeded in the short term not because financial repression is inherently powerful against sanctions, but because the sanctions failed to close off Russian export income; Russia kept receiving substantial foreign currency from energy sales, reducing the pressure on the tools of repression. The structural gap in the sanctions regime was the failure to curtail Russian export revenues.The "What's Next for Ukraine?" seriesListen to our three-part series based on papers presented at the 1st Economic Policy: Papers on European and Global Issues Conference, Paris, December 2025.Giacomo Anastasia, Tito Boeri, and Oleksandr Zholud: what the data from Ukraine's wartime labour market reveal about employment, displacement, and the economic costs of the war. Also in the series: Maurice Obstfeld and Yuriy Gorodnichenko on financial inflows, integration, and the growth prospects of a westward-facing Ukraine. Also in the series: Edward Glaeser, Martina Kirchberger, and Andrii Parkhomenko on how to rebuild Ukraine's cities, and why the choice of what to reconstruct matters as much as the scale of investment. 

Ukraine has lost close to a quarter of its civilian workforce since the invasion. Three and a half million workers left government-controlled areas: mobilised into the armed forces, displaced inside the country, gone abroad as refugees, or killed. Giacomo Anastasia, Tito Boeri, and Oleksandr Zholud draw on an unprecedented wartime dataset to document how Ukraine's labour market adapted under that pressure. What they find is not what you might expect. Aggregate matching efficiency fell by only about 15%; less than the decline recorded in the United States during the 2008 financial crisis. Firms hired women into roles previously closed to them by law, took on older workers and people with disabilities, and expanded remote work to keep displaced employees and refugees connected to Ukrainian payrolls. The collapse was real, but concentrated: in contested territories near the frontline, employment fell to less than half its pre-war level and vacancy postings dropped to virtually zero. The question the paper poses for reconstruction is how to sustain that resilience, absorb close to a million returning soldiers, and begin to reverse what five years of disrupted schooling has done to a generation.The research behind this episode:Anastasia, Giacomo M., Tito Boeri, and Oleksandr Zholud. 2026. "A Wartime Labor Market: The Case of Ukraine." Economic Policy: Papers on European and Global Issues, special issue: "What's Next for Ukraine?"To cite this episode:Phillips, Tim. 2026. "What's Next for Ukraine: A Wartime Labour Market." Economic Policy: Papers on European and Global Issues (podcast).Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestsGiacomo Anastasia is a PhD student in Economics at Columbia University and Columbia Business School. His research interests include public economics, labour economics, and industrial organisation.Tito Boeri is Professor of Economics at Bocconi University and one of Europe's leading authorities on labour markets, unemployment insurance, and welfare state reform. He served as President of INPS, Italy's national social security institution, from 2015 to 2019.Oleksandr Zholud is a researcher at the National Bank of Ukraine. He was central to maintaining the economic data systems that continued to function through the war, and which made the empirical work in this paper possible. Research cited in this episodeThe civilian labour force contraction is estimated at roughly twenty to twenty-five per cent of the pre-war workforce in government-controlled areas, equivalent to a loss of around 3.5 million workers. The calculation combines refugees abroad (between six and seven million, of whom approximately seventy per cent are of working age), military mobilisation (at least 800,000 since 2022, up from 250,000 before the war), and combat casualties. The authors note that a shock of this scale has almost no modern precedent; the closest comparisons are Serbia's losses in the First World War and the economic disruption caused by the 1994 Rwandan genocide.Work.ua is the largest online job-search platform in Ukraine, covering around 125,000 firms and 4.5 million workers. The paper draws on weekly data from Work.ua on vacancy postings, job-seeker resumes, and offered and expected wages to track labour market dynamics across sectors and regions throughout the war. This platform data continued to be updated through the conflict and provided the primary source for the paper's matching analysis, replacing the State Statistics Service household survey, which suspended publication after the invasion.The InfoSapiens household survey, commissioned by the National Bank of Ukraine since 2021, serves as the wartime replacement for the State Statistics Service quarterly Labour Force Survey. It interviews around 1,000 individuals per quarter on employment, unemployment, and labour force participation, stratified by gender, age, region, and settlement size. Despite its smaller sample, it remains the primary regular survey-based source on Ukraine's labour market since the full-scale invasion.The State Employment Service (SES) firm survey, conducted in January 2025 in cooperation with Helvetas Swiss Intercooperation, covered 55,000 enterprises employing 4.2 million workers plus 70,000 registered unemployed persons. This cross-sectional survey provided the paper's evidence on how recruitment practices, remote work adoption, and workforce composition changed after the invasion; it is described in the paper as one of the largest wartime enterprise surveys of its kind.Air raid alarm data are used as the paper's proxy for regional exposure to the war. When missiles or drone attacks are detected, sirens activate across affected areas; the authors use the frequency and duration of these alarms to classify Ukrainian regions on a spectrum from low-exposure (western oblasts such as Lviv) to high-exposure (eastern regions such as Kharkiv) to contested (partially or fully occupied territories including parts of Donetsk and Luhansk). This classification is the basis for the paper's finding that war intensity is the primary driver of differences in labour market outcomes across regions.Matching efficiency is a standard labour economics measure of how effectively the market converts a given stock of unemployed workers and open vacancies into new hires. A fall in matching efficiency means that jobs and workers exist but find each other more slowly. The paper estimates that Ukraine's aggregate matching efficiency declined by about fifteen per cent after the invasion; a smaller fall than the more than twenty per cent recorded in the United States during the 2008 financial crisis, though with severe deterioration concentrated in frontline and contested regions, where matching efficiency dropped by close to twenty-five per cent.Remote work as a retention mechanism. A survey of Ukrainian refugees abroad found that roughly forty per cent of those in employment were working for Ukrainian firms remotely. Those maintaining an employment link to a Ukrainian company reported a significantly higher intention to return to Ukraine after the war compared with refugees employed by foreign firms. Anastasia argues this makes remote work not only an economic adaptation but a tool for sustaining the connection between displaced workers and the country they may one day return to rebuild.More in the "What's Next for Ukraine?" seriesThis episode is the third and final in a series based on papers presented at the inaugural Economic Policy winter conference, Paris, December 2025.Episode 1, with Yuriy Gorodnichenko and Maurice Obstfeld: why $40 billion a year in investment is more achievable than it sounds, why deep debt restructuring is a prerequisite for attracting private capital, and what the Euroclear frozen assets could unlock. Episode 2, with Edward Glaeser, Martina Kirchberger, and Andrii Parkhomenko: why the right model for rebuilding Ukraine's cities is postwar Tokyo rather than postwar Berlin or Warsaw, and why directing reconstruction spending towards the most damaged regions would be rebuilding in the wrong direction. Related reading on VoxEUThe labour market in Ukraine: Rebuild better, the companion VoxEU column by Anastasia, Boeri, and Zholud, summarising the paper's findings on matching efficiency, firm adjustment, and the policy priorities for reconstruction. You only live twice: A growth strategy for Ukraine, Gorodnichenko and Obstfeld's companion column to Episode 1, making the case for $40 billion a year in investment and explaining why EU and NATO accession momentum is the key enabling condition.Rebuilding cities in Ukraine, a VoxEU column on the spatial and urban decisions that will shape how Ukraine's cities develop in the decades after the war, and why the Tokyo model of decentralised land readjustment is the right precedent.

Ukraine's cities were failing long before the Russian invasion began. Kyiv and Lviv ranked among the 40 most congested cities in the world, yet neither makes the top 100 by population. Ninety per cent of Ukraine's housing stock was built before 1990. Its urban infrastructure was designed for a Soviet economy and never properly adapted for the one that followed. So when reconstruction begins, the question is not simply how to repair what was there: it is whether repairing what was there is the right goal.Edward Glaeser of Harvard, Martina Kirchberger of Trinity College Dublin, and Andrii Parkhomenko of the University of Southern California argue that the most instructive precedent is not post-USSR Warsaw, or postwar Berlin, it is postwar Tokyo. Firebombed into ruin, Tokyo rebuilt in a way that was strikingly decentralised: master plans quickly abandoned, local communities empowered to combine small lots through land readjustment, and figure it out from the bottom up. Before the war, Ukraine's economic activity was already shifting away from heavy industry and the east, towards services and the west. Reconstruction that concentrates investment where the damage is greatest, rather than where people want to build a new life, would repair the buildings and miss the point.The research behind this episode:Glaeser, Edward L., Martina Kirchberger, and Andrii Parkhomenko. 2025. "Rebuilding Ukraine's Cities: Maximizing Benefits and Minimizing Costs." Economic Policy: Papers on European and Global Issues, special issue: "What's Next for Ukraine?" To cite this episode:Phillips, Tim. 2026, "What's Next for Ukraine: Reconstruction." Economic Policy: Papers on European and Global Issues (podcast). Assign this as extra listening: the citation above is formatted and ready for a reading list or VLE.About the guestsEdward Glaeser is Fred and Eleanor Glimp Professor of Economics at Harvard University and a Research Associate of the National Bureau of Economic Research. He is one of the world's leading urban economists, with a research agenda spanning cities, housing markets, economic growth, and governance.Martina Kirchberger is a CEPR Research Affiliate and Assistant Professor in Economics at Trinity College Dublin. Her research focuses on structural transformation, urban economics, and development in low- and middle-income countries.Andrii Parkhomenko is Assistant Professor of Real Estate at the USC Marshall School of Business and a researcher at the Kyiv School of Economics. His work centers on urban and spatial economics, with a particular focus on housing markets and city growth.Research cited in this episodeUkraine Rapid Damage and Needs Assessment, World Bank Group, European Commission, and UN, 2024. The source of the physical damage figure cited in this episode: approximately $175 billion by the end of 2024, with estimates for end-2025 likely exceeding $200 billion. Some independent projections cited by Glaeser run to $500 billion or above.The concept of investing-in-investing, referenced by Kirchberger, originates in work by Paul Collier on how resource-rich developing countries can scale up capital investment effectively. It refers to the prior investments in institutions, skills, and capacity that must be made before large-scale capital flows can be productively absorbed. The implication for Ukraine: there is work to do now, before reconstruction begins at scale.The Tokyo land readjustment model, which Glaeser cited as the most instructive reconstruction precedent, allowed owners of small fragmented lots to pool their land, redevelop it jointly, and receive a share of the new property in exchange for their stake in the old. It enabled large-scale urban reconstruction without central expropriation, and without waiting for government direction. The mechanism remains in active use in Japanese urban planning.The Solidere reconstruction of central Beirut was raised as a cautionary counterexample: a centralised, top-down rebuild that produced a high-end commercial district with questionable benefit to ordinary Lebanese, and which substantially enriched its private shareholders. The contrast with Tokyo's decentralised model is the episode's sharpest illustration of what reconstruction can and cannot achieve when organised from above.More in the "What's Next for Ukraine?" seriesThis episode is the second in a three-part series based on papers presented at the inaugural Economic Policy winter conference, Paris, December 2025.Episode 1: Yuriy Gorodnichenko and Maurice Obstfeld on the investment and financing challenge: $40 billion a year, debt restructuring as a prerequisite for private capital, and why the number is more achievable than it sounds.Episode 3: Demobilisation and the labour market: getting soldiers back into work without breaking the economy that kept the country going. Related reading on VoxEURebuilding cities in Ukraine: A VoxEU column on the urban reconstruction challenge, including the spatial decisions that will shape how Ukraine's cities develop in the decades after the war.A blueprint for the reconstruction of Ukraine: A comprehensive VoxEU overview of the reconstruction architecture: what institutions are needed, how international financing can be coordinated, and what the sequencing of investment should look like.Completing Ukraine's reconstruction architecture: On the remaining gaps in the international framework for financing and coordinating Ukraine's rebuild, and what needs to happen before reconstruction can begin at the required scale.Lessons for rebuilding Ukraine from economic recoveries after natural disasters: What the evidence from post-disaster reconstruction in other countries tells us about what works, what fails, and how quickly economies can return to their pre-shock trajectories.

Ukraine will emerge from this war with enormous debt. The conventional wisdom treats that as an obstacle: investors weigh it before committing capital, and the burden slows the recovery before it starts. Yuriy Gorodnichenko and Maurice Obstfeld of UC Berkeley argue the opposite. A thorough restructuring of Ukraine's war debts – including, for sufficiently large obligations, outright forgiveness – is not just politically defensible but economically essential for attracting private investment. The bill for rebuilding and growing Ukraine, Gorodnichenko estimates, is $40 billion a year: $20 billion to replace destroyed capital, $10 billion to stop Ukraine falling behind its Eastern European peers, and $10 billion to start closing the gap. Put that figure next to what Poland absorbed in FDI during its post-communist transition, or the €200 billion of Russian state assets currently immobilised in Euroclear, or the budgetary support Ukraine has been receiving since 2022 – and it looks achievable. The harder challenge, they argue, is not raising $40 billion. It is directing it: towards investment rather than consumption. Ukraine didn’t grow in the post-Soviet era at the rate that its neighbours achieved. EU accession momentum and secure borders can be a signal to investors that this time the trajectory will be different.The research behind this episode:Gorodnichenko, Yuriy, and Maurice Obstfeld. 2026. "You Only Live Twice: Financial Inflows and Growth in a Westward-Facing Ukraine." Economic Policy: Papers on European and Global Issues, special issue: "What's Next for Ukraine?"To cite this episode:Phillips, Tim. 2025. "You Only Live Twice: Financial Inflows and Growth in a Westward-Facing Ukraine." Economic Policy: Papers on European and Global Issues (podcast).Assign this as extra listening — the citation above is formatted and ready for a reading list or VLE.About the guestsYuriy Gorodnichenko is a CEPR Research Fellow and Professor of Economics at the University of California, Berkeley, where he leads CEPR's Ukraine Initiative. His research spans monetary policy, fiscal policy, and the macroeconomics of growth and business cycles.Maurice Obstfeld is a CEPR Distinguished Fellow and Class of 1958 Professor of Economics at the University of California, Berkeley. He served as Chief Economist of the International Monetary Fund from 2015 to 2018, and as a member of the Council of Economic Advisers under President Obama from 2014 to 2015. He is also a Fellow of the Econometric Society and the American Academy of Arts and Sciences.Research cited in this episodeThe discussion of debt overhang draws on a body of work from the 1980s developing-country debt crises, notably the insight that for sufficiently indebted countries, debt reduction can increase the expected value of what creditors recover. Gorodnichenko and Obstfeld apply this framework directly to Ukraine's war debts, arguing that deep restructuring – supported by bilateral official creditors, many of whom are European – is a prerequisite for private investment to follow.The €200 billion figure for immobilised Russian central bank assets held at Euroclear is the basis for Obstfeld's proposal of a reparations loan that would give Ukraine immediate access to large-scale resources, with repayment contingent on Russian reparations. This is discussed in more detail in the related reading below.More in the "What's Next for Ukraine?" seriesThis episode is the first in a three-part series based on papers presented at the inaugural Economic Policy winter conference, Paris, December 2025. Episodes 2 and 3, on rebuilding and the labour market, are forthcoming.Related reading on VoxEUYou only live twice: A growth strategy for Ukraine — Gorodnichenko and Obstfeld's own VoxEU column summarising the key arguments in this paper: why $40 billion a year is achievable, what the policy levers are, and why the window matters.Euroclear and the geopolitics of immobilised Russian assets — The legal and financial context behind the €200 billion of Russian central bank assets frozen at Euroclear, and what it would take to use them for a reparations loan to Ukraine.Using the returns of frozen Russian assets to finance the victory of Ukraine — A VoxEU proposal for channelling the interest income generated by frozen Russian assets to finance Ukraine's needs, without requiring the more politically contested step of confiscating the assets themselves.Ukraine's recovery challenge — An earlier VoxEU overview of the reconstruction task: the scale of damage, the role of EU accession, and the two-phase approach to restoring growth.

Recorded live at the CEPR Annual Symposium. We seem to be talking about the behaviour of alpha males on social media a lot recently. But what happens when we put them in charge of a country? The work of Mario Carillo of Universitat Autònoma de Barcelona attempts to answer that question. He talks to Tim Phillips about when and why voters choose alpha males, and how they respond to being given power.

What type of manager would you be? An experiment in Ethiopia set out to measure the management traits of young professionals by setting them challenges in a video studio, and along the way also uncovered valuable (and surprising) information about the type of manager that employees and employers preferred.Simon Quinn of Imperial College London and CEPR and Tom Schwantje of Bocconi University were two of the researchers. They tell Tim Phillips about why it is important to develop better managers, and how we might do that for young professionals.

Recorded live at the CEPR Annual Symposium in Paris. When VoxTalks Economics visits a symposium or conference, we try to find the most interesting new research from economists who are just starting out in their careers. In Paris we invited three of them to the CEPR Office to tell us about their work.In this episode, Tim Phillips talks to Lucie Giorgi, Aix-Marseille School of Economics (AMSE), whose research tracks the impact of sex segregation in French elementary schools; Alishuba Philip of the University of Zurich, who has investigated why slum redevelopment often doesn’t benefit the people who live there, and Ali Bakhtawar – also of AMSE – about Lawfare in Pakistan.