We show an equivalence result in the representative-agent New-Keynesian model after demand, wage-markup and correlated price-markup and TFP shocks: assuming sticky prices and flexible wages yields identical allocations for GDP, consumption, labor, inflation and interest rates to the opposite case—flexible prices and sticky wages. This equivalence arises with identical price and wage Phillips-curve slopes and generalizes to any slopes' pair whose sum and product are identical. Equilibrium profits and wages are, however, substantially different; equivalence breaks when these factor-distributional implications matter for aggregate allocations, e.g. in New-Keynesian models with heterogeneous agents, endogenous firm entry, and non-constant returns to scale.

This paper develops a micro-founded framework linking price-cost and wage markups to intangible assets. Intangible assets, once created, are a source of firm rents. Owing to limits to enforceable ownership and the non-rival nature of knowledge, these rents can be both retained by the origin firm and transferred to a competitor through poaching of workers. Search and matching frictions affect labor mobility and result in bargaining over rents between the firm and the worker. This environment generates hold-up in intangible asset creation and motivates rent sharing. Under non-compete agreements, poached workers face start delays that weaken outside options. Using microdata from the Netherlands, we document higher price-cost and wage markups in more intangible-intensive firms and lower wages for workers with non-compete agreements, consistent with the model.

Firms increasingly rely on markets for technology to acquire innovations developed outside their boundaries, yet acquiring intellectual property rights alone often does not guarantee successful implementation. Many technologies depend on tacit know-how that must be supplied by the provider after the transaction is completed. This paper examines whether common ownership between a technology provider and a potential adopter mitigates this implementation problem. I develop a model in which overlapping institutional investors cause the provider to partially internalize the adopter’s gains from successful implementation, strengthening incentives to transfer tacit know-how. This mechanism operates only when know-how is unverifiable – absent this friction, common ownership leaves matching and outcomes unchanged. Under moral hazard, the model predicts that common ownership increases the likelihood of technology transfer to a given adopter, that this effect is stronger when tacit know-how is more important, and that common ownership improves post-transfer outcomes conditional on adoption. I test these predictions using U.S. patent reassignments between publicly traded firms. Using within-deal variation across competing potential adopters and plausibly exogenous variation from passive index-fund holdings, I show that common ownership increases the likelihood that a firm acquires a technology, particularly when the transferred bundle is more tacit. Common ownership predicts stronger subsequent innovation and higher future firm value, especially when ownership overlap is concentrated among investors with stronger incentives to monitor the provider. These findings show how ownership structure shapes interfirm technology transfer by affecting not only who acquires a technology, but also how much value is created.

We study how deposit competition affects a bank's decision to securitize mortgages. Exploiting the state-specific removal of deposit market caps across the U.S. as a source of competition, we find a 7.1 percentage point increase in the probability that banks securitize mortgage loans. This result is driven by an 11 basis point increase in deposit costs and corresponding reductions in banks' deposit holdings. Our results are strongest among banks that rely more on deposit funding. These findings highlight a hitherto undocumented and unintended regulatory cause that motivates banks to adopt the originate-to-distribute model.

This study provides new evidence on the impact of the EU Cohesion Policy on income growth in less developed regions. Our panel includes data from all European regions for the years 1989-2020. Using a fuzzy Regression Discontinuity Design, we model treatment dynamics by applying a random effects estimator. Based on digitized historical data, we precisely replicate the policy rule and correctly classify the regions’ eligibility status. Results show that the policy has a moderate positive effect on GDP per capita growth in the targeted regions.

This paper examines the macroeconomic and distributional effects of the European Union’s transition to Net Zero emissions through a gradually increasing carbon tax. I develop a New Keynesian Environmental DSGE model with two household types and distinct energy and non-energy sectors. Five alternative uses of carbon tax revenues are considered: equal transfers to households, targeted transfers to Hand-to-Mouth households, subsidies to green energy firms, and reductions in labor and capital income taxes. In the absence of technological progress, the carbon tax policy induces a persistent increase in energy prices and a reduction in GDP, investment, and consumption. Headline inflation falls below zero in the medium run, reflecting weaker aggregate demand. Distributional outcomes vary significantly depending on the implemented revenue recycling scheme: targeted transfers are the most progressive but entail larger macroeconomic costs, while subsidies and tax cuts mitigate output and investment losses but are less effective in narrowing the consumption gap. A limited foresight scenario, in which agents learn about policy targets sequentially, generates more volatile adjustment paths and temporary inflationary spikes around announcements, but long-run outcomes remain close to the baseline.

Climate policy design must balance emissions mitigation with concerns for fairness, particularly as climate change disproportionately affects the poorest households within and across countries. Integrated Assessment Models used for global climate policy evaluation have so far typically not considered inequality effects within countries. To fill this gap, we develop a global Integrated Assessment Model representing national economies and subnational income, mitigation cost, and climate damage distribution and assess a range of climate policy schemes with varying levels of effort sharing across countries and households. The schemes are consistent with limiting temperature increases to 2 °C and account for the possibility to use carbon tax revenues to address distributional effects within and between countries. We find that carbon taxation with redistribution improves global welfare and reduces inequality, with the most substantial gains achieved under uniform taxation paired with global per capita transfers. A Loss and Damage mechanism offers significant welfare improvements in vulnerable countries while requiring only a modest share of global carbon revenues in the medium term. The poorest households within all countries may benefit from the transfer scheme, in particular when some redistribution is made at the country level. Our findings underscore the potential for climate policy to advance both environmental and social goals, provided revenue recycling mechanisms are effectively implemented. In particular, they demonstrate the feasibility of a welfare improving global climate policy involving limited international redistribution.

Vietnam, a lower-middle-income economy, faces severe climate risks from heat waves, sea-level rise, and tropical cyclones, which are expected to intensify under ongoing global warming. Using a dynamic general equilibrium model, we analyze economic transition dynamics from 2015 to 2100, incorporating heat-induced labor productivity losses, agricultural land loss, and cyclone-related property damage. We compare a Paris-compatible scenario limiting warming to below 2 °C with a high-emission scenario reaching 4–5 °C. While output and investment impacts remain highly uncertain and statistically indistinguishable across scenarios until 2100, consumption losses are significantly larger under high emissions, mainly driven by heat-related productivity declines, with cyclones contributing most to uncertainty. These findings underscore the importance of considering multiple impact channels beyond output damages in climate-development research.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

This paper introduces a novel measure to quantify firms’ sensitivity to shifts in bilateral trade flows between the United States and its trading partners. We exploit the 2016 U.S. presidential election as an exogenous shock to trade policy expectations and assess the stock market reactions of firms across 52 countries. Our findings indicate that firms with higher trade policy sensitivity experienced significantly more negative stock returns surrounding the election. These results are robust to variations in event windows, return model specifications, and alternative estimations of trade policy sensitivity.