We study how monetary policy shapes macroeconomic outcomes in a two-sector small open economy hit by export shocks —due, e.g., to export tariffs, geopolitical tensions, or a recession in destination countries— allowing the shock to have both aggregate and distributional effects. Imperfect worker mobility across sectors, coupled with incomplete markets against aggregate and idiosyncratic shocks, implies that export contractions (i) spill over across sectors due to households’ precautionary response and (ii) affect income and consumption inequalities within and across sectors —in addition to their usual asymmetric effects on sectoral outputs and wages. In this context, exchange-rate flexibility provides insurance against inefficient fluctuations in consumption inequality, which increases the social value of floating-rate regimes. Relative to a nominal exchange-rate peg, flexible inflation targeting helps mitigate the rise in consumption inequality after an export contraction, especially among tradable-sector workers. However, even flexible inflation targeting does not in general provide sufficient exchange-rate flexibility relative to the optimal monetary policy.
We analyze the fiscal and welfare implications of the size of public debt in stochastic OLG models with distortionary taxation. The government borrowing rate is realistically sensitive to debt issuance and lower than the growth rate. The risky rate is much higher due to convenience benefits of public debt, idiosyncratic return risk, and aggregate risk. Although free-lunch deficits can reduce tax distortions, welfare-maximizing debt (WMD) is considerably lower than deficit-maximizing debt (DMD) in our baseline model calibrated to the US economy. A detailed decomposition of ex ante welfare reveals the forces shaping WMD, the strongest being the positive risk-sharing effect and the negative crowding-out effect. We identify key drivers, such as pension policy or risk premia, and quantify their differential impact on WMD and DMD. Extending the model to account for market power substantially reduces WMD. When wealth inequality is included in the model, the rich favor much higher debt than the middle class.
How do import tariffs affect retail prices? We combine daily product-level posted prices from seven major Canadian retailers with product-level tariff exposure to estimate tariff effects in a difference-in-differences framework. Prices of tariffed goods rose gradually, peaking at 6% after three months, implying pass-through of roughly one quarter of the 25% tariff. We find little spillover to untariffed substitutes and a rapid reversal of price effects after tariff removal. Adjustment occurred mainly through the frequency of price changes. Pass-through shifted with trade-policy news and was larger for products labeled “Tariffed”, showing that tariff-induced inflation depends on policy expectations and tariff salience.
This paper studies the effects of replacing the Earned Income Tax Credit (EITC) with a Universal Basic Income (UBI) in an environment where EITC take-up is incomplete. In contrast, UBI ensures full participation by design, extending benefits to households who do not take up the EITC, at the cost of higher fiscal expenditures. I study this trade-off between expanded coverage and fiscal cost using a life-cycle model with an endogenous EITC participation decision that replicates the observed eligibility and take-up patterns. The results show that a generous UBI ($12,000 annually) reduces welfare due to the substantial fiscal burden it entails, whereas a modest UBI ($3500 per year) improves welfare by reaching previously uncovered households at only a moderate fiscal cost. These findings highlight the importance of the program participation margin in evaluating policy reforms.
This paper documents five empirical facts about the role of strategic complementarities in firms’ price-setting behavior, using administrative data on Chilean firms. (1) Strategic complementarities play a dominant role in price setting, exerting a stronger influence than changes in marginal costs. (2) While the strength of strategic complementarities varies across sectors, it consistently outweighs the role of cost changes. (3) In high-inflation environments, firms become more responsive to changes in their competitors’ prices. (4) Firms respond more strongly to competitors’ price increases than to price decreases, mirroring the ‘rockets and feathers’ phenomenon of costs. (5) Strategic complementarities are stronger among firms with fewer competitors, larger market shares, and broader customer bases. These findings suggest that strategic complementarities — a source of real rigidities — are quantitatively important, state-dependent, asymmetric, and shaped by market structure.
Empirical evidence commonly cited as indicating that inflation expectations have become better anchored includes the declining sensitivity of expectations to inflation surprises over time, particularly around the adoption of inflation targeting. These patterns are typically attributed to the influence of explicit or implicit inflation targets on inflation expectations. We show that this evidence is consistent with a model of experience-based learning in which individuals learn solely from their life-time history of realized inflation, without anchoring their expectations to an announced inflation target. In this model, the prolonged experience of low short-run inflation persistence in the pre-COVID decades renders long-run expectations insensitive to inflation surprises, matching the patterns observed in empirical anchoring tests. A unique prediction of the experience-based learning model is also borne out in the data: the decline in surprise sensitivity since the 1980s is strongest among younger individuals. The memory of low inflation persistence experiences further explains why long-run inflation expectations remained stable in the face of the post-COVID inflation surge. At the same time, simulations indicate that the sensitivity of long-run expectations to inflation surprises would rise sharply if individuals were to experience another sustained episode of highly persistent inflation. Overall, long-run inflation expectations may be less firmly anchored than commonly believed.
We examine how policymakers’ speeches and monetary policy announcements at official policy meetings transmit to financial markets and the real economy in the euro area. Using high-frequency intraday data across a broad cross-section of financial assets, we introduce the Euro Area Extended Monetary Policy Event-Study Database (EA-EMPD). We refine the identification of monetary policy surprises by exploiting granular, quote-level data on individual market participants’ bid and ask quotes. This novel dataset expands the set of identifiable policy events by an order of magnitude relative to databases restricted to rate-setting meetings. Our analysis yields three main findings. First, central bank speeches move asset prices across all maturities by magnitudes comparable to those of official policy announcements. Second, the relative importance of surprises associated with policy decisions and speeches differs markedly between euro area and U.S. financial markets. Third, speech-induced short-rate shocks transmit to the real economy similarly to official policy shocks, and combining the two sources of policy shocks materially improves the precision of inference. Importantly, even under this much broader definition and measurement of monetary policy impulses, monetary policy shocks account for only a negligible share of fluctuations in real economic activity.
Between 2021 and 2024, the United States experienced one of the most severe inflation episodes in decades, coinciding with renewed debate over the role of economic conditions for electoral outcomes. This paper studies the political economy of inflation and real wages using U.S. county-level data on family budget costs, nominal income, and electoral results for President and Congress during this period. Exploiting within-state, cross-county variation in changes in local prices over time, it examines how inflation, real wage growth, and purchasing power relate to changes in vote shares of incumbents, vote margins, and turnout. Real wage decline, rather than higher inflation itself, is predictive of Republican electoral gains, in line with an economic voting rationale. Inflation, however, retains an association with presidential vote shares beyond pure economic voting.
This paper develops a general equilibrium framework to examine the welfare implications of aggregate-demand information derived from payment data. The disclosure of such information tends to improve aggregate welfare when interest rates are low but may reduce welfare when rates are high. If a central bank controls information disclosure, it can craft noisy messages that encourage investment and benefit welfare through a form of Bayesian persuasion. If the information is instead produced by private banks and traded in a competitive market, excessive information production may happen. Finally, privacy concerns may dissuade some consumers from using digital payments, thereby decreasing the precision of aggregate-demand information, but it need not reduce welfare.
Asymmetries play an important role in many macroeconomic models. We show that assumptions on household and firm expectations play a key role in determining the effects of these asymmetries on macroeconomic outcomes. If households and firms have perfect foresight and hence do not account for the possibility of future shocks, then the implied longer-run averages and distributions can differ significantly from their rational expectations counterparts. We first derive this result analytically in a standard New Keynesian environment under either an asymmetric monetary policy rule or a nonlinear Phillips curve before numerically examining some of the key nonlinearities featured in the recent literature.
We develop a framework for decision-making under model uncertainty, grounded in Wasserstein distributionally robust optimization. In contrast to standard Kullback-Leibler approaches constrained by absolute continuity, Wasserstein ambiguity sets constructed via optimal transport permit the adversary to physically relocate probability mass to new states, capturing economically relevant support-shifting distortions-structural breaks, regime shifts, and catastrophic tail events-that the reference model excludes entirely. We integrate these state-shift distortions into both static and recursive dynamic decision problems, where agents optimize against the worst-case distribution within a transport budget. A data-driven bootstrap calibration procedure disciplines the degree of robustness by linking the ambiguity radius to statistical confidence sets. Applying the framework to asset pricing, we show that endogenous belief distortions amplify perceived variance, inducing precautionary savings that lower the risk-free rate and widen the equity premium. The geometric uncertainty propagates through persistent state dynamics, generating endogenous stochastic volatility akin to long-run risk. The resulting pricing kernel aligns with empirical Hansen-Jagannathan bounds without requiring implausibly high risk aversion or non-standard preferences.
U.S. product markets have become more concentrated since the late 1990s, with higher markups, weaker entry, and slower productivity growth. This paper studies whether consolidation in the U.S. banking sector has contributed to these trends. Empirically, I show that higher bank concentration is associated with rising product market markups, lower entry, and a reallocation of market power toward large incumbents. I interpret these patterns in a quantitative endogenous growth model with imperfect bank competition and heterogeneous firms. The key mechanism is a widening loan-deposit spread: even as interest rate levels decline, greater bank market power increases the wedge between the borrowing costs faced by small bank-dependent firms and the opportunity cost of funds faced by large internally financed firms, shifting competition toward large incumbents and weakening innovation incentives. In calibrated counterfactuals, the rise in bank concentration explains about 14% of the increase in product market concentration and 5% of the slowdown in productivity growth over the post-2000 period.
Nowadays, many producing firms do not hire workers, but rent them from “staffing agencies”, whose main activity is to find, employ, and rent out workers to producing firms. That way, producing firms do not bear the direct costs of search and instead rent workers in a competitive market. We employ standard search-and-matching theory to analyze this phenomenon, with producing firms that have decreasing returns to scale. We find that the mere presence of staffing agencies has an important impact on the bargaining between the firms and their hired, “in-house” workers: firms can strategically use rented workers to weaken the bargaining position of in-house workers, tilting bargaining in their own favor. This has first-order effects on wages and employment. If rented and in-house workers are identical in production and in search markets, the unique equilibrium is one where producing firms only use rented workers. If renting workers involve additional costs, the equilibrium features an interior solution where each firm procures workers from two sources.
This paper documents how racial differences in labor income may simultaneously explain both crime and wealth disparities between Black and White individuals. Using an overlapping generations model that endogenously determines crime rates alongside consumption and savings decisions, we find that equalizing labor incomes results in a significant decline in both the Black crime rate and the proportion of Black individuals in the lowest wealth quintile. Higher crime and incarceration rates of Black individuals, on the other hand, do not significantly contribute to their low wealth. This is primarily because most crimes are committed by already poor and young individuals who are not saving in any case.
Mihet et al. (2025) offer a substantive contribution to our understanding of the mechanisms driving market power in the age of artificial intelligence (AI) and big data. The paper expands the horizon in three ways, as I see it. First, the paper takes a deep dive into the difference between “raw” and “processed” data, highlighting the cost of possessing the raw data and the ability of firm-level AI capability to turn raw data into processed data. This distinction among raw data, AI capability, and processed data links the literature on information entropy and the value of data, as I discuss below. A second contribution is the modeling of a secondary market for trading processed data among firms, which is highly relevant for policy discussion on how to facilitate and regulate data sharing platforms, such as via API or data vendors. Finally, the paper makes an effort to empirically test the model’s implications for how improvements in firms’ ability to obtain raw versus processed data offer opposite predictions on industry concentration. Below, I comment on each contribution by benchmarking it against the current literature.
The COVID-19 pandemic led to unprecedented disruptions in global supply chains (GSC), coupled with large fiscal stimulus. This paper employs a proxy structural VAR model to examine GSC shocks, the Federal Reserve's response, and their propagation under two counterfactual policy rules. Large fiscal stimulus amplifies inflation while cushioning the downturn from GSC shocks. Historically, the Fed has looked through price surges and adopted a loose stance. The first counterfactual, which stabilises inflation, entails less accommodation and yields a more favourable inflation-output trade-off, reflecting greater price flexibility and limited output losses. The second, which minimises a dual-mandate loss function under inflation targeting (IT) or average inflation targeting (AIT), calls for greater initial easing. Relative to IT, AIT implies a looser policy that generates more persistent inflation and ultimately requires a contractionary response, worsening the trade-off.
This paper studies how rising returns to scale contributed to declining business dynamism and increasing markups and expenditures devoted to customer acquisition in the U.S. economy. It introduces a firm dynamics model with heterogeneous markups and customer accumulation based on directed search, in which larger firms gain a competitive edge from higher returns to scale. This makes markets less contestable for new firms and leads to the rise of superstar firms. The model quantitatively accounts for a substantial share of these trends, and the underlying micro-level mechanisms align with empirical evidence.
We use a large granular dataset to analyze the households’ choice between cash and card payments. Empirically, both the size of the transaction and the amount of cash on hand appear as significant covariates of the payment choice. We unveil a novel interaction between these two variables: the critical size for a card purchase depends on the amount of cash on hand. We present a tractable model of payment choices, featuring non-universal acceptance of cards by merchants, and a random expenditure flow. The model generates a precautionary motive for holding a cash buffer: cards are used to avoid “running out of cash”, accounting for the interaction discussed before. We use a calibrated version of the model to quantify the benefits of card ownership, the welfare costs of imperfect card acceptance by merchants, and to identify conditions under which a cashless economy emerges.
