We explore the mechanics of empathy. We show that information about an outgroup can activate and magnify empathy when presented in conjunction with an experience simulating their struggles. This response increases the willingness to help the struggling group. We provide evidence for this effect in an immersive virtual reality experiment where participants (“witnesses”) experience a simulation of the struggle of unauthorized migrants (“protagonists”), then replicate these results in a series of controlled lab experiments. We show that information enhances the witnesses’ empathetic response and drives them to engage in more prosocial behavior when it increases their perceived interpersonal similarity, or relatability to the protagonist — an effect we trace to attention: eye-tracking data reveals that information provision concentrates witnesses’ gaze on the struggles of the protagonist instead of searching through peripheral elements of the scene. Conversely, only information packages that strengthen perceived relatability — an effect that can vary across subgroups with heterogeneous attributes — magnify empathy. Together, our evidence suggests that the ability to put oneself in the shoes of another person or group can be enhanced by activating empathy through simple, targeted, information provision.

In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an intangible asset to devise a tractable recursive representation of the data economy. The model rationalizes why apps are often “free” and why even non-digital economic activity might be greater than GDP suggests. Calibrating the model using a combination of macroeconomic and financial moments suggests that the mis-measurement in U.S. GDP due to missing value of data has been as high as 6% in 2018.

This paper studies the impact of income-based criminal punishments on crime. In Finland, speeding tickets become income-dependent if the driver’s speed exceeds the speeding limit by more than 20 km/h, leading to a substantial jump in the size of the speeding ticket. Contrary to predictions of a traditional Becker model, individuals do not bunch below the fine hike. Instead, the speeding distributions are smooth at the cutoff. However, I demonstrate that the size of the realized speeding ticket has sizable, but short-lived, impacts on reoffending. I use a regression discontinuity design to show that fines that are, on average, 200 euros larger decrease reoffending by 15 percent in the following six months. The drop in reoffending is driven by high-income individuals facing the highest fine at the cutoff. I estimate that a fixed fine hike that matches the current deterrence effect would raise the fine increase faced by the bottom income quartile by about 300% at the cutoff, relative to the increase they face under the current income-based schedule. My empirical results are consistent with an explanation that people operate under information frictions. To illustrate this, I construct a Becker model with misperception and learning that can explain all the empirical findings.

We study structural change in production networks for intermediate inputs (inputoutput network) and new capital (investment network). For each network, we document that the share of output produced by services (relative to goods) is rising over time. While the relative prices of services that produce intermediates and consumption are rising, we find that the relative price of services that produce investment is falling over time. We then develop a multi-sector growth model to study these trends and their implications for economic growth. To match the relative price trends, inputs to intermediates production are complements and inputs to investment production are substitutes. Hence, structural change endogenously reallocates resources to the slowest growing intermediates producers and the fastest growing investment producers. Growth accounting exercises reveal that investment-specific technical change accounts for an increasing share of U.S. aggregate growth, with 20% of aggregate growth since 2000 due to investment structural change. Growth projections from our model show that structural change within investment networks alone can offset stagnating or declining growth in other sectors due to Baumol’s cost disease.

Can markets discipline socially costly misbehavior abroad? We explore the market penalty associated with major human rights violations—specifically, the assassination of mining activists, a context where formal legal recourse is rare and events often do not involve counterparties. We show that firms featured in media coverage of these incidents experience significant, negative abnormal stock returns. Whereas reactions to related forms of corporate misconduct may be transitory or muted, we find that the market responses are both substantial and persistent, with a median 10-day loss exceeding USD 100 million. Since formal legal sanction is exceedingly rare, we consider the role of market penalties. We highlight three mechanisms that are consistent with reputational costs: (1) Media attention magnifies the market response. (2) Information-sensitive institutional investors systematically divest following assassination events. (3) Events reduce future trade, leading to a 19% decline in new contracts with counterparties. Despite these costs, events persist. We find that assassinations increase with dependence on mining royalties, suggesting that local rents sustain conflict despite market pressure. Thus, reputational sanctions may be significant—even in weakly institutionalized settings—yet may not fully deter misbehavior when local and global incentives diverge.

Using a heterogeneous agent model calibrated to match spending dynamics over four years following an income shock (Fagereng, Holm, and Natvik (2021)), we assess the effectiveness of three fiscal stimulus policies implemented during recent recessions. Unemployment insurance (UI) extensions are the “bang for the buck” winner when the metric is effectiveness in boosting utility. Stimulus checks are second‐best and have two advantages (over UI): they arrive faster, and are scalable. A temporary (two‐year) cut in wage taxation is considerably less effective than the other policies and has negligible effects in the version of our model without a multiplier.

An easy‐to‐interpret rule for the optimal risk‐adjusted social cost of carbon is derived using perturbation analysis. This rule internalizes the adverse effects of global warming on the risk of recurring climate‐related disasters, the risk of irreversible cascading climate tipping points, and the usual effect on total factor productivity. It and its three components approximate the true numerical optimum well, especially if the small parameters (i.e., the share of damages in GDP, the sensitivity of the risk of disasters to temperature and the risk of climate tipping) are small enough and the discount rate is not too small. The rule is also accurate if applied to AK models with a different supply side, for example, with ongoing technical progress in fossil‐fuel production or multiple economic sectors. With a growth‐adjusted discount rate of 2%/year, the SCC is $172/tCO 2 , 70% of which is due to recurring climate disasters and 9% to climate tipping risk.

Seasonal migration is a common strategy to mitigate rural seasonal deprivation, but migrants need to remit money during the lean season to family members facing food shortages. We observe counterintuitively low remittances in rural Nepal during periods of seasonal hunger, and migrants return with remittances later during harvest when food is relatively abundant. To indirectly overcome this apparent constraint in remittance timing, we provide a $90 consumption loan to randomly selected rural households during the pre‐harvest lean season. Loan‐recipient households increase pre‐harvest investments in fertilizer and time spent working on their own farm, smooth consumption, and save more of their migration income to bring it back home. Food security, subjective well‐being, rice harvest, and revenues improve. Ninety‐eight percent of beneficiaries repay the loan with the increased harvest‐period remittance. In a two‐period model of household decision making, we show that remittance frictions—a market failure—are necessary to qualitatively match our experimental results.

We provide a complete description of the set of all solutions to a vector autoregressive law of motion. Every solution is shown to be the sum of three components, each corresponding to a directed flow of time. One component flows forward from the arbitrarily distant past, one flows backward from the arbitrarily distant future, and one flows outward from time zero. The three components are obtained by applying three complementary spectral projections to the solution, these corresponding to a separation of the eigenvalues of the autoregressive coefficient matrix according to whether they are inside, outside, or on the unit circle. We establish a one‐to‐one correspondence between the set of all solutions and a finite‐dimensional space of initial conditions.

We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be achieved via a very simple loss‐based reweighting of logistic regression or state‐of‐the‐art machine learning techniques. We apply our analysis to algorithmic fairness in pretrial detentions.

Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and related measures in large structural VARs identified with a large number of structural restrictions on impulse responses. The methodology is illustrated using a 35‐variable VAR with over 100 sign and ranking restrictions to identify 8 structural shocks.

In density forecasts, respondents are asked to assign probabilities to a response scale with pre‐specified ranges of inflation. In two large‐scale experiments, one conducted in the US and one in Germany, we show how the specifics of the response scale determine the results: Shifting, compressing, or expanding the scale leads to shifted, compressed, and expanded forecasts. Quantifying this effect using the scale most widely used among central banks, we find that mean forecast, uncertainty, and disagreement can vary by several percentage points. Density forecasts may severely misestimate the “true” inflation expectations, and may generate spurious changes in reported inflation uncertainty. The analysis suggests several ways to reduce this bias.

The theory of infinitely repeated games may lack predictive power due to its insensitivity to, for example, changes in some game parameters, the timing of players' moves, and communication possibilities. We propose a new approach by studying an infinitely repeated prisoner's dilemma game and its variants where preferences for cooperation are heterogeneous, and strategic risk arises from incomplete information about opponents' preferences. Our model generates a rich set of comparative static predictions in a variety of settings. We show that, unlike standard theory and other existing models, our approach organizes the findings of a host of experiments including our novel experiments.

We adapt Double Machine Learning to macroeconomic time series by combining regularized nuisance estimation with Reverse Cross-Fitting. This deterministic scheme exploits time reversibility to use time-reversed auxiliary blocks and, unlike neighbor-deletion designs, avoids buffer blocks, thereby improving sample usage. We derive conditions for asymptotic validity and show in simulations that the estimator performs well in realistic finite samples across the designs considered, including cases with misspecification, heteroskedasticity, and state dependence. We also show that, in high dimensions, predictive tuning metrics do not minimize bias in the causal score. We therefore propose a calibration rule targeting a Goldilocks zone of tuning parameters delivering stable partialled-out signals and reduced small-sample bias. We extend the method to residualized Local Projections and apply it to estimate the dynamic effects of a rise in Tier 1 regulatory capital. The results illustrate the usefulness of the approach for macroeconomic time-series inference.

We present a new approach for modelling dynamic conditional correlations. It spans the whole space of allowable correlation matrices, and yet it is computable efficiently. We compare the new model, which we call dynamic spectral conditional correlations (DSCC), to the popular dynamic conditional correlations (DCC) in three ways. First, we demonstrate analytically the difference in span (or coverage) of the space of positive-definite correlation matrices. Second, we introduce a general numerical criterion, based on volumes of elliptopes, to benchmark how close any volatility model gets to filling the allowable space of positive-definite correlation matrices. We use it to illustrate how substantial this gap can be here: DSCC achieves the full potential which can be up to double the coverage of DCC for three-dimensional matrices. Third, we present the methodology for DSCC’s forecasting then apply it. We construct dynamically minimum-variance portfolios of up to 1,000 stocks, showing that we attain systematically higher returns than the best of DCC-based methods, for a comparable risk, and with more stable portfolio allocations. The gains are substantial, almost double DCC’s portfolio value over the sample period.

We construct a continuous-time, monetary model with frictional goods and labour markets to revisit the long-run relationship between inflation and unemployment. By endogenising the value of consumers’ outside options and market power in accordance with standard consumer search theory, we generate novel predictions for the slope of the long-run Phillips curve, optimal monetary policy, and outcomes at the frictionless limit. The relationship between inflation and unemployment is non-monotone and, at low inflation rates, an increase in inflation reduces unemployment. The Friedman rule is suboptimal when firms’ average bargaining power across markets is low. Markups and markdowns vanish as frictions disappear.

Restrictive immigration policies can force organisations to alter their workforce composition, with unclear effects on organisational performance. We study the effects of the 2016 Brexit referendum, which reduced the share of EU-nationality nurses in English hospitals. Using administrative patient-level data and a continuous difference-in-differences design, exploiting hospitals’ pre-referendum exposure, we estimate the causal impact of a negative labour supply shock on care quality. Hospitals with higher pre-referendum EU nurse shares experienced higher post-referendum emergency patient mortality, equivalent to 1,238 additional deaths annually in the three years after the referendum. Consistent with theoretical predictions, hospitals responded to labour shortages by relaxing hiring standards: foreign nurses recruited after the referendum were appointed to lower salary grades, suggesting lower skills and experience.

Dispute resolution in low-income countries is typically done by either a costly and slow formal court or an informal institution without state-sanctioned enforcement powers. Can access to justice be increased by combining the best aspects of formal and informal institutions? We evaluate the effects of “Village Courts” (VCs) in rural Bangladesh using a large-scale field experiment. The introduction of VCs more than doubles the share of disputes resolved in state-sanctioned courts, but an informal institution called shalish remains dominant. There is some substitution from shalish to VCs, but congestion in higher-level courts, village social dynamics, and economic activity remain unaffected.

This paper develops a framework for monetary policy normalization in which liquidity conditions shape aggregate demand through a liquidity channel. The model implies that central bank balance sheet operations can stabilize demand even away from the zero lower bound, making reserves an independent policy instrument alongside interest rates. Optimal balance sheet size therefore depends not only on private reserve demand, but also on fiscal interactions and liquidity management. Following shocks that generate a liquidity trap, optimal policy expands reserves at the lower bound, initiates quantitative tightening before rate liftoff, and normalizes rates and reserves together, clarifying balance sheets’ stabilization role.

Social norms that are costly for individuals are held in place because of social pressure to conform. Historical examples include duelling in Europe and footbinding in China; contemporary examples include female genital cutting and wasteful consumption. Such norms exhibit varied dynamics. Some collapse after staying unchanged for centuries, while others erode gradually. Still others escalate gradually before collapsing. This paper develops a theoretical framework that explains these patterns in terms of different forces of social influence and lends itself to empirical estimation. The analysis can guide the design of policies aimed at mitigating or eliminating costly norms, and shows that some interventions can be counterproductive.