We build a general equilibrium production-based asset pricing model with heterogeneous rms that jointly accounts for rm-level and aggregate facts emphasized by the recent macroeconomic literature, and for important asset pricing moments. Using administrative rm-level data, we establish empirical properties of large negative idiosyncratic shocks and their evolution. We then demonstrate that these shocks play an important role for delivering both macroeconomic and asset pricing predictions. Finally, we combine our model with data on the universe of U.S. seaborne import since 2007, and establish the importance of supply chain disasters for the cross-section of asset prices.
We characterize the revenue-maximizing information structure in the second-price auction. The seller faces a trade-off: more information improves the efficiency of the allocation but creates higher information rents for bidders. The information disclosure policy that maximizes the revenue of the seller is to fully reveal low values (where competition is high) but to pool high values (where competition is low). The size of the pool is determined by a critical quantile that is independent of the distribution of values and only dependent on the number of bidders. We discuss how this policy provides a rationale for conflation in digital advertising.
Virtually all theories of economic growth predict a positive relationship between population size and productivity. In this paper, I study a particular historical episode to provide direct evidence for the empirical relevance of such scale effects. In the af- termath of the Second World War, 8 million ethnic Germans were expelled from their domiciles in Eastern Europe and transferred to West Germany. This inflow increased the German population by almost 20%. Using variation across counties, I show that the settlement of refugees had large and persistent effects on the size of the local popula- tion, manufacturing employment, and income per capita. These findings are quantita- tively consistent with an idea-based model of spatial growth if population mobility is subject to frictions and productivity spillovers occur locally. The estimated model im- plies that the refugee settlement increased aggregate income per capita by about 12% after 25 years and triggered a process of industrialization in rural areas.
We study order statistics (OS) from independent non identically distributed (INID) samples for two large classes of statistical distributions: Exponentiated Distributions (ED) and Proportional Hazard Rate Models (PHRM). We show that for the analytical solution for the CDF (PDF) of OSs in ED and PHRM: i) each OS's CDF (PDF) depends on all shape parameters; ii) the CDF (PDF) of each OS is a weighted average of CDF (PDF) within the same family and with shape parameters equal to a partial sum of the original shape parameters; and iii) the weights are integers and sum up to 1. These properties allows for a clear analytical solution and allows a simple parameter estimation in these classes of distributions.
To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study network resilience and the consequences of reshoring of supply chains. In our model, firms exit due to exogenous shocks or the propagation of shocks through the network, while firms can replace suppliers they have lost due to exit subject to switching costs and search frictions. Applying our model to a large international firm-level production network dataset, we find that restricting buyer–supplier links via reshoring policies reduces output and increases volatility and that volatility can be amplified through network adaptivity.
The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper sheds light on its versatility in trend-cycle determination, explaining in a simple manner both HP filter smoothing and the consistency delivered by boosting for general trend detection. Applied to a universe of time series in FRED databases, boosting outperforms other methods in timely capturing downturns at crises and recoveries that follow. With its wide applicability the boosted HP filter is a useful automated machine learning addition to the macroeconometric toolkit.
United States households’ consumption expenditures and car purchases collapsed during the Great Recession and more so than income changes would have predicted. Using CEX data, we show that both the extensive and the intensive car spending margins contracted sharply in the Great Recession. We also document significant crosscohort differences in the impact of the Great Recession including a stronger reduction in car spending by younger cohorts. We draw inference on the sources of the Great Recession by investigating which shocks can explain household choices in a 60 period life-cycle model with idiosyncratic and aggregate shocks fitted to aggregate and lifecycle moments. We find that the Great Recession was caused by a combination of large aggregate income and wealth shocks, while cross-cohort adjustment patterns imply a role for life-cycle income profile shocks. We also find a role for car loan premia shocks in accounting for car spending and car loans.
We propose a model of intermediated digital markets where data and heterogeneity in tastes and products are defining features. A monopolist platform uses superior data to match consumers and multiproduct advertisers. Consumers have heterogenous preferences for the advertisers' product lines and shop on- or off-platform. The platform monetizes its data by selling targeted advertising space that allows advertisers to tailor their products to each consumer's preferences. We derive the equilibrium product lines and advertising prices. We identify search costs and informational advantages as two sources of the platform's bargaining power. We show that privacy-enhancing data-governance rules, such as those corresponding to federated learning, can lead to welfare gains for the consumers.
The present study analyzes the impact of carbon pricing along with other policies on the value of fossil fuel resources, CO2 emissions, and economic welfare. It employs a model based on the Hotelling analysis of resource values and calibrates this approach to data on fossil resources, costs, demands, and CO2 emissions. Total fossil-fuel resource rents are estimated to be $17 trillion (2021 US$) without carbon pricing. Oil and gas rents are unchanged for low carbon taxes but would decline by 40% with a $100/tCO2 price. The losses in producer values would be only about 10% of the carbon tax revenues. The study also shows that other policies – such as ones involving ethical investing or subsidies for renewable energy – are very inefficient and poor substitutes for carbon pricing.
Using a large-scale hybrid laboratory and online trust experiment with and without preplay communication, we investigate how the passage of time affects trust. Communication (predominantly through promises) raises cooperation, trust, and trustworthiness by about 50 percent. This result holds even when three weeks pass between the time of the trustee's message/the trustor's decision to trust and the time of the trustee's contribution choice and even when this contribution choice is made outside of the lab. Delay between the beginning of the interaction and the time to reciprocate neither substantially alters trust or trustworthiness nor affects how subjects communicate.