Topics in Macroeconomics
Paper Session
Sunday, Jan. 3, 2027 8:00 AM - 10:00 AM (EST)
- Chair: Andrew Foerster, Federal Reserve Bank of San Francisco
The Past and Future of U.S. Structural Change: Compositional Accounting and Forecasting
Abstract
"We explore the evolving significance of different production sectors within the U.S. economy since World War II and provide methods for estimating and forecasting these shifts. Using a compositional accounting approach, we find that the well-documented transition from goods to services is primarilydriven by two compositional changes: 1) the rise of Intellectual Property Products (IPP) as an input
producer, replacing Durable Goods almost one-for-one in terms of input shares in virtually all sectors;
and 2) a shift in consumer spending from Nondurable Goods to Services. A structural model replicating
these shifts reveals that the rise of IPP at the expense of Durable Goods is largely explained by increases
in the efficiency of IPP inputs used in production: input-biased technical change. Trend variations in
sectoral total factor productivity, and their attendant effects on relative prices and income, are the main
driver of evolving consumption patterns. Both reduced-form and structural forecasts project these trends
to continue over the next two decades, albeit at lower rates, indicating a slower pace of structural change."
Uncertain Network Dynamics
Abstract
I study the dynamics of flows on networks under uncertainty. I analyze the dynamic transmission across a network of shocks with uncertain dynamic properties, which can capture both exogenous stochastic shocks and uncertainty in the network structure. I show how to map uncertain network dynamics into a control system, which allows me to apply tools from robust control theory to analyze system performance. I study two main classes of applications: production networks and consensus problems with local information diffusion. I characterize the response of these networks to perturbations, whether due to (1) stochastic shocks with specified properties, where performance is measured by the volatility of the network output, or (2) uncertain but norm-bounded inputs, leading to a robust measure of worst-case shock amplification.I analyze several examples to illustrate how the network topology affects shock propagation and amplification, then apply the methods to empirical networks. For the US production network, at a coarse level of aggregation, the US economy appears to have become less fragile over time. But at a finer disaggregation, fragility has not declined but has become more localized. I also show that Twitter, a directed social network, is more fragile and more sensitive to targeted disruption than Facebook, where connections are undirected and more diffuse.Will AI Adoption Intensify or Weaken Market Competition?
Abstract
We study how AI adoption affects market competition based on a general equilibrium framework with heterogeneous firms facing idiosyncratic productivity and variable markups. Firms choose the AI technology subject to fixed costs, where AI production requires data and energy inputs. Our model predicts non-monotonic relations between AI adoption and industry concentration. As AI usage rises from an initially low level, large firms that are incumbent adopters gain market shares. When AI usage is sufficiently diffused, the entry of smaller, new AI adopters erodes the market share of incumbents, reducing industry concentration. The non-monotonic relations are robust even when firms can complement AI with their own data. In comparison, the impact of AI adoption on the average markup depends on whether the adoption is driven by demand or supply factors. In our calibrated model featuring markup distortions and data externality, a modest revenue subsidy of 2 percent for AI adopters maximizes social welfare.JEL Classifications
- E3 - Prices, Business Fluctuations, and Cycles
- E1 - General Aggregative Models