In this paper we study the relationship between task complexity and the occupational wageand employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980-2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.

In this paper we study the relationship between task complexity and the occupational wage-and employment structure. Complex tasks are dened as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate eectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980-2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a signicant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market eects of automatization.

We characterize optimal monetary policy when agents have extrapolative beliefs about asset prices. Such boundedly rational expectations induce inefficient asset price and aggregate demand fluctuations. We find that the optimal monetary policy raises interest rates when expected capital gains or the level of current asset prices is high, but does not eliminate deviations of asset prices from their fundamental value. When the asset is in elastic supply, optimal policy also leans against the wind, tolerating low inflation and output when asset prices are too high. Optimal policy can be reasonably approximated by simple interest rate rules that respond to capital gains. Our results are robust to a wide range of belief specifications.

Explaining asset price booms poses a dicult question for researchers in macroeconomics: how can large and persistent price growth be explained in the absence large and persistent variation in fundamentals? This paper argues that boom-bust behavior in asset prices can be explained by a model in which boundedly rational agents learn the process for prices. The key feature of the model is that learning operates in both the demand for assets and the supply of credit. Interactions between agents on either side of the market create complementarities in their respective beliefs, providing an additional source of propagation. In contrast, the paper shows why learning involving only one side on the market, which has been the focus of most of the literature, cannot plausibly explain persistent and large price booms. Quantitatively, the model explains recent experiences in US housing markets. A single unanticipated mortgage rate drop generates 20 quarters of price growth whilst capturing the full appreciation in US house prices in the early 2000s. The model is able to generate endogenous liberalizations in household lending conditions during price booms, consistent with US data, and replicates key volatilities of housing market variables at business cycle frequencies.

In this note we question the emerging view that automation is a primary driver of wage and employment outcomes in labor markets.

We characterize optimal monetary policy when agents are learning about endogenous asset prices. Boundedly rational expectations induce inefficient equilibrium asset price fluctuations which translate into inefficient aggregate demand fluctuations. We find that the optimal policy raises interest rates when expected capital gains, and the level of current asset prices, is high. The optimal policy does not eliminate deviations of asset prices from their fundamental value. When monetary policymakers are information-constrained, optimal policy can be reasonably approximated by simple interest rate rules that respond to capital gains. Our results are robust to a wide range of belief specifications as well as to the inclusion of an investment channel.

We characterize optimal monetary policy when agents learn about endogenous asset prices. Learning leads to inefficient asset price fluctuations and distortions in consumption and investment decisions. We find that the policy-relevant natural real interest rate increases with subjective asset price beliefs. Optimal monetary policy therefore raises interest rates when expected capital gains are high. When the asset is not in fixed supply, optimal policy also "leans against the wind". In a simple calibration of the model, a positive response to capital gains in simple interest rate rules is beneficial. Our results are robust to alternative belief specifications.

We characterize optimal monetary policy when agents are learning about endogenous asset prices. Boundedly rational expectations induce inefficient equilibrium asset price fluctuations which translate into inefficient aggregate demand fluctuations. We find that the optimal policy raises interest rates when expected capital gains, and the level of current asset prices, is high. The optimal policy does not eliminate deviations of asset prices from their fundamental value. When monetary policymakers are information-constrained, optimal policy can be reasonably approximated by simple interest rate rules that respond to capital gains. Our results are robust to a wide range of belief specifications as well as to the inclusion of an investment channel.