Program Meeting: Monetary Economics

NBER Reporter: Summer 2000

Monetary Economics

The NBER's Program on Monetary Economics met in Cambridge on April 28. Christopher D. Carroll, NBER and Johns Hopkins University, and Stephen G. Cecchetti, NBER and Ohio State University, organized the program and chose the following papers for discussion:

Athanasios Orphanides, Federal Reserve Board of Governors, "The Quest for Prosperity without Inflation"

Discussant: Lars E. O. Svensson, NBER and Princeton University

James Bullard, Federal Reserve Bank of St. Louis, and Kaushik Mitra, University of York, "Learning About Monetary Policy Rules"

Discussant: Michael Woodford, NBER and Princeton University

Mark Zbaracki, University of Pennsylvania; Mark Ritson, London Business School; Daniel Levy, Bar-Ilan University; Shantanu Dutta, University of Southern California; and Mark Bergen, University of Minnesota; "The Managerial and Customer Dimensions of the Cost of Price Adjustment: Direct Evidence from Industrial Markets"

Discussant: Julio J. Rotemberg, NBER and Harvard University

Michael Fratantoni, Fannie Mae, and Scott Schuh, Federal Reserve Bank of Boston, "Monetary Policy, Housing Investment, and Heterogeneous Regional Markets"

Discussant: James H. Stock, NBER and Harvard University

David B. Gross, University of Chicago, and Nicholas S. Souleles, University of Pennsylvania, "Consumer Response to Changes in Credit Supply: Evidence from Credit Card Data"

Discussant: Simon Gilchrist, NBER and Boston University

David I. Laibson, NBER and Harvard University; Andrea Repetto, University of Chile; and Jeremy Tobacman, Harvard University; "A Debt Puzzle"

Discussant: Stephen P. Zeldes, NBER and Columbia University

In recent years, activist monetary policy rules in response to inflation and the level of economic activity have been advanced as a means of achieving effective output stabilization without inflation. Advocates of such policies suggest that their flexibility may yield substantial stabilization benefits while avoiding the excesses of overzealous discretionary fine-tuning that characterize the experience of the 1960s and 1970s. Orphanides demonstrates that these conclusions are misguided. He constructs a database of information that was available to policymakers in real time from 1965 to 1993. He then performs counterfactual simulations under alternative assumptions about the knowledge that policymakers likely had about the state of the economy when they made policy decisions. His results overturn findings favoring activist policies in favor of prudent policies that ignore short-run stabilization concerns. The evidence suggests that the primary underlying cause of the 1970s' inflation was misperceptions of the economy's productive capacity. Further, apparent differences in the framework governing monetary policy decisions during the 1970s as compared to the more recent past have been greatly exaggerated, he concludes.

Bullard and Mitra study macroeconomic systems with forward-looking private sector agents and a monetary authority that is trying to control the economy through the use of a linear policy feedback rule. A natural question about this scenario is: will policy responses that are too aggressive actually destabilize the economy? Using stability under recursive learning as a criterion for evaluating monetary policy rules in this context, Bullard and Mitra find that considering learning substantially alters the evaluation of alternative policy rules. In some situations, overly aggressive rules indeed can destabilize the economies that they model. They also find that a certain type of rule is robustly associated with both determinacy and "learnability": an active, Taylor-type rule, with only a small positive reaction to variables other than inflation.

Zbaracki, Ritson, Levy, Dutta, and Bergen study a large U.S.-based industrial manufacturer and its customers. Using field interviews, nonparticipant observations, and analysis of corporate communications, data, and records used in the price change process, they show that the costs of price adjustment are much more complex than previously has been believed. They identify three types of managerial costs -- information gathering, decisionmaking, and communication costs -- and two types of customer costs -- communication, and negotiation costs. They find that the managerial costs of price adjustment are more than six times, and the customer costs more than 20 times, the actual physical costs associated with changing prices. In dollar terms, the authors' estimate that the total annual cost of price adjustment for this manufacturer in 1997 was $1,233,245. Of this amount, 3.3 percent is the physical cost of changing prices, 22.7 percent is the managerial cost, and the remaining 74 percent is the customer cost. In relative terms, these costs of price adjustment comprise 1.23 percent of the company's revenue and 20.3 percent of the company's net margin. Each price change costs between $2.47 and $12.33. The authors' evidence suggests that many managerial and customer dimensions of the costs of price adjustment are convex -- that is, they increase with the size of the price change -- while many of the physical costs are nonconvex.

Fratantoni and Schuh quantify the importance of heterogeneity in regional housing markets for the conduct of monetary policy. Their model integrates a national financial market with regional housing markets, imposing all exact aggregation conditions. Monetary policy is transmitted to the real economy through the mortgage rate. The effect of monetary policy on the real economy depends on the extent and nature of regional heterogeneity, both of which vary over time. Using longitudinal data for specific U.S. regions, the authors estimate the effects of time variation and state dependence on the dynamic responses of their model. These estimates, and aggregation bias, provide plausible and tangible explanations for "long and variable" lags in monetary policy.

Gross and Souleles use a unique new dataset on credit card accounts to analyze how people respond to changes in credit supply. The data consist of a panel of several hundred thousand individual credit card accounts from several different card issuers with associated credit bureau data included. These accounts were followed monthly for two to three years. The authors find that increases in credit limits generate an immediate and significant rise in debt. This response is sharpest for people starting near their credit limit, showing that liquidity constraints are binding. However, even people starting well below their credit limit respond significantly. There are other results that conventional models cannot easily explain though, such as the fact that many credit card borrowers simultaneously hold other low-yielding assets. Unlike most other studies, this paper shows strong effects from changes in account-specific interest rates. Debt is particularly sensitive to large declines in interest rates, which can explain the widespread use of "teaser rates." The long-run elasticity of debt to the interest rate is about -1.3. Less than half of this elasticity represents balance-switching across cards, with most of it reflecting net changes total borrowing. Overall, the results imply that the consumer plays a potentially important role the transmission of monetary policy and other credit shocks.

Over 60 percent of U.S. households with credit cards are currently borrowing on those cards (that is, paying interest). Laibson, Repetto, and Tobacman attempt to reconcile this high rate of credit card borrowing with observed levels of life-cycle wealth accumulation. They simulate a life-cycle model with five properties that create demand for credit card borrowing: 1) their calibrated labor income path follows a trajectory that is upward sloping early in life; 2) their income path has transitory shocks; 3) they introduce an illiquid asset that will attract substantial investment, but cannot be used to smooth transitory income shocks; 4) they give consumers the opportunity to declare bankruptcy, making credit card borrowing less costly; and 5) their simulated households have relatively more dependents early in the life cycle. The authors' calibrated model predicts that 20 percent of the population will borrow on their credit card at any point in time, far less than the observed rate of over 60 percent. The resolution to this puzzle is hyperbolic time preferences: simulated hyperbolic consumers borrow actively in the revolving credit card market and accumulate relatively large stocks of illiquid wealth, matching observed data.

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