The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Behavioral Macroeconomics and Macroeconomic Behavior
Abstract
Think about Richard Scarry’s Cars and Trucks and Things That Go. Think about what that book would have looked like in sequential decades of the last century had Richard Scarry been alive in each of them to delight and amuse children and parents. Each subsequent decade has seen the development of ever more specialized vehicles. We started with the Model T Ford. We now have more models of backhoe loaders than even the most precocious fouryear-old can identify. What relevance does this have for economics? In the late 1960’s there was a shift in the job description of economic theorists. Prior to that time microeconomic theory was mainly concerned with analyzing the purely competitive, general-equilibrium model based upon profit maximization by firms and utility maximization by consumers. The macroeconomics of the day, the so-called neoclassical synthesis, appended a fixed money wage to such a generalequilibrium system. “Sticky money wages” explained departures from full employment and business-cycle fluctuations. Since that time, both microand macroeconomics have developed a Scarry-ful book of models designed to incorporate into economic theory a whole variety of realistic behaviors. For example, “The Market for ‘Lemons’ ” explored how markets with asymmetric information operate. Buyers and sellers commonly possess different, not identical, information. My paper examined the pathologies that may develop under these more realistic conditions. For me, the study of asymmetric information was a very first step toward the realization of a dream. That dream was the development of a behavioral macroeconomics in the original spirit of John Maynard Keynes’ General Theory (1936). Macroeconomics would then no longer suffer from the “ad hockery” of the neoclassical synthesis, which had overridden the emphasis in The General Theory on the role of psychological and sociological factors, such as cognitive bias, reciprocity, fairness, herding, and social status. My dream was to strengthen macroeconomic theory by incorporating assumptions honed to the observation of such behavior. A team of people has participated in the realization of this dream. Kurt Vonnegut would call this team a kerass, “a group of people who are unknowingly working together toward some common goal fostered by a larger cosmic influence.” In this lecture I shall describe some of the behavioral models developed by this kerass to provide plausible explanations for macroeconomic phenomena which are central to Keynesian economics. For the sake of background, let me take you back a bit in time to review some history of macroeconomic thought. In the late 1960’s the New Classical economists saw the same weaknesses in the microfoundations of macroeconomics that have motivated me. They hated its lack of rigor. And they sacked it. They then held a celebratory bonfire, with an article entitled “After Keynesian Macroeconomics.” The new version of macroeconomics that they produced became standard in the 1970’s. Following its neoclassical synthesis predecessor, New Classical macroeconomics was based on the competitive, general-equilibrium model. But it differed in being much more zealous in insisting that all decisions—consumption and labor supply by † This article is a revised version of the lecture George A. Akerlof delivered in Stockholm, Sweden, on December 8, 2001, when he received the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel. The article is copyright © The Nobel Foundation 2001 and is published here with the permission of the Nobel Foundation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
The Market for "Lemons": Quality Uncertainty and the Market Mechanism
Akerlof, G. A. (1970) · The Quarterly Journal of Economics · doi:10.2307/1879431 · Cited by 23,016
Self-citationCredit Rationing in Markets with Imperfect Information
Stiglitz, J. E., & Weiss, A. (1981) · Columbia University · doi:10.7916/d8v12ft1 · Cited by 12,769
Myopia and Inconsistency in Dynamic Utility Maximization
Strotz, R. H. (1955) · The Review of Economic Studies · doi:10.2307/2295722 · Cited by 2,834
Akerlof, G., & Akerlof, G. (1991) · Unknown · doi:10.22004/ag.econ.294661 · Cited by 849
Self-citationExperiences of collaborative research
Kahneman, D. (2003) · American Psychologist · doi:10.1037/0003-066x.58.9.723 · Cited by 149
Cited by
Publication Selection Bias in Minimum‐Wage Research? A Meta‐Regression Analysis
Doucouliagos, H., & Stanley, T. D. (2009) · British Journal of Industrial Relations · doi:10.1111/j.1467-8543.2009.00723.x · Cited by 507
Selling at the Farmgate or Traveling to Market
Fafchamps, M., & Hill, R. V. (2005) · American Journal of Agricultural Economics · doi:10.1111/j.1467-8276.2005.00758.x · Cited by 350
Experiences of collaborative research
Kahneman, D. (2003) · American Psychologist · doi:10.1037/0003-066x.58.9.723 · Cited by 149
Metadata
- Authors
- Akerlof, G. A.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2002-05-01
- Volume
- 92
- Issue
- 3
- Pages
- 411-433
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W2319837793
- Cited by
- 664
- References
- 6
- FWCI
- 45.47
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.