An Integration of Econometric and Behavioral Economic Research
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Making Sense of Unemployment Data
PAGE ONE Economics® Making Sense of Unemployment Data Scott A. Wolla, Ph.D., Senior Economic Education Specialist GLOSSARY “Unemployment is like a headache or a high temperature—unpleasant and Cyclical unemployment: Unemployment exhausting but not carrying in itself any explanation of its cause.” associated with recessions in the business —William Henry Beveridge, Causes and Cures of Unemployment cycle. Discouraged worker: Someone who is not working and is not looking for work because of a belief that there are no jobs Job growth has been healthy for five years.1 However, many people still available to him or her. express concern over the health of the overall labor market. For example, Employed: People 16 years and older who Jim Clifton, CEO of Gallup, states that the “official unemployment rate, as have jobs. reported by the U.S. Department of Labor, is extremely misleading.”2 He Frictional unemployment: Unemployment proposes the Gallup Good Jobs rate as a better indicator of the health of the that results when people are new to the labor market. At the heart of Clifton and others’ concern is what the official job market, including recent graduates, or are transitioning from one job to another. unemployment rate actually measures and whether it is a reliable indicator. Labor force: The total number of workers, including both the employed and the The Labor Force: Are You In or Out? unemployed. To measure the unemployment rate, the U.S. Bureau of Labor Statistics Labor force participation rate: The percent- (BLS) surveys 60,000 households—about 110,000 individuals—which age of the working-age population that is in the labor force. -
Notes on Behavioral Economics and Labor Market Policy
Notes on Behavioral Economics and Labor Market Policy Linda Babcock, Carnegie Mellon University William J. Congdon, The Brookings Institution Lawrence F. Katz, Harvard University Sendhil Mullainathan, Harvard University December 2010 We are grateful to Jeffrey Kling for extensive discussions of these topics. We thank the Alfred P. Sloan Foundation and the Russell Sage Foundation for their support of this project. 0. Background and motivation Recent years have been trying ones for American workers. The unemployment rate has reached double digits for the first time in over a quarter of a century. Worker compensation growth has all but stalled. The human costs of labor market turbulence have rarely been clearer, and the value of public policies, such as unemployment insurance and job training programs, that assist workers in managing that turbulence, gaining new skills, and navigating the labor market have rarely been more apparent. And, even in the best of times, the United States’ labor market is a dynamic and turbulent one, with high rates of turnover (over five million separations and five million new hires in a typical month in normal times) but substantial frictions as well. As a result, labor market programs and regulations are key components of economic policy. Such policies help support the unemployed, provide education and training opportunities, and ensure the fairness, safety, and accessibility of the workplace. The challenge for policymakers is to design such policies so that they meet these goals as effectively and as efficiently as possible. Labor market policies succeed in meeting their objectives, however, only to the extent that they accurately account for how individuals make decisions about work and leisure, searching for jobs, and taking up opportunities for education and training. -
Ten Tips for Interpreting Economic Data F Jason Furman Chairman, Council of Economic Advisers
Ten Tips for Interpreting Economic Data f Jason Furman Chairman, Council of Economic Advisers July 24, 2015 1. Data is Noisy: Look at Data With Less Volatility and Larger Samples Monthly Employment Growth, 2014-Present Thousands of Jobs 1,000 800 Oct-14: +836/-221 600 400 200 0 -200 Mar-15: Establishment Survey -502/+119 -400 Household Survey (Payroll Concept) -600 Jan-14 Apr-14 Jul-14 Oct-14 Jan-15 Apr-15 Source: Bureau of Labor Statistics. • Some commentators—and even some economists—tend to focus too closely on individual monthly or weekly data releases. But economic data are notoriously volatile. In many cases, a longer-term average paints a clearer picture, reducing the influence of less informative short-term fluctuations. • The household employment survey samples only 60,000 households, whereas the establishment employment survey samples 588,000 worksites, representing millions of workers. 1 2. Data is Noisy: Look Over Longer Periods Private Sector Payroll Employment, 2008-Present Monthly Job Gain/Loss, Seasonally Adjusted 600,000 400,000 200,000 0 -200,000 12-month -400,000 moving average -600,000 -800,000 -1,000,000 2008 2010 2012 2014 Source: Bureau of Labor Statistics. • Long-term moving averages can smooth out short-term volatility. Over the past year, our businesses have added 240,000 jobs per month on average, more than the 217,000 per month added over the prior 12 months. The evolving moving average provides a less noisy underlying picture of economic developments. 2 2. Data is Noisy: Look Over Longer Periods Weekly Unemployment Insurance Claims, 2012-2015 Thousands 450 400 Weekly Initial Jobless Claims 350 300 Four-Week Moving Average 7/18 250 2012 2013 2014 2015 Source: Bureau of Labor Statistics. -
Macroeconomics Course Outline and Syllabus
City University of New York (CUNY) CUNY Academic Works Open Educational Resources New York City College of Technology 2018 Macroeconomics Course Outline and Syllabus Sean P. MacDonald CUNY New York City College of Technology How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/ny_oers/8 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] COURSE OUTLINE FOR ECON 1101 – MACROECONOMICS New York City College of Technology Social Science Department COURSE CODE: 1101 TITLE: Macroeconomics Class Hours: 3, Credits: 3 COURSE DESCRIPTION: Fundamental economic ideas and the operation of the economy on a national scale. Production, distribution and consumption of goods and services, the exchange process, the role of government, the national income and its distribution, GDP, consumption function, savings function, investment spending, the multiplier principle and the influence of government spending on income and output. Analysis of monetary policy, including the banking system and the Federal Reserve System. COURSE PREREQUISITE: CUNY proficiency in reading and writing RECOMMENDED TEXTBOOK and MATERIALS* Krugman and Wells, Eds., Macroeconomics 3rd. ed, Worth Publishers, 2012 Leeds, Michael A., von Allmen, Peter and Schiming, Richard C., Macroeconomics, Pearson Education, Inc., 2006 Supplemental Reading (optional, but informative): Krugman, Paul, End This Depression -
Decision Making and Keynesian Uncertainty in Financial Markets: an Interdisciplinary Approach
Decision making and Keynesian uncertainty in financial markets: an interdisciplinary approach Ms Eirini Petratou, PhD candidate in Economics division, Leeds University Business School Abstract: This interdisciplinary research examines decision-making in financial markets, regarding secondary markets and financial innovation in Europe, so as to unveil the multiple dimensions of this complexly-layered world of financial interactions. Initially, I synthesise different literatures, including Post-Keynesian economics, behavioural economics and finance, and decision-making theory, in order to shape a more complete understanding of behaviours and outcomes in financial markets. Particularly, I focus on an analysis of Keynesian uncertainty in order to show the need of economic science for interdisciplinary research, on theoretical and methodological levels, and also to highlight potential links between Post-Keynesian theory and behavioural economics. After presenting the active role of decision-making under uncertainty into the creation of financial bubbles, as incorporated in Post-Keynesian economics, I support my argument by presenting findings obtained by a series of semi-structured interviews I conducted in 2016 in the UK, in order to investigate financial traders’ beliefs, perspectives and terminology about decision-making under uncertainty in financial markets. The goal of this qualitative project is to identify market professionals’ beliefs about market uncertainty and how they deal with it, to find out their knowledge about causes of the -
Pandemic 101:A Roadmap to Help Students Grasp an Economic Shock
Social Education 85(2) , pp.64–71 ©2021 National Council for the Social Studies Teaching the Economic Effects of the Pandemic Pandemic 101: A Roadmap to Help Students Grasp an Economic Shock Kim Holder and Scott Niederjohn This article focuses on the major national economic indicators and how they changed within the United States real GDP; the over the course of the COVID-19 pandemic. The indicators that we discuss include output from a Ford plant in Canada is not. Gross Domestic Product (GDP), the unemployment rate, interest rates, inflation, and Economists typically measure real GDP other variations of these measures. We will also present data that sheds light on the as a growth rate per quarter: Is GDP get- monetary and fiscal policy responses to the pandemic. Graphs of these statistics are ting bigger or smaller compared to a prior sure to grab teachers’ and students’ attention due to the dramatic shock fueled by the quarter? In fact, a common definition of pandemic. We will explain these economic indicators with additional attention to what a recession is two quarters in a row of they measure and the limitations they may present. Teachers will be introduced to the declining real GDP. Incidentally, it also Federal Reserve Economic Database (FRED), which is a rich source of graphs and measures total U.S. income (and spend- information for teacher instruction and student research. Further classroom-based ing) and that explains why real GDP per resources related to understanding the economic effects of the COVID-19 pandemic capita is a well-established measure used are also presented. -
1 GEORGE A. AKERLOF Addresses
1 GEORGE A. AKERLOF Addresses: Department of Economics 549 Evans Hall -- #3880 University of California Berkeley, California 94720-3880 (510) 642-5837 E-mail: [email protected] Education: l966 Ph.D., M.I.T. 1962 B.A., Yale University Employment: l980- Professor, University of California at Berkeley 1994- Senior Fellow, The Brookings Institution l978-l980 Cassel Professor with respect to Money and Banking, London School of Economics l977-l978 Visiting Research Economist, Special Studies Section, Board of Governors of the Federal Reserve System l977-l978 Professor, University of California, Berkeley l973-l974 Senior Staff Economist, Council of Economic Advisors l970-l977 Associate Professor, University of California at Berkeley l969-Summer Research Associate, Harvard University l967-l968 Visiting Professor, Indian Statistical Institute 1966-1970 Assistant Professor, University of California at Berkeley Honors and Awards: Senior Adviser, Brookings Panel on Economic Activity Executive Committee of the American Economics Association Vice-President, American Economic Association Associate Editor, American Economic Review, Quarterly Journal of Economics, Journal of Economic Behavior and Organization 2 Co-editor, Economics and Politics Guggenheim Fellowship Fulbright Fellowship Fellow of the Econometric Society North American Council of the Econometric Society Fellow of the American Academy of Arts and Sciences 1990 Ely Lecturer of the American Economic Association Associate, Economic Growth Program, Canadian Institute for Advanced Research -
Neuroeconomics and the Study of Addiction
Neuroeconomics and the Study of Addiction John Monterosso, Payam Piray, and Shan Luo We review the key findings in the application of neuroeconomics to the study of addiction. Although there are not “bright line” boundaries between neuroeconomics and other areas of behavioral science, neuroeconomics coheres around the topic of the neural representations of “Value” (synonymous with the “decision utility” of behavioral economics). Neuroeconomics parameterizes distinct features of Valuation, going beyond the general construct of “reward sensitivity” widely used in addiction research. We argue that its modeling refinements might facilitate the identification of neural substrates that contribute to addiction. We highlight two areas of neuroeconomics that have been particularly productive. The first is research on neural correlates of delay discounting (reduced Valuation of rewards as a function of their delay). The second is work that models how Value is learned as a function of “prediction-error” signaling. Although both areas are part of the neuroeconomic program, delay discounting research grows directly out of behavioral economics, whereas prediction-error work is grounded in models of learning. We also consider efforts to apply neuroeconomics to the study of self-control and discuss challenges for this area. We argue that neuroeconomic work has the potential to generate breakthrough research in addiction science. Key Words: Addiction, behavioral economics, delay discounting, limbic targets, especially the ventral striatum (3). One hypothesis is neuroeconomics, prediction error, substance dependence that individuals with relatively weak responses to reward “tend to be less satisfied with natural rewards and tend to abuse drugs and he behavior of someone with an addiction can be frustrating alcohol as a way to seek enhanced stimulation of the reward path- and perplexing. -
Uncertainty, Evolution, and Behavioral Economic Theory
UNCERTAINTY, EVOLUTION, AND BEHAVIORAL ECONOMIC THEORY Geoffrey A. Manne, International Center for Law & Economics Todd J. Zywicki, George Mason University School of Law Journal of Law, Economics & Policy, Forthcoming 2014 George Mason University Law and Economics Research Paper Series 14-04 Uncertainty, Evolution, and Behavioral Economic Theory Geoffrey A. Manne Executive Director, International Center for Law & Economics Todd J. Zywicki George Mason University Foundation Professor of Law George Mason University School of Law Abstract: Armen Alchian was one of the great economists of the twentieth century, and his 1950 paper, Uncertainty, Evolution, and Economic Theory, one of the most important contributions to the economic literature. Anticipating modern behavioral economics, Alchian explains that firms most decidedly do not – cannot – actually operate as rational profit maximizers. Nevertheless, economists can make useful predictions even in a world of uncertainty and incomplete information because market environments “adopt” those firms that best fit their environments, permitting them to be modeled as if they behave rationally. This insight has important and under-appreciated implications for the debate today over the usefulness of behavioral economics. Alchian’s explanation of the role of market forces in shaping outcomes poses a serious challenge to behavioralists’ claims. While Alchian’s (and our) conclusions are born out of the same realization that uncertainty pervades economic decision making that preoccupies the behavioralists, his work suggests a very different conclusion: The evolutionary pressures identified by Alchian may have led to seemingly inefficient firms and other institutions that, in actuality, constrain the effects of bias by market participants. In other words, the very “defects” of profitable firms — from conservatism to excessive bureaucracy to agency costs — may actually support their relative efficiency and effectiveness, even if they appear problematic, costly or inefficient. -
Title the Possibility of Behavioral New Institutional Economics Sub Title Author 菊沢, 研宗
Title The possibility of behavioral new institutional economics Sub Title Author 菊沢, 研宗(Kikuzawa, Kenshu) Publisher Society of Business and Commerce, Keio University Publication year 2011 Jtitle Keio business review No.46(2011) ,p.25(1)- 42(18) Abstract Behavioral economics has recently been the subject of considerable research with the consequence that theories in behavioral economics and finance have complementarily developed to comprise a research field known as 'behavioral finance'. Subsequent studies seeking to integrate game theory and behavioral economics come under the 'behavioral game theory' umbrella, while those wanting to integrate contract theory and behavioral economics fall under 'behavioral contract theory'. Given such circumstances, the remaining avenue to explore is the integration of behavioral economics and new institutional economics, the latter consisting of transaction cost economics, agency theory, and the theory of property rights. This paper pursues this remaining possibility and indeed proves that 'behavioral new institutional economics' can be developed and would be a fruitful new field of research. Notes Genre Journal Article URL https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=AA002 60481-20110000-0025 慶應義塾大学学術情報リポジトリ(KOARA)に掲載されているコンテンツの著作権は、それぞれの著作者、学会または出版社/発行者に帰属し、その 権利は著作権法によって保護されています。引用にあたっては、著作権法を遵守してご利用ください。 The copyrights of content available on the KeiO Associated Repository of Academic resources (KOARA) belong to the respective authors, academic societies, or publishers/issuers, and these rights are protected by the Japanese Copyright Act. When quoting the content, please follow the Japanese copyright act. Powered by TCPDF (www.tcpdf.org) (25)1 KEIO BUSINESS REVIEW Final version received on 30th November 2011 No. 46, 2011 pp.25–42. -
Methodology for the National Accounts Main Aggregates Database CONTENTS
Methodology for the National Accounts Main Aggregates Database CONTENTS Page I. INTRODUCTION A. Background ................................................................................................................................................................ 2 B. System of National Accounts ..................................................................................................................................... 2 C. Scope of the database ................................................................................................................................................. 2 D. Collection of data ....................................................................................................................................................... 3 E. Comparability of the national estimates ..................................................................................................................... 3 F. Nomenclature ............................................................................................................................................................. 4 G. Country coverage ....................................................................................................................................................... 5 H. Country groupings ...................................................................................................................................................... 5 I. Revisions ................................................................................................................................................................... -
Chapter 1 the Demand for Economic Statistics
Chapter 1 The Demand for Economic Statistics The media publish economic data on a daily basis. But who decides which statistics are useful and which are not? Why is housework not included in the national income, and why are financial data available in real time, while to know the number of people in employment analysts have to wait for weeks? Contrary to popular belief, both the availability and the nature of economic statistics are closely linked to developments in economic theory, the requirements of political decision-makers, and each country’s way of looking at itself. In practice, statistics are based on theoretical and interpretative reference models, and if these change, so does the picture the statistics paint of the economic system. Thus, the data we have today represent the supply and demand sides of statistical information constantly attempting to catch up with each other, with both sides being strongly influenced by the changes taking place in society and political life. This chapter offers a descriptive summary of how the demand for economic statistics has evolved from the end of the Second World War to the present, characterised by the new challenges brought about by globalisation and the rise of the services sector. 1 THE DEMAND FOR ECONOMIC STATISTICS One of the major functions of economic statistics is to develop concepts, definitions, classifications and methods that can be used to produce statistical information that describes the state of and movements in economic phenomena, both in time and space. This information is then used to analyse the behaviour of economic operators, forecast likely movements of the economy as a whole, make economic policy and business decisions, weigh the pros and cons of alternative investments, etc.