Credibility Revolution
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Credibility Revolution
In economics, the credibility revolution was the movement towards improved reliability in empirical economics through a focus on the quality of research design and the use of more experimental and quasi experimental methods. Developing in the 1990s and early 2000s, this movement was aided by advances in theoretical econometric understanding, but was especially driven by research studies that focused on the use of clean and credible research designs. Studies driving the credibility revolution have made use of better quality data, and also econometric techniques such as difference in differences, instrumental variables, regression discontinuity, natural experiments, and even, when funding and opportunity permit, true randomized experiments. These techniques have made it possible (in principle) to distinguish between correlation and causality better than methods previously used. The 2021 Nobel Prize in Economics was awarded to David Card, Joshua Angrist and Guido Imbens for th ...
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David Card
David Edward Card (born 1956) is a Canadian-American labour economist and the Class of 1950 Professor of Economics at the University of California, Berkeley, where he has been since 1997. He was awarded half of the 2021 Nobel Memorial Prize in Economic Sciences "for his empirical contributions to labour economics", with Joshua Angrist and Guido Imbens jointly awarded the other half. Early life and career David Card was born in Guelph, Ontario, in 1956. His parents were dairy farmers. Card is a graduate of John F. Ross Collegiate Vocational Institute, he attended it between the years of 1970 to 1975. Card was originally pursuing a degree in physics, before eventually switching to economics. He then earned his Bachelor of Arts degree from Queen's University in 1978 and his Ph.D. degree in economics in 1983 from Princeton University, after completing a doctoral dissertation titled "Indexation in long term labor contracts" under the supervision of Orley Ashenfelter. Card bega ...
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Instrumental Variables
In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term (endogenous), in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable (is correlated with the endogenous variable) but has no independent effect on the dependent variable and is not correlated with the error term, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable. Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when: # changes ...
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Experimental Economics
Experimental economics is the application of experimental methods to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law (experimental law and economics). A fundamental aspect of the subject is design of experiments. Experiments may be conducted in the field or in laboratory settings, whether of individual or group behavior. Variants of the subject outside such formal confines include natural and quasi-natural experiments. Experimental topics One can loosely classify economic experiments using the following topics: * Markets * Games * Evolutionary game theory * Decision making * Bar ...
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Journal Of Economic Perspectives
The ''Journal of Economic Perspectives'' (''JEP'') is an economic journal published by the American Economic Association. The journal was established in 1987. The JEP was founded by Joseph Stiglitz, Carl Shapiro, and Timothy Taylor. It is oriented around the twin goals of "providing perspective on current economic research, and explaining how economics provides perspective on questions of general interest." According to its editors its purpose is: #to synthesize and integrate lessons learned from active lines of economic research; #to provide economic analysis of public policy issues; to encourage cross-fertilization of ideas among the fields of thinking; #to offer readers an accessible source for state-of-the-art economic thinking; #to suggest directions for future research; #to provide insights and readings for classroom use; #and to address issues relating to the economics profession. Its current editors are Heidi Williams and Jeffrey Kling, and its managing editor ...
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Nobel Prize In Economics
The Nobel Memorial Prize in Economic Sciences, officially the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (), commonly referred to as the Nobel Prize in Economics(), is an award in the field of economic sciences administered by the Nobel Foundation, established in 1968 by Sveriges Riksbank (Sweden's central bank) to celebrate its 300th anniversary and in memory of Alfred Nobel. Although the Prize in Economic Sciences was not one of the original five Nobel Prizes established by Alfred Nobel's will, it is considered a member of the Nobel Prize system, and is administered and referred to along with the Nobel Prizes by the Nobel Foundation. Winners of the Prize in Economic Sciences are chosen in a similar manner to and announced alongside the Nobel Prize recipients, and receive the Prize in Economic Sciences at the Nobel Prize Award Ceremony. The laureates of the Prize in Economic Sciences are selected by the Royal Swedish Academy of Sciences, which ...
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Randomized Experiments
In science, randomized experiments are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important in experimental design and in survey sampling. Overview In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups. For example, if an experiment compares a new drug against a standard drug, then the patients should be allocated to either the new drug or to the standard drug control using randomization. Randomized experimentation is ''not'' haphazard. Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers). Randomization also produces ignorable designs, which are valuable in model-based statistical inference, especially Bayesian or likelihood-based. In the design of experiments, the ...
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Natural Experiments
A natural experiment is a study in which individuals (or clusters of individuals) are exposed to the experimental and control conditions that are determined by nature or by other factors outside the control of the investigators. The process governing the exposures arguably resembles random assignment. Thus, natural experiments are ''observational studies'' and are not controlled in the traditional sense of a randomized experiment (an ''intervention study''). Natural experiments are most useful when there has been a clearly defined exposure involving a well defined subpopulation (and the absence of exposure in a similar subpopulation) such that changes in outcomes may be plausibly attributed to the exposure. In this sense, the difference between a natural experiment and a non-experimental observational study is that the former includes a comparison of conditions that pave the way for causal inference, but the latter does not. Natural experiments are employed as study designs when ...
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Regression Discontinuity
In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned. By comparing observations lying closely on either side of the threshold, it is possible to estimate the average treatment effect in environments in which randomisation is unfeasible. However, it remains impossible to make true causal inference with this method alone, as it does not automatically reject causal effects by any potential confounding variable. First applied by Donald Thistlethwaite and Donald Campbell (1960) to the evaluation of scholarship programs, the RDD has become increasingly popular in recent years. Recent study comparisons of randomised controlled trials (RCTs) and RDDs have empirically demonstrated the internal validity of the design. Example ...
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Difference In Differences
Difference in differences (DID or DD) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. It calculates the effect of a treatment (i.e., an explanatory variable or an independent variable) on an outcome (i.e., a response variable or dependent variable) by comparing the average change over time in the outcome variable for the treatment group to the average change over time for the control group. Although it is intended to mitigate the effects of extraneous factors and selection bias, depending on how the treatment group is chosen, this method may still be subject to certain biases (e.g., mean regression, reverse causality and omitted variable bias). In contrast to a time-series estimate of the treatment effect on subjects (wh ...
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Alan Krueger
Alan Bennett Krueger (September 17, 1960 – March 16, 2019) was an American economist who was the James Madison Professor of Political Economy at Princeton University and Research Associate at the National Bureau of Economic Research. He served as Assistant Secretary of the Treasury for Economic Policy, nominated by President Barack Obama, from May 2009 to October 2010, when he returned to Princeton. He was nominated in 2011 by Obama as chair of the White House Council of Economic Advisers, and served in that office from November 2011 to August 2013. He was among the 50 highest ranked economists in the world according to Research Papers in Economics. He made innovative use of natural experiments in economics, including influential research in the 1990s that challenged the dominant perspective in economics at the time that minimum wage adversely affected employment. He also made prominent contributions to research on inequality and the economic effects of education. Early life ...
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Econometrics
Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. M. Hashem Pesaran (1987). "Econometrics", '' The New Palgrave: A Dictionary of Economics'', v. 2, p. 8 p. 8–22 Reprinted in J. Eatwell ''et al.'', eds. (1990). ''Econometrics: The New Palgrave''p. 1 p. 1–34Abstract ( 2008 revision by J. Geweke, J. Horowitz, and H. P. Pesaran). More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships." Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today. A basic tool for econometrics is the multiple linear regression model. ''Econome ...
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Quasi-experiment
A quasi-experiment is a research design used to estimate the causal impact of an intervention. Quasi-experiments share similarities with experiments and randomized controlled trials, but specifically lack random assignment to treatment or control. Instead, quasi-experimental designs typically allow assignment to treatment condition to proceed how it would in the absence of an experiment. Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. In other words, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes. This is particularly true if there are confounding variables that cannot be controlled or accounted for. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteri ...
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