Empirical Work In Economics Research Articles

Confirmation Bias, Data Manipulation and the Kuznets Curve: Original, Environmental and Financial

Empirical work in economics and finance involves data manipulation in ways that make it possible to confirm prior beliefs. The results typically turn out to be sensitive to model specification, sample period, variable definitions and estimation methods. The status quo provides the motivation for dealing with this problem using three versions of the Kuznets curve for the purpose of illustration. The underlying hypotheses are represented by quadratic functions of income per capita, with a different dependent variable for each version of the Kuznets curve. Both time series and cross-sectional data are used to estimate the equations. The results turn out to be highly sensitive to a number of factors, which provides an incentive for being selective in the reporting of results while exhibiting confirmation bias. To overcome the model uncertainty problem one can resort to the use of one of several methods that are based on the reporting of the distribution rather than the point estimation of the coefficients.

Confirmation Bias, Data Manipulation and the Kuznets Curve: Original, Environmental and Financial

Empirical work in economics and finance involves data manipulation in ways that make it possible to confirm prior beliefs. The results typically turn out to be sensitive to model specification, sample period, variable definitions and estimation methods. The status quo provides the motivation for dealing with this problem using three versions of the Kuznets curve for the purpose of illustration. The underlying hypotheses are represented by quadratic functions of income per capita, with a different dependent variable for each version of the Kuznets curve. Both time series and cross-sectional data are used to estimate the equations. The results turn out to be highly sensitive to a number of factors, which provides an incentive for being selective in the reporting of results while exhibiting confirmation bias. To overcome the model uncertainty problem one can resort to the use of one of several methods that are based on the reporting of the distribution rather than the point estimation of the coefficients.

Average marginal tax rates in the U.S., 1913–2019

Measuring marginal tax rates at the economy-wide level, while useful for empirical work in economics and beyond, is a non-trivial exercise. This paper revises and updates through 2019 the average marginal income and Social Security tax rates originally conceived by Barro and Sahasakul (1983, 1986) and Seater (1982, 1985) and subsequently updated by Stephenson (1998). The update extends previous estimates of the marginal tax rate series over two decades—thus providing more than a century of data going back to 1913. This update also revises the estimates reported in previous studies to provide historically consistent series following more recent comprehensive revisions of the National Income and Product Accounts. The newly updated series reflect tax legislation over the last two decades. Generally, tax cuts in the early 2000s along with the Great Recession led to a decrease in overall average marginal tax rates from levels achieved in the late 1990s. The Trump tax overhaul in 2017 lowered average marginal tax rates by about one percentage point. • Data on average marginal tax rates for the U.S. individual income tax, 1913–2019. • Data on average marginal tax rates for the Social Security tax, 1937–2019. • Average marginal comprehensive (income plus Social Security) tax rates.

Discussion on “ Transparency in Structural Research” by I. Andrews, M. Gentkow and J. Shapiro

We provide a complementary approach to global sensitivity analysis that should be useful for empirical work in economics.

Robust Inference Using Inverse Probability Weighting

Inverse probability weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of “small denominators,” trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this article, we first show that the IPW estimator can have different (Gaussian or non-Gaussian) asymptotic distributions, depending on how “close to zero” the probability weights are and on how large the trimming threshold is. As a remedy, we propose an inference procedure that is robust not only to small probability weights entering the IPW estimator but also to a wide range of trimming threshold choices, by adapting to these different asymptotic distributions. This robustness is achieved by employing resampling techniques and by correcting a non-negligible trimming bias. We also propose an easy-to-implement method for choosing the trimming threshold by minimizing an empirical analogue of the asymptotic mean squared error. In addition, we show that our inference procedure remains valid with the use of a data-driven trimming threshold. We illustrate our method by revisiting a dataset from the National Supported Work program. Supplementary materials for this article are available online.

Incentives Matter, But What Do They Mean? Understanding the Meaning of Market Coordination

ABSTRACT This article argues that the discussion of incentives in economics neglects a crucial question: why are some incentives felt as powerful reasons to alter actions, while other incentives have little, or even counterproductive, effect? We argue that an answer to this question can be found in recent empirical work in economic sociology, institutional economics and Austrian economics. This work studies the meaning of incentives in particular social settings and shows that incentives become meaningful in relation to those settings. We demonstrate that it illuminates why certain incentives are perceived as powerful reasons for action, while others are mostly ignored. We also explain why incentives are typically tied to certain social roles that can be identified through ideal-type analysis, and why situations of high uncertainty are of particular use for studying the activity of actors. In order to understand entrepreneurial action, financial markets, and why monetary rewards and market exchange are sometimes perceived as the wrong type of incentive, research should focus on how an uncertain future is understood by actors. We identify four building blocks in the work of Alfred Schutz, and suggest they yield a constructive research program at the intersection between economics and sociology.

Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics

In this essay I discuss potential outcome and graphical approaches to causality, and their relevance for empirical work in economics. I review some of the work on directed acyclic graphs, including the recent The Book of Why (Pearl and Mackenzie 2018). I also discuss the potential outcome framework developed by Rubin and coauthors (e.g., Rubin 2006), building on work by Neyman (1990 [1923]). I then discuss the relative merits of these approaches for empirical work in economics, focusing on the questions each framework answers well, and why much of the the work in economics is closer in spirit to the potential outcome perspective. (JEL C31, C36, I26)

Ensemble Methods for Causal Effects in Panel Data Settings

In many prediction problems researchers have found that combinations of prediction methods (“ensembles”) perform better than individual methods. In this paper we apply these ideas to synthetic control type problems in panel data. Here a number of conceptually quite different methods have been developed. We compare the predictive accuracy of three methods with an ensemble method and find that the latter dominates. These results show that ensemble methods are a practical and effective method for the type of data configurations typically encountered in empirical work in economics, and that these methods deserve more attention.

The fragility of results and bias in empirical research: an exploratory exposition

ABSTRACTThe results of empirical work in economics and finance are typically sensitive, inter alia, to model specification, sample period, variable definitions and estimation methods. If the underlying issue attracts bias of some sort, the reporting of the results tends to be selective, intended to confirm prior beliefs, which may be ideologically-driven, or to make the paper more publishable. In this paper the fragility (variability and sensitivity) of empirical results is demonstrated and the process whereby it enables research selection bias is described. For this purpose, several research areas are considered, including the determinants of economic growth, the effect of gender on risk aversion, the determinants of capital structure, the J-curve effect, the effect of corruption on foreign direct investment, and the Kuznets curve.

Inference in Linear Regression Models with Many Covariates and Heteroscedasticity

ABSTRACTThe linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker–White heteroscedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroscedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroscedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is provided, and the proposed methods are also illustrated with an empirical application. Supplementary materials for this article are available online.

The Empirical Economist’s Toolkit

While historians of economics have noted the transition toward empirical work in economics since the 1970s, less understood is the shift toward “quasi-experimental” methods in applied microeconomics. Angrist and Pischke (2010) trumpet the wide application of these methods as a “credibility revolution” in econometrics that has finally provided persuasive answers to a diverse set of questions. Particularly influential in the applied areas of labor, education, public, and health economics, the methods shape the knowledge produced by economists and the expertise they possess. First documenting their growth bibliometrically, this paper aims to illuminate the origins, content, and contexts of quasi-experimental research designs, which seek natural experiments to justify causal inference. To highlight lines of continuity and discontinuity in the transition, the quasi-experimental program is situated in the historical context of the Cowles econometric framework and a case study from the economics of education is used to contrast the practical implementation of the approaches. Finally, significant historical contexts of the paradigm shift are explored, including the marketability of quasi-experimental methods and the 1980s crisis in econometrics. JEL: B21, B23, B4, C1

Maternal Socio-Economic Status and the Well-Being of the Next Generation(S)

A rich literature in economics and the social sciences has shown that improvements in women's socio-economic status (SES) can also improve the well-being of their children. This chapter identifies several channels for this effect, drawing on both theoretical and empirical work in economics. Empirical evidence on the effects of maternal SES on child outcomes like health, education, and labor market success is presented, with a focus on recent work using new data sets and methodological innovations that allow for credible identification. The chapter also discusses emerging evidence that shocks to maternal well-being can affect not only a woman's own children, but future generations as well. Finally, the chapter highlights several fertile areas for future work.

The Empirical Economist's Toolkit: From Models to Methods

While historians of economics have noted the transition toward empirical work in economics since the 1970s, less understood is the shift toward methods in applied microeconomics. Angrist and Pischke (2010) trumpet the wide application of these methods as a credibility revolution in econometrics that has finally provided persuasive answers to a diverse set of questions. Particularly influential in the applied areas of labor, education, public, and health economics, the methods shape the knowledge produced by economists and the expertise they possess. First documenting their growth bibliometrically, this paper aims to illuminate the origins, content, and contexts of quasi-experimental research designs, which seek natural experiments to justify causal inference. To highlight lines of continuity and discontinuity in the transition, the quasi-experimental program is situated in the historical context of the Cowles econometric framework and a case study from the economics of education is used to contrast the practical implementation of the approaches. Finally, significant historical contexts of the paradigm shift are explored, including the marketability of quasi-experimental methods and the 1980s crisis in econometrics.

Political Arithmetic: Simon Kuznets and the Empirical Tradition in Economics by Robert Fogel, Enid Fogel, Mark Guglielmo, and Nathaniel Grotte

Reviewed by: Political Arithmetic: Simon Kuznets and the Empirical Tradition in Economics by Robert Fogel, Enid Fogel, Mark Guglielmo, and Nathaniel Grotte Steven G. Medema Robert Fogel, Enid Fogel, Mark Guglielmo, and Nathaniel Grotte. Political Arithmetic: Simon Kuznets and the Empirical Tradition in Economics. Chicago: University of Chicago Press, 2013. xiii + 148 pp. ISBN 13-978-0-226-25661-0, $32.00 (cloth); 978-0-226-02072-3, $30.00 (ebook). Robert Fogel is best known as an economic historian, one whose researches transformed the field of economic history and ultimately earned him the economics Nobel. In Political Arthimetic: Simon Kuznets and the Empirical Tradition in Economics, however, Fogel, along with co-authors Enid Fogel, Mark Guglielmo, and Nathaniel Grotte, pursue a rather different path—the intellectual history of economics in the twentieth century and the important role that Simon Kuznets played within that tradition. The book’s title, Political Arithmetic, takes us back to the origins of empirical economics in the work of Sir William Petty and others of the Baconian tradition, who attempted to put the emerging subject of political economy on an empirical footing. Even though their methods were crude by modern standards, and their data limited by the collection vagaries of the period, these scientists recognized that taking the measure of the world around them was essential for understanding what had be theorized about and for putting policy on solid footing. As Fogel’s discussion reveals, Kuznets was very much Petty’s intellectual heir. Kuznets received the Nobel Prize in 1971—the third prize awarded in economics—for his contributions to our understanding [End Page 472] of economic growth. His legacy is much broader than this, though, as Fogel and his co-authors bring out very nicely. Born in Russia in 1901, Kuznets emigrated to the United States in 1922 and completed his undergraduate and graduate education at Columbia University, earning his Ph.D. in 1926. His mentor at Columbia was Wesley Clair Mitchell, a pioneer of modern empirical analysis and a founder of the National Bureau of Economic Research (NBER), from which much of the earliest empirical work in economics in the United States emanated. Over the course of his career, Kuznets held academic appointments at the University of Pennsylvania, Johns Hopkins, and Harvard. However, he was far from the isolated academic. His career included stints at the U.S. Department of Commerce during the depression and with the Bureau of Planning and Statistics during World War II, as well as service on a range of other governmental and international agency commissions, and consultancies with governments around the globe. No less important was Kuznets’s work as an institution builder; he was a major national and international force in bringing together groups of scholars and policy makers to examine and construct data related to fundamental problems related to processes of economic growth and development. Historians of economics and of the social sciences generally have been rather slow in coming to Kuznets as a subject of analysis. The recent efforts of Glen Weyl to bring to publication Kuznets’s previously unpublished work on the economic history of the Jews1 are accompanied by his excellent introductory essay setting Kuznets’s work against the backdrop of his larger body of scholarship. However, Kuznets’s contributions to economics, although frequently referenced as pieces of larger historical narratives, have never been made the subject of serious analysis. The present volume begins to fill this lacuna—although, at 105 pages of text, it does not pretend to do so in exhaustive fashion. Instead, it locates the man and his work in the political, economic, and professional contexts of the middle third of the twentieth century and provides the reader with an overview of Kuznets’s contributions and how they fit within these contexts. The book opens with two chapters that describe the evolution of economics in the United States during the nineteenth century and the first third of the twentieth, with the second focusing in particular [End Page 473] on the empirical turn of the latter period and furtherance of this that came with the founding of the NBER. Kuznets enters the story, at last but only sporadically, in the...

The Envelope Theorem and Identification: Implications for Some Empirical Work in Finance and Economics

This paper is about identification and endogeneity in models that estimate the effects of instrument choice on performance (or wealth or welfare) in situations where the instruments are chosen to optimize performance. Such models occur frequently in corporate finance, analysis of happiness and effectiveness of economic policy. The instruments are endogenous because of the optimization. We suggest an impossibility theorem: that identification is not possible for this class of models, and that in a well-specified model the expected values of estimated impacts of instruments choices will be exactly zero. This is a direct application of the envelope theorem. We follow with four applications to empirical work in economics and finance.

Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments

Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. Sequential Monte Carlo comes very close to this ideal whereas other approaches like Markov chain Monte Carlo do not. This paper presents a sequential posterior simulator well suited to this computing environment. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm is robust to pathological posterior distributions, generates accurate marginal likelihood approximations, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.

SUBSTITUTION BETWEEN OFFLINE AND ONLINE ADVERTISING MARKETS

Online advertising is increasingly subject to antitrust scrutiny. Despite this scrutiny, in their 2010 article in this Journal, James Ratliff and Daniel Rubinfeld emphasized that there is a lack of empirical analysis on whether the large offline advertising market disciplines the online advertising market. In this comment, we summarize recent empirical work in economics that begins to fill this void by showing an effect of offline market restrictions on online advertising markets.

STRUCTURAL, EXPERIMENTALIST, AND DESCRIPTIVE APPROACHES TO EMPIRICAL WORK IN REGIONAL ECONOMICS

ABSTRACT The three general approaches to empirical work in economics are structural, experimentalist, and descriptive. This paper provides an overview of how empirical work in regional economics fits into these three categories. In particular, I examine a single issue in the field, the nature of agglomeration benefits and the productivity gains from agglomeration, and analyze the advantages and drawbacks of following each of these three empirical approaches. I also discuss potentially fruitful ways empirical work in regional economics might advance.

Exploring different visions of the model–empirics nexus: Solow versus Lipsey

Does empirical work in economics both provoke and test theoretical models, or does model development proceed according to a theory-oriented research program, with little interaction with empirics? Robert Solow and Richard Lipsey have articulated different visions of this relationship. This paper: (i) describes these competing Solow versus Lipsey views; (ii) presents examples illustrating each view; and (iii) draws inferences about factors promoting a close relation between empirics and modeling. Three examples are examined in detail: the ‘nursing shortage’ literature; Lind's analysis of recent rent control models; and a wide-ranging evaluation of ‘is there too little theory in development economics?’ by leading development economists. Various factors promoting or inhibiting a close connection between modeling and empirics are identified.

Invitation to economics: understanding argument and policy

List of Boxes. Preface. 1. Introduction. 1.1 What This Book Provides. 1.2 Looking around Corners. 1.3 Ideological Stance. 1.4 Economics Embedded in Philosophy and Sociology. 1.5 My Own Biases. 1.6 What Is to Come?. Part I: The Subject of Economics:. 2. What Economists Do. 2.1 What Economics Encompasses. 2.2 How Economists Work. 2.3 Some Characteristics of Economics. Appendix 2.1: Unrealistic Assumptions a Good Theory Make. Appendix 2.2: Is the Stock Market Efficient?. 3. How Well Do They Do It?. 3.1 Forecasting. 3.2 Choosing the Right Policies: The Problem of Value Judgments. 3.3 Implementing Economic Policies. 3.4 Biases. 3.5 Disagreement among Economists. Appendix 3.1: A Survey of American Academic Economists' Opinions. Part II: Some Infrastructure:. 4. Beguiling Words. 4.1 Some Widely Used Terms and Phrases. 4.2 Some Terms and Phrases Relating to Economics and Politics. 5. Important Economic Ideas: Often Misunderstood. 5.1 Levels versus Rates of Change. 5.2 Stocks versus Flows. 5.3 Real versus Nominal. 5.4 Future versus Current Dollars. 5.5 The Price Level versus Relative Prices. 5.6 Stable Prices versus Low Prices. 5.7 Gross versus Net and Single versus Double Counting. 5.8 Opportunity Costs versus Monetary Costs. 5.9 Cost--Benefit Analysis. 5.10 Wage Rates versus Labor Costs. 5.11 Capital Goods versus Human Capital. 5.12 Marginal versus Average Utility. 5.13 Marginal versus Average Costs. 5.14 Big Business versus Market Power. 5.15 Pro-market versus Pro-business. 6.16 and Can versus Will. 5.17 Comparative versus Absolute Advantage. Part III: Economic Theory and Policy:. 6. The Crown Jewel of Economics: The Price Mechanism. 6.1 An Overview of the Price Mechanism. 6.2 Equilibrium. 6.3 Shortages. 6.4 A Digression: The Equilibrium of the Firm. 6.5 What Happens in the Absence of a Price Mechanism?. 6.6 The Dark Side of the Price Mechanism. 6.7 The Upshot. Appendix 6.1: More on Supply and Demand. 7. Risk Taking and Incentives. 7.1 Risk Bearing and Entrepreneurship. 7.2 Incentives. 8. Looking around Corners. 8.1 Do Easier Bankruptcy Laws Help the Poor?. 8.2 Should Artists Receive a Share of the Capital Gain When Their Work Is Resold?. 8.3 Do We Want Fewer People Exploiting the Poor?. 8.4 Cut the Gas Tax to Slow the Rise of Soaring Gas Prices?. 8.5 A Costless Way to Alleviate Destitution in Third World Countries?. 8.6 Ban or Fine All Speculators?. 8.7 Is Limiting Tobacco Advertising an Effective Policy?. 8.8 Would Importing Pharmaceuticals from Canada Substantially Lower Drug Prices?. 8.9 An Efficient and Humane Way to Reduce Cocaine Production?. 8.10 Protecting Lives by Requiring Use of Seatbelts?. 8.11 Do You Really Want Your Bid to Win?. 8.12 Is Fast Chinese Growth Bad for the United States?. 8.13 In Emergencies Should Prices Ration Supply, Thus Letting the Rich Outbid the Poor?. 8.14 Is Gaining from Someone's Misfortune a Moral Wrong?. 8.15 Should Those Close at Hand Have a Special Responsibility to Alleviate Misery?. 8.16 Seven Short Ones. 8.17 On the Other Hand. 9. Natural Resources and Environmental Economics. 9.1 Running Out of Privately Owned Natural Resources. 9.2 The Tragedy of the Commons. 9.3 Pollution Permits and Taxes on Pollutants. 9.4 Tail-pipe Pollution. 9.5 Protecting Endangered Species. Part IV: Looking at Data:. 10. Empirical Economics. 10.1 Role of Empirical Work in Economics. 10.2 Types of Empirical Evidence. 10.3 What to Watch Out for in Statistics: Some General Problems. Appendix 10.1: Survey Data. Appendix 10.2: Measuring GDP, Savings Rates, Price Indexes, Employment, and Poverty. 11. Some Simple (?) Ways of Presenting Data: Percentages, Figures, and Graphs. 11.1 Percentages. 11.2 Figures and graphs. 12. Samples and Their Problems. 12.1 Selecting a Representative Sample. 12.2 Watching for Inappropriate Samples. 12.3 The Regression Fallacy: Building a Bias into the Sample. 12.4 Sample Size and Coincidence. 12.5 Sampling Error, Confidence Intervals, and Significance. 12.6 Some Warnings about Significance Tests. Appendix 12.1: The Normal Distribution. 13. Regressions: The Workhorse of Empirical Economics. 13.1 The Regression Coefficient. 13.2 How Well Does the Regression Fit the Data?. 13.3 What Should You Look at in a Regression?. 13.4 A Summing Up. 13.5 Looking the Workhorse in the Mouth. 13.6 In Conclusion. 14. The Workhorse in Action: Some Examples of Empirical Economics. 14.1 Consumption and Saving. 14.2 Should You Choose a Large Mutual Fund?. 14.3 The Slave Trade and African Economic Development. 14.4 The Effect of Tax Changes on Real GDP. Epilogue. Index