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Nber Working Paper Series Natural Experiments In NBER WORKING PAPER SERIES NATURAL EXPERIMENTS IN MACROECONOMICS Nicola Fuchs-Schuendeln Tarek Alexander Hassan Working Paper 21228 http://www.nber.org/papers/w21228 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 June 2015 Chapter prepared for the Handbook of Macroeconomics. The chapter has benefitted from helpful discussions and comments from Yuriy Gorodnichenko, Daron Acemoglu, Jonathan Dingel, Yuriy Gorodnichenko, Chang-Tai Hsieh, Nathan Nunn, Elias Papaioannou, Rob Vishny, Mirko Wiederholt, and seminar participants of the Handbook of Macroeconomics Conference at Stanford and at the Deutsche Bundesbank. Leonhard Czerny, Denis Gorea, and Philip Xu provided excellent research assistance. Fuchs-Schündeln gratefully acknowledges financial support from the Cluster of Excellence "Formation of Normative Orders" and the European Research Council under Starting Grant No. 262116. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2015 by Nicola Fuchs-Schuendeln and Tarek Alexander Hassan. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Natural Experiments in Macroeconomics Nicola Fuchs-Schuendeln and Tarek Alexander Hassan NBER Working Paper No. 21228 June 2015 JEL No. C1,C9,E21,E62,H31,O11,O14,O43,O50 ABSTRACT A growing literature relies on natural experiments to establish causal effects in macroeconomics. In diverse applications, natural experiments have been used to verify underlying assumptions of conventional models, quantify specific model parameters, and identify mechanisms that have major effects on macroeconomic quantities but are absent from conventional models. We discuss and compare the use of natural experiments across these different applications and summarize what they have taught us about such diverse subjects as the validity of the Permanent Income Hypothesis, the size of the fiscal multiplier, and about the effects of institutions, social structure, and culture on economic growth. We also outline challenges for future work in each of these fields, give guidance for identifying useful natural experiments, and discuss the strengths and weaknesses of the approach. Nicola Fuchs-Schuendeln Goethe University Frankfurt House of Finance 60323 Frankfurt Germany [email protected] Tarek Alexander Hassan Booth School of Business University of Chicago 5807 South Woodlawn Avenue Chicago, IL 60637 and NBER [email protected] Contents 1 Introduction 4 2 Verification: The Permanent Income Hypothesis 7 2.1 Reaction of Consumption to Unexpected Income Shocks . 9 2.1.1 Unexpected Temporary Income Shocks . 9 2.1.2 An Unexpected Permanent Income Shock: the Natural Experiment of German Reunification . 10 2.2 Reaction of Consumption to Expected Income Changes . 13 2.2.1 Random Treatment: Determining an Appropriate Control Group . 15 2.2.2 The Presence of Liquidity Constraints . 19 2.2.3 Overview of Natural Experiment Studies of the Permanent Income Hypothesis . 22 2.2.4 Violation of Rational Expectations or Need for Model Extension? . 23 3 Quantification: The Fiscal Multiplier 28 3.1 Permanent Income Hypothesis Studies and the Fiscal Multiplier . 29 3.2 Military News Shocks as Natural Experiments . 31 3.3 Local Fiscal Multipliers . 32 4 Identification: Causal Factors in Economic Growth 38 4.1 The Fundamental Causes of Growth . 38 4.2 Institutions and Political Economy . 41 4.2.1 The Effect of Institutions on Growth . 42 4.2.2 The Effect of Institutions on Business Cycles and Conflict . 47 4.2.3 Persistent Effects of Historical Institutions . 48 4.2.4 Determinants and Dynamics of Institutions . 50 4.3 Social Structure . 55 4.3.1 The Effect of Social Ties on Growth . 56 4.3.2 The Effect of Social Ties on Trade and Other Aggregates . 60 4.3.3 The Effect of Internal Social Structure on Institutions and Growth 64 4.4 Trust and Civic Capital . 66 4.4.1 The Effect of Trust on Growth . 67 4.4.2 Effect of Trust on Financial Development and Other Aggregates . 69 4.4.3 Determinants and Dynamics of Trust . 70 2 4.5 Multiple Equilibria and Path Dependence . 73 5 Conclusion 77 3 1 Introduction Establishing causality is a major challenge in economics, especially in macroeconomics, where the direction of various important causal relationships is widely debated, as illus- trated, for example, by large-scale debates about the causal effects of monetary and fiscal policies. Most empirical applications of macroeconomic models focus on matching con- ditional correlations and improving the fit of models to a set of data moments. Despite substantial advances in this area in recent years, these conditional correlations often can- not identify causal chains. For example, New Keynesian models and real business cycle models can match similar sets of conditional correlations but have very different predic- tions about the causal effects of fiscal or monetary policies. This lack of identification of clear causal channels is especially troubling when one is providing policy advice. In applied microeconomic fields, causality is often established by designing laboratory or field experiments. In these types of experiments, the researcher consciously influences the economic environment in a way that allows the establishment of causality. The most prevalent and clearest method in this spirit is to randomly allocate agents into a treatment group and a control group, and then analyze the effect of the treatment by directly com- paring the relevant outcome variables between both groups, or the change in the outcome variables of both groups coinciding with the introduction of the treatment in a difference- in-differences approach. Field experiments randomize treatment in a real-world economic environment, whereas laboratory experiments do so in a controlled environment. Both of these methods are mostly unavailable to macroeconomists for fairly obvious reasons. Because macroeconomics deals with phenomena that affect the economy at large (e.g., economic growth, unemployment, monetary policy, fiscal policy), any field interventions would be very expensive and would have far-reaching consequences because they cannot easily be targeted at a specific small group, making it unlikely that anyone would agree to carry them out. Bringing key features of the economic environment into the labora- tory is also complicated in macroeconomics, where the interplay of different agents and markets often plays a key role (see Duffy (2008) for a survey of laboratory experiments in macroeconomics). Natural experiments are an alternative to field and laboratory experiments. For the purposes of our discussion, we define natural experiments as historical episodes that pro- vide observable, quasi-random variation in treatment subject to a plausible identifying assumption. The \natural" in natural experiments indicates that a researcher did not consciously design the episode to be analyzed, but researchers can nevertheless use it to 4 learn about causal relationships. The episode under consideration can be a policy inter- vention carried out by policy makers (e.g., changes in the tax law), historical episodes that go beyond simple policy measures (e.g., the fall of Communism), or a so-called \natural natural" experiment that arises from natural circumstances (rainfall, earth quakes, etc.). Whereas the main task of a researcher carrying out a laboratory or field experiment lies in designing it in a way that allows causal inference, the main task of a researcher analyzing a natural experiment lies in arguing that in fact the historical episode under considera- tion resembles an experiment, and in dealing with weaknesses of the ex-post experimental setup that one would have avoided a priori in a designed experiment. To show that the episode under consideration resembles an experiment, identifying valid treatment and control groups, that is, arguing that the treatment is in fact randomly assigned, is crucial. Establishing such quasi-random treatment involves showing that two groups are comparable along all relevant dimensions for the outcome variable except the one involving the treatment. The methods used to do this are often adapted from the micro-econometric literature on field and laboratory experiments. Rather than attempting to cover all papers in macroeconomics that feature natural experiments (which would be a formidable task), we instead select three specific lines of enquiry that use natural experiments for three different purposes: to verify underlying model premises (\verification”), to quantify specific policy parameters (\quantification”), and to identify causal mechanisms that operate outside conventional models (\identifica- tion"). The first line is the literature on the Permanent Income Hypothesis. In contrast to the simple Keynesian consumption theory, the Permanent Income Hypothesis assumes agents are rational and forward-looking when making their consumption decisions. Therefore, not only current income and current assets, but also the expected value of future income plays a role for the optimal consumption choice today. This forward-looking behavior can be subjected to a very simple test if a preannounced income change happens: the
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