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Sophie Langenskiold AKADEMISK AVHANDLING PEER INFLUENCE ON SMOKING: CAUSATION OR CORRELATION? Sophie Langenskiold AKADEMISK AVHANDLING som for avlaggande av ekonomie doktorsexamen vid Handelshogskolan i Stockholm framlaggs for offentlig granskning fredagen den 13 j anuari 2006, kl 15.15 i sal KAW, Handelshogskolan, Sveavagen 65, Stockholm '~\ST.OCKHOLMSC.HOOL (:"I:J OF ECONOMICS ~~.'" HANDELSH0GSKOLAN I STOCKHOLI\.1 EFI, The Economic Research Institute EFI Mission EFI, the Economic Research Institute at the Stockholm School of Economics, is a scientific institution which works independently of economic, political and sectional interests. It conducts theoretical and empirical research in the management and economic sciences, including selected related disciplines. The Institute encourages and assists in the publication and distribution of its research findings and is also involved in the doctoral education at the Stockholm School of Economics. At EFI, the researchers select their projects based on the need for theoretical or practical development of a research domain, on their methodological interests, and on the generality ofa problem. Research Organization The research activities at the Institute are organized in 23 Research Centres. Centre Directors are professors at the Stockholm School ofEconomics. EFIResearch Centre: Centre Director: Management and Organisation (A) Sven-Erik Sjostrand Centre for Ethics and Economics (CEE) Hans de Geer Centre for Entrepreneurship and Business Creation (E) Carin Holmquist Public Management (F) Nils Brunsson Information Management (1) Mats Lundeberg Centre for People and Organization (PMO) Andreas Werr (acting) Centre for Innovation and Operations Management (T) Christer Karlsson Centre for Risk Research (CFR) Lennart Sj oberg Economic Psychology (P) Guje Sevon Centre for Consumer Marketing (CCM) Magnus Soderlund Centre for Information and Communication Research (CIC) Per Andersson (acting) Marketing, Distribution and Industrial Dynamics (D) Bjorn Axelsson Centre for Strategy and Competitiveness (eSC) Orjan Salvell Centre for Business and Economic History (BEH) Hakan Lindgren Accounting and Managerial Finance (B) Johnny Lind Centre for Financial Analysis and Managerial Economics in Kenth Skogsvik Accounting (BFAC) Finance (FI) Clas Bergstrom Centre for Health Economics (CHE) Bengt Jonsson International Economics and Geography (LEG) Mats LundaW Economics (S) Lars Bergman Economic Statistics (ES) Anders Westlund Law (RY) Erik Nerep Centre for Tax Law (SR) Bertil Wiman Chair ofthe Board' Professor Carin Holmquist Director: Associate Professor Filip Wijkstrom Address EFI, Box 6501, SE-113 83 Stockholm, Sweden • Homepage: www.hhs.se/efi/ Telephone: +46(0)8-736 9000 • Fax: +46(0)8-31 62 70 • E-mail [email protected] PEER INFLUENCE ON SMOKING: CAUSATION OR CORRELATION? Sophie Langenskiold l:it, STOCKHOLM SCHOOL \~i:J OF ECONOMICS ~.~~.~ HANDELSHOGSKOLAN I STOCKHOLM EFI, The Economic Research Institute ,,";?ii3-~~ f'S'~'S'l Dissertation for the Degree of Doctor of Economics ".~~~.!' Stockholm School of Economics ABSTRACT: In this thesis, we explore two different approaches to causal inferences. The traditional approach models the theoretical relationship between the outcome variables and their explanatory variables, i.e., the science, at the same time as the systematic differences between treated and control subjects are modeled, i.e., the assignment mechanism. The alternative approach, based on Rubin's Causal Model (RCM), makes it possible to model the science and the assignment mechanism sepa­ rately in a two-step procedure. In the first step, no outcome variables are used when the assignment mechanism are modeled, the treated students are matched with similar control students using this mechanism, and the models for the science are determined. Outcome variables are only used in the second step when these pre-specified models for the science are fitted. In the first paper, we use the traditional approach to evaluate whether a husband is more prone to quit smoking when his wife quits smoking than he would have been had his wife not quit. We find evidence that this is the case, but that our analysis must rely on restrictive assumptions. In the subsequent two papers, we use the alternative RCM approach to evaluate if a Harvard freshman who does not smoke (observed potential outcome) is more prone to start smoking when he shares a suite with at least one smoker, than he would have been had he shared a suite with only smokers (missing potential outcomes). We do not find evidence that this is the case, and the small and insignificant treatment effect is robust against various assumptions that we make regarding covariate adjustments and missing potential outcomes. In contrast, we do find such evidence when we use the traditional approach previously used in the literature to evaluate peer effects relating to smoking, but the treat­ ment effect is not robust against the assumptions that we make regarding covariate adjustments. These contrasting results in the two latter papers allow us to conclude that there are a number of advantages with the alternative RCM approach over the traditional approaches previously used to evaluate peer effects relating to smoking. Because the RCM does not use the outcome variables when the assignment mechanism is modeled, it can be re-fit repeatedly without biasing the models for the science. The assignment mechanism can then often be modeled to fit the data better and, because the models for the science can consequently better control for the assignment mechanism, they can be fit with less restrictive assumptions. Moreover, because the RCM models two distinct processes separately, the implications of the assumptions that are made on these processes become more transparent. Finally, the RCM can derive the two potential outcomes needed for drawing causal inferences explicitly, which enhances the transparency of the assumptions made with regard to the missing potential outcomes. KEYWORDS: Peer effects, smoking, Harvard freshmen, quasi-experimental study, Rubin's Causal Model, imputation, propensity score matching, Mahalanobis-metric matching, Bonferroni-adjusted p-Ievels. vii @EFI and the author, 2005 ISBN NR 91-7258-692-3 PRINTED BY: Elanders Gotab, Stockholm 2005 DISTRIBUTED BY: EFI, The Economic Research Institute Stockholn1 School of Economics POBox 6501, SE-113 83 Stockholn1 www.hhs.se/efi Contents Acknowledgements xi Part 1. Introduction 1 Description of thesis 3 Summary of papers 7 References 13 Part 2. Papers 15 Causal inference according to the traditional approach 17 1. Introduction 17 2. Theory 18 3. Empirical strategy 20 4. Data 23 5. Empirical results 25 6. Conclusion 31 7. Tables 32 References 39 Designing a study using Rubin's Causal Model (Part I) 41 1. Introduction 41 2. Theory and perspectives 44 3. The template experiment 49 4. Covariate data preparation 52 5. Replicating a randomized study 54 6. "Practice" outcome analyses 60 7. Conclusion 63 8. Tables 67 References 103 Analyzing the results using Rubin's Causal Model (Part II) 105 1. Introduction 105 2. Theory 107 3. Preparation 113 4. Analysis 116 ix x CONTENTS 5. "Hunting for results" 118 6. Conclusion 121 7. Tables 124 References 131 Part 3. A quasi-experimental study 133 A freshman study at Harvard College 135 1. Applying for approval 135 2. Recruiting students 136 3. Interviewing participants 138 4. Questionnaires 138 5. Informed consent forms 154 References 161 Acknowledgements Before I began my graduate studies, I knew that the experience would be a challenge. But I never quite understood how daunting a challenge it would be. In fact, the first semester was one of the hardest times in my life. Because I started my graduate stud­ ies five years after I finished my undergraduate studies, I had forgotten much of the basic requirements for the first two courses in mathematics and microeconomics. As a consequence, I sat through lectures that were completely incomprehensible to me from beginning to end. But, I got through these two courses, the rest of the coursework, and my thesis work. I would never have seen it through, however, without the support that I was fortunate enough to have from the people around me. I have been fortunate to have Professor Magnus Johannesson at Stockholm School of Economics as my advisor. Magnus has always been very dedicated to my work and available to discuss research and to comment on my written material on short notice. His support has been vital to n1e for finishing n1Y dissertation. Magnus also helped me get the most out of my graduate studies by encouraging my wish to spend nearly four years at Harvard University. Without his willingness to direct my research over the phone and his involvement in my applications for funding and other critical factors, I would not have had this opportunity. I have also had the opportunity to have Professor Donald Rubin at Harvard University as my advisor during the last couple of years. Donald has been very involved in the two last papers in this thesis and he has been tremendously generous, not only with his time, but also with his ideas and feedback. I have learned enormously from the many discussions that we have had, as well as the detailed feedback that I received from him on written work. Not only have I gained a deeper knowledge about the field that Donald has been instrun1ental in developing, but from him I also learned about the art of writing a paper and the responsibility that comes along with being a researcher. Donald's intuition for statistics and his intuitive
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