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Market Consequences of ICT Innovations ifo Beiträge 70 zur Wirtschaftsforschung Market Consequences of ICT Innovations Constantin Mang Institut Leibniz-Institut für Wirtschaftsforschung an der Universität München e.V. Herausgeber der Reihe: Clemens Fuest Schriftleitung: Chang Woon Nam ifo Beiträge 70 zur Wirtschaftsforschung Market Consequences of ICT Innovations Constantin Mang Institut Leibniz-Institut für Wirtschaftsforschung an der Universität München e.V. Bibliografische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar ISBN-13: 978-3-95942-017-4 Alle Rechte, insbesondere das der Übersetzung in fremde Sprachen, vorbehalten. Ohne ausdrückliche Genehmigung des Verlags ist es auch nicht gestattet, dieses Buch oder Teile daraus auf photomechanischem Wege (Photokopie, Mikrokopie) oder auf andere Art zu vervielfältigen. © ifo Institut, München 2016 Druck: ifo Institut, München ifo Institut im Internet: http://www.cesifo-group.de Preface This volume was prepared by Constantin Mang while he was working at the Ifo Institute. It was completed in 2014 and accepted as a doctoral thesis by the Department of Economics at the University of Munich. It includes a short introduction and four self-contained chapters that focus on ICT innovations in different markets. Chapter 1 gives a short introduction into the topic and lays out some general aspects about the methodological frameworks used in this volume. Chapter 2 is focused on the effects of broadband Internet on the housing market in order to quantify the value of broadband access in terms of a premium on housing rents. Methodologically, the chapter uses micro data from Germany’s largest online platform for real estate advertisements and estimates a hedonic model with rent prices. Chapter 3 investigates the association of online job search and matching quality using individual-level data from the German Socio-Economic Panel (SOEP). It measures matching quality by the respondent’s evaluation of his new job compared to his former job. The results show that job changers who found their new job online are better matched than their counterparts who found their new job through traditional channels. Chapter 4 provides evidence on the effectiveness of PC use in schools across countries. Using data from the international student achievement test TIMSS, it estimates the effect of PC use on student test scores and finds that using a PC for some activities has positive effects on student achievement, whereas using a PC for other activities has negative effects. Chapter 5 investigates the effects of a mobile Internet infrastructure upgrade on the take-up of location-based online services. Using data from the largest German online platform for restaurant reviews, it finds that upgrading the mobile Internet infrastructure increases the number of restaurant reviews and the share of reviews written on smartphones. Keywords: Broadband Internet, mobile Internet, value, hedonic pricing, job search, matching quality, PC use in classrooms, e-learning, infrastructure upgrade. JEL-No.: L86, L96, R31, R42, H54, O18, I21, I28, J64. Acknowledgements First and foremost, I would like to thank my supervisor Ludger Woessmann for his constant guidance, advice and support during my PhD studies. I am also deeply grateful to Oliver Falck, my co-supervisor. His helpful comments, inspiring ideas and continuous encouragements were an enormous help to me. Furthermore, I thank Monika Schnitzer for joining the dissertation committee. I sincerely thank Oliver Falck and Ludger Woessmann for their collaboration on Chapter 4 and Martin Micheli for his collaboration on Chapter 2 of this thesis. I also owe many thanks to my current and former colleagues at the Ifo Center for the Economics of Education and Innovation for inspiring conversations and helpful suggestions. Particularly, I would like to thank Nadine Fabritz for supporting me in any possible way during the finalization phase of this thesis. I have greatly benefitted from working together with Francesco Cinnirella, Nina Czernich, Bernhard Enzi, Alexandra Heimisch, Erik Hornung, Stefan Kipar, Franziska Kugler, Philipp Lergetporer, Susanne Link, Andreas Mazat, Natalie Obergruber, Marc Piopiunik, Jens Ruhose, Benedikt Siegler, Martin Schlotter, Ruth Maria Schüler, Guido Schwerdt, Benedikt Siegler and Katharina Werner. I also received comments and suggestions from many other friends and colleagues that unfortunately cannot all be mention here. I gratefully acknowledge financial support from Deutsche Telekom AG. I owe my deepest gratitude to my parents and my sister Isabelle for their unconditional support that was the basis of my work on the PhD thesis. Market Consequences of ICT Innovations Inaugural-Dissertation zur Erlangung des Grades Doctor oeconomiae publicae (Dr. oec. publ.) an der Ludwig-Maximilians-Universität München 2014 vorgelegt von CONSTANTIN MANG Referent: Prof. Dr. Ludger Wößmann Korreferent: Prof. Dr. Oliver Falck Promotionsabschlussberatung: 5. November 2014 Contents 1 Introduction 1 1.1 Consequences for Markets . 2 1.2 Methodological Framework . 6 1.3 Outline of the Thesis . 8 2 The Value of Broadband Internet - Evidence from the Housing Market 13 2.1 Related Literature . 16 2.2 A Hedonic Market for Broadband Internet . 19 2.3 Data . 23 2.4 Empirical Strategy . 26 2.4.1 Estimation of the Hedonic Price for Broadband . 26 2.4.2 Historical Features of Broadband Infrastructure in Germany . 27 2.4.3 Exploiting Variation in the Distance Between Properties and MDFs . 29 2.4.4 Exploiting Variation in the Roll-out Costs for Properties that are Far Away from their MDF . 31 2.4.5 Exploiting Variation from a Technological "Mistake" in Eastern Germany . 32 2.5 Results . 33 2.5.1 The Effect of DSL Availability on Property Prices . 33 2.5.2 Interpretation of the Estimates Size . 36 2.5.3 Comparison of our Estimates with Estimates from Previous Studies . 38 2.6 Validation of Results with Survey Data from SOEP . 39 2.6.1 Description of SOEP Data . 39 2.6.2 The Value of Broadband for SOEP Households . 40 2.7 Conclusion . 41 Figures and Tables . 43 Appendix . 52 3 Online Job Search and Matching Quality 57 3.1 Data and Methodology . 60 3.1.1 Data on Job Search Methods and Matching Quality . 60 3.1.2 Estimation Strategy . 62 3.2 The Association of Internet Job Search and Matching Quality . 64 3.2.1 Baseline Results . 64 3.2.2 Results in Matched Samples . 65 3.2.3 Effect Heterogeneity . 66 3.2.4 How Internet Job Search Compares to Other Search Methods . 69 3.3 Discussion of Potential Selection Issues . 70 3.3.1 Systematic Differences in Job Seeker Characteristics . 70 3.3.2 Selection into Search Intensities . 72 i ii 3.3.3 Selection of Advertised Jobs . 74 3.4 Conclusion . 76 Figures and Tables . 77 4 PC use in Classrooms and Student Achievement 91 4.1 Related Literature . 95 4.2 Data . 97 4.3 Empirical Framework . 102 4.4 Results . 107 4.5 Student Heterogeneities . 110 4.6 Country Heterogeneities . 113 4.7 The Fourth-Grade Sample . 119 4.8 Conclusion . 122 Figures and Tables . 125 Appendix . 136 5 The Effect of Mobile Internet Infrastructure on Local Online Services 141 5.1 Related Literature . 144 5.2 Data . 146 5.2.1 Restaurant and Review Data from Qype . 147 5.2.2 Spatial and Regional Data . 148 5.2.3 Descriptive Statistics . 150 5.3 Identification Strategy . 152 5.4 Effects on the Number of Restaurant Reviews . 157 5.5 Mobile Reviews and the Restaurant Rating . 160 5.5.1 Do Mobile Reviewers Give Better Ratings? . 161 5.5.2 Are Restaurants with More Reviews Better Rated? . 164 5.6 Robustness . 165 5.6.1 Threats to the Exogeneity of a Clear Line-of-Sight . 165 5.6.2 Sensitivity to Different Sample Restrictions . 168 5.7 Conclusions . 170 Figures and Tables . 172 References 190 List of Tables 2.1 Descriptive statistics of ImmoScout24 data in the full sample and three estimation samples . 46 2.2 OLS and IV estimates in the distance sample of ImmoScout24 properties . 47 2.3 OLS and IV estimates in the above-threshold sample of ImmoScout24 properties . 48 2.4 OLS and IV estimates in the OPAL sample of ImmoScout24 properties . 49 2.5 Comparison of OLS estimates for each sample in east and west Germany . 50 2.6 OLS and IV estimates for west and east Germany in the SOEP sample . 51 A2.1 Descriptive statistics of SOEP 2008 survey data on rented properties . 54 A2.2 The association between Internet use intensity and distance to the MDF . 55 3.1 All job changers and job changers who found their job through the Internet . 81 3.2 Sample means by job search method . 82 3.3 The association between online job search and several matching outcome variables . 83 3.4 Regression-adjusted matching samples . 84 3.5 Effect heterogeneity with the dependent variable "skill use" . 85 3.6 Different job search channels compared to the employment office . 86 3.7 Reduced sample for comparison of Internet with newspaper and friends . 87 3.8 Internet job finders and personality traits . 88 3.9 Controlling for respondent’s assessment of working time as a way to account for bias from systematic differences in job changer characteristics . 89 3.10 Controlling for whether job changers were actively searching for a job . 90 4.1 PC availability and usage intensities by subject for eighth-grade students in TIMSS . 127 4.2 Cross-sectional OLS and seemingly unrelated regressions for eighth-grade students . 128 4.3 Within-student between-subject effect of PC use activities for eighth-grade students . 129 4.4 The effect of PC use activities in subsamples of top-performing and of disadvantaged eighth-grade students . 130 4.5 The effect of PC use activities for eighth-grade students in subsamples of countries with specific characteristics .
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