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UCLA Electronic Theses and Dissertations UCLA UCLA Electronic Theses and Dissertations Title Firm Heterogeneity in Macroeconomics Permalink https://escholarship.org/uc/item/8th5n0n2 Author Tran, Allen Publication Date 2014 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California University of California Los Angeles Firm Heterogeneity in Macroeconomics A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Economics by Allen Tran 2014 c Copyright by Allen Tran 2014 Abstract of the Dissertation Firm Heterogeneity in Macroeconomics by Allen Tran Doctor of Philosophy in Economics University of California, Los Angeles, 2014 Professor Hugo Hopenhayn, Chair Macroeconomic models are often estimated with aggregate data, aligning the ag- gregated behavior of firms and households in models to the data. However, using aggregate data alone can overlook important details of firm behavior that are cru- cial for understanding issues in macroeconomics. In this dissertation, I use data on firms at the micro-level to more accurately capture firms behavior and their interactions with one another. This approach is applied to answer questions that relate to the monetary policy transmission mechanism, economic growth from new entrants and welfare gains from new technology. A substantial literature exists which suggests that imperfect information across firms is capable of generating large monetary non-neutralities. In Chapter One, the level of imperfect information is taken from micro-data and used to discipline a standard menu cost model augmented with information frictions. In the model, imperfect information has a negligible effect and real responses to a monetary shock are small and transient in contrast to the bulk of the imperfect information literature. The selection effect dominates the effects of imperfect information as the level of dispersion in inflation expectations in the data is tiny. This result still holds even when the level of dispersion is set to that of the maximal observed levels of dispersion. ii Chapter Two presents data that suggests new entering establishments compete for customers, rather than inputs in order to grow. Consistent with the data, I present a model where customers satisfice in forming relationships with establish- ments in the presence of search frictions. The extent of these search frictions is a new margin that affects selection and allocative efficiency. As search becomes less random and more directed, customers are less willing to satisfice, improving allocative efficiency and inducing exit of slower growing firms. When search fric- tions in product markets are increased to match establishment dynamics in Chile, output falls by roughly 14 per cent relative to the model calibrated to the US, reflecting decreased allocative efficiency. Chapter Three studies the impact of online retail on aggregate welfare. I de- velop a new measure of store level retail productivity and with a spatial model, calculate each store's equilibrium response to increased competitive pressure from online retailers. From counterfactual exercises mimicking improvements in ship- ping and increased internet access, I estimate that improvements in online retail increased aggregate welfare from retail activities by 13.4 per cent. Roughly two- thirds of the increase can be attributed to welfare improvements holding fixed market shares, with the remainder due to reallocation. Surprisingly, 8.2 percent of firms actually benefit as they absorb market share from closed stores. Finally, I estimate that the proposed Marketplace Fairness Act would claw back roughly one-third of sales that would otherwise have gone to online retailers between 2007- 12. iii The dissertation of Allen Tran is approved. Andrew Atkeson Ariel Burstein Pablo Fajgelbaum Hanno Lustig Hugo Hopenhayn, Committee Chair University of California, Los Angeles 2014 iv Table of Contents 1 Dispersion in Beliefs and Price Setting ::::::::::::::: 1 1.1 Introduction . 1 1.2 Model . 4 1.2.1 Consumers . 5 1.2.2 Monetary policy . 6 1.2.3 Firms . 7 1.2.4 Market clearing . 9 1.2.5 Solution algorithm . 10 1.2.6 Parameter selection . 11 1.3 Results . 17 1.3.1 Discussion . 22 1.4 Conclusion . 24 2 Product Market Frictions, Selection and Allocative Efficiency 26 2.1 Introduction . 26 2.1.1 Related literature . 32 2.2 Data . 34 2.2.1 Rivalrous growth . 37 2.2.2 Wages do not drive selection . 42 2.2.3 Evolution of cumulative growth . 44 2.3 Model . 46 2.4 Estimation . 56 v 2.5 Results . 63 2.6 Conclusion . 68 2.7 Appendix . 70 2.7.1 Definition of equilibrium . 71 2.7.2 Solution algorithm . 71 3 The Aggregate Impact of Online Retail ::::::::::::::: 72 3.1 Introduction . 72 3.1.1 Related literature . 75 3.2 Data . 77 3.3 Model . 78 3.3.1 Consumers . 80 3.3.2 Retail stores . 83 3.3.3 Aggregate welfare . 86 3.3.4 Equilibrium . 87 3.4 Estimation . 88 3.4.1 Demand system . 88 3.4.2 Fixed costs . 98 3.5 Results . 103 3.5.1 Improvements in online retail . 104 3.5.2 Effects of the Marketplace Fairness Act (2013) . 121 3.6 Conclusion . 123 3.7 Appendix . 127 3.7.1 Estimates of internet access by zip code . 127 3.7.2 Proof of uniqueness and existence . 129 vi 3.7.3 BLP algorithm with endogenous prices . 131 3.7.4 Entry and exit algorithm . 133 3.7.5 Results at bounds of fixed costs . 135 vii List of Figures 1.1 Dispersion in inflation expectations . 14 1.2 Impulse response of the aggregate price to a monetary shock . 19 1.3 Impulse response of output to a monetary shock . 20 2.1 Exit rates and spread in 80th-50th percentile cumulative growth (log units) . 39 2.2 Evolution of cumulative growth in USA . 45 2.3 Evolution of cumulative growth in Chile . 46 2.4 Spread of growth estimated for US (top) and Chile (bottom) . 64 2.5 Effect of θ on moments relative to values at estimated parameters 65 2.6 Effect of θ on model relative to values at estimated parameters . 66 3.1 Growth of online retail sales . 73 3.2 Estimated improvements in internet access across zip codes (2007 to 2012) . 106 3.3 Shipping and fulfillment expansion at Amazon.com . 108 3.4 Productivity improvements at FedEx and UPS . 109 3.5 Online market shares by industry . 111 3.6 E-commerce sales growth across products (2007 to 2011) . 112 viii List of Tables 1.1 Calibrated Parameters . 11 1.2 Estimated Parameters . 16 1.3 Moments from Data and Model . 16 1.4 Law of Motion for Inflation . 17 1.5 Variance decomposition . 20 1.6 Signal to noise ratio . 22 2.1 Fixed effects regression . 40 2.2 Probit regression on exit . 42 2.3 Regression for average wages (log units) . 43 2.4 Entrant size distribution . 45 2.5 Moments from the data and the model . 62 2.6 Estimated and calibrated parameter values . 63 3.1 List of moments . 92 3.2 Summary of parameter estimates and standard errors . 94 3.3 Relative demand to a store 0 miles away . 95 3.4 Implied dollar costs of traveling to stores . 95 3.5 Variation in prices across markets . 96 3.6 Markups and demographics . 97 3.7 Mean store-level surplus and demographics . 98 3.8 Estimates of fixed costs . 103 3.9 Results from counterfactual exercises . 115 3.10 Regression on store sales . 116 ix 3.11 Welfare decomposition . 119 3.12 Decomposing aggregate profits . 120 3.13 Distribution of profit changes at surviving firms . 121 3.14 Average state tax rates inclusive of city and county rates . 122 3.15 Estimated potential effects of Marketplace Fairness Act (2007-12) 123 3.16 Internet access and demographics . 127 3.17 Results from counterfactual exercises . 136 x Acknowledgments I wish to thank my dissertation committee and the faculty at UCLA for help and guidance throughout my graduate studies. In particular, Hugo Hopenhayn for expanding my view of macroeconomics and inspiring my fascination with data, Pablo Fajgelbaum for honest criticism that made my research more rigorous and ambitious, and Andy Atkeson for insight that often improved my understanding of my own research. For her help at the California Census RDC, I thank Abigail Cooke. I am grateful to Emily, her family and Yuki for making Los Angeles a second home, and my teammates at Wikipedia Brown for providing a constant distraction from research. My deepest gratitude is to my family for their love and support. Kelvin and Tina, for demonstrating that the world was larger than Lalor, and my mother and father, who sacrificed everything for a better life for their children. Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. xi Vita 2002{2007 Bachelor of Commerce, Bachelor of Information Systems, Uni- versity of Melbourne. 2008{2009 Research Economist, Reserve Bank of Australia. 2012 Masters of Arts (Economics), UCLA, Los Angeles, California. Publications Reconciling Microeconomic and Macroeconomic Estimates of Price Stickiness (with Adam Cagliarini and Tim Robinson), Journal of Macroeconomics, 2011, 33(1), pages 102-120. xii CHAPTER 1 Dispersion in Beliefs and Price Setting 1.1 Introduction There exists a wide literature on models where nominal rigidities are the key fric- tion in generating monetary non-neutralities. Using a menu cost model capable of fitting the micro-data on price setting, Golosov and Lucas (2007) show that the mechanism by which nominal rigidities are implemented is important. Real responses to monetary shocks are small and transient in the menu cost model compared to the large and persistent responses in an equivalent Calvo-based pric- ing model.
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