The initial wireless phone industry in the United States had many competitors, but due to mergers and acquisitions the industry has become highly consolidated. This paper documents the history of the consolidation. More importantly, I use the geographic path of consolidation to distinguish whether consolidation has been motived by retail market power or efficiency explanations. One efficiency explanation is that consumers prefer national coverage areas. I use data on roaming agreements in the early cellular industry to analyze whether contracts can substitute for roaming agreements. Finally, in joint work with Patrick Bajari and Stephen Ryan we estimate the consumer valuation for national coverage areas using plan demand data.
"The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers' heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available"--National Bureau of Economic Research web site.
We study the dual relationship between market structure and prices and between market structure and investment in mobile telecommunications. Using a uniquely constructed panel of mobile operators' prices and accounting information across 33 OECD countries between 2002 and 2014, we document that more concentrated markets lead to higher end user prices. Furthermore, they also lead to higher investment per mobile operator, though the impact on total investment is not conclusive. Our findings are not only relevant for the current consolidation wave in the telecommunications industry. More generally, they stress that competition and regulatory authorities should take seriously the potential trade-off between market power effects and efficiency gains stemming from agreements between firms.
This dissertation studies the U.S. wireless telecommunications industry. I propose a novel model, which I then estimate. I analyze potential counterfactual results in the first chapter of my dissertation. Second (joint with Nick Doudchenko) and third (joint with Dmitry Arkhangelsky and Lanier Benkard) chapters develop methodology that is necessary for the estimation of the models alike the one analyzed in the first chapter. The first chapter studies the outcomes of a hypothetical T-Mobile/Sprint and AT& T/T-Mobile mergers in the U.S. wireless telecommunications industry. I propose a model in which consumers trade off price and network coverage, so firms have to compete on both price and investment. The key finding is that had T-Mobile and Sprint merged in 2009, consumers would have benefited from expanded network coverage. The two firms would have increased profits due to less duplication on the investment side. An acquisition of T-Mobile by AT& T, on the other hand, would have harmed consumers because it would not have resulted in better coverage. Additionally, the outcomes of the T-Mobile/Sprint merger vary across geographic areas. Markets with high population density or flat terrain typically have a strong initial Sprint or T-Mobile presence, and would therefore experience lower, often negative, changes in consumer surplus as a result of the merger. Conversely, markets where the merging parties struggle to enter separately, mainly those with lower population density and harder to cover terrain, benefit more because the merger would diversify carrier choices. In the second chapter we propose a new method of estimation for discrete choice demand models when individual level data are available. The method employs a two-step procedure. Step 1 predicts the choice probabilities as functions of the observed individual level characteristics. Step 2 estimates the structural parameters of the model using the estimated choice probabilities at a fixed point and the moment restrictions. In essence, the method uses nonparametric approximation (followed by) moment estimation. Hence the name--NAME. We use simulations to compare the performance of NAME with the standard methodology. We find that our method delivers an improved precision as well as a substantially faster convergence time. We supplement the analysis by providing the large sample properties of the proposed estimator. In the third chapter we investigate the finite sample properties of iterative two-step procedures. We show how iterations of the fixed point equation might reduce the first-order bias in the problem. Based on this, we propose a new iterative estimator that works in games and achieves fully parametric properties even if the initial first stage estimator is not accurate. The estimator has several appealing properties such as bias reduction, stability, and computational feasibility. Some known results, such as iterative estimators by policy function iterations in the single agent dynamic discrete choice models Aguirregabiria and Mira (2002) or recursive projections methods from Kasahara and Shimotsu (2009) are special cases of our corrected procedure. We test the performance of our estimator in several examples via simulations.
