Measuring the Efficiency of an FCC Spectrum Auction

FCC spectrum auctions sell licenses to provide mobile phone service in designated geographic territories. We propose a method to structurally estimate the deterministic component of bidder valuations and apply it to the 1995–1996 C-block auction. We base our estimation of bidder values on a pairwise stability condition, which implies that two bidders cannot exchange licenses in a way that increases total surplus. Pairwise stability holds in many theoretical models of simultaneous ascending auctions, including some models of intimidatory collusion and demand reduction. Pairwise stability is also approximately satisfied in data that we examine from economic experiments. The lack of post-auction resale also suggests pairwise stability. Using our estimates of deterministic valuations, we measure the allocative efficiency of the C-block outcome.

Do Input Quality and Structural Productivity Estimates Drive Measured Differences in Firm Productivity?

Firms in the same industry can differ in measured total factor productivity (TFP) by multiples of 3. Griliches (1957) suggests one explanation: the quality of inputs differs across firms. Labor inputs are traditionally measured only as the number of workers. We investigate whether adjusting for the quality of labor inputs substantially decreases measured TFP dispersion. We add labor market history variables such as experience and firm and industry tenure, as well as general human capital measures such as schooling and sex. We also investigate whether an innovative structural estimator for productivity due to Olley and Pakes (1996) substantially decreases measured residual TFP. Combining labor quality and structural estimates of productivity, the one standard deviation difference in residual TFPs in manufacturing drops from 0.70 to 0.67 multiples. Neither the structural productivity measure nor detailed input quality measures explain the very large measured residual TFP dispersion, despite statistically precise coefficient estimatesproduction function estimation; total factor productivity; input quality; structural estimates of productivity

Identifying Demand with Multidimensional Unobservables: A Random Functions Approach

We explore the identification of nonseparable models without relying on the property that the model can be inverted in the econometric unobservables. In particular, we allow for infinite dimensional unobservables. In the context of a demand system, this allows each product to have multiple unobservables. We identify the distribution of demand both unconditional and conditional on market observables, which allows us to identify several quantities of economic interest such as the (conditional and unconditional) distributions of elasticities and the distribution of price effects following a merger. Our approach is based on a significant generalization of the linear in random coefficients model that only restricts the random functions to be analytic in the endogenous variables, which is satisfied by several standard demand models used in practice. We assume an (unknown) countable support for the the distribution of the infinite dimensional unobservables.

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

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%u2019 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.

Estimating Matching Games with Transfers

Economists wish to use data on matches to learn about the structural primitives that govern sorting. I show how to use equilibrium data on who matches with whom for semiparametric estimation of match production functions in many-to-many, two-sided matching games with transferable utility. Inequalities derived from equilibrium necessary conditions underlie a maximum score estimator of match production functions. The inequalities do not require data on transfers, quotas, production levels, or unmatched agents. The estimator does not suffer from a computational or data curse of dimensionality in the number of agents in a matching market, as the estimator avoids solving for an equilibrium and estimating first-stage match probabilities. I present an empirical application to automotive suppliers and assemblers.

Estimating the Employer Switching Costs and Wage Responses of Forward-Looking Engineers

I estimate the relative magnitudes of worker switching costs and how much the employer switching of experienced engineers responds to outside wage offers. Institutional features imply that voluntary turnover dominates switching in the market for Swedish engineers from 1970--1990. I use data on the allocation of engineers across a large fraction of Swedish private sector firms to estimate the relative importance of employer wage policies and switching costs in a dynamic programming, discrete choice model of voluntary employer choice. The differentiated firms are modeled in employer characteristic space and each firm has its own age-wage profile. I find that a majority of engineers have moderately high switching costs and that a minority of experienced workers are responsive to outside wage offers. Younger workers are more sensitive to outside wage offers than older workers.

Consolidation in the Wireless Phone Industry

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

Identifying Heterogeneity in Economic Choice Models

We show how to nonparametrically identify the distribution that characterizes heterogeneity among agents in a general class of structural choice models. We introduce an axiom that we term separability and prove that separability of a structural model ensures identification. The main strength of separability is that it makes verifying the identification of nonadditive models a tractable task because it is a condition that is stated directly in terms of the choice behavior of agents in the model. We use separability to prove several new results. We prove the identification of the distribution of random functions and marginal effects in a nonadditive regression model. We also identify the distribution of utility functions in the multinomial choice model. Finally, we extend 2SLS to have random functions in both the first and second stages. This instrumental variables strategy applies equally to multinomial choice models with endogeneity.

Does Input Quality Drive Measured Differences in Firm Productivity?

Firms in the same industry can differ in measured productivity by multiples of 3. Griliches (1957) suggests one explanation: the quality of inputs differs across firms. We add labor market history variables such as experience and firm and industry tenure, as well as general human capital measures such as schooling and sex. We also use the wage bill and worker fixed effects. We show adding human capital variables and the wage bill decreases the ratio of the 90th to 10th productivity quantiles from 3.27 to 2.68 across eight Danish manufacturing and service industries. The productivity dispersion decrease is roughly of the same order of magnitude as some competitive effects found in the literature, but input quality measures do not explain most productivity dispersion, despite economically large production function coefficients. We find that the wage bill explains as much dispersion as human capital measures.

Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93649/1/ECTA8585.pd