The paper develops the first structural model of organizational buying to study innovation diffusion in a B2B market. Our model is particularly applicable for routinized exchange relationships, whereby centralized buyers periodically evaluate and choose contracts, then downstream users or- der items on contracted terms. The model captures different utility tradeoffs for users and buyers while accounting for how buyer and user choices interact to impact user adoption/usage and buyer contracting. Further, the paper considers the dynamics induced by share of wallet (SOW) pricing contracts, commonly used in B2B markets to reward customer loyalty with discounts for buying more than a threshold share from a supplier. We assemble novel panel data on surgeon usage, SOW contracts, contract switching, and hospital characteristics. We find two segments of hospitals in terms of the relative power of surgeons and buyers: a buyer-centric and a surgeon-centric segment. Further, innovations diffuse faster in teaching hospitals and when surgeries are concentrated among a few surgeons. Finally, we answer such questions as: Should the marketer focus on push (buyer-focused) or pull (user-focused) strategies? Do SOW contracts hurt the innovations of smaller firms? Surprisingly, we find that the contracts can help speed the diffusion of major innovations from smaller players.
We study how organizational boundaries affect pricing decisions using comprehensive data from a large U.S. airline. We document that the firm’s advanced pricing algorithm, utilizing inputs from different organizational teams, is subject to multiple biases. To quantify the impacts of these biases, we estimate a structural demand model using sales and search data. We recover the demand curves the firm believes it faces using forecasting data. In counterfactuals, we show that correcting biases introduced by organizational teams individually have little impact on market outcomes, but coordinating organizational outcomes leads to higher prices/revenues and increased deadweight loss in the markets studied.
We derive the optimal unilateral policy in a general equilibrium model of trade and climate change where one region of the world imposes a climate policy and the rest of the world does not. A climate policy in one region shifts activities—extraction, production, and consumption—in the other region. The optimal policy trades off the costs of these distortions. The optimal policy can be implemented through: (i) a nominal tax on extraction at a rate equal to the global marginal harm from emissions, (ii) a tax on imports of energy and goods, and a rebate of taxes on exports of energy but not goods, both at a lower rate than the extraction tax rate, and (iii) a goods-specific export subsidy. The policy controls leakage by combining supply-side and demand-side taxes to control the price of energy in the non-taxing region. It exploits international trade to expand the reach of the climate policy. We calibrate and simulate the model to illustrate how the optimal policy compares to more traditional policies such as extraction, production, and consumption taxes and combinations of those taxes. The simulations show that combinations of supply-side and demand-side taxes are much better than simpler policies and can perform nearly as well as the optimal policy.
We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors for real-time measurement and rapid drop in storage costs. In particular, we consider the setting where the time series data on N entities is generated from a Gaussian mixture model with autocorrelations over k clusters in Rd. Our main contribution is an algorithm to construct coresets for the maximum likelihood objective for this mixture model. Our algorithm is efficient, and, under a mild assumption on the covariance matrices of the Gaussians, the size of the coreset is independent of the number of entities N and the number of observations for each entity, and depends only polynomially on k, d and 1/ε, where ε is the error parameter. We empirically assess the performance of our coresets with synthetic data.
This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/ε (where ε is the error parameter) and the number of regression parameters – independent of the number of individuals in the panel data or the time units each individual is observed for. Our approach is based on the Feldman-Langberg framework in which a key step is to upper bound the “total sensitivity” that is roughly the sum of maximum influences of all individual-time pairs taken over all possible choices of regression parameters. Empirically, we assess our approach with synthetic and real-world datasets; the coreset sizes constructed using our approach are much smaller than the full dataset and coresets indeed accelerate the running time of computing the regression objective.
This paper studies the endogenous timing of moves in a game with competition in basic research between a university and a commercial firm. It examines the conditions under which the two entities end up investing in innovation at equilibrium, both under simultaneous and sequential moves. It argues that when the innovation process is not too costly, under any timing, the firm conducts research despite the opportunities for complete free riding. The two sequential move games with either player as leader emerge as equilibrium endogenous timings, with both entities realizing higher profits in either outcome than in a simultaneous move game. Each entity also profits more by following than by leading. Finally, as a proxy for a welfare analysis, we compare the propensities for innovation across the three scenarios and find that university leadership yields a superior performance. This may be used as a selection criterion to choose the latter scenario as the unique outcome of endogenous timing.
New methods are developed for identifying, estimating and performing inference with nonstationary time series that have autoregressive roots near unity. The approach subsumes unit root (UR), local unit root (LUR), mildly integrated (MI) and mildly explosive (ME) specifications in the new model formulation. It is shown how a new parameterization involving a localizing rate sequence that characterizes departures from unity can be consistently estimated in all cases. Simple pivotal limit distributions that enable valid inference about the form and degree of nonstationarity apply for MI and ME specifications and new limit theory holds in UR and LUR cases. Normalizing and variance stabilizing properties of the new parameterization are explored. Simulations are reported that reveal some of the advantages of this alternative formulation of nonstationary time series. A housing market application of the methods is conducted that distinguishes the differing forms of house price behavior in Australian state capital cities over the past decade.
The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d ≥ 1 2: Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1; the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.