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This paper studies regulatory policy interventions that are aimed at protecting sticky consumers who are exposed to the risk of being taken advantage of. We model heterogeneous consumer switching costs alongside asymmetric market shares. This setting encompasses many markets in which established firms are challenged by new entrants. We identify circumstances under which such interventions can be counterproductive: with regard to the stated consumer\ud protection objective and also with regard to the complementary aim to promote competition.

There is a useful but not widely known framework for jointly implementing Durbin-Wu-Hausman exogeneity and Sargan-Hansen overidentification tests, as a single artificial regression. This note sets out the framework for linear models and discusses its extension to non-linear models.

This paper studies regulatory policy interventions aimed at protecting vulnerable consumers who are disengaged and thus exposed to exploitation. We model heterogeneous consumer switching costs alongside asymmetric market shares. This setting encompasses many markets in which established firms are challenged by new entrants. We identify circumstances under which such interventions can be counterproductive, both with regard to the stated consumer protection objective and the complementary aim to promote competition.

A common reform used to increase consumer choice and competition in public services has been to allow private providers to compete with public incumbents. However, there remains a concern that not all consumers are able to benefit equally from wider choice. We consider the case of publicly funded elective surgery in England, where reforms in the 2000s enabled privately owned hospitals to enter the market. We show that, post-reform, poor and ethnic minority patients were much less likely to choose private hospitals; and that dominant drivers of sorting between public and private providers are health based criteria for treatment by private providers and the geographic distribution of hospitals. Counterfactual simulations suggest differences in health explain 18% of the difference in the use of private providers between rich and poor patients, while the geographic distribution of hospitals explains 61% once other sorting mechanisms - ethnicity, patient preferences, physician referral patterns - are accounted for. Although much of the observed sorting does not appear to be the result of market frictions, limited variation in payments made to hospitals according to patient health means that sorting is estimated to cost public hospitals in excess of $426,426 ($625,000) per year.

This manuscript derives closed form solutions for conditional expectations of order statistics in models that are based on the extreme value and generalized extreme value distributions. Such conditional expectations are of interest in empirical anal-yses when the (identity of the) maximal statistic is observed, but the econometric model also relies on lower-rank order statistics which are unobserved. This is the case, for example, in some (sequential) bargaining models (e.g. Beckert, Smith and Takahashi (2015), for which this manuscript is a companion piece), or in empirical auctions models.\ud \ud The manuscript also provides an algorithm to derive the density of the GEV cumulative distribution function. This density is required to simulate nested logit models following the MCMC approach proposed by McFadden (1999).

This paper considers the micro-econometric analysis of discrete choice problems in which the choice set is strategically pre-selected by a third-party advisor or expert. It delineates measures of efficiency loss arising from (i) the sets of relevant choice attributes being imperfectly aligned between ultimate beneficiary of the choice outcome and third-party expert, and (ii) pre-selected choice-sets being uniform across ultimate beneficiaries who differ in their subjective evaluations of relevant choice attributes. And it identifies inconsistencies in estimation when strategically pre-selected choice sets are treated as exogenous. Some applications to choice in healthcare and financial services markets are sketched.

This paper considers parametric estimation problems with independent, identically nonregularly distributed data. It focuses on rate efficiency, in the sense of maximal possible convergence rates of stochastically bounded estimators, as an optimality criterion, largely unexplored in parametric estimation. Under mild conditions, the Hellinger metric, defined on the space of parametric probability measures, is shown to be an essentially universally applicable tool to determine maximal possible convergence rates. These rates are shown to be attainable in general classes of parametric estimation problems.

This paper analyses equilibria in sequential take-it-or-leave-it sales and sequential auctions when demand is stochastic. It is shown that equilibria in the former mechanism trade-off allocative efficiency and competing buyers' opportunities to acquire an item to be sold, permitting prices and expected revenue above those of one-shot offers. Hence Coase-type conjectures are invalid in this setting. Moreover, sequential take-it-or-leave-it sellers can achieve expected revenue in early periods in excess of those in sequential auctions, and, if sufficiently patient, higher expected revenue in the entire game than slightly less patient auction sellers. This provides one explanation why some goods are typically sold in take-it-or-leave-it deals, while others are sold in auctions. An asymptotic revenue equivalence result is shown to reconcile the two mechanisms as the time horizon of the dynamic game gets large.

This paper considers structural nonparametric random utility models for continuous choice variables. It provides sufficient conditions on the structural model to yield reduced-form systems of non-parametric stochastic demand functions that constitute a global homeomorphism between demands and random utility components. Such homeomorphic relationships are essential for global identification of the structural model, the existence of well-specified probability models for choice variables and for the analysis of revealed stochastic preference.

The presence of sticky, often labelled ‘unengaged’, consumers is arguably one of the most intractable issues faced by competition regulators, in that it entrenches incumbency advantage. We develop a spatial linear model of heterogeneous switching costs that allows for asymmetric distributions of heterogeneous switching costs. We not only model uniform pricing and history-based price discrimination, but also the impact of regulatory intervention aimed at making it easier for customers to be upgraded to a better tariff from their current service provider, something we call ‘leakage’. Finally, we analyse firms’ incentive to adopt history-based price discrimination and voluntarily permit ‘leakage’.

There exists a useful framework for jointly implementing Durbin-Wu-Hausman exogeneity and Sargan-Hansen overidentification tests, as a single artificial regression. This note sets out the framework for linear models and discusses its extension to non-linear models. It also provides an empirical example and some Monte Carlo results.

This paper studies discriminatory and non-discriminatory pricing when firms' customers have heterogeneous switching costs and market shares are asymmetric. This setting encompasses many markets in which established firms are challenged by disruptive entrants and have yet come under regulatory scrutiny. We identify circumstances under which regulatory interventions to protect ``back-book'' customers from exploitation are counterproductive. And we show how most-favoured customer clauses can be discriminatory and would benefit firms, but firms do not have an incentive to implement them unilaterally.