Berlin IO Day

The Berlin IO Day is a one-day workshop sponsored by the Berlin Centre for Consumer Policies (BCCP) and supported by the Berlin's leading academic institutions, including DIW Berlin, ESMT Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Technische Universität Berlin. The aim is to create an international forum for high quality research in Industrial Organization in the heart of Berlin, one of Europe's most vibrant and intellectually lively cities.

Program

(available as PDF here)

Abstracts & Downloads

Reclassification Risk in the Small Group Health Insurance Market
Sebastian Fleitas (with Gautam Gowrisankaran and Anthony Lo Sasso)
Abstract:   We evaluate reclassification risk in the small group health insurance market from a period before ACA community rating regulations. Reclassification risk in this setting is of key policy relevance and also a matter of debate. We use detailed claims and premiums data from a large insurance company and control non-parametrically for selection. We find a pass through of 16% from changes in health risk to changes in premiums, with a stronger equilibrium relationship between premiums and risk. This pattern is consistent with the insurer implicitly offering “guaranteed renewability” contracts with one-sided pricing commitment. We further find that groups whose health risk decreases have premiums that are more responsive to risk, which the guaranteed renewability model attributes to ex post renegotiation. The observed pricing policy adds 60% of the consumer welfare gain from community rating relative to experience rating. The welfare gains are limited because employers and employees switch coverage frequently.
Paper is accessible here: https://bit.ly/3xRBBDk

The Patent Bazaar: Incentives, Motivated Agents and Bargaining in the Patent System
Mark Schankerman (with William Matcham)
Abstract (to be revised):   This paper develops and estimates a dynamic structural model of the patent screening process. The model incorporates incentives, intrinsic motivation and bargaining. We estimate the model using novel negotiation-round-level data on examiner decisions and text data from 24 million patent claims. From the claim text data, we use modern natural language processing methods to develop a new measure of patent distance, which is used to characterise strategic decisions by patent applicants and examiners. The parameter estimates imply substantial variation in intrinsic motivation , with senior examiners less intrinsically motivated than juniors, on average. Using counterfactual simulations, we calculate the impact of changes to examiner incentives, applicant fees, and the bargaining structure on the timeliness and quality of patent screening, including errors from granting patent claims that do not meet the patentability standard, and not granting claims do. Among other findings, we show that a reduction in the allowed number of negotiation rounds would improve both the timeliness and the quality of the patent screening process. This paper is part of a broader research agenda to show how structural models can be used to study the role of incentives, motivation and organisational design in public agencies.

Price Recommendations and the Value of Data
Willy Lefez
Abstract:   This paper presents a novel methodology to regulate data collection for e-commerce platforms based on the marginal value of information. I consider a platform that discloses buyer information to sellers, via price recommendations, to influence their prices. I determine the gain in profit from acquiring datasets with marginally more buyers under two business models. First, when a platform charges buyers and sellers, it discloses information efficiently, but undervalues data. In this case, a subsidy scheme restores efficiency. Second, when a platform provides free access to buyers, it discloses information and ranks datasets inefficiently. I develop a classification of datasets depending on if they increase welfare or consumer surplus.
Paper is accessible via SSRN link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4154435

Buyer-Optimal Platform Design
Daniele Condorelli (with Balazs Szentes)
Abstract:   A platform matches a unit-mass of sellers, each owning a single product of heterogeneous quality, to a unit-mass of buyers with differing valuations for unit-quality. After matching, sellers make takeit-or-leave-it price-offers to buyers. Initially, valuations of buyers are only known to them and the platform, but sellers make inferences from the matching algorithm. The efficient matching is positiveassortative, but buyer-optimal matchings are, often, stochastically negative-assortative (i.e., compared to lower-quality sellers, high-quality ones are matched to buyers with lower expected valuation). Albeit everyone trades when the platform has full-information, generating rents for the side lacking bargaining power results in inefficient matching.
Paper is accessible here: https://www.condorelli.science/BOMTSP.pdf

Optimal Policy for Mergers with Potential Competitors
Chiara Fumagalli (with Massimo Motta and Emanuele Tarantino)
Abstract:   A start-up and an incumbent negotiate over an acquisition price. The acquisition may result in shelving the start-up’s project or developing a project that would otherwise never reach the market. The optimal merger policy commits to standards of review that prohibit high-price takeovers, even if they may be welfare-beneficial ex-post. Ex ante this pushes the incumbent to acquire start-ups that cannot develop independently, increasing expected welfare. This insight is robust to bidding competition and start-up innovation incentives. We also propose empirical tests to identify high-price takeovers that are more likely to exacerbate the acquirer’s market power