Berlin IO Day

Program

Program (PDF, 88.15 KB)

Abstracts & Links

Demand Estimation with Text and Image Data (Download this paper)
Stephan Seiler (with Giovanni Compiani and Ilya Morozov)
We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit model with product-pair specific nesting parameters that depend on the image and text similarities between products. Our framework does not require collecting product attributes for each category and can capture product similarity along dimensions that are hard to account for with observed attributes. We apply our method to a dataset describing the behavior of Amazon shoppers across several categories and show that incorporating texts and images in demand estimation helps us recover a flexible cross-price elasticity matrix.

Buyer-Optimal Algorithmic Consumption
Shota Ichihashi (with Alex Smolin)
An algorithm recommends a product to a buyer based on the product's value to the buyer and its price. We characterize an algorithm that maximizes the buyer's expected payoff and show that it strategically biases recommendations to incentivize lower prices. Under optimal algorithmic consumption, informing a seller about the buyer's value does not affect the buyer's expected payoff but leads to a more equitable distribution of payoffs across different values. These results extend to Pareto-optimal algorithmic consumption and multi-seller markets.

Can Usage Based Pricing Reduce Congestion? Evidence from a Large Field Experiment
Shoshana Vasserman (with Itai Ater, Adi Shany, Brad Ross, and Eray Turkel)
This paper analyzes the effects of the largest field experiment to date that incentivizes drivers to limit driving during peak hours and congested areas via usage-based congestion pricing. The experiment monitored the driving behavior of 10,000 Israeli drivers who were recruited over the course of 2020. During the first six months of a driver's participation in the experiment, their driving behavior is monitored and recorded; afterwards, drivers receive an annual budget and are charged for each kilometer driven during historically congested times in congested areas. Whatever remains in each driver's budget is paid to them a year later. We use comprehensive data on driving behavior of participating drivers over the course of 2020 and 2021 to evaluate how usage-based congestion pricing affects driving behavior. The staggering of driver recruitment facilitates identifying treatment effects via a difference-in-differences approach. We find that drivers decrease their congested driving behavior by approximately 10\% across a myriad of outcomes designed to detect both extensive margin (i.e. whether to take a trip) and intensive margin (i.e. when to take a trip and via which route) responses. We also find that there is significant treatment effect heterogeneity across drivers that can be predicted by pre-treatment driving behavior. The most affected drivers tend to be those who contributed more to congestion and who appear to have more flexibility in their driving choices and easier access to public transit, but they are not disproportionately socioeconomically advantaged or disadvantaged.

An Empirical Analysis of Merger Efficiencies (Download this paper)
Alon Eizenberg (with Omri Zvuluni)
We develop an econometric method to study merger efficiencies. Classification techniques are employed first to determine the sign of the merger’s effect on output levels in specific markets. These classifications are then combined with familiar oligopoly theory results to yield bounds on marginal cost savings. Applying this framework to the 2013 merger of US Airways and American Airlines we find that the merger led to output expansions in more than half of the markets where the sign of the output effect could be determined, and in at least 44% of the total number of markets that were directly affected by the merger and where the market structure was otherwise stable. Pro-competitive effects were more prevalent in larger markets and, to some extent, in markets that serve the merging carriers’ hubs. Averaging across the markets experiencing output expansions, the lower bound on the marginal cost reduction was slightly above 2 USD, capturing 0.8% of the market price. The analysis provides insights regarding the nature and magnitude of merger efficiencies.

Reputation and Competitive Selection (Download this paper)
Joyee Deb (with Jack Fanning)
We demonstrate how selection in competitive markets can impede long-run reputational incentives. In our model, competent firms can exert costly effort to improve expected product quality, but inept firms cannot. With low entry barriers, we show competent firms cannot persistently exert effort. Such effort would cause consumers to select firms based on reputations for competence, eventually selecting a monopolist with an arbitrarily high reputation, who would subsequently not exert effort. More generally, reputation-based selection rules out long-run effort, which then undermines the basis for such selection. Intermediate entry barriers provide stronger persistent reputational incentives than unfettered competition and no competition.

About Berlin IO Day

The Berlin IO Day is a one-day workshop sponsored by the Berlin Centre for Consumer Policies (BCCP) and the Vereinigung der Freunde e.V. (VdF) des DIW Berlin 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 which takes place twice a year, in the Spring and in the Fall.

For each Berlin IO Day, we will invite four or five speakers to present their recent work on a variety of IO topics, followed by a general discussion. 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.

Organizers:

• Tomaso Duso, Jo Seldeslachts, Hannes Ullrich (DIW Berlin)
• Michał Grajek, David Ronayne, Stefan Wagner (ESMT Berlin)
• Andreas Asseyer (Freie Universität Berlin)
• Daniel Guhl, Daniel Klapper, Sebastian Schweighofer-Kodritsch, Roland Strausz (Humboldt-Universität zu Berlin)
• Radosveta Ivanova-Stenzel (Technische Universität Berlin)

Host of the 17th Berlin IO Day

Many thanks to David Ronayne, who is the local organizer this time, and to ESMT Berlin for providing additional financial support.