A Model of Search with Two Stages of Information Acquisition and Additive Learning

This paper presents a model of choice with two stages of information acquisition. In this model, the choice problem can be interpreted as a variant of a more general multiarmed bandit problem. We assume that information acquisition takes a simple “additive form”—the value of an alternative is the sum of two components, which the decision maker can learn by undertaking two stages of information acquisition. This assumption yields a model that is tractable for the purposes of structural estimation. One possible application of the model is to online purchasing on e-commerce sites. For a consumer on an e-commerce website, there are potentially two stages of information acquisition: the consumer can obtain information about an alternative from (i) browsing the search results page and (ii) clicking on the alternative. By way of contrast, in much of the literature on structural econometric models of online purchasing, there is typically only one stage of information acquisition. Our paper may, therefore, provide a more realistic theory for modeling search, at least for those types of search—such as online purchasing—that involve two stages of information acquisition. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Matching Returning Donors to Projects on Philanthropic Crowdfunding Platforms

We propose an approach to match returning donors to fundraising campaigns on philanthropic crowdfunding platforms. It is based on a structural econometric model of utility-maximizing donors who can derive both altruistic (from the welfare of others) and egoistic (from personal motivations) utilities from donating—a unique feature of philanthropic giving. We estimate our model using a comprehensive data set from DonorsChoose.org—the largest crowdfunding platform for K–12 education. We find that the proposed model more accurately identifies the projects that donors would like to donate to on their return in a future period, and how much they would donate, than popular personalized recommendation approaches in the literature. From the estimated model, we find that primarily egoistic factors motivate over two-thirds of the donations, but, over the course of the fundraising campaign, both motivations play a symbiotic role: egoistic motivations drive the funding in the early stages of a campaign when the viability of the project is still unclear, whereas altruistic motivations help reach the funding goal in the later stages. Finally, we design a recommendation policy using the proposed model to maximize the total funding each week considering the needs of all projects and the heterogeneous budgets and preferences of donors. We estimate that over the last 14 weeks of the data period, such a policy would have raised 2.5% more donation, provided 9% more funding to the projects by allocating them to more viable projects, funded 17% more projects, and provided 15% more utility to the donors from the donations than the current system. Counterintuitively, we find that the policy that maximizes total funding each week leads to higher utility for the donors over time than a policy that maximizes donors’ total utility each week. The reason is that the funding-maximizing policy focuses donations on more viable projects, leading to more funded projects, and, ultimately, higher realized donors’ utility. This paper was accepted by Kartik Hosanagar, information systems.

Is there a market power in the Philippine Rice Industry?

Rice, as a staple food for the Filipinos, is widely studied from production to consumption. However, observations of the National Food Authority domestic procurement and price stabilization policy, as well as results of the marketing and market-related studies, still reveal some gaps which call forth for an in-depth investigation and analysis. One ofthese is the possible presence ofmarket power, a market inefficiency in rice. Hence, this study aimed to ascertain the presence of market power in the Philippine rice industry. Secondary data published by the Philippine Statistics Authority from 1990 to 2015 were utilized. A structural econometric model using a time series approach was used in estimating the presence of market power. Results revealed the presence of market power in non-major rice-producing regions for well-milled and regular-milled rice in major rice-producing areas. The more the demand curve becomes inelastic the more the market power becomes apparent. The price elasticity of demand in the non-major rice-producing regions is -0.63 for both well-milled and regular-milled rice and -0.83 and -0.59, respectively, in the major rice producing areas. To minimize, if not solve market power, a substitute staple for rice may be introduced, programs/policies that will encourage more palay traders may be implemented, and farmers may be trained to operate like industry clusters.

AN EMPIRICAL ANALYSIS OF MERGERS: EFFICIENCY GAINS AND IMPACT ON CONSUMER PRICES

In this article, we extend the literature on merger simulation models by incorporating its potential synergy gains into structural econometric analysis. We present an integrated approach. We estimate a structural demand and supply model dealing with two-part tariff contracts between manufacturers and retailers as in Bonnet and Dubois (2010). This model allows us to recover the marginal cost of each differentiated product. Then we estimate potential efficiency gains using the data envelopment analysis approach of Bogetoft and Wang (2005), and some assumptions about exogenous cost shifters. In the last step, we simulate the new price equilibrium post-merger by taking into account synergy gains, and derive price and welfare effects. We use a home scan data set of dairy dessert purchases in France, and show that synergy gains could offset the upward pressure on prices post. Moreover, in this market, the increase in industry profit due to the merger is more driven by its induced synergy gains than by the market power increase.

A Structural Econometric Analysis of Network Formation Games Through Subnetworks

The objective of this paper is to identify and estimate network formation models using observed data on network structure. We characterize network formation as a simultaneous‐move game, where the utility from forming a link depends on the structure of the network, thereby generating strategic interactions between links. With the prevalence of multiple equilibria, the parameters are not necessarily point identified. We leave the equilibrium selection unrestricted and propose a partial identification approach. We derive bounds on the probability of observing a subnetwork, where a subnetwork is the restriction of a network to a subset of the individuals. Unlike the standard bounds as in Ciliberto and Tamer (2009), these subnetwork bounds are computationally tractable in large networks provided we consider small subnetworks. We provide Monte Carlo evidence that bounds from small subnetworks are informative in large networks.

Dynamic Decision Making in Sequential Business-to-Business Auctions: A Structural Econometric Approach

We develop a dynamic structural model of competitive bidding in multiunit sequential business-to-business auctions. Our model accounts for two notable characteristics of these auctions: (i) bidders have multiple purchase opportunities for the same product, and (ii) winning bidders in each round can acquire multiple units of the same product. We apply the model to bidding data from the world’s largest flower wholesale market at which trades are facilitated through fast-paced, sequential, Dutch auctions. Using a two-step estimation approach, we are able to recover the structural parameters effectively and efficiently. We then conduct policy counterfactuals to evaluate the performance of alternative design choices. The results suggest that the current auction practice still has ample room for improvement. In light of this, we propose an optimization framework that can facilitate auctioneers’ decisions in making the trade-off between revenue maximization and operational efficiency. This paper was accepted by Lorin Hitt, information systems.

On Heckits, LATE, and Numerical Equivalence

Structural econometric methods are often criticized for being sensitive to functional form assumptions. We study parametric estimators of the local average treatment effect (LATE) derived from a widely used class of latent threshold crossing models and show they yield LATE estimates algebraically equivalent to the instrumental variables (IV) estimator. Our leading example is Heckman's (1979) two‐step (“Heckit”) control function estimator which, with two‐sided non‐compliance, can be used to compute estimates of a variety of causal parameters. Equivalence with IV is established for a semiparametric family of control function estimators and shown to hold at interior solutions for a class of maximum likelihood estimators. Our results suggest differences between structural and IV estimates often stem from disagreements about the target parameter rather than from functional form assumptions per se. In cases where equivalence fails, reporting structural estimates of LATE alongside IV provides a simple means of assessing the credibility of structural extrapolation exercises.