Search results

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

We propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals. Our estimator is based on the stability conditions in matching models; we consider both transferable (TU) and nontransferable utility (NTU) models. In both cases, the stability conditions yield moment inequalities which can be taken to the data. The preference parameters are partially identified. We consider simple illustrative examples, and also an empirical application to aggregate marriage markets.

This paper shows how a shorter fecundity horizon for females (a biological constraint) leads to age and educational disparities between husbands and wives. Empirical support is based on data from a natural experiment commencing before and ending after China’s 1980 one-child law. The results indicate that fertility in China declined by about 1.2–1.4 births per woman as a result of China’s anti-natalist policies. Concomitantly spousal age and educational differences narrowed by approximately 0.5–1.0 and 1.0–1.6 years, respectively. These decreases in the typical husband’s age and educational advantages are important in explaining the division of labor in the home, often given as a cause for the gender wage gap. Indeed, as fertility declined, which has been the historical trend in most developed countries, husband-wife age and educational differences diminished leading to less division of labor in the home and a smaller gender wage disparity. Unlike other models of division of labor in the home which rely on innately endogenous factors, this paper’s theory is based on an exogenous biological constraint.

We propose a set-estimation approach to supermodular games using the restrictons of rationalizable strategies, which is a weaker solution concept than Nash equilibrium. The set of rationalizable strategies of a supermodular game forms a complete lattice, and are bounded above and below by two extremal Nash equilibria. We use a well-known alogrithm to compute the two extremal equilibria, and then construct moment inequalities for set estimation of the supermodular game. Finally, we conduct Monte Carlo experiments to illustrate how the estimated confidence sets vary in response to changes in the data generating process.

Platforms in two-sided markets are known to provide subsidies to either buyers or sellers, in order to take advantage of cross-group externalities inherent in such industries. Online travel agents can be thought of as platforms facilitating trade between passengers and travel service providers (airlines). This chapter evaluates the effects of a buyer subsidy provided by one major US online travel agent – a low-price guarantee offered by Orbitz. We find evidence consistent with increased airline participation with this travel agent upon implementation of the low-price guarantee policy. Our results also confirm the theoretical claims that most-favored customer low-price guarantee policies are procompetitive.

The authors propose an Markov Chain Monte Carlo (MCMC) method for estimating a class of linear sum assignment problems (LSAP; the discrete case of the optimal transport problems). Prominent examples include multi-item auctions and mergers in industrial organizations. This contribution is to decompose the joint likelihood of the allocation and prices by exploiting the primal and dual linear programming formulation of the underlying LSAP. Our decomposition, coupled with the data augmentation technique, leads to an MCMC sampler without a repeated model-solving phase.