José J. Canals-Cerda

The Value of a Good Reputation Online: An Application to Art Auctions

Using a unique dataset of art auctions on eBay, we conduct an empirical analysis of the value of a sellers online reputation. Several aspects distinguish our work from most existing research. We analyze a heterogeneous panel data consisting of a large number of observations over a large period of time, including significant variation in reputation across and within sellers. The panel structure of our dataset allows us to employ fixed effects techniques to control for observed and unobserved differences across auctions. The existing literature suggests a marginally significant, and small in magnitude, impact of reputation on sale price. In contrast, our results point to a highly significant, and sizable, impact of reputation on the behavior of buyers and sellers and on market outcomes. The data suggest that sellers on eBay exert significant effort to avoid a negative feedback. Our analysis reveals a significant impact of the eBay feedback rating large enough to be consistent with this observation.

The dynamics of school and work in rural Bangladesh

Canals-Cerd? and Ridao-Cano investigate the effect of work on the school progress of rural Bangladeshi children. They specify a dynamic switching model for the sequence of school and work outcomes up to the end of secondary school, where the switching in each school level is determined by the endogenous work history of the child up to that level. This approach allows the authors to evaluate the dynamic effects of work on school progress. They find that work has a negative and sizable effect on school progress and are able to measure this effect for different groups of children. Their results highlight the relevance of policies aimed at increasing school progress through reductions in child work and the importance of accompanying these policies by efforts to improve the adverse environment that working children face. The authors evaluate the dynamic effects of three policies: compulsory primary schooling, compulsory school entry at age six, and universal access to secondary school. They find that these policies have a sizable effect on school progress and child labor.

Semiparametric Competing Risks Analysis

In this paper we analyse a semi-parametric estimation technique for competing risks models based on series expansion of the joint density of the unobserved heterogeneity components. This technique allows for unrestricted correlation among the risks. The finite sample behavior of the estimation technique is analysed in a Monte Carlo experiment using an empirically relevant data-generating process. The estimator performs well when compared with the Heckman--Singer estimator. Copyright Royal Economic Society 2007

Arriving in Time: Estimation of English Auctions with a Stochastic Number of Bidders

We develop a new econometric approach for the estimation of second-price ascending-bid auctions with a stochastic number of bidders. Our empirical framework considers the arrival process of new bidders as well as the distribution of bidders’ valuations of objects being auctioned. By observing the timing of bidder arrival, the model is identified even when the number of potential bidders is stochastic and unknown. The relevance of our approach is illustrated with an empirical application using a unique dataset of art auctions on eBay. Our results suggest a higher impact of sellers’ reputation on bidders’ valuations than previously reported in cross-sectional studies but the impact of reputation on bidder arrival is largely insignificant. Interestingly, a seller’s reputation impacts not only the actions of the bidders but the actions of the seller as well. In particular, experience and a good reputation increase the probability of a seller posting items for sale on longer-lasting auctions, which we find inc...

Congestion Pricing in an Internet Market

This paper analyzes a unique dataset of art auctions on eBay. We study the behavior of buyers and sellers, demand and supply, by means of a novel structural estimation approach. Our empirical framework considers the process of arrival of new bidders as well as the distribution of bidder valuations of artworks being auctioned. We use this empirical framework to quantify the e(ect of market congestion, and congestion pricing strategies implemented by the market intermediary. Because we explicitly model the process of arrival of new bidders, we can estimate the e(ect of congestion pricing on the number of bidders, the distribution of bidders’ valuations, and the final selling price. Using the structural model we can also measure the impact of congestion pricing on the revenues of the artists and the market intermediary, as well as its e(ect on consumer surplus. Our results indicate that the congestion pricing policy acts as a coordination mechanism that facilitates the match between buyers and sellers.

Credit risk analysis of credit card portfolios under economic stress conditions

We develop an empirical framework for the credit risk analysis of a generic portfolio of revolving credit accounts and apply it to analyze a representative panel data set of credit card accounts from a credit bureau. These data cover the period of the most recent deep recession and provide the opportunity to analyze the performance of such a portfolio under significant economic stress conditions. We consider a traditional framework for the analysis of credit risk where the probability of default (PD), loss given default (LGD), and exposure at default (EAD) are explicitly considered. The unsecure and revolving nature of credit card lending is naturally modeled in this framework. Our results indicate that unemployment, and in particular the level and change in unemployment, plays a significant role in the probability of transition across delinquency states in general and the probability of default in particular. The effect is heterogeneous and proportionally has a more significant impact for high credit score and for high-utilization accounts. Our results also indicate that unemployment and a downturn in economic conditions play a quantitatively small, or even irrelevant, role in the changes in account balance associated with changes in an account’s delinquency status, and in the exposure at default specifically. The impact of a downturn in economic conditions and, in particular, changes in unemployment on the recovery rate and loss given default is found to be large. These findings are of particular relevance for the analysis of credit risk regulatory capital under the IRB

Credit Risk Modeling in Segmented Portfolios: An Application to Credit Cards

The Great Recession offers a unique opportunity to analyze the performance of credit risk models under conditions of economic stress. We focus on the performance of models of credit risk applied to risk-segmented credit card portfolios. Specifically, we focus on models of default and loss and analyze three important sources of model risk: model selection, model specification, and sample selection. Forecast errors can be significant along any of these three model-risk dimensions. Simple linear regression models are not generally outperformed by more complex or stylized models. The impact of macroeconomic variables is heterogeneous across risk segments. Model specifications that do not consider this heterogeneity display large projection errors across risk segments. Prime segments are proportionally more severely impacted by a downturn in economic conditions relative to the subprime or near-prime segments. The sensitivity of modeled losses to macroeconomic factors is conditional on the model development sample. Models estimated over a period that does not incorporate a significant period of the Great Recession may fail to project default rates, or loss rates, consistent with those experienced during the Great Recession.

Forecasting Credit Card Portfolio Losses in the Great Recession: A Study in Model Risk

Credit card portfolios represent a significant component of the balance sheets of the largest US banks. The charge‐off rate in this asset class increased drastically during the Great Recession. The recent economic downturn offers a unique opportunity to analyze the performance of credit risk models applied to credit card portfolios under conditions of economic stress. Specifically, we evaluate three potential sources of model risk: model specification, sample selection, and stress scenario selection. Our analysis indicates that model specifications that incorporate interactions between policy variables and core account characteristics generate the most accurate loss projections across risk segments. Models estimated over a time frame that includes a significant economic downturn are able to project levels of credit loss consistent with those experienced during the Great Recession. Models estimated over a time frame that does not include a significant economic downturn can severely under-predict credit loss in some cases, and the level of forecast error can be significantly impacted by model specification assumptions. Higher credit-score segments of the portfolio are proportionally more severely impacted by downturn economic conditions and model specification assumptions. The selection of the stress scenario can have a dramatic impact on projected loss.