Ke-Wei Huang

Pricing Digital Goods: Discontinuous Costs and Shared Infrastructure

In this paper, we analyze a model of usage pricing for digital products with discontinuous supply functions. This model characterizes a number of information technology-based products and services for which variable increases in demand are fulfilled by the addition of blocks of computing or network infrastructure. Such goods are often modeled as information goods with zero variable costs; in fact, the actual cost structure resembles a mixture of zero marginal costs and positive periodic fixed costs. This paper discusses the properties of a general solution for the optimal nonlinear pricing of such digital goods. We show that the discontinuous cost structure can be accrued as a virtual constant variable cost. This paper applies the general solution to solve two related extensions by first investigating the optimal technology capacity planning when the cost function is both discontinuous and declining over time, and then characterizing the optimal costing for the discontinuous supply when it is shared by several business profit centers. Our findings suggest that the widely adopted full cost recovery policies are typically suboptimal.

Engineer/Scientist Careers: Patents, Online Profiles, and Misclassification Bias

Through an inventor survey, we find substantial error in tracking mobility of engineers and scientists by patents. These errors cause misclassification of mobility -- false positives (wrongly recording change of employer), and false negatives (failing to record change of employer). Errors are higher among inventors with shorter patent careers. Econometric theory shows that misclassification of the dependent variable causes systematic bias in regression estimates (not merely attenuation). We introduce LinkedIn as a more accurate source of career data. Taking LinkedIn as a benchmark, the rate of false positives in patent measures of mobility is 12 percent, while the rate of false negatives is 83 percent. Measuring mobility by LinkedIn, we investigate the effect of aspects of human capital previously found to affect mobility. One previous finding is robust: that mobility is higher among Silicon Valley inventors than those elsewhere. Other findings are sensitive to sample or misclassification. We interpret our results as the outcome of targeted retention of human capital.

Optimal Criteria for Selecting Price Discrimination Metrics When Buyers Have Log-Normally Distributed Willingness-to-Pay

This paper investigates how a monopoly seller should determine the optimal set of pricing variables (pricing metrics) for third-degree price discrimination applications in which buyers have log-normally distributed willingness-to-pay (WTP). In a setup that closely resembles linear and probit regressions, this paper shows that when the monopoly seller is restricted to using one metric and no price discrimination cost exists, the pricing metric that best reduces the residual variance of buyers’ willingness-to-pay is the one that maximizes revenue. Equivalently, the explanatory power of willingness-to-pay is the ordering criterion. This paper also shows that this criterion is not universally true when willingness-to-pay follows other distributions. When the seller incurs price discrimination costs associated with different metrics, the ordering criterion becomes the explanatory power of each pricing metric divided by its cost. This paper also discusses how to apply this model to solve real-world pricing problems with contingent valuation models or using probit regression.

Do economic recession and gender influence the likelihood of entry job in IT for IT graduates

Attracting and retaining women is a major concern in the IT profession. Existing literature has noted that female students are less likely to choose IT as an undergraduate major, while female IT professionals are more likely to leave the IT workforce. There is little study of an intermediate step i.e., if gender affects whether IT graduates take up their first job in IT. At the same time, the recession in developed economies has prompted research interest on its effects on IT jobs. This paper examines the intersection of these two phenomena i.e., it seeks to understand the influence of both gender and the recent recession on the likelihood of IT graduates taking up their first jobs in IT. It hypothesizes direct and interaction effects of gender and recession on the dependent variable. The hypotheses are tested through analyzing data from annual surveys of undergraduate students majoring in IT at a large public university over a 5-year period from 2007-2011 that includes the recent recession. As hypothesized, female IT graduates were found to be 7.5% less likely to enter IT jobs over this period than their male counterparts. We also found that the economic recession of 2009 has significant interaction with gender i.e., female IT students were 20.9% less likely to enter an IT job in 2009 than male IT students. We contribute to the literature by demonstrating that economic downturn and gender could significantly affect likelihood of entry job in IT, while most of the previous research considers the effects of individual characteristics. Our findings provide implications for IT students, IT employers, and government policy makers.