Knowledge that Transforms

We quantify how user mobile Internet usage relates to unique characteristics of the mobile Internet. In particular, we focus on examining how the mobile-phone-based content generation behavior of users relates to content usage behavior. The key objective is to analyze whether there is a positive or negative interdependence between the two activities. We use a unique panel data set that consists of individual-level mobile Internet usage data that encompass individual multimedia content generation and usage behavior. We combine this knowledge with data on user calling patterns, such as duration, frequency, and locations from where calls are placed, to construct their social network and to compute their geographical mobility. We build an individual-level simultaneous equation panel data model that controls for the different sources of endogeneity of the social network. We find that there is a negative and statistically significant temporal interdependence between content generation and usage. This finding implies that an increase in content usage in the previous period has a negative impact on content generation in the current period and vice versa. The marginal effect of this interdependence is stronger on content usage (up to 8.7%) than on content generation (up to 4.3%). The extent of geographical mobility of users has a positive effect on their mobile Internet activities. Users more frequently engage in content usage compared to content generation when they are traveling. In addition, the variance of user mobility has a stronger impact on their mobile Internet activities than does the mean. We also find that the social network has a strong positive effect on user behavior in the mobile Internet. These analyses unpack the mechanisms that stimulate user behavior on the mobile Internet. Implications for shaping user mobile Internet usage behavior are discussed.

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, we extract constant maturity S&P 500 implied distributions and transform them to the corresponding risk-adjusted ones. Then we form optimal portfolios consisting of a risky and a risk-free asset and evaluate their out-of-sample performance. We find that the use of risk-adjusted implied distributions times the market and makes the investor better off than if she uses historical returns' distributions to calculate her optimal strategy. The results hold under a number of evaluation metrics and utility functions and carry through even when transaction costs are taken into account. Not surprisingly, the reported market timing ability deteriorated during the recent subprime crisis. An extension of the approach to a dynamic asset allocation setting is also presented.

Recent evidence has shown that entrants into self-employment are disproportionately drawn from the tails of the earnings and ability distributions. This observation is explained by a multitask model of occupational choice in which frictions in the labor market induce mismatches between firms and workers, and misassignment of workers to tasks. The model also yields distinctive predictions relating prior work histories to earnings and to the probability of entry into self-employment. These predictions are tested with the Korean Labor and Income Panel Study, from which we find considerable support for the model.

We examine the roots of gender segregation in the screening process by using a longitudinal data set of candidates considered for temporary projects at a staffing firm and following their progress through the hiring pipeline. Theories invoked to explain gender segregation across jobs traditionally rely on firm-specific human capital and expectations of future commitment to explain this phenomenon. These do not apply in this setting. Yet we find that the staffing firm is more likely to shortlist women for low-paid projects and less likely to do so for high-paid ones. These effects are due to women being considered for different projects than men, and associated at least partially to the level of competition within vacancies. Although client companies also exhibit some gender-sorting behavior in the later steps of the hiring process, they are more likely to prefer women and less likely to sort them into lower-paid projects. Our findings are consistent with “anticipatory gender-sorting” mechanisms, by which first screeners generate segregation when narrowing down the pool of candidates for later decision makers. We discuss the implications of this case for theories of gender stratification and workplace inequality, especially in mediated labor markets.

We study the impact of financial innovations on real investment decisions within the framework of an incomplete market economy comprised of firms, investors, and an intermediary. The firms face unique investment opportunities that arise in their business operations and can be undertaken at given reservation prices. The cash flows thus generated are not spanned by the securities traded in the financial market and cannot be valued uniquely. The intermediary purchases claims against these cash flows, pools them together, and sells tranches of primary or secondary securities to the investors. We derive necessary and sufficient conditions under which projects are undertaken due to the intermediary's actions, and firms are amenable to the pool proposed by the intermediary, compared to the no-investment option or the option of forming alternative pools. We also determine the structure of the new securities created by the intermediary and identify how it exploits the arbitrage opportunities available in the market. Our results have implications for valuation of real investments, synergies among them, and their financing mechanisms. We illustrate these implications using an example of inventory decisions under random demand.

We formulate and carry out an analytical treatment of a single-period portfolio choice model featuring a reference point in wealth, S-shaped utility (value) functions with loss aversion, and probability weighting under Kahneman and Tversky's cumulative prospect theory (CPT). We introduce a new measure of loss aversion for large payoffs, called the large-loss aversion degree (LLAD), and show that it is a critical determinant of the well-posedness of the model. The sensitivity of the CPT value function with respect to the stock allocation is then investigated, which, as a by-product, demonstrates that this function is neither concave nor convex. We finally derive optimal solutions explicitly for the cases in which the reference point is the risk-free return and those in which it is not (while the utility function is piecewise linear), and we employ these results to investigate comparative statics of optimal risky exposures with respect to the reference point, the LLAD, and the curvature of the probability weighting.

In many services, the quality or value provided by the service increases with the time the service provider spends with the customer. However, longer service times also result in longer waits for customers. We term such services, in which the interaction between quality and speed is critical, as customer-intensive services. In a queueing framework, we parameterize the degree of customer intensity of the service. The service speed chosen by the service provider affects the quality of the service through its customer intensity. Customers queue for the service based on service quality, delay costs, and price. We study how a service provider facing such customers makes the optimal “quality–speed trade-off.” Our results demonstrate that the customer intensity of the service is a critical driver of equilibrium price, service speed, demand, congestion in queues, and service provider revenues. Customer intensity leads to outcomes very different from those of traditional models of service rate competition. For instance, as the number of competing servers increases, the price increases, and the servers become slower.

The Lerner relationship linking the profit-maximizing price to marginal cost and the elasticity of demand generalizes to the price-setting newsvendor, and the result resolves the puzzle over the different effects of additive and multiplicative uncertainty on the solution. Multiplicative uncertainty increases the optimal price because it increases the marginal cost of a unit sold and does not affect the markup factor. Additive uncertainty has no effect on the marginal cost of a unit sold and lowers the markup factor because it increases the elasticity of the average quantity sold with respect to price.

Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique data set from Amazon containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers' relative preferences for different product features and also how text can be used for predictive modeling of future changes in sales.

To what extent are firms kept out of a market by patents covering related technologies? Do patents held by potential entrants make it easier to enter markets? We estimate the empirical relationship between market entry and patents for 27 narrowly defined categories of software products during the period 1990–2004. Controlling for demand, market structure, average patent quality, and other factors, we find that a 10% increase in the number of patents relevant to market reduces the rate of entry by 3%–8%, and this relationship intensified following expansions in the patentability of software in the mid-1990s. However, potential entrants with patent applications relevant to a market are more likely to enter it. Finally, patents appear to substitute for complementary assets in the entry process, because patents have both greater entry-deterring and entry-promoting effects for firms without prior experience in other markets.