Factor price

About: Factor price is a research topic. Over the lifetime, 2764 publications have been published within this topic receiving 86176 citations.

Why Aren't the Prices of the Same Item the Same at Me.com and You.com?: Drivers of Price Dispersion Among E-Tailers

Abstract: Frictionless e-commerce implies that price dispersion for identical products sold by different e-tailers should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) suggests that such a phenomenon may be due to heterogeneity among e-tailers in such factors as shopping convenience, consumer awareness, and trust. These hypotheses, however, remain untested. In this paper, we extend previous research by developing a comprehensive framework of the drivers of online price dispersion that includes market characteristics such as number of competitors, consumer involvement, and product popularity, in addition to e-tailer characteristics and product category differences. We also empirically test our propositions in a more comprehensive manner than prior research by using a range of price dispersion measures covering 6,739 price quotes for 581 products from 105 e-tailers in a variety of product categories including books, CDs, DVDs, desktop computers, laptop computers, PDAs, computer software, and consumer electronics. Specifically, we (1) identify some key dimensions of e-tailer heterogeneity using factor analysis; (2) identify clusters of e-tailers on these dimensions using cluster analysis; (3) analyze how market factors affect price dispersion using regression analyses; and (4) examine how heterogeneity among e-tailers is related to their prices using hedonic regressions by category and by cluster. Our results show that e-tailer services can be characterized by five underlying factors, namely, shopping convenience, reliability in fulfillment, product information, shipping and handling, and pricing policy. There are three clusters of e-tailers who target different consumer groups and position themselves differently along these five factors. Even after controlling for e-tailer characteristics, online price dispersion is large. Market characteristics drive a large portion of this e-tailer price dispersion. Specifically, price dispersion increases with involvement or average price level of items, albeit at a decreasing rate, and decreases with the number of competitors, but at a diminishing rate. The models explain over 92% of the variance in price dispersion. E-Tailers charge prices in line with their characteristics, but do not necessarily command higher prices for superior services. The drivers of e-tailer prices also vary significantly by the cluster to which the e-tailers belong.

"The Bigger They Are, The Harder They Fall": How Price Differences Across U.S. Cities Are Arbitraged

Abstract: Recent empirical work has made headway in exploring the non-linear dynamics of deviations from the law of one price and" purchasing power parity that are apt to arise from transaction costs. However, there are two important facets of this work that need improvement. First, the choice of empirical specification is arbitrary. Second, the data used are typically composite price indices which are subject to potentially serious aggregation biases. This paper examines the evidence for transport-cost-induced nonlinear price behavior within the U.S. We address both of the above shortcomings. First, we use a simple continuous-time model to inform the choice of empirical specification. The model indicates that the behavior of deviations from price parity depends on the relative importance of fixed and variable transport costs. Second, we employ data on disaggregated commodity prices, yielding a pure' measure of the deviations from price parity. We find strong evidence of nonlinear reversion in these deviations. The nature of this reversion suggests that fixed costs of transportation are integral to an understanding of law-of-one-price deviations.

Price setting behaviour in the Netherlands: results of a survey

Abstract: Using a survey among Dutch firms on price setting behaviour in the Netherlands the study identifies how sticky prices are, which prices are sticky and why they are sticky. The most distinctive feature of the Dutch survey is its broad coverage of the business community (seven sectors and seven size classes), including the service sector and small firms. Our primary finding is that price setting behaviour depends critically on both a firm's size and the competitive environment it faces. Small firms in particular adopt more rigid pricing policies, and the weaker the competition a firm faces, the stickier a company's price will be. Furthermore, we find that wholesale and retail prices are more flexible than those for business-to-business services. The survey suggests that explicit and informal contracting are the most important sources of price stickiness. Menu costs and psychological pricing—two prominent explanations of price stickiness in the literature—are of minor importance. Finally, there is clear evidence of asymmetries in shocks driving price increases and decreases. Copyright © 2009 John Wiley & Sons, Ltd.

List Prices, Sale Prices and Marketing Time: An Application to U.S. Housing Markets

Abstract: ∗∗∗ ∗∗∗ ∗∗∗∗ Many goods are marketed after first stating a list price, with the expectation that the eventual sales price will differ. In this article, we first present a simple model of search behavior that includes the seller setting a list price. Holding constant the mean of the buyers’ distribution of potential offers for a good, we assume that the greater the list price, the slower the arrival rate of offers but the greater is the maximal offer. This trade-off determines the optimal list price, which is set simultaneously with the seller’s reservation price. Comparative statics are derived through a set of numerical sensitivity tests, where we show that the greater the variance of the distribution of buyers’ potential offers, the greater is the ratio of the list price to expected sales price. Thus, sellers of atypical goods will tend to set a relatively high list price compared with standard goods. We test this hypothesis using data from the Columbus, Ohio, housing market and find substantial support. We also find empirical support for another hypothesis of the model: atypical dwellings take longer to sell. Although the theory of how the seller of an asset searches for a buyer is well developed,ithasfocusedlessfrequentlyonhowlist(ask)pricesaredetermined. However, setting a list price that differs from the expected selling price is a common occurrence in the U.S. economy. One of the largest such examples is the housing market. In 2007, 6.43 million existing and new homes were sold, each having a seller-determined list price. 1 In the vast majority of cases, the list price exceeded the sales price. Setting a list price that differs from the expected sales price also is common for automobiles and the fine art market and it occurs in some Internet auctions.