Magnitude Of Network Effect Research Articles

Spatial spillover effects of global forest product trade

Spatial spillover is widespread in forest product trade. We employ a spatial autoregressive (SAR) interaction model to analyze spatial spillover effects of global forest product trade in 2004 and 2014 and their underlying determinants. The SAR interaction model, specified with three types of spatial dependence (origin-to-origin, destination-to-destination and origin-to-destination) can account for the complex spatial interconnection of forest product trade, leading to unbiased and consistent estimates of effects of determinants on trade flows. Although all the spatial dependence parameters are statistically significant, the spatial interconnectivity was much stronger among destination countries than across origin countries. The identified spatial dependence reflects a core-periphery structure of the trade network. The effect decomposition shows that changes in the principal explanatory variables in the SAR interaction model had a greater total effect (TE) on trade flows in 2014 than in 2004. This attributes to a substantial increase of the origin effect (OE), destination effect (DE) and network effect (NE) from 2004 to 2014. Especially, NE rose by a big margin during this period and accounted for approximately 45% of TE in 2014 for each determinant. Based on the magnitude of NE that represents the spatial spillover effect, gross domestic product (GDP) ranked first, followed by gross national income (GNI) per capita, roundwood production per capita and tariffs. Besides the strong evidence of spatial spillover effects, we also find that countries tended to mimic their neighbors in forest product trade engagements. Our findings shed new light on the spatial interconnection of global forest product trade and offer implications for future trade and forest conservation policy design.

Estimating Network Effects in Two-Sided Markets

ABSTRACTThe proliferation of the Internet has enabled platform intermediaries to create two-sided markets in many industries. Time-series data on the number of customers on both sides of the markets allow platform intermediaries for estimating the direction and magnitude of network effects, which can then support growth predictions and subsequent information technology (IT) or marketing investment decisions. This article investigates the conditions under which this estimation of same-side and cross-side network effects should distinguish between its impact on the number of new customers (i.e., acquisition) and existing customers (i.e., their activity). The authors propose an influx-outflow model for doing so and conduct a simulation study to benchmark the new model against the traditional model. Further they compare the models in an illustrative empirical study in which they study the growth of an Internet auction platform. The results show that this separation of effects is beneficial because the existing customers on both sides of the market can influence the acquisition and dropout of other customers asymmetrically. The paper thus makes an important contribution that should impact the way how researchers and business practitioners measure network effects in two-sided markets.

Network Effects, Nonlinear Pricing and Entry Deterrence

A number of products that display positive network effects are used in variable quantities by heterogeneous customers. Examples include corporate operating systems, infrastructure software, web services and networking equipment. In many of these contexts, the magnitude of network effects are influ- enced by gross consumption, rather than simply by user base. Moreover, the value an individual customer derives on account of these network effects may be related to the extent of their individual consumption, and therefore, the network effects may be heterogeneous across customers. This paper presents a model of nonlinear pricing in the presence of such network effects, under incomplete information, and with the threat of competitive entry. Both homogeneous and heterogeneous network effects are modeled. Conditions under which a fulfilled-expectations contract exists and is unique are established. While network effects generally raise prices, it is shown that accompanying changes in consumption depend on the nature of the network effects — in some cases, it is optimal for the monopolist to induce no changes in usage across customers, while in others cases, network effects raise the usage of all market participants. Optimal pricing is shown to include quantity discounts that increase with usage, and may also involve a nonlinear two-part tariff. These results highlight the impact of network effects on the standard trade-off between price discrimination and value creation, and have important implications for pricing policy. The threat of entry generally lowers profits for the monopolist, and increases customer surplus. When network effects are homogeneous across customers, the resulting entry-deterring monopoly contract is a fixed fee and results in the socially optimal outcome. However, when the magnitude of heterogeneous network effects is relatively high, there are no changes in total surplus induced by the entry threat, and the price changes merely cause a transfer of value from the seller to its customers. The presence of network effects, and of a credible entry threat, are also shown to increase distributional efficiency by reducing the disparity in relative value captured by different customer types. Regulatory and policy implications of these results are discussed.