291 research outputs found
Exclusivity and exclusion on platform markets
We examine conditions under which an exclusive license granted by the upstream producer of a component that some consumers regard as essential to one of two potential suppliers of a downstream platform market can make the unlicensed supplier unprofitable, although both firms would be profitable if both were licensed. If downstream varieties are close substitutes, an exclusive license need not be exclusionary. If downstream varieties are highly differentiated, an exclusive license is exclusionary, but it is not in the interest of the upstream firm to grant an exclusive license. For intermediate levels of product differentiation, an exclusive license is exclusionary and maximizes the upstream firmβs payoff
Null Models of Economic Networks: The Case of the World Trade Web
In all empirical-network studies, the observed properties of economic
networks are informative only if compared with a well-defined null model that
can quantitatively predict the behavior of such properties in constrained
graphs. However, predictions of the available null-model methods can be derived
analytically only under assumptions (e.g., sparseness of the network) that are
unrealistic for most economic networks like the World Trade Web (WTW). In this
paper we study the evolution of the WTW using a recently-proposed family of
null network models. The method allows to analytically obtain the expected
value of any network statistic across the ensemble of networks that preserve on
average some local properties, and are otherwise fully random. We compare
expected and observed properties of the WTW in the period 1950-2000, when
either the expected number of trade partners or total country trade is kept
fixed and equal to observed quantities. We show that, in the binary WTW,
node-degree sequences are sufficient to explain higher-order network properties
such as disassortativity and clustering-degree correlation, especially in the
last part of the sample. Conversely, in the weighted WTW, the observed sequence
of total country imports and exports are not sufficient to predict higher-order
patterns of the WTW. We discuss some important implications of these findings
for international-trade models.Comment: 39 pages, 46 figures, 2 table
Heterogeneity and the dynamics of technology adoption
We estimate the demand for a videocalling technology in the presence of both network effects and heterogeneity. Using a unique dataset from a large multinational firm, we pose and estimate a fully dynamic model of technology adoption. We propose a novel identification strategy based on
post-adoption technology usage to disentangle equilibrium beliefs concerning the evolution of the network from observed and unobserved heterogeneity in technology adoption costs and use benefits. We find that employees have significant heterogeneity in both adoption costs and network benefits, and have preferences for diverse networks. Using our estimates, we evaluate a number of counterfactual adoption policies, and find that a policy of strategically targeting the right subtype for initial adoption can lead to a faster-growing and larger network than a policy of uncoordinated or diffuse adoption
Peer influence in network markets: a theoretical and empirical analysis
Network externalities spur the growth of networks and the adoption of network goods in two ways. First, they make it more attractive to join a network the larger its installed base. Second, they create incentives for network members to actively recruit new members. Despite indications that the latter "peer effect" can be more important for network growth than the installed-base effect, it has so far been largely ignored in the literature. We address this gap using game-theoretical models. When all early adopters can band together to exert peer influence-an assumption that fits, e.g., the case of firms supporting a technical standard-we find that the peer effect induces additional growth of the network by a factor. When, in contrast, individuals exert peer influence in small groups of size n, the increase in network size is by an additive constant-which, for small networks, can amount to a large relative increase. The difference between small, local, personal networks and large, global, anonymous networks arises endogenously from our analysis. Fundamentally, the first type of networks is "tie-reinforcing," the other, "tie-creating". We use survey data from users of the Internet services, Skype and eBay, to illustrate the main logic of our theoretical results. As predicted by the model, we find that the peer effect matters strongly for the network of Skype users-which effectively consists of numerous small sub-networks-but not for that of eBay users. Since many network goods give rise to small, local networks
ΠΠ±Π·ΠΎΡ ΠΌΠ΅ΡΡΠ½ΡΡ ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΡΡ ΡΡΠ΅Π΄ΡΡΠ², ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ Π² Π»Π΅ΡΠ΅Π½ΠΈΠΈ Π³Π½ΠΎΠΉΠ½ΠΎ-Π½Π΅ΠΊΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° Π΄ΠΈΠ°Π±Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠΎΠΏΡ
The article providesΒ an overview of local medicines used in woundsβ treatmentΒ for patients with purulo-necrotic complications of diabetic foot syndrome (DFS) and the requirementsΒ for these preparationsΒ at the present-day stage of wound healing process development and also the principles of monitoringΒ ofΒ wounds of different etiology. The above material also allows you to get acquainted with the classification of local medicines groups, their brief characteristics with highlighting the peculiarity of each group, strengths and weaknesses, indications, contraindications and possible mistakes of their using. Here is given the detailed description ofΒ main groups modern wound coverings based on alginates, hydrocolloids, hydrogels and histo-equivalent-bioplastic material of hyaluronic acid. There were made conclusions about the lack of information on the real clinical effectiveness of modern dressings and aboutΒ numerous and not systematic approaches for measuring the course of the wound healing process using various biological models. This explains the urgency to improving the modern diagnostic algorithm for measuring the course of the wound healing process and the need to develop an universal model that allows to identify its peculiarities and regularities, including efficiency assessment for local treatment of DFS purulo-necrotic complications.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΎΠ±Π·ΠΎΡ ΠΌΠ΅ΡΡΠ½ΡΡ
ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ², ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π΄Π»Ρ Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π½ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π³Π½ΠΎΠΉΠ½ΠΎΠ½Π΅ΠΊΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΡΠΌΠΈ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° Π΄ΠΈΠ°Π±Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠΎΠΏΡ (Π‘ΠΠ‘), ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ, ΠΏΡΠ΅Π΄ΡΡΠ²Π»ΡΠ΅ΠΌΡΠ΅ ΠΊ ΡΡΠΈΠΌ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ°ΠΌ Π½Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΠ΅Π½ΠΈΡ ΠΎ ΡΠ°Π½Π΅Π²ΠΎΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°Ρ
Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ°Π½ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ. Π ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΌ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π΅ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ·Π½Π°ΠΊΠΎΠΌΠΈΡΡΡΡ Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠ΅ΠΉ Π³ΡΡΠΏΠΏ ΠΌΠ΅ΡΡΠ½ΡΡ
ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ², ΠΈΡ
ΠΊΡΠ°ΡΠΊΠΎΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΎΠΉ Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ Π³ΡΡΠΏΠΏΡ, ΡΠΈΠ»ΡΠ½ΡΡ
ΠΈ ΡΠ»Π°Π±ΡΡ
ΡΡΠΎΡΠΎΠ½, ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΈΡΠΌΠΈ, ΠΏΡΠΎΡΠΈΠ²ΠΎΠΏΠΎΠΊΠ°Π·Π°Π½ΠΈΡΠΌΠΈ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌΠΈ ΠΎΡΠΈΠ±ΠΊΠ°ΠΌΠΈ ΠΈΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ. ΠΠ°Π½ΠΎ ΠΏΠΎΠ΄ΡΠΎΠ±Π½ΠΎΠ΅ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
Π³ΡΡΠΏΠΏ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ°Π½Π΅Π²ΡΡ
ΠΏΠΎΠΊΡΡΡΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π»ΡΠ³ΠΈΠ½Π°ΡΠΎΠ², Π³ΠΈΠ΄ΡΠΎΠΊΠΎΠ»Π»ΠΎΠΈΠ΄ΠΎΠ², Π³ΠΈΠ΄ΡΠΎΠ³Π΅Π»Π΅ΠΉ ΠΈ Π³ΠΈΡΡΠΎΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½Ρ-Π±ΠΈΠΎΠΏΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π³ΠΈΠ°Π»ΡΡΠΎΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ. Π‘Π΄Π΅Π»Π°Π½Ρ Π²ΡΠ²ΠΎΠ΄Ρ ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΏΠΎΠ²ΠΎΠ΄Ρ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ² Π΄Π»Ρ ΠΌΠ΅ΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅Π΄ΠΈΠΊΠ°ΠΌΠ΅Π½ΡΠΎΠ·Π½ΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ, ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΡΠΈΡΠ»Π΅Π½Π½ΡΡ
ΠΈ Π½Π΅ Π²ΡΠ΅Π³Π΄Π° ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°Ρ
ΠΊ ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π½Π΅Π²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ. ΠΡΠΈΠΌ ΠΎΠ±ΡΡΡΠ½ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π½Π΅Π²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅ΠΉ Π²ΡΡΠ²Π»ΡΡΡ Π΅Π³ΠΎ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΡΠ΅Π΄ΡΡΠ² ΠΌΠ΅ΡΡΠ½ΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ Π³Π½ΠΎΠΉΠ½ΠΎ-Π½Π΅ΠΊΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ Π‘ΠΠ‘
- β¦