economic networks

Economic Networksby

Hema Jayaprakash

Outline Introduction Socioeconomic Perspective

R&D Project Game Theory

Complex Network Perspective Interbank Network International Financial Network International Economic Integration

New Methodology Summary

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Introduction Dynamic Interaction of a large number of

different agents. Systemic behavior are hard to predict. More fundamental insight into the system

dynamics. How they can be traced back to the structural

properties of the underlying interaction network.

Economic Networks: The New Challenges by Frank Schweitzer, Giorgio Fagiolo, Didier Sornette,Fernando Vega-Redondo,Alessandro Vespignani,Douglas R. White

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Introduction

Economic Networks are studied from two Perspectives Economics and Sociology Physics and Computer Science

In both perspective Nodes represent different individual agents (firms,

banks and countries) Link between the nodes represent mutual

interactions, trade, ownership, R&D alliances or credit-debt relationships.


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Socioeconomic Perspective How the strategic behavior of the interacting

agents is influenced by relatively simple network architectures.

Example: Star Network


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http://upload.wikimedia.org/wikipedia/commons/6/66/NetworkTopology-Star.png

Socioeconomic Perspective Microeconomics Approach

Individual system elements and their detailed network of relations

Macroeconomics Approach Statistical regularities of the network as a whole


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Socioeconomic Perspective – R&D Project World bank has set up several projects to foster the

development of a collaboration network between firms from the least developed countries and partners from the strong economies

Inter-firm networks play an important role in international technological development and economic growth.

Collaborative R&D enables firms to avoid the duplication of research investments and to exploit complementarities between technology stocks.

The structure and dynamics of the global network of inter-firm R&D partnerships 1989-2002 - Bojanowski, Micha l, Corten, Rense and Westbrock, Bastian Department of Sociology, Utrecht University, Utrecht School of Economics

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Socioeconomic Perspective – R&D Project Investigate major structural properties of the inter-firm R&D

alliance network on a global scale and over the extensive time period 1989-2002 for firms from 52 countries situated in different parts of the world

Connectedness - how the rate at which new partnerships have been added to the network changed over time

Concentration - whether the concentration of collaborative activity is also reflected on the level of countries and world regions

Integration - concerning the extent to which the global network of R&D partnerships connected firms from different countries and regions in the period 1989-2002


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Socioeconomic Perspective – R&D Project

Anglo-Saxon countries: United States, Canada, United Kingdom, Australia, New Zealand, and Israel;

East Asia: Hong Kong, Japan, South Korea, and Singapore;

Western Europe: Finland, France, Germany, Italy, Netherland, Sweden and Switzerland;

Agriculture, Forestry, Fishing, Mining, Construction, Manufacturing, Transportation, Communications, Finance, Insurance, Real EstateThe structure and dynamics of the global network of inter-firm R&D partnerships 1989-2002 - Bojanowski, Micha l, Corten, Rense and Westbrock, Bastian Department of Sociology, Utrecht University, Utrecht School of Economics

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Socioeconomic Perspective – R&D Project

Number of newly formed R&D partnerships and average degree over time.

Connectedness


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SocioEconomic Perspective – R&D Project

Firms and collaborators in the global network of R&D partnerships

Connectedness


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Concentration

Distribution of R&D partnerships and regional average degrees in 1989-2002.The structure and dynamics of the global network of inter-firm R&D partnerships 1989-2002 - Bojanowski, Micha l, Corten, Rense and Westbrock, Bastian Department of Sociology, Utrecht University, Utrecht School of Economics

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Integration

Regional and worldwide average homophily over time


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Issues with macroeconomic approach Economic networks were often viewed as the result of a network

formation game among competing and cooperating agents Their links are added or deleted as the consequence of

purposeful decisions attempting to maximize their payoffs Agents must rely on (and be able to) anticipate what others may

do Use information about their environment (which may be limited) Frame the problem within some necessarily bounded time

horizon Learn from the past, which may create a biased experience if

similar situations are encountered later These considerations tended to result in a dramatically large

number of options that agents must choose from on the basis of limited information


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Socioeconomic Perspective – Game Theory

micro analysis of economic networks relies on game theory, which aims at identifying Nash equilibrium (i.e.,situations that are strategically stable in the sense that no agent has an incentive to deviate)

Game TheoryA game consists of a set of players, a set of moves (or strategies) available to those players, and a specification of payoffs for each combination of strategiesGame theory attempts to mathematically capture behavior in strategic situations, in which an individual's success in making choices depends on the choices of others

Bargaining in a network of buyers and sellers by Margarida Corominas-Bosch Department of Economics, Spain

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Issues with Microeconomic approach As the number of nodes and possible links scales up, such

problems become very difficult to solve, and classical approaches are unsatisfactory

Highlighted the crucial role of incentives in the endogenous and induced behavior of socioeconomic networks

This micro approach has not typically been integrated with macro approaches that can identify the complex systemic forces at work

Cannot fully understand important issues, such as the conflict between individual incentives and aggregate welfare, or their impact on the overall efficiency in the performance of the network at large

This problem is exacerbated if the underlying environment is subject to persistent volatility, and if agents are out of equilibrium, as in most real world situations

Agents are unable to attain efficient configurations, despite their continuous efforts to adapt to an ever-changing situation


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Issues with Microeconomic approach


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Complex Network Perspective

Complex-systems approach that may provide predictions for large-scale networks.

These predictions are made from the testing of stochastic rules that affect link formation randomness the characteristic features of the agents, such as their

degree of connectivity (number of links) or their centrality, as measured on the basis of the importance of a node which, in turn, can be affected by its links to other nodes


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Complex Network Perspective – Interbank network Empirical analysis of the network structure of

the Austrian interbank market. Data – 10 Liability matrices between 2000

and 2003 Seven Sectors:

savings banks (S), Raiffeisen (agricultural) banks (R), Volksbanken (VB), joint stock banks (JS), state mortgage banks (SM), housing construction savings and loan associations (HCL), and special purpose banks (SP).

eight federal states (B,St,K,V,T,N,O,S)

The Network Topology of the Interbank Market by Michael Boss, Helmut Elsinger, Martin Summer, and Stefan Thurner

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Complex Network Perspective – Interbank network

The banking network of Austria . Clusters aregrouped (colored) according to regional and sectorial

organization RB yellow

RSt orangeRKlight orange

RV gray RT dark green

RN black RO light green RS light yellow

VB-sector:dark gray

S-sector: orange-brown

other: pink.


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Complex Network Perspective – Interbank network


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Complex Network Perspective – Interbank network Clustering Coefficient: A high clustering coefficient means that two banks that

have interbank connections with a third bank, have a greater probability to have interbank connections with one another, than will any two banks randomly chosen on the network

C = 0.12 + 0.01 (mean and standard deviation over the 10 data sets) – small

Two small banks have a link with their head institution there is no reason for them to additionally open a link among themselves.


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Complex Network Perspective – Interbank network Average Shortest Path Length: L = 2.26 + 0.03 Austrian interbank network looks like a very small

world with about three degrees of separation Sector organization with head institutions and

sub-institutions apparently leads to short interbank distances via the upper tier of the banking system and thus to a low degree of separation.


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Complex Network Perspective - International financial network

European Union members (red), NorthAmerica (blue), other countries (green)


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Complex Network Perspective - INTERNATIONALECONOMIC INTEGRATION

HPAE — high-performing Asian economiesLATAM—Latin American countriesBilateral trade data for 171

countries over the 1980–2005 period are used to build the trade matrix for the countries considered

Columns represent importing countries, while rows denote exporting countries

ASSESSING THE EVOLUTION OF INTERNATIONAL ECONOMIC INTEGRATION USING RANDOM WALK BETWEENNESS CENTRALITY: THE CASES OF EAST ASIA AND LATIN AMERICA by JAVIER REYES, STEFANO SCHIAVO, GIORGIO FAGIOLO

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Country integration (centrality) in the World Trade Network (WTN) by means of random walk betweenness centrality (RWBC)

RWBC is a measure of node centrality that captures the effects of the magnitude of the relationships that a node has with other nodes within the network as well as the degree/strength of the node in question

RWBC exploits (randomly) the whole length of the trade chains present in the network for country i and, therefore, is a good measure for the degree of integration that a given node has within the WTN


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Average random walk betweenness centrality (RWBC).


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Complex Network Perspective -Issue A focus on centrality or other such properties

of networks can only provide a first order classification that emphasizes the role of fluctuations and randomness and cannot predict the underlying dynamics of the agents, whether they are firms or countries


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Complex Network Perspective – New Methodology Merge the description of individual agents

strategies with their coevolving networks of interactions

Predict and propose economic policies that favor networks structures that are more robust to economic shocks and that can facilitate integration or trade


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Complex Network Perspective – Massive Data Analysis Transition from a qualitative to a quantitative and evidence-

based science Large-scale network data can be gathered for different levels of

the economy (e.g., firms, industries, and countries), and models can be tested through the generation of large, synthetic, data sets

Possible to gather individualized data on specific interactions over time such as employee flows, R&D collaborations, and so on within a business or firm-bank credit market interactions

Manipulate the huge scale of available information reflecting agent interactions and network properties

Databases containing this information may complement both theoretical economic network experiments and empirical economic network studies and provide large-scale observations in real-time


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Complex Network Perspective – Time and Space A time-dependent resolution of the properties

of economic networks will help to move beyond a single snapshot approach

Identify the evolutionary path of networks through the combination of complementary information sources

R&D networks in the field of human biotechnology, which follow a predictable life cycle related to the timing of the exchange and integration of knowledge


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Complex Network Perspective - Structure Identification Extracting network topology from reported

data, in particular for aggregated economic data is very difficult

banking sector, where detailed accounts of debt-credit relations are not publicly available

In an evolving economic network, information about agents’ roles, their function and their influence are needed

quantify both direct and indirect influence


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Complex Network Perspective - Structure Identification promising steps have begun to identify

functional roles played by interactive agents that relate to specific patterns in the link structure of their multirelational interaction network

Mapping a large network as a homologous small one, with statistically optimal sets of distinctive roles, gives a statistical correspondence.


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Complex Network Perspective – Beyond Simplicity All economic networks are heterogeneous

with respect to both their agents and interaction strength and can also strongly vary in time

Previous studies of efficient (i.e., not wasteful) and equilibrium (or strategically stable) networks assumed homogeneity

Heterogeneities of agents can turn out to become a source of stability


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Complex Network Perspective – Systemic Feedbacks Simple amplification mechanisms can

dominate the network dynamics at large, despite the best intentions of the agents electricity in a power grid or credit debt in a

banking network most stable dynamic network models account

for only the addition or removal of a single agent to or from the network at each instance of time


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Summary Interaction between agents’ behavior and the

dynamic interactions among them. Massive data analysis, theory encompassing the

appropriate description of economic agents and their interactions, and a systemic perspective bestowing a new understanding of global effects as coming from varying network interactions are needed

Such studies will create a more unified field of economic networks that advances our understanding and leads to further insight

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economic networks

Documents

collaboration network

interfirm rd alliance

collaborative rd

underlying interaction

rd alliances

new challenges

new partnerships

simple network architectures