Economic Networks

1
Economic Networks

• by
• Hema Jayaprakash

2
Outline

• Introduction
• Socioeconomic Perspective
• RD 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
4
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, RD alliances or
  credit-debt relationships.

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
5
Socioeconomic Perspective

• How the strategic behavior of the interacting
  agents is influenced by relatively simple network
  architectures.
• Example Star Network

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
6
Socioeconomic Perspective

• Microeconomics Approach
• Individual system elements and their detailed
  network of relations
• Macroeconomics Approach
• Statistical regularities of the network as a whole

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
7
Socioeconomic Perspective RD 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 RD 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 RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
8
Socioeconomic Perspective RD Project

• Investigate major structural properties of the
  inter-firm RD 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 RD partnerships connected
  firms from different countries and regions in the
  period 1989-2002

The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
9
Socioeconomic Perspective RD 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 Estate
The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
10
Socioeconomic Perspective RD Project
Connectedness
Number of newly formed RD partnerships and
average degree over time.
The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
11
SocioEconomic Perspective RD Project
Connectedness
Firms and collaborators in the global network of
RD partnerships
The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
12
SocioEconomic Perspective RD Project
Concentration
Distribution of RD partnerships and regional
average degrees in 1989-2002.
The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
13
SocioEconomic Perspective RD Project
Integration
Regional and worldwide average homophily over time
The structure and dynamics of the global network
of inter-firm RD partnerships 1989-2002 -
Bojanowski, Micha l, Corten, Rense and Westbrock,
Bastian Department of Sociology, Utrecht
University, Utrecht School of Economics
14
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
15
Socioeconomic Perspective Game Theory
Game Theory A 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 strategies Game theory attempts to
mathematically capture behavior in strategic
situations, in which an individual's success in
making choices depends on the choices of others
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)
Bargaining in a network of buyers and sellers by
Margarida Corominas-Bosch Department of
Economics, Spain
16
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
17
Issues with Microeconomic approach
Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
18
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
19
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
20
Complex Network Perspective Interbank network
The banking network of Austria . Clusters
are grouped (colored) according to regional and
sectorial organization
RB yellow RSt orange RKlight orange RV gray
RT dark green RN black RO light green RS light
yellow VB-sector dark gray S-sector
orange-brown other pink.
The Network Topology of the Interbank Market by
Michael Boss, Helmut Elsinger, Martin Summer, and
Stefan Thurner
21
Complex Network Perspective Interbank network
The Network Topology of the Interbank Market by
Michael Boss, Helmut Elsinger, Martin Summer, and
Stefan Thurner
22
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.

The Network Topology of the Interbank Market by
Michael Boss, Helmut Elsinger, Martin Summer, and
Stefan Thurner
23
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.

The Network Topology of the Interbank Market by
Michael Boss, Helmut Elsinger, Martin Summer, and
Stefan Thurner
24
Complex Network Perspective - International
financial network
European Union members (red), North America
(blue), other countries (green)
Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
25
Complex Network Perspective - INTERNATIONALECONOM
IC INTEGRATION
HPAE high-performing Asian economies LATAMLatin
American countries
Bilateral trade data for 171 countries over the
19802005 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
26
Complex Network Perspective - INTERNATIONALECONOM
IC INTEGRATION

• 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

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
27
Complex Network Perspective - INTERNATIONALECONOM
IC INTEGRATION
Average random walk betweenness centrality
(RWBC).
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
28
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
29
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
30
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, RD 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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
31
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
• RD networks in the field of human biotechnology,
  which follow a predictable life cycle related to
  the timing of the exchange and integration of
  knowledge

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
32
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
33
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.

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
34
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
35
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

Economic Networks The New Challenges by Frank
Schweitzer, Giorgio Fagiolo, Didier
Sornette,Fernando Vega-Redondo, Alessandro
Vespignani,Douglas R. White
36
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