BACKGROUND:
In the light of the failure
of the Doha Development Round of the World Trade Organisation (WTO), preferential
trade agreements (PTAs) have proliferated in the last 25 years. From around 50 PTAs
in force in 1991, this number has grown to 481 in 2019. The PTAs now form a
complex network as shown below:
PTAs have evolved from
“shallow” agreements that cover tariff and non-tariff barriers to “deep”
agreements that cover a host of other issues like labour, environment and
intellectual property rights. [1]
Many PTAs now cover
environmental aspects such as trade in environmental goods and services, and
compliance with multilateral environmental agreements to which the signatories
of the PTA are already a party. Bilateral and Multilateral Environmental
Agreements (BEAs and MEAs) also form a complex and overlapping network and may include
trade in environmental goods and services.
However, traditionally
growth in trade and environmental protection have been seen as opposing forces rather
than complementary goals. Are the most central actors in the trade network also
providing leadership in trade in environmental goods?
NETWORK QUESTIONs:
1)
Which
network has greater effect in increasing trade in environmental goods and
services – PTAs or BEAs?
2)
Are the
most central actors in trade networks also providing leadership in trade of
environmental goods and services? Or can we identify new actors in this space?
RESEARCH
METHODOLOGY:
Data will be used from
the following sites:
4)
World Bank Database of World
Trade statistics: https://wits.worldbank.org/countrystats.aspx?lang=en
NETWORK MEASURES:
The network of PTAs as
well as the network of MEAs are two-mode, nondirectional networks, which may be
given a value as deep or shallow agreement. This will be correlated with the
network of actual flows of environmental goods and services across borders,
which is a one-mode, directional network, valued by amount of trade (low =1,
medium =2, high = 3).
Centrality
Centrality measures will
help us see whether the same actors are leaders in signing both PTAs and MEAs
and whether this translates into actual leadership in trade in environmental
goods. Here, degree centrality is not of as much use as signing of treaties is
no guarantee of actual increase in trade. A country could sign a large number
of treaties with actors that are peripheral in the international trades network.
Hence, Eigenvector centrality will be given more importance. A large betweenness
score could indicate that the country acts as an important trade centre in
connecting two otherwise loosely connected economies.
Density &
Centralisation
Density will help us
understand the potential linkages while Centralisation will identify hubs in
the trade network.
Subgroup Analysis
It will be useful to
identify cliques in the trade of environmental goods and whether flows are
predominantly one of the four:
1)
North-North
2)
North-South
3)
South-North
4)
South-South
My hypothesis is the
first two types of flows dominate when it comes to trade in environmental goods
and services. Subgroup analysis will help us identify the hubs and spokes of
the system or identify a core-periphery structure if it exists, in trade in
environmental goods and services.
WHY SNA?
Social Network
Analysis is a useful tool here besides traditional economic analysis because it
can help identify a core-periphery structure in the trade of environmental
goods through centrality, whole network and subgroup analysis. It can also help
highlight potential linkages in the network and identify new opportunities and
potential leaders/hegemons in trading environmental goods.
LIMITATIONS:
If I take the second
module, I will only be restricting my analysis to Eurasia due to shortage of
time.
REFERENCES:
1 comment:
Sapna, this is a very good idea that you have thought more about since we initially discussed it. You've incorporated net measurements well. I'm assuming that the data is readily available and that you can come up with a sensible approach to coding it. You might get some help from Alex Kersten on that. If the visualizations are complex, you might also consider using Gephi, which does a much better job on complex maps than NetDraw.
Look forward to having you participate in the second module.
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