Slide from my trade networks presentation...

So, today I gave my first presentation of work that I have been doing on the evolution of hierarchy and community structure in international trade networks.  I have included my slides below...you can also skip down to just below my slides and find a short description of the various plots (if you are interested)...feedback and comments are appreciated as always...
Network Fragility Short)


Some additional thoughts on the CCC plots...
Below is the plot of the CCC for OECD countries from 1962-2009 using data from UN Comtrade.  Grey bars represent U.S. recessions as defined by NBER.   I will focus my discussion on this plot (the other is similar...one of the nice things about limiting analysis to OECD countries is that the results are not dependent on choice of data!).  Note that five countries have yet to report for 2009. 

The evolutionary theory that I am testing predicts that environmental change will cause an increase in the modularity of the trade network.  Here modularity (really hierarchy) is measured by CCC.  He (2010) supposes that U.S. recessions are an indicator of environmental change.  I have a couple of issues with this.  First, he is using U.S. recessions which may or may not be a good indicator of global recessions (to my knowledge there is not a universally accepted measure of global recessions).  More importantly, while the CCC does indeed increase during recessions, the largest increases in the CCC seem to occur outside of recessions...see for example the entire decade of the 1960's!  Generally speaking, the economic environment in which global trade is being conducted is constantly changing and forcing individuals and companies to adapt along with it.  I think a better measure of environmental change is needed.

Not that this is the answer, but I think I will plot some measure of real oil price shocks against CCC ans see what that looks like...           

Computational Model of Trade...

Just need to jot down the specifics of an idea I had about a computational model of trade with endogenous network formation...

The world consists of an exchange economy with N agents arranged in a circle.  Each agent is described by the following parameters:

1. Parameter, r~U[0,1] describing their level of risk aversion.
2. Parameter, v~U[a,b] describing their vision.  This vision parameter tells how many agents to the left and right are in an agents neighbourhood.  Vision could also be the same fixed v for all agents to simplify things.  Assume that agent's have perfect information about the endowments etc of all other agents in their neighbourhood/vision.
3. Each agent is endowed with some amount of two goods sugar and spice.  Endowments could be distributed uniform on some interval.
4. Agent have the same utility function which takes the amount of sugar and spice as arguments, and also must include risk aversion somehow.  I am open to suggestions as to what utility function would be most appropriate.

Agents would then have to decide whether or not to trade (if they wanted to trade at all) at "home" within their neighbourhood/vision or "abroad" by linking up with some other agent about which they know nothing (except the distributions of risk aversion, vision, etc.).  Of interest to me are the following...

1. Do agents with higher levels of risk aversion trade at home more often? Do agents with less risk aversion trade abroad more often?
2. What type of trade networks evolve through this process? Network structure within time steps and network structure aggregated across time steps would be of interest.
3. What are the equilibrium properties of such a model?  Is there meaningful convergence?  If so, how fast?  Is the equilibrium Pareto efficient/Pareto optimal?

I wrote this down very fast, and no doubt left out details necessary to close the model, but I thought I better write it down lest I forget...

International Trade Network: A First Pass...

I will be presenting a talk on the structure of international trade at a networks workshop being held here at the University of Edinburgh.  What follows is a dump of my thoughts related to the subject which so far are largely based on my attempt to replicate recently published work on the structure of international trade network.

International Trade Network Data...
There are two principle sources of network data on international trade (that I have been able to find):

1. Prof. Kristian Skrede Gleditsch at the University of Essex:  Data are from 1948-2000 and the primary source is the IMF.
2. UN Comtrade: Data are from 1962-2009 and the primary source is of course the UN.  
3. There is a third data source, the Economics Web Institute, that I would like to dissuade people from using even though they use Prof. Gleditsch's IMF data (without going into too much detail, the Economics Web Institute, seems to use an inconsistent methodology to assign edge weights (trade values) to countries when converting from Prof. Gleditsch's raw .asc files to .xls workbooks). 

If you would like to hear the gory details on the data validity issues feel free to contact me.

Hierarchical Clustering and Trade Network Density:
For the moment I am simply trying to replicate the work of the Deem et al. (2010) paper (linked to above).  I have applied a average linkage hierarchical clustering algorithm to the OECD international trade network as outlined in their paper.  Below are some plots of the cophenetic correlation coefficient (CCC) and network density for the international trade network for OECD countries using two different data-sets.  The first plot uses data entirely from the UN Comtrade database.  NBER recessions are marked with gray bars.  I downloaded the data by hand from Comtrade (commodity code is SITC ver. 1 AG0) and then used a Python script to clean and reorganize the .xls spreadsheets into more manageable text files.  Statistical analysis is done using SciPy, network analysis (so far) has been done using NetworkX, and plotting has been done using Matplotlib.     

The plot below is the CCC and network density for the international trade network for OECD countries using Prof. Gleditsch's IMF data for 1948-2000 and then UN Comtrade from 2001-2009 (commodity code this time is SITC ver. 3 AG0). 

There are some differences between the two plots of the CCC (I have not yet tested whether or not they are significantly different nor have I tested whether or not the CCC jumps significantly during/after recessions...this is on my to do list!).

Still to come:

1. Dendrogram of identified clusters
2. Results of community structure algorithm applications
3. Weighted clustering algorithms and other graph measures

Basically I am just trying to learn to do network analysis using Python by applying the tools to an interesting topic...

Slight PhD Research Detour...

My PhD research has taken a slight detour over the last couple of days.  In order to do theory I need to work with data, and there is just not a lot of publicly available data on financial networks at the moment.  So I decided that for the time being I am going to do some empirical research on trade networks using data from the UN Comtrade database. 

The inspiration for my research comes from the the following paper on the evolution of international trading networks.  The paper basically postulates that international trade is best described as a specific type of evolutionary system that satisfies the following three requirements:

1. The dynamics of the international trade system are "slow" to respond to environmental change
2. That environmental change is present
3. Information is exchanged between agents in the system

The authors have studied this type of evolutionary system in previous research and two of their papers on the details of their evolutionary theory can be found here and here.  Their theory makes three specific predictions about the evolution of international trade networks:

1. Decreased modular/hierarchical structure in the world trade network increases the sensitivity of the network to recessionary shocks
2. Decreased modular/hierarchical structure decreases the rate of recovery from shocks
3. Recessions (negative shocks) should spontaneously increase the modular/hierarchical structure in the trade network

According to the authors, all three of these predictions are borne out in the data...I am going to attempt to replicate their results.  Their theory implies that the modular/hierarchical structure that forms in response to environmental shocks (recessions) increases the resistance to and rate of recovery from shocks (recessions).  Globalization reduces modular/hierarchical structure in the global trade network and thus should lead to increasingly large recessions and a decreased rate of recovery from these recessions.

I have already written Python scripts to download the UN trade data and combine it into a single text file for use in the analysis.  I will be building an on-line code repository in the near future where people can come and download my code so that they can attempt to replicate MY results...

I would be interested in comments from readers concerning what standard economic theory I could bring to bear on this problem...I suspect that there is quite a bit of support for this line of research in more mainstream economics, but I could be wrong...