Thanks to the attention my paper on the cost of Somali piracy has received, a lot of people have approached me to ask how I computed the maritime routes. It is not a very difficult task using R. The key ingredient is a map of the world, that can be rasterized into a grid; all the landmass needs to be assigned an infinite cost of crossing and last but not least — one needs to compute the actual routes.

What packages do I need?

The package gdistance does most of the actual work of computing the routes. The wrld_simpl map provides what is needed to generate a raster.

Generating a Raster

After the raster is generated, we can proceed by making landmass impassable for vessels.

There are a few more things to do, such as opening up the Suez Canal and some other maritime passages — one needs to find the right grid cells for this task. In the next step we can transform the raster into a transition layer matrix, that comes from the gdistance package. It is a data construct that essentially tells us how one can move from one cell to the other — you can allow diagonal moves by allowing the vessel to move into all 8 adjacent grid cells. There is also a geo-correction necessary, as the diagonals are longer distances than the straight-line moves.

Well — and thats basically it — of course, there are a few bits and pieces that need additional work — like adding heterogenuous costs as one can imagine exist due to maritime currents and so on. Furthermore, there is a whole logic surrounding the handling of the output and the storing in a local database for further use and so on.

But not to bore you with that — how can I obtain the distance between A and B? This uses Dijkstra’s Algorithm and is called through the gdistance function “shortestPath”.

Using this output, you can then generate fancy graphs such as …

I found it useful to have multiple Stata instances running on my Mac, in particular, if I use one instance to clean the data before running merge commands. It is always annoying if the merging does not work out or throws an error and then, one would have to clear the current instance and open the DTA file that was messing up the merge.

Its a simple command that allows you to open multiple Stata instances on a Mac:

You can also define an alias command in your .bash_profile,

I have started to work with R and STATA together. I like running regressions in STATA, but I do graphs and setting up the dataset in R. R clearly has a strong comparative advantage here compared to STATA. I was writing a function that will give me a (balanced) panel-structure in R. It then simply works by joining in the additional data.tables or data.frames that you want to join into it.

It consists of two functions:

This first function generates the time-vector, you need to tell it what time-steps you want it to have.

This second function then generates the panel-structure. You need to give it a group vector, such as for example a vector of district names and you need  to pass it the time vector that the other function created.

Hope this is helpful to some of you.

We are working on finalizing our paper on Somalian piracy and the effects on the shipping industry. We believe that our paper is the first serious attempt to identify the cost of piracy using a novel dataset and a novel approach. We find that the direct and indirect cost may be between $ 1.8 – $3.0 billion. This is a lot of money compared with the mere $150 – $250 million that the piracy activity generates for the pirates. This highlights how large the welfare gains from having a functioning state with working institutions and an established monopoly of power can be.

Clearly, the paper is not capturing all the adjustments that are taking place. In particular, we find that local and regional trade is a lot stronger affected by piracy then, e.g. trade from Asia to Europe. This suggests that the piracy burden is especially born by regional economies, such as Yemen, Kenya, the Seychelles and so on. This got me thinking and I started looking at the impact of piracy on the fishing industry — I first started off with Yemen, however, the data quality is very poor.

Here is something very primer…The graph depicts quarterly reported fish catches by the Ministry of Fisheries of the Seychelles and the number of piracy attacks in the vicinity of the Seychelles (roughly in a radius of around 500 miles). Do we believe that the rise in piracy was causing this drop in fish catches, which appears to be persistent?

Jon de Quidt and I recently looked a bit into the data from the paper of De Mel, Woodruff and McKenzie (2008) in Sri Lanka. It is quite an influential paper, experimentally administering capital shocks to microenterprises in Sri Lanka in order to estimate the returns to capital.

Their paper highlighted that the returns to capital “at the bottom of the Pyramid” could be very large indeed. In their favorite specification they find that these could be as high as 50 – 63 % per year (in real terms). This suggests that these investments pay-off on average. It was one of the first papers that used experimentally generated variation in capital stocks to estimate these returns. Thats why it became so influential and the authors have a set of papers on Mexico and Ghana, performing similar estimates.

They point out that there is a lot of heterogeneity in the estimated treatment effects. In particular, they observe that for female entrepreneurs, there is virtually no (average) treatment effect. This and the reasons that could be underlying this observation are explored in a second paper.

We had a look at the De Mel et al (2008) data, which is available here. We essentially ran their regressions of (reported) real profits on the treatment dummy. However, we did this iteratively for each individual treated person, using the whole group of non-treated individuals as counterfactual. This allow us to get an estimate of the treatment effect for each individual treated.

From this, we get a distribution of treatment effect estimates. The average of this should be the treatment effect that De Mel et al (2008) report in their paper. And indeed it comes quite close. However, what we are intrigued by is the significant heterogeneity in the point estimates for the treatment effect for different individuals.

A key observation is that the treatment effects are very heterogeneous – the mass of enterprises who saw a drop in profits is almost as large as the mass of enterprises that saw a rise, however, some saw a very significant and large rise in real profits. The vertical line is the average of the treatment effect, which here, as we lumped the cash treatments together, is around 900 rupees.  The median treatment effect however, is only  332 rupees. Thats some food for thought.

I was a bit annoyed by the error when loading a dataset that contains multi-byte characters. R basically just chokes on them. I have not really understood the intricacies of this, but it was basically just an annoyance and since I did not really use these characters in the strings containing them, I just wanted to remove them.

The easiest solution was to use Vim with the following search and replace:

s/[\x80-\xFF]//g

Max Weber has shaped the distinction between functioning and failed states. In his words ”a state is a human community that (successfully) claims a monopoly of the legitimate use of physical force within a given territory”. For Somalia the effective monopoly of power has not been with any form of state since 1991 as it has been topping the list of the failed states index for the past five consecutive years. The disastrous US intervention in Mogadishu in October 1993 lead to a shift in US foreign policy, towards non-intervention in Somalia. Unchecked by outside forces, the state further fragmented into several smaller regions that were dominated by war lords. As such it became a refuge for radical islamists and organised crime. It was only through the rise in piracy throughout the first decade of this century, and in particular due to a sharp increase in piracy attacks in 2008, that the world seemed to notice and care again about the situation in Somalia. The interest may be partly driven by the romantic ideas that Hollywood’s pirates inspire, but mostly, as we argue, because Somalian piracy has been an externality. But how costly can such an externality from statelessness be? What is the tax rate at with which Somalian piracy taxes world-trade? And how does this tax rate compare to an optimal tax rate?

Our recent research aims to answer these questions, thus shedding light on the key questions about the role of institutions in securing trade from predation and theft (see e.g. Dixit (2004) ).

Piracy at a Maritime choke point 

Our window into obtaining estimates of the “piracy tax” comes from micro-data on individual shipping contracts. This approach is methodologically more powerful than indirect accounting approaches such as the various One Earth Future Foundation Reports (2010, 2011), which lack any counterfactual. We consider the direct link between the risk of piracy attacks and the cost of shipping by studying the impact on chartering rates on maritime routes that vary by (1) whether and (2) the extent over time to which they are exposed to Somalian piracy. Most of the trade between Asia and Europe has to go through the Gulf of Aden and is thus, potentially affected by Somalian piracy. The fact that the Gulf of Aden is one of the business shipping routes becomes clear by inspection of our data on chartering contracts. Roughly 25% of our ships are travelling through the Gulf of Aden.

This graphical representation highlights how important the Suez Canal is for world trade. Anything that disrupts trade through the Suez Canal has thus the potential of disturbing patterns of trade. The role of the Suez Canal and its impact on trade has been studied in a related study by Feyrer (2009), who looks at the Suez Canal closure as a natural experiment.

We argue that the upsurge in piracy in spring 2008, which becomes evident when studying the monthly time series of attacks, has been disrupting trade and has lead to several reactions by the shipping industry. The cost of these reactions are passed on to the charteres and eventually to the consumers of traded goods. The impact on shipping rates is our window through which we estimate the “piracy tax”.

Bringing both of data-sets together, we find that piracy caused an increase in the transport cost by around 8%. We identify this increase in the cost both from the sudden increase in violence intensity in spring 2008, but also from seasonal variation in the intensity.

In particular, early summer is a period of relative little piracy activity. We show that this is due to the Monsoon season, which just makes it difficult for pirates to operate in their small vessels. Hence, the estimated effect varies significantly with the season as the risks are lower. This is illustrated in the following picture. The piracy tax is lower in the Monsoon season. We check that this drop in shipping rates is not due to less shipping through the area.

Taking econometric studies seriously. 

Taking our estimates seriously, the overall costs of Somalian piracy can be obtained by scaling up the point estimate by aggregate measures of trade through the region. Clearly, such estimates will have very large standard errors – however, their foundation is a solid micro-econometric estimate. Given this we find that, at the lowest level, the $120 million in net revenues that pirates generate are far offset by the costs borne by the shipping industry, which lie between $ 0.9 billion to $ 3.3 billion.

One could compare the revenues generated by piracy to an equivalent tax rate on traffic through the Gulf of Aden or the broader Somalian territorial waters. If we follow this avenue, we estimate that the equivalent tax rate on traffic would be well below 1%. What does this mean? It highlights that a functioning state is able to implement redistributive policies a lot more efficiently than a “roving bandit” ( Olson, 2000). Hence, this exercises provides a sense of how much value is generated by installing functioning institutions and a functioning state.

Conclusions

Is there a solution to Somalian piracy? As Somalian piracy is a classical externality, there is a need for cooperation to adress the problem. Many commentators argue that the only long lasting solution is to provide support on the ground in Somalia. However, international cooperation is difficult to muster, due to varying geo-political interests of the players involved.

History is full of anecdotes suggesting that this problem is not new. Consider for example, the correspondent report on Chinese piracy in The London and China Telegraph from 4th February 1867 noted that

Besides we are not the only Power with large interests at stake. French, Americans, and Germans carry on an extensive trade [...] Why should we then incur singly the expense of suppressing piracy if each provided a couple of gunboats the force would suffice for the safety foreign shipping which is all that devolves upon [..] why should the English tax payer alone bear the expense?

This sentiment highlight the public good aspect of travels on global water ways. This research will fall short of providing policy suggestions, however, it highlights that piracy is most likely one of the costliest ways of making transfers to Somalia.

I really disklike iTunes, it is the worst piece of software I have ever come accross.  I would say that Windows has been getting better and better. I had the following problem: I uploaded quite a few pictures via iTunes onto my iPad, just because its nice to look at pictures on that machine. However, the machine with which I did the syncing broke and needed repair and somehow, I forgot to save these pictures onto a  hard drive for backup. So the only place where these pictures now rest is on my iPad.

iTunes wont allow you to copy pictures on your iPad onto a machine (only the pictures that you atually take with the iPad). This is because, these pictures *should*  be on the machine with which you synced your iPad in the first place.

However, this was not true in my case anymore. Now you could either invest some money and purchase an app that allows you to copy your picture albums from the iPad onto a Windows machine.

There is e.g. CopyTrans Suite, which is a bit costly and in the version I tried, did not copy the full resolution of the pictures (which is a rip-off!).

So I was looking into a cheap and quick solution to get the original full resolution pictures down from my iPad.

Setting things up: installing free app “WiFi Photo”
This app basically makes your photo albums available on a local webserver. Once you start the app on the iPad, it tells you an URL to brows to on your local machine. There you can see all the pictures that are on your iPad.

You could now use this app to manually download the pictures, however, it is limited to 100 pictures at once and you will not get the full resolution pictures if you do a batch download.

If you browse through the app, you will notice that the URL to the full resolution pictures has the following form:

http://192.168.1.6:15555/0/fr_564.jpg

where the “0″ stands for the album ID. If you have, say 2 albums on the iPad, this would take values “0″ or “1″. Images are stored as consecutive numbers in each album, so the following link would go to picture number 564 in full resolution in album 0. So we will exploit this structure to do an automated batch download.

Doing an automated batch download

First, in order for this to work you need to get a a local PHP installation up and running. If you are really lazy, you could just install XAMPP. However, you can implement the code in any other coding language, e.g. in R as well.

To download all the pictures, you need to adjust and run the following script

As I have to be in hospital for a few days in Germany, I bought myself a SIM card and a month mobile internet flatrate. I have a netbook, a EEPC 1000go with an integrated mobile broadband card.

However, in hospital I rather wanted to use my iPad and my iPhone to read papers and so on. As both are from the UK and I have not unlocked them, I wanted to indirectly use the internet on these devices through my netbook. I was amazed by how quick and easy it was setting things up….

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I gave it another go, trying to get a map that looks a bit nicer. This time, I tried to compute something like a density or intensity in a certain area. On the previous map, this was not visible very well. I used ggplot2 and a bit of R code, together with RGoogleMaps to produce the following picture:

This map displays the intensity of microfinance institution headquarter distribution across India. The data comes from the MIX Market.

The fact that many MFIs are clustered around in the south is highlighted quite strongly. What this graph does not take into account however, is their variable size. This is problematic and I agree that this needs further refinement, i.e. that the intensity takes into account how big an MFI is. However, I would conjecture that this merely makes the contrasts in such a map just stronger.

I am working on a review paper on microfinance in India and use data from the MIX market. Today, I was amazed by how quick I conjured a map of India with the headquarters of the microfinance institutions that report data to the MIX market depicted on that map. Ideally, I would have more geolocation data – but this is hard to come by. But what we can clearly see is the clustering of institutions in big cities and in the south, which was hit hardest by the recent crisis.

Microfinance Institutions across India

I dont think anybody has produced such a map before. In fact, I can do this for all institutions reporting data around the world, which may be interesting to see. Also, I already tried to make the size of the dot proportional to e.g. measures of real yield or color-coding the nearest neighborhood (say the neigbhouring districts) by the average loan sizes reported. Lots of things to do. Maybe thats something for the guys at MIX Market or for David Roodman who, I think has finished his open book.

The key difficulty was actually not in plotting the map (though it took some time), but in obtaining geo-data on where the headquarters of the microfinance institutions are located. I managed to obtain this data – though its not perfect – by making calls to the Google MAP API via a PHP script., basically using the following two functions:

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I just came accross this amazing animation, which depicts lending flows from Kiva lenders to Kiva borrowers in the field. I have been working on a few pieces of research with my colleague Jon de Quidt using Kiva data. However, that work has stalled a bit as prioritization moved it towards the end of the queue. However, this animization is indeed inspiring and it is somewhat awaking the urge in me, not to wait too long to continue work on Kiva.

Intercontinental Ballistic Microfinance from Kiva Microfunds on Vimeo.

On another note, I am going to try to do some more work with R on mapping data, trying out several packages:

• http://crantastic.org/packages/RgoogleMaps
• http://cran.r-project.org/web/packages/maps/index.html
• http://crantastic.org/packages/RgoogleMaps

I found working with ArcGIS way too clumsy…

I thought this was a bit provoking and quite funny…where would you place yourself on that?

As seen on http://i.imgur.com/vRTRP.jpg