Last September, as the big honchos of the financial world - the regulators and the bankers - were witnessing the imminent collapse of Lehman Brothers, they realized that they were facing a far bigger problem: The possible collapse of American International Group (AIG), the biggest insurance company in the world. If AIG collapsed, so the theory went, it would cause a chain-reaction that could potentially prove fatal to large number of financial institutions all over the world, plunging the world economies into a financial abyss!

Fear of financial meltdown was the reason given then for the taxpayer paid bailout of AIG, that would eventually rise to more than $160 billion. However what was not clear then, in what way AIG bailout would save the rest of the world. Even as some in the media did expose the role played by the independently operating but wholly owned subsidiary AIGFP and its unit in London headed by Mr. Cassano, under the name of innovative financial products and services was essentially gambling with AIG investors money. And yet it was not clear who were the partners in this gambling scheme who would ultimately get the counterparty claims.

Both AIG and the Feds hemmed and hawed around to release the names of these counterparties under the legalistic language of confidentiality agreements. And it would have continued that way till the AIG bonus-babies scandal broke last week, forcing AIG to issue the press release that gave partial list of trading partners/counter parties.

Below are the maps that show locations of financial institutions who were paid by AIG in the counterparty claims for the complex financial transactions such as Credit Default Swaps (CDS), repurchase of mortgage-backed securities and security lending obligations.

From these maps it would appear that the problem is limited to Western Europe and the U.S. And yet nearly $22 billion was paid to other counter parties, who probably are scattered all across the globe. The partial list of Western European institutions and those in the U.S. reads like who’s who in the financial world. A cross check with Madoff’s clients and TARP list reveals that many of these are the same players who lost billions in Madoff’s ponzi scheme and are also getting TARP money and its quite likely that these same set of players will show up for TARP-II, the TALF!

Talk about small-world! In the name of spreading risk, they have collectively managed to plunge the U.S. and rest of the world into the worst financial crisis since the Great Depression.

Your tax $ at work: AIG’s payment to financial institutions for Credit Default Swaps (CDS)

View the map in Maker! here

Your tax $ at work: AIG’s payment to financial institutions for Mortgage-backed securities

View the map in Maker! here

Your tax $ at work: AIG’s payment to financial institutions for Security lending obligations

View the map in Maker! here

Explore interactive map on the Maker!
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Dataset of the Day: Australia’s Wildfires

February 11th, 2009by Brendan Lewis

Australia’s southern state of Victoria has struggled to contain the brush fires that are tearing through the state. Police officials have confirmed the death of 181 people thus far, and expect that number to exceed 200.

Police suspect arsonists played a role in starting the blazes that have quickly gotten out of control. The fires have become one of the nations worst natural disasters. Brush fires are not an uncommon occurrence in Australia, however the magnitude of these fires is unusually high. Gary Morgan, chief executive of the Brushfire Cooperative Research Centre, is quoted in a Wall Street Journal article saying, “climate change and drought are altering the nature, the ferocity and the duration of the brushfires.”

There are many theories on why these particular fires have gotten out of hand. Some point to the abnormal weather conditions over this past weekend, in which temperatures set record highs and humidity levels plummeted.

Others believe that extended climate changes over time are to blame. Blaming the lack of precipitation in the area along with rising temperatures in population centers. The area around Melbourne has suffered from drought conditions for nearly a decade.

Here is a map of Australia mashing together the recorded fires from the previous 7 days / 48 Hours / 24 hours along with rainfall measurements. As you can see the southern part of Australia collected very little rain this month. Please zoom in for a better perspective.

Others go further and believe that the shifting population in in Australia is to blame. They believe that the recent trend of people moving into more rural areas to escape the city is to blame. Stating that these newcomers are less familiar than longtime rural residents with precautions needed to prevent wildfires and are less prepared to escape when fires occur.

Whatever the causes lets hope that the fires are brought under control quickly.

Click here to view a few data sets in the Finder! platform regarding the fires in Australia.

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Dataset of the Day: Collegiate Rowing Revenues

December 16th, 2008by William Benjamin

The U.S. Department of Education has a link on their website to The Equity in Athletics Data Analysis Cutting Tool, which allows visitors to download spreadsheets that show financial information about equity in college athletics. The universities that the data accounts for are US college universities that receive Title IV funding. What that basically means is that these colleges participate in federal student aid programs, which a majority of college universities do.

Considering that I was a rower in college, I was pleased to find this data. I was particularly interested in seeing what it would look like if I mapped out the college universities that have rowing programs and then by using proportion symbols, I could see which colleges had the biggest revenues. The following map displays revenues of collegiate rowing teams for both male and female programs combined in 2007:

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(Click Finder! to view the data set)

Now to give you an idea of what each college rowing program revenue looks like by gender, the following map is broken down by female revenue and male revenue using proportion symbols to show the amount of revenue comparatively.

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(Click map or Maker! to view map)

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Dataset of the Day: International Unemployment

December 16th, 2008by Emily Sciarillo


Global economic crisis! Record level unemployment in the U.S.!

With our latest dataset on unemployment levels for select countries from 1995 to 2008 from the U.S. Department of Labor, I decided to take a look at what has been happening to unemployment in this economic environment.

The next three maps show unemployment levels for three different years at the same scale.

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Then too see the more short term effects of the current crisis on unemployment rates, I made a map based on the percent change of unemployment rates from the first quarter of 2008 to the third quarter of 2008.

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It is clear that globally things have worsened since 2000, however they still have not reached the levels seen in 1995. Also, the U.S. still has much lower unemployment rates than many European countries, such as Spain, France, Portugal, Germany, Greece and Italy (this may change with the latest figures for the fourth quarter of 2008).

Although comparatively, the U.S. has lower rates of unemployment than many European countries; it is important to note that the U.S. has a much less significant safety net for the unemployed (in the area of health care for example) so that the social effects may be as devastating.

The U.S. is also one of the countries that has seen the largest percent increase in unemployment rates since the beginning of 2008. Only Spain, Portugal and Ireland have had larger increases in unemployment rates than the U.S. (Italy does not have data after the second quarter of 2008). Since this data is based on self reporting from each country, figures may be inflated or deflated, such as the case of the U.S. It is important to note that this data does not represent unemployment in poorer countries where increasing unemployment may be more devastating.

Take a look at these maps yourself or go to Maker! and make your own maps from the dataset.

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