Michael Ewens

The AEA’s JOE postings present the near-population of jobs available for newly-minted economic PhDs.  I used the XML data available for download to create a mash-up of job locations on Google Maps.  I break the posting down into US full-time academic, international full-time academic and non-academic.  Here is how I create the maps: 

1. Select the subset of the data you want (e.g. US academic) and download the XML file.
2. Fix some validation errors: take out the “<” and “>” within the text of nodes (I use TextMate for this).
3. Parse the XML file with a custom PHP script that creates a csv file with school, position, location and url to posting. Here is my simple script for the academic XML file.
4. Save the csv file produced by the script in step 3 as an Excel spreadsheet (Google Docs doesn’t like csv’s).  Add a “Latitude” and “Longitude” column to the spreadsheet.
5. Upload the Excel file to Google docs.
6. Follow these directions to populate the latitude and longitude of each position+location.
7. Publish the Google spreadsheet and save the unique id in the url that Google gives you.
8. Sign up for a Google Maps API account.
9. Follow these directions to produce a Google map of your postings.

UPDATE: This service may make this process a bit easier, produce cleaner maps and allow the incorporation of more information.

Maybe the AEA can follow these directions to produce these maps after this year.  Contact me with any suggestions or questions.

From the Indeed job listing aggregation site, one can look at trends in both supply (employers) and demand (potential employees). The demand-side is slightly tricky to interpret because it is a function of not only real employment demand, but the popularity of Indeed as a search platform. Here are some interesting facts:

Finance: Supply of jobs falls, demand increases

Financial Services and Banking job postings have decreased 39% since March 2008.

Clicks on Financial Services and Banking jobs have increased 96% since March 2008.

Construction employment falls

builder Job Trends

builder jobs

Healthcare bucks the trends

healthcare Job Trends

healthcare jobs

Tax-related employment is volatile

tax Job Trends

tax jobs

SimplyHired.com also has a trends section with similar patterns.

Also see my own take on apartment rental supply and demand using online listings + web scraper.

One of my research papers studies spinoffs in the venture capital industry. I seek to understand the process of spinoff formation and the performance of spinoff firms versus both their parent firms and other new firms.  If the data permits, I also hope to study the characteristics and investment performance of spinoff founders.

The data was a lot more difficult to collect that I originally planned for, so the following graph is quite exciting:

New firms and spinoffs in VC. Shows the total number of new firms and spinoffs from 1992 - 2007.

Of all new firms each year, some 15% are spinoffs founded by employees of existing VC firms.  Defining a spinoff requires rich data on the partners who found the firms and the investment activity of all actors over time.  I define a spinoff in the following way:

1. New firm post-1992
2. Has a partner that sat on an entrepreneurial firm within two years of the firm’s founding
3. Any partner has past experience at an existing VC firm (the “parent”) and did not found any previous VC firm

Right now, I assume that if the partners found in step 2 satisfy step 3, they are founders.  Of course, it could be the case that the partner was hired by the new firm by the real founders.  Fortunately, at least one of these partners is a founder, so the number of spinoff firms is basically known.  So, I have to use Mechanical Turk to identify the true founders from the set of “potential founders.”   There are some 390 such individuals for which I know had past VC employment before working at this new firm, but it is unknown whether they are simply an employee or a founder.  So, I will use Mechanical Turk to:

1. Collect the biographies of each potential founder
2. Read each bio to determine whether they founded the firm in question.

I will update this post with my progress — and new graphs — soon.

Amazon’s Mechanical Turk is a services that allows you to hire 100s of people from across the world to tag photos, complete surveys or find websites. According to Wikipedia:

The Amazon Mechanical Turk (MTurk) is one of the suite of Amazon Web Services, a crowdsourcing marketplace that enables computer programs to co-ordinate the use of human intelligence to perform tasks which computers are unable to do. Requesters, the human beings that write these programs, are able to pose tasks known as HITs (Human Intelligence Tasks), such as choosing the best among several photographs of a storefront, writing product descriptions, or identifying performers on music CDs. Workers (called Providers in Mechanical Turk’s Terms of Service) can then browse among existing tasks and complete them for a monetary payment set by the Requester. To place HITs, the requesting programs use an open Application Programming Interface, or the somewhat limited Mturk Requester site.

Why would economists find this service useful? An example from my own work might help. I am collecting all the individuals employed by new VC firms founded from 1992 – 2007. I need demographic information and employment histories for each VC partner. After scraping the web to get the VC firm websites and “team pages” I have a set of locations for an individual to find the online biography of each of some 3000 VC partners. I submit a job to Mechanical Turk that asks the Turk’er to go to the website, find the individual’s biography and copy and paste the text. Further jobs could ask the Turk to read the bio and answer questions like: 1) Does this person have an MBA or PhD? 2) Male or Female? 3) Founder of firm? Unfortunately, submitting HITs to the Turk system is somewhat difficult. Enter Smartsheet.

Smartsheet as a Frontend to Mechanical Turk

Although the Mechanical Turk service has a system for submitting HITs, it is a little cubersome and requires a bit of strange formatting steps. If you value your time just a little (say, $10/hour) I recommend using the project management webapp Smartsheet’s service SmartSourcing. Again, I refer to my research. I created an Excel file with columns like “Name”, “Firm Name”, “Website” and an empty column “Biography.” I upload this file to Smartsheet, select the rows for which I need biography filled in and walk through their SmartSourcing steps. In about 3 minutes I have submitted a HIT to 1000s of workers that will be complete in 12 hours. I can approve or reject the responses while I watch them populate the online spreadsheet.

Such a service is not free. For a $9.95/month fee (or $99/annual…ask them for a non-profit coupon), you get access to SmartSourcing. Then, on top of the standard Turk fees, you have Smartsheet charges:

Any paid Smartsheet subscriber has access to the Crowdsourcing feature. Monthly charges include Amazon fees plus the cost for work performed (number of tasks completed * cents paid per task) and a low Smartsheet processing fee ($.01 + 10% per task completed – usually $10-$30 per 1,000 tasks).

For example, I paid about $7 for 116 biographies of VC partners. It sounds relatively expensive, but this service has:

• increased the potential sample size of my studies
• expanded the set of possible control variables
• gives you the ability to request multiple workers per task for error checking
• kept me sane by outsourcing mundane data tasks

Since March 2008, the S&P 500 is down 36%, the Nasdaq is down 32% and the Dow is down 35%.  Venture capital returns — proxied by exit rates — are down significantly more:

-There were just 68 M&A deals, the lowest total for a quarter since at least 1999. That’s 35% fewer deals than in the year-ago period.

-Liquidity – the amount of money generated in M&A deals and IPOs, if there were any - fell for the fifth straight quarter, plumetting 65% from a year ago to $3.2 billion. That is the lowest quarterly amount since the first quarter of 2003.

I am slightly more confident in the beta of 2.4 that I find in my VC returns paper.

Jim Hamilton is concerned that the Fed is changing the way it intervenes in the market and suggests the TALF may generate the false security of securitization that got us where we are now:

But the whole premise behind those Aaa ratings– that securitization could isolate a “safe” component of a pool of fundamentally risky loans– was deeply flawed. It is impossible to diversify away aggregate or systemic risk. All that the device did was to mislead investors into thinking they were protected from those nondiversifiable risks and push those risks onto the taxpayers and the Fed. Before we decide that securitization is the road out of our present difficulties, I would like a detailed and convincing explanation of why the past mistakes are not going to be repeated again.

And frankly, this plan doesn’t make survival look all that likely at anything remotely approaching GM’s current size–not if by “survival” we mean “weaning itself from taxpayer cash”.  The government can guarantee warrantees.  But it will be less effective at shedding all sorts of obligations than bankruptcy court.  The administration won’t be reorganizing the way a bankruptcy does; it will be negotiating.  To be sure, bankruptcy judges, too, negotiate, particularly among the various creditors.  But they do so with the power to cramdown firmly in their hands.  If the administration wants to wield that kind of power, it will have to find new and inventive threats to level at the creditors.  They may not find them.  And if they do, it’s not really in the best interests of the nation for the government to find lots of new and innovative ways to threaten private investors.

~ Megan McArdle on the  GM Bailout

I have been called the devil by strangers and “the Facilitator” by friends. It’s not uncommon for people, when I tell them what I used to do, to ask if I feel guilty. I do, somewhat, and it nags at me. When I put it out of mind, it inevitably resurfaces, like a shipwreck at low tide. It’s been eight years since I compiled a program, but the last one lived on, becoming the industry standard that seeded itself into every investment bank in the world.I wrote the software that turned mortgages into bonds. More here.

Newspaper Advertising Revenues Falling Across the Board

In Fama and French’s new paper “Luck versus Skill in the Cross Section of Mutual Fund Alpha Estimates,” they find that mutual fund managers do not skills to earn expected returns above the market portfolio.  This result contrasts with the findings in Kosowski, Timmermann and White (2006) who find a set of mutual fund managers for 1975 – 2002 did outperform the market (not because of luck). Fama and French argue that these earlier results are suspect because the bootstraps ignored “common variation is fund returns.” Also, the different sample period and assumptions for sample inclusion may create data issues and survival bias. It will be interesting to see how this paper does in the next year or so.