Here’s the presentation I’ll be giving. It’s based on some of my newer work that looks at the connections between local entrepreneurial cultures and the reasons why entrepreneurs decide to start their firms in the first place. Hopefully I’ll find an outlet to publish it in soon.

I wanted to highlight two tables from the paper. The first was part of the lit review. Like I said, there have been dozens of papers since the 1980s that have examined the regional causes of entrepreneurial activity. Normally these are regressions based on census or tax data on a metropolitan level, but some of the more advanced work employ high level statistical approaches to giant, micro-level datasets. But, there has yet to be a serious attempt to synthesize this research. The challenge is that these papers employ a variety of datasets and examine a variety of countries at a variety of times, making it difficult to really compare. But after many, many hours spent reading articles and working with spreadsheets, I was able to create this table:

Significant findings of past research on regional entrepreneurial determinates

The big takeaway from this table is (1) it’s easy to see that things that proxy economic growth, like population growth, and the presence of other startups, are generally constantly associated with higher levels of entrepreneurial activities. We also see interesting differences between countries. Personal wealth has almost no effect on German entrepreneurship, but it is shows to cause it in countries like Sweden and the US. It’s a difficult task to tease out if this is more related to differing national economies, or due to the different statistics and methods used by the various papers.

The second table are the results from Canada.

I argue in this paper that Canadian self-employment appears to be mostly driven by local economic growth. Population growth, a fairly good proxy for economic growth (people aren’t moving to Fort MacMurray for the culture) has a positive effect and unemployment has a negative effect. Nothing too surprising there. Barriers to entry are important too, economies dominated by a few large employers have less entrepreneurship than those with a pre-existing base of small businesses. Most surprising was the role of taxes, I found that areas with higher commercial-to-residential tax ratios had higher rates of self-employment than other regions. I don’t know what to make of this last finding: it’ll take some more work to figure out if this is a real issue or just a statistical artifact.

And now, just a few seconds ago, I saw an article in the Ottawa Citizen about the lack of angel investors in Canada’s Capital City. I’ve touched on this topic before, and I’m happy to say that I’ll have a chapter in volume 22 of Advances in the Study of Entrepreneurship, Innovation and Economic Growth on it. It’s called “A Series of Unfortunate Events: The Growth, Decline, and Rebirth of Ottawa’s Entrepreneurial Institutions” because I’m a sucker for titles that contain other titles.

So, credentials: established. The article in The OC basically bemoans the lack of angel investors in the Ottawa region. In essence, in addition to the lack of medium and late-term venture capital investment that plagues the rest of Canada, Ottawa also has a problem in that there’s very little early-stage seed money to help entrepreneurs transition from a raman-based development process to something more resembling a real, human, life. In 99% of cases, this money comes locally from either the investor’s family and friends, and after that, by a wealthy individual looking to get in at the very early stages of a firm with huge growth potential.

The article seems to place the blame on the fact that the area’s richest individuals aren’t acting as angel investors. That’s barking up the wrong tree. First, local tycoon Terry Mathews does invest in startups, they’re not necessary local. He’s Murchock to an A-team of super-smart and motivated technology workers. He brings them together, gives them resources, and points them to a problem he’s identified as needing to be solved.

But the biggest point that this article misses is what happened to the angel community in Ottawa. There used to be one! Indeed, it was one of the most active in the country, made up of successful Nortel execs looking for something to spice up retirement and successful entrepreneurs looking for some post-sell-out fun. Indeed, the large federal workforce in Ottawa meant that it was fairly easy to find a friend willing to invest a bit, since they have pretty nice salaries and the best job security in the world. Ottawa saw a whole bunch of angel investment plays, both formal and informal, throughout the dotcom boom when everyone and their younger brother was starting a web startup. Everyone was going to be the new GeoCities!

But the crash happened. Firms that had taken on angel investment went under and obviously, the angels lost their dollars. However, the sadder story is what happened with companies that had taken on venture capital. The venture capitalists (I’m imagining them as something like this) structured the deals to protect them at the cost of the original founding entrepreneurs and the early angel investors. In the death spiral of the dotcom age, they were able to force the company to sell or liquidate and take back their entire investment, often leaving those orignal investors with nothing. This had the effect of mostly shattering the local network of angel investors. Those that had money left to invest became very gun shy, hesitiate to go through that again. Upsell altert: the forthcoming chapter discusses why a community of angel investors is so critical.

This has caused a decade of entrepreneurs, an entire generation, to be unable to get any early stage angel investment. They’ve had to re-adjust their growth strategies to be able to use only organic revenues to grow the company. Essentially, they chose business strategies that would let them grow without needing any investment. Companies like Shopify, Trustifier, or Klipfolio (note: maybe I interviewed the founders of these firms, maybe I didn’t. I’ll never tell) took a cloud-based or Software as a Service route as a way to lower initial startup costs and provide a predictable path to growth. When they realized they couldn’t get anglels

So, the problem of Ottawa is not just there aren’t enough deranged millionaires throwing pennies down from their air-zeppelins. Rather, it’s the fact that many medium sized potential investors, people who could write a check from $10,000 to $100,000, either got burned a decade ago, or have been hearing stories about how other people got burned a decade ago. It’s a bigger problem than just a lack of investors, it’s a lack of will. That’ll take much longer to fix.

I’m mostly posting this here so I can find it again in the future. This is like classroom discussion gold.

Maybelline is already Occupy Wall Street themed ads. What ever you might think about capitalism, it will adapt to anything.

My philosophy for using technology to deal with this mess situation is to realize that I’m good at a some things and that my computer is good at other things. The computer, with its gigabytes and gigahertz, is fantastically good at keeping track of things. With a bit of high-tech processing, it can be pretty good at finding connections between things. But,  the computer is less good at figuring out if those things are important and what they contribute to a bigger picture. For that you need the human brain with its analog processing.

My term for this is turning dumb data into smart data. By this I mean that the data has to be converted from its original format to another format that can be interpreted and used by a computer program. This can take the form of something completely automatic, like OCRing a scan of an article so that the text can be understood by a computer, to manually coding and classifying an interview so it can be used by Qualatative Analysis Software (QAS) like DeDoose or nVivo. The advantage of smart data is that it can be analyized by both a computer and a human, each doing what they do best, producing better research faster.

In this post, I’m going mainly focus on a program called Devon Think, a document management program. I am not so much a fan of Devon as I am completly and hopelessly dependent on it. Despite the fact that my database is backed up in 6 different location, including on-site, near-site, off-site, and in The Cloud, and I’m pretty sure it could survive a limited nuclear war, I would most likely drop out of grad school if the database got corrupted beyond recovery. Basically, Devon Think is a document management program, like Yojimbo, Papers, or even a folder with a bunch of PDFs in it. But what separates Devon from others is it’s ability to generate a limited understanding of the document and do two things: suggest other documents that are similar and (2) suggest what folder a new document belongs in. I don’t use the first feature too much, finding links between articles is something better suited for the human brain than the cold, robotic logic of a computron, but the second feature is invaluable.

The thing about any pile of documents, whether made of actual paper or of tiny bits on a hard drive, is that it quickly gets unmanageable. My RSS feeder subscribes to 39 journals, and I get e-mail updates of several working paper series, along with my regular trolling for interesting articles  Even if I only add a few new articles per month (there are months with very few additions, but when I’m starting a new project I can easily add several dozen a day), this still builds up, As close as I can estimate, my DevonThink database contains about 625 academic articles and notes on about 500 of those. These papers are thrown into folders that are largely created on-demand as I begin new projects and explore new topics. To give you an example, here is the folder structure for research relating to my dissertation on culture and entrepreneurship (I can’t get lists to work properly, so > means a subfolder>:

• Culture
• Bourdieu
• Bourdieu and Geography
• Bourdieu and Entrepreneurship
• Cultural Turn
• Defining Culture
• Economic Geography and Culture
• Institutional Economic Geography
• Markusen Debate
• Mitchell Debate
• Management and Culture
• Ethnic and National Entrepreneurship
• Hofstede Debate
• Family Entrepreneurship
• And this list goes on for another 20 or so lines. It turns out I have a lot of folders I forgot about

The point of all that is to show that even if I started my dissertation research by sitting down and trying to make logical, sensible categories, all those plans get thrown out the window when you start working on a paper or a proposal. You start exploring other avenues you hadn’t thought of and your organization system gets increasingly complicated. DevonThink’s ability to suggest what folder a paper best fits is a lifesaver because it avoids the eternal purgatory that is the ‘other’ or ‘read later’ folder or pile. Even if I don’t get around to reading a paper immediately, just being in a topical folder means that I see it when I’m looking at what I’ve read about the subject and being seen means reading.

The bigger problem is that as my filing system expands to fit my needs, it becomes much easier to lose track of papers. There is no way to keep the details of the several hundred papers I’ve read or downloaded, and it’s very easy to forget about papers.If a paper gets misfiled, in all likelihood I’ll never see it again, if I do happen across while looking for something else, I’ll ignore it because that wasn’t what I was looking for. Even with well organized, topical folders, it’s easy to not see the one paper the one paper that will provide the exact citation that I need because it’s just one document among many. For this, DevonThink’s search function is a life saver. If I just remember one snippet of text, one term that was used in a paper, I can find it no matter how many years ago I read it or how deeply buried in a file structure it is.  It’s a snap to call up all the papers I have by a single author, quickly scan through them for keywords, and identify the ones that I need. DevonThink has some kind of statistical AI, so that it’s just not looking for how often a word like “institutions” is used, but in what context and if it’s near other words I’m searching for. Unanalyzed, the words in a document (or on an unscanned book or un-OCRed PDF) are dumb: sure I can read them, but the computer can’t. It can’t do anything with them. But when you start using a computer program that is designed to do something with those words, suddenly you have smart data that can be used in all sorts of ways.

Now, it would be theoretically possible to do all of this through Finder in OS X, or even with the actual, physical folders in my actual physical desk. . There would be a few things that would be harder (DevonThink lets you make duplicates of documents, so that any changes are reflected in the multiple duplicates, and put them in different folders, for example. This would be hard to do in Finder and impossible with physical files), but it’s feasible and thousands of people do it this way. But the point is that DevonThink lets me take advantage of my computer’s ability to almost instantaneously scan through documents and compare it with others in a way that my brain can’t. A computer is a perfect tool for organizing documents. It can’t do everything; it can’t define the scope of the project or know how *I* want to organize a project based on *my* needs, but I can tell it how to do these things.

That’s the essence of my thoughts on smart data. Smart data means my computer can do something with the data. It can make my life easier, help me find exactly what I need quicker than if it were dumb. The computer thus is able to make life easier for me, in the same way that spellcheck makes my life easier: the computer is able to do something better than I can and so I let it do that.

109 interviews, that’s 80 entrepreneurs, 13 economic development officials, 4 angel investors, 7 bankers and 7 venture capitalists.

Average length of interview: 40 minutes and 23 seconds.

Total tape collected: 69 hours and 40 minutes.

Shortest interview: 20 minutes, 35 seconds.

Longest interview: 78 minutes, 6 seconds

Interviews delayed due to earthquake – 1 (I didn’t feel it, but they evacuated the building)

Now I’ve got till next May to go through all of this and write a dissertation, before they stop paying me.