Mapping UK Startups

Since I first washed up on the chalky (more peaty, I guess) British shores, I've been doing my best to get an overview of the geography of UK startup activities. That's my job after all: to figure out where the entrepreneurship hots spots are and why those places are great areas for startups. I forgot about this for a while after being buried in other work and teaching, but I was reminded about this by a recent report by Startup Britain about the the UK's entrepreneurial hotspots. They were kind enough to release the underlying dataset, which was produced by Companies House. The data is a report of how many new firms were registered in every postal code area in the UK.

This data set helped me rediscover the joy and the pain of making maps while watching re-runs of Law and Order.

Plugging the data from Startup Britain into QGIS (a nice, open source GIS platform that actually runs on OS X!) produces a nice visualization of where the UK's entrepreneurs are. UK Startups

This is a pretty diverse geography of startups, but it's about what we'd expect. High levels of entrepreneurship in the Southwest and up into the Midlands, lower levels of entrepreneurship in the Northeast and in the Highlands.

We can make this a bit simpler to get an even broader overview of the UK's entrepreneurial geography. This is an equal area map of the average number of startups in the postal code areas contained within 25 KM hexes I think this is the prettiest map I've ever made.

With this, you can see a very clear pattern of high rates of startup activity in the area between London and Manchester, with fewer activity elsewhere.

But XKCD teaches us that most maps just map population.... XKCD teaches us every lesson.

So, we've got to control for population. This is where I ran into the wall of horrible data collection. It's pretty dang easy to get population for postal code areas England and Wales from NOMIS. But, because of Events over the past 700 years, Scotland gets it's own census and it's not very good at showing what data they have and letting you have it. After several hours of yelling at the computer, I finally found what I needed and could make a map of the number of startups per 1000 people in every area code in the UK (except for northern Ireland, Gibraltar, and the Channel Islands, because I just couldn't bring myself to care.) Startups per 1000 people

This is..... ummm.....less interesting. London is really the only place where we see huge deviations from the mean of 20.66 new firms per 1000 people. Indeed, if we look at a histogram of the log of startups per capita, we see it's really concentrated around the mean. Has anyone writen a history of histograms?

This is because there is a very clear relationship between the population of a postal code area and the number of startups. The correlation coefficient is 78%! This is very apparent when you graph population against startups. The colors! From the graph, it's clear that there are very few regions that have an exceptionally high rates of startups per capita, but there are plenty of regions in the North and the North West which have very low rates.

This is even more apparent when we make a box plot of startups per capita by region. I guess it's more of a violin plot than a boxplot. London does have a lot of areas with exceptionally high levels of entrepreneurship per capita. Of the 6 area codes that have more than 1 reported startup per person, 5 are in London (EC1V, SW1Y, EC4A, W1B, W1S) and one is in Birmingham (B2). I imagine these codes are some weird corporate or historical zones where no one actually lives (maybe just the Queen and her Dogs), which totally throws off the per capita calculation. But even with that, the average startups per capita in London is still significantly higher than the national mean.

So, where do we go from here. The first thing I want to do is try to break this down by industry. In terms of economic development, all new firms aren't created equally. A consulting LLC will likely never employ more than a few people, but a new manufacturing firm can employ many people and export products abroad. We also need to look at firm births as well as death. What regions are gaining startups and which are losing them? We also need more data to figure out what's driving entrepreneurship. High populations do mean more economic activity, but this doesn't help policy makers figure out how to encourage entrepreneurship. We need to look at things like education, levels of immigration and migration, and that fun stuff.

So, I've got a lot of librarians and statisticians to yell at. I want to thank everyone on the twitter-sphere who encouraged me to make these maps, it was a great excuse to learn some new tools and data sources.

Two new papers on Entrepreneurial Cultures & The Financial Crisis

I got an early Christmas present a few weeks ago when my paper in Entrepreneurship and Regional Development was published. The paper, Bourdieuian approaches to the geography of entrepreneurial cultures, is part of a special issue on entrepreneurial cultures. My paper develops a new framework to understand how entrepreneurial cultures develop within regions, influence entrepreneurs' practices, and change over time. The second paper, Economic Geography and the Financial Crisis: Full Steam Ahead?, is a contribution to a special issue of The Professional Geographer that came out of the 2010 Summer Institute of Economic Geography in Sunny Vancouver. Along with Chris Muellerleile, Kendra Strauss, Thomas. Narins, we discuss how economic geographers can best contribute to global understandings of the 2008 Financial Crisis.

Everywhere is an ecosystem

I hate analogies. To quote Britta Perry, "It's a thought.....with another thought's hat on it." Ot, as Ron Swanson said this week, "That's why my favorite book is Moby Dick: No froo foo symbolism, just a good simple tale about a Man who hates an animal" And yes, to answer your questions, I did not exactly excel in high school English classes. The biggest issue for me is when biological concepts are used as analogies for social or economic processes. When we borrow a basic concept from biology, like evolution, we also mentally import a lot of the scientific perspective on that concept that doesn't really apply to the social world. Evolution only occurs between generations, but evolutionary economics allows for change within firms during their lifetimes (who are the animal in this analogy). Yes we still think of firm evolution in terms of spinoffs and deaths. Should we be talking about Darwinian or Lamarkian evolutionary economics? What about Lysenkoisms?

I've been thinking a lot about the problems of analogies in the context of entrepreneurial ecosystems. The term ecosystem is decidedly biological. To quote the hive mind, an ecosystem is a:

community of living organisms...in conjunction with the non-living components of their environments...interacting as a system.

The entrepreneurial ecosystem is a combination of living (hopefully) actors like entrepreneurs, investors, and workers and non-living institutions like social networks, government polices and universities, that contribute to a vibrant entrepreneurial community. At its base, an ecosystem is a pretty good metaphor for what we're looking for, a biological ecosystem. An entrepreneurial ecosystem should be self-sustaining and depend on complex interactions between the various living and institutional components that reenforce and reproduce their functions.

However, the usefulness of the ecosystem concept starts to break down once we think about it a bit more. Much of the writing on entrepreneurial ecosystems are based on the question of how do we build ecosystems in new areas. How can policy makers and community leaders foster the institutions and people that will help build a strong ecosystem the likes of Silicon Valley, Waterloo, or New York City? This is based on the assumption that only a few communities have ecosystems, but we should all be working towards building them where ever possible.

There is where the analogy starts to fall away for me. In nature, everywhere has an ecosystem. There's not a place on the earth (from the atmosphere to deep sea trenches) which don't have some kind of ecosystem. Sometimes these are rich, vibrant, and sustainable ecosystems with lots of components, like those in a rain forest or savannah. Others are thin, with few components and resources, like a desert or the arctic. Some human-designed ecosystems, like those of a sorghum farm, could not exist without continual human intervention and involve a number of species (including bacteria) that you could count on two hands.

From this perspective, instead of saying we should build entrepreneurial ecosystems, we should instead recognize that all regional communities already have an ecosystem. Some of these ecosystems support the kind of high-growth, innovation-based entrepreneurship that we like to associate with successful regional economies. Others discourage entrepreneurship, either because there is no support infrastructure to help people start new ventures or there is a cultural discomfort towards the risks of entrepreneurship. In most cases I imagine, the ecosystem has no positive or negative influence on entrepreneurship, the ecosystem is simply neutral towards starting new firms.

A successful entrepreneurial ecosystem isn't created out of whole cloth: it requires the transformation of an already existing economic and social ecosystem within a region. While it's fun and interesting to read about success stories like San Francisco, Denver, or New York City, each region is fundamentally unique. You've got to look at what social, economic, and cultural resources already exist and how they contribute to how entrepreneurs are perceived. Only then can you start to build something new.

Freedom for Silicon Valley! Freedom From Silicon Valley!

Reading Gawker's Silicon Valley / Silicon Alley gossip blog ValleyWag is one of my favourite diversions from reading research about Silicon Valley / Silicon Alley. There isn't enough critical thought about its growing bubble economy and its brogrammer environment. A talk (non-TED, thankfully) by a Stanford lecturer about how The Valley must get around government restrictions and red tape is emblematic of this lack of critical thought and reflection. Entrepreneur / lecturer Balaji Srinivasan called for the need to build "opt-in society, outside the US, run by technology."

I'll admit that I wasn't in the talk (I'm in sunny, warm Edinburgh), but it strikes me that this kind of thought is part of a larger techno-libertarianism that's been a popular feature of the technology industry since the very start. This viewpoint generally sees new technology and innovation as an unalloyed good and anything that gets in the way of new technology (skeptical investors, government regulators, liberal arts majors,  Underwriters Labs) as a barrier at best and an evil at worst. The increasing fetishization of Disruption With a Capital D, especially for disrupting urban life through things like Uber (the car sharing / unregulated taxi business) or AirBnB (the room sharing / cheep hotel service) is a major component of this.

But this movement is largely ahistorical. The desire for Silicon Valley to secede from government regulations is ahistorical, ignoring the critical role of the US government in the creation of Silicon Valley. One of the best histories of this traces the development of Silicon Valley to the establishment of military bases in the 1910s. Even if we don't go back that far, the emergence of the original transistor economy in the region wouldn't have existed without federal support of the original research and as one of the main buyers. Today, 83% of Stanford's research budget comes from public sources.

Utopianism is important: it allows us to envision a better world and then (rarely) take steps to create that world. But utopias are literal (well, figurative) no-places. They cannot be real. There will never be a entrepreneurial ecosystem or innovation hub that exists outside the presence of public investment in education, research or infrastructure and without government procurement. Yes, that means there will never be a (successful, non-norovirus infected) floating tech utopia or Reddit Island.

World Weary Waterloo Waits and Wonders: When Will RIM's Worries Wane?

First, apologies for the lack of posts here. Since the last post, I've moved my entire life to Edinburgh and started a new job in the University of Edinburgh Business School. I've started an experiment in using Tumblr to make short comments on interesting articles about culture, entrepreneurship. I'll eventually find some way to integrate the two. Second, thing are....um....not going well for Blackberry (ńee RIM). Losses of almost a billion dollars in the last quarter, reports of planned layoffs of 40% of the workforce, disappointment and delays on new products, this has not been a great week for the Beleaguered Smartphone Company©.

At this point, it seems likely that recovery is unlikely at best and that RIM (sorry, not calling it Blackberry) will cease to be an independent company at some point in the near future. This may take the form of a complete selloff to a private equity firm or someone in the smartphone industry or the spinoff and selling off of the remaining profitable areas (Blackberry OS, BBM and the cafeteria?).

The question for me, even on the other side of the Atlantic is: what does this mean for Waterloo's entrepreneurial ecosystem and culture? While the 4500 cut jobs will take place around the world, there's no debate that many, if not most, will be in Waterloo.  RIM, along with the University of Waterloo, are seen as the twin pillars of the region's entrepreneurial community.  The presence of a home grown startup that became a global force is a vital narrative in the community: it shows the possibilities of entrepreneurship and the potential rewards of leaving a stable job for the risks of starting your own company.

As I've said before, Ottawa after the collapse of Nortel is the easiest comparison, but I don't think Waterloo will suffer the same fate. After Notel began it's long decline, there was an initial exodus of skilled workers out of the region in search of other jobs. Other highly skilled engineers stayed in the region due to family ties or the fact that they actually liked living there (?!). These people looked for jobs where they could and turned to entrepreneurship, mostly as small time consultants, when they couldn't find a place in another big company or the government.

Waterloo can expect a different kind of exodus. It's proximity to Toronto (an hour on the 401) means that people can stay in Waterloo but become highway warriors and work in offices in Mississagua or Oakville. It's not a pleasant drive, but it's doable. Many major companies like Microsoft and Google already have large Toronto offices and will look to scoop up some of RIM's talented engineers. We're already seeing reports of smaller firms opening up Waterloo offices, I'm sure with the hope of picking up laid off cell phone engineers and programers as well. 

It's also likely that many of the region's economic development programs like Communitech, will try to help the recently laid off workers become entrepreneurs. The logic is seductive: take the experienced human capital of RIM workers, combine it with the social capital and experience of the region's talented entrepreneurial mentors, and help create new, high-tech businesses.

However, it's a mistake to see this as the Great Hope of economic recovery. RIM is an interesting beast: it's a major part of the discourse and legend of Entrepreneurial Waterloo (along with the Mennonites and the Germans), but the company itself seems to discourage entrepreneurship. I can only think of one spinoff from RIM, KIK Messenger, and RIM sued them! Similarly, very few people leave RIM and start their own firms. In my extensive interviews in the region, I only heard of one person who did so (the aforementioned KiK). This is to say that people who have been working at RIM may not want to be entrepreneurs. They want to be people who design high quality cell phones and messaging infrastructure that works a lot of the time, and leave the dirty work of actually running a company to someone else.

The same thing happened with Ottawa and the ex-Nortel workers. The dreams of seeing startups escape the bloated caucus of Nortel like so many baby spiders in a nature documentary never happened. But the region kept on promoting this kind of entrepreneurship without a second thought.

If Waterloo wants to make the best out of a bad situation, they need to figure out a way to help the soon-to-be laid off RIM workers. Entrepreneurship training is a big part of this, but it's not the only part. The community should be working to convince other high-tech companies to take advantage of this situation and open local offices to snatch them up. The local government needs to work with its provincial and federal counterparts to try to encourage Canadian firms in the region to expand their operations to take them on. Despite the region's celebration of entrepreneurship, it shouldn't see entrepreneurship as the only way forward.

Finishing up and starting again

I haven't posted for quite a long time, but I do have the best excuses in the world. I was busy defending my dissertation and interviewing for jobs! I'm happy to say that I defended successfully and am now a Doctor of Philosophy and even more importantly, I've accepted a position as Chancellor's Fellow at the University of Edinburgh Business School. I'll be working on the development of entrepreneurial ecosystems and the relationship to firm strategy in Canada and the dusky moors and wrens of Scotland (I'm still developing my Scottish accent). And now that I'm an official Expert in Entrepreneurship, I'd like to say how much I agree with this article by Melba Kurman about the dark side of entrepreneurship policy. In our constant desire to boost technology entrepreneurship, we often forget that there's a large population of people who really can't benefit from this kind of entrepreneurship: people without the human capital to start or work in high-tech firms, poor people without the savings to endure the wait for revenues to start flowing in or the low pay and high insecurity of startups; single mothers unable to work the long hours these kinds of startups require.

More than that, I think we also may over estimate the actual economic development created by these kinds of firms. In the extreme, you have startups like Instagram, which only had 13 workers when it was acquired for a billion dollars. The value of internet companies is in their IP, not their capital or equipment. Even in the most fortuitous circumstances, when an internet startup gets all the VC investment and angels and invitations to TED talks, they may be worth a lot of money but employ very few people and therefore have limited economic spillovers to the community.

There are exceptions to this. Miovision in Waterloo has all the sparkle of a UW technology spinoff (which it is), but employs a lot of people in manufacturing and maintenance, as well as in engineering and development. However, companies like this don't fit well into the existing accelerator to incubator to VC pipeline many technology entrepreneurship programs are implicitly designed around.

Big Data and Deep Data

I'm officially done with my dissertation — It's been handed into to ml committee and I couldn't change anything, even if I wanted to. This puts me in an odd position: for the past 24 months most of my days were spent working on my dissertation, either analyzing my interviews, outlining my ideas, writing or editing. Being done with this has left a pretty big hole in my daily schedule. I've started work on a few other projects to fill this gap, projects that have me working with entirely new types of data than in my dissertation. My dissertation research was interview based. I conducted 110 interviews which produced something like 70 hours of tape and over 3000 pages of transcripts. I have lots of detail on the 80 entrepreneurs I talked to. I know how and why they started their company, how they raised money from investors or why they've avoided it, the challenges they've faced and what they did to overcome them, if they've networked with other entrepreneurs and what they talked about.

This data is amazingly deep, but in the grand scheme of things it's very small. I talked to about 1/3 of the high-tech entrepreneurs in each city who happened to be on a business directory I used. So, when I found really cool things in my interviews, like the fact that most entrepreneurs in Waterloo actively searched through their own social networks to find mentors but those in Ottawa mostly relied on their parents or former business partners to provide business advice, it's hard to say if this is something True for everyone in the city or if it was just a coincidence. There are a few statistical tests to try to figure out what's real and what's an illusion, but they can only go so far.

The new project I'm working on gives me access to fantastic datasets about innovation and economic development in Canada. This includes the famous Dun and Bradstreet directory, which is the biggest dataset I've ever played with. Clocking in at 1.5 gigabytes, it contains information on more than 1.5 million Canadian firms. I would consider this to be on the very small end of 'big data.' For someone studying entrepreneurship, this is a godsend. I can now tell you, for instance, between 2001 and 2006, there were 669 new high tech firms founded in Toronto* and that the average sales of these firms are around $360,000. I can also make really cool pictures like this, which shows that there is a positive relationship between the proportion of immigrants in a region and the proportion of high tech firms in every province except Saskatchewan and New Brunswick.

But as I work more and more with this data, I'm beginning to see its limitations. I know things about a whole lot of firms, but I don't know much about them. With the D&B data, I essentially know a firm's name, it's address, what year it was founded, what industry it's in, how many employees they have and a guess about their sales number. In aggregate, these data can tell me many things — which regions have the most startups, which industries seem to grow the fastest, what's the relationship between workers and sales across the entire country. But it also raises lots of questions that the data can never answer.

Looking at one record at random, I know that Bait Consulting Inc. of Thornhill is a consulting company that was formed in 2001 and which has one employee and an estimated 120,000 in sales. But unlike in my dissertation research, I don't know anything more. I don't know why the company was founded, I don't know why it was founded in Thornhill instead of Toronto or Mississagua or Cambridge. I don't know how its founder learns about the market or finds new customers.It's difficult to figure out if a government policy is working from this data, or how an entrepreneur is affected by where they live.

That's the big difference between big data and what I'd call deep data. Big data can tell you a small number of things about a whole lot of things. You can do a whole lot with this, but you always need to be aware what it's not telling you. Only so many different questions can be asked on surveys — the more you ask, the fewer people will respond.

Qualitative data collected through long, semi-structured interviews, is deep data. I know a lot of about the people I talked to. Not everything, and many of the responses are biased by the respondent wanting me to think they are really skilled entrepreneurs. I know more than a binary variable, I know what they did, why they did it, and what that has caused. I can understand what practices they took to start and grow their firm and relate those back to their larger cultural context. But again, there's that tradeoff: I know a lot about a very small number of people. And I have it easy, people doing ethnography or observational research will have hundreds and hundreds of hours of recordings or notes about an even smaller range of people.

It would be nice to think that we can meet in the middle, but working with big qualitative datasets requires a totally different set of skills than working with big quantitative datasets. Very few people are equally as able to produce a grounded analysis of a collection of interviews and a Baysian analysis of a census dataset. But there is value in each, and the challenge is being able to figure out the right way to collect data to solve a problem. The platonic ideal is for quantitative and qualitative data to be used together to prove a larger point, but this kind of research is expensive and rare. But it might be the only way to get a real sense of what's going on in the world around us.

*This seems really low to me and I'm already working with librarians and others to figure out the proportion of all firms the D&B directory accounts for

Book Review: Startup Communities: Building an Entrepreneurial Ecosystem in Your City

I just finished reading Startup Communities. It dovetails nicely with what I've been thinking about, that entrepreneurship relies on an entire community surrounding the entrepreneur. Here's my mini-review for all you busy business people: I agree with the first part of the title and disagree with the second part. I believe startup communities are vitally important, but I'm not sure you can build one in your community.

Let's start with the first point. Schumpter talked about the "Heroic Economic Superman" who boldly innovate, releasing the winds of creative destruction upon the world. However, the successful entrepreneur is not so much a Superman working in his Fortress of Solitude, but rather like Batman, a smart, skilled mortal who relies on a team for support (and is also likely deeply psychologically damaged). Entrepreneurs rely on their family and friends to not only slip them a few dollars when they need it, but also to  accept the fact that they'll miss holidays, birthdays, and everyday social events as the entrepreneur builds the business. They rely on employees willing to accept the lower pay and increased risk of working at a startup instead of a traditional company. They rely on customers taking on the risk of buying from a startup when they could often just stick with IBM or Sysco. They rely on suppliers to trust them enough to offer them credit or other kinds of support. They rely on local lawyers and accountants having the knowledge to advise them on challenges unique to small, growing firms.  Without these things, it is very hard for an entrepreneur to build a successful startup that does more than provide a decent income for themselves.

However, the promise of the book's subtitle is that you can build this kind of ecosystem in your community. I'm not so sure about this. I've looked at plenty of communities who try to jump start an entrepreneurial culture that ends in nothing more than a lot of breakfast meet-and-greets sponsored by the local economic development agencies. Ottawa springs to mind, where the local economic development agency has a laser-like focus on fostering an entrepreneurial community in the telecommunications market and has missed the fact that technology entrepreneurship there has now moved to software and social media. One entrepreneur there told me that:

OCRI [the local development organization] as an organization that has done this area a true disservice because it believes chips and wires and cables were going to come back.

But, Brad Field, the book's author, has seen this too. He specifically and rightly warns that this kind of environment has to be led by the entrepreneurs themselves. And I've seen some amazing people starting some amazing grass roots organizations to create entrepreneurial environments. In particular, Calgary has seen the formation of some great groups in the past year, like the A100 and Startup Calgary. However, they're butting up against an entrepreneurial culture based around the oil industry. This means that investors are used to investing in oil wells, not startups. I heard tales like this constantly during my fieldwork there:

See that [oil] hole over there? I’ll thrown $100,000 down that hole tomorrow on 24 hours analysis because I know I have the map, I know where, I know who the players are, I know generally speaking what the risk parameters are. But you tell me that this software guy with this platform that’s going to match up with this and that or this little black box is going to take the world by storm, how do I know that? I don’t know anything about it.

Similarly, it's hard to keep employees at a small tech startup when they all know that they could call up a friend at one of the big oil companies and be earning six-figures with 6 weeks of vacation tomorrow (I'm not kidding, the salaries there are mind blowing if you've got the right skills). Grassroots organizations can help increase the social prestige of tech entrepreneurship — which I found to be very low there — but I don't think that they can change the region's culture, which is far more focused on making money than making cool technology. Or if they can, it'll take years.

I'm not saying that it's impossible to build an entrepreneurial community; I firmly believe that there are options besides the Waterloo or Silicon Valley model of "start 50 years ago." However, I think it takes more than DemoCamps and Third Tuesday drink nights. However, I'd be lying if I told you I knew what that was.

Wither Waterloo?

Research in Motion is not a healthy company. It makes a product no one particularly wants for a price no one particularly wants to pay. The reason for the company's decline will no doubt be chronicled in a thousand MBA case studies, but I imagine at the end of the day it will simply be a tale of complacency in the face of change, over-confidence in the face of challenge, and stagnation during the punctured part of punctuated evolution of the mobile device market. But, I'm not the guy to talk to about that. I don't know from management. But I do know from regional development, and especially the role of small firms and entrepreneurship in regional development. The major question amongst nerds like me is that if RIM does implode, if it either dies a quick natural death (unlikely), or if some corporate raider takes advantage of its low share price to acquire the company and strip it for assets (likely), or it slowly shrinks over a period of several years until it's simply another has-been (99.9% chance), what will happen to Waterloo?

Christine Dobby, Mark Hartley and someone called "Financial Post Staff" think that it'll be good times! Waterloo, as you must know if you're reading this (since I'll likely be the only reader and I know this) is what you might call an 'entrepreneurial hot zone.' There are a huge number of small software startup firms in the region and these firms are all desperate for workers. I intervened around 30 entrepreneurs, investors, and economic development officials from the area as part of my dissertation and almost all of them mentioned the difficulty of hiring skilled workers. This was in part RIM's fault: they would suck up all the best workers, leaving slim pickings for the rest of the region's economy.

Ottawa is the model for this. When Nortel Networks died its slow death throughout the past decade, there was always the hope that the thousands of workers laid off from the company's Ottawa HQ would enter the local tech market, either by working for other local firms or by starting up their own firms. The former plan didn't end up working out because most of Ottawa's software economy was based around the TelCom sector, whose decline in 2001 had sealed Nortel's fate. Just as a massive labor pool of highly trained engineers was available, there was no one looking to hire. The entrepreneurship thing didn't really happen either. Nortel employees were used to working in a very large firm, many of them did not want the lifestyle of an entrepreneur. It's hard to accept 100-hour workweeks for no salary when the federal employee who lives next door is out by 4:30, get's a month's vacation and *gasp* has a pension. What entrepreneurship did exist was really an outcome of people who loved living in Ottawa but who needed to create jobs for themselves. These were largely small consultancies that will never grow or produce jobs.

I predict Waterloo will have a similar experience (few laid off workers joining the local labor force or starting local firms), but for different reasons. Waterloo's technology economy is far more diversified than Ottawa's. So it's not that no one will be hiring. It's just that there is a complete mismatch between the skills and expectations working at RIM and the skills and expectations of working for a small firm. Small firms pay less, offer fewer benefits, and expect workers to be far more flexible in what they do and how they do it. Not everyone thrives in such an environment, especially if they've spent a good deal of their career in a large, hierarchical company like RIM. One of the chief complaints I heard from entrepreneurs in Waterloo regarding employees was that it was hard to find workers who could work successfully in small firms.

But you know where there are high-tech jobs in large organizations? Just down the 401 in Mississagua and Toronto. Microsoft, Google, IBM, Intel, they all have offices or campuses in the GTA that are a reasonable commute from Waterloo. Not a nice commute, the Queen's Express Way is pretty much the worst stretch of highway in North America, but a reasonable commute none the less. Unlike ex-Nortellers in Ottawa, former RIM employees won't have to uproot their lives to find a similar employment situation. They'll just have to drive eest to suburban Toronto's plentiful office parks.

RIM's decline will hurt Waterloo, a lot. I think that as a whole, the entire regional economy is resilient enough to survive this. They have great institutions, institutions like the University of Waterloo and Communitech that will always do a great job of attracting talented people to the region and encouraging growth. And I think it is critical that the region try to support former RIM employees' local endeavours, from joining existing firms to starting their ow. However, it's clear that simply having a large pool for very skilled workers isn't an economic panacea. These workers can't simply be slotted in to existing job openings. It will be an ongoing process, one that will have far more failures than successes.

To New York I go

I'm heading down to New York City tomorrow for the 2012 Association of American Geographers conference. 7,000 geographers. 5 days. 1 city. It's always an interesting time. 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.

New article: The sources of regional variation in Canadian self-employment

I just got the final version of my new paper in the International Journal of Entrepreneurship and Small Business (Vol 15, issue 3, pages 340-361 for those keeping track at home. E-mail me for a copy). This is my first solo paper and the first paper that I controlled from start to finish. It's not directly related to my dissertation, but rather an outcome of what I saw as a gap in the literature: the lack of any research on what regional economic and social factors are associated with local levels of entrepreneurship and self employment. There is research on this topic from dozens of countries, but none yet in Canada. 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. Regression results of non-agricultural self-employment in Canadian census metropolitan areas

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.

Angels in the back field

I love it when newspapers provide great examples of economic geography. In just the past few weeks, I've seen a cornocopia of great articles that really exemplify why economic geography is so amazing. We have Adam Davidson's Making it in America cover story in the Atlantic (which I'm currently forcing 200 students to read and then write about its connection with the transition to Post-Fordism in the American sunbelt), and the NYTimes' fantastic investigations into Apple's use of Chinese labor and even Paul Krugman is getting into the game, talking about clusters and such. 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.

New Article: The Spatial Economy of North American Trade Fairs

I've been busy over the last few weeks teaching my first class ever, but I got a pleasant surprise that an article that I had written last year has finally been published in The Canadian Geographer. The Spatial Economy of North American Trade Fairs uses a unique dataset to track the location, size, and types of trade fairs over the past decade. The paper is specifically interested in showing how a relational event (trade fairs are temporary gatherings of international actors) still have a real, every-day geography to them. Lots of pretty maps and graphs! E-mail me for a copy if you don't have free access to the article.

A quick thought

I don't want to do many of these short questions designed only to provoke, but I'm reading the Steve Jobs biography and it's hard not to feel somewhat philosophical. Here it is. There is only one question that matters when studying the geography of entrepreneurship: If John and Clara jobs had not moved back from Wisconsin in 1952, would Steve Jobs be The Steve Jobs, or would he be the best used car salesman that northwestern Wisconsin had ever seen?

Using technology in the qualitative social sciences - I

So I'm a geek. This means that I have an peculiar relationship with technology. Despite much evidence to the contrary, I see technology as a source of all things good and pure in this world. However, I'm also a social scientist who uses primarily qualitative methods: I interview entrepreneurs and investors and through those interviews try to better understand the connections between their actions and the cultural and economic environments they're embedded in (yes, I assure you this is geography. There will be a map.)  The data I collect is fairly voluminous, the 109 interviews I've conducted take up 70 hours of tape and come out to about 1200-1300 pages. In addition to this, I've read many hundreds and hundreds of articles, reports and working papers and even took some notes on a few. All this means that I have several thousand megabytes, representing some 10,000s of pages of very, very messy data. This is beyond what can be usefully comprehended by my brain 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.

A little something from the lab

I haven't posted much - a trip drive from Calgary to Toronto was followed by throwing myself into the dissertation work. I'll try to do more short posts instead of fewer longer ones. Here's a little thing that I dug up for a class I'm prepping for next semester called "New Economic Spaces." This is a graph of trademark registrations in the United States from 1883-2009, drawn from data from WIPO. It's a fairly amazing dataset, with information from over 100 countries, but the spreadsheet is laid out in such a way as to make importing it to a GIS very annoying, so no pretty map this time. Anyways, here is a good example of the rising importance of symbolic content in the valuation of commodities. Want to know more? Plenty of time to apply to U of T and enrole in the class.  

Finally there

Well, it took 1 year, 2 months and 23 days, but I finally finished my PhD fieldwork. Here are some stats. 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.