Sunday, January 20, 2013

Community Imprinting: Why do some Communities Work better than others?

It is possible to find a list of the best and worst-run cities in the US on the following blog:
The best run city is Plano, Texas, followed by Madison, Wisconsin; the worst was San Bernardino, California, followed by Miami, Florida. How was the list made? To quote, “we looked at factors like the city’s credit rating, poverty, education, crime, unemployment, and regional GDP.” That seems a bit unfair. Poverty, unemployment, and regional GDP are outcomes of the local economy, which may be influenced by city government but is surely not run by it. None of these cities have a centrally planned economy.

But if it is not a list of the best and worst run cities in the US, it might be one of the best and worst functioning cities, by those criteria at least. That's interesting to know, especially if you are planning to move to any of these cities. But then it would also be interesting to learn how stable the ranking is, because looking at the list the year you move would make no sense if it was reshuffled every year. And also, the list of criteria could be expanded if we wanted to know where to move: What about schools, voluntary associations, and cultural life?

As it turns out, communities are stable in how well they function, so looking at the list for one year helps a lot. Communities also have stable differences in voluntary and mutual associations. And remarkably, these features seem to be stable not just from one year to the next, but from one generation to the next. Yes, I wrote generation, meaning 30 years. In a recent paper in American Journal of Sociology, Hayagreeva Rao and I look at some of the past work on community stability in governance and community life. 
We also report our own research on how communities have stable differences in the founding of mutual organizations. We took data from Norway, where mutual insurance firms and savings banks were founded in many communities in the 19th century. Retail cooperatives (coops) were founded in many communities in the 20th century. We found that even as we controlled for every other difference between communities that we could measure, it was still true that pioneer communities in banks and insurance mutuals were also pioneers in coops.

This is important because it suggests community imprinting: some communities end up with more community organizations than others.  Because those organizations are made to improve the community, this is obviously helpful for the residents. Imprinting may also occur for commercial organizations - we did not show that, but work like that of Pino Audia and coauthors suggests this effect. It means that we could be on our way to explaining some of the differences between the social, economic, and governance performance of nearby communities. What is the underlying mechanism? We think that community organizations create networks of trained activists who can use their skills to create other organizations. We also think that they leave a culture of community entrepreneurship. Now that we know about community imprinting – and it seems important, for how well communities work and where you should locate – we can look more closely at how it happens. That could be the next topic of research.

Sunday, January 13, 2013

Why Entrepreneurs Fail: On Average Correct, but Overconfident most of the Time

Autobiographies by successful entrepreneurs often depict their business success as being "against all odds," accomplished only through extraordinary effort and sometimes also luck at critical junctures. They are right. Successfully starting an entrepreneurial venture is against the odds, as least as far as we can tell from the statistics that are available. Although the numbers differ by nation, the 50-5 rule that more than 50% of all new ventures are gone in 5 years is pretty good (perhaps a bit optimistic). A venture that closes within its first five years has probably lost money. On the upside, the ventures that make it past the 5-year mark are highly likely to survive the next five year.

These statistics have made people wonder whether entrepreneurs are overly confident, given the poor odds of success. In a new article in Organization Science, Robin Hogarth and Natalia Karelaia show that entrepreneurs are indeed overly confident, but this is – or could be – a result of them being right on average. This statement is not as paradoxical as it seems. The point is that even when our judgments are right on average, as they often are, they are not exactly right.  There is some error, plus or minus. I may assess my chances of success at some task as too high at one time, and too low at another, but in the long run it averages out. You and I make look at the same task at the same time and make too high and too low judgments of success, but on average we may be right.  (We are not always right on average, but for the sake of argument, let's assume we are.)

Now suppose the task in question was to start a new venture. You and I would only do this if we thought our chances of success were good. On average we are right. But that means that if we are both looking at the same opportunity at once, the one who is more confident of success will move. If I am looking at the same opportunity more than once, I will move when I am more confident of success. Suppose the value of the venture is exactly zero, or even negative. Because we only enter when we are confident of success, those who become entrepreneurs will on average be overconfident of success even if all potential entrepreneurs are – all the time – on average correct.

It is a neat model that helps us understand overconfidence in entrepreneurs. It also happens to be a rediscovery. In a 1984 article in Administrative Science Quarterly, J. Richard Harrison and James G. March made a model of decision makers that are on average correct about the value of different alternatives before making a choice. They take the highest-value alternative. as we all do. But if there is some error in their judgments, even if the error is unbiased, the act of choosing the alternative with the highest value will usually make them disappointed in their choice, because the alternative that seems to have highest value is also the one most likely to be overestimated. This is the same insight as Hogarth and Karelaia, except it is more general: instead of choosing between doing nothing and entering a business, the Harrison and March model is a choice between a number of different alternatives.

Rediscovery or not, it is important to be aware of this trap. The way we make choices sets us up for disappointments, even when we are on average right. So entrepreneurs can be on average right, but also too confident of entering new businesses. And scientists can be on average right, but also too confident of having new findings.