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We recently had a class assignment – Analyze Alan Greenspan’s decision to keep the interest low for a long time and raising it very slowly. Many say this policy along with a lackluster regulatory policy led to the current housing led credit crisis.

It was a very interesting exercise and though I wasn’t able to reach the “right” conclusion, I felt that the he did indeed keep the interests low for a longer time than needed. This conclusion came based on my analysis of the GDP gap as perceived (based on Taylor rule and then data) by Greenspan vs. my own estimate of the GDP gap. The increasing natural rate of unemployment also provided some direction towards my conclusion. I cannot exactly share my data, charts, and analysis here because … it was an assignment that may be given next year also and I don’t yet know my grades on the assignment .

What I want to discuss here is one of the statements made during the class –  The economists have not yet build a model that can capture the true relationship between Alan Greenspan’s decisions and the housing crisis. My question is – Can such a model be built? Can it be predictive in nature, and not just retrospective? Lets start by asking a more fundamental question…

Can consumer behavior be modeled? To some extent, yes. Marketers tend to model behaviors all the time in order to sell consumers exactly what they want, and do not want, in the most (un)appropriate times. Numerati (Stephen Baker’s book) talks about various such models and how it is revolutionizing the industry. But this behavior is always predicted based on certain other behavioral patterns and not on demographics, access to cash, and spending power (Note that such models also do exist but may not be the right way to segment consumers).

How can you predict what a consumer will do when he is given easy access to large amount of cash? We look for patterns. We look around ourselves and see what do people do in such cases. When a person gets a great job that comes with large salary incentives, what does he purchase first? When a new generation enters the workforce, what do parent recommend them they do with the money earned? When somebody wins a lottery, what is the one thing he invests in? Does ‘buying a house’ answer all the questions above to some extent? Probably yes.

How can we test this hypothesis? Can this pattern be quantified in an equation? Can it lead to building a model that can actually prove that Greenspan is the root cause of this disaster? Thoughts?

During my first semester here at Goizueta, I took a class on Statistics taught by Prof. Steve Stuk. Not only did I enjoy the class immensely, my interest in the subject was greatly enhanced. Prof. Stuk showed us numerous real world examples where statistical analysis is used and how it impacts our day to day lives. During the same time, my marketing teacher, Prof. Sundar Bharadwaj, referred me to a book – The Numerati by Stephen Baker – that talks about strides this new science is making in today’s context.

I recently finished reading the book and I have to say, I am greatly impressed. Stephen starts with asking everyone of us as to how much in our opinion do we leave a trail of data ‘explaining’ our tastes and behavior. Our clicks on the humble IE/Firefox/et all are getting captured and monitored by numerous numerati (number crunchers) to understand and gain information about us. He then goes on to mention how marketers are using the same data to provide us with targetted, contextual advertisement to get our business.

Then the author takes us on a journey – as a patient, blogger, lover, and even a terrorist. How the different companies and agencies are tracking information that we unconciously and willfully give to clearly identify us. Clearly many of the applications and systems designed are going to be for our own benefits. But an ethical question is also raised – Do we really want someone to process the information and join the dots to know a private ‘us’?

The digital age is changing the way businesses evaluate the needs of their customers. With the advent of Google and other data rich companies, a completely new discipline that intermixes core skills such as statistical analysis, anthropology, psychology, et cetra is taking birth. What this sector is creating and what impact will it have on our lives is the basic premise of the book. The book is organized very well and provides for an easy read.