My background is in Econometrics. I get asked this question a lot. Sometimes it comes directly, sometimes in puzzled stares. Sometimes in glazed eyes where they’re regretting their first question for fear of a barrage of heavy statistical terminology. Let’s simplify it without the fear.
Econometrics uses economic theory, mathematics and statistical inference to quantify economic phenomena. IMF’s1 words, not mine. To put it simpler, it is the practice of modeling economic data for solving economic problems. Those who spend most of their time doing so are called Econometricians. Quite a mouthful. Don’t worry, they won’t mind if you call them statisticians. They are after all statisticians, mathematicians, economists who have chosen to hone their skills in the niche area of the confluence all 3 disciplines.
What is economic data? Any data related to the economy - not only GDP (Gross Domestic Product) but also health related data, education levels, resource distribution, population, among others.
What are some economic problems that I have worked on?
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I quantified the the importance of world crisis - how this importance is defined by the nation where a crisis occurs, & its geopolitical impact radius, rather than the severity of the crisis in terms of human & economic cost. It is sad, but true. We see this from the aid & intervention received by victims of natural disasters & wars across the world [my dataset] - this aid is not always proportional to the crisis severity (some disasters become old news for the rest of the globe faster than a tide), but in fact in sync with the international importance of its origin point (country, in my dataset). What does this say about humans’ evaluation of other human lives? Spoiler: nothing celebratory.
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In dire need to lift my spirits, next I studied the pricing models of multiplexes (multiscreen movie theatres). What has movie pricing got to do with an economy, you ask?
You better not regret thisAlright, alright, I’ll make it brief:
Depending on the health of an economy, movie tickets may sometimes be normal or even luxury goods. As an economy improves, (more wealth distribution, income opportunities across the population) people are more likely to spend on entertainment like, movies. And movie ticket pricing models are more likely to be sensitive to changing income levels. Conversely, inflation is likely to increase the costs of operating a multiplex - and like many products, this increased cost may be shifted to the movie goer in terms of increased ticket prices. These days, OTTs (online streaming services) are a major substitute to visiting theatres - naturally, affecting demand & price. Then again, multiplexes are offering unique experiences (like, dine & watch, private screening, etc.) to complement their movie ticket purchase - adding another agent to the factors that influence movie priding models.
How all of these (& other factors) interact, which forces exert more direct vs indirect pressure - was the goal of this analysis. And if I watched a few more movies than usual, it was purely for field study.
Econometrics was my first eye opener to applied statistics. It empowered me to find relationships between seemingly disparate activities, to create models that served as mirrors to de-mystify our complex world, to better understand the human nature that influences & gets influenced by our several man-made systems for world order or anarchy.
Econometrics became my stepping stone to move to quantitative market research (consultancy). No, not for more profits - the job was simply at the right place at the right time - and I found several like minded people sharing my love for transforming raw data into meaning. Gradually this paved the way from consulting (for external clients) to building an in-house product analytics team (within a software organization). Both work places have taught me a lot & I continue to apply my econometrics foundations to my everyday dealings with data. First loves are difficult to shake off.
Now a days, I more often cross-apply my statistical & mathematical skills to micro-organization problems than to macro-economic ones.