Modeling Economic Growth

I often see the students and faculty writing their papers and thesis on the topics like ‘Impact of ABC on economic growth’; with a trivial conclusion that ‘ABC is having impact on growth therefore we should focus on improving ABC’. In fact there are number of problems with this kind of research which make it very odd topic for research.

First, the choice of topics indicates that the researcher considers the economic growth as ultimate objective and every other variable is subservient to growth. To me, it is not sensible to treat the growth as ultimate objective of economic policies. It is making more sense to assume the humanity as the ultimate objective instead of the growth. 

For example you may find many papers with the title similar to ‘Human Capital and Economic Growth’ with the conclusion that human capital improves economic growth therefore we should have focus on improving the human capital. But what is human capital? It consists of the measures of health and education of the humanity. It is actually a measure of the well-being of humanity and therefore it closer to the ultimate objective. That is, if someone is writing ‘there should be growth because it improves the public health’, it makes more sense. But if someone is writing ‘we should focus on health because it will improve the growth’ it looks very odd.

Sometimes, the growth does not have very strong relationship with the happiness and wellbeing of the public. In 2008, the President of France Mr. Nicholas Sarkozy formulated a commission ‘The Commission on the Measurement of Economic Performance and Social Progress’ to revise the measure of human wellbeing.  The commission consisted of two Nobel laureates Amartya Sen and Joseph Stiglitz and and another well-known economist Jean Paul Fitoussi. The report highlights several flaws of the conventional measures of GDP to be used as the measure of well-being. They cite an example of a couple who is living in their home happily; they grow most of their food in kitchen garden, cook the meal for the family at home and enjoy reading newspaper together. All of these activities are not marketed, therefore did not count to GDP.  In contrast, consider a person who lives in a hostel, eats unhealthy fast food, visits the prostitute and goes to the bar for the entertainment and while coming back from the bar, due to overdrinking, had a serious accident and goes to the mechanic to repair his car. All of these activities are the market activities and would count to GDP. One can very easily judge that the life of the couple is much better than the life of lonely young man, but the GDP would consider the young man to be better than the couple. So GDP is neither the ultimate objective nor is it a good measure of happiness and wellbeing.

The services of female at home are among the most valuable services, as they prepare and recruit the future generations. But these activities are not marketed, therefore don’t count to GDP. The same services would be counted if provided at a marketplace. The high percentage of economic growth might be a reflection of conversion of home activities into market activities. It may not indicate any improvement in the living standards of the people.

Beside the false philosophy of taking the GDP as ultimate objective, many times the research question itself is trivial. For example, you might have seen the research papers like ‘Impact of Financial Development on Economic Growth’. But what is the financial development? It is usually proxied by the profits on financial assets and these profits are already a part of GDP. The GDP includes all the goods and services produced in an economy and the financial assets are also part of the economy. So it is useless to ask whether or not GDP will increase with the increase in profits on financial assets. It is not possible to increase the financial development without having same increase in the overall GDP. Same is the case of the questions like ‘Energy consumption and economic growth’. The energy consumption is a part of GDP and an increase in energy consumption must increase the GDP. No unknown research question is addressed by this kind of research.  

At the third place there is issue of the methodology of estimating the models. There are literally dozens of theories for economic growth and hundreds of variables are the candidates of explaining economic growth. In fact this is no surprise to have so many models for economic growth because everything produced in an economy ultimately counts to the growth. Numbers of haircuts at a barber shop also counts to the economic growth, and it indicates that there is no harm in developing a theory ‘haircut theory of economic growth’. Most probably, the regression of GDP on haircuts will yield very high correlation with growth.

But when you have so many determinants of a variable of interest, estimating a model without any of these variables would be subject to serious missing variable bias. Any model based on single theory is inherently subject to missing variable bias. You have to take care of all determinants of Y, even if they are not a part of your research question.

On the other hand it is also not easy to avoid this missing variable because there are so many theories and models for growth and a model encompassing all of these variables in one model is rarely feasible to estimate. This illustrates the inherent difficulty of estimating a growth model. Estimating a growth model with sensible procedures is not an easy job. Only few people have attempted the growth model in a serious mode, considering all important determinant of growth and one of those is Sala-i-Martin study titled as ‘I just ran 2 million regressions’. The title illustrates the difficulty of estimating a growth model in a sensible way, i.e. you may need to run 2 million regressions to get valid determinants of growth.

Same difficulty arise in many other kinds of economic models such as the model for inflation, consumption and others where there are so many theories to explain the variable of interest. But the academic journals are accepting the papers without any care of these considerations and people are increasing the lengths of their CVs by putting names of so many papers in it. 

In fact having so many models for a variable of interest provides a unique opportunity of doing novel research, but such a research may need longer time and longer efforts. A note on selecting appropriate variables coming from different theories can be found in my blogs [1] [2].

Suppose there are three theories for a variables of interest, it is easy to produce a paper based on theory 1. But a sensible research should take the variables from the theory 1,2 and 3 simultaneously and should come up with a final model.

In my previous blogs, I have explained how to do research in presence of multiple theories by constructing the Generalized Unrestricted Model. However, sometimes it is not possible to construct the generalized model. In the next few blogs, I will explain how we can do research in an area where there are so many models and constructing GUM is not possible. Stay tuned