Last Monday the Sveriges Riksbank Prize for Economic Sciences was awarded in memory of Alfred Nobel 2021. The winners were David Card, “For his empirical contributions to labor economics” and on the other hand, jointly, Joshua D. Angrist and Guido W Imbens “For his methodological contributions to the analysis of causal relationships.”
Despite addressing different topics in their research, the work of the three converges in the same aspect, more methodological than thematic: The findings and advances in economic science that favor empirical and experimental research, to try to analyze the effects that a condition or policy may have on the behavior of economic variables.
One of the long-standing topics of discussion in economics is the fact that, while, for example, medical science can carry out experiments with homogeneous groups to which they apply a drug and a second control group is given a placebo, and this allows us to contrast, at a statistical level, the effects of the drug; in economics it is extremely complex to have homogeneous groups on which to experiment with the application of public policy or not.
David Card has carried out studies to corroborate in an experimental way between similar populations to which a certain economic situation was presented or not fortuitously, to understand, for example, the effect of the growth of minimum wages in employment, or the effect of immigration also in employment.
In the same way, part of the studies by Angrist and Imbens try to solve another problem in economic research. And this is the one that, in an effort to find simplified explanations about the relationship between economic phenomena, many investigations tend to simplify the analysis methodology to the extent of finding causal relationships where they do not exist, confusing correlation with causality.
A simple way to explain the difference between correlation and causality is with the following example. If we analyze the data in a city of the sales of sunglasses, with the sales of popsicles, we will find that there is a similar statistical behavior. Possibly we will find a certain seasonality, where both data grow during the summer and decrease in the winter.
For a superficial analysis, this correlation of variables (simultaneous occurrence) can be confused with causality and, therefore, it could infer that the purchase of lenses causes the consumption of pallets.
This, which in the example is obviously incorrect, in the scientific analysis of economic phenomena, frequently leads to oversimplifications that distort the interpretation of reality and this error is transferred to diagnoses of economic problems and erroneous public policies.
The investigations carried out by Angrist and Imbens are focused on creating better econometric and experimental tools, experimenting and analyzing finding true causalities between economic phenomena and variables.
Empirical economic research allows a better approach than models to understand economic phenomena, but when it is conceptually simplified with models, it is forgotten that these are to understand reality, but are not fully explanatory of it.
Is it possible to increase wages without generating inflation? Is it possible to raise taxes to have more distributive capacity towards disadvantaged segments, but without affecting investment and employment? How do economic agents respond in times of uncertainty to monetary policy decisions aimed at containing inflation?
More experimentation, less oversimplification and, above all, less economic dogmas, are essential to create policies that effectively contribute to improving the living conditions of the population.
CEO of Fibra Educa and President of the Council for the Promotion of Educational Savings
The author is a political scientist, marketer, financier, specialist in behavioral economics and professor at the UNAM Faculty of Economics. CEO of Fibra Educa and President of the Council for the Promotion of Educational Savings.
Follow him on Twitter: @martinezsolares
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The Nobel Prize in Economics