Nobel laureates in economics have successfully reversed the knowledge gained in mainstream economics
Does the entry of immigrants reduce employment and lower wages for local workers? Does the introduction of a minimum wage, designed to protect workers, harm them by reducing employment? Does compulsory schooling affect schooling and earnings? If people got a basic income, would they stop working for a living? Graduates of private universities earn more than graduates of public universities in the US. Does this mean that attending private universities carries a salary premium?
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These are among the many important and deep questions that have been examined by the three Nobel Prize winners in economics for 2021 – David Card, Joshua Angrist and Guido Imbens. The answer to such questions involves accurately establishing causality. One way to draw causal conclusions is through experiments or randomized controlled trials (RCTs), the prominence of which was recognized by the Nobel Committee in the field of empirical economics in 2019. However, many big-picture and urgent questions cannot be assessed through RCTs. For ethical, logistical or financial reasons.
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Apart from experiments, researchers have to rely on real-world data which is messy. Drawing true causal inferences requires comparison between groups (those who did not stay in school longer, or states where the minimum wage has increased compared to states where it has not increased, and ahead). But since individuals or states differ in many dimensions, careful comparisons need to be made to avoid comparing apples with oranges. Additionally, adjustments need to be made for self-selection and omitted variables that may confound causal inference. The 2021 Nobel Prize winners have been appropriately recognized for their pioneering contributions to methods to uncover causality using real-world observational data. In doing so, his path-breaking studies have successfully questioned established conservatism and overturned the wisdom gained in mainstream economics.
The most influential 1992 study by Professor Card and Alan Kruger estimated the effect of a minimum wage increase. The two economists used a ‘natural experiment’ (in which individuals are randomly exposed to change caused by nature, institutions, or policy changes): in this case, a policy change in New Jersey that has reduced its – Raised the minimum wage for skilled workers. Rather than comparing employment change in New Jersey before and after the wage increase, as it can be influenced by many other factors, they compared the double difference (‘gap-gap’): before and after the policy. Employment in New Jersey compared to neighboring Pennsylvania, where wages did not change over the same period. Contrary to established wisdom, he found that an increase in the minimum wage did not lead to a reduction in employment. This study has been replicated by other researchers over several rounds of minimum wage hikes and each time, the result has been the same, i.e. no adverse effect on employment.
Read also: Three share economics Nobel for research on “natural experiment” to study cause and effect
Why is textbook prediction not afforded by data? There are several reasons for this: one is that the mythically competitive labor market, where firms are price takers, i.e. they have no autonomy in wage determination, does not actually exist. It turns out that monopolistic firms (very large employers with market power) can set wages lower than competitive wages and earn surpluses. Therefore, when the government imposes minimum wages, there is no reduction in the number of workers employed, but a part of the surplus is now transferred to the workers.
A source of great concern in the contemporary world is the fear that the entry of immigrants will adversely affect the employment and wages of non-immigrant residents. Pro. Card’s analysis of another natural experiment – the Mariel Boat Lift, which brought 1,25,000 Cubans to the US in 1980, half of whom settled in Miami – showed this concern to be invalid. As a result of the boat lift, the Miami workforce grew by 7%, but it had no adverse effect on the wages or employment of non-Cuban-born workers.
Pro. Imbens and Prof. Both Angrist have made innovative methodological contributions to causation inference that have enabled the exploration of big-picture questions. For example, in the US, private university graduates earn 14% higher salaries than public university graduates. Does this mean that private universities cause pay hikes? Pro. Angrist’s research corrected for ‘selection bias’, that is, adjusted for the fact that SAT scores and family income are higher for private university entrants. Compared to the likes, the study finds that attending private universities does not bring a salary premium.
These causal techniques are based on a comparison of observed results with counterfactuals: ‘what if’ scenarios or ‘likely outcomes’ that are not observed. Such comparisons are logistically compelling, and these methods have been replicated in a variety of contexts by hundreds of researchers, validating their effectiveness in separating causal effects from messy observational data.
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Nobel laureates have also contributed to transform pedagogy. Learning econometric techniques through his work has made econometrics less abstract, more relatable and interesting. Pro. Angrist is an excellent communicator through his highly popular textbooks (co-authored with Steve Piske) and short videos in the Frontier Revolution University series on ‘Mastering Econometrics’ (as Master Joshway). He begins with a real-world problem and leads us through the techniques of analysis, thereby reversing the approach of standard econometrics textbooks, starting with dry, math-heavy abstract proofs, until chapter examples. Until the end, the students lose.
In the age of Big Data and Machine Learning, are econometric techniques of causal inference increasingly redundant? Pro. Angrist argues that while data science helps with ‘curve fitting’, that is, it shows a pattern, it does not provide insight into causality. In other words, it neither enables us to understand why we observe a particular pattern, nor does it allow us to evaluate counterfactual scenarios. For that we need econometrics, which relies not on big data but on new ways of analyzing data. Pro. Angrist believes that econometrics will remain relevant regardless of any advances in data science.
The work of three Nobel Prize winners demonstrates the immense power of good, rigorous empirical work and reminds us that rigor need not be viewed as opposed to relevance, and that careful analysis can successfully challenge existing conservatism.
Ashwini Deshpande, Professor of Economics and Director, Center for Economic Data and Analysis, Ashoka University
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