Getting to the heart of causality

Does the entry of immigrants reduce employment and lower the wages of native workers? Does the introduction of minimum wage, designed to protect workers, end up harming 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 U.S. Does this mean that attending private universities bestows a wage premium?

Drawing causal inference

These are among the many significant and deep questions that the three Economics Nobel Laureates for 2021 — David Card, Joshua Angrist and Guido Imbens — have investigated. Answering such questions involves establishing causality accurately. One way to draw causal inference is through experiments or randomised controlled trials (RCTs), the predominance of which in the field of empirical economics was recognised by the Nobel Committee in 2019. However, several big-picture and urgent questions cannot be evaluated through RCTs because of ethical, logistical or financial reasons.

Also read: Nobel Prizes 2021

Outside of experiments, researchers have to rely on real-world data which is messy. Drawing the correct causal inference entails comparisons between groups (those who stayed longer in school compared to those who did not, or States where the minimum wage increased compared to States where it did not increase, and so forth). But since individuals or States differ along many dimensions, comparison needs to be done carefully to avoid comparing apples with oranges. Additionally, adjustments need to be made for self-selection and omitted variables that might confound causal inference. The 2021 Nobel Laureates have been justly recognised for their pioneering contribution to the methodologies to uncover causality using real-world observational data. In doing so, their path-breaking studies have successfully questioned established orthodoxy and upturned received wisdom in mainstream economics.

Professor Card and Alan Krueger’s most influential 1992 study estimated the effect of minimum wage increases. The two economists utilised a ‘natural experiment’ (in which individuals are randomly exposed to a change caused by nature, institutions, or policy changes): in this case, a policy change in New Jersey that raised minimum wages for its low-skilled workers. Instead of comparing change in employment in New Jersey before and after the wage increase, as that could be affected by several other factors, they compared a double difference (‘difference-in-differences’): employment in New Jersey before and after the policy change compared to neighbouring Pennsylvania, where wages did not change, over the same period. Contrary to established wisdom, they found that an increase in minimum wages did not lead to a reduction in employment. This study has been replicated since by other researchers across several rounds of minimum wage increase and each time, the result has been the same, viz., no adverse impact on employment.

Also read: Three share Economics Nobel for research on "natural experiment" to study cause and effect

Why is the textbook prediction not borne out by data? There are many reasons: one is that the mythical perfectly competitive labour market, where firms are price-takers, i.e. they have no autonomy in wage setting, does not exist in reality. It turns out that monopsonistic firms (very large employers with market power) can set wages lower than the competitive wage and earn a surplus. Therefore, when the government imposes a minimum wage, the number of workers employed does not necessarily decrease but a part of the surplus now gets transferred to the workers.

A source of great anxiety in the contemporary world is the apprehension that entry of immigrants will adversely affect employment and wages of non-immigrant residents. Prof. Card’s analysis of another natural experiment — the Mariel boat lift that brought 1,25,000 Cubans to the U.S. in 1980, half of whom settled in Miami — showed this anxiety to be invalid. As a result of the boat lift, the Miami workforce increased by 7% but this had no adverse impact on the wages or employment of the non-Cuban native workforce.

Both Prof. Imbens and Prof. Angrist have made innovative methodological contributions to causal inference that have enabled explorations of big-picture questions. For example, in the U.S., private university graduates earn 14% higher wages than public university graduates. Does that mean private universities cause wages to go up? Prof. Angrist’s research corrected for ‘selection bias’, i.e. adjusted for the fact that SAT scores and family incomes are higher for private university entrants. Comparing like with like, the study finds that attending private universities does not confer a wage premium.

These causal techniques are based on a comparison of observed outcomes with counterfactuals: the ‘what if’ scenarios or ‘potential outcomes’ that are not observed. Such comparisons are logically compelling, and these methods have been replicated by hundreds of researchers across a variety of contexts, validating their effectiveness in disentangling causal effects from messy observational data.

Making metrics fun, relevant

The Nobel Laureates have also contributed to transforming pedagogy. Learning econometric techniques through the body of their work has made econometrics less abstract, more relatable and interesting. Prof. Angrist, through his immensely popular textbooks (co-authored with Steve Pischke) and short videos in the Marginal Revolution University series on ‘Mastering Econometrics’ (as Master Joshway), is an excellent communicator. He starts with a real-world problem and takes us through techniques of analysis, thereby inverting the approach of the standard econometrics textbooks that begin with dry, math-heavy abstract proofs, losing students along the way by the time the chapters end with examples.

In the age of big data and machine learning, are econometric techniques of causal inference increasingly redundant? Prof. Angrist argues that while data science helps ‘curve fitting’, i.e. it illustrates a pattern, it does not provide insights into causation. In other words, it neither enables an understanding of why we see a particular pattern, nor does it allow us to evaluate counterfactual scenarios. For that, we need econometrics, which does not rely on big data but on innovative ways of analysing data. Prof. Angrist believes that econometrics will continue to be relevant regardless of any advances in data sciences.

The work of the three Nobel Laureates demonstrates the immense power of good, rigorous empirical work and reminds us that rigour need not be seen in opposition to relevance, and that careful analysis can successfully challenge existing orthodoxy.

Ashwini Deshpande is Professor of Economics and Director, Centre for Economic Data and Analysis, Ashoka University

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Printable version | Dec 3, 2021 6:24:56 AM |

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