Sukanta Chandra Swain
Associate Professor, School of Humanities & Social Sciences
The Sveriges Riksbank Prize in Economic Sciences in the memory of Alfred Nobel for 2021 is awarded to three economists: (1) David Card of University of California, Berkeley, USA “for his empirical contributions to labour economics,” and to (2) Joshua D. Angrist of Massachusetts Institute of Technology, Cambridge, USA, and (3) Guido W. Imbens of Stanford University, USA “for their methodological contributions to the analysis of causal relationships.” Their main contribution lies in observing the effects of policy experiments in natural settings (natural experiments or randomized field experiments), establishing causality, and drawing inference regarding the effectiveness of policy interventions.
Natural experiments are like randomized control trials that are used mainly in bio-medical and now in economics research. The two types of experiments are common in that both have a control group and a treatment group each. But, unlike the latter, in natural experiments the researcher does not assign the subjects to the groups in a random manner; and to belong to a group is a subject’s discretion. However, random assignment occurs naturally because of policy changes implemented in some regions of a country and not in another, citizens adopting a particular policy depending on personal choices, etc. In such natural settings, not all people for whom a policy intervention is directed (the perceived treatment group) comply with the intervention, while some people for whom the policy intervention is not targeted (the control group) may comply with the intervention. Thus, we can discern four categories of people: Compilers, Always-Takers, Never-Takers, and Defiers (Table 1).
Table 1: Categories of Treated and Untreated Persons in Treatment and Control Groups
| Assigned to Treatment | Assigned to Control | |
| Compliers Always-Takers Never-Takers Defiers | Treated Treated Not Treated Not Treated | Not Treated Treated Not Treated Treated |
Non-compliance to policy interventions make it difficult to estimate the average treatment effect on the population. The Nobel Laureates developed a method to compute the Local Average Treatment Estimate (LATE), also called the Complied Average Causal Effect (CACE), with the help of which the causal effect of a policy intervention can be judged. They applied this method to show real-world economic impacts concerning labour economics, particularly on the relationship between years of education and future earnings and between minimum wage and level of employment. Their seminal works show how causation can be generalized from observational data in real-world natural experiments.
David Card
Born in 1956 in Guelph, Canada, and a Ph. D. in 1983 from Princeton University, USA, Professor David Card is known for his seminal works on immigration, wages and education, and differences pertaining to gender race in the labour market. Research findings of Card and his co-researchers could strike down the conventionally accepted relationship between hike in the minimum wage and the rate of unemployment. Typically, it was believed that as minimum wages increase, demand for workers gets reduced. exhibiting an inverse relationship between them. However, in a study in 1994, Card and Krueger did not find any evidence for this inverse relationship as the rise in New Jersey’s minimum wage did not reduce employment at the fast-food restaurants in the state.
Reviewing the historical records of Canadian emigration to the United States, Card found that Canadian immigrants in the United States were comparatively well educated. Difference in skill sets between the two countries had been accentuated due to outflows of relatively well-educated men and women from Canada. There had been remarkable hike in the relative wages of younger college-educated workers in the U.S., which had caused economic incentives for Canadians and other people from all around the world. Consequently, people from across the globe were preferring to move to the U.S. Compared to the U.S. natives with similar observed skills, Canadian immigrants in the U.S. earned more. Many Canadians were concerned that the brain drain from Canada would result in an imminent skill shortage in Canada. However, considering wages in the U.S. and Canada, there was no indication of a dearth of educated workers in Canada. At the same time, in spite of Canada having high levels of education on the scale of world standards, it is substantially falling short in educational attainment as compared to the U.S.. The gap is prominent at the lower end of the distribution, i.e., in high school drop-outs, Canada is much ahead of the U.S. From the age-group or education-group perspectives, as of the early 1980s, there were larger wage differentials in the U.S. than Canada. It means there were lucrative economic incentives to immigrate for highly skilled Canadians. Moreover, the incentives got reinforced by the provision of lower taxes and lower social benefits in the U.S. as compared to Canada. Thus, it is inferred that the economic incentives for Canadians’ immigration to the U.S. were strengthened for decelerated average wages in Canada as compared to the U.S., broadening wage differences in the U.S., continuant wage inequality in Canada, and tributary fiscal set up in the U.S..
Card’s work that highlighted the importance of schooling for changing labour markets was also seminal. Card showed that the average return to education is not much below the estimate that emerges from a standard human capital earnings function fit by OLS. It may, however, so happen that marginal returns to schooling for certain subgroups—particularly relatively disadvantaged groups with low education outcomes—are higher than the average marginal returns to education in the population as a whole.
Joshua D. Angrist
Born in 1960 in Columbus, Ohio, USA, and a Ph. D. in 1989 from Princeton University, USA, Joshua David Angrist was a former doctoral student of Card and Krueger and is one of the top economists in labour economics, urban economics, and the economics of education. Well known for methodological contributions to the analysis of causal relationships, he has used quasi-experimental research designs (such as instrumental variables) to study the effects of public policies and changes in economic or social circumstances. He is a co-founder and co-director of the MIT’s School Effectiveness & Inequality Initiative, which studies the relationship between human capital and income inequality in the U.S. He has worked on issues from research on the determinants of student learning to identifying causal relationships in labour markets with his quasi-natural experiments.
Guido Wilhelmus Imbens
Born in 1963 in the Netherlands, and a Ph. D. in 1991 from Brown University, Providence, USA, Guido Wilhelmus Imbens is currently a Professor of Applied Econometrics and Professor of Economics at Stanford Graduate School of Business, Stanford University. He is specialized in Econometrics, particularly in methods specifically meant for drawing causal inference. A regular collaborator of Krueger and Angrist, he was instrumental in devising the methodologies to perform the natural experiments that were used to determine causal relationships in the real world. in collaboration with Angrist, he pioneered the model of Local Average Treatment Effect (LATE) that facilitated researchers to draw causal inferences from observational data. Imbens also developed econometrics tools so that researchers could link qualitative statements to quantitative data.
Sources:
- Butcher, K. F. and Card, D. (1991). Immigration and Wages: Evidence from the 1980’s, The American Economic Review, Vol. 81, No. 2, pp. 292–296. Papers and Proceedings of the Hundred and Third Annual Meeting of the American Economic Association.
- Card, D. (1999). The Causal Effect of Education on Earnings, Handbook of Labor Economics, in: O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics, Edition 1, Elsevier, Vol. 3, Chapter 30, pp. 1801–1863,.
- Card, D. and Krueger, A. B. (1994), Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania, The American Economic Review, Vol. 84, No. 4, pp. 772 – 793.
- Imbens, G. W. & J. D. Angrist (1994), Identification and Estimation of Local Average Treatment Effects, Econometrica, Vol. 62, No. 2, pp. 467 – 475.
- https://www.nature.com/articles/d41586-021-02799-7
- https://www.nobelprize.org/prizes/economic-sciences/2021/press-release/