Zhao Zhong, a professor at the National People's Congress, explained in detail why the 2021 Nobel Prize in Economics was awarded

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Author: Zhao Zhong, Professor, School of Labor and Personnel, Chinese Min University

Small discussion of the 2021 Nobel Prize in Economics

Author: Zhao Zhong

This year's Nobel Prize in Economics was awarded to Three Economists, David Card of the University of California, Berkeley, Joshua D. Angrist of the Massachusetts Institute of Technology, and Guido W. Imbens of Stanford University, for their contributions to empirical research in labor economics, And Joshua D. Angrist and Guido W. Imbens for their contributions to causal inference methodologies. All three economists teach in the United States, but at the same time are extremely international. David Card was born in Canada, Guido W. Imbens was born in the Netherlands, and Joshua D. Angrist, though born in the United States, has dual U.S. and Israeli citizenship. David Card and Joshua D. Angrist, both Ph.D., from Princeton University under the tutelage of renowned labor economist Orley Ashenfelter, and Dr. Guido W. Imbens graduated from Brown University as a student of the renowned econometric economist Tony Lancaster.

The two fields awarded to the three scholars are not relevant at first glance, but are actually closely related. The award can be seen in part as recognition of the three scholars' groundbreaking research in what Joshua D. Angrist and Jörn-Steffen Pischke called The Credibility Revolution in Empirical Economics.

David Card graduated from Princeton University in 1983 and has been engaged in empirical research in labor economics, and in 1995 he was awarded the John Bates Clark Prize, known as the Little Nobel Prize, which was awarded every two years to distinguished economists under the age of 40. His research covers almost all important topics in labor economics, from immigration to labor supply, from strikes to collective bargaining, from technological progress to income inequality, from the minimum wage to changes in wage structure, from education to job training.

Throughout his research is the innovative use of econometric and statistical methods. Creatively use exogenous shocks caused by "natural experiments" to clearly identify and estimate causal relationships between economic variables. This short essay, of course, is not long enough to cover David Card's main contributions, so I have to make a brief account of his well-known study of the minimum wage. The minimum wage policy in the United States is a widely concerned and controversial policy, of which whether the implementation of the minimum wage policy will have a negative impact on employment is one of the focal points. David Card and Alan Kruger's 1994 American Economic Review article focused on two neighboring states, New Jersey and Pennsylvania, where New Jersey adjusted the minimum wage in 1992 and Pennsylvania did not. By comparing changes in employment in the fast food industry in the two neighboring states, it was found that the increase in the minimum wage did not lead to a decline in employment. This is the most famous academic study in the field of minimum wages and one of the most famous studies in the empirical study of labor economics. The above change in the minimum wage is an example of a "natural test".

The Nobel Prize in Economics was awarded to Joshua D. Angrist for his and Guido W. Imbens' contributions to causal inference methodologies. In fact, Joshua D. Angrist was first and foremost a labor economist. His best-known labor economics paper is probably his 1991 paper with Alan Kruger using the U.S. compulsory education system to estimate the rate of return on education in the Quarterly Journal of Economics. Human capital is an important driver of modern economic growth, and education is an important form of human capital. Papers on estimating the rate of return on education have been plentiful since Jacob Mincer's seminal research, but it is not easy to accurately estimate the impact of education on income. From the perspective of empirical research, the main difficulty is that the number of years of education received is chosen by the individual himself, rather than randomly allocated, and some factors affecting the choice are usually not observed, such as the ability of the individual often mentioned in the literature, which will lead to the inability to distinguish whether the observed difference in income is due to different abilities or different educational attainments, that is, the endogenous nature of education causes the estimation bias. Is it possible to find exogenous events that affect an individual's educational attainment while not being under the control of the individual, i.e. "natural experiments"? If such "natural experiments" exist, they can be used to solve the above endogenous problems. Joshua D. Angrist and Alan Kruger found that the U.S. compulsory education system generally requires students to drop out of school after a certain age (e.g., 16 years old), and states impose parental penalties if they violate compulsory education regulations. Based on the U.S. compulsory education system, if a person is born in the first quarter, he reaches the legal age of dropping out of school before the end of the school year, and is born in the second half of the year, he must reach the legal age of dropping out of school after the end of the school year. In this way, the time of birth, an exogenous factor that is not under their control, will affect a person's educational level, and this difference in exogenous educational attainment can be used to estimate the rate of return on education. Moreover, this birth time, compared with the above-mentioned minimum wage adjustment, is caused by the forces of nature, so the literature calls such "natural experiments" natural "natural experiments".

Guido W. Imbens and Joshua D. Angrist's contributions to causal inference methodology, most notably and most influentially, were their collaboration while teaching at Harvard University, published in a 1994 paper on the effects of local projects in Econala. Papers in international economic journals, authors are generally sorted alphabetically by last name, but this article Guido W. Imbens ranks ahead of Joshua D. Angrist, to some extent reflecting the relative contributions of the two scholars. Considering the heterogeneity of causal effects, the paper combines the traditional instrumental variable method in econometrics with the potential effect analysis framework pioneered by Jerzy Neyman in statistics from 1923 and further systematized by Donald Rubin in 1974, namely Rubin Model, and systematically discusses the conditions identified by the tool variable estimation method in the presence of heterogeneity, the statistical properties of the estimation and the interpretation of parameters. At this time, the conditions identified by the model need to meet the monotonic conditions in addition to the conditions of the traditional instrumental variable method; the estimated causal effect is a local project effect, not a global effect; and more profoundly and to some extent, it is also frustrating that it is not only the "local project effect" that is estimated at this time, but also the applicable group of this "local project effect" does not exist in reality and cannot be observed. This study greatly advanced the study of causal inference and deepened the understanding of the instrumental variable method commonly used in economics.