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The Nobel Prize-2021 was given to economists, whose work in Russia is well known, students study them. They seem to be working in a neighboring office, and their topics have become so firmly embedded in our daily life that we lost the feeling that these are new, recently developed ideas. David Card was awarded a separate award for empirical research in labor economics, and Joshua Angrista and Guido Imbens were awarded for the econometric design of new research. There is a feeling that one was given for the solution of substantive issues, and the other for methodological work. In fact, both have done a lot for labor economics. Most of the works of Angrist and Imbens are not highbrow theory, but applied research, and Card’s themes overlap very strongly with those of Angrist and Imbens.
About salary and education
One of the questions scientists are working on is whether education brings additional income? It would seem like a simple question. We know that in all studies that ask questions about how many years a person studied and what their salary is, there is a positive correlation – the longer people studied, the higher their salaries. Can this correlation be interpreted as a causal effect of education, or are there many more factors? Of course, any correlation cannot be interpreted as a causal relationship. Some of the increase in wages is related to education, and some is not. Those who went to university were smarter and did better in school, so it was easier for them to go to university, or they had rich parents who hired tutors, or created such an atmosphere in the family that the child was interested in studying.
And we see that it’s not just education – people are just different. For university graduates, salaries without a university would most likely have been higher due to personal aptitude and family factors. Conversely, those who went to work immediately after school and graduated from university would still receive less because they are not so smart, or ended up in a bad university and their education is of no value. It is necessary to distinguish between the effect of education and the effect of selection. The Nobel Prize was received, among other things, for research and proof of these differences. In addition, general methods have been proposed for separating causality from simply observable relationships in economic data, and this began with labor economics and then expanded to other areas.
About employment and minimum wages
Card’s first studies looked at whether raising the minimum wage led to declining employment. In the classic textbooks of microeconomics, they write that where wages have been raised, employment should fall, because employers have rising costs, and it becomes unprofitable to hire so many people. In reality, an employer can have different reasons to keep hiring people. In addition, the rise in the minimum wage itself often happens during recessions when the government is trying to support workers’ incomes, i.e. the actual relationship is the opposite: a decrease in employment during a recession provokes an increase in the minimum wage. Therefore, before drawing conclusions, you need to see if employment is really declining. Of this observed decline, we must also single out the part that is associated precisely with the increase in the minimum wage.
David Card proceeded from such substantive questions and problems and intuitively developed this methodology in order to calculate the effect of raising the minimum wage. He used the fact that in the United States the minimum wage can be set at the state level. In the early 1990s, New Jersey raised the minimum wage, but neighboring Pennsylvania did not. And if we compare how employment changes in these states (with the same trend before the increase), we will see that the trends in these states change in different ways. It is logical that if other factors in these two states did not change, then the increase in the minimum wage was the cause of the diverging trend.
This simple idea was presented in the work. Comparison approaches between control and experimental groups have already been widely used, for example, in medicine, where randomized clinical trials are conducted, but this is the first time this has happened in economic research. In general, data analysis is more difficult in economics than in medicine or natural sciences, where researchers can control the experimental conditions. Economists are dealing with data in which the influence of different factors is mixed, and it is necessary to isolate the causal effect of one of them. This is where what economists call natural experiments comes to the rescue – situations that arise as a result of some natural events, peculiarities of legislation, ongoing reforms, and so on. These events and changes provoke new patterns in the data that can be used to identify causal relationships and effects.
About labor migrants, veterans and wages
In the early 1990s, David Card published another work in which he examined the impact of immigration on local wages. He took advantage of the situation when, in the early 1980s, Fidel Castro allowed Cubans to emigrate to the United States. One hundred thousand Cubans who came to the United States settled in the same city of Miami, where there was already a large Cuban community. How did this affect local wages? Economic theory would say that there are more potential workers and wages should be reduced, including for local ones. By examining the dynamics of wages and employment in Miami and other cities where there was no such influx of migrants, Card showed that local workers were not affected by the influx of migrants. If anyone has suffered, it is the immigrants of the previous waves.
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