the introduction of the minimum wage would affect employment. The study was based on the fact that on April 1, 1992, New Jersey was supposed to raise the minimum wage from $4.25 to $5.05 per hour. The researchers collected data on the employment rate at fast food restaurants in New Jersey and Pennsylvania before and after the increase in New Jersey. The result showed that the increase in the minimum wage led to an increase in employment, since it actually increased in New Jersey.
Another fundamental task, which is generally expressed as the search for the causal relationship between decisions (individual or public) and economic results, was investigated by Joshua Angrist. “Cause-and-effect” issues required a special language. Angrist in his works relied on the model of “potential outcomes” invented in the seventies by Donald Rubin.
The initial assumptions of the model were the following: each individual has a set of potential outcomes that can happen to him, depending on what his decision will be. For example, if a person has a toothache, then he can either take an analgesic pill, or go to the dentist, or do nothing. Each of these solutions will lead to some potential result. The actual data show only one of these outcomes, we do not know the outcome if the same individual would have made a different decision. This fundamental problem of causal analysis has no solution, we will never be able to measure the impact effect for a particular individual. At the same time, under certain conditions, we can measure some average effect.
In their 1994 paper, Imbens and Angrist show how to apply this methodology to instrumental variables. Instead of coming up with potential prerequisites to evaluate the effect for everyone, the researchers turned the task around and asked for whom we can evaluate the effect with reasonable prerequisites. The answer turned out to be simple and intuitive: the average effect can be calculated for those individuals who changed their decision under the influence of the tool — the so-called Local Average Treatment Effect.
Based on the new methodology, Angrist and Kruger studied the impact of education on wages. Their task was to exclude other influencing factors — a person's abilities or his family background. Scientists decided to use data on the year of birth of students to predict how many students will study at school. It was assumed that the year of birth is not related to the origin and innate abilities, respectively, it does not affect the success and salary level of a person in the future. They calculated for a large sample that in fact the impact of training on earnings turned out to be greater than previously estimated using traditional methods. This is how the standard for this kind of analysis was established.
Instead of coming up with potential prerequisites to evaluate the effect for everyone, the researchers turned the task around and asked for whom we can evaluate the effect with reasonable prerequisites. The answer turned out to be simple and intuitive: the average effect can be calculated for those individuals who changed their decision under the influence of the tool.
New ideas were not immediately recognized by the official science. In the early nineties, the work of David Card caused a lot of criticism from researchers, including Nobel laureates, as well as the approach of Imbens and Angrist to the analysis of cause-and-effect relationships. But over the years, these ideas have stood the test of time, and today it is difficult to imagine empirical work that does not rely to some extent on the research of Card, Imbens and Angrist. This approach to applied research has been called the “credibility revolution". “Thanks to them, the role of empirical research in economics has undergone revolutionary changes. Their work has radically changed the approach of scientists to finding answers to empirical questions based on data from natural and field experiments," the Nobel Committee stated.
Economic research over the past half century has shifted into the zone of empirical hypothesis testing. The very purpose of empirical research has also changed. There has been a shift in the attention of science from the description of variations of a particular value (income, unemployment, etc.) to the zone of identification of cause-and-effect relationships, from the description of attributes to the zone of experiments.
The global methodology for assessing Impact effects of socio-economic processes is moving from attributive approaches with the formulation of a controlled experiment towards natural experiments and counterfeit modeling. Here we can recall another Nobel laureate, Daniel Kahneman, who defined a new direction of research in economics — “behavioral economics". Kahneman proved by a simple experiment that a real person, and not some abstract economic person, is prone to irrational choice and that about 80–90 % of people do not follow rational choice.
It is worth noting that the rationality in economics differs from this concept in everyday life. It means a set of some axioms that indicate how to make a choice. We have all the alternatives; we can compare them taking into account all significant indicators and mathematically evaluate the benefits of each option. And as Kahneman's experiments have shown, real people evaluate the probabilities of certain alternatives differently. In the case of a win, for example, people tend to a guaranteed option more often than a risky one (less likely), even if a larger win is embedded in the risk. At the same time, with losses, these same people are more willing to take risks in choosing, expecting that there is a chance that there will be no losses. With his research, Kahneman seemed to combine mathematized approaches of economics with all strict rules and not always rational paradigms of social sciences. A new broad view of socio-economic processes was initiated.
In conclusion, it is worth noting that other scientists researched the idea of human irrationality — Adam Smith, John Maynard Keynes, Herbert Simon and Richard Thaler, as well as many others. “A person is not
