• KDI Journal of Economic Policy
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• v.30 no.1
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• pp.171-209
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• 2008

The Great Depression is one of the most important economic incidents in the twentieth century. A significant and long-lasting impact of this event is the rise of the government intervention to the economy. Under the catastrophic downturn of the economic condition worldwide, people required their government to play an active role for economic recovery, and this $mentalit{\acute{e}}$ prolonged even after the Second World War. Social science textbooks taught at Korean high schools mostly referred to the Great Depression for explaining the reason of government intervention in economy. However, the mainstream view commonly found in the textbooks provides a misleading theological interpretation. It argues that inherent flaws of the market economy causes over-production/under-consumption, and that this mismatch ends up with economic crisis. The chaotic situation was resolved by substitution of the governments for the market, and the New Deal was introduced as the monumental example ('laissez-faire economy ${\rightarrow}$over-production${\rightarrow}$the Great Depression${\rightarrow}$government intervention${\rightarrow}$economic recovery'). Based on economic historians' researches for past three decades, I argue that this mainstream view commits the fallacy of ex-post justification. Unlike what the mainstream view claims, the Great Depression was neither the result of the 'market failure', nor the recovery from the Great Depression but was due to successful government policies. For substantiating this claim, I suggest three points. First, blaming the weakness or instability of the market economy as the cause of the Great Depression is groundless. Unlike what the textbooks describe, the rise of the U.S. stock price during the 1920s cannot be said as a bubble, and there was no sign of under-consumption during the 1920s. On the contrary, a new consensus emerging from the 1980s among economic historians illustrates that the Great Depression was originated from 'the government failure' rather than from the 'market failure'. Policymakers of European countries tried to return to the gold standard regime before the First World War, but discrepancies between this policy and the reality made the world economy vulnerable. Second, the mainstream view identifies the New Deal as Keynesian interventionism and glorifies it for saving the U.S. economy from the crisis. However, this argument is not true. The New Deal was not Keynesian at all. What the U.S. government actually tried was not macroeconomic stabilization but price and quantity control. In addition, New Deal did not brought about economic recovery that people generally believe. Even after the New Deal, industrial production or employment level remained quite low until the late 1930s. Lastly, studies on individual New Deal policies show that they did not work as they were intended. For example, the National Industrial Recovery Act increased unemployment, and the Agricultural Adjustment Act expelled tenants from their land. Third, the mainstream view characterizes the economic order before the Great Depression as laissez-faire, and it tends to attribute all the vice during the Industrial Revolution era to the uncontrolled market economy. However, historical studies show that various economic and social problems of the Industrial Revolution period such as inequality problems, child labor, or environmental problems cannot be simply ascribed to the problems of the market economy. In conclusion, the remedy for all these problems in high school textbooks is not to use the Great Depression as an example showing the weakness of the market economy. The Great Depression should be introduced simply as a historical momentum that had initiated the growth of government intervention. This reform of high school textbooks is imperative for enhancing the right understanding of economy and history.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.