• Land and Housing Review
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• v.13 no.2
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• pp.31-47
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• 2022

This paper examines residential environment quality and housing expenses of young households through longitudinal analysis. Using the 5^th and 15^th Korea Welfare Panel Study (KoWePS), this research compared their housing outcomes with those of the Korean households. The statistical analysis revealed that most young households were highly-educated, salaried workers who were predominantly married men in their early 30s. There was a sharp rise in the number of female householders and one-person households. Also, the young households were largely renters of mid-sized multi-family housing with two bedrooms in non-Seoul Metropolitan Area. Their housing expense was slightly higher than the national average. As a proportion of renters of multi-family housing (exclusive of apartments) rose, the proportion of young households who spent more than 25% of their income increased faster than the national average. The proportion of young households in the Seoul Metropolitan Area outpaced the national average. Their monthly rental arrangements grew in contrast to no change in the nationwide monthly rental arrangement over the survey period, resulting in their high burden on housing expenses. Their homeownership rate was below the national average, and it decreased while the overall homeownership rate increased nationwide, implying that their housing affordability was worsened, which made it difficult for them to move up the housing ladder. Thus, this research suggests housing policies that scale up support for young households.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.91-102
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• 2020

This study constructed production frontiers of inputs and outputs in a sequential manner, measured inefficiencies by applying a non-radial sequential weighted Russell directional distance function to these frontiers, and analyzed Luenberg productivity indices and the contribution of each of input and output factor based on these distances. The results are as follows. First, the productivity of banks increased due to technical changes after the global financial crisis. Second, productivity growth decreased between 2009 and 2014 due to technical changes after the recession, as previous studies have shown that technology progressed before the global financial crisis but then largely decreased or remained the same thereafter. After 2014, the productivity of banks improved. This result may be due to both technology improvement after 10 years of stagnation and reduction of inputs and non-performing loans. Third, the 3.6% annual of productivity growth for 10 years was comprised of 1.77% household loans, 0.67% corporate loans, 0.98% manpower, 1.18% non-performing loans, -0.5% total deposits, and -1.25% securities. Finally, this study has limitations since it could not control risks such as capital structure and interest volatility.

• KDI Journal of Economic Policy
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• v.32 no.3
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• pp.1-32
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• 2010

The global financial crisis has exerted enormous impacts on the attainment of inflation target in Korea. The annual average CPI inflation was 3.3% during the targeting period of 2007-2009 and the target was $3.0{\pm}0.5%$. Thus Korea has succeeded in keeping annual average CPI inflation just below the upper limit of the 2007-2009 target under the global crisis. This paper intends to evaluate the performance of the inflation targeting system in Korea. First, it estimates the conventional call rate reaction equation under the global crisis and finds that the policy interest rates never reacted to expected inflation, output gap, and won/dollar exchange rate, as expected by theory. Second, it identifies the shock of global financial crisis into core and non-core, applying the structural VAR model. The core shock was defined to have no (medium- to) long-run impact on real output. The core shock was identified to have the character of the demand shock, since it has the positive impact on the inflation and output in the short run. The structural core inflation due to core shock was an attractor of headline inflation, not vice versa. Therefore, the structural core inflation that reflects the demand-side shock would be the better intermediate target for the final headline inflation target than the official core inflation that excludes the volatile inflation of agricultural and oil-related products. During the inflation targeting period of 2007-2009, the structural core inflation was more volatile than the official core inflation, because the global crisis has very large negative impacts on the domestic demand as well as the prices of agricultural and oil-related products. This paper shows that the negative core shock during the fourth quarter of 2008 was larger than that in the financial crisis in 1998. But the core shock turned into positive very quickly in 2009, as the Korean economy recovered very quickly from crisis. The volatile changes in structural core inflation suggests that the Bank of Korea barely managed to attain the 2007-2009 inflation target, owing to the very large negative impacts of the global financial crisis on the domestic demand. It also suggests that the rapid rise in core inflation with the rapid recovery of the Korean economy will lead to rapid rise in headline inflation.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.