• Korean Journal of Social Welfare
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• v.66 no.3
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• pp.133-158
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• 2014

Over the past decades, changes in economic, social and demographic structures have pushed the growth of care employment across countries around the world. Women's increasing labor force participation has squeezed the time so far available for unpaid caregiving and led to increased demand for paid care services. Population aging and increasing needs for pre-school education also have contributed to the growth in demand for care services. As a result, care workers now comprise a large and growing segment of the labor force in many countries including South Korea. But, there are not a few problems. Especially, we take underpaid and undervalued care work very seriously. care work has been generally characterized as underpaid and undervalued compared with other work in developed and developing countries alike. This study tries to show current situation of care work and estimate the wage penalty for doing care work in Korea using official employment micro-data and applying propensity matching analysis. Especially, recent expansion of social service is a big step up for Korean Welfare State. But, there are not a few problems. Especially, we take underpaid and undervalued care work very seriously. This presentation tries to show current situation of care work and estimate the wage penalty for doing care work in Korea using official employment micro-data and applying propensity matching analysis.

• Korean Journal of Social Welfare
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• v.63 no.4
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• pp.301-326
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• 2011

The major purpose of this study is to evaluate the impact of the long-term care insurance program. In order to estimate the impact of policy accurately, certain bias which might hamper the validity of this study has been removed by Propensity Score Matching(PSM) and Double Difference(DD) method from the semi-experimental design. To study the effects of long-term care insurance on the elderly and their family members as social outcome variable sand the quality of life of their family and satisfaction in family relationships, the third and fourth waves of Korea Welfare Panel are used to match experimental and comparative groups by the propensity score matching. Then, DD method, using the panel fixed effects model, is applied to estimate the differences of those groups'treatment effects before and after the policy implementation. As a result, it was found that the Quality of life on the elderly and their family members is statistically meaningless, while the satisfaction in family relationships has much increased after the policy implementation. The result has a limitation in that this evaluation is performed at the point when the long-term insurance program has not been ripened enough. However, there is an important implication on the significance of realizing the main goal of the long-term care insurance to improve the quality of life of the elderly and their family members and as for the potentiality of further system improvements.

• Journal of Labour Economics
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• v.41 no.2
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• pp.61-88
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• 2018

This paper examines the impact of internal migration on wage growth among college graduates using Propensity Score Matching methods. We define migration as moving between Seoul Metropolitan Area (SMA) and non-SMA based on the locations of graduates' first and second jobs. We also take the direction of migration into account for examining the wage premium in SMA. In order to estimate the impact of migration on wage growth, we use the Graduate Occupational Mobility Survey (2010GOMS) coupled with other supplementary data such as College Scholastic Ability Test score and local characteristic variables. The results reveals that graduates moving from SMA to non-SMA do not experience significant wage growth. However, we find that graduates moving from non-SMA to SMA experience an increase in their monthly wage about 170,000~186,000 KRW on average (9.5~10.3% of their monthly wage on the first job).

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Journal of Technology Innovation
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• v.19 no.1
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• pp.29-53
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• 2011

This paper analyzes the effects of government-sponsored R&D on firm's employment and management performance, using the panel data of manufacturing firms in the area of Busan, Ulsan and Gyungnam. The paper applies PSM to estimate the treatment effects(ATT) without sample selection bias. The findings show that government-sponsored R&D in the area has positive effects on the participating firms' employment and R&D for several years after completing R&D projects.

• 한국사회정책
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• v.19 no.3
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• pp.85-117
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• 2012

This study attempts to examine the relationship between giving & volunteering behavior and charitable bequest intention. For the examination, this study used '2011 Korean National Social Survey' that was randomly sampled from the population of Korean in 2011. In examining the relationship, this study employed the method of Propensity Score Matching that permits the comparisons between experimental group and control group. In this study, the experimental groups consist of six different combinations of philanthropic behaviors as follows: donating only; volunteering only; participating both; regular donating only; regular volunteering only; and doing both regularly. The results show that all the types of philanthropic behaviors have statistically significant positive effect on charitable bequest intention. First, more active philanthropic behavior such as regular behavior causes higher charitable bequest intention. Second, those who participate in both philanthropic behaviors (combined behavior of donating and volunteering) have stronger effect on charitable bequest intention in comparison to participating only one philanthropic behavior (either donating or volunteering). Third, giving have relatively stronger relationship with charitable bequest intention than volunteering. Throughout more careful examination of the causal relationship from philanthropic behavior to charitable bequest intention, this study provides new understanding on the effect of daily philanthropic behavior on charitable bequest and practical implication to nurture charitable bequest. Indeed, this study contributes to building a knowledge foundation for future research on charitable bequest.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.923-936
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• 2021

A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a nonparametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies.

• Journal of Labour Economics
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• v.32 no.2
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• pp.125-150
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• 2009

This article analysed the effect of government intervention on the market failure in firm training in Korean. I used the Employment Insurance DB and the KISLINE DB and joined two data by the firm identification number 2004-2006 year. I estimated the effect of intervention of government by propensity score matching and difference-in-difference method to avoid of participation selection and endogeneity problem. The result is that government intervention on the firm training have made positive effect but it is not significant statistically. We have to investigate the market failure in firm training and to reassess the level of optimal firm training in Korea. After the study, the government intervention on the firm trainig will have to be rearranged.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Journal of Labour Economics
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• v.42 no.3
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• pp.75-98
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• 2019

This paper examines the impact of on-the-job training (OJT) programs on turnover rates and employment status in the labor market. Exploiting the administrative data (the Employment Insurance Database), we apply the propensity score matching method to investigate 1) whether OJT participation increases the probability of remaining in the labor market after the job training, and 2) whether trainees are more likely to transition to a new employer. Our findings reveal positive effects of OJT on the continuous employment (2.4~5.3%p). We also observe that trainees show lower rates of turnover for some part of the study period, from 2008 to 2015.