• Korean Journal of Social Welfare Studies
• /
• v.44 no.3
• /
• pp.421-450
• /
• 2013

One of the challenging tasks of the National Health Insurance Corporation(NHIC). the only public insurance institution administrating the Korea's compulsory national health insurance(NHI) system, is to make those NHI beneficiaries who fail to make a scheduled monthly premium payment to pay. For this purpose, the NHIC has been using a measure known as 'Benefit Limit Measure(BLM)' in which those who miss premium payment for six or more month's in total are classified as 'late payer' and are sent warnings and late payer status notices. If the late payers fail to make a full payment of missed premiums even after receiving the written notices, the NHIC can order a temporary seizure of the late payer's property until all missed premiums plus interest are paid. Recently, the BLM has been criticized by the public of its cruel nature, and its effectiveness has been questioned because no empirical evidence has been collected. In this study, the authors using the NHIC data set attempted to analyze the effectiveness of the BLM. Those late payers for whom the BLM was administered were compared to those not in terms of the likelihood of paying missed premium payments with a series of logistic regression analyses models. Data analyses results showed that the likelihood of paying one or more month's unpaid premium of the former group was 14 to 46 times higher than the latter. It, however, was also found that the BLM was only effective to make no more than 12% of the late payers to pay at all. Based on the study findings, the authors made a few recommendations regarding the BLM.

• Health Policy and Management
• /
• v.13 no.2
• /
• pp.85-100
• /
• 2003

The purpose of this study is to (1) identify socio-demographic, economic, village-effect variables that influence the late payment of the National Health Insurance contribution, (2) to develop the model to predict the probability of a household to make late payment of the contribution. Data is composed of information on 78,858 households, Gangnam branch, National Health Insurance Corporation, as of September 30, 2001. We analyzed the data by using multivariate logistic regressions. The major findings are as follows; (1) an older or female householder whose family consists of smaller number of members is more likely to pay the contribution late than others, (2) as for income, one who belongs to a lower income group or nm a private business tend to pay it late, (3) more attention should be paid to a householders who does not have his/her own house or automobile so as to prevent late payment, (4) lastly, those who live in villages such as Nonhyun-l-dong are less likely to pay the contribution prior to due date.

• Health Policy and Management
• /
• v.33 no.4
• /
• pp.450-456
• /
• 2023

Background: This study aims to develop a "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions" for the National Health Insurance Service to enhance administrative efficiency in protecting and collecting contributions from livelihood-type defaulters. Additionally, it aims to establish customized collection management strategies based on individuals' ability to pay health insurance contributions. Methods: Firstly, to develop the "Predictive Model for the Possibility of Collection Delinquent Health Insurance Contributions," a series of processes including (1) analysis of defaulter characteristics, (2) model estimation and performance evaluation, and (3) model derivation will be conducted. Secondly, using the predictions from the model, individuals will be categorized into four types based on their payment ability and livelihood status, and collection strategies will be provided for each type. Results: Firstly, the regression equation of the prediction model is as follows: phat = exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction) / [1 + exp (0.4729 + 0.0392 × gender + 0.00894 × age + 0.000563 × total income - 0.2849 × low-income type enrollee - 0.2271 × delinquency frequency + 0.9714 × delinquency action + 0.0851 × reduction)]. The prediction performance is an accuracy of 86.0%, sensitivity of 87.0%, and specificity of 84.8%. Secondly, individuals were categorized into four types based on livelihood status and payment ability. Particularly, the "support needed group," which comprises those with low payment ability and low-income type enrollee, suggests enhancing contribution relief and support policies. On the other hand, the "high-risk group," which comprises those without livelihood type and low payment ability, suggests implementing stricter default handling to improve collection rates. Conclusion: Upon examining the regression equation of the prediction model, it is evident that individuals with lower income levels and a history of past defaults have a lower probability of payment. This implies that defaults occur among those without the ability to bear the burden of health insurance contributions, leading to long-term defaults. Social insurance operates on the principles of mandatory participation and burden based on the ability to pay. Therefore, it is necessary to develop policies that consider individuals' ability to pay, such as transitioning livelihood-type defaulters to medical assistance or reducing insurance contribution burdens.