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본 연구에서는 가계 채무불이행 위험의 결정요인을 실증적으로 분석하였다. 이론적으로는생애주기 경제이론의 관점에서 가계 채무불이행 위험을 분석하고 해석하고자 하였다. 본 실증연구에서는 채무불이행 요인을 파악하기 위해 가구별 제 특성과 신용활동에 관한 가구별미시정보를 포함하는 관련 조사․행정자료를 최초로 분석하였다. 가구수준에서 제 조사․행정자료를 연계하여 구축한 본 가구미시자료는 가구수준의 신용활동과 인구․가구구조, 소득․ 고용활동, 주택․주거 특성 등의 가구 미시정보를 포괄한다. 본 자료의 활용은 가계부채 관련기존 연구가 가구설문조사 또는 부채 측면 정보에 주로 한정된 CB자료 분석에 국한되었음을고려할 때 보다 엄밀하면서도 종합적인 분석을 가능케 한다. 본 분석결과의 주요 시사점은 다음과같다. 첫째, 채무불이행 예방 및 관련 위험 해소를 위해서는 가계에 가해지는 경제적 충격의빈도 및 강도를 줄이는 한편 충격을 흡수할 수 있는 보험기제를 확충하고 금융교육․상담을활성화하는 등 종합적인 접근이 필요하다. 둘째, 채무불이행 위험은 소득․고용과 재무적 측면의경제적 취약성 뿐 아니라 인구사회학적 속성과 주택․주거 특성에 의해서도 영향을 받으므로채무불이행 위험의 해소를 위해서는 금융․경제적 접근 뿐 아니라 인구사회학적 및 주거특성까지 함께 고려한 다양한 정책방안 마련이 필요하다.
We analyze household level micro-data to understand the drivers of households’ default risk. From a theoretical viewpoint, we approach household credit risk based on such an economic framework as the life-cycle theory, and examine the relevant risk factors thereof. Households may be unable to honor their credit commitments when they can not sustain the minimum level of livelihood, or minimum consumption level, with given lifetime resources. With this viewpoint in mind, we analyze and interpret the factors driving households’ default risk. In order to analyze the risk factors behind household credit risk, we constructed household level micro-data that cover information on various aspects of household characteristics and activities, including households’ credit activities, household characteristics, income & work status, housing and financial characteristics, and etc. This study is the first one that analyzes the risk of households’ default by using the combined administrative data from Korea Credit Information Services-that is the public credit registryand from Statistics Korea-that is the national statistical office. The use of these data allows a more rigorous and comprehensive analysis of the household debt issues, given that most previous studies are based on household survey with a relatively small sample size or the Credit Bureau data, which mostly include individuals’ liability-side information. In this study, the default probability model is estimated to analyze the factors driving household credit risk. A logistic regression model is estimated with a binary dependent variable. The main estimation results are as follows. With respect to household characteristics, the larger the number of household members and the lower the education level were, the higher the risk of delinquency was. Also, the risk of delinquency was high for the households with no spouse (single, bereaved, or divorced) and with male household heads. As for the income status, the lower the income level was, the higher the risk of delinquency was. The risk of delinquency in relation to the status of workers was different by the level of income. Within high income household groups, the risk of delinquency was higher for self-employed workers, whereas within low income household groups, the risk of delinquency was higher for wage earners. This result implies that the risk of insolvency is higher for temporary and daily workers, who earn low income and have a high earnings volatility in comparison with selfemployed workers in the same income group. Regarding housing characteristics, the risk of delinquency was higher when households own no house or a smaller number of houses. In addition, the risk of delinquency was higher for renters than for owner- or Jeonse- occupiers. Regarding house types, residents in apartment houses had lower delinquency rates than in other types. In terms of financial characteristics, the risk of delinquency was high in households with unsecured loans, nonbank loans, and with a larger number of loans. The main implication of the analyses are as follows. Households with frequent exposure to economic shocks, with poor capacity to absorb shocks, or with insufficient understanding and judgment of finance are more likely to default in comparison with others. According to the results of the analysis, the risk factors that drive households’ default are as much different as the characteristics of households are different. First, the intensity and frequency of the economic shocks that can be experienced by each household are different. In addition, the risk of default can be different across households as their capabilities to absorb shocks are different, even against the same economic shock. For example, differences in patterns of fixed expenditure, labor supply capacity, and assets held by households may lead to differences in their ability to cope with shocks. Hence, it may be necessary to invigorate households’ capacity to absorb shocks by using insurance-like mechanisms in addition to efforts to reduce the frequency and intensity of shocks in order to resolve the risk of being in default. Furthermore, the lack of financial comprehension and poor decision making can be another cause of over-indebtedness; hence, it is necessary to revitalize financial education and financial advisory services for households. In addition, as suggested by the above results, such variables indirectly related to economic conditions as household or housing-related characteristics as well as such variables directly reflecting economic vulnerabilities as income, employment, and financial characteristics are confirmed the major risk factors that drive the default probability of households. Hence, we may need to have a policy approach that takes into account non-financial factors such as socio-demographic and housing-related characteristics as well as purely financial factors.
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Household Debt, Default Probability, Delinquency, Over-indebtedness
