본 연구는 최근 연구 동향을 반영하여 국내 은행의 무수익여신(NPL) 상승률의 결정요인에 대한 연구를 수행하였다. 본 연구는 NPL의 상승률에 관한 거시변수 가설과 은행변수 가설을 2단계 실증절차를 사용하여 순차적으로 분석하였다. 그리고 패널 VAR 모형을 사용하여 NPL의 영향이 실물 경제로 이어지는 전달 효과를 분석하였다. 본 연구의 표본은 2000년부터 2016년까지 164개 일반은행과 저축은행의 반기 관측치이다. 패널 VAR 모형을 바탕으로 직교화된 충격반응함수를 측정한 결과, 은행 시스템에서 발생한 충격이 약 1년 동안 실물 경제에 부정적 영향을 주는 것으로 나타났다. 이것은 개별은행이 NPL을 효과적으로 관리하는 것이 거시경제적으로 긍정적인 영향을 미친다는 것을 의미한다. 또한, 물가 경로와 이자율 경로에서 전달 효과가 뚜렷했는데, 이는 통화 정책을 수행할 때 실물 경제의 부담을 경감하기 위해 먼저 은행 시스템을 점검해야 함을 시사한다.

This paper examines the determinants of non-performing loans (hereafter NPLs) in the perspective of the macroeconomic and bank-level variables. Our contributions to the literature are in three folds. First, we employ the fixed effect model controlling for bank-level heterogeneity and the dynamic panel model controlling for autocorrelation to examine the determinants of NPLs growth in the Korean banking sector. On top of that, we extend the sample to commercial banks and savings banks. Including savings bank is crucial since those banks were distressed during the sample periods. Third, this paper analyzes the feedback effects of the banking sector on the real economy using panel vector autoregressive model. The results show that, among other macroeconomic factors, ‘growth rate of GDP’, ‘growth rate of KOSPI’, ‘growth rate of HPI’, ‘inflation’, ‘risk-free rate’, ‘corporate sector leverage’, ‘government spending-to-taxes’, ‘non-financial sector credit-to-GDP’, and ‘growth of nominal exchange rate’ support the macroeconomic hypotheses. For the bank-level hypotheses, it was found that 1) ‘non-interest expenses-to-total assets’ and ‘total expenses-to-total income’ satisfy the skimping hypothesis, 2) ‘bank size in log’ satisfies the too-big-to-fail hypothesis, 3) ‘liquid assets-to-total deposits’ satisfy the liquidity hypothesis, and 4) ‘total loans-to-total assets’, ‘total loans-to-total deposits’, and ‘interest income-to-total loans’ satisfy the lending supply hypothesis. Furthermore, as for the impact of the banking sector on the real economy, a simultaneous negative relationship was found between bank's NPLs and GDP. The causal relationship between the NPLs and GDP, on the other hand, was only found in the inflation and credit channels. According to the impulse response functions estimated, shocks in the banking sector result in negative consequences of the real economy for about one year. This implies that there exists the positive macroeconomic effect if individual banks effectively manage their NPLs.