The Anatomy of Household Debt Build Up: What Are the Implications for the Financial Stability in Croatia? Ivana Herceg and Vedran Šošić* *Views expressed in this paper are solely those of the authors’ The 16th Dubrovnik Economic Conference, June, 2010

Intro & motivation  traditionally household lending has never been identified as a cause of banking system distress  recently scrutinized due to rapid growth of household indebtedness  as it raised concerns about potential implications on the stability of financial system  “macro” vs. “micro” approach in the literature  micro approaches identify different vulnerability indicators that move out of line with each other and suggest different levels of vulnerable households overlap very little as demonstrated on Croatian data  goal: to explore implications of the rapid debt accumulation by the households for the financial stability in Croatia by observing changes in the household debt determinants and debt distribution

Distribution of household debt Source: authors' calculations based on the Household Budget Survey

Methodology  Step 1. Quantile regression PROBLEM! Sample selection bias SOLUTION! Sample selection model  Step 2. Heckman’s two-step procedure PROBLEM! Normality assumption SOLUTION! Semiparametric estimator where; correction term

Methodology  Step 3. Final model corrected for sample selection bias  Step 4. Machado-Mata (MM) decomposition ; debt changecovariates effectcoefficient effect

Data  Household budget survey for 2005 and 2008  sample size:  dependant variable: 1. binary variable (0,1) - selection equation 2. log (household's loan)- output equation  explanatory variables: 1. socio-economic characteristics 2. demographic characteristics  additional variables in selection equation: 1. rural area of resident 2. life insurance sample all households indebted households

Selection equation results

Output equation results  statistically significant on almost the entire debt distribution and with expected sign are variables  when corrected for sample selectivity bias the significance of explanatory variables, together with the size of their estimated coefficients, changes significantly current disposable income age of the household's head type of activity the head is working in part-time work dummy tenure status number of household's bank loans dummy identifying household with housing loan taken during the last 12 months

Output equation results  disposable income and dummy variable identifying homeowners that repay housing loan remain the most important variables in explaining the amount of debt hold in both years  dummy variable indicating households with new housing loan was also important in determining the size of debt along the entire debt distribution, especially in 2005  different age brackets have statistically more significant explanatory power at different conditional quantile in 2008, together with part-time work

MM decomposition with no sample selectivity correction  improvement of households’ characteristics along the entire debt distribution  however, relaxation of banks' lending standards and/or greater appetite of some households to take more credit were the main drivers of households debt growth between 2005 and 2008  their effect was the strongest among the highly indebted households whose improvement of creditworthiness was at the same time the slowest Source: authors' calculations based on the Household Budget Survey

Decomposition of the improved households’ creditworthiness  growth of household current disposable income had positive effect upon household debt across almost the whole distribution  rising number of bank loans had positive effect on the amounts of household debt, but higher number of loans cannot be considered as an improvement of households' creditworthiness  if the impact of the number of loans household carries is taken into account, households' creditworthiness observed at the highest conditional quantiles would actually deteriorate Source: authors' calculations based on the Household Budget Survey

MM decomposition corrected for sample selection bias  relative improvement of households' creditworthiness would be much stronger if selection of households to which loans were granted was random  banks entered into more risky segments of population in with new loans, significantly shrinking differences in characteristics between their customers and households without any loans Source: authors' calculations based on the Household Budget Survey

Conclusions  Although there was some improvement of households' creditworthiness over the observed period, major impact on debt growth came from relaxation of lending standards, especially for the highly indebted households, whose creditworthiness even deteriorated  So, most of the debt build-up during the recent lending splurge was the result of more lenient lending and more optimistic households willing to take more debt  Also, control for the sample selection bias shows that banks have entered into more risky segments of population with new loans, as creditworthiness of indebted households deteriorated relative to general population

Household debt as % of GDP Sources: Eurostat and Croatian National Bank

Different vulnerability indicators Source: authors' calculations based on the Household Budget Survey

Overlapping of different vulnerability criterion Source: authors' calculations based on the Household Budget Survey

2008 Source: authors' calculations based on the Household Budget Survey