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통계연구(2014), 제19권 제1호, 107-137 The Impact of Expense Shocks on the Financial Distress of Korean Households Jane Yoo1) Abstract The main objective of this study is to examine the major determinants of household financial distress, particularly expense shocks that increase the probability of failure in loan repayment. Using the survey of household finances published by Statistics Korea, the study investigates expense shocks that result in a large proportion of household income being allocated to debt repayment, thus limiting the funds available for consuming goods and/or saving. I use a bootstrapped random effect probit model and two-stage least squares model. In addition, in finding a variable’s optimal spit value and goodness of fit, I use an early warning signal methodology. The test results present the following three categories of expense shocks are sensitive to financial vulnerability: i) secured loans for debt repayment, medical expenses, and daily expenses; ii) unsecured credits for debt repayment, medical expenses and rents; iii) credit card loans for debt repayment, and rents. Key words : Household financial stress, Unsecured credit, Logit model, Early warning signal 1. Introduction Since the recent financial crisis, changes in household debt levels have become an important indicator to inform us the current state of an economy. It also contains the prominent implications on its future stability because total consumption and investment is influenced by a household’s saving decision: when a substantial proportion of household income is allocated to debt repayment, households have fewer funds available to purchase goods or to start a business. A high debt-to-income ratio increases the market’s vulnerability to unexpected negative productivity shocks, as households are more likely to fail on their debt obligations when they suffer unanticipated misfortune such as job losses or illness. In addition, when household debt relative to income is high and unemployment is rising, lenders may respond to the expected increase in the number of insolvent accounts by limiting the availability of credit, which may further reduce total spending. A household feels more vulnerable or less confident when her 1) Contact: [email protected]. An assistant professor in the department of Financial Engineering, School of Business, Ajou University. 108 Jane Yoo consumption expenditure soars under this stressful environment. The household financial distress is also related to the structure and sophistication of financial markets. For example, in the housing mortgage market, the root of the recent US-led subprime crisis, lenders have developed products that broaden the base of household debt by enabling borrowers to purchase homes despite them having a low credit score or limited funds to make a down payment on the home. Advances in home equity lending have also allowed borrowers to extract equity-financing more easily from their homes, and this market has expanded to the secondary market of cash-out refinancing at high interest rates. In the same vein, car purchasers have a greater number of finance choices than they did in the past, such as leasing or borrowing through installment loans. Meanwhile, numbers of revolving accounts are also growing, according to the annual report on credit card usage (G19 published by the Federal Reserve Bank, New York) by both financial and non-financial businesses. As a result, it underlies the connection between a household’s stress from using a specific loan type according to expense shocks. This study contributes to the body of knowledge on this topic by examining the situations that a household feels vulnerable or is afraid of losing her creditworthiness due to the inability to repay debts. By the recent technological development, we can encompass a wide range of the origins of these shocks including medical expenses, wedding costs, educational spending, and other investment plans. Analyzing these expense shocks has been shown important by their strong relation to household financial distress in much macroeconomic theoretical literature (Athreya (2002), Li and Sartre (2006), and Athreya, et al. (2012)). Fay, Hurst and White (2002) built a general equilibrium model with the unexpected expenditure such as legal fees for divorce, or medical expenses for accidents or hospitalization. The expenses raised the probability of insolvency, the degree of poverty, and the rates of bankruptcy filings particularly when a household was lack of buffer stock saving (Carroll (1997)). Some behavioral finance literature emphasized a psychological factor when a household resolved the stress in an uncertain environment. Clustering, herding, and flocking2) in a financial market are the aggregate phenomena by the combination of a psychological factor and a lack in credible information in a bad economy. To the best of my knowledge, this paper is the first attempt to find a measurable financial health indicator that is strongly related to a psychological factor in resulting the financial distress. The paper provides details about the indicator including the goodness of fit in predicting the severe stress, and the optimal split value in screening a vulnerable household. 2) See Banerjee (1992), and Shiller (2000) for more details in explaining the impact of a psychological factor on a financial market. The Impact of Expense Shocks on the Financial Distress of Korean Households 109 <Figure 1.1> Fitted Savings Plots on the Disposable Income 3) Figure 1.1 shows savings as a function of disposable income according to the level of financial stress. The red dotted line represents savings of a representative household who is under the financial distress. At the same level of disposable income, a household, who is free from the stress, accumulates more savings than those under the stress and the amount increases as her disposable income increases (shown by black crosses). Given the lack of buffer stock for a financially constrained household, Figure 1.2 shows the use of a disaggregated loan corresponding to the level of income by the level of stress. Financially constrained households, whose loans are drawn by the red dotted line, tend to borrow more secured loans than a household without the constraint of the black plots (in crosses). In the examination of the difference with other types of loans, unsecured credit lines and credit card loans, the difference is negligible. Despite the small size of the difference, credit card usage can be an efficient indicator at margin to distinguish a household under the severe stress from those who are not. This paper provides the detailed information about these determinants by focusing on the goodness of fit rather than the simple marginal impact. An indicator’s threshold can play a key role in minimizing the related costs when a policy maker targets the reduction of the stress. 3) Source: The survey of household finance in 2010 and 2011. The Figure represents the fitted line of household saving amounts on the disposable income after robust OLS regression. Before regression, data are interpolated on 1000 grid points to minimize noise with respect to clustered data around a low income quartile. 110 Jane Yoo The empirical model of the paper studies which expense shock is particularly related with the stressful situations. Some examples may include wedding, purchasing home, or paying for huge medical expenses. Here, severe stress refers to a household’s lack of confidence in repaying its loans within one year. Using Korean survey of household finance, the present study examines those expense shocks that result in a large proportion of household income being allocated to debt repayment, thus limiting the funds available for consuming goods and/or saving and resulting in the stress caused by the existence of a higher debt service ratio. <Figure 1.2> Comparison of the Fitted Loans Plots between household with and without the financial stress 4) In addition to the marginal effects of expense shocks on the probability of 4) Source: The survey of household finance in 2010 and 2011. Each figure represents the fitted plot of the labeled loans (from the left one: secured, unsecured credit except credit cards, and the loans through credit cards) on the amount of disposable income. The red dotted line of each graph represents the fitted loan amount in association with household disposable income for households who are under the financial distress. The black crossed line shows that of households who are free from the stress. The line is drawn with the intercept and slope obtained by the OLS regression results with interpolated data into 1000 grid points. The Impact of Expense Shocks on the Financial Distress of Korean Households 111 being under the stress, the empirical evidences presented in this study differ from those of previous works in that it suggests an optimal split for each expense category between safe and risky accounts for managing stress. The shortcomings of the traditional binary response model include its limited analysis of the marginal effect evaluated at the mean, under a restricted assumption on error terms, and its inability to identify which indicators are more suitable for predictions. I resolve these shortcomings by applying nonparametric estimations to our data. The results of the EWS model suggest that the following three sudden expense shocks explain a substantial degree of household vulnerability: i) secured loans for debt repayment, medical expenses, and daily expenses; ii) unsecured credits for debt repayment, medical expenses and rents; iii) credit card loans for debt repayment, and rents. Based on these results, this study suggests that policy makers target resolving these expense shocks, thus diminishing credit risks in the economy. The empirical evidence presented herein emphasizes that using unsecured credit accounts should be more closely analyzed when a lender or creditor attempts to predict a borrower’s ability and willingness to repay debts. The study then develops a screening tool that could be used before implementing a public policy that targets the relief of certain types of debts. The remainder of the paper comprises six sections. The next section briefly discusses previous studies of household-level financial stresses and their determinants. Section 3 describes the Korean data used in the study. Our methodological models and strategies are presented in Section 4, and Section 5 discusses our results. Section 6 concludes the paper. 2. Literature review There are far fewer empirical studies than theoretical works on the impact of the type of loan in association with expense shocks on the household financial stress. It is mainly because of limited access to the relevant microdata5) and a lack of data on household-level credit management. Domowitz and Sartian (1999) developed qualitative choice models by using sample cases compiled by the United States General Account Office and a survey conducted by Consumer Finance (related theoretical studies include Athreya (2002) and Li and Sartre (2006)). They showed that medical and credit card debts were the strongest contributors to bankruptcy. In the nested logit results in Fay et al. (2002), the financial benefit 5) In Korea, The Statistics Korea only began collecting micro data regularly in 2010, following their first collection in 2006. 112 Jane Yoo from the value of the debt of non-exempt assets affected the probability of filing for bankruptcy. Credit card issuers and credit score reporting agencies also studied this problem with their in-house data on customers’ credit history, income, employment status, length of employment, and home-ownership. Similarly, Gross and Souleles (2002) studied unsecured credit lines of delinquent debtors, and Dawsey and Ausubel (2004) demonstrated a strategic delinquency choice. These studies are closely related to drawing delinquency profiles based on consumption and saving over a lifetime (see Attanasio and Browning (1995), Attanasio et al. (1999), and Fernandez-Villaverde and Krueger (2011)). A few studies on precautionary saving or the lifetime wealth, such as Carroll (1997) and Gourinchas and Parker (2002), are also related with the literature. Despite their findings on the marginal impact of explanatory variables, there have been discussions on developing a more sophisticated methodology for better estimation. Some economists in the International Monetary Fund and monetary authorities concern on how to detect an efficient indicator, and how to determine the criteria of the indicator in examining a household’s financial health. In order to evaluate an explanatory power and to find the optimal split value, a traditional regression models are not sufficient. The level of household debt in Korean credit market has received attention since the recent financial crisis. There are many studies that find the determinants of insolvent accounts as well as ways in which to help those households. Kim and Jeon (2000), for example, empirically examined the effectiveness of credit measurements. By using the results provided by Lee and Jeong (2005) on the current state of personal bankruptcy in Korea, they showed that the rate of credit card usage, the most popular type of unsecured credit, and its sensitivity to interest rate changes were the important factors in determining credit risk. It was comparable with personal bankruptcy data from Japan and the US. Some empirical studies, which use aggregate variables, support these micro-level findings by emphasizing the role of commercial banks in buffering an adverse shock to the market by releasing surplus funds before loanable funds dry up, which would result in a worse constraint when the leverage is limited. In addition, most dynamic analyses on the stress caused by household debt use dynamic general equilibrium models with a Bayesian framework to evaluate the impulse response function and variance decomposition given a shock (see Kim (2012), Kim and Kim (2010)). Further, those models extended to cover empirical analyses most often used logit or probit models, correcting for the heteroskedasticity (Kim et al. (2009)). However, the work is limited because it used the aggregate level of debt and probability of bankruptcy. The Impact of Expense Shocks on the Financial Distress of Korean Households 113 3. Data As the government became less confident about the distribution of loans to households from the data provided by commercial banks in representing the distribution of the credit market as a whole, it started to collect the data on household budgets including assets, incomes, and debt levels. Statistics Korea, the Bank of Korea, and the Financial Supervisory Service jointly conduct the Survey of Household Finances to elaborate data on household-level credit risks. The survey contains micro-level variables including demographic characteristics that are not easily captured by aggregate variables. It also provides the information on the household’s financial health: their financial distress and attitude to resolving risks and stresses when managing a loan repayment schedule. Since the first publication in 2006, the random sample was next drawn in 2010, and again in 2011. The attrition rate from 2010 to 2011 was approximately 10 percent. In 2012, Statistics Korea expanded Survey of Household Finances by compiling it with Living Condition data. It over-samples the population from the lowest quintile to measure poverty factors in the country, thus helps developing a social security program. This expansive sample includes detailed questions on consumption expenditure and monthly interest payments as well as the selective indicators of unemployment such as a job-seeking period. Given this structural change in the survey, only the survey of household finance collections of 2010 and 2011 are used in our empirical tests. Despite its advantage, only few studies have employed the micro-level works using the survey. For example, Lee and Kim (2012) used the survey to measure household credit risk, while Lee (2012) used the data in order to investigate changes in the real estate prices from 2010 to 2011. He concluded that a high proportion of the elderly were exposed to higher volatility risks in the housing market. According to his analysis, the portfolio of the elderly, which is heavily weighted by non-financial assets, is one of the major risks in the aggregate economy, particularly when a market is experiencing real estate price risks. Similarly, Park et al. (2012) used the survey to examine the features of lenders in a financial sector and assessed their expected losses to measure the credit risks of these lending institutions. Income in this study refers to the conventional definition used by the Federal Reserve Board since 1995. The survey of Korean household finance provides data on household income, which may include wages/salaries, earnings from a business/farm/sole proprietorship, non-tax investments, interest income, dividends, gains or losses from the sale of stocks and bonds, net rent/royalties, compensation, child support/alimony, and any other earnings. The category of 114 Jane Yoo financial assets may include savings in transaction accounts (checking accounts, if more than one), certificates of deposits, investments in saving bonds, bonds, stocks (both public and equities associated with business ownership), pooled investment funds, brokerage accounts, retirement accounts, other managed assets, and any other saving plans. If a household has used a loan to purchase a financial asset, the loan amount may be subtracted from the total asset value. Finally, the survey also collects the market values of non-financial assets including vehicles, primary residence, other properties, and equity in non-residential property at a cost-based value. <Table 3.1> Summary statistics of demographic variables 6) Variable Mean Standard Deviation Min Median Max Financial Stress 0.38 0.49 0 0 1 Age 51.32 14.67 17 50 95 Education 4.19 1.60 1 4 7 Number of Members in a Household 2.96 1.32 1 3 10 Marital Status (1 if married, 0 otherwise) 0.73 0.44 1 0 1 Number of Observations 19435 Total consumer debt comprises two major types: revolving and non-revolving debt. Revolving credit plans include a prearranged limit and debt repayment plans in one or more installments. This type of credit may be further classified into unsecured and secured credit. The most popular types of unsecured, revolving credit are credit card loans or cash advances through a card, which increase the cumulative cost of borrowing as the length of repayment plan is extended. In terms of secured credit, most households have revolving mortgage payment plans; 6) Source: The survey of household finance in 2010 and 2011. In the summary of statistics, the unit of a variable refers to the structure of the survey. Because the survey collects the data of a household balance sheet from a household head, a demographic variable represents the characteristic of a head. Educational indicator is classified into seven categories such as, 1: no education, 2: an elemetary level, 3: a junior high level, 4: high school level, 5: college-educated, 6: an undergraduate level, 7: a post-graduate level. There were four types with indicating a martial status, 1: single, 2: married, 3: divorced, and 4: widowed. In this descriptive table, 1 if married, and 0 otherwise. The Impact of Expense Shocks on the Financial Distress of Korean Households 115 however, unless there is any indication of deferring or extending the schedule, I have classified this type of loan as non-revolving debt. The most common type of non-revolving credit is closed-end credit, which is repaid on a prearranged schedule. This credit can be secured or unsecured. Vehicle, education, and consumer loans make up the majority of this type of credit, but other loans are included as well such as personal loans and loans for debt repayment, business management, and miscellaneous purposes. In this paper, the empirical analysis uses only three types of loans, secured loans, unsecured credit loans except credit card payments, and credit card loans. The total amount of loans is free from perfect multicollinearity with the linear combination of these loans because the miscellaneous loans, and borrowing for non-home-equity-financing are excluded in a regression model. Table 3.1 and 3.2 summarize the descriptive statistics of variables. In addition, I define the financial distress of Korean households according to responses to the question in the survey, “Do you think that you are willing to/able to repay the outstanding amount of debt in a given schedule within the next year?” If a household answers “yes,” I take this to indicate “no or minimal stress.” On the contrary, if the answer is “no or unlikely,” then this is taken to mean “high or significant stress.” <Table 3.2> Summary statistics of variables in the Survey of Household Finance, in ten thousands Won Standard Deviation Median Variable Mean Net worth 23,658 48111 12,399 Financial assets 6,372 11,714 3,056 Home equity value 11,541 19,505 4,500 The amount of saving 4,600 10,244 1,846 Disposable income 3,158 3,567 2,471 Expenditure 627 824 435 Debt 4,890 17,891 530 The total amount of loans 3,368 16,107 117 Secured loans 2,685 14,559 0 Unsecured credit loans (except credit card loans) 577 5,696 0 Credit card loans 45 359 0 The number of observations 19435 116 Jane Yoo Given the small number of miscellaneous cases, I suggest the collinearity diagnostics after the random-effects logit regression by presenting the variance inflation factors. I assume that the market value of a loan is the outstanding amount of the loan, reported as the balance still owed. Finally, the net worth is obtained by subtracting total debt amount from total assets, which includes both non-financial and financial assets. I use the inflation-adjusted data as per the consumer price index published by the Bank of Korea. <Table 3.3> Pairwise Correlation 7) Variable (1) (1) Income 1 (2) Disposable Income 0.9515 1 (3) Expense 0.6236 0.4330 1 (4) Debt 0.2952 0.0667 0.3430 1 (5) Loans 0.2387 0.0087 0.2649 0.9400 1 (6) Secured Loans (7) Unsecured Credit Loans (8) Credit Card Loans (9) Extra Loans (10) Miscellaneous (2) (3) (4) (5) (6) (7) (8) (9) (10) 0.2387 0.0036 0.2682 0.8841 0.9350 1 0.0597 0.0123 0.0581 0.3904 0.4300 0.0837 1 -0.0123 -0.0241 -0.0222 0.0414 0.0468 0.0163 0.0262 1 0.0796 0.0638 0.0913 0.0707 0.0692 0.0437 0.0081 0.0225 1 0.0520 0.0512 0.0129 0.0266 0.0194 0.0180 0.0061 0.0368 0.0023 1 Debt In Table 3.3, I summarize the pairwise correlation between the selected variables. Income, the net present value of labor productivity, has a significantly positive relationship with variables except extra loans and credit card loans. Credit card loans have a negative correlation with (1) income, (2) disposable income, and (3) household expenditure. A household tends to use more secured channels than a 7) The number in bold text means that the statistic is significant at 0.5% level The Impact of Expense Shocks on the Financial Distress of Korean Households 117 credit card. In addition, Table 3.4 suggests the summary statistics of the proportion of these loans, that is for a specific purpose. Alternative loan usages are home financing, non-home equity financing, rental payment, stock investment, debt repayment, business management, wedding expense, educational expense, medical expense, and daily expenses. Although the fraction of debts used for miscellaneous expenses is not reported in Table 3.4, it is ranged from 4 to 5 percent for the total debt amounts on average. A lot of households have suffered debt-repaying burdens from the loans made for business management; the burden is universally found from all types of loan. In purchasing houses in Korea, most of households prefer a secured loan. On the other hand, both unsecured credits and credit card loans are made for daily expenses. <Table 3.4> Descriptive Statistics for the classification of loans, in percent Secured: Home financing Secured: Non-home equity financing Secured: Rental payment Secured: Stock investment Secured: Debt repayment Secured: Business management Secured: Wedding expense Secured: Medical expense Secured: Educational expense Secured: Daily expense Number of Observations 6758 6758 6758 6758 6758 6758 6758 6758 6758 6758 Unsecured Credit: Home financing Unsecured Credit: Non-home equity financing Unsecured Credit: Rental payment Unsecured Credit: Stock investment Unsecured Credit: Debt repayment Unsecured Credit: Business management Unsecured Credit: Wedding expense Unsecured Credit: Medical expense Unsecured Credit: Educational expense Unsecured Credit: Daily expense 4423 4423 4423 4423 4423 4423 4423 4423 4423 4423 5.68 2.56 8.38 1.19 5.25 23.84 0.99 2.30 8.75 33.72 22.82 15.58 27.25 10.61 21.50 41.98 9.72 14.71 27.51 46.35 Credit Card: Home financing Credit Card: Non-home equity financing Credit Card: Rental payment Credit Card: Stock investment Credit Card: Debt repayment Credit Card: Business management Credit Card: Wedding expense Credit Card: Medical expense Credit Card: Educational expense Credit Card: Daily expense 1304 1304 1304 1304 1304 1304 1304 1304 1304 1304 0.72 0.16 3.17 1.39 5.96 12.75 0.25 2.94 6.48 57.66 8.45 4.01 17.33 11.66 23.18 33.05 5.03 16.80 24.19 48.83 Variable Standard Mean Deviation 42.55 48.54 10.90 30.31 9.72 29.36 0.66 7.83 3.39 17.53 16.31 36.13 1.59 12.17 0.96 9.54 2.95 16.31 7.37 25.38 118 Jane Yoo 4. Estimation This section describes two methodological strategies of the paper in estimating the marginal impact of expense shocks on household vulnerability. First, I introduce a traditional binary response model linked to a logistic or normal distribution of errors. Although this model is widely used in the literature, it has limits for examining a variable’s predictive power. The section provides the details. The “signals” approach is thus introduced to overcome these limits, and this section then suggests an optimal division value and goodness of fit for each explanatory variable. The signals approach exploits the information in the interactions between indicators and uses a distress index without strong statistical or distributional assumptions about the predictors. 4.1 Panel model: random-effects probit model Based on the survey of household finance from 2010 to 2011, this subsection investigates what is the relationship between the severe financial stress and the changes in household assets, liabilities, net worth, and income. Further, based on the summary documents on outstanding loan amounts for households, I examine both revolving and non-revolving credit. When analyzing a borrower’s decision whether to consume or to invest in risky assets, if a certain proportion of the current income stream is limited by an expense shock, I interpret this as a combination of both the marginal impact of expense shocks and the substantial financial distress. The popular statistical methodologies used to find the significant factors of a binary dependent variable are the probit and logit models. These models provide the marginal effects and predict the correlation matrix of the variables by maximizing the likelihood based on the Newton–Raphson iterative procedure. In this study, I use the probit model. It is different from a logit model because it explains the sample at tails, where probabilities are close to zero. The logit model fits better with the fat-tailed distribution. Because the analysis is for a random sample of households in a two-years sample (panel-household data), an estimator is appropriate to figure out the marginal impact of explanatory variables, but only on average. By integrating out the individual effects as i.i.d random variables, the random-effect estimators present the margins of responses, which are also known as predictive margins or adjusted predictions based on the estimated coefficients and the intercept (if there is). Standard errors are obtained by using the delta method under the assumption that the values at which the covariates are evaluated to obtain the marginal The Impact of Expense Shocks on the Financial Distress of Korean Households 119 responses are fixed. To use these results in interpreting the interaction between the variables and a stress-indicator, I must make two key assumptions: i) the relation between the explanatory variables and the dependent variable is linear and ii) the distribution of the residuals is well behaved in large samples. However, these assumptions may not apply, or may be inappropriate, in the estimation because i) the sample is unbalanced (i.e., it has many more zeros than ones or vice versa) or ii) the relationship between delinquency and age is likely to be non-monotonic. I express this nonlinear relationship as follows: (4.1) where refers to whether household i in year t is under the severe stress and is an intercept. I assume that the error term, , follows a logistic distribution. Even if a vector, , controls certain factors in a matrix, , the shape of may require a strong assumption (point ii) above) despite it not necessarily having a linear relationship with matrix . I am thus only able to deduce a nonlinear relationship between these indicators in predicting the stressful situation indicator, . An unbalanced sample also prohibits us from making an assumption about the distribution of , such as a normal or any similar distribution with a thick tail, since it is difficult to predict a zero (or one) by using the prediction rule based on traditional qualitative choice models. Moreover, a number of new techniques based on the availability of high-quality panel data sets on microeconomic behavior within this modeling framework require strong conditions, including an assumption about heterogeneity in the random effects model and the incidental parameters problem that affects the fixed effects model. 4.2 Early warning signal model In this paper, I use a strategy widely deployed in sovereign debt crisis analysis to overcome the problems suggested above and thus derive nonlinear relationship. A country-level crisis is similar to consumer delinquency in many respects, from outright default on external debt to illiquidity. In the latter case, the solvent balance sheet of a country worsens after a liquidity crunch until it verges on default because of lenders’ unwillingness to roll over short-term debts coming to maturity. However, the EWS model can detect the determinants of a debt crisis, as its objective is to monitor those critical indicators that determine whether a country may experience a potential crisis. Among the various models available for 120 Jane Yoo conducting a forward-looking vulnerability assessment, I use the EWS model developed by Kaminsky et al. (1998) that, according to Berg et al. (2005), possesses the greatest predictive power.8) The seeking process is first run to select the optimal cut-off value for any indicator, , and then to predict severe stress if . For example, given the assumption that the greater income means a good household budget or a lower probability of experiencing the severe stress, the income is set up as a larger-better indicator. The methodology initiates the process by picking an arbitrary level of income, say 1,000,000 Won. According to this threshold, every household is sorted into two groups, a no-stress group (indicated by 0) if their income is greater than the threshold and a stressed group (indexed by 1) otherwise. A screening test proceeds by comparing the empirical index, which is obtained from the data with the estimated index. The optimal cut-off value is set by minimizing the loss function derived from the errors in predicting the true stress index. The process is applied for all explanatory variables. The model is useful to compute the percentage sum of errors for each indicator in fitting the stress index. The reverse of the percentage sum of errors is used to refer the goodness of fit. The variables are sorted by the fit, which finally works as the explanatory weight to be imposed on a corresponding variable. The model automatically seeks the best weight for an indicator while minimizing the percentage sum of errors. In detail, the errors refer to the probability of incorrectly calling a bad state in a safe account and of calling a good state in a risky account. The model then ranks the indicators and aggregates them into a score according to the goodness of fit of the threshold rule with weight . Here, represents the proportion of stress missed added to the proportion of solvent stress misclassified. Finally, with the weighted average of the indicators for each person, I predict who is highly likely to be exposed to severe stress in a certain year by assessing whether this composite index is at or above the weighted optimal split level. In many practical applications of the statistical model, type I errors are known to be more delicate than type II errors. In contrast, in this model, both the proportion of stress missed and the proportion of a false stress called are important to maximize the goodness of fit. In other words, the goodness of fit with weight is determined by the sampling process to obtain the that is limited in the sample of the household finance survey in 2010 and 2011. 8) As an extension of this method that allows interactions between the various explanatory variables, Ghosh and Ghosh (2003) developed a binary recursive tree (BRT). Then, Manasse et al. (2003) identified macroeconomic variables able to predict the most recent sovereign debt crisis episodes based on the BRT model. The Impact of Expense Shocks on the Financial Distress of Korean Households 121 The EWS sampling errors or the proportions, thus, not necessarily follow a normal distribution. 5. Results This section presents the estimated marginal impacts of demographic characteristics such as wealth, income, and debt levels on the probability of failure in repaying debts. First, the summary statistics from the random-effects probit regression are presented along with demographic and other budget-related variables in order to find the probability of having the severe financial distress. I then present the results from the probit regression on the stress according to specific expense shocks within each loan category. The second section focuses on the results of a EWS model. The section elaborates the optimal split value for each variable by sorting the indicators according to how well they predict actually severe stress. 5.1 Estimating the major budget components Table 5.1 shows the marginal impacts of certain important components that capture a household budget: some demographic factors, outstanding debt, net worth, financial assets, and loans. All variables except the demographic variables and loan amounts are normalized by the current level of income. To check the robustness of the estimation, several collinearity diagnostic measures are examined. Two commonly used measures are tolerance and a variance inflation factor. The tolerance is an indicator of how much collinearity that a regression analysis can tolerate. For a particular variable, it is calculated by “1 minus the R-squared” that results from the regression of the other variables on that variable. Variance inflation factor (VIF) is an indicator of how much of the inflation of the standard error could be caused by collinearity. The corresponding VIF is simply 1/tolerance. If all of the variables are orthogonal to each other or completely uncorrelated with each other, both the tolerance and VIF are 1. On the other hand, if a variable is very closely related to another variable(s), the tolerance goes to 0. As a rule of thumb, a variable whose VIF values are greater than 10 may need futher investigation. On the right two columns of Table 5.1 summarizes the collinearity diagnostics on the baseline Probit model. Tolerance present the consistent evidences that there is no such a perfect linear relationship among the predictors, and that the estimates for a regression model can be uniquely computed. 122 Jane Yoo <Table 5.1> Probit Model: Baseline 9) Probit Model VIF Tolerance 0.983*** (0.003) 1.40 0.7162 -0.168*** (0.025) 0.846*** (0.021) 1.31 0.7648 Number of family members 0.174*** (0.026) 1.190*** (0.031) 1.07 0.9370 Net worth-to-income ratio -0.001 (0.001) 0.999 (0.001) 1.24 0.8051 Financial assets-to-income ratio -0.029*** (0.009) 0.971*** (0.009) 1.23 0.8112 Loan-to-debt ratio 3.120*** (0.115) 22.65*** (2.603) 1.03 0.9731 Year effect -0.607*** (0.053) 0.545*** (0.029) 1.00 0.9987 Constant -0.440* (0.257) 0.644* (0.166) - - Mean VIF 1.19 Variables (1) Marginal Effects (2) Odds Ratio Age -0.018*** (0.003) Education Number of Observations 12,017 Wald Statistic 1722.97 (degrees of freedom = 11) Despite the insignificant role of the net worth-to-income ratio, most of indicators imply their significant impact on the financial stress (either increasing or decreasing) at 0.01% significant level. In particular, financial assets, age and the number of education years decrease the probability of being exposed to the financial distress. The marginal impact of holding net worth is shown to be small because it is concentrated among people with a mobile home, who are at the high-income quintile, as there is a barrier to Korean housing market. The size of a one-unit increase in financial assets relative to income lowers the probability of experiencing the stress by 3 percent (see the odds ratio in column (2)). Financial market participants are relatively wealthy and not suffering from a severe stress, while paying for the high transaction costs in trading stocks and other sophisticated instruments. 9) The number of observations is smaller than that of the sample due to some households with missing variables. Robust standard errors in parentheses. * p-value < 0.01, ** p-value < 0.05, *** p-value < 0.1 The Impact of Expense Shocks on the Financial Distress of Korean Households 123 Age and years of education have the consistent implications. Being one year older or being educated one more year means the lower probability. The marginal impact of education years is substantial to bring down the probability to feel vulnerable in a stressful situation according to the odds ratio of 0.85. The result is consistent with the more educated in a financial market. Note that the year effect is controlled for by including the dummy variable of 2010, which implies the higher probability for a representative household to be in a stressful situation in 2011. However, having one more person in the household increases financial stress by approximately 19 percent. It is related to the greater consumption expenditure to lower the household’s saving amount. Finally, Table 5.1 shows the marginal impact of a change in total amount of loans relative to outstanding debt. I find that the average Korean household feels more vulnerable when they have made a lot of loans to increase their subjective stress level by 23 times. It is a noteworthy size to be analyzed according to a type of loan that is particularly significant to bring the substantial marginal impact on the household vulnerability. <Table 5.2> Second-Stage Estimation of TSLS Probit Model: Robustness Checks 10) Dependent Variable Is this household is under sever stress? (1 if yes, 0 if no) (Result I) IV: Fraction of secured loans-to-debt (1) Odds Marginal (2)Ratio Effects 0.005*** 1.005*** Age (0.001) (0.001) -0.03** 0.976** Education (0.011) (0.011) Number of family 0.062*** 1.064*** members (0.011) (0.012) Net worth-to-income 0.0001 1.000 ratio (0.001) (0.001) Financial -0.006 0.995 assets-to-income ratio (0.004) (0.004) 2.803*** 16.50*** Loan-to-debt ratio (0.092) (1.510) -0.250*** 0.779*** Year effect (0.027) (0.021) -2.122*** 0.120*** Constant (0.158) (0.019) 12,017 Number of Observations 2171.32 (degrees of Wald Statistic freedom = 7) (Result II) IV: Fraction of unsecured credit loans-to-debt (1) Odds Marginal (2)Ratio Effects (Result III) IV: Fraction of credit card loans-to-debt -0.011*** 0.989*** (0.002) (0.002) -0.084*** 0.919*** (0.011) (0.01) 0.078*** 1.081*** (0.010) (0.011) -0.0004 1.000 (0.0005) (0.001) -0.015*** 0.985*** (0.003) (0.003) 1.028*** 2.795*** (0.133) (0.372) -0.257*** 0.773*** (0.249) (0.019) 0.294 1.342 (0.205) (0.275) 12,017 (1) Odds Marginal (2)Ratio Effects 0.006 1.006 (0.004) (0.004) -0.0203 0.980 (0.017) (0.017) 0.061*** 1.062*** (0.012) (0.012) 0.0005 1.000 (0.001) (0.001) -0.006 0.994 (0.004) (0.004) 2.86*** 17.39*** (0.396) (6.880) -0.242*** 0.785*** (0.027) (0.021) -2.234*** 0.107*** (0.555) (0.060) 12,017 1387.50 (degrees of freedom = 7) 649.94 (degrees of freedom = 7) 10) The number of observations is smaller than that of the sample due to some households with missing variables. Robust standard errors in parentheses. * p-value < 0.01, ** p-value < 0.05, *** p-value < 0.1 124 Jane Yoo With respect to the high correlation between a loan with the total debt outstanding, Table 5.2 presents the results from the two stage instrumental variable probit model. In this regression, the debt is fitted by the fraction of each loan and other demographic variables (which are assumed as exogenous ones) and then plugged into the second stage regression of estimating its marginal impact on the stress. In Table 5.2, the TSLS results are presented according to the instrumental variable for the loan-to-debt ratio. Specifically, for each estimation, I use the first stage results on the estimated loan-to-debt ratio that is fitted by the fraction of secured loans-to-debt in (Result I), the fraction of unsecured loans-to-debt in (Result II), and finally the fraction of credit card loans-to-debt in (Result III). For each estimation of the loan-to-debt ratio, the demographic variables and other flow variables are controlled. With the smaller degrees of freedom, the results show that the baseline random-effects probit estimation is valid to emphasize the different effect by a loan category. Although the statistical significance, and sign of an impact are consistent with them from the probit model. there are some changes in the size of the coefficient. For example, the impacts of the financial-asset-to-income ratio and the debt-to-income ratio significantly increase. On the other hand, the number of family members and years of education show a smaller impact on the stress. The marginal impacts of other variables are consistent with those shown in Table 5.1. Further, as discussed, the impact of loan-to-debt ratio can be different when it is estimated by a different type of loan. For example, when the ratio is instrumented by the fraction of secured loans-to-debt amount, it impacts on the probability of household’s being under a sever stress to increase it by 16.5 times greater than when it has not made a secured loan. The impact’s size of loan-to-debt ratio is shown to be similar when it is instrumented by the credit card loans (including cash advances, revolving accounts, and installment payment) to report its marginal effect by 2.86 at 0.01% significant level. In summary, when a household is afraid of that she cannot to repay loans through easily revolving, but expensive, cost-based credit card loans, her stress indicates a severe stress. On the other hand, all else constant, the odds ratio is 2.795 when a household delivers money through unsecured credit loans, which are not related to credit cards. Table 5.3 presents the first-stage estimation results of the TSLS probit model. Regarding the marginal impact of a specific loan, in general, the loan-to-debt ratio is in a positive relationship with all types of loans. When the loan-to-debt ratio is regressed on the fraction of secured loans-to-debt, it tends to increase by 34 percent when there is a unit increase in the fraction. Interestingly, although the marginal impact of the fraction, which is obtained by the credit card loans-to-debt The Impact of Expense Shocks on the Financial Distress of Korean Households 125 ratio, is only 20 percent, the loan-to-debt ratio in the first stage brings a significant change in a household’s subjective probability of being under stress (by 2.86 at the marginal effect, Result III of Table 5.2). In other words, for the households of who are exposed at a high possibility to be vulnerable, the credit card loans could be critical at margin in spite of its small impact on the aggregate loan among debt. <Table 5.3> First-Stage Estimation of TSLS Probit Model 11) Dependent Variable = Loan-to-debt ratio Marginal Effects Marginal Effects Marginal Effects Age -0.0088*** (0.0002) -0.0085*** (0.0003) -0.0088*** (0.0003) Education -0.035*** (0.002) -0.0312*** (0.002) -0.0334*** (0.0025) Number of family members -0.0033 (0.002) 0.0113** (0.003) 0.0109*** (0.0026) Net worth-to-income ratio -0.0005*** (0.0001) -0.0003*** (0.0001) -0.0004*** (0.0001) Financial assets-to-income ratio -0.006*** (0.0006) -0.005*** (0.0007) -0.0051*** (0.0008) Fraction of secured loans to debt 0.338*** (0.007) - - Fraction of unsecured loans to debt - 0.2545*** (0.0085) - Fraction of credit card loans to debt - - 0.2002*** (0.0180) Year effect -0.006 (0.0057) -0.0080 (0.0061) -0.009 (0.0063) Constant 1.255*** (0.0214) 1.276*** (0.023) 1.35*** (0.024) Number of Observations, 12,017 12,017 12,017 0.2605 (degrees of freedom = 7) 0.1585 (degrees of freedom = 7) 0.1048 (degrees of freedom = 7) Adjusted R-squared Table 5.4 provides more specific, but the consistent information about the factors. More specifically, EWS results show that Korean households feel 11) The number of observations is smaller than that of the sample due to some households with missing variables. Robust standard errors in parentheses. * p-value < 0.01, ** p-value < 0.05, *** p-value < 0.1 126 Jane Yoo vulnerable when their financial debt approaches 4,190 thousands won per household in a year, which is responsible for a debt-to-income ratio of 213 percent. The threshold ratio provides the valuable information, which cannot be captured by the level of debt and income separately owing to outliers with the extreme debt-to-income ratio. The optimal split of the debt-to-net worth ratio is lower in Korea where most households hold non-financial assets in home equity value (the threshold is 19 percent level of the debt-to-net worth ratio). These debt-related ratios imply a predictive power of between 59 and 75 percent. To develop an efficient screening procedure, I finally examine the goodness of fit for all indicators and conclude that the financial debt-to-financial assets ratio (with the optimal threshold of 24.22 percent), the debt-to-income ratio (the threshold of 213 percent), the outstanding debt (the threshold of 4,190 thousands won), and the debt-to-net worth ratio (the threshold of 19 percent) are significant. In this analysis, the proportion of unsecured credits to the total amount of household loans turns out the sensitive one to show a level of vulnerability. However, savings, assets, net worth, income, expenditure, and the proportion of secured loans may not be sensitive indicators at margin because most households in Korea already own housing assets and use earning wages and salaries to maintain consumption. The results of the present study thus emphasize that lenders should pay particular attention to credit card usage or unsecured credit when investigating a borrower’s willingness and ability to repay debt. Furthermore, this criteria could be an efficient one for policymakers when they find an appropriate indicator to reduce the household financial distress level and credit-related vulnerabilities. The Impact of Expense Shocks on the Financial Distress of Korean Households 127 <Table 5.4> Baseline Optimal Split and Goodness of Fit by an Early Warning Signal Method 12) Variable (1) Optimal Split Value (2) Proportion of a stress indicator missed (3) Proportion of non-stress indicators called in error (4) Goodness of Fit Financial debt-tofinancial assets ratio 24.22% 0.032 0.216 0.7524 Debt-toincome ratio 213% 0.034 0.326 0.6399 Debt 419 0.02878 0.337 0.6342 Debt-to-net worth ratio 19% 0.118 0.287 0.5949 0.0034% 0.559 0.247 0.1938 0% 0.791 0.094 0.1151 Savings 576 0.137 0.783 0.0802 Financial assets 847 0.152 0.784 0.0646 Net worth -0.5513) 0.927 0.009 0.0644 Disposable income 536.61 0.113 0.870 0.0173 Daily expenses 1.66 0.036 0.960 0.0072 Home equity value 95.15 0.020 0.975 0.0048 Income 913.03 0.055 0.942 0.0034 100% 0.001 0.998 0.0004 Proportion of (unsecured) credits Proportion of credit card usage Proportion of secured loans 12) Source: Author’s calculation obtained by the early warning signal method. Indicators are sorted by their absolute goodness of fit to truly call household financial distress. The optimal split values of flow variables are in ten thousand Won. See text for more details in estimation. 13) The negative value of the optimal threshold is related to the debt-to-net worth ratio, which is higher than 100%. In other words, some households could maintain a budget free from the financial stress even when they have more debt than assets. This is consistent with the findings with the U.S household data. 128 Jane Yoo 5.2 Estimation of expense shocks Our major findings about expense shocks are presented in Table 5.5 and 5.7. For each type of loan, the impacts of expense shocks are explained after age, years of education, number of family members, net worth, and outstanding debt are controlled. All flow variables except years of education are normalized by taking its ratio relative to income. The model also controls for the year effect by including the 2010-year dummy variable as before. In this paper, we present the results with the sample of households who do have loans or debts: all variables except demographic ones are a natural log transformed. The results are significant with the results with the whole sample or without the transformation and the results are available from authors upon request. All demographic variables examined with expense shocks delivered through secured and unsecured debt are significant in line with the previous model. No matter what type of loan is used, the number of family members and years of education are significant to increase the probability of being in the stress. The size of the marginal effect increases particularly when a household use credit card loans according to their odd ratios (1.457 for the number of family members; 1.343 for the debt-to-income ratio). Although the probability of having the stress is lowered when a household has more net worth (by 2.2 percent) or financial assets (by 5.1 percent) relative to income particularly when they use a secured borrowing channel, it implies the different meaning for a household with unsecured credits including credit cards. More specifically, the impact of net worth decreases for the households with unsecured credits in contrast to that of financial assets is more significant (by 15.4 percent). Moreover, their effects are not significant or negligible for a household whose borrowing is based on credit card loans. Interestingly, the credit card loans may give the significant stress when a household uses a card for rental payment, (the odd ratio of 1.024), debt repayment (1.026). On the other hand, if those loans are used for business management the household stress level decreases by 1.8 percent. (the odd ratio of 0.982). The decrease in the business-owners’ vulnerability is explained by some entrepreneurs with the substantial amount of business equities/assets to prove their creditworthiness. The evidence is consistent with the results from other types of loans; no matter what type of loan is financed, a business-owner household is relatively free from stresses even when they borrow. These expenses are more closely related with investment costs that a household expects a good income stream. For households who have borrowed money either through a secured channel or through unsecured one, some situations are stressful: home-financing, medical The Impact of Expense Shocks on the Financial Distress of Korean Households 129 expenses, educational expenses, and daily expenses. Here, daily expenses contain the daily consumption expenditure on general food and service items. Subsequently, if a household needs to borrow money for this particular aim, she is highly likely to belong the low income quintile. The expense shocks that arise from financing non-home equities and miscellaneous cases are omitted to avoid the nearly perfect multicollinearity with other explanatory variables. Particularly, it is interesting to find the medical expenses to increase the household financial stress because it means the double burden for her from i) a large amount of sudden expenditure; ii) a potentially sudden stop of labor income stream if a household head is not able to work any more by hospitalization. <Table 5.5> Probit Model by Expense shock 14) Variables Secured (1) Marginal Effects -0.0163*** Age (0.004) Years of -0.260*** education (0.0312) The number of 0.115*** family members (0.034) Debt-to-income 0.236*** ratio (0.0189) Net -0.022*** worth-to-incom (0.004) e ratio Financial -0.051*** asset-to-income (0.0161) ratio Debt used For: Home financing Debt used For: Rental payment Debt used For: Stock investment Debt used For: Debt repayment Debt used For: Business management Debt used For: Wedding expense Debt used For: Medical expense Debt used For: Educational expense Debt used For: daily expenses Year effect Observations Pseudo R-squared Debt (2) Odds Ratio 0.984*** (0.004) 0.771*** (0.024) 1.122*** (0.038) 1.266*** (0.024) Unsecured (3) Marginal Effects -0.021*** (0.005) -0.290*** (0.039) 0.146*** (0.039) 0.316** (0.028) Credit (4) Odds Ratio 0.979*** (0.005) 0.748*** (0.029) 1.157*** (0.045) 1.371*** (0.039) Credit Card Debt (5) Marginal (6) Odds Effects Ratio -0.019 0.981 (0.015) (0.015) -0.258** 0.773** (0.129) (0.1) 0.376*** 1.457*** (0.136) (0.198) 0.295*** 1.343*** (0.081) (0.109) 0.978*** (0.004) -0.0302*** (0.006) 0.970*** (0.006) 0.006 (0.023) 1.006 (0.023) 0.950*** (0.015) -0.154*** (0.025) 0.858*** (0.022) -0.087 (0.059) 0.917 (0.054) 0.0063*** (0.001) 0.0058*** (0.002) 1.006*** (0.001) 1.006*** (0.002) 0.005* (0.002) 0.0068** (0.002) 1.005* (0.002) 1.007*** (0.002) -0.0153 (0.014) 0.023** (0.012) 0.985 (0.014) 1.024** (0.012) 0.00173 (0.0043) 1.002 (0.004) -0.003 (0.004) 0.997 (0.004) -0.015 (0.0105) 0.985 (0.014) 0.0123*** (0.003) 1.012*** (0.003) 0.012*** (0.003) 1.012*** (0.003) 0.026** (0.011) 1.026** (0.011) -0.009*** (0.0013) 0.991*** (0.001) -0.008*** (0.002) 0.992*** (0.002) -0.017*** (0.006) 0.982*** (0.006) 0.0067** (0.003) 1.007*** (0.003) -0.0004 (0.004) 1 (0.004) 0.205 (180.7) 1.227 (221.7) 0.017*** (0.005) 1.017*** (0.003) 0.011*** (0.004) 1.011*** (0.004) 0.0001 (0.008) 0.999 (0.008) 0.007*** (0.003) 1.007*** (0.003) 0.004* (0.002) 1.004* (0.002) 0.006 (0.007) 1.006 (0.007) 0.008*** (0.002) -0.716*** (0.072) 1.008*** (0.002) 0.489*** (0.035) 0.005*** (0.002) -0.780*** (0.091) 1.005*** (0.002) 0.458*** (0.042) 0.002 (0.005) -0.866*** (0.28) 1.002 (0.005) 0.420*** (0.118) 6,749 4,409 1,301 0.1358 0.1637 0.1469 14) The number of observations is smaller than that of the sample due to some households with missing variables. Robust standard errors in parentheses. * p-value < 0.01, ** p-value < 0.05, *** p-value < 0.1 130 Jane Yoo With respect to the near-multicollinearity between the fractions, which indicate the particular reason of borrowing, Table 5.6 presents the collinearity diagnostics on a probit model by the expense shock. Tolerance, defined as 1/VIF, is used by many researchers to check on the degree of collinearity. In Table 5.6, there is no indicator with a tolerance value lower than 0.1 (it is comparable to a VIF of 10). It means that the variable is not considered as a linear combination of other independent variables. <Table 5.6> Collinearity Diagnostics on Secured Debt Variables VIF SQRT Toleranc VIF e Probit Model by Expense shock Unsecured Credit VIF SQRT Toleranc VIF e Credit Card Debt VIF SQRT Toleranc VIF e Age 1.39 1.18 0.7216 1.34 1.16 0.7469 1.23 1.11 0.8123 Years of education 1.30 1.14 0.7713 1.32 1.15 0.7549 1.20 1.09 0.8350 The number of family members 1.10 1.05 0.9128 1.08 1.04 0.9286 1.08 1.04 0.9260 Debt-to-income ratio 1.39 1.18 0.7203 1.44 1.20 0.6935 1.05 1.02 0.9538 Net worth-to-income ratio 1.64 1.28 0.6092 1.47 1.21 0.6817 1.24 1.11 0.8090 Financial asset-to-income ratio 1.26 1.12 0.7945 1.07 1.04 0.9329 1.15 1.07 0.8692 Debt used For: Home financing 2.32 1.52 0.4303 1.49 1.22 0.6707 1.08 1.04 0.9293 Debt used For: Rental payment 1.59 1.26 0.6291 1.71 1.31 0.5838 1.35 1.16 0.7429 Debt used For: Stock investment 1.04 1.02 0.9584 1.11 1.06 0.8979 1.14 1.07 0.8775 Debt used For: Debt repayment 1.19 1.09 0.8382 1.44 1.20 0.6959 1.58 1.26 0.6316 Debt used For: Business management 1.81 1.34 0.5532 2.65 1.63 0.3772 2.13 1.46 0.4686 Debt used For: Wedding expense 1.11 1.05 0.8988 1.10 1.05 0.9118 1.03 1.01 0.9722 Debt used For: Medical expense 1.07 1.03 0.9385 1.21 1.10 0.8261 1.32 1.15 0.7597 Debt used For: Educational expense 1.16 1.08 0.8630 1.70 1.30 0.5872 1.66 1.29 0.6036 Debt used For: daily expenses 1.61 1.19 0.7094 2.91 1.71 0.3440 3.17 1.78 0.3151 Year effect 1.01 1.00 0.9928 1.00 1.00 0.9916 1.01 1.01 0.9890 Mean VIF 1.36 1.50 1.40 The Impact of Expense Shocks on the Financial Distress of Korean Households 131 Table 5.7 supports the evidences suggested in Table 5.5. In the EWS analysis, the top three expense shocks, namely credit card loans for medical expenses, risky investments in stocks, and unsecured loans for wedding-related expenses. Although they affect a small proportion of the population, they are efficient to call stresses: they are at the moment of loaning money for these objectives is crucial for household to cover emergency costs. The predictive power of the other types of loans in resolving expense shocks are too close to be narrowed to a set of informational indicators. However, in general, loans secured by equities are ranked highly as their loan amount endogenously fluctuates with the collateral value. When the collateral value changes over the business cycle, a debt limit or leverage is constrained, regardless of whether personal productivity restricts consumption. This makes households feel vulnerable to an unexpected expense shock. <Table 5.7> Goodness of Fit of the Expense shock in an Early Warning Signal Model 15) (1) Number of Stress indicator Observati ons (2) Total Number of Non-Missing Observations (3) Proportion of a severe stress missed (4) Proportion of nonstress called in error (5) Goodness of Fit ln(credit cards: medical expense) 33 40 0.364 0 0.636 ln(credit cards: stock investment) 12 18 0.333 0.167 0.5 ln(credit loans: wedding expense) 35 50 0.457 0.133 0.410 ln(secured loans: daily expenses) 526 619 0.317 0.333 0.349 ln(credit cards: rental payment) 41 43 0.659 0 0.341 ln(credit cards: daily expenses) 680 778 0.335 0.327 0.338 ln(secured loans: educational expense) 203 248 0.429 0.289 0.283 ln(credit loans: stock investment) 33 57 0.394 0.333 0.273 ln(credit loans: medical expense) 90 104 0.267 0.5 0.233 ln(credit loans: daily expenses) 1319 1636 0.357 0.416 0.227 ln(secured loans: home) 2425 2985 0.509 0.279 0.213 Variable 15) Source: Author’s calculation obtained by the early warning signal method. Indicators are sorted by their absolute goodness of fit to truly call household financial distress. See text for more details in estimation. 132 Jane Yoo ln(secured loans: stock purchase) 41 58 0.683 0.118 0.199 ln(secured loans: wedding expense) 97 119 0.227 0.591 0.182 ln(credit cards: educational expense) 83 93 0.831 0 0.169 ln(credit loans: debt repayment) 243 273 0.654 0.2 0.146 ln(secured loans: business) 780 1232 0.490 0.372 0.139 ln(secured loans: debt repayment) 242 272 0.533 0.333 0.134 ln(secured loans: medical expense) 66 73 0.727 0.143 0.130 ln(secured loans: rental payment) 534 676 0.788 0.085 0.127 ln(credit loans :educational expense) 347 444 0.591 0.299 0.110 ln(credit loans: business management) 705 1106 0.343 0.554 0.103 ln(credit cards: business management) 121 166 0.322 0.578 0.100 ln(credit loans: rental payment) 310 390 0.187 0.775 0.038 ln(credit loans: home) 215 264 0.884 0.082 0.035 ln(credit cards: debt repayment) 85 87 0 1 0 5.3 Robustness checks This section has two aims, to verify the robustness of the estimation results, and to confirm the usefulness of the EWS methodology in screening household financial health. Table 5.8 presents the correlation between the vulnerability flags predicted by various estimation. If these predictions have a high correlation with the real stress index, the estimation results are robust and useful to examine a household’s financial health in the future studies. In the examination, I compare the factor, EWS, and logit models. Factor analysis can explain data in the form of few variables because it finds a few common factors that linearly reconstruct the matrix made by the original explanatory variables. In this process, the model finds the diagonal matrix of uniqueness by computing the leading eigenvectors, scaled by the square root of the appropriate eigenvalue. Under a factor model, the system of regression equations are established by the factor-loaded variables matrix. The predicted flags are compared with the actual financial distress index. I find that the flag raised by the EWS has a 69 percent correlation with actual delinquency reports, which is lower than that of the factor regression models, but The Impact of Expense Shocks on the Financial Distress of Korean Households 133 higher than that of the probit or logit models. Given our aim of predicting the precise size and characteristics of a vulnerable population, it is worth noting that the EWS prediction has a reasonable correlation with the traditional models, while still providing the relative marginal effects without making assumptions about the statistical model or errors. Even though these assumptions do not harm generality, however, the flags predicted by using these traditional models may contain different implications for real stressful cases, as it is difficult to find significant indicators with the expected sign. For example, the summary results from the logit model show the non-significant marginal effects of many determinants, whereas the EWS model provides results from a factor analysis by comparing each indicator’s unique predictive power with a higher forecasting power.16) <Table 5.8> Correlation of the Vulnerability Flags 17) Regression Models Data Stress Index Early Warning Signal Factor Regression Data: Stress Index 1.0000 Early Warning Signal 0.6937 1.0000 Factor Regression 0.7126 0.9869 1.0000 Logit Regression 0.5335 0.7441 0.7201 Logit 1.0000 In order to verify the degree of matching between the EWS prediction and the real stress index, I draw the age-density based on the EWS prediction in Figure 5.1 and compare it with the density drawn by the real stress index which is shown in Figure 5.2. The graph on the right hand of Figure 5.1 shows the model’s capability to explain the generations who are under the financial distress in that of Figure 5.2. Particularly, when a household head is around 40 – 50 years old, that household tends to go through the severe financial distress. Although the prediction results on the population free from the stress match well with the data, the model overestimates the stress of the retired, who are about 70 years old. The error, however, is within 0.05% range. 16) A factor analysis extracts meaningful information using fewer variables, which can be reduced by linear combinations of the variables. 17) Note: The flag is the predicted value with the marginal effects estimated by each regression model. 134 Jane Yoo <Figure 5.1> Comparison of the estimated kernel age density between household with and without the financial stress from the EWS estimation 18) <Figure 5.2> Comparison of the empirical kernel age density between household with and without the financial stress from the survey of household finance 19) 18) Source: Author’s calculation using the survey of household finance in 2010 and 2011. The kernel density is obtained by the fitted sample population over a household head’s age. After sorting the major determinants from an early warning signal method according to the goodness of fit, I examine whether a component of each household’s balance sheet is over/under the indicator’s optimal threshold. The weight on an indicator allows us to determine which indicator is more valuable in sorting out the vulnerability of a household. Finally, the age density is drawn by this examination, not by the subjectively measured financial stress, with a gaussian kernel 19) Source: The survey of household finance in 2010 and 2011. The kernel density indicates The Impact of Expense Shocks on the Financial Distress of Korean Households 135 6. Conclusion This study examined the specific expense shocks that are strongly linked to the financial distress of Korean households. Accoridng to the bootstrapped random effect probit model and EWS estimation, some expenses, particularly medical and miscellaneous daily expenses through unsecured and credit card loans, significantly influence household distress: when a household is using these loans, it is highly likely that they feel to fail in repaying debt on schedule. The main results are The results of the EWS model suggest that the following three sudden expense shocks explain a substantial degree of household vulnerability: i) secured loans for debt repayment, medical expenses, and daily expenses; ii) unsecured credits for debt repayment, medical expenses and rents; iii) credit card loans for debt repayment, and rents. Based on these results, this study suggests that the study then develops a screening tool that could be used before implementing a public policy that targets the relief of certain types of debts. Further, it aims at diminishing credit risks in the economy. A specific threshold level and goodness of fit of the indicators are also found by the EWS model. Policy makers that aim to provide debt relief should thus consider establishing a social safety net to help mitigate these expense shocks. Indeed, the results of the presented analysis imply that it is more important for policy makers to understand the source of expense shocks rather than the source of funds if their main objective is to implement an efficient and effective preemptive strategy to diminish household financial distress. This work, however, is limited to panel data in 2010 and 2011 in South Korean households (the Survey of Household Finance). Expanding the dataset would allow researchers to analyze the impact of expense shocks on the probability of bankruptcy by considering the household dynamics of lifetime income, consumption, and wealth. (2014년 1월 3일 접수, 2014년 2월 13일 수정, 2014년 3월 26일 채택) Acknowledgement This work was (partially) supported by the new faculty research fund of Ajou University. An author appreciate the valuable comments from three unknown referees, Jung-soon Shin, and Jun-ho Hahm. All remaining errors are my own. the age density according to the level of financial distress, which is reported by a household’s head in the survey. 136 Jane Yoo References Athreya, K. B. (2002). Welfare implications of the bankruptcy reform act of 1999. The Journal of Monetary Economics, 49(8):1567-1595. Attanasio, O., Banks, J., Meghir, C., and Weber, G. (1999). Humps and bumps in lifetime consumption. The Journal of Business & Economic Statistics, 17(1):22-35. Attanasio, O. P. and Browning, M. (1995). Consumption over the life cycle and over the business cycle. The American Economic Review, 85(5):1118-1137. Banerjee, A. J. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3):797-817. 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