05. The Impact of Expense Shocks on the Financial Distress

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 Income3)

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 stress4)

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.


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.


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 variables6)

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.


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

Variable Mean Standard Deviation Median

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 Correlation7)

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(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

Loans0.2387 0.0036 0.2682 0.8841 0.9350 1

(7)

Unsecured

Credit Loans

0.0597 0.0123 0.0581 0.3904 0.4300 0.0837 1

(8) Credit

Card Loans-0.0123 -0.0241 -0.0222 0.0414 0.0468 0.0163 0.0262 1

(9) Extra

Loans0.0796 0.0638 0.0913 0.0707 0.0692 0.0437 0.0081 0.0225 1

(10) Misc-

ellaneous

Debt

0.0520 0.0512 0.0129 0.0266 0.0194 0.0180 0.0061 0.0368 0.0023 1

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


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

Variable Number of Observations Mean Standard

Deviation

Secured: Home financing 6758 42.55 48.54

Secured: Non-home equity financing 6758 10.90 30.31

Secured: Rental payment 6758 9.72 29.36

Secured: Stock investment 6758 0.66 7.83

Secured: Debt repayment 6758 3.39 17.53

Secured: Business management 6758 16.31 36.13

Secured: Wedding expense 6758 1.59 12.17

Secured: Medical expense 6758 0.96 9.54

Secured: Educational expense 6758 2.95 16.31

Secured: Daily expense 6758 7.37 25.38

Unsecured Credit: Home financing 4423 5.68 22.82

Unsecured Credit: Non-home equity financing 4423 2.56 15.58

Unsecured Credit: Rental payment 4423 8.38 27.25

Unsecured Credit: Stock investment 4423 1.19 10.61

Unsecured Credit: Debt repayment 4423 5.25 21.50

Unsecured Credit: Business management 4423 23.84 41.98

Unsecured Credit: Wedding expense 4423 0.99 9.72

Unsecured Credit: Medical expense 4423 2.30 14.71

Unsecured Credit: Educational expense 4423 8.75 27.51

Unsecured Credit: Daily expense 4423 33.72 46.35

Credit Card: Home financing 1304 0.72 8.45

Credit Card: Non-home equity financing 1304 0.16 4.01

Credit Card: Rental payment 1304 3.17 17.33

Credit Card: Stock investment 1304 1.39 11.66

Credit Card: Debt repayment 1304 5.96 23.18

Credit Card: Business management 1304 12.75 33.05

Credit Card: Wedding expense 1304 0.25 5.03

Credit Card: Medical expense 1304 2.94 16.80

Credit Card: Educational expense 1304 6.48 24.19

Credit Card: Daily expense 1304 57.66 48.83

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


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 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: Baseline9)

Variables

Probit Model

VIF Tolerance(1) Marginal

Effects (2) Odds Ratio

Age -0.018*** (0.003)

0.983*** (0.003) 1.40 0.7162

Education -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) - -

Number of Observations

Wald Statistic

12,017

1722.97(degrees of freedom = 11)

Mean VIF 1.19

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


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 Checks10)

Dependent Variable Is this household is under sever stress? (1 if yes, 0 if no)

(Result I) IV: Fraction of

secured loans-to-debt

(Result II)IV: Fraction of unsecured credit loans-to-debt

(Result III)IV: Fraction of credit card loans-to-debt

(1) Marginal Effects

(2) Odds Ratio


(2) Odds Ratio


(2) Odds Ratio

Age 0.005***(0.001)

1.005***(0.001)

-0.011***(0.002)

0.989***(0.002)

0.006(0.004)

1.006(0.004)

Education -0.03**(0.011)

0.976**(0.011)

-0.084***(0.011)

0.919***(0.01)

-0.0203(0.017)

0.980(0.017)

Number of family members

0.062***(0.011)

1.064***(0.012)

0.078***(0.010)

1.081***(0.011)

0.061***(0.012)

1.062***(0.012)

Net worth-to-income ratio

0.0001(0.001)

1.000(0.001)

-0.0004(0.0005)

1.000(0.001)

0.0005(0.001)

1.000(0.001)

Financial assets-to-income ratio

-0.006(0.004)

0.995(0.004)

-0.015***(0.003)

0.985***(0.003)

-0.006(0.004)

0.994(0.004)

Loan-to-debt ratio 2.803***(0.092)

16.50***(1.510)

1.028***(0.133)

2.795***(0.372)

2.86***(0.396)

17.39***(6.880)

Year effect -0.250***(0.027)

0.779***(0.021)

-0.257***(0.249)

0.773***(0.019)

-0.242***(0.027)

0.785***(0.021)

Constant -2.122***(0.158)

0.120***(0.019)

0.294(0.205)

1.342(0.275)

-2.234***(0.555)

0.107***(0.060)

Number of Observations

Wald Statistic

12,017


12,017


12,017





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


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 Model11)

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)


-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,

Adjusted R-squared

12,017


12,017


12,017


Table 5.4 provides more specific, but the consistent information about the

factors. More specifically, EWS results show that Korean households feel




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.


<Table 5.4> Baseline Optimal Split and Goodness of Fit

by an Early Warning Signal Method12)

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-to-

financial assets ratio 24.22% 0.032 0.216 0.7524

Debt-to-

income 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

Proportion of

(unsecured) credits 0.0034% 0.559 0.247 0.1938

Proportion of

credit card usage 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

Proportion of

secured loans 100% 0.001 0.998 0.0004

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


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 shock14)

Variables Secured Debt Unsecured Credit Credit Card Debt (1) Marginal

Effects (2) Odds

Ratio (3) Marginal

Effects (4) Odds

Ratio (5) Marginal

Effects (6) Odds

Ratio

Age -0.0163***

(0.004) 0.984***(0.004)

-0.021***(0.005)

0.979***(0.005)

-0.019(0.015)

0.981(0.015)

Years of education

-0.260*** (0.0312)

0.771***(0.024)

-0.290***(0.039)

0.748***(0.029)

-0.258**(0.129)

0.773**(0.1)

The number of family members

0.115*** (0.034)

1.122***(0.038)

0.146***(0.039)

1.157***(0.045)

0.376***(0.136)

1.457***(0.198)

Debt-to-income ratio

0.236***(0.0189)

1.266***(0.024)

0.316**(0.028)

1.371***(0.039)

0.295***(0.081)

1.343***(0.109)


-0.022***(0.004)

0.978***(0.004)

-0.0302***(0.006)

0.970***(0.006)

0.006(0.023)

1.006(0.023)

Financial asset-to-income ratio

-0.051***(0.0161)

0.950***(0.015)

-0.154***(0.025)

0.858***(0.022)

-0.087(0.059)

0.917(0.054)

Debt used For: Home financing

0.0063***(0.001)

1.006***(0.001)

0.005*(0.002)

1.005*(0.002)

-0.0153(0.014)

0.985(0.014)

Debt used For: Rental payment

0.0058***(0.002)

1.006***(0.002)

0.0068**(0.002)

1.007***(0.002)

0.023**(0.012)

1.024**(0.012)

Debt used For: Stock investment

0.00173(0.0043)

1.002(0.004)

-0.003(0.004)

0.997(0.004)

-0.015(0.0105)

0.985(0.014)

Debt used For: Debt repayment

0.0123***(0.003)

1.012***(0.003)

0.012***(0.003)

1.012***(0.003)

0.026**(0.011)

1.026**(0.011)

Debt used For: Business management

-0.009***(0.0013)

0.991***(0.001)

-0.008***(0.002)

0.992***(0.002)

-0.017***(0.006)

0.982***(0.006)

Debt used For: Wedding expense

0.0067**(0.003)

1.007***(0.003)

-0.0004(0.004)

1(0.004)

0.205(180.7)

1.227(221.7)

Debt used For: Medical expense

0.017***(0.005)

1.017***(0.003)

0.011***(0.004)

1.011***(0.004)

0.0001(0.008)

0.999(0.008)

Debt used For: Educational expense

0.007***(0.003)

1.007***(0.003)

0.004*(0.002)

1.004*(0.002)

0.006(0.007)

1.006(0.007)

Debt used For: daily expenses

0.008***(0.002)

1.008***(0.002)

0.005***(0.002)

1.005***(0.002)

0.002(0.005)

1.002(0.005)

Year effect -0.716***(0.072)

0.489***(0.035)

-0.780***(0.091)

0.458***(0.042)

-0.866***(0.28)

0.420***(0.118)

Observations 6,749 4,409 1,301 Pseudo R-squared 0.1358 0.1637 0.1469




130 Jane Yoo

Variables

Secured Debt Unsecured Credit Credit Card Debt

VIFSQRTVIF

Tolerance

VIFSQRTVIF

Tolerance

VIFSQRTVIF

Tolerance

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


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

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 Probit Model by Expense shock


Variable

(1) Number of Stress indicator Observati

ons

(2) Total Number of

Non-Missing Observations

(3) Proportion of a severe

stress missed

(4) Proportion of

non-stress 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

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

Model15)

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


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 Flags17)

Regression Models Data Stress

Index

Early

Warning

Signal

Factor

Regression Logit

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 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 estimation18)

<Figure 5.2> Comparison of the empirical kernel age density between household

with and without the financial stress from the survey of household finance19)

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


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 수, 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

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Documents

05. The Impact of Expense Shocks on the Financial Distress