Discussion Paper No. 817

GENERALIZED HYPERBOLIC DISCOUNTING,
BORROWING AVERSION,AND DEBT HOLDING

Shinsuke Ikeda
Myong-Il Kang

October 2011
Revised October 2011

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Generalized Hyperbolic Discounting, Borrowing Aversion, and Debt Holding*

Ikeda, Shinsuke**and Myong-Il Kang***

ISER, Osaka University
andDepartment of Business Administration, Korea University

Korea University
ISER, Osaka University

29 Sep. 2011 (1st version), 27 Oct. 2011 (this version)*

We are very grateful to D.J. Flath for helpful discussions. We would give thanks to K. Onoshiro forassistance. We appreciate financial supports from Grants-in-Aid for Scientific Research (B No. 21330046)
from the Japan Society for the Promotion of Science and the 21st COE Program and the Joint
Usage/Research Center Project of ISER from the Ministry of Education, Culture, Sports, Science andTechnology.
**

Corresponding author: S. Ikeda, The Institute of Social and Economic Research, Osaka University, 6-1

Mihogaoka, Ibaraki, Osaka 567-0047, Japan. Telephone: 81-6-6879-8568, Fax: 81-6-6879-8583. E-mail:<ikeda@iser.osaka-u.ac.jp>.
***

M. Kang, Department of Business Administration, Korea University, 1-700 Ogawa-cho, Kodaira,Tokyo 187-8560, Japan. Telephone: 81-42-341-1331. E-mail: <mkang@korea-u.ac.jp>

Abstract

Analysis of an original, broad, internet-based survey reveals that debt holding is related to three aspects oftime discounting: (i) present bias, measured by the degree of declining impatience in the generalized
hyperbolic discount function; (ii) borrowing aversion, captured by a sign effect - discounting future losses
at a lower rate than future gains; and (iii) impatience, measured by the overall discount rate. Present-biased
respondents are classified as naïve if their answers reveal them to be time-inconsistent procrastinators, andclassified as sophisticated otherwise. Naïve respondents with more steeply declining impatience are more
likely to be debtors, and are likely to have larger amounts of debt, whereas sophisticates display only
insignificant positive association between declining impatience and debt holding. Responses indicative of asign effect are negatively associated with debt holding. The marginal effect on debt of such a sign effect islarger in magnitude than the effect of one standard deviation increases both in declining impatience and in
impatience. Survey responses indicative of high or declining impatience are associated with highdebt-to-income ratios, borrowing on credit cards, and the experiences of having borrowed unsecured
consumer loans, of having engaged in debt-restructuring, or of having declared personal bankruptcy.

KEYWORDS: Debt; hyperbolic discounting; sign effect; naive; internet survey.
JEL Classification: D91, D14, D03.

1

1. Introduction

We conduct an original nationwide internet survey of Japanese adults to empirically examine how debt
behavior is related to personal time discounting. Our focus is on the association between debt behavior andthree aspects of personal time discounting: (i) hyperbolic discounting or declining impatience, where aperson is less patient in immediate future choices than in distant future choices (e.g., Thaler, 1981; Benzion
et al., 1989; and Kinari et al., 2009); (ii) the sign effect, where a person discounts positive payoffs more
intensely than negative payoffs (e.g., Khwaja et al. 2007; and Ikeda et al., 2010); and (iii) impatience,measured by the overall discount rate. In the internet survey, we ask respondents four hypotheticalquestions regarding intertemporal choices, which enables us to detect each respondent`s time discounting
biases, and to precisely infer each`s degree of impatience. We also ask detailed questions about their debt
holdings, their credit history, and their economic status.
Intertemporal choice theory predicts that time discounting affects borrowing through three channels.
In the classic microeconomic framework, impatience, measured by time preference, is an important
determinant of consumers' saving and borrowing (e.g., Fisher, 1930). In the more recently developed
behavioral economics framework, hyperbolic discounting (or declining impatience) and the resulting
self-control problem are predicted to cause overconsumption, undersaving and overborrowing (e.g.,
Laibson, 1996, 1997; Krusell et al., 2002; Laibson et al., 2003, 2007). Also the behavioral economics
predicts that gain-loss asymmetry in time discounting, captured by the sign effect, makes consumers averse
to intertemporal trades, and hence leads to "borrowing aversion" (Loewenstein and Prelec, 1992). In sum,
theory predicts that indebtedness has positive association with hyperbolic discounting and impatience andnegative association with the sign effect. Although the theoretical predictions are logical and testable, there
have been few attempts at direct empirical confirmation.
The contribution of our research is to directly and systematically measure associations between time
discounting and borrowing behavior. In so doing, we add three novelties. First, we specify the discountfactor in the form of a "generalized hyperbolic discount function" (Loewenstein and Prelec, 1992), which
is characterized by two parameters. One parameter indicates the degree of hyperbolic deviation fromexponential discounting (α) and the other determines the intercept (η). We adduce the two parameters for2each individual from responses to hypothetical questions regarding immediate future choices and distantfuture choices. Each individual's α value measures his degree of declining impatience. We find that thismeasure is associated with debt behavior in the way predicted. The inferred individual values of parameterη are combined with other discount rates to construct each's degree of impatience.
Second, we distinguish the naïve, who do not expect that as time passes their impatience for given
points in the future is going to rise, and the sophisticated, who correctly anticipate the future incidence of
their own preference reversals and behave consistently with what their future impatient "selves" would do.
Theory predicts that naïve hyperbolically discounting consumers display time-inconsistent overborrowing,whereas sophisticated people may somehow forestall their own undesirable inclination to overborrow.1 To
examine the validity of the theoretical predictions, we ask respondents two questions regarding their
behavioral tendencies. The one is how likely they were in fact to procrastinate by putting off doing onerous
homework assignments during vacations in their school days. The other is how late they planned to do thesame homework assignments at that time. Using the response data, we divide hyperbolic respondents intonaïve ones, who self-reported unplanned procrastination, and sophisticated ones, who did not. We show
how the effect of hyperbolic discounting on debt holdings depends on whether the respondent is naïve or
sophisticated.
Third, we examine association between time discounting and inclination toward overborrowing,
which is revealed by high debt-to-income ratios, borrowing on credit cards, and the experiences of having
borrowed unsecured consumer loans, of having being denied a loan, of having engaged in
debt-restructuring, or of having declared personal bankruptcy.
We first find that, for the naïve respondents, both debt holding and overborrowing inclination areassociated positively with the degree of declining impatience. For example, for the naïve respondents, anincrease in the degree of declining impatience by one standard deviation leads to: a 3.6 percentage-point
higher probability of being a debtor; a 2.1 percentage-point higher probability of having credit-card debts;
1 See, e.g., Phelps and Pollack (1968), O'Donophue and Rabin (1999), and Heidhue and Kőszegi (2010).

As noted by these theoretical works, however, being sophisticated can affect saving and borrowing ineither direction, i.e., it can either mitigate or exaggerate the undersaving and overborrowing problems thatnaïve people would face.

3and a JPY0.10 million (USD1,233) larger debt amount. Notably, the marginal effect on the likelihood of
having credit card debts is not that small compared with the prevalence rate of credit-card debtors in thesample (8.6%). In contrast, the sample of the sophisticated respondents displays only insignificant positive
association between declining impatience and borrowing. This implies that being sophisticatedsignificantly weakens the positive association between declining impatience and debt holding.
We also find that borrowing aversion, revealed by the incidence of the sign effect, negatively relates
to actual debt holding, as predicted by Loewenstein and Prelec (1992). The probability of respondents whoare subject to the sign effect being indebted is 6.1 percentage-points lower than the corresponding
probability for those who are not subject to the effect. The marginal effect of the presence of the sign effecton the respondents' debt amount is JPY0.24 million (USD2,959). These marginal effects of the presence ofthe sign effect are all larger than the marginal effect of a one-standard-deviation increase both in the degreeof declining impatience and in the degree of impatience.
The degree of impatience, measured by the discount rate, positively relates not only to debt holding
but also to overborrowing inclination. A one-standard-deviation increase in impatience leads to: a 5.4
percentage-point higher probability of being a debtor; a JPY0.18 million (USD2,219) larger amount of
debt; and a 2.2 percentage-point higher probability of having credit card debts.
The robustness of the regression results are checked in two ways. First, regressions are re-conductedby using for a regressor the present bias dummy, instead of the degree of declining impatience. Second, torule out the possibility that our results simply reflect that responses indicative of present bias or highdiscount rates are a consequence of overborrowing rather than the cause of it, we re-estimate debt
equations by excluding from the sample any respondent with a credit problem or a troubled credit history.
Our results are robust against these considerations.
This research relates to the previous literature as follows. By calibration, Angeletos et al. (2001) and
Laibson et al. (2003) show that the model of hyperbolic discounting consumers has the potential ofresolving "the debt puzzle" that over 60% of US households are borrowing on credit cards. Using micro
data provided by payday lenders, Skiba and Tobacman (2008) analyze the behavior of payday-loanborrowers by Method of Simulated Moments to accept partially-naïve hyperbolic-discounting models.
These studies successfully present indirect evidence that hyperbolic discounting relates to (over)borrowing.

4The evidence, however, is not based on micro data of personal discount rates.
Meier and Sprenger (2010) report that debt levels on revolving accounts have positive correlation
with present bias. The study is important especially because they match credit bureau data of actual debt
holding and the discount-rate data elicited using choice experiments. Our debt data are self-reported andthus possibly contain under-reporting bias. Nevertheless, if we detect positive correlation between present
bias (or declining impatience) and debt, true association can be regarded as also positive insofar as theunderreporting of debt is not negatively correlated with the present bias. Although our study is similar in
spirit to Meier and Sprenger's, we add new insights by controlling for whether the respondents are naïve or
sophisticated and by incorporating the degree of declining impatience and the sign effect.2
The sign effect has been shown to relate negatively to smoking (Khwaja et al., 2007; and Kang and
Ikeda, 2011) and to obesity (Ikeda et al. 2010). Although Ikeda et al. (2010) also report negative
association between the incidence of the sign effect and debt holding, neither hyperbolic discounting nor
sophistication is controlled for in the regression analysis.
Our way of identifying the naïve and the sophisticated is similar to that of Wong (2008). He identifies
whether in his class on macroeconomics are subject to the self-control problem and how naïve the students
with self-control problem might be by asking them three questions: (i) when they should ideally start2

See also Ottaviani and Vandone (2011), which reports significant associations between impulsivity andunsecured debt holding. A similar association to theirs is detected for here in an economic model withpresent bias and other discounting factors. By estimating a simple hyperbolic discount function from alaboratory task experiment, Chabris et al. (2009) show that individuals' elicited "discount rates" predict
inter-individual variations in various field behaviors, including credit-card debt holding. However, theirfocus is not on debt but rather on health-related behaviors. They do not distinguish the overall discount rate
and present bias nor control for the degree of naiveté or for the sign effect. Tsutsui et al. (2007) discussed
on associations between time discounting and debt behavior using cross-section data. Using panel survey
data, Ikeda et al. (2010) show that the effect on debt holding of an exogenous reduction in the usury cap,
which was brought about by Japan's supreme-court decision in 2009, depends on the debtor's
procrastinating tendency, a proxy of present bias. See also Tanaka and Murooka (2010), which provides acomprehensive survey on consumption-saving decisions under the self control problem.

5

midterm preparation; (ii) knowing themselves, when they themselves predict to start it, whatever the ideal
action may be; and (iii) when they actually did start it. By matching these self-reported data to the students'
actual grades, he shows that the time inconsistency problem and naiveté negatively affect both actual classperformance and final grade. By applying this simple idea, where our couple of questions on when to do
onerous homework assignments can be regarded as corresponding to Wong's questions (ii) and (iii), we
work out differences in borrowing behavior between naïve respondents and sophisticated ones.
The remainder of the paper is structured as follows. In Section 2, the theoretical relation between time
discounting and debt holding behavior is briefly discussed. Section 3 explains the data. Section 4 shows
the regression results. Section 5 checks the robustness of the results. Section 6 concludes the paper.

2. Time discounting and debt holding behavior

When we make intertemporal consumption decisions, the subjective discount rate or time preference, as ameasure of impatience, plays a key role in determining how much resource is consumed for present
gratifications, and saved for future gratifications. We hypothesize that debt holding is, in part, related totime discounting. To detect for the association between time discounting and debt holding, we focus on
three aspects of time discounting: (i) hyperbolic discounting, (ii) the sign effect, and (iii) impatience. In
this section, we first demonstrate theoretical backgrounds of our empirical analysis.

2.1. Hyperbolic discounting and impatience

Consider consumers whose discount factor for future felicity with delay τ is given by the generalized
hyperbolic discount function ƒ(τ):

f (τ ; α ,η ) = (1 + ατ ) −η , τ ≥ 0, α ≥ 0,η ≥ 0(1)

The discount rate ρ, which is obtained by computing -ƒ'(τ)/ƒ (τ), is given as:ρ (τ ; α ,η ) =αη
1 + ατ

(2)

In (2), three points are noteworthy. First of all, the discount rate is declining in delay τ, which6represents the usual property (e.g., Ainslie, 2001; and Benzion et al., 1989) of hyperbolic discounters thatthey are less patient in immediate future choices than in distant future choices. Secondly, the degree of
declining impatience is higher as α is larger. Especially, for two distinct delays τ1 and τ2 (τ1>τ2), the relative
discount rates ρ(τ₁)/ρ(τ₂)(=(1+ατ2)/(1+ατ1)), which are smaller than one, depends solely on α: a large α
implies a small ρ(τ₁)/ρ(τ₂). We thus refer to α as the degree of declining impatience. Thirdly, the discountrate equals αη when τ=0, which implies that, for given α, η determines the discount rate for infinitesimallyshort horizon. For given α and τ, the discount rate ρ has the same information as η. To ease interpretation,and from the data availability, we focus on the relationship of debt behavior and (α, ρ), rather than (α, η).
The discount factor depends negatively on both declining impatience α and impatience ρ.3 The positive
association between impatience and debt holding follows from the standard theory of intertemporal choice(e.g., Fisher, 1930).
As for the effect of declining impatience, we should consider whether the hyperbolic discounter is anaïve person, who do not expect that, as time passes, their impatience is going to rise, and hence that thepreference reversal will take place, or a sophisticated one, who expects correctly the future incidence of thepreference reversal and behaves consistently with what future impatient "selves" would do (see,O'Donophue and Rabin, 1999).
With declining impatience, hyperbolic discounters face a self-control problem: "selves" in different
points in time always have weaker preferences toward immediate savings than they would have toward
future savings. When they are naïve, the declining impatience causes overconsumption, undersaving, andoverborrowing in a time-inconsistent way. Thus, for naïve hyperbolic discounters, debt holdings are
expected to depend positively on the degree of declining impatience (α).
When hyperbolic discounters are sophisticated, it may be plausible to hypothesize that the positiveeffect of declining impatience on undersaving and overborrowing becomes somewhat weaker than it would
be if they were naïve. It is true firstly because, if the agent is sufficiently risk-averse, the present "self" has3

When we rewrite ƒ(τ; α, η) as F(τ, α, ρ(τ; α, η)), the discount factor F satisfies ∂F(τ, α, ρ)/ ∂τ=ƒτ<0,∂F(τ, α, ρ)/ ∂α=ƒα<0, and ∂F(τ, α, ρ)/ ∂ρ=((1+ατ)/α)ƒη<0.

7

a saving incentive for future selves as to retain their consumption levels.4 Secondly, sophisticates have an
incentive to commit to his lifetime consumption plan by using some devices (e.g., illiquidity assets,savings accounts, 401(k), not using credit cards, etc.). In that case, they can prevent future selves fromoverconsuming and overborrowing due to the preference reversal (see Laibson, 1995; Sourdin, 2008).5
In sum, we hypothesize the followings regarding the relationships between the debt holding behaviorand (α, ρ):

H1 The degree of impatience, measured by the discount rate ρ, positively relates to debt holding.
H2 For naïve respondents, the degree of declining impatience, measured by α, positively relates to debt
holding and overborrowing inclination.
H3 For sophisticated respondents, positive association between the degree of declining impatience anddebt holding is somewhat weaker than for the naïve.

2.2. The sign effectBehavioral economists have reported that losses are discounted at a lower rate than gains. For example,
Thaler (1981) elicited discount rates for gains that were three to ten times higher than those for losses.
Several of his subjects revealed negative discount rates for loss, which implying that an immediate loss ispreferred to a delayed loss of the same value. Loewenstein (1988) reported that, for his subjects, receiving
USD 100 today was indifferent to receiving USD 157 in a year, whereas loosing USD 100 immediately
was indifferent to USD 133. This prevalent gain-loss asymmetry is referred to as the sign effect (Frederick,4

In the literature (e.g., Phelps and Pollack, 1968; and O'Donophue and Rabin, 1999), it is known thatbeing sophisticated, rather than naïve, mitigates the self-control problem and undersaving if the utility
function is more concave than the log utility. As is known in the empirical studies on the equility premium
puzzle, actual estimates of the relative risk aversion parameter are usually much higher than one. Being
sophisticated is thus considered to mitigate, rather than exaggerate, the undersaving behavior of the naïve.
5

In Appendix A, consumption/borrowing behavior of the naïve and the sophisticated under generalized

hyperbolic discounting are discussed formally by using a three-period model.

8et al., 2002).
With the sign effect, people are reluctant to intertemporal trade-offs, which causes aversion to both
lending, i.e., giving up a part of present consumption for future consumption, and borrowing, i.e., giving
up a part of future consumption for present consumption. Loewenstein and Prelec (1992) show analyticallythe property by using a "temporal prospect" model. We hypothesize that respondents whose time
preferences display the sign effect are likely to hold smaller debt.6

H4 The incidence of the sign effect negatively relates to debt holding and overborrowing.

3. The data

Our empirical research is based on our original nationwide internet survey, titled as the Japan Internet
Survey on Preferences Relating to Time and Risk 2010 (hereafter JPTR 2010). Nikkei Research, Inc., arepresentative Japanese private research company, which deals with economic surveys to constructdatabase for academic and non-academic purposes, carried out the survey during October 21 to 27, 2010,by using questionnaires that we prepared. The respondents were 2387 Japanese people between the ages of
20 and 65, who had been enrolled in the Nikkei Research Access Panel, which was composed totally of

6

The temporal prospect model of Loewenstein and Prelec (1992) describes choices among sequences of

dated adjustments to consumption, rather than the entire lifetime utility maximization problem. Although
there is no theoretical discussions on the issue, one simple way to incorporate the sign effect into the entire
lifetime utility maximization problem is to assume that there is an exogenous reference consumption
level c͞ and to specify the discount factor for period utility u(cτ - ͞c ) with delay τ, u(cτ- ͞c ), asg (τ ) = (1 + kθ ) f (τ ) ,where, with θ denoting a binary indicator which takes one for cτ < ͞c and zero otherwise, k(>0) represents
the sign effect, which weakens time discounting when cτ is smaller than the reference level c͞ ; and ƒ(τ) is
the discount factor in (1). When ͞c < current total income yτ, cτ < ͞c implies cτ < yτ and hence borrowing cτ-yτ
is positive. It follows that consumers apply lower discount rates when choosing how much to borrow.

9about 155,000 registrants.7 Cash voucher are provided as incentives by lottery to respondents. The sample
was selected by stratified random sampling such that the age and sex distribution is as close as possible tothat of the Japanese census. We included various questions in the survey to elicit information about: therespondents' preferences regarding time discounting and risk aversion; 8 their economic attributes,
including debt holding behavior, income, and asset holding; and demographic and social attributes.
The summary statistics of background attributes of the respondents are listed in panel A of Table 1.
Males occupied 49.9% of the respondents with the average age of 41.8.

Insert Table 1. Summary statistics

3.1. Debt

In JPTR 2010, we asked respondents to indicate how much they had debt other than mortgages bychoosing one of nine ranges, from (1) "no debt" to (9) "more than JPY 10 million." To construct the data ofdebt amounts, we assign median to each of the nine individual ranges that the respondents chose.9 As seenfrom panel B of Table 1, around a 20.2% of the respondents have debts, the average of which amounts to
JPY 2.45 million (converted to around USD 30,265 by the average yen/dollar rate (81.1) in October2011).10
7

During the survey period of October 21 to 27, 2010, 11,090 registrants were notified about the survey by

e-mail. During October 5 to 12, 2010, we conducted a pre-survey, in which 177 responses were collectedfrom 902 samples sent. The sample of the pre-survey was excluded from the main survey.
8

To elicit the degree of risk aversion, we asked respondents to make sequential three binary choices, as in

Figure A1 of Appendix B for discount rates, on whether they buy lottery "A" that gives JPY100,000(USD1233) with probability 0.5 at given prices, specified as JPY10 to JPY50,000.
9As for the highest range (9): "more than JPY 10 million", we simply take it as [JPY 10 million, 12.5

million), where the width of JPY 2.5 million is determined such that it equals to the width of the range of(8): [JPY 7.5 million, JPY 10 million).
10

Hereafter we use the conversion rate (81.1 JPY/USD) when converting JPY values into USD values.

10We also asked the respondents with debts to indicate for what purposes they borrowed, from sevenoptions (multiple responses are allowed): (i) purchasing cars; (ii) purchasing other goods; (iii) financing
living costs; (iv) financing business activities, (v) financing amusements, (vi) paying off other debts; and(vii) others. Debts for cars (6.6%), other goods (8.0%), and living costs (4.1%) are relatively frequent
among the seven (see panel B of Table 1).
To quantify the respondents' inclination toward overborrowing, we construct an excess debt indicatorwhich equals one if the debt amount held is larger than 30% of annual household income, and zerootherwise.11 As seen in panel B of Table 1, excess borrowers in this sense amount to 8.1 % in the sample.
Besides, we asked all the respondents: (i) whether or not they have credit-card debt including debt on arevolving-payment account; (ii) whether or not they have ever borrowed unsecured consumer loans; (iii)
whether or not they have ever been denied a loan for whatever reason; and (iv) whether or not they haveever engaged in (out-of-court) debt-restructuring, or declared personal bankruptcy.
As seen from the table, credit-card debtors occupy only 8.6% in the sample, which is much smaller
than in the U.S. case. For example, based on the actual credit report data from the U.S. credit bureaus,
Meier and Sprenger (2010) report that 41% of their 541 respondents have credit-card debts on revolving
accounts. Similarly, the rate of respondents who have ever borrowed unsecured consumer loans (8.7%) andthat of respondents who have experienced debt-restructuring or personal bankruptcy (2.1%) are not high.
As conjectured from the finding of Karlan and Zinman (2006) that nearly a half of their respondents
underreported borrowing of high interest consumer loans, our self-reported data may containunderreporting bias. Notwithstanding the concerns about possible self-reporting bias, we shall use theself-reported data because of limited data availability. This may be a limit of our empirical study. But notethat if we detect positive association between present bias (or impatience) and debt, then actual association
can also be taken as positive so long as underreporting of debt is not negatively associated with present
bias (or impatience).1211

In June, 2010, the Japanese government revised the Money Lending Business Control Law to ban

individuals from newly borrowing when the outstanding amounts of their debts exceed one third of theirannual income.
12

To see this, suppose first that underreporting of debt is independent of time discounting. Then,

11

3.2. Time discounting

In the survey, we try to elicit the respondents' time discounting using four questions on intertemporalchoice: two (Q1 and Q2) are designed to detect the degree of declining impatience (α); and the other two(Q5 and Q6) are to detect the incidence of the sign effect (θ). We also measured the degree of impatience(ρ) from their responses to the four questions.
As noted in the literature (e.g., Frederick et al., 2002; and Anderson et al., 2008), the discount rate
will be overestimated unless the effect of the curvature (risk aversion) of the utility function is controlled.
Nevertheless, to make it easier to calculate the parameters of the generalized hyperbolic discount function,
we do not control for the effect of risk aversion in eliciting the personal discount rates. Instead, we copewith the problem in two ways. First, when estimating association between time discounting and debt byregression, we incorporate as a control variable the degree of risk aversion which is inferred from
responses to a query on risky choice. Then, the estimated coefficients of time discounting variables
represent partial correlation after controlling for the effects of risk aversion both on time discounting andon debt. Secondly, we will also conduct regression later by using for a regressor the binary indicator for
present bias (α>0), instead of the degree of declining impatience (α) itself. As the value of the binary
indicator for present bias is not affected by the degree of risk aversion (see Eisenhauer and Ventura, 2006),
the reexamination would work as a robustness check.

3.2.1. Declining impatienceestimated association, if detected, between time discounting and debt behavior can be taken as theunbiased estimate of true association. Next, suppose instead that underreporting of debt is positively
associated with present bias, i.e., that hyperbolic respondents are more likely to underreport debt than
exponential ones. Then association between present bias and debt would be underestimated (oroverestimated) if true association is positive (or negative). Therefore, if positive association is detectedfrom the self-reported data, it implies that true association is also positive.

12

Questions Q1 and Q2 consist of sequential three binary decisions on immediate future trade-offs and of
distant future trade-offs, respectively. As illustrated in Appendix B, in Q1, respondents are asked to choose
between: (A) getting JPY 1,000 (around USD 12.3) today; and (B) getting JPY 1,000 plus a certain amounta week later; whereas in Q2 the options are: (A) getting JPY 1,000 a year later; and (B) getting JPY 1,000
plus a certain amount a year plus one week later. Let XQ1 and XQ2 be the delayed money amounts in Q1 andQ2, respectively, which are taken as subjectively equivalent to JPY 1,000 in options (A). Then, the degreeof declining impatience α in (1) is obtained by solving jointly

1000
1000 ƒ(365, α, η)=

XQ1 ƒ(7, α, η),= XQ2 ƒ(372, α, η),which are combined to a non-linear equation of α,ln(1000) − ln( X Q1 )ln(1000) − ln( X Q 2 )=ln(1 + 7α ).
ln(1 + 365α ) − ln(1 + 372α )In Table 2, panel A summarizes elicited parameters which characterize the generalized hyperbolic
discount function. Note that a positive α implies declining impatience or present biased preferences. The
sample mean of α equals 0.018, which differs significantly from zero (p<0.00). The average respondents
are thus present biased (α>0). A 40.1% (N=960) of the 2386 respondents are present-biased (see Table 3
below).13

Insert Table 2.

By substituting the sample mean of (α, η) into (2), Figure 1 illustrates the discount rates of the average
respondent, the average debtor, and the average non-debtor, as decreasing functions of delay τ. As is also
shown in panel A of Table 2, the debtor's discount rate schedule is characterized by its stronger concavity
13

The rate of present biased respondents is comparable with that which Meier and Sprenger (2010)

estimate for the US sample (36%), and is somewhat higher than that which Eisenhauer and Ventura (2006)
report (22.8% for the Italian sample and 18.4% for the Dutch sample).

13

(i.e., a larger α) and higher intercept (i.e., a larger αη).

Insert Figure 1.

3.2.2. Naïve or sophisticatedTo identify whether each of the hyperbolic respondents is naïve or sophisticated, we asked two queries Q3
and Q4 as to: (i) how early used to do homework assignments during school vacation; and (ii) how early tofinish up homework assignments used to plan during school vacation, respectively:

Q3 Thinking about when you were a child and you were given an assignment during school
vacation, how early did you usually finish up the assignment? (X ONE Box)
1

Got it done right away.

2

Tended to get it done early, before the due date.

3

Worked on it daily up until the due date.

4

Tended to get it done toward the end.

5

Got it done at the last minute.

Q4 Thinking about when you were a child and you were given an assignment during school
vacation, how early to finish up did you plan for the assignment? (X ONE Box)
1

I planned to get it done right away.

2

I planned to get it done rather early, before the due date.

3

I planned to work on it daily up until the due date.

4

I planned to get it done rather toward the end.

5

I planned to get it done at the last minute.

6

I didn't make any plans.

After excluding 109 hyperbolic respondents who did not make any plans (i.e., who chose "6" in Q4)

14

from the sample, we take present-biased respondents (i.e., those with α>0) who chose a larger number inQ3 than in Q4 as being naïve, because they could be regarded as those who were not cognizant of theirself-control problem and tended to procrastinate onerous jobs in a time-inconsistent way. The other
respondents, who are non-hyperbolic (α≤0) or did choose a weakly smaller number in Q3 than in Q4, areidentified as being sophisticated. The naïve (N=576) occupy 60.0% of 960 hyperbolic respondents who
used to make plans over performing assignments.14

3.2.3. The sign effect

To detect the sign effect, we elicit the discount rates for future receipts and future payments by asking
queries Q5 and Q6, respectively. In doing so, we follow the literature (e.g., Harrison et al., 2002) in asking
respondents to make nine of binary decisions in each of two payoff tables, where the money amounts withfront-end delay are commonly set to JPY 1 million (around USD 12,330). See Appendix C for the payoff
table of Q6.
As summarized in panel B of Table 2, the sample mean of the discount rates for future receipt, elicitedfrom Q5, is 8.8%, whereas that of the discount rates for future payments, elicited from Q6, is 0.1%. The
difference of the two means is highly significant (p<0.000). The average respondent thus displays the signeffect. The proportion of the respondents who exhibited the sign effect (N=1859) amounts to 81.1%.

3.2.4. Impatience

We quantify each respondent's degree of impatience by taking the standardized average of the four
14

The proportion of the naïve might be underestimated because in Q4 and Q5 the respondents might

self-report their past behavior as more rational and time-consistent than it actually were. For example, theproportion of the naïve is comparable with, but somewhat smaller than, what Wong (2008) shows byconducting a field survey in the university class on macroeconomics. In his sample, naïve subjects occupy
86.4% of the students with the time-inconsistency problem in actual midterm preparation. See also Hey
and Lotito (2009), which estimates by conducting experiments.that 50% of their subjects are naïve.

15

discount rates that are implied from queries Q1, Q2, Q5, and Q6: Two are discount rates for τ=7 and 372
elicited from Q1 and Q2, i.e., ρ(7) and ρ(372) computed from (2); and the other two are the discount rates
for future receipts and payments implied from Q5 and Q6.15

4. Results

4.1. Means in stratified sample

Let us start with examining simple associations between time discounting and debt behavior. Panel B of
Table 3 compares average debt behavior among respondents stratified by whether their impatience isdeclining (α>0) or not (α≤0); whether impatience ρ is higher than the average or not; and whether the signeffect is present (θ=1) or not (θ=0). The table shows that debt holding and time discounting are associatedconsistently with our hypotheses H1 to H4. First, naïve respondents with a positive α are more likely to be
debtors and have larger amounts of debt than both sophisticated hyperbolic respondents andnon-hyperbolic respondents. Both associations are significant. For example, in the sample of the naïve, theproportion of debtors is 23.8%, which is higher than 20.3% in the sample of the hyperbolicallysophisticated and 18.1% in the non-hyperbolic sample. For all the overborrowing indices ("Debt-to-income
ratio >30%" to "Having ever engaged in debt restructuring or declared personal bankruptcy"), respondents
with overborrowing inclination occupy larger proportions in the naïve sample than both in thesophisticated sample and in the non-hyperbolic sample (α≤0). The associations are all significant.

Insert Table 3.

Secondly, in all the cases, less patient respondents (ρ > mean) display stronger inclinations towardborrowing and overborrowing than the more patient. For example, the average debt amount of the high-ρ
15

Note that ρ(7) and ρ(372) are daily discount rates while the discount rates implied from Q5 and Q6 are

expressed in annual rates. As we standardize the rates, this difference does not matter when computing the
average of the four rates.

16

respondents (JPY 0.73 million) is around twice as large as that of the low-ρ respondents (JPY 0.37 million).
In the sample of the high-ρ respondents, the proportion of the respondents who have the experience of
having engaged in debt restructuring and/or declared personal personal bankruptcy (3.8%) is nearly threetimes as high as that in the low-ρ sample (1.3%). Except that the difference in average debt amounts
between high- and low-respondents is insignificant for the debtor sample, the positive associations
between (over)borrowing and impatience are significant.
Thirdly, the incidence of the sign effect is shown to be associated with borrowing aversion, as
expected. For example, debtors occupy 18.9% in the sample with the sign effect, whereas they do 23.4% in
the sample without the sign effect. However, the negative correlation between overborrowing and the signeffect is not valid except for "Debt-to-income ratio >30%".

4.2. Regression results

4.2.1. Debt holding

To detect associations between debt holding behavior and each of three time-discounting variables (α, ρ, θ),
we estimate models (A) and (B). In model (B), the product term of declining impatience (α) and thedummy variable for the naïve (DN) is added to the set of independent variables. In both models, control
variables for other personal attributes are included. The controls include: (i) the degree of risk aversion; (ii)demographic factors, including age, gender, and education; and (iii) economic factors, including householdincome, household real asset holding, and household financial asset holding.
For example, letting Debtorί represent a binary indicator which equals one if the respondent ί is adebtor, and zero otherwise, we estimate the probability that respondent ί with time discounting attributes(αί, ρί, θί) and controlled attributes xί is a debtor by using probit models (A) and (B):Pr( Debtori = 1 | α i , ρ i ,θ i , xi )Φ (c + β α α i + β ρ ρ i + β θ θ i + γxi )⎧=⎨⎩Φ (c + β α α i + β N α i DNi + β ρ ρ i + β θ θ i + γxi )for model (A),for model (B).

where Φ represents the cumulative normal distribution. In the specification of model (B), with the productterm αί∗DNί being added, coefficient βα captures correlation for sophisticated respondents between the17likelihood of being a debtor and αί, whereas the corresponding correlation for the naïve are given by βα+βN.
We also estimate the debt holding function by using debt amounts for the dependent variable. In sodoing, the interval tobit regression model is estimated, because the debt amount data are right-censored atzero.
Panel A of Table 4 summarizes the marginal effects of increases in declining impatience (α),
impatience (ρ), and the sign effect (θ) on the probability of being a debtor and on the amount of debt
holding. As we shall explain below in order, we can confirm that our hypotheses H1 to H4 are supported athigh significance levels.

Insert Table 4.

As consistent with H1 and H2 (i.e., the hypotheses concerning associations between α and debt
holding of the naïve/sophisticated), the results of model (B) show that, for naïve respondents, declining
impatience has positive correlations with both the probability of being a debtor and the amounts of debt
holding, whereas, for sophisticated respondents, the corresponding correlations are insignificant. Either forthe probability of being a debtor or for the amounts of debt holding, associations between α and debt
holding differ between the naïve and sophisticated respondents at the 10% significance level (not listed in
the table). Quantitatively, for naïve respondents, an increase in α by one unit of standard deviation (0.046)
leads to: (i) a 3.6 percentage-point (=0.046∗0.780) higher probability of being a debtor; (ii) a JPY 0.10(=0.046∗2.12) million larger debt amount of respondents; and (iii) a JPY 0.12 (=0.046∗2.61) million larger
debt amount of debtors.
Positive association between impatience (ρ) and debt holding (i.e., H3) can also be confirmed at highsignificance levels. With other personal attributes being equal, respondents who are less patient by one unitof standard deviation than the average: (i) are debtors with a 5.4 (or 5.5) percentage-point higher
probability in model (A) (or (B)); and (ii) have a JPY 0.22 (or 0.23) million larger amount of debt in model(A) (or (B)) for the debtor sample.
As predicted by H4, the respondents' debt holding has strong negative correlations with the incidenceof the sign effect. The probability of respondents who are not subject to the sign effect being indebted is18

6.3 (6.1) percentage-points higher than the corresponding probability of those who are subject to the effectin model (A) (or (B)). The marginal effects of the absence of the sign effect on the debtors' debt amounts
are JPY 0.26 million for model (A) and JPY 0.27 million for model (B). Borrowing aversion associatedwith the sign effect is so strong that all of the estimated marginal effects of the absence of the sign effectare larger than the marginal effects of one-standard-deviation increase both in α and ρ.

4.2.2. Debt purposes

We also estimate the probability functions of debt holding for various purposes. The results are
summarized in panel B of Table 4. As the proportion of borrowers for each specific purpose is small (atmost 8% from Table 1), the estimation results are not so strong as those for overall debt. However, the signconditions predicted from H1 through H4 are met in almost all cases. Although it seems difficult to findout strong tendency from the results, it might be noteworthy that declining impatience is significant for
debts for cars and amusements whereas it is not for living costs and business. To be rough, the effect ofpresent bias seems to occur more clearly for debt holding which is less closely related to subsistence.

4.2.3. Inclination toward overborrowing

We detect for the marginal effects of the three time-discounting variables (α, ρ, θ) on the respondents'
inclination toward overborrowing. To do so, we estimate probit models for the probabilities of: displaying
a debt-to-income ratio being higher than 30%; having credit-card debts; having the experience of borrowed
unsecured consumer loans; having the experience of having been denied a loan; and the experience of
having engaged in debt-restructuring or of declared personal bankruptcy.
Table 5 summarizes the results. Consistent with hypothesis H2, an increase in declining impatience α
is shown to lead naïve respondents to have stronger inclination to overborrow. It is valid in almost all cases
at high significance levels. For example, naïve respondents whose α is higher by one unit of standard
deviation (0.046) are 2.31 (=4.6∗0.503) percentage-points more likely to have credit-card debts. The
marginal effect is more than one fourth of the prevalence rate of credit-card debtors (8.6% from Table 1).

19In contrast, as expected by hypothesis H3, the sophisticated respondents does not show significant
association between declining impatience and overborrowing inclination, except for the case of the default
experience (debt restructuring or personal bankruptcy). Sophisticated respondents with a higher degree of
declining impatience are less likely to have the default experience. The result is somewhat puzzling andmight be hard to understand theoretically.

Insert Table 5.

For all cases, overborrowing inclination is highly correlated with impatience ρ. For model (A) (or (B)),
an increase in ρ by one standard deviation is associated with a 2.5 (or 2.4) percentage-point higher
probability of exhibiting higher debt-to-income ratios than 30 %. The marginal effect is not that small
when compared with the corresponding unconditional probability, 8.1% (see Table 1): the marginal effectrelative to the unconditional probability amounts to 30.9%. The marginal effect on the probability of
having credit-card debts is 2.2 percentage points for model (A) and 1.9 percentage points for (B), which
are around one fourth of the unconditional probability (8.6%). The marginal effects on the probability of
having ever borrowed unsecured consumer loans are around 1%.
As for the sign effect, expected negative association is observed for the probability of exhibiting
higher debt-to-income ratios than 30%: the incidence of the sign effect is associated with a 3.2
percentage-point decrease in the probability, which is greater in magnitude than the marginal effect of
one-standard-deviation increase both in α and ρ. However, association with the other overborrowing
variables are not detected.

5. Discussions

For robustness check, we re-examine the above analysis in two ways. Firstly, we re-conduct regression
using as a regressor a binary indicator for the present-biased respondent (α>0), instead of declining
impatience α. This enables us to focus on the effect of having present-biased preferences under a weakercondition. Secondly, to rule out the possibility that the above results simply reflect that responses

20indicative of declining and/or low impatience are a consequence of overborrowing rather than the cause ofit, we conduct the same regression as in the previous section by excluding from the sample any respondent
who could be taken as seriously credit-constrained.

5.1. Regressions with the present-bias dummy

In the previous section, we have estimated the generalized hyperbolic discount function, which enables us
to detect the marginal effect of a parametric increase in the degree of declining impatience on borrowing
behavior. However, the results may depend on the specification of the discount function. To focus on theeffect of the incidence of present bias on borrowing behavior by giving up detecting the effect ofdifferences in the degree of declining impatience αί, let us use for a regressor the binary indicator, insteadof αί , which equals one if the respondent is present biased (αί >0) and zero otherwise.
Table 6 summarizes the results, where in the column of "Present bias (α>0) =1" the marginal effects
compared with the case without present bias are shown. The results are consistent with our hypotheses andthe results of Table 4 in the previous section. In particular, naïve respondents are 4.7 percentage-points
more likely to have debt, and have a JPY 0.11 million larger amounts of debt, than respondents without
present bias, whereas there is no significant difference both in the probability of being indebted and theamounts of debt holding between sophisticates with present bias and respondents without present bias.

Insert Table 6.

The marginal effects on overborrowing inclination are summarized in Table 7. Except for that themarginal effects of the incidence of present bias on the probability of having the experiences of having
borrowed unsecured consumer loans are insignificant, the results are consistent in that the naïve are more
likely to overborrow.

Insert Table 7.

21

The result on credit-card debts is comparable with that of Meier and Sprenger (2010), which reportsthat the presence of present bias leads to a 16 percentage-point increase in the probability of having
credit-card debts. In Table 7, the corresponding marginal effect of the presence of present bias amounts to
4.8 percentage points for naïve respondents and 3.1 percentage points for average respondents. Although
the effects are much smaller than Meier and Sprenger's estimate, the relative magnitudes to the prevalence
rate of credit-card debtors (i.e., the unconditional probability of having credit card debts), i.e., 38.4%(=4.8/12.5) for naïve respondents and 36.0% (=3.1/8.6) for average respondents, are just as high as thecorresponding ratio that is computed from Meier and Sprenger's estimate (16/41=39.0%).16

5.2. Excluding credit-constrained respondents

It should be noted that the observed association between time discounting and debt holding could beinterpreted as that overborrowing causes high and/or declining impatience: Credit-constrained respondents
with excessive debt might be likely to choose smaller amounts of money with front-end delay to pay off
outstanding debt and/or live on today, which may lead them to reveal high discount rates and cause
positive association between debt and impatience. When the credit problem is very serious, the respondents
might be much more impatient in immediate future choices than in distant future choices, which may leadthem to reveal declining impatience and cause positive association between debt and declining impatience.
To consider this problem, recall first that in Questions Q1 and Q2, from which we estimate the degreeof declining impatience, the smaller amount of money with front-end delay is set to JPY1,000 (around
USD12.3). It might not be a plausible interpretation to take that intertemporal choices of such small money
are strongly affected by their credit conditions unless the respondents face such a serious financial
difficulty that they hardly live on the current week or month.
Given our data restriction, one possible way to check the robustness against the problem of our main
results above is to reestimate equations by excluding credit-constrained respondents from the sample. To
do it, we exclude from the sample any respondent with a credit problem or a troubled credit history who
16

As seen by comparing the log pseudolikelihood values of Tables 4 and 6, the regression model with αί

fits better than the model with the present bias dummy, especially for the overall debt holding.

22

has borrowed to pay off other debts; has the experiences of having denied a loan for whatever reason; or
has the experiences of having engaged in debt restructuring or declared personal bankruptcy. The numberof the respondents excluded amounts to 280.
Table 8 shows the results. Our main results, the validity of hypotheses H1 through H4, are shown to
be robust for overall debt behavior captured by the probability of being a debtor and by the amount of debt
holding. Although the results for the overborrowing inclination become somewhat weaker, the coefficientsof the degree of declining impatience are significant for the probability of exhibiting a debt-to-income ratio
higher than 30% and that of having credit-card debts. It is also confirmed that less patient respondents are
more likely to have credit-card debts. In this sample, however, the probability of having the experiences of
having borrowed unsecured loans does not have significant correlation with any time discounting
variables.17

Insert Table 8.

6. Conclusions

We have shown that consumers' borrowing behavior have expected correlations with time discounting,including decision biases such as hyperbolic discounting and the sign effect. Although these time
discounting variables have been often emphasized in theory as important determinants of indebtedness,
empirical examination has not been conducted in a direct way. By identifying naïve and sophisticatedrespondents, our study has quantified substantial positive associations of (over-)indebtedness withhyperbolic discounting and impatience, and substantial negative correlations with the sign effect.
Three novelties are there. Firstly, respondents preferences are parameterized by using the generalized
hyperbolic discount function, so that the elicited degree of declining impatience is used to detect
17

We also estimated debt holding equations by excluding from the sample respondents whose

debt/income ratios are greater than 30% as credit-constrained respondents. The results are again supportiveof our results obtained in the previous section regarding overall debt holding as well as overborrowing
inclination.

23

association between the degree of present bias and debt holding behavior. Secondly, we try to distinguishnaïve and sophisticated respondents by using self reporting data on the gap between plan and actual
behavior over onerous assignments. Thirdly, borrowing aversion associated with the sign effect, i.e., thegain-loss asymmetry in time discounting, is also detected.

24Appendix A: An illustrative model

In this appendix, we illustrate theoretically relations between declining impatience and borrowing
behavior of the naïve and the sophisticated using a simple three-period model. Consider a hyperbolic naïve
consumer in the three period setting. Let cτ and u(cτ) denote period-τ consumption the period utility
function, respectively. The gross rate of interest R is constant. In the initial period 1, the naïve consumer
determines his "optimal" consumption c1 from the intertemporal optimality conditions,u ′(c1 )(1 + α )η = R
u ′(c1 )(3)ηu ′(c2 ) ⎛ 1 + 2α ⎞⎜⎟ =Ru ′(c3 ) ⎝ 1 + α ⎠

(4)

c2 c3
+
= W1
R R2

(5)

and the lifetime budget constraint,

c1 +where W1 is initial wealth holding. As 1+α > (1+2α)/(1+α), the conditions imply that the consumer puts agreater weight on the immediate gratification from c1 than he expects to put on the period-2 immediate
gratification from c2. Since this tendency is stronger with α, a larger α results in larger debt holdings in the
period.
In period 2, the naïve consumer re-determines (c2, c3) from the the period-2 optimality condition,u′(c2 )
(1 + α )η = Ru′(c3 )

(5)

which differs from the period-1 condition (4): gratification from c2 is evaluated more highly than in (4).
The realized c2 is thus larger than the level which was planned in the previous period, whereas the opposite
is true for c3.
To be more specific, if u(cτ) is specified by the power function,u (c ) =c1−γ − 1, γ > 0,1− γ

the naïve consumer's "optimal" consumption rates are given by25

c1N = λ11W1 ,(6)

c2N = λ22W2N ,(7)

c3N = (1 − λ22 ) RW2N ,(8)where W2N is wealth in period 2, R(W1-c1N); and λ11and λ22 represent the realized marginal propensities to
consume (MPC) in periods 1 and 2, respectively:

λ11 =

λ22 =

1
1 + R {Rf (1)}

1/ γ

−1

{

}

+ R − 2 R 2 f ( 2)

1/ γ

,

1
.
1/ γ
1 + R {Rf (1)}
−1

(9)

(10)

From these solutions, as ƒ(τ)’ s are decreasing in α, a large α implies a large c1N and a small c3N.18 In thissense, naïve consumers with stronger declining impatience are likely to hold larger debts.
When the consumer is a sophisticate, who expects correctly the incidence of the preference reversal
from (4) to (5), he first solves (5) for (c2, c3) by taking c1 and hence period-2 wealth W2 as given. Once theoptimal c2 and c3 are obtained as functions of c1, by substituting functions c2(c1) and c3(c1) into the lifetime
utility maximization problem, the consumer determines the optimal c1 and hence c2 and c3.
In the illustrative model, the sophisticate's optimal consumption ctS in period t is obtained as

c1S = λ11′ W1 ,(11)

c2S = λ22W2S ,(12)

c3S = (1 − λ22 ) RW2S ,(13)

where W2S=R(W1- c1S); and the MPC λ'11 in period 1 is defined in the same way as in (9) by using effective
discount factors ƒS(τ) (τ=1,2), instead of ƒ(τ):′ f (1) + (1 − λ22 ) f (2) / f (1) ,f s (1) = λ22
f s (2) = f (1) f S (1) .
The solution differs from the naïve consumer's solution only in the MPC in period 1: it is defined by
18

The effect of an increase in α on c2 are ambiguous because it raises the marginal propensity to consume

from W2 whereas it reduces W2 by raising c1.

26

using the effective discount factors ƒS(τ), instead of ƒ(τ). The effective discount factor ƒS(1) for period 2 isa weighted average of the corresponding original discount factor ƒ(1) and the original future one-period
discount factor for period 3 computed as ƒ(2)/ƒ(1). Since, as is easily seen, ƒS(1)>ƒ(1) and ƒS(2)>ƒ(2),which means that the sophisticate behaves as if he had a lower discount rate when comparing felicities
from c1 and c2 and a higher discount rate when comparing felicities from c2 and c3 than he would if hewere naïve. Due to these opposite effects, it is theoretically ambiguous whether the sophisticatedconsumer's MPC in period 1 (λ'11) is smaller than the naïve one's (λ11).19
Nevertheless, it may be natural to hypothesize that the sophisticated consumer would weakensomehow overconsumption/undersaving and overborrowing because period-1 self has an incentive to usesome devices to commit to his lifetime consumption plan. In fact, when there is an commitment device, theperiod-1 self commits to his lifetime consumption plan which is obtained from (3) and (4) (not (5)), so thatthe sophisticate's solution can be shown to satisfy:

c1S = c1N , c2S < c2N , and c3S > c3N , with commitment devices
meaning that the sophisticate restrains c2S, and mitigates undersaving and/or overborrowing.20

19

As shown in the literature (e.g., Phelps and Pollack, 1968; Laibson, 1996; and O'Donoghue and Rabin,

1999), the solution of the naive and that of the sophisticated coincide when the relative risk aversion γ
equals one, i.e., when the felicity function is logarithmic. We can verify that our solutions satisfy the same
property.
20

From (3), (4), and the lifetime budget, the solution with commitments are obtained as c2S =λ21RW1 andc3S =(1-λ11-λ21)R2W1, where λ11 is given by (9); andλ21 =R −1{Rf (1)}1/ γ

1 + R −1{Rf (1)}

1/ γ

{

}

+ R − 2 R 2 f ( 2)

1/ γ

.

From this and (8), it can be shown that, with commitment devices, c3S - c3N ∝ ƒ(2) 1/γ- ƒ(1) 2/γ>0.

27Appendix B: Questions in Q1 and Q2

Insert Figure A1.

28

Appendix C: Q6 and its payoff table

Q6.

Suppose you have two options to pay some money. You may choose Option "A", topay JPY 1 million today; or Option "B", to pay a different amount in a year. Compare theamounts and timing in Option "A" with Option "B" and indicate which amount you would
prefer to pay for each of all 9 choices.

Option A - paying today

A

B

Option B - paying in a year

JPY1,000,000 (USD12,330)□□

JPY 920,000 (USD11,344)

JPY1,000,000 (USD12,330)□□

JPY 970,000 (USD11,961)

JPY1,000,000 (USD12,330)□□

JPY1,000,000 (USD12,330)

JPY1,000,000 (USD12,330)□□

JPY1,001,000 (USD12,343)

JPY1,000,000 (USD12,330)□□

JPY1,005,000 (USD12,392)

JPY1,000,000 (USD12,330)□□

JPY1,010,000 (USD12,454)

JPY1,000,000 (USD12,330)□□

JPY1,050,000 (USD12,947)

JPY1,000,000 (USD12,330)□□

JPY1,100,000 (USD13,564)

JPY1,000,000 (USD12,330)□□

JPY1,300,000 (USD16,029)29

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32

Table 1
Summary statistics
Panel A. Background attributes

Mean

Age

41.763 (12.46)
2386
0.499
(0.50)
2386
0.546(0.50)
2386
6.90(4.09)
2361
14.53 (23.67)
2345
21.15 (29.54)
2351

Male
University grad.
Household income (in JPY million)
Household's financial assets (in JPY million)
Household's real assets (in JPY million)

N(S.D.)

Panel B. Debt-related attributes
Debtors
Debt amounts (in JPY million)
Debt amounts when debtor=1 (in JPY million)

0.202(0.40)
2386
0.50(0.17)
2386
2.45(0.32)
483

Debt for:
Cars 0.066(0.25)
2386
Other goods 0.080(0.27)
2386
Living costs 0.041(0.20)
2386
business 0.017(0.13)
2386
Amusements 0.016(0.13)
2386
Paying off other debt 0.020(0.14)
2386
Debt-to-income ratio > 0.3
0.081(0.27)
2386
Having credit-card debts
0.086(0.28)
2364
Having experiences of having borrowed
0.087(0.28)
2386
unsecured consumer loans
Having experiences of having been denied a
0.118(0.32)
2386
loan for whatever reason
Having experiences of having engaged in debt
0.021
(0.14)
2378
restructuring or declared personal bankruptcy
Note : Data source: The original internet survey, The Japan Internet
Survey on Preferences Relating To Time and Risk 2010 (JPTR2010). Mortgage loans are excluded fron debt.

Table 2.
Generalized hyperbolic discounting, the sign effect, and impatience.
All

Debtors

Non-debtors

Debtor's > Non-debtor's ?

αDeclining impatience (α>0) ?
α*η
Intercept
Discount rates
ρ(7) ((1))ρ(372) ((2))
Panel B. Implied discount rates from Q5 and Q6

0.018 (0.001)
Yes
0.050 (0.001)
0.039 (0.001)
0.029 (0.001)

0.025 (0.003)
Yes
0.065 (0.004)
0.049 (0.002)
0.032 (0.003)

0.016 (0.001)
Yes
0.046 (0.002)
0.037 (0.001)
0.028 (0.001)

Yes*

Discount rates for:0.088 (0.004)
0.010 (0.002)
Yes

0.120 (0.009)
0.027 (0.005)
Yes

0.079 (0.004)
0.005 (0.002)
Yes

Yes
Yes

-0.007 (0.014)

0.159 (0.034)-0.048 (0.015)

Yes

Panel A. Generalized hyperbolic discount function
Declining impatience

receipts Q5 ((3))
payments Q6 ((4))

The sign effect ((3) > (4)) ?
Panel C. The degree of impatience
Impatience ρ (standardized average of (1)-(4))

Yes
Yes
Insignificant**

Note: Standard errors in paretheses. See equations (1) and (2) for the meanings of parameters α, η, andρ. * All the 'Yes's represent that the differences are
significent with P-values smaller than 0.000. ** 'Insignificant' represents that the corresponding P-value equals 0.15.

Table 3
Summary statistics stratified by time discounting variables.
Declining impatience (α)α>0α≦0χ2 testNaif
Sophisticate
2
Mean (S.D.) Mean (S.D.) Mean (S.D.) χ statistics
NNN(P-value)

Impatience (ρ)ρ>meanMean(S.D.)
N

Sign effects (θ)ρ≦meanMean

2

χ test(S.D.) χ statisticsN(P-value)

θ=1

2

Mean

(S.D.)
N

θ=0Mean

χ2 test(S.D.) χ statisticsN(P-value)2

Panel A. Background attributes
Age
Male
University grad.
Household income (in JPY million)
Household's financial assets (in JPY million)
Household's real assets (in JPY million)

45.68(12.44)576
0.516 (0.50)576
0.523 (0.50)
576
7.00(4.32)
565
13.08 (22.17)560
22.46 (28.98)
562

44.1 (12.65)384
0.521 (0.50)384
0.531 (0.50)384
7.26 (4.11)
381
16.22 (23.03)
380
24.93 (31.57)
382

39.26 (11.87)1317
0.472 (0.50)1317
0.541 (0.50)
1317
6.75 (3.97)
1307
14.86 (24.62)
1298
19.52 (29.31)
1299

198.84(0.00)
4.61(0.10)
1.10(0.58)
29.59(0.13)
29.64(0.04)
36.13(0.01)

45.15(12.32)703
0.578 (0.49)703
0.498 (0.50)703
6.71(4.15)
693
13.96 (24.69)
687
21.35 (30.55)
692

40.32 (12.19)1588
0.463 (0.50)
1588
0.55 (0.50)
1588
7.00 (4.07)
1574
15.01 (23.43)
1564
21.48 (29.59)
1566

116.69(0.00)
25.67(0.00)
6.08(0.01)
17.44(0.10)
31.81(0.00)
12.43(0.19)

41.82 (12.51)
1859
0.501 (0.50)
1859
0.528 (0.50)
1859
6.91 (4.09)
1838
14.31 (23.26)
1825
21.01 (29.29)
1832

41.72 (12.09)432
0.486 (0.50)
432
0.56 (0.50)
432
6.94 (4.15)
429
16.32 (26.07)
426
23.28 (32.24)
426

39.47
(0.70)
0.3(0.58)
1.63(0.20)
4.13(0.97)
10.80(0.29)
8.11(0.52)

0.238(0.43)
576
0.58(1.83)
576
2.45(3.08)
137

0.203 (0.40)384
0.45 (1.50)
384
2.21 (2.68)
78

0.181 (0.38)
1317
0.48 (1.81)
1317
2.63 (3.55)
238

8.25
(0.02)
32.29(0.01)
23.24(0.06)

0.277(0.45)
703
0.73(2.08)
703
2.63(3.27)
195

0.162 (0.37)
1588
0.37 (1.50)
1588
2.28 (3.09)
258

39.76
(0.00)
45.63(0.00)
5.09(0.65)

0.189 (0.39)
1859
0.43 (1.59)
1859
2.28 (3.03)352

0.234 (0.42)432
0.69 (2.13)432
2.96 (3.58)
101

4.37
(0.04)
18.7(0.02)
12.66(0.08)

0.064

0.076 (0.26)384
0.073 (0.26)
384
0.029 (0.17)384
0.018 (0.13)384
0.01 (0.10)384
0.01 (0.10)384
0.089 (0.28)384
0.073 (0.26)381
0.081 (0.27)384
0.104 (0.31)384
0.01 (0.10)384

0.059 (0.24)1317
0.071 (0.26)
1317
0.032 (0.18)1317
0.014 (0.12)1317
0.01 (0.10)1317
0.021 (0.14)1317
0.068 (0.25)
1317
0.07 (0.26)1308
0.072 (0.26)
1317
0.101 (0.30)1317
0.019 (0.14)
1310

1.35
(0.51)
6.81(0.03)
17.49(0.00)
0.93(0.63)
12.85(0.00)
2.12(0.35)
7.30(0.03)
16.22(0.00)
5.01(0.08)
15.75(0.00)
6.33(0.04)

0.087

0.054 (0.23)
1588
0.062 (0.24)
1588
0.025 (0.16)
1588
0.01 (0.10)
1588
0.011 (0.10)1588
0.016 (0.12)1588
0.062 (0.24)
1588
0.064 (0.24)
1576
0.065 (0.25)
1588
0.09 (0.29)
1588
0.013 (0.11)
1584

8.79
(0.00)
20.58(0.00)
20.75(0.00)
15.04(0.00)
8.47(0.00)
3.35(0.07)
20.58(0.00)
24.12(0.00)
18.96(0.00)
27.69(0.00)
16.25(0.00)

0.056 (0.23)
1859
0.076 (0.26)
1859
0.034 (0.18)
1859
0.017 (0.13)
1859
0.016 (0.12)
1859
0.018 (0.13)
1859
0.073 (0.26)
1859
0.081 (0.27)
1840
0.085 (0.28)
1859
0.114 (0.32)
1859
0.021 (0.14)
1856

0.1

11.09
(0.00)
1.01(0.32)
2.85(0.09)
0.07(0.79)
0.01(0.93)
1.11(0.29)
4.81(0.03)
0.43(0.51)
0.12(0.73)
0.02(0.89)
0.10(0.75)

Panel B. Debt-related attributes
Debtors
Debt amounts (in JPY million)
Debt amounts when debtor=1 (in JPY million)
Debt for:
Cars
Other goods
Living costs
business
Amusements
Loan repayment
Debt-to-income ratio > 0.3
Having credit-card debts
Having experiences of having borrowed
unsecured consumer loans
Having experiences of having been denied a loan
for whatever reason
Having experiences of having engaged in debt
restructuring or declared personal bankruptcy(0.25)
576
0.106 (0.31)
576
0.071 (0.26)
576
0.019 (0.14)576
0.031 (0.17)576
0.023 (0.15)576
0.104 (0.31)
576
0.125 (0.33)566
0.109 (0.31)
576
0.163 (0.37)576
0.033 (0.18)575Note : Mortgage loans are excluded from debt. Bold digits for χ2 statistics represent the significance at the 10 % level.

(0.28)
703
0.117 (0.32)703
0.064 (0.24)703
0.033 (0.18)703
0.027 (0.16)703
0.027 (0.16)703
0.117 (0.32)703
0.125 (0.33)694
0.127 (0.33)703
0.165 (0.37)703
0.038 (0.19)702(0.30)
432
0.09 (0.29)432
0.051 (0.22)432
0.019 (0.13)432
0.016 (0.13)432
0.025 (0.16)432
0.104 (0.31)432
0.091 (0.29)
430
0.081 (0.27)432
0.111 (0.31)432
0.019 (0.14)
430

Table 4
The marginal effects of time discounting on debt holding behavior.
Model

Declining impatience（α）

Impatience (ρ)

Sign effect (θ) Log pseudolikelihood #obs

Regresson methods

Panel A. Overall debt holding
Prob. of being a debtor

(A)
(B)

Debt amounts (in JPYmillion)

1.22 **(2.58)(A)(B)

Debt amounts in the debtor
sample (in JPYmillion)

0.454 ***
(2.83)
Naifs
Sophisticates
0.203
0.780 ***
(3.58)(0.79)

(A)
(B)

Naifs
2.12 ***(3.66)

Sophisticates
0.37(0.42)

1.48 ***(2.59)
Naifs
Sophisticates2.61 ***(3.71)

0.46(0.42)

0.054 ***
(4.45)-0.063 ***(-2.80)-1030.81

2245

Binary probit

0.055 ***
(4.52)-0.061 ***(-2.71)-966.72

2139

Binary probit

0.18 ***(4.64)-0.23 ***(-3.06)-3948.54

2245

Interval tobit

0.19 ***(4.75)-0.24 ***(-3.04)-3700.62

2139

Interval tobit

0.22 ***(4.76)-0.26 ***(-3.25)-3948.54

2245

Interval tobit

0.23 ***(4.88)-0.27 ***(-3.23)-3700.62

2139

Interval tobit

0.011(1.72)-0.053 ***(-3.45)-495.49

2245

Binary probit

0.010(1.59)-0.048 ***(-3.20)-498.2

2139

Binary probit

0.017 ***
(2.72)-0.017(-1.30)-571.122245

Binary probit

0.018 ***
(2.88)-0.021(-1.58)-540.12

2139

Binary probit

0.003(1.36)-0.008(-1.36)-308.22

2245

Binary probit

0.003(1.32)-0.008(-1.31)-292.92139

Binary probit

0.006 ***
(2.94)-0.002(-0.39)-172.212245

Binary probit

0.005 **
(2.54)-0.003(-0.67)-156.92

2139

Binary probit

0.002(1.28)-0.000(-0.01)-160.69

2245

Binary probit

0.002(1.02)

0.001(0.18)-140.13

2139

Binary probit

Panel B. Debt purposes
Prob. of having debts for:
Cars

(A)
(B)

Other goods

Sophisticates-0.013(-0.09)0.026(1.03)
Sophisticates-0.065(-1.01)

Naifs
0.059
(1.40)
0.022(0.78)(A)(B)

Amusements

Naifs
0.143
(1.37)(A)(B)

Business

0.066(0.80)(A)(B)

Living costs

0.218 ***
(2.56)
Sophisticates
Naifs
0.256 **
0.212 *
(1.86)(2.11)

Naifs
0.035(1.08)

Sophisticates
0.031(0.65)
0.038(1.72)

(A)
Naifs
(B)

0.054 **
(2.16)

Sophisticates-0.014(-0.37)Note : The estimated marginal effects are listed with z values in parentheses. Control variables include: the degree of risk aversion, age, gender, education, householdincome, household real asset holding, household financial asset holding. *, **, *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 5
The marginal effects of time discounting on overborrowing inclination.
Probability of:
Debt-to-income ratio > 30%

Model

(A)
(B)

Having experiences of
having borrowed unsecured
consumer loans

0.098 *
(1.83)
Naifs
0.179 ***
(2.65)

Sophisticates-0.024(-0.27)

0.236 **
(2.05)
Naifs
0.458 ***
(3.12)

Sophisticates-0.111(-0.52)-0.035(1.06)(A)(B)

Sophisticates
0.009(0.05)

0.278 ***
(2.77)
SophisticatesNaifs-0.1510.503 ***
(4.07)(-0.85)(A)(B)

Having experiences of
having engaged in debt
restructuring or declaredpersonal bankruptcy

Naifs
0.309 **
(2.56)(A)(B)

Having experiences of
having been denied a loan
for whatever reason

0.142(1.49)(A)(B)

Having credit-card debts

Declining impatience（α）

Naifs-0.010(-0.43)

Sophisticates-0.247 *(-1.76)

Impatience (ρ)

Sign effect (θ) Log pseudolikelihood #obs

Regresson methods

0.025 ***
(3.52)-0.032 **(-2.16)-581.95

2245

Binary probit

0.024 ***
(3.40)-0.033 **(-2.19)-550.5

2139

Binary probit

0.022 ***
(3.02)-0.016(-1.08)-612.48

2245

Binary probit

0.019 ***
(2.61)-0.014(-0.92)-572.45

2139

Binary probit

0.010 ***
(2.61)-0.001(-0.07)-414.29

2245

Binary probit

0.009 **
(2.40)-0.003(-0.33)-527.87

2245

Binary probit

0.032 ***
(3.80)-0.007(-0.42)-721.78

2245

Binary probit

0.027 ***
(3.11)-0.002(-0.13)-684.05

2139

Binary probit

0.005 **
(2.37)-0.000(-0.02)-197.79

2240

Binary probit

0.003 *
(1.83)-0.001(-0.27)-179.13

2134

Binary probit

Note : The estimated marginal effects are listed with z values in parentheses. Control variables include: the degree of risk aversion, age, gender, education, householdincome, household real asset holding, household financial asset holding. *, **, *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 6
The marginal effects on debt holding: Estimation with the present bias dummy.
Model

Present bias ( α>0) = 1(A)

0.038 **
(2.20)

Impatience (ρ)

Sign effect (θ)

0.056 ***
(4.72)

Log pseudolikelihood#obs

Regresson methods-0.063 ***(-2.81)-1032.07

2245

Binary probit

0.059 ***
(4.87)-0.061 ***(-2.69)-970.79

2139

Binary probit

0.19 ***(4.86)-0.23 ***(-2.99)-3950.81

2245

Interval tobit

0.20 ***(5.03)-0.23 ***(-2.95)-3704.62

2139

Interval tobit

0.23 ***(5.00)-0.26 ***(-3.17)-3950.81

2245

Interval tobit

0.25 ***(5.18)-0.26 ***(-3.14)-3704.62

2139

Interval tobit

0.013 **
(2.02)-0.051 ***(-3.36)-497.812245

Binary probit

0.012 *
(1.91)-0.046 ***(-3.05)-461.18

2139

Binary probit

0.017 ***
(2.78)-0.018(-1.40)-570.40

2245

Binary probit

0.019 ***
(2.99)-0.023 *(-1.70)-538.76

2139

Binary probit

0.003(1.37)-0.008(-1.36)-307.45

2245

Binary probit

0.004(1.36)-0.008(-1.34)-291.42

2139

Binary probit

0.006 ***
(2.92)-0.002(-0.33)-172.45

2245

Binary probit

0.006 **
(2.56)-0.003(-0.56)-157.55

2139

Binary probit

0.003(1.43)-0.000(-0.10)-159.142245

Binary probit

0.002(1.26)

0.000(0.05)-139.40

2139

Binary probit

Panel A. Overall debt holding
Prob. of being a debtor(B)

Debt amounts (in JPYmillion)

Naifs
0.047 **
(2.29)
0.07(1.27)(A)(B)

Debt amounts in the debtor sample(in JPYmillion)

Naifs
0.11 *(1.76)

Sophisticates-0.01(-0.15)0.08(1.28)

(A)
Naifs
(B)

Sophisticates
0.013(0.56)

Sophisticates

0.14 *(1.78)-0.01(-0.15)

Panel B. Debt purposes
Prob. of having debts for:
Cars

(B)

Other goods

BusinessNaifs
0.022 *
(1.92)

Sophisticates
0.002(0.13)
0.004(1.31)
Sophisticates-0.002(-0.38)

Naifs
0.009
(1.58)
0.001(0.25)(A)(B)

Amusements

0.013(1.38)(A)(B)

Sophisticates
0.001(0.76)

Naifs
0.006(0.54)(A)(B)

Living costs

0.013(1.29)

(A)

Naifs
0.001
(0.15)

Sophisticates-0.000(-0.04)0.007 *
(1.83)

(A)
Naifs
(B)

0.008 **
(1.98)

Sophisticates
0.001(0.32)

Note : The estimated marginal effects are listed with z values in parentheses. Control variables include: the degree of risk aversion, age, gender, education, household income,household real asset holding, household financial asset holding. *, **, *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 7
The marginal effects on overborrowing inclination: Estimation with the present bias dummy.
Probability of:
Debt-to-income ratio > 30%

Model

(A)
(B)

Having experiences of having
borrowed unsecured consumer
loans

0.009
(1.36)
Naifs
0.009(1.26)

Sophisticates
0.006(0.75)
0.022 *
(1.68)

Naifs
0.040 **
(2.59)

Sophisticates-0.003(-0.19)0.001(0.32)(A)(B)

Sophisticates
0.013(0.91)

0.031 ***
(2.68)
Sophisticates
Naifs
0.004
0.048 ***
(3.45)(0.25)(A)(B)

Having experiences of having
engaged in debt restructuring or
declared personal bankruptcy

Naifs
0.025 *
(1.85)(A)(B)

Having experiences of having
been denied a loan for whateverreason0.019 *
(1.67)(A)(B)

Having credit-card debts

Present bias ( α>0) = 1

Naifs
0.003
(1.07)

Sophisticates-0.006(-1.36)

Impatience (ρ)

Sign effect (θ) Log pseudolikelihood#obs

Regresson methods

0.026 ***
(3.64)-0.034 **(-2.23)-581.46

2245

Binary probit

0.026 ***
(3.61)-0.034 **(-2.22)-551.75

2139

Binary probit

0.023 ***
(3.30)-0.018(-1.17)-612.28

2245

Binary probit

0.022 ***
(3.02)-0.016(-1.07)-574.26

2139

Binary probit

0.011 ***
(2.80)-0.000(-0.05)-415.09

2245

Binary probit

0.011 ***
(2.71)-0.002(-0.22)-384.99

2245

Binary probit

0.033 ***
(4.04)-0.007(-0.43)-722.41

2245

Binary probit

0.029 ***
(3.42)-0.004(-0.24)-685.27

2139

Binary probit

0.005 **
(2.35)-0.001(-0.14)-198.18

2240

Binary probit

0.004 **
(1.98)-0.002(-0.43)-179.11

2134

Binary probit

Note : The estimated marginal effects are listed with z values in parentheses. Control variables include: the degree of risk aversion, age, gender, education, householdincome, household real asset holding, household financial asset holding. *, **, *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 8
The marginal effects of time discounting on debt holding and overborrwoing inclination: Credit-constrained respondents excluded from the sample.
Model

Declining impatience（α）

Impatience (ρ)

Sign effect (θ) Log pseudolikelihood #obs

Regresson methods

Panel A. Overall debt holding
Prob. of being a debtor(B)

Debt amounts (in JPYmillion)

Naifs
0.670 ***
(2.83)

Naifs
1.95 ***(2.94)

Sophisticates-0.39(-0.53)0.97(1.49)(A)(B)

Sophisticates
0.015(0.06)

0.71(1.48)(A)(B)

Debt amounts in the debtor sample(in JPYmillion)

0.283 *
(1.71)(A)

Naifs
2.69 ***(2.97)

Sophisticates-0.54(-0.53)0.040 ***
(3.25)-0.040 *(-1.80)-819.77

1965

Binary probit

0.042 ***
(3.34)-0.040 *(-1.81)-769.25

1876

Binary probit

0.11 ***(3.11)-0.15 **(-2.06)-2884.94

1965

Interval tobit

0.12 ***(3.20)

0.15 **(-2.08)-2710.33

1876

Interval tobit

0.16 ***(3.15)-0.19 **(-2.17)-2884.94

1965

Interval tobit

0.17 ***(3.25)-0.20 **(-2.20)-2710.33

1876

Interval tobit

0.011(1.58)-0.020(-1.44)-407.421965

Binary probit

0.012(1.64)-0.023(-1.62)-386.96

1876

Binary probit

0.015 **
(2.13)-0.004(-0.27)-433.211965

Binary probit

0.015 **
(2.10)-0.004(-0.28)-406.94

1876

Binary probit

0.004(1.11)

0.003(0.52)-227.63

1965

Binary probit

0.003(0.91)

0.001(0.21)-205.88

1876

Binary probit

Panel B. Overborrowing inclination
Prob. of:Debt-to-income ratio > 30%(B)Having credit-card debts

Naifs
0.350 ***
(2.89)

Naifs
0.376 ***
(3.02)

Sophisticates-0.151(-0.94)0.020(0.45)

(A)
Naifs
(B)

Sophisticates-0.078(-0.49)0.142(1.48)(A)(B)

Having experiences of having
borrowed unsecured consumer
loans

0.146
(1.59)(A)

0.054(0.99)

Sophisticates
0.027(0.39)

Note : In regressions, any respondent with a credit problem or a troubled credit history (having borrowed to pay off other debts, having the experience of having engaged in
debt restructuring or declared personal bankruptcy, or having the experience of having been denied a loan for what ever reason) is excluded from the sample. The estimatedmarginal effects are listed with z values in parentheses. Control variables include: the degree of risk aversion, age, gender, education, household income, household real
asset holding, household financial asset holding. *, **, *** represent statistical significance at the 10%, 5%, and 1% levels, respectively.

Figure 1

Declining discount rates under generalized hyperbolic discounting.
0.06

0.05

0.04

0.03

0.02

0.01

1
8
15
22
29
36
43
50
57
64
71
78
85
92
99
106
113
120
127
134
141
148
155
162
169
176
183
190
197
204
211
218
225
232
239
246
253
260
267
274
281
288
295
302
309
316
323
330
337
344
351
358
365
372
379
386

τ
0

Average ρ

Debtor's ρ

Non-debtor's ρ

Note: The schedules depict discount rates ρ=αη/(1+ατ) of average respondents, average debtors, and average non-debtors, where the (α, η) values are the sample means of thecorresponding samples: (0.018, 2.842) for average respondents; (0.018, 2.240) for average debtors; and (0.016, 2.938) for average non-debtors.

Figure A1
Questions to elicit the generalized hyperbolic discount factor.
Q1 (Questions on discount rates for immediate future)
Which one do you prefer? Choos one you prefer.

Q1-2-1

Q1-2(A) To get JPY 1,000 today.
(B) To get JPY 3,000 a week later.

Q2-2

Q2-1
Q2
1
Q1-3-1Q1-3-2(A) To get JPY 1,000 today.
(B) To get JPY 1,200 a week later.

Q2-2-2(A) To get JPY 1,000 a year later.
(B) To get JPY 1,700 a year + a week later.

Q2-3-1(A) To get JPY 1,000 a year later.
(B) To get JPY 1,400 a year + a week later.

(A) To get JPY 1,000 a year later.
(B) To get JPY 1,500 a year + a week later.

(A) To get JPY 1,000 today.
(B) To get JPY 1,400 a week later.

(A) To get JPY 1,000 today.
( ) To g(B)
get JPY 1,300,
a week later.

(A) To get JPY 1,000 a year later.
(B) To get JPY 3,000 a year + a week later.

(A) To get JPY 11,000000 a year later
later.
(B) To get JPY 2,000 a week later.

(A) To get JPY 1,000 today.
(B) To get JPY 1,700 a week later.

(A) To get JPY 1,000 today.
(B) To get JPY 1,500 a week later.

Q1-3

Q2-2-1(A) To get JPY 11,000000 today
today.
(B) To get JPY 2,000 a week later.

Q1-2-2Q1-1
Q1
1Q2 (Question on discount rates for distant future)
Which one do you prefer? Choos one you prefer.

Q2-3(A) To get JPY 1,000 a year later.
((B)) To get
g JPY 1,300,
a yeary + a week later.

Q2-3-2(A) To get JPY 1,000 a year later.
(B) To get JPY 1,200 a year + a week later.