ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E21-6

Is "I Don't Know" a Signal of
Lack of Financial Knowledge?:
Some Preliminary FindingsbyHideki Endo
Kenichiro Chinen

August 2021

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Is "I Don't Know" a Signal of Lack of Financial Knowledge?:
Some Preliminary Findings
Hideki Endo*

Kenichiro Chinen†
August 5, 2021

Abstract
Previous studies indicated “I don’t know” (DK) item in an option to financial literacy question is a proxy of lack offinancial knowledge. However, is the DK option a proxy for only a lack of knowledge? We examined whether persons
who frequently answered DK to financial literacy questions were at the level of "don't know the answer at all" or "narrow down the choices to some extent” based on a web survey in Japan. Then we compared the factors that leadto the selection of DK with the factors that form financial literacy and explored the impact of those factors. The
results indicated that the group chose DK was at a knowledge level where they could narrow down their answers
to some extent, did not check it box with “a pure guesswork.” Respondents with investment experience and withrich experience in economics or finance courses had higher financial literacy score and lower the number of DK
responses. This suggests that both knowledge and experience are important in the formation of appropriate
financial literacy.

JEL classification: G41, G53
Keywords: financial literacy, “I don’t know” option, experience of investment, experience in taking economics orfinance courses

Faculty of Economics, Nihon Fukushi University. E-mail address: endo@n-fukushi.ac.jp. This research was supported by JSPS
KAKENHI Grant Number JP24730281. The author is very grateful to Economic Research Center of Graduate School of
Economics, Nagoya University and Eiji Mangyo for giving me the opportunity to conduct the research.
† College of Business Administration, California State University, Sacramento. E-mail address: ken_chinen@yahoo.com.
*

1. Introduction
Due to the spread of COVID-19 identified worldwide in 2020, the number of households whose incomes
declined and whose livelihoods have become unstable has increased. According to the US Census
Bureau's Household Pulse Survey conducted from April 14 to July 5, 2021 (Phase 3.1), "the percentage ofadults living in households not current on rent or mortgage where eviction or foreclosure in the next twomonths is either very likely or somewhat likely" is 35.8%, and approximately 4.74 million adults were
projected to face that risk. The Japan Housing Finance Agency announced that the number of borrowers
who have changed their repayment method for 35-year fixed-rate mortgages handled by the agency has
increased rapidly since April 2020, reaching more than 10,000 at the end of March 2021. Financial
planning is “the process of determining whether and how an individual can meet life goals through theproper management financial resources” (Certified Financial Planner Board of Standards, 2013). To
prepare for an unexpected personal financial crisis, sufficient knowledge of financial planning is essential(Luu, Lowe, Butler, and Byrne, 2017).
A number of previous studies show that financial literacy influences individual’s financial planning.
Agarwal, Amormin, Ben-David, Chomsisengphet, and Evanoff (2010) examined an education program inIndiana and indicated that providing financial education to borrowers with low credit scores and incomes,
who are likely to default on their mortgages and other debts in the future, reduce their risks. Lusardi and
Mitchell (2007a, 2011) showed that those who lack basic financial knowledge are more likely to faceinadequate retirement planning (e.g., no plan, no savings). In Japan, Sekita (2011) verified the results of
Lusardi et al. by Japan Household Panel Survey on Consumer Preferences and Satisfaction conducted by
the Global COE Program " Human Behavior and Socioeconomic Dynamics" at Osaka University and foundsimilar results to their studies.
Questions appropriate for measuring financial literacy have been proposed by Huston (2010),
Lusardi and Mitchell (2011), Knoll and Houts (2012), and so forth. However, very few studies havefocused on choices of the questions. When measuring financial literacy, multiple-choice tests are often
adopted. In the test methods, the literacy scores are calculated based on the assumption that a respondent
who chooses a correct answer to a question understands which is the correct answer to it, but it is notclear whether "guesswork" due to lack of knowledge is included.
In this regard, Lusardi and Mitchell (2011), Lusardi (2015) and Sekita (2011) suggested including
an "I don't know"(hereafter, DK) item in an option to each financial literacy question, and they indicatedthat DK option was expected to reduce the number of correct answers due to guesswork, and we got the
picture of the tendency of respondents to choose DK. Lusardi and Mitchell (2011) and Lusardi (2015)
measured American middle-aged and elderly's understanding of basic financial literacy (compounding,inflation, and risk diversification investing) introduced as modules of the 2004 Health and Retirement
2Study. The results showed that there was a very strong tendency to answer DK to questions on acompound interest calculation and the meaning of inflation, and a very weak tendency for those who
answered a question incorrectly to also answer another question incorrectly.
In addition to the above questions, Lusardi and Mitchell (2007b) also asked eight questions about
investment. The results showed that the choice of DK was strongly related to the correct answer. Afterthe confirmation of several statistics, Lusardi et al. made an assertion that those who were not financiallyliterate tended to choose DK instead of the incorrect item, and DK choice as a proxy for “financial
illiteracy”.
Financial illiteracy, or lack of financial knowledge, leads to "ignorance," the inability to foresee
future risks in personal finance. The cognitive bias that leads to overconfidence through the illusion of "Iknow" without being aware of that ignorance is known as the Dunning-Kruger effect (Kruger andDunning, 1999). Balasubramnian and Sargent (2020) investigated persons who perceive their financial
literacy to be "high" in spite of their low literacy tend to engage in high-risk financial behavior. Kramer(2016) indicated that persons who estimate their own financial literacy higher are less likely to seek
financial advice, and Kim, Lee and Hanna (2020) reported that overconfidence in one's financial literacy
increases the probability of the delay in mortgage payments.
However, is the DK option a proxy for only a lack of knowledge? Sanchez and Morchio (1992), who
conducted a study on the choice of political party support (decision-making), showed that wheninterviewers probe DK responses to knowledge questions, their probing behaviors lead answers of
respondents to pure guesswork. Roy and Zeckhauser (2015) also pointed out that when persons
recognized their ignorance, they become indecisive and unable to make up their minds.
In a consignment survey without the intervention of an interviewer, if the question does not include
DK as an option (and the respondents cannot refuse to answer), it may mandatorily lead respondents
who would check the “DK” box to choose other options by guesswork. On the other hand, when theoptions include DK, respondents who recognize their lack of financial knowledge may not be able to makea decision on the option they consider to be the correct answer and may choose DK.
When DK is included in the choices, it is considered that the degree of awareness of ignorance
depends on whether a respondent does not know the correct answer at all or has narrowed it down to
the middle. If they do not know the answer at all, they are likely to recognize their ignorance strongly andchoose DK. However, if they have narrowed down the answers to the middle of the questionnaire yet are
still indecisive, there is a possibility that they choose DK with less aware of their ignorance. Whenanswering multiple financial literacy questions, respondents who choose DK more often for the latterreason are more likely to have a severe cognitive illusion of “I know”.
Balasubramnian and Sargent (2020) indicated that those with heavy cognitive illusions about
financial literacy tend to engage in poor financial behaviors such as imprudent borrowing and poor3

expenditure management. However, in lots of studies on financial literacy, respondents who choose DK
are treated as incorrect answers (simple lack of knowledge), and there are few studies that examine thereasons that respondents choose the option. In this study, we survey different groups of respondents
with and without DK as each option for questions on financial literacy. By comparing the distribution of
the financial literacy scores (the number of correct answers to the questions) of the two groups, we try
to determine whether the answer DKs are pure guesswork or are due to other reasons.
Then, we also check the factors that decrease the number of DK choices. The determinants offinancial literacy scores have been examined in previous studies and were comprehensively reviewed in
Lusardi and Mitchell (2014). If respondents tend to answer with guesswork when they do not know thecorrect answer to a question, there is a possibility that they have a strong recognition of ignorance,otherwise, they recognize their ignorance weakly. In the latter case, the impact of financial illiteracy as
pointed out by Lusardi and Mitchell (2007b) is heavy, and it is significant to clarify the factors that reduce
the number of DK choices.
Section 2 describes the survey method conducted in this study and the questions and options
regarding financial literacy. Then, in Section 3, we examine whether there is a significant difference in the
distribution of financial literacy scores when the question options include DK and when they do not. We
also show that factors forming financial literacy supported by previous studies, such as experience intaking economics or finance courses, also have the effect of reducing the DK choice. Section 4 is a
discussion.

2. Data
2.1 Survey method
In December 2012, we conducted 2012 Survey on Basic Knowledge of Economy and Finance (hereafter, BK
survey) among men and women aged 18-69 in Japan and the United States. The BK survey wascommissioned by Cross Marketing Inc. and conducted as a web survey. We use only Japanese data.
As pointed out by Bethlehem and Biﬃgnandi (2012) and others, respondents in web surveys areneeded to pay attention with respect to their representativeness to the population. First, selection bias
arises when persons who do not have any access devices to the Internet are excluded from the survey(undercoverage). According to 2011 Communications Usage Trend Survey by The Ministry of Internal
Affairs and Communications, showed that the penetration rate was almost 95% or higher for those in
their 20s to 40s, but it dropped to 86.1% among those in their 50s, and to 73.9% and 60.9% among thosein their early and late 60s, respectively. It is rather difficult to survey the condition of people in their 60s
and older via web surveys.
4

In this regard, van Rooij et al. (2011) reported that those who are financially literate are more likely
to make use of information in newspapers and the Internet. In addition, Hastings and Tejeda-Ashton(2008) indicated in a survey in Mexico that those who used Internet banking and Internet reservations
were more likely to cite financial health, not low fees or high yields, as a reason for selecting a pension
management company. According to these previous studies, it is considered that web surveys target
groups with a relatively high level of financial literacy.
Next, when we outsource a web survey to a research company, the respondents of the survey aresampled from a group of persons (called “monitors”) who have registered for the purpose of participatingin various surveys from the research company. There is a difference between the composition of themonitors and the population distribution of 2010 Population Census of Japan. Compared to the census,
most of the monitors were in their 20s to 40s, and the number of them in their 50s and above decreased
significantly. In addition, the monitors slightly concentrated in Kanto region include Tokyo metropolitanarea, and many of the monitors were employed or commuting to school. The number of monitors aged
18-69 was 1,584,175, which was 1.9% of the census population of the age group1.
In collecting the completed questionnaires, the distribution of respondents was made close to theage distribution by 10-year age groups based on the census population. A total of 500 responses werecollected. Specifically, we asked 5,000 stratified randomly selected respondents from among themonitors to respond to the survey and ended the survey when we received 500 valid responses.
In the next section, we clarify the characteristics of the respondents of the BK survey that are causedby the selection bias described above. The preliminary survey for this study was conducted in October
2012, and 200 responses were collected. Since the survey target, survey method, and survey items are
the same for both the preliminary survey and the BK survey, we treat them as a total sample of 700.

2.2 Characteristics of respondents
We examine the characteristics of the respondents in the BK survey in comparison to the census. First,
the average age of people aged 18-69 in the 2010 Census was 44.4 years for men and 44.9 years for
women. In the BK Survey, on the other hand, the average ages were slightly higher at 45.7 and 46.0,
respectively. However, our data was collected according to the 10-year age group distribution of thecensus, and there is no difference in the shape of the distribution between the two surveys.
Next, Table 1 shows the educational attainment and employment status. The percentage of
respondents with a bachelor’s degree or a postgraduate degree is considerably larger in the BK survey,
44.7%, compared to one-fifth of the total in the census. Ishida et al. (2009), after examining the results ofseveral mail survey and web survey methods, pointed out that the educational background of the web

1 Appendix A provides tables comparing the distribution of the monitors and 2010 Population Census.

5

Table 1: Educational attainment and employment status(a) Educational attainment
BK survey (%)
High school graduate or less34.3
Some college
15.3
Bachelor’s degree or higher
44.7
Others (Unknown)
5.7
N
700

Census (%)
51.0
15.4
20.4
13.2
80,785,085(b) Employment status
BK survey (%)
Employed
66.9
Unemployed
3.2
Not in the labor force
29.9
Housework
19.0
Students
2.6
Others
8.3
N695

Census (%)
70.0
4.9
25.1
15.6
3.9
5.6
80,717,222

Note: Survey on Basic Knowledge of Economy and Finance (the BK survey) was conducted among aged 18 to 69 as a web survey
in December 2012. Population Census (Census) was surveyed in October 2010.

Relative frequency of households (%)

Source: Statistics Bureau of Japan (2010) 2010 Population Census.

20.0
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

BK survey (N=612)

Comprehensive Survey of Living Conditions

Note: The distributions of household income were displayed by Survey on Basic Knowledge of Economy and Finance (the BK
survey) and 2011 Comprehensive Survey of Living Conditions.
Source: Ministry of Health, Labour and Welfare (2012) 2011 Comprehensive Survey of Living Conditions.

Figure 1: Distribution of household income

survey's respondents tended to be higher than that of the mail survey methods. With regard to
employment status, the percentage of the housework is 3.4 points larger than in the census. A similar
situation was confirmed in other studies dealing with web surveys, such as Honda and Motokawa (2005).
Finally, Figure 1 shows the distribution of household income. Comparing this survey with 2011
Comprehensive Survey of Living Conditions by Ministry of Health, Labour and Welfare of Japan as thecondition of the country, the FY2011 distribution of household income (including tax) in this survey is
predominantly in the range of 2 to 10 million yen, with a sharp decline in the under 2 million yen income
6groups. The average household income of those aged 18-69 was 5,742 thousand yen, compared to 5,648
thousand yen in overall country. Ishida et al. (2008), Bethlehem and Biﬃgnandi (2012), and othersurveys in Japan and abroad showed that the number of web survey monitors is low among low-income
groups.

2.3 Survey questions on financial literacy
Referring to Huston (2010) and Knoll and Houts (2012), the questions in the previous studies are broadly
categorized into basic literacy questions such as prices, household money management, and saving for
retirement, and applied questions such as insurance, credit card, and mortgage structures. Lusardi and
Mitchell (2011) asked the fewest number of questions, three. Lusardi et al. presented questions oncompound interest calculation, inflation, and risk diversification as the basis of financial literacy andobtained various findings. Therefore, we included the questions of Lusardi et al. in the BK survey.
However, Knoll and Houts (2012) pointed out that the questions asked by Lusardi et al. do not include
enough items related to household budget management. Therefore, we extracted seven questions on"income" and "savings and protection of household purchasing power" from thirty-one questions of theJump$tart survey2 presented in Mandell (2008) and added them to the questionnaire of the BK survey.
Therefore, the financial literacy questions addressed in our study consist of ten questions and areprimarily focused on measuring basic financial literacy (see Appendix B for each question). In accordancewith previous studies, all questions are multiple-choice tests.

3. Empirical analysis
3.1 Financial literacy score
For each of the ten financial literacy questions, one point is added to the score for each correct answer,
and the total score is the financial literacy score (hereafter, Score): up to seven points for the Jump$tart
survey questions and up to three points for the Lusardi and Mitchell (2011) questions. Score is zero toten points. Figure 2 shows a histogram of the distribution of two groups’ Scores. The left figure is thegroup 𝐷𝐾𝑒𝑥 that does not include DK as an option, and the right figure is the group 𝐷𝐾𝑖𝑛 that includes DK.
The number of respondents is 500 in the group 𝐷𝐾𝑒𝑥 and 200 in 𝐷𝐾𝑖𝑛 .
The group 𝐷𝐾𝑒𝑥 has a mean of Score of 6.162 with a standard deviation of 1.765, and the coefficientof variation is calculated to be 0.286. The group 𝐷𝐾𝑖𝑛 has a mean Score of 5.085 with a standard deviation

2 The Jump$tart survey (Survey of Personal Financial Literacy among College Students) was conducted in the U.S. from 1997 to2008 by the Jump$tart Coalition for Personal Financial Literacy, a non-profit organization that aims to improve personal financial
education for school-age students.

7

𝐷𝐾𝑒𝑥

𝐷𝐾𝑖𝑛
Financial literacy score (0-10)

Note: The group 𝐷𝐾𝑒𝑥 that does not include "I don't know (DK)" as an option in each question on financial literacy is shown on
the left (𝑁 = 500), and the group that includes DK (𝐷𝐾𝑖𝑛 ) is shown on the right (𝑁 = 200). The financial literacy score is 0 to 10
points.

Figure 2: Distributions of financial literacy scoreof 2.198 (coefficient of variation 0.432). 𝐷𝐾𝑖𝑛 has a lower mean and a larger relative variation in Score.
However, in the group 𝐷𝐾𝑒𝑥 , if an option chosen as a pure guesswork by a respondent who did not know
the correct answer was correct, we are compelled to treat the respondent knew the correct answer. The
group 𝐷𝐾𝑖𝑛 reduces this concern because it includes the option DK. When a respondent recognizes theirlack of financial knowledge, it is possible for him/her to choose DK because he/she become indecisive
about even narrowed down options.
We estimate an imputed score based on the hypothesis that all the respondents chose DK answered
the question by a pure guesswork in the group 𝐷𝐾𝑖𝑛 . When there are 𝑘 alternatives except DK, theprobability that a respondent who answers by guesswork choose each option is 1⁄𝑘, and the imputed
score is calculated by randomly assigning each DK response (i.e., DK response in each question is assigned
randomly to other options).
If most of respondents chose DK by a pure guesswork, there would be insignificant difference
between the mean of the imputed financial literacy score and of Score of the group 𝐷𝐾𝑒𝑥 . Figure 3 is the
distribution of the imputed and the group 𝐷𝐾𝑒𝑥 's score. The mode of the imputed score’s distribution is
6 and it of the group 𝐷𝐾𝑒𝑥 is 7. The imputed score is a mean of 5.835 with a standard deviation of 1.815.

8

0.25
0.2
0.15
0.1

0.05
0
1

2

3

4

5

Scoreofofgroup
groupDK_ex
𝐷𝐾𝑒𝑥
Score

6

7

8

9

10

Imputed score

Note: The group 𝐷𝐾𝑒𝑥 does not include "I don't know” (DK) as an option in each question on financial literacy. The imputed scoreis calculated by randomly assigning each DK response. The number of the group 𝐷𝐾𝑒𝑥 is 500 and the number of the group that
DK responses are randomly assigned to other options is 200.

Figure 3: Distribution of the imputed and the group 𝐷𝐾𝑒𝑥 's financial literacy score

Note: The graph is drawn based on 200 respondents in the group that includes “I don’t know” (𝐷𝐾𝑖𝑛 ). The number of “I don’tknow” in financial literacy questions is 0 to 10 points.

Figure 4: Number of “I don’t know” in financial literacy questions

Here, when we test the difference in mean between the two groups, the t-value is 2.197 (𝑃 = 0.03)
and the difference is significant at the 5% level. These results suggest that when respondents are unsureof the answer to a financial literacy question, they do not always choose from options by guesswork. If
the respondents who chose DK had tended to answer by guesswork, then there would have been a strong9possibility of recognizing ignorance. However, since this is not the case in this survey, it is possible thatthey are less aware of ignorance.

3.2 Response options for survey questions
The group 𝐷𝐾𝑖𝑛 includes DK as an option for questions on financial literacy. The distribution of thenumber of DK responses by each respondent (hereafter, 𝐶𝑜𝑢𝑛𝑡) is illustrated in Figure 4. The number of
respondents with zero count of DK is 26.5% of 200 respondents in the group 𝐷𝐾𝑖𝑛 . Restricting to Lusardi
et al.'s three questions, the percentage rises to 44.5%. Sekita (2011) which used Japan Household Panel
Survey on Consumer Preferences and Satisfaction found that the percentage was 38.5%. The BK survey has
a slightly higher percentage than Sekita (2011). Though no one responded DK to all ten questions, there
are 11.5% of respondents did not know more than half of the questions.
Three out of four respondents had financial literacy questions that they were not able to answer
clearly, and one in ten did not determine the correct answer to most questions. In the following section,
we examine the that influence on the number of DK choices for the financial literacy questions using thegroup 𝐷𝐾𝑖𝑛 (200 respondents).

3.3 Statistical method
The estimated equation for the formative factors of financial literacy is as follows:
𝐿𝑖 = 𝛼 + 𝑷𝑖 𝜷 + 𝑺𝑖 𝜸 + 𝑭𝑖 𝜽 + 𝑒𝑖 .
𝐿𝑖 is the respondent 𝑖 ’s financial literacy indices ( 𝑆𝑐𝑜𝑟𝑒 or 𝐶𝑜𝑢𝑛𝑡 ), 𝑷𝑖 is the vector of personal
characteristics, 𝑺𝑖 is the vector of the experience in taking economics or finance courses and theexperience of investment, 𝑭𝑖 is the vector of the family backgrounds in which each respondent grew up,
and 𝑒𝑖 is the error term. We use the ordinary least squares method (OLS) to the equation with 𝑆𝑐𝑜𝑟𝑒 as
the explained variable.
Here, we create a correlation matrix to find out if respondents tend to choose DK for one questionof financial literacy when they also choose DK for another question. As a result, 46.7% of all combinations
have a correlation coefficient below 0.2, and 15.6% of all combinations have a correlation coefficient
above 0.4. Therefore, we consider that respondents who chose DK for one question would not necessarily
tend to choose DK for other choices, and treat 𝐶𝑜𝑢𝑛𝑡 as count data that takes a non-negative integer value.
In this case, it is known that the use of OLS causes bias in the estimated values, such as loss of
efficiency. Though the Poisson regression model (PRM) using the Poisson distribution is known as a countdata analysis, it must satisfy the relationship that the conditional mean and variance of the explained
variable are equal. The problem of overdispersion is pointed out, where the variance becomes larger in
actual data. When overdispersion is observed, PRM is not appropriate. One way to deal with this problem
is to use the negative binomial regression model (NBRM) that allows the variance to exceed the mean.
10

Table 2: Descriptive statistics of explanatory variables of the group 𝐷𝐾𝑖𝑛
Gender (Female=1)
Age
Educational attainment
High school graduate or lessSome college
Bachelor’s degree or higher
Employment status (employed=1)
Equivalent income (million yen)
Experience of investment (Yes=1)
Experience in taking economics or financecourses (Frequently or sometimes=1)
Parent’s educational attainment
Father: bachelor’s degree or higher
Mother: bachelor’s degree or higher
Parent’s retirement savings (I don’t know=1)

Mean
0.475
45.540Std. dev.
0.501
13.499

0.345
0.225
0.430
0.675
3.500
0.430

0.477
0.419
0.496
0.470
1.822
0.496

0.110

0.314

0.285
0.110
0.405

0.453
0.314
0.492

Note: The group 𝐷𝐾𝑖𝑛 includes "I don't know (DK)" as an option in each question on financial literacy. The number of the group
𝐷𝐾𝑖𝑛 is 200. The number of respondents answered their income in FY 2011 is 179.

Therefore, both PRM and NBRM are used for estimation with 𝐶𝑜𝑢𝑛𝑡 as the explained variable, and themore appropriate model is selected by testing for overdispersion.

3.4 Descriptive statistics
We accept from the literature review of Chinen and Endo (2011) as the explanatory variables: experienceof investment, experience in taking economics or finance courses, and factors related to parents of each
respondent. Gender, age, educational attainment, employment status, and income are set as personal
characteristics. These variables were also listed by Lusardi and Mitchell (2014) as important factors informing financial literacy.
Table 2 shows the descriptive statistics of explanatory variables. Describing the personal
characteristics of the 200 respondents of the group 𝐷𝐾𝑖𝑛 , 47.5% are female and their average age is
45.540 years with 13.5 years per standard deviation. The educational attainments of the respondents are
34.5% high school graduates or less, 43.0% college graduates or higher, and 67.5% are employed. The
equivalent income, obtained by dividing the household income by the square root of the number ofhousehold members, is 3.5 million yen. Respondents reported their income including tax for FY2011. The
number of respondents answered their income is 179.
The percentage of respondents with experience of investment is 43.0%. According to the 2009
National Survey on Securities Investment (Shoken Toushi ni Kansuru Zenkoku Chosa) by the Japan
Securities Dealers Association, 21.1% of respondents hold domestic or foreign securities or financial

11

products denominated in foreign currencies. Since our BK survey asked about the experience of holding
investment products, the percentage is higher than the percentage of current holders.
“Experience in taking economics or finance courses” is a dichotomous variable that is defined as 1
if the respondent answered "frequently" or "sometimes" to the question "Have you taken classes on
economy, management, or finance either in high school, college, or graduate school?” and the rest as 0.
The percentage of respondents answered "frequency" or “sometimes” is 11.0%.
The remaining variables are related to the respondents' parents. Regarding parental education,
28.5% of the respondents have a father with a bachelor’s degree or higher and 11.0% have a mother withthe degree. We also apply the variable of whether or not the respondent answered DK to the question"Do you have parent(s) who had stocks or retirement savings as you grew up (between age 12 and 17)?".
This variable is used to grasp whether parents provided in-home education on financial matters, such as
giving information on retirement preparedness to their children (respondents). Bowen (1996) points outthat savings are the most frequent financial management topic as parents discuss with their children.
About 40% of the respondents answered, "I don't know if my parents invested or saved for retirement.”

3.5 Results
First, we indicate the results of the regression analysis with 𝑆𝑐𝑜𝑟𝑒 as the explained variable. The left
column of Table 3 shows that for personal characteristics, female respondents have lower 𝑆𝑐𝑜𝑟𝑒 andrespondents with higher equivalent income have higher 𝑆𝑐𝑜𝑟𝑒. Age and educational attainment are notsignificant to 𝑆𝑐𝑜𝑟𝑒. Both experience of investment and experience in taking economics and financecourses are expected to increase financial literacy, but only experience of investment has a significant
effect on 𝑆𝑐𝑜𝑟𝑒.
Many of the variables related to parents have significant effects on 𝑆𝑐𝑜𝑟𝑒 at the 10% level. The
results show that when both parents have a bachelor’s degree or higher, or when the father has thedegree, it significantly increases 𝑆𝑐𝑜𝑟𝑒. The respondents who did not know whether their parents wereprepared for retirement tend to have lower 𝑆𝑐𝑜𝑟𝑒.
Next, we regress 𝐶𝑜𝑢𝑛𝑡 on each explanatory variable to examine the factors to lead respondents tochoose DK for the financial literacy questions. We test whether the PRM or NBRM is more appropriate.
Table 3 shows 𝛼, which indicates the degree of overdispersion, is 0.380. As a result of the likelihood ratio
test with 𝛼 = 0 as the null hypothesis, the test statistic is 31.423 (𝑃 = 0.000), and the null hypothesis isrejected. Therefore, since the constraint that the mean and variance of the explained variable are equal
is not satisfied, we adopt the estimation results from NBRM.
The results in the right column of Table 3 indicate that, the variation in 𝐶𝑜𝑢𝑛𝑡 is contributed similar
determinants to 𝑆𝑐𝑜𝑟𝑒: gender, the experience of investment, father has a bachelor’s degree or higher,

12

Table 3: Estimation results
Explained variable

𝑆𝑐𝑜𝑟𝑒
OLS

Gender (Female=1)-0.716

Age

𝐶𝑜𝑢𝑛𝑡
PRM

**

0.270

NBRM
*

0.330

(0.327)(0.145)(0.167)

0.019

0.002

0.002

(0.013)(0.006)(0.006)0.186-0.166-0.159(0.407)(0.186)(0.200)0.111-0.092-0.066(0.375)(0.153)(0.174)-0.566

0.043

0.047

(0.358)(0.154)(0.174)-0.112-0.102(0.100)(0.104)**

Educational attainment
Some college
Bachelor’s degree or higher
Employment status (employed=1)
Equivalent income (million yen)

0.483

**

(0.241)
Experience of investment (Yes=1)
Experience in taking economics or finance(Frequently or sometimes=1)

0.751

**

-0.383**-0.420(0.341)(0.158)0.652-0.716(0.506)(0.303)(0.307)-0.470-0.485***(0.158)**-0.647**

Parent’s educational attainment
Parents: bachelor’s degree or higher

1.117

*

(0.659)Father: bachelor’s degree or higher
Mother: bachelor’s degree or higher
Parent’s retirement savings (I don’t know=1)

0.739(0.502)*(0.180)(0.196)0.381-0.332-0.315(0.598)(0.482)(0.474)-0.594
3.975*

𝑅 / Pseudo 𝑅

2

0.363

**

(0.142)
***

0.855

0.415

***

0.799

(0.314)(0.350)

179

179

179

0.207

0.099

0.056

-351.013

335.302

Log pseudolikelihood

**

***

(0.147)(0.753)N
2-0.415(0.416)(0.335)Constant-0.364(0.327)
**

**

0.380

𝛼
Likelihood ratio test of 𝛼 = 0 (P-value)

31.423 (0.000)

Note: Standard errors are reported in parentheses. Asterisks (*, **, ***) indicate that the difference is significant at 10%, 5% and
1% levels. 𝑆𝑐𝑜𝑟𝑒 is the financial literacy score, and 𝐶𝑜𝑢𝑛𝑡 is the number of the choice of "I don't know" in questions on financial
literacy. PRM is the Poisson regression model and NBRM is the Negative binomial regression model.

13

Table 4: Effects of the experience of investment and experience in taking economics or finance courses
Explained variable
Investment (No) - Taking courses (Frequently or sometimes)

𝑆𝑐𝑜𝑟𝑒

𝐶𝑜𝑢𝑛𝑡

OLS

NBRM

0.354

179

-0.636(0.392)-0.418(0.164)-1.081(0.453
)
179

0.209

0.056

(0.736)
Investment (Yes) - Taking courses (Not frequently or sometimes)

0.673

*

(0.365)Investment (Yes) - Taking courses (Frequently or sometimes)

1.624(0.541)N
𝑅 / Pseudo 𝑅
2

2

**

**
**

Note: Standard errors are reported in parentheses. Asterisks (*, **) indicate that the difference is significant at 10% and 5%
levels. 𝑆𝑐𝑜𝑟𝑒 is the financial literacy score, and 𝐶𝑜𝑢𝑛𝑡 is the number of the choice of "I don't know" in questions on financial
literacy. NBRM is the Negative binomial regression model. Other explanatory variables in Table 3 are included.

and recognition to parent’s retirement savings. For example, the number of predictions in 𝐶𝑜𝑢𝑛𝑡 isreduced 0.657 times3 for respondents with the experience of investment. If the respondent’s father has abachelor’s degree or higher, 𝐶𝑜𝑢𝑛𝑡 decreases by almost the same amount.
Respondents who did not know whether their parents were taking action to save money for theirretirement when the respondents were high school students have 1.514 times 4 increase in 𝐶𝑜𝑢𝑛𝑡 .
Equivalent income contributes to higher financial literacy, but the impact on 𝐶𝑜𝑢𝑛𝑡 is not significant. On
the other hand, the effect of experience in taking economics or finance courses on 𝑆𝑐𝑜𝑟𝑒 is not significant,but the effect of the variable is significant for 𝐶𝑜𝑢𝑛𝑡 and is expected to reduce the number of DK choices
by 47.7%5.
The difference in 𝑆𝑐𝑜𝑟𝑒 is not significant for respondents who had taken more courses on
economics or finance than for those who had not. The effect of the cross-term of the experience ofinvestment and the experience of taking economics or finance courses on 𝑆𝑐𝑜𝑟𝑒 and 𝐶𝑜𝑢𝑛𝑡 is verified inTable 4, where 𝑆𝑐𝑜𝑟𝑒 is significantly positive for respondents with only investment experience comparedto those with no investment experience and little experience of taking courses. As both the experience ofinvestment and the course-taking experience increases, 𝑆𝑐𝑜𝑟𝑒 increases 5.073 times, where 𝐶𝑜𝑢𝑛𝑡decreases 0.339 times6.

3𝑒𝑥𝑝(−0.420) = 0.657.

4

𝑒𝑥𝑝(0.415) = 1.514.

5

1 − 𝑒𝑥𝑝(−0.647) = 0.477.

6

𝑒𝑥𝑝(1.624) = 5.073. 𝑒𝑥𝑝(−1.081) = 0.339.

14

4. Discussion
Previous studies, such as Lusardi and Mitchell (2011), showed that the rate of choosing "I don't
know“ (DK), rather than the rate of wrong answers, is strongly related to the rate of correct answers toquestions on financial literacy. Therefore, Lusardi et al. pointed out that the DK option in financial literacy
questions is a proxy indicator of lack of knowledge.
Unrecognition of ignorance leads to overconfidence (Kruger and Dunning, 1999; Balasubramnianand Sargent, 2020). Though Roa García (2013) indicated that advice is effective in the financial behavior
of consumers with low financial literacy, Kramer (2016) reported that persons who overestimate theirown literacy are less likely to seek financial advice. If financial advice is not delivered to persons whoneed it, they are likely to continue to make financial decisions with insufficient knowledge. If we identify
the factors that cause people to choose DK for financial literacy questions, we expect to mitigate the lackof knowledge through those factors.
To study if the choice of DK option truly represents a respondent’s lack of financial knowledge, we
examined whether those who frequently chose DK to the questions were at the level of "don't [actually]know the answer at all" or "narrow down the choices to some extent” based on a web survey in Japan(the BK survey). Then we compared the factors that lead to the selection of DK with the factors that form
financial literacy and explored the impact of those factors. The results by the BK survey indicated that thegroup chose DK was at a knowledge level where they could narrow down their answers to some extent,
did not check it box with “a pure guesswork.” Our results and the findings from previous studies suggests
the possibility that respondents choose DK with their indecision and yet have weak recognition ofignorance.
Gender and investment experience were influenced both on the number of the correct answers forfinancial literacy questions (𝑆𝑐𝑜𝑟𝑒) and the number of the DK option for those questions (𝐶𝑜𝑢𝑛𝑡). The
income level affected only 𝑆𝑐𝑜𝑟𝑒. The experience in taking economics or finance courses affected only
𝐶𝑜𝑢𝑛𝑡 . According to Beal and Delpachitra (2003), the upper-income class is likely to make more
appropriate choices through trial and error because of experiencing facing lots of financial decisions, suchas considering the purchase of expensive goods and preserving savings against the removal of full deposit
insurance. However, it is difficult to find a clear answer from our study as to why income does not affect
𝐶𝑜𝑢𝑛𝑡, but only 𝑆𝑐𝑜𝑟𝑒.
Respondents who did not know whether their parents saved money for their retirement when the
respondents were 12-17 years old showed a lower 𝑆𝑐𝑜𝑟𝑒 and a higher 𝐶𝑜𝑢𝑛𝑡. Bowen (1996) showed thatthe most common financial management parents taught their children was saving, but saving was not themost common financial management teenagers remembered being taught by their parents. Even if

15

Note: The horizontal axis shows the number of the choice of "I don't know" in questions on financial literacy (𝐶𝑜𝑢𝑛𝑡), and thevertical axis shows the difference between the distribution based on the observed values and the distribution derived from thepredicted values by the Poisson regression model (PRM) and the Negative binomial regression model (NBRM).

Figure 5: The difference between the observed and the predicted values by PRM and NBRM

parents try to educate their children on money management to their children, it is not easy to share theinformation between parents and children. However, the above results suggest that sustained efforts byparents to make their children recognized the importance of saving money for retirement build theirchildren's financial literacy.
Respondents with investment experience and with rich experience in economics or finance courses
had higher 𝑆𝑐𝑜𝑟𝑒 and lower 𝐶𝑜𝑢𝑛𝑡. According to Forbes and Kara (2010), investment self-efficacy (thebelief that a person is able to take appropriate action and achieve his/her goals with respect to an
investment) is not enhanced by knowledge of the investment, but is reached by gaining experience basedon the knowledge. This suggests that both knowledge and experience are important in the formation of
appropriate financial literacy.
Our study found that the Japanese respondents selected the DK option after narrowing their choices
to some extent. Previous studies suggest that people with weak financial literacy tend to be less aware of
their insufficient financial literacy skills, which often leads them to be overconfident. However, oursurvey did not include appropriate questions to verify this.
Lastly, we provide our observations regarding statistical methods. In analyzing our data, we appliedboth the Poisson regression model (PRM) and the negative binomial regression model (NBRM) and found16

that the NBRM was a more appropriate model to use after testing for overdispersion. Figure 5 shows how
close the predicted values of 𝐶𝑜𝑢𝑛𝑡 by PRM and NBRM are to the observed values. The horizontal axis
shows 𝐶𝑜𝑢𝑛𝑡, and the vertical axis shows the difference between the distribution based on the observed
values and the distribution derived from the predicted values by PRM and NBRM. The more the graph isnear zero, the higher the prediction accuracy. Figure 5 indicates that NBRM is a more appropriate model
because the line graph drawn the difference between the observed values and the predictions by NBRM
is drawn nearer to zero than the graph by PRM. However, there is room for reconsideration in theapplication of the model, because both PRM and NBRM underpredicts when 𝐶𝑜𝑢𝑛𝑡 = 0. It is necessary toapply more appropriate models for the explained variables, including the Zero-Inﬂated Count model.
These issues will be discussed in the future.

17

Appendix
A. Comparison of the Population Census and “monitors”
We outsourced our web survey to a research company. The respondents of the survey are sampled froma group of persons (called “monitors”) who have registered for the purpose of participating in various
surveys from the research company.
A-1: Age group
Age
10-19
20-29
30-39
40-49
50-59
60+
N

Census
10.3
11.8
15.6
14.4
14.0
33.8
116,198,442

A-2: Region
Monitors
3.5
24.4
38.4
22.2
8.9
2.6
1,655,757

RegionHokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku
Shikoku
Kyushu / Okinawa
N

Census
4.4
7.3
34.0
17.7
16.3
5.9
3.1
11.3
115,023,245

Monitors
4.7
5.2
41.8
15.4
17.8
4.8
2.3
8.1
1,655,757

A-3: Employment status
StatusEmployed
UnemployedStudents
Unemployed and others
N

Census
57.3
17.1
6.3
19.3
104,071,474

Monitors
62.3
14.8
11.7
11.2
1,655,757

Note: “Census” is 2010 Population Census and “Monitors” is a group of persons who have registered for the purpose ofparticipating in various surveys from Cross Marketing Inc., the research company. The census is as of October 2010, and themonitor is as of December 2012.

18

B. Questions on financial literacy
Q1

Item
Rebecca has saved $12,000 for her college expenses byworking part-time. Her plan is to start college next year andshe needs all of the money she saved. Which of the following
is the safest place for her college money?

Options

𝐷𝐾𝑒𝑥

𝐷𝐾𝑖𝑛

Locked in her closet at home

28

10

Stocks175Corporate bonds

15

5

A bank savings account

440

168

I don't know
Q2

Which of the following types of investments would bestprotect the purchasing power of a family's savings in theevent of a sudden increase in inflation?

12

A 10-year bond issued by a corporation

71

19

A certificate of deposit at a bank

300

71

A 25-year corporate bond

10

2

A house financed with a fixed-rate mortgage

119

32

I don't know
Q3

Many people put aside money to take care of unexpected
expenses. If Juan and Elva have money put aside for
emergencies, in which of the following forms would it be of
the LEAST benefit to them if they needed it right away?

76

Invested in a down payment on a house

173

58

Stocks251

87

Savings account48

18

Checking account28

7

I don't know
Q4David just found a job with a take-home pay of $2,000 per
month. He must pay $900 for rent and $150 for groceries
each month. He also spends $250 per month ontransportation. If he budgets $100 each month for clothing,$200 for restaurants, and $250 for everything else, how long
will it take him to accumulate savings of $600?

30

One month90

Two months

27

7

Three months

39

18

Four months

425

147

I don't know
Q5

Q6

Rob and Mary are the same age. At age 25, Mary begansaving $2,000 a year while Rob saved nothing. At age 50, Rob
realized that he needed money for retirement and started
saving $4,000 per year while Mary kept saving her $2,000.
Now they are both 75 years old. Who has the most money in
his or her retirement account?

Don and Bill work together in the finance department of thesame company and earn the same pay. Bill spends his freetime taking work-related classes to improve his computer
skills, while Don spends his free time socializing withfriends and working out at a fitness center. After five years,
what is likely to be true?

They would each have the same amountbecause they put away exactly the same64

13

Rob, because he saved more each year

33

11

Mary, because she has put away each year

48

18

Mary, because her money has grown for alonger time at compound interest

355

120

I don't know
Don will make more because he is moresocialDon will make more because Bill is likely to
be laid off

36

8

15

0

Bill will make more money because he ismore valuable to his company

298

100

151

35

Don and Bill will continue to make the samemoney
I don't know
Q7

Sara and Joshua had a baby. They received money as baby
gifts and want to put it away for the baby's education. Whichtends to have the highest growth over periods of time aslong as 18 years?

38

57

Stocks104

38

A government savings bond

116

38

Savings account

264

71

Checking account16

6

I don't know

19

28

47

Q8

Item
Suppose you had $100 in a savings account and the interest
rate was 2% per year. After five years, how much do you
think you would have in the account if you left the money togrow?

Options

𝐷𝐾𝑒𝑥

𝐷𝐾𝑖𝑛

More than $102

388

148

Exactly $102

45

12Less than $102

67

16

I don't know
Q9

Imagine that the interest rate on your savings account was
1% per year and inflation was 2% per year. After one year,
you would be able to buy ________ today with the money inthis account. Please select the one that applies to ________.

24

More than

66

10

Exactly the same as

61

10Less than

373

112

I don't know
Q10

Do you think that the following statement is true or false?
'Buying a single company stock usually provides a safer
return than a stock mutual fund.'

68

True

94

11

False

406

94

I don't know

95

Note: The group 𝐷𝐾𝑒𝑥 excludes "I don't know" from each question's options, and the group 𝐷𝐾𝑒𝑥 includes "I don't know" option.
Q1-7 and Q8-10 referred to Mandell (2008) and to Lusardi and Mitchell (2011), respectively.

20

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22