Cash demand and financial literacy: A case study using Japanese survey data

Journal Pre-proof Cash demand and financial literacy: A case study using Japanese survey data Hiroshi Fujiki

PII:

S0922-1425(20)30003-7

DOI:

https://doi.org/10.1016/j.japwor.2020.100998

Reference:

JAPWOR 100998

To appear in:

Japan & The World Economy

Received Date:

22 February 2019

Revised Date:

15 January 2020

Accepted Date:

2 February 2020

Please cite this article as: Fujiki H, Cash demand and financial literacy: A case study using Japanese survey data, Japan and amp; The World Economy (2020), doi: https://doi.org/10.1016/j.japwor.2020.100998

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Cash demand and financial literacy: A case study using Japanese survey data Hiroshi Fujiki

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Faculty of Commerce, Chuo University

Corresponding author: Hiroshi Fujiki

Abstract

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Cash demand is positively associated with financial literacy in Japan. Hoarding by financially literate households may explain this positive association. We compare the distribution of cash holdings for three levels of financial literacy. We assume hoarding show up in the higher percentile of the cash distributions. We impute financial literacy variable by matching two survey data sets.

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Highlights

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742-1 Higashinakano, Hachioji-shi, Tokyo 192-0393, Japan, Tel: +81-42-674-3602, Fax: +81-42-674-3651, email: [email protected].

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Understanding the statistical relationship between cash demand and financial literacy is important in addressing policy concerns in Japan because the government has tried to reduce cash demands for day-to-day transactions and hoarding. However, few scholars have investigated this point because the available statistics on household cash demand do not contain the standard proxy variable for financial literacy and they do not distinguish cash demands among various households for day-to-day transactions and hoarding. Findings from this research fill these gaps by first imputing the missing financial literacy variable from other surveys and then by comparing the distribution of cash holdings for the high, middle, and low levels of financial literacy groups. In comparing the distributions of cash holdings by the three groups, cash distributions were also compared after dropping the observations above higher percentiles, expecting that the cash demand for hoarding would show up in the higher percentile of the cash distributions. Thus, it was possible to examine cash demands for day-to-day transactions. A comparison of the distribution of the ratio of cash demands to total financial assets holdings plus cash demands (hereafter cash ratio) across three levels of financial literacy groups was also made. It was found that a person with a higher level of financial literacy tends to have a large amount of cash holdings, perhaps mainly for the sake of hoarding; however, the

person also tends to have other kinds of financial assets and thus a lower cash ratio value. Taking those findings at face value, the promotion of financial literacy and cashless payments for day-to-day transactions would reduce the relatively small amount of cash demands for dayto-day transactions, but it would not necessarily reduce the amount of cash demands for hoarding.

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Keywords: cash demand; cash hoarding; financial literacy; matching; imputation

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1. Introduction Understanding the statistical relationship between cash demand and financial literacy is important to addressing policy concerns in Japan because the government has tried to reduce cash demands related to two motives: day-to-day transactions and hoarding. With both motives, higher levels of financial literacy could be associated with fewer cash demands. First, the Ministry of Economy, Trade and Industry of Japan (METI) subsidized cashless payments in some registered retail shops for nine months following the increase in the consumption tax rate on October 1, 2019, with plans to increase the cashless

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payment ratio from 20% to 40% by 2025. Second, the Financial Services Agency (FSA)

of Japan has been actively promoting investments in FSA-selected, no-load, and simple

investment trusts through tax exemptions on dividend and interest earnings on securities, hoping that many Japanese will stop placing their savings into safe but low-return

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financial products, including cash and bank deposits.

Previous research across the globe shows that a higher level of financial literacy

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is associated with the use of noncash payment methods (see Henry et al. [2018]) and a higher number of risky financial assets holdings (see Lusardi and Mitchell [2014] for the

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international survey). Based on these results, a negative relationship was anticipated between cash demand and financial literacy. If such a relationship were identified from Japanese data, and if it were a causal one, it could be argued that the promotion of

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financial literacy would be a desirable step toward achieving the government’s objectives of running a cashless society and influencing higher risky assets holdings by the Japanese. However, few scholars have established an empirical relationship between cash

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demands and financial literacy in Japan because of two shortcomings of the data from the Survey of Household Finance (hereafter SHF) from the Central Council for Financial

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Services Information (CCFSI). First, the SHF does not contain the standard proxy variable for financial literacy and it only provides household knowledge about the Deposit Insurance Corporation of Japan (hereafter DICJ). This is problematic because it is not possible to compare the results (based on the household’s knowledge about the DICJ) with other studies (based on the standard proxy variable for financial literacy). Second, the SHF does report the outstanding amount of cash held at home but it does not distinguish each household’s cash demand by motives (day-to-day transactions or 2

hoarding). This is problematic because the authors of recent research on cash demands placed emphasis on the demands for cash and payment instruments by motives and payment contexts (See Fujiki and Nakashima [2019], Fujiki [2020], and Koulayev et al. [2016]).

Moreover, cash hoardings could explain about 40% of Japanese cash in

circulation as of 2017, as shown by Fujiki and Tomura (2017). The findings from this research contribute to the literature by proposing a few remedies for these two difficulties to establish an empirical relationship between cash demands and financial literacy in Japan, given the restriction that the SHF is the only

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readily available data on cash demands held by households. Regarding the first difficulty, it was noticed that the 2010 wave of the Preference Parameters Study (hereafter PPS 2010) of Osaka University’s 21st Century Center of Excellence (COE) Program ‘Behavioral Macrodynamics Based on Surveys and

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Experiments’ and its Global COE project ‘Human Behavior and Socioeconomic Dynamics’ and the Financial Literacy Survey 2016 (hereafter FLS 2016) from the CCFSI contained the data the standard proxy variable for financial literacy. The missing financial

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literacy variable for the SHF in 2010 and 2016 was imputed by matching the data in the PPS 2010 and the FLS 2016 using four methods. Then, households were grouped

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according to three levels of imputed financial literacy: high literacy, middle literacy, and low literacy.

Regarding the second difficulty, an indirect method was proposed for separating

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each household’s cash demands for day-to-day transactions from those related to hoarding. First, a comparison was made of the distribution of cash holdings among the

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three financial literacy groups. Next, a comparison of the distribution of cash holdings for the three levels of financial literacy groups was made after dropping the observations above higher percentiles, hoping to be more likely to examine the cash distribution for

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day-to-day transactions by the three levels of financial literacy groups. This method was proposed because it was reasonable to expect that cash hoarding should show up in the higher percentile of the cash distributions. Therefore, the observations were dropped above higher percentiles and there was a higher likelihood of examining the cash distributions for day-to-day transactions. The next comparison involved the distribution of the ratio of cash demands to total financial assets holdings plus cash demands 3

(hereafter, cash ratio) by the three groups. This method was proposed because it was anticipated that if a financially literate person engaged in cash hoarding, that person would also have large amount of other types of financial assets. The reason is that findings from previous research tell us that financial literacy is positively associated with financial asset holdings, and thus the cash ratio of a financially literate person would be lower and closer to zero. On the contrary, if a person with lower level of financial literacy demands cash for both day-to-day transactions and savings, the ratio for that person would be one. Based on those methodologies, three findings emerged. First, it was noted that the

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imputed financial literacy variable tends to take higher values for households with better knowledge of the DICJ. This finding helped with comparing the results of Fujiki (2019), in that the households with better knowledge of the DICJ tended to have higher cash holdings conditional on the choice of payment methods among cash, electronic money,

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and credit cards as well as other household demographic variables. A second finding was that a household that belonged to a higher financial literacy group tended to have a higher

amount of cash holdings in many cases. However, there was not a clear relationship

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between cash holdings and financial literacy when the distribution of cash holdings after dropping the observations above the median (50 thousand yen for family household and

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30 thousand yen for single-person household) were examined; these should be close to the distribution of cash demands for day-to-day transactions. Third, it was found that households that belonged to a higher financial literacy group tended to have lower values

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of the cash ratio compared to the other two groups in the data set for year 2016. Taken together, these results suggest that a person with higher financial literacy

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tends to have a large amount of cash holdings perhaps mainly for the sake of hoarding; however, this same person tends to have other kinds of financial assets and a lower cash ratio value. Cash hoarding seems to constitute a part of the financially literate household’s

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financial assets. Note that we do not provide any causal evidence. However, taking those conclusions at face value, our results also suggest that the promotion of financial literacy and cashless payments for day-to-day transactions would only reduce a relatively small amount of cash demands for day-to-day transactions. Hence, the Japanese government needs other policy tools if it pursues a cashless society without cash hoarding.

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This paper is most closely related to the work of Henry et al. (2018) who used the 2017 Methods-of-Payment Survey in Canada and found that persons with higher financial literacy in terms of a standard measure tended to have smaller cash holdings in their wallets and used noncash payment methods. This study also relates to those on cash demands and the use of noncash payment methods. Research based on Japanese SHF data (Fujiki and Tanaka [2018], Fujiki [2019]) shows that households with the following characteristics tend to be credit card users rather than cash-only users for day-to-day transactions; (1) better financial knowledge as

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measured by knowledge about the DICJ; (2) a higher disposable income; (3) greater financial assets; (4) a younger household head; (5) a female household head; (6) higher

educational attainment; (7) not being self-employed; (8) living in a large city; and (9) living in areas with more passengers per kilometer. It also shows that holding household

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characteristics constant, households with better financial knowledge as measured by knowledge about the DICJ tend to have a larger amount of cash holdings.

The studies on cash demands and noncash payment methods outside Japan also

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show that households with a younger household head and higher educational attainment tend to use noncash payment methods rather than cash; Esselink and Hernández (2017)

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for the Eurozone; Greene et al. (2017), Koulayev et al. (2016) and Schuh and Briglevics (2014) for the US; Henry et al. (2018), Wakamori and Welte (2017), and Chen et al. (2017) for Canada; Jonker et al. (2018) for the Netherlands; and Lippi and Secchi (2009)

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for Italy. However, Henry et al. (2018) is the only study that includes a standard measure of financial literacy.

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Regarding the studies on financial literacy, as summarized in an extensive survey by Lusardi and Mitchell (2014), economists often find that personal financial literacy affects financial decisions, even after controlling for individual educational attainment

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(see Lusardi, Michaud, and Mitchell [2017] for research involving US data; Kadoya et al. [2017] for research involving the PPS 2010 data; and Sekita et al. [2018] for research involving the FLS 2016 data. Also see Sekita [2011, 2013]; Kadoya and Khan [2017, 2018, 2019]; and Yoshino et al. [2017]). Most of the studies in this area employ three questions relating to compound interest, inflation, and stock risk to measure personal

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financial literacy. As of December 2019, as far as it is known, responses to these three standard questions are available from five surveys conducted in Japan. These include: (1) the PPS 2010; (2) the FLS 2016 and 2019, both of which are from the CCFSI; (3) the 2010 wave of the National Survey on Work and Family (hereafter NSWF 2010) conducted by the Nihon University Population Research Institute (analyzed by Clark, Matsukura, and Ogawa [2013]); and (4) the 2009 wave of the Japanese Study on Aging and Retirement (hereafter

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JSTAR 2009) by the Research Institute of Economy, Trade and Industry, Hitotsubashi University, and the University of Tokyo, which was studied by Shimizutani and Yamada (2018).

NSWF 2010 and JSTAR 2009 were not used for this study because NSWF 2010

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does not provide information on the outstanding amount of financial assets and JSTAR

2009 does not provide data from individuals below age 50. Gan et al. (2018) used a survey data set and investigated the relationships among financial literacy, wealth accumulation,

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and investment returns conditional on risk aversion. This study is the first known to impute personal financial literacy using the SHF and to examine the relationship between

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cash demand and financial literacy.

The remainder of this paper is organized as follows: Section 2 discusses the data sets. Section 3 explains the empirical methods and the data used for the empirical

2. Data

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investigation. Section 4 provides the results. Section 5 concludes.

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The PPS 2010 and the FLS 2016 were used in this study because they contain three standard questions to compute the financial literacy index and impute the missing

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financial literacy variable for the SHF 2016 and 2010. These were also used because the data from respondents ages 20 to 69 years are included as well as key demographic variables including the outstanding amount of financial assets; this is in line with the SHF 2010 and 2016. The SHF 2010 and 2016 were used for examining cash holdings and total financial assets excluding cash holdings. 2.1 PPS 2010 6

The purpose of Osaka University’s PPS is to calculate the preference parameters in utility functions. The PPS questions respondents on their risk aversion, time preferences, habit formation, externalities, and demographic information. The survey began in 2003, with panels of respondents chosen from men and women ages 20 to 69 years. Conducted from January to February 2010, the 2010 wave of the PPS surveyed 6,134 households; 5,386 responded. In the PPS 2010, Questions 26 through 28 best fit the following three standard financial literacy questions: Question 26: “Suppose you had ¥10,000 in a savings account and the interest rate

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is 2% per year and you never withdraw money or interest payments. After 5 years, how much would you have in this account in total?” Choose from: more than ¥10,200, exactly ¥10,200, less than ¥10,200, do not know, refuse to answer.

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Question 27: “Imagine that the interest rate on your savings account was 1% per year and the inflation was 2% per year. After 1 year, how much would you be able

to buy with the money in this account?” Choose from: more than today, exactly

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the same, less than today, do not know, refuse to answer.

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Question 28: “Please indicate whether the following statement is true or false: ‘Buying a company stock usually provides a safer return than a stock mutual fund.’” Choose from: true, false, do not know, refuse to answer.

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The variable Financial Literacy Index was constructed for this study based on the number of correct responses to Questions 26, 27, and 28. Consequently, the Financial

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Literacy Index takes values of 0, 1, 2, or 3, with the most financially literate receiving the highest score.

The PPS 2010 also provides the following demographic information: annual

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before-tax household income, 1 household holdings of financial assets, gender, age, employment status, and educational attainment. Other information includes the household prefecture of residence; whether there are any loans in the household; and whether the

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For the sake of consistency with the FLS and SHF data, the respondents’ income data were not included.

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respondent is a homeowner. Note that the PPS 2010 also asks about the number of household members living with the respondent. Only 260 out of 5,386 respondents replied that they lived alone; thus, those samples were removed so as to focus on the analysis of family households. 2.2 FLS 2016 The FLS 2016 is a web survey that was administered in Japan from February 29 to March 17, 2016 to about 25,000 individuals ages 18 to 79. The survey included true/false questions on financial knowledge and financial decision-making skills, along with

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behavioral and attitudinal questions. Some of the questions are comparable to US

Financial Industry Regulatory Authority surveys and the Organization for Economic Cooperation and Development survey summarized by Atkinson and Messy (2012).

Questions 18 through 21 in the FLS 2016 was used to construct the variable Financial

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Literacy Index:

Question 18: “Suppose you put 1 million yen into a savings account with a

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guaranteed interest rate of 2% per year. If no further deposits or withdrawals are made, how much would be in the account after 1 year, once the interest payment

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is made? Disregard tax deductions. Answer with a whole number.” Question 19: “Then, how much would be in the account after 5 years? Disregard

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tax deductions.” Choose only one answer from the following options: More than 1.1 million yen, Exactly 1.1 million yen, Less than 1.1 million yen, Impossible to tell from the information given, Do not know.

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Question 20: “Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to

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buy with the money in this account?” Choose only one answer from the following options: More than today, Exactly the same, Less than today, Do not know. Question 21 (4): “Please indicate whether you think the following statements are

true or false. ‘Buying a single company’s stock usually provides a safer return than a stock mutual fund.’”

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The Financial Literacy Index variable was constructed based on the number of correct answers to Questions 19, 20, and 21(4). To identify respondents who provided a correct response to Question 19, we required the respondent to correctly answer both Questions 18 and 19. In addition to information on financial literacy, the FLS provides the following demographic information: household annual pretax income, household total financial asset holdings, gender, age, employment status, educational attainment, area of residence

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by prefecture, and whether the respondent’s household has any loans. 2.3 SHF 2010 and 2016

The SHF 2010 and 2016, conducted by the CCFSI, was also used for this study. It includes

questions about household financial assets and liabilities with a breakdown of financial

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products and their outstanding amounts. It also includes questions about various

household characteristics, including annual after-tax income, area of residence, and the

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age, gender, education, and employment status of household heads.

There are two weak points of the SHF. First, it does not contain the standard proxy

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variable for financial literacy and it only provides information on the household’s knowledge about the DICJ. The following values were assigned to the variable Deposit Insurance: 1—respondent knows about the role of the DICJ; 2—respondent has heard

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about the DICJ; 3—respondent does not know about the DICJ; and 4—respondent refuses to answer. A second weak point regarding this survey is that while the SHF does report the outstanding amount of cash held at home, it does not distinguish among the

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household’s motivations for cash demands (day-to-day transactions or hoarding). For each survey year, the SHF data comprises family and single-person household

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data sets. For the family household data set, the SHF uses a stratified two-stage random sampling method to select 500 survey areas. Then, 16 households are randomly selected (each household consists of two or more people from each area), producing a total of about 8,000 samples. In the 2016 survey, 192 sample households in the Kumamoto and Oita Prefectures were dropped because the disasters caused by the Kumamoto earthquake in April 2016 made it difficult to conduct the survey as planned.

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In 2010 and 2016, 4,035 and 3,497 households responded to the survey, respectively. By contrast, the single-person household data set selected each survey year comprises 2,500 respondents from a pool of individuals aged 20 to 69 years who registered with a survey company through the Internet. The distribution of respondent ages, genders, and regions is determined in such a way as to reflect the population characteristics of the Japanese census. 3. Estimation Method Methodology for imputation

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3.1.

The PPS 2010 and the FLS 2016 were used to impute the Financial Literacy Index for the SHF 2016 and 2010. Next, the PPS 2010 and the SHF 2010 family household data

were combined, as were the FLS 2016 and the SHF 2016 family household data and the

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FLS 2016 and the SHF 2016 single-person household data.

To impute the Financial Literacy Index using the SHF, the value of the Financial Literacy Index was first set to zero in the SHF 2010 and 2016 because the SHF did not

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contain the information needed to construct the Financial Literacy Index. Then, a common set of demographic variables was provided (𝑋𝑖𝑡 ) to help identify whether an

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individual i in year t was surveyed by the PPS 2010, the FLS 2016, the SHF 2010, or the SHF 2016.

𝑋𝑖𝑡 includes the household income of respondents (note that the SHF is for after-

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tax income; the PPS 2010 and the FLS 2016 are for before-tax income); the outstanding amount of total household financial assets; and whether the household has any loans. It

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also includes gender, age, employment status, educational attainment, and area of residence (or the household head in the SHF), which are available in the PPS 2010, the 2016, and the SHF (both 2010 and 2016). A dummy variable was also used to indicate

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homeownership; this is available in both the PPS 2010 and the SHF 2010. The details of these variables are reported in Appendix 1 and the mean value of those data sets are reported in Appendix Tables 1 through 3. To impute the Financial Literacy Index value for the data obtained from the PPS 2010 and the FLS 2016, a treatment effects framework was applied for imputing missing data (see Cameron and Trivedi [2005] and Hoshino [2007] for details). It was assumed 10

that the observations from the PPS 2010 and the FLS 2016 were treated groups, whose outcome variable was the Financial Literacy Index. It was also assumed that the SHF 2010 and 2016 data samples were nontreated groups. Because these samples were not surveyed by the PPS 2010 and the FLS 2016, their outcome variable, Financial Literacy Index, was unobserved. A dummy indicator variable, Findata, was then constructed. Its value was 1 for data from the PPS 2010 and the FLS 2016; its value was 0 for data from the SHF 2010 and 2016. The propensity score 𝑝(𝑋𝑖𝑡 ) was defined for an individual in the PPS 2010 or

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the FLS 2016 data, rather than the SHF 2010 or 2016 data: 𝑝(𝑋𝑖𝑡 ) = Prob(𝐹𝑖𝑛𝑑𝑎𝑡𝑎 = 1| 𝑋𝑖𝑡 ).

(1)

It was assumed that the ignorability assumption was satisfied so that participation in the SHF 2010 or 2016 data and the value of Financial Literacy Index were independent,

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conditional on the covariate 𝑋𝑖𝑡 . Thus, conditional independence given 𝑝(𝑋𝑖𝑡 ) holds.

To compute the propensity score 𝑝(𝑋𝑖𝑡 ) = Prob(𝐹𝑖𝑛𝑑𝑎𝑡𝑎 = 1| 𝑋𝑖𝑡 ), a logit

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treatment model was estimated whose dependent variable was Findata and whose independent variable was 𝑋𝑖𝑡 . All of the common covariates were specified as the

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explanatory variable 𝑋𝑖𝑗𝑡 , because there was no hint that any of these were superior in predicting a particular data sample surveyed in one of the two data sets. If some variables had an absolute value of standardized difference after matching of more than 0.1, the

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variable with the largest absolute value of the standardized difference was dropped and matched again using the remaining common covariates as explanatory variables. This continued until all of the absolute values of the standardized differences after matching

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were less than 0.1, as suggested by Austin (2011). Propensity score matching was then conducted to estimate the treatment effects

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on the Financial Literacy Index if the household was in the PPS 2010 or the FLS 2016, rather than the SHF 2010 or 2016. As the value of the Financial Literacy Index in the SHF 2010 and 2016 datasets was set to zero, the treatment effects were natural values for imputing the Financial Literacy Index for individuals in the SHF 2010 and 2016. This is because the treatment effect estimators obtained by propensity score matching imputed the missing potential outcome of the Financial Literacy Index for each of the SHF samples by using an average of the outcomes of the Financial Literacy Index with the 11

PPS 2010 or FLS 2016 samples that included close values of the propensity score. For estimation, the teffects psmatch command in STATA 14 was used. As a robustness check for the standard propensity score matching, propensity score matching with the Epanechnikov kernel function was also used. The bandwidth and matching weight were selected using the kmatch ps command in STATA 14 (see Jann [2017] for details). In the case of kernel matching, estimates of the potential outcome differences were employed as the imputed value for the Financial Literacy Index for individuals in the SHF 2010 or 2016.

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As a further robustness check, nearest-neighbor matching and multivariatedistance (Mahalanobis distance) matching with the Epanechnikov kernel function were

conducted to estimate the treatment effects. The expectation was that nearest-neighbor

matching and multivariate-distance matching would yield similar results as propensity

nnmatch command in STATA 14.

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score matching. Standard nearest-neighbor matching was conducted using the teffects

Multivariate-distance matching with the

14 (see Jann [2017] for details).

Methodology for the analysis of cash demands using the imputed financial literacy variable

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3.2.

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Epanechnikov kernel function was conducted using the kmatch nn command in STATA

Respondents with a better understanding of the DICJ were examined and noted to have

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higher levels of the imputed financial literacy variable; results based on the DICJ were compared to those that were based on the imputed Financial Literacy Index developed for this study. Behind this examination lies a result in Yamori (2014) that questioned the

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validity of knowledge of the DICJ as a proxy for financial literacy. He showed that the variable tends to have higher values in regions with many bank failures in the late 1990s

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and early 2000s, and thus, it might merely reflect personal experiences with bank failure and not general financial literacy. Note that the statistical distributions of imputed financial literacy variables were

unknown, and thus nonparametric methods were used to test the hypothesis that respondents with a better understanding of the DICJ have a higher level of imputed financial literacy variable. Therefore, the Mann-Whitney rank sum test was used to examine whether the sum of the ranking (from low values to high values) of the imputed 12

variables for households with a better understanding of the DICJ tended to have a higher value than those from households with a poor understanding of the DICJ. Second, respondents were grouped into three levels of financial literacy: high financial literacy (more than or equal to the 67th percentile); middle financial literacy (less than the 67th percentile and more than or equal to the 34th percentile); and low financial literacy (less than the 34th percentile). The distribution of cash holdings was then compared for the three groups. The distributions of cash holdings reported in the SHF were also compared for the

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three levels after dropping the observations above the 99th, 95th, 90th, and 75th percentiles and the median to distinguish cash demands (for day-to-day transactions and

hoarding). It was assumed that most of the demand for cash for day-to-day transactions

tended to have lower values (for example, below 10 thousand yen), while cash demands

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for hoarding would vary from household to household. Remember only the data on cash demands could be used (which adds cash for day-to-day transactions and for hoarding). However, it was expected that examining the cash distribution for day-to-day transactions

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by the three levels of financial literacy groups was more likely if an evaluation of the cash distribution after dropping the observations above higher percentiles was applied.

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In comparing the cash distribution by the three levels of financial literacy groups, the Mann-Whitney rank sum test was used. This was because the cash distribution was right-skewed and many respondents answered zero value, which made the inference

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based on normal distribution impossible. Zero cash holdings might sound odd; however, note that the FLS asks for the amount of cash holdings in units of 10 thousand yen.

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Therefore, if a person had 4 thousand yen, he/she might have replied that he/she had zero cash holdings.

Third, a comparison of the distributions of cash ratio (the ratio of cash demand to

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total financial assets holdings plus cash demand) was made by considering data across the three levels of financial literacy. If a person with a higher level of financial literacy demands cash for both day-to-

day transactions and hoarding and demands a large amount of other financial assets for savings, the cash ratio for this person would be low and possibly close to zero. On the contrary, if a person with a lower level of financial literacy demands cash for both day13

to-day transactions and hoarding, and demands small amount of other financial assets for savings the cash ratio for this person would be close to one. In comparing the cash ratios of each of the three financial literacy groups, the Mann-Whitney rank sum test was used. This was because the distribution of the cash ratio was bimodal, having two spikes at the values of zero and one, which again made the inference based on normal distribution impossible. The cash ratio of zero represented a household with financial assets other than cash as well as zero cash holdings. The cash ratio of one corresponded to a household who had no financial assets other than cash.

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4. Results 4.1 Imputation Results

An estimate of the logistic treatment model regressing the indicator variable Findata on

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𝑋𝑖𝑗𝑡 was made as specified in equation (1) for the propensity score matching and for the

propensity score matching with the Epanechnikov kernel function for three combinations of data sets. Results are reported in Appendix Table 4 through 6, where the goodness of

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fit statistics demonstrate the moderate fits of the models to the data. Appendix Figure 1 also reports the overlap plot of the logistic treatment model for the propensity score

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matching, indicating overlap between the two data sets even though the data from PPS 2010 were concentrated across small values of the propensity score for the SHF 2010 data. Appendix Tables 4 through 6 also detail the standardized differences of the

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covariates to compute the treatment effects or the potential outcome differences after the propensity score matching using the logit treatment model, propensity score matching

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with the Epanechnikov kernel function, nearest-neighbor matching, and multivariatedistance matching (Mahalanobis distance) with the Epanechnikov kernel function. The data in these Tables show that absolute values of the standardized differences after

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matching were less than 0.1. Figure 1 shows the average imputed value of the Financial Literacy Index

according to the value of Deposit Insurance, using four imputation methods. This shows that a household that knows about the DICJ and what it does (shown as blue bars) tended to have higher imputed values of the Financial Literacy Index. The only exception was the combination of the SHF 2010 family household data samples as the base group and the PPS 2010 as the treated group using propensity score matching. These showed that a 14

household that had heard about the DICJ (shown in yellow) tended to have lower imputed values of the Financial Literacy Index than a household that did not know about the DICJ (shown in red). However, even in this case, households that knew about the DICJ and what it does tended to have higher imputed values of the Financial Literacy Index than households that did not know about it. A formal statistical test for the difference in the average of imputed financial literacy variables based on the different values of Deposit Insurance could not be provided because the statistical distributions of imputed values of the Financial Literacy

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Index were unknown. However, the Mann-Whitney rank sum test was used to examine whether the sum of the ranking (from low values to high values) of the imputed variables

for households with a better understanding of the DICJ tended to have higher values than those households with a poor understanding of it. Specifically, the following null

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hypotheses were tested: (1) The rank sum of a household that knows about the DICJ and what it does and that of those who have heard about the DICJ are equal; and (2) The rank sum of a household that heard about the DICJ and that of a household that do not know

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about the DICJ are equal. The results are summarized in Table 1.

Because the rank was assigned from low values to high values of the imputed

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variables, a group who had higher values of imputed financial literacy was expected to have a higher rank sum. Therefore, positive and statistically significant results for the first null hypothesis in Table 1 imply that a household that knows about the DICJ and what it

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does would have higher values compared to a household that heard about the DICJ. Positive and statistically significant results for the second null hypothesis imply that a

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household that heard about the DICJ would have higher values compared to a household that did not know about the DICJ. Table 1 shows that except for the four cases shaded for the SHF 2010 family household data based on the propensity score matching method and

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the propensity score kernel matching method, the imputed variables for households with better understanding of the DICJ tended to have higher values than those with a poor understanding of the DICJ at 10% level of statistical significance. These results support the comparison of the results with the analysis that used the knowledge of the DICJ as a proxy of financial literacy, such as in the work of Fujiki (2019) and Fujiki and Tanaka (2018). 15

4.2 Cash holdings and financial literacy Among studies of the relationship between financial literacy and cash holdings, Henry et al. (2018) used the 2017 Methods-of-Payment Survey in Canada and found that persons with higher financial literacy in terms of a standard measure tended to have smaller cash holdings in their wallets. The SHF only reports the cash holdings held by households (not only in the wallets of household members, but elsewhere in their home). Armed with the imputed Financial Literacy Index for the SHF 2010 and 2016 data, the relationship between the level of Financial Literacy Index and cash holdings was analyzed.

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Figure 2 shows the average and median cash holdings of households with a high

level of imputed Financial Literacy Index (more than or equal to the 67th percentile); a middle level of imputed Financial Literacy Index (less than the 67th percentile and more

than or equal to the 34th percentile); and a low level of imputed Financial Literacy Index

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(less than the 34th percentile) for three combinations of data sets using four imputation

methods. Except for the combination of the SHF 2010 family household data and the PPS

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2010 using propensity score matching with the Epanechnikov kernel function and nearest-neighbor matching, the mean cash holdings of households with a high level of

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imputed Financial Literacy Index (shown with blue bars) were higher than those with a middle level of imputed Financial Literacy Index (shown in yellow) and those with a low level of imputed Financial Literacy Index (shown in red). In contrast, the median cash

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holdings of households with a high level of imputed Financial Literacy Index (shown as diamonds) were not lower than those with a middle level of imputed Financial Literacy Index (shown as triangles) and those with a low level of imputed Financial Literacy Index

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(shown as circles). These results are consistent with the finding by Fujiki and Tanaka (2018) that a household with better knowledge about the DICJ tends to have higher cash

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holdings conditional on various demographic variables using the SHF family household data from 2007 to 2014. Unfortunately, the cash distribution is right-skewed and many respondents

answered zero value, which made the inference based on normal distribution impossible. For example, Figure 3 shows the right-skewed cash distributions by the three financial literacy groups using the combination of SHF 2016 single-person household data and the FLS 2016 using propensity score matching. In Figure 3, more observations are seen in 16

the right end of distributions for higher literacy groups than those of the lower financial literacy group, as anticipated. Therefore, a comparison was made of the cash distribution among the three financial literacy groups using a Mann-Whitney rank sum test for robustness checks. The distribution of cash holdings for the three levels of financial literacy groups was also compared after dropping the observations above higher percentiles (99th, 95th, 90th, and 75th) and the median to distinguish the cash demands for day-to-day transactions from those related to hoarding. Incidentally, the SHF provides a measure of cash hoarding (hereafter referred to It asks the respondents whether they increased their cash

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as a “mattress deposit”).

holdings to reduce investment risk to themselves, either by reducing asset holdings or by suspending additional investment in other financial products, or not. A dummy indicator

variable, Mattress Deposit, was constructed from this question. It took a value of one for

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the respondents who responded affirmatively to this question and zero otherwise. Fujiki (2019) and Fujiki and Tanaka (2018) both found that a household with one value of Mattress Deposit had, on average, a higher cash demand by about 250 thousand yen than

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a household with zero value of Mattress Deposit, conditional on various demographic variables. Therefore, when comparing the distribution of cash holdings for the three levels

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of financial literacy after dropping the observations above higher percentiles, the samples with one value of Mattress Deposit were also dropped. The following null hypotheses were tested: (1) The rank sum of cash holdings of

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households with a high level of imputed Financial Literacy Index and that of middle level of imputed Financial Literacy Index are equal; and (2) The rank sum of cash holdings of

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households with a middle level of imputed Financial Literacy Index and that of low level of imputed Financial Literacy Index are equal. The results are summarized in Tables 2 and 3.

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Table 2 shows the comparison of the distribution of cash holdings for the three

levels of financial literacy groups using whole observations and the distributions of cash holdings after dropping the observations above: (1) the 99th percentile (200 thousand yen for the SHF 2010 and the SHF family household data 2016, and 500 thousand yen for the SHF single-person household data 2016); 17

(2) the 95th percentile (50 thousand yen for the SHF 2010, 30 thousand yen for the SHF family household data 2016, and 100 thousand yen for the SHF single-person household data 2016); and (3) the 90th percentile (23 thousand yen for the SHF 2010 and the SHF family household data 2016, and 50 thousand yen for the SHF single-person household data 2016). Table 3 shows the comparison of the distribution of cash holdings for the three levels of financial literacy after dropping the observations above:

single-person household data 2016); and

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(1) the 75th percentile (10 thousand yen for the SHF 2010, the SHF family and

(2) the median (50 thousand yen for the SHF 2010 and the SHF family household

data 2016, and 30 thousand yen for the SHF single-person household data

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2016).

Because the rank was assigned from lower cash holdings to higher cash holdings, positive and statistically significant results imply that households with a higher level of

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financial literacy tended to have higher amounts of cash holdings, as shown in Tables 2 and 3.

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The results in Table 2 suggest that in many cases, households with higher levels of financial literacy tended to have higher amounts of cash holdings. The combination of the SHF 2016 single-person data and the FLS 2016 support this conclusion for all cases.

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The combination of the SHF 2016 family household data and the SHF 2016 and the combination of the SHF 2010 family household data and the PPS 2010 data support this

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conclusion if the nearest-neighbor matching method is used. The results make sense if cash hoarding is a rational response by a financially literate person who considers that long-lasting low interest rates make the investment into cash an attractive option.

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However, the results in Table 2 demonstrate several cases for which there aren’t

clear answers. The shaded figures in Table 2 show results which are not statistically significant at 10% level. Results with an asterisk (Table 2, panels 1 through 3, the combination of the SHF 2010 family household data and the PPS 2010 data using propensity score kernel) imply that a household with a high level of imputed Financial Literacy Index would have a lower amount of cash holdings compared to a household 18

with a middle level of imputed Financial Literacy Index. This is consistent with the analysis of mean values shown in Figure 2. For the sake of robustness checks, the first panel of Table 2 was recreated using the subsamples of households with lower age (below 40 years), middle age (from 40 to 59 years), and old age (over 59 years). The results were similar to those reported in the first panel of Table 2. However, for the combination of the SHF 2016 family household data and the FLS 2016, the null hypothesis that the ranks sum of cash holdings of households with a high level of imputed Financial Literacy Index and that of middle level

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of imputed Financial Literacy Index were equal were not rejected by the young age subsample in all cases. Note that the SHF 2016 single-person household data were taken from an Internet survey made for respondents below age 70. Perhaps reflecting this

limitation, the rank sum test statistics using the old age subsample were inconclusive for

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the combination of the SHF 2016 single-person household data and the FLS 2016.

Table 3 illustrates a comparison of the distribution of cash holdings for the three groups after dropping the observations above the 75th percentile and the median. The

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second panel of Table 3 shows that households with a higher level of financial literacy did not tend to have higher cash holdings when dropping the observations above the

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median because many test statistics were inconclusive at 10 % significance level as shown by the shaded figures. The combination of the SHF 2016 single-person data and the FLS 2016 support this conclusion except for the case of comparing the high literacy group and

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the middle literacy group using the propensity score kernel matching method when dropping the observations above the median. The combination of the SHF 2016 family

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household data and the SHF 2016 support this conclusion except for the case of using nearest-neighbor matching kernel method. The combination of the SHF 2010 family household data and the PPS 2010 data support this conclusion except for the case of using

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nearest-neighbor matching kernel method and the comparison of the middle literacy group and the low literacy group using propensity score matching kernel method and nearest-neighbor matching method. For the sake of robustness checks, the second panel of Table 3 was recreated using the subsamples of households with lower age, middle age, and old age. The results were similar to those reported in the second panel of Table 3. If one is more likely to examine the cash distribution for day-to-day transactions by the three 19

levels of financial literacy by examining the cash distribution after dropping the observations above the median, it could be said that the cash demand for day-to-day transactions for the three levels of financial literacy might not differ so much. Taken together, the results suggest that the positive association between cash holdings and financial literacy seems to occur through the cash hoarding by households with higher levels of financial literacy, rather than the cash demand for day-to-day transactions. Cash hoardings could explain about 40% of Japanese cash in circulation as of 2017, as shown by Fujiki and Tomura (2017). Given the long-lasting low-interest rates

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in Japan since the mid-1990s and the declining trend in stock prices following the collapse of the asset price bubble in the late 1980s through the early 2010s, cash hoarding might be regarded as a rational saving strategy for financially literate Japanese households. 4.3 Cash ratio and financial literacy

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Distributions of the cash ratio were compared among the three levels of financial literacy using the Mann-Whitney rank sum test. This is because the distribution of cash ratio is

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bimodal and has two spikes at the values of zero and one. Again, this made the inference based on normal distribution impossible. For example, Figure 4 shows the bimodal cash

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ratio distributions by the three levels using the combination of the SHF 2016 singleperson household data and the FLS 2016 using propensity score matching. A lower cash ratio corresponded to a household with financial assets other than cash and a small amount

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of cash holdings. One cash ratio corresponds to a household who has no financial assets other than cash. Figure 4 also shows that the value of the cash ratio close to zero was more frequent for the group with a higher level of financial literacy, possibly because a

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household with higher financial literacy would also have other kinds of financial assets than cash. It also demonstrates that the value of a cash ratio close to one was more frequent

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in the group with lower financial literacy, possibly because that is associated with small amounts of investment into financial assets other than cash. One cash ratio might sound odd; however, Appendix Table 3 shows that the households with zero financial assets, whose value of variable Asset_0 is one, account for 49% of the household surveyed in the SHF 2016 single-person household data. Note that the FLS asks for the amount of financial assets for savings. Thus, even if a person had bank deposit to pay for utility bills, he/she might have replied that he/she had zero financial asset holdings. 20

Table 4 shows the results of testing two additional null hypotheses: (1) The cash ratio of households with a high level of imputed Financial Literacy Index and that of a middle level of imputed Financial Literacy Index are equal; and (2) The cash ratio of households with a middle level of imputed Financial Literacy Index and that of low level of imputed Financial Literacy Index are equal. The shaded figures in this table correspond to statistically insignificant results at 10% level. Because rank was assigned from lower cash ratio to higher cash ratio, a negative and statistically significant test result implied that a household with a higher level of

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financial literacy tended to have a lower cash ratio. Both the combination of the SHF 2016 single-person data and the FLS 2016 and the combination of the SHF 2016 family household data and the SHF 2016 imply that a household with higher level of financial

literacy tended to have a lower cash ratio, except for the cases of using nearest-neighbor

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matching analyzing the middle literacy versus low literacy for the SHF 2016 singleperson data and the FLS 2016. The combination of the SHF 2010 family household data and the PPS 2010 data did not yield statistically significant results in most cases.

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The results obtained from the combination of the SHF 2016 single-person data and the FLS 2016 and the combination of the SHF 2016 family household data and the

of cash ratio.

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SHF 2016 suggest that a person with higher financial literacy tended to have a lower value For the sake of robustness checks, the second panel of Table 4 was

recreated using the subsample of households with lower age, middle age, and old age.

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The results were similar to those reported in the second panel of Table 4. However, for the combination of the SHF 2016 family household data and the FLS 2016, the null

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hypothesis that the ranks sum of cash holdings of households with a high level of Financial Literacy Index and that of middle level of imputed Financial Literacy Index were equal were not rejected by the young age subsample in three cases. The rank sum

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test using the old age subsample were inconclusive in many cases for the combination of the SHF 2016 single-person household data and the FLS 2016. Taken together with the evidence from Tables 2 and 3, a person with higher

financial literacy tended to have a large amount of cash holdings, perhaps mainly for the sake of hoarding; however, the person tended to have other kinds of financial assets and thus a lower value of cash ratio. Hoarded cash could be considered as part of the 21

financially literate household’s financial assets. It was also found that a person with a lower level of financial literacy could have a large amount of cash holdings; however, that person would not necessarily have other kinds of financial assets and thus would have a higher cash ratio value. 5. Conclusion and limitations In few studies has an empirical relationship been established between cash demands and financial literacy in Japan because the SHF does not contain the standard proxy variable for financial literacy; it also does not distinguish a household’s cash demand by

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motivation (day-to-day transactions or hoarding). The findings of this study contribute to the literature by proposing remedies for these two difficulties in establishing an empirical

relationship between cash demand and financial literacy in Japan. The missing financial

literacy variable was first imputed for the SHF in 2010 and 2016 by matching the data in

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the PPS 2010 and FLS 2016 using four matching methods. Then, in comparisons of the distribution of cash holdings for the three levels of imputed financial literacy groups, it

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was noted that the observations at the higher percentiles of cash distribution should reflect cash hoarding. An additional comparison involved the distribution of cash holdings for

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the three levels after dropping the observations above higher percentiles was done. It was hoped that by dropping the observations above higher percentiles, the cash distribution for day-to-day transactions by the three levels were compared. A further comparison was

literacy.

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made of the distribution of the cash ratio among those in the three levels of financial

Based on these methodologies, three findings emerged. First, the imputed

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financial literacy variable tended to take higher values for households with better knowledge of the DICJ. This finding helped with comparing the results of Fujiki (2019),

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in that the households with better knowledge of the DICJ tended to have higher cash holdings conditional on the choice of payment methods among cash, electronic money, and credit cards as well as other household demographic variables. Second, it was found that in many cases, a household that belonged to a higher

financial literacy group tended to have a higher amount of cash holdings. This result makes sense if cash hoarding is a rational response by a financially literate person who considers that long-lasting low interest rates make the investment into cash an attractive 22

option. However, there was not a clear relationship between cash holdings and financial literacy when examining the distribution of cash holdings after dropping the observations above the median (50 thousand yen for family household and 30 thousand yen for singleperson household), which should be close to the distribution of the cash demand for dayto-day transactions. This result could be interpreted as meaning that the cash demands for day-to-day transactions for the three levels of financial literacy might not differ so much. A third finding was that a household that belonged to a higher financial literacy group tended to have a lower cash ratio, based on the combination of the SHF 2016 single-

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person data and the FLS 2016 and the combination of the SHF 2016 family household data and the SHF 2016. Cash that is hoarded could be considered as part of the assets of a financially literate household.

Note that we do not provide any causal evidence here. However, taking those

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conclusions at face value, the promotion of financial literacy and cashless payments for day-to-day transactions would reduce the relatively small amount of cash demands for day-to-day transactions, but it would not necessarily reduce the amount of cash demands

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for hoarding. If the government were to pursue a cashless economy that included cash hoarding, it should address the reasons that a financially literate household hoards cash

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(such as low interest rates or the aging of the population).

These results should be regarded with caution as there were some measurement issues. First, the SHF data for the family household may not have been measured

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correctly. For example, it is hard to imagine a situation in which a husband knows how much cash his family members have. Moreover, the difference in the number of

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household members was ignored when comparing the cash demands of family households.

Second, the standard three questions that were used to construct the Financial

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Literacy Index variable were taken as given, without discussion of the measurement of financial literacy. It is simply not known whether these results are robust with regard to changes in the measurement of the Financial Literacy Index variable, or whether other methods used to measure personal financial literacy in addition to the standard three questions would be important, as discussed in the work of van Rooija, Lusardi, and Alessie (2012) and that of Calcagnoa and Monticonec (2015). Using an example from 23

Japan, Yamori (2017) estimated the level of financial literacy for life insurance using the data from the Survey on Life Protection on the self-assessment of a respondent’s financial literacy. Kadoya and Khan (2017) examined the factors affecting financial literacy in terms of financial knowledge, financial attitudes, and financial behavior using the FLS 2016. Sekita et al. (2018) proposed taking into account variables related to behavioral economics. Finally, bias potentially arising from the use of online sampling methods (as with the FLS 2016 and the single-person household data in the SHF 2016) was ignored, rather

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than using stratified random sampling methods (as for the PPS 2010 and the SHF 2010 and 2016 family household data). This could be important in that Kitamura and Uchino

(2014) compared the SHF 2006 (random sampling) and the SHF 2007 single-person household data (web sampling methods) and concluded that the bias could be as great as

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25% of the mean for some questions. Moreover, the SHF single-person household data were taken from an Internet survey made for respondents below age 70, and thus the data set did not include the demands for cash made by older, single-person households.

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Acknowledgments: A part of this draft was formerly circulated as “Imputation of a financial literacy index: A case study using Japanese survey data.” I would like to thank Shinichi Fukuda and an anonymous referee for their useful comments on the earlier draft. I would also like to thank Nobuyoshi Yamori for sharing his ongoing research on financial literacy, and Arito Ono, Akiko Kamesaka, Yukinobu Kitamura, Heng Chen, Ben Fung, Kim P. Huynh, Angelika Welte, and participants in the Bank of Canada Retail Payments Workshop for helpful discussions on the matter. This research utilizes the micro data from the Preference Parameters Study of Osaka University’s 21st COE Program ‘Behavioral Macrodynamics Based on Surveys and Experiments’ and its Global COE project ‘Human Behavior and Socioeconomic Dynamics.’ I acknowledge the program and project contributors: Yoshiro Tsutsui, Fumio Ohtake and Shinsuke Ikeda. While undertaking this research, I participated in the Research Group on the Survey of Household Finances and received permission from the Central Council for Financial Services Information to use the data in the Survey of Household Finances and the Financial Literacy Survey 2016, for which I am most grateful. I also acknowledge the financial support of the Japan Society for the Promotion of Science through KAKENHI Grant No. 18K01704.

24

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28

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Table 1. Knowledge of the DICJ and imputed financial literacy variables

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SHF 2016 Family household data H0: Know about DI = Heard about DI

pr

Nearestneighbor

Nearestneighbor kernel

TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

1.500

0.134

0.401

0.688

5.208

0.000

9.614

0.000

-0.702

0.483

1.450

0.147

2.441

0.015

9.109

0.000

2.654

0.000

3.352

0.000

6.281

0.000

6.784

0.000

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SHF 2010 Family household data H0: Know about DI = Heard about DI H0: Heard about DI = Don't Know about DI

Propensity score kernel

e-

Mann-Whitney rank-sumtest

Propensity score

Pr

Imputation methods

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0.002

0.004

8.234

0.000

10.716

0.000

e-

pr 3.721

2.856

2.754

0.006

3.069

0.002

2.469

0.014

6.370

0.000

6.859

0.000

4.459

0.000

8.657

0.000

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Notes: DI stands for DICJ.

0.000

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SHF 2016 Single-person household data H0: Know about DI = Heard about DI H0: Heard about DI = Don't Know about DI

4.833

Pr

H0: Heard about DI = Don't Know about DI

1

f

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Table 2. Cash demand by the group of imputed financial literacy variables 1. All observation

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SHF 2010 Family household data

TestPTeststatistics value statistics

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H0: High literacy = Middle literacy H0: Middle literacy = Low literacy

Nearestneighbor

Nearestneighbor kernel

PTestPTestPvalue statistics value statistics value

Pr

Mann-Whitney rank-sumtest

Propensity score kernel

e-

Propensity score

pr

Imputation methods

0.229

0.819 -2.636* 0.008*

3.553

0.000

11.530

0.000

1.353

0.176

3.634

0.000

5.267

0.000

2.356

SHF 2016 Family household data

2

0.019

f

H0: Middle literacy = Low literacy

3.507

0.001

2.053

0.040

8.572

0.000

9.409

0.000

3.740

0.000

6.409

0.000

7.725

0.000

Pr

SHF 2016 Single-person household data

oo

0.000

pr

5.174

e-

H0: High literacy = Middle literacy

3.506

0.001

2.129

0.033

3.018

0.025

3.413

0.006

H0: Middle literacy = Low literacy

2.783

0.005

4.656

0.000

2.959

0.003

4.149

0.000

Jo ur

na l

H0: High literacy = Middle literacy

3

f oo

2. Dropping households with mattress deposit and above 99th percentile (200 thousand yen for SHF 2010 and SHF Family 2016, and 500 thousand yen for SHF Single 2016)

Pr

TestPTeststatistics value statistics

na l

SHF 2010 Family household data

Propensity score kernel

e-

Mann-Whitney ranksumtest

Propensity score

pr

Imputation methods

Jo ur

H0: High literacy = Middle literacy H0: Middle literacy = Low literacy

Pvalue

Nearestneighbor

Nearestneighbor kernel

TestPTestPstatistics value statistics value

0.190

0.850 -2.627* 0.009*

3.911

0.000

11.728

0.000

1.263

0.207

3.064

0.002

4.964

0.000

2.086

SHF 2016 Family household data

4

0.037

f H0: Middle literacy = Low literacy

3.247

0.001

2.123

0.034

8.296

0.000

9.479

0.000

4.029

0.000

6.388

0.000

7.506

0.000

Pr

SHF 2016 Single-person household data

oo

0.000

pr

5.451

e-

H0: High literacy = Middle literacy

3.210

0.001

2.056

0.040

2.936

0.003

3.510

0.000

H0: Middle literacy = Low literacy

2.874

0.004

4.638

0.000

3.151

0.002

4.106

0.000

Jo ur

na l

H0: High literacy = Middle literacy

3. Dropping households with mattress deposit and above 95th percentile (50 thousand yen for SHF 2010, 30 thousand yen for SHF Family 2016, and 100 thousand yen for SHF Single 2016)

5

f

Propensity score

Mann-Whitney ranksumtest

Propensity score Nearest-neighbor Nearest-neighbor kernel kernel

oo

Imputation methods

e-

pr

TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

Pr

SHF 2010 Family household data

4.873

0.000

1.541

0.123

7.706

0.000

9.112

0.000

H0: Middle literacy = Low literacy

3.186

0.000

4.364

0.000

6.747

0.000

7.178

0.000

-2.265* 0.024*

3.479

0.001

11.104

0.000

Jo ur

na l

H0: High literacy = Middle literacy

SHF 2016 Family household data

H0: High literacy = Middle literacy

-0.017

0.986

6

f oo

1.530

0.126

2.068

0.039

3.642

0.000

4.719

0.000

pr

H0: Middle literacy = Low literacy

e-

SHF 2016 Single-person household data

3.198

H0: Middle literacy = Low literacy

2.606

Pr

H0: High literacy = Middle literacy

na l

0.001

0.007

3.293

0.001

3.433

0.001

4.365

0.000

3.035

0.002

3.950

0.000

Jo ur

0.009

2.713

7

f

oo

4. Dropping households with mattress deposit and above 90th percentile (23 thousand yen for SHF 2010 and SHF Family 2016, and 50 thousand yen for SHF Single 2016)

Mann-Whitney rank-sumtest

Propensity score kernel

pr

Propensity score

Nearest-neighbor

Nearest-neighbor kernel

TestTestTestTestP-value P-value P-value P-value statistics statistics statistics statistics

e-

Imputation methods

Pr

SHF 2010 Family household data

4.977

H0: Middle literacy = Low literacy

2.700

Jo ur

na l

H0: High literacy = Middle literacy

0.000

2.107

0.035

7.055

0.000

8.934

0.000

0.000

3.794

0.000

6.571

0.000

6.953

0.000

SHF 2016 Family household data

8

f

H0: Middle literacy = Low literacy

1.781

0.075

oo

0.587

-1.371

0.171

2.777

0.006

9.883

0.000

0.072

4.409

0.000

4.655

0.000

pr

0.543

1.799

e-

H0: High literacy = Middle literacy

Pr

SHF 2016 Single-person household data

3.265

0.001

2.382

0.017

3.057

0.002

3.412

0.001

H0: Middle literacy = Low literacy

2.950

0.003

4.648

0.000

2.949

0.003

3.391

0.007

Jo ur

na l

H0: High literacy = Middle literacy

9

f

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Table 3. Cash demand by the group of imputed financial literacy variables

1. Dropping households with mattress deposit and above 75th percentile (10 thousand yen for SHF 2010, SHF Family 2016, and SHF

Mann-Whitney rank-sumtest

Jo ur

H0: High literacy = Middle literacy H0: Middle literacy = Low literacy

Nearestneighbor

Nearestneighbor kernel

TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

na l

SHF 2010 Family household data

Propensity score kernel

e-

Propensity score

Pr

Imputation methods

pr

Single 2016)

1.181

0.237

1.250

0.211

6.233

0.000

5.413

0.000

1.873

0.061

2.686

0.007

2.646

0.008

4.695

0.000

SHF 2016 Family household data

10

f H0: Middle literacy = Low literacy

1.507

0.132

oo

0.882

-0.818

0.413

pr

0.149

1.424

0.154

7.256

0.000

0.059

H0: High literacy = Middle literacy

2.618

0.009

2.192

0.028

0.513

0.608

1.789

0.074

H0: Middle literacy = Low literacy

2.636

0.008

4.936

0.000

3.690

0.002

2.802

0.005

Jo ur

na l

SHF 2016 Single-person household data

1.888

3.095

0.002

4.542

0.000

Pr

e-

H0: High literacy = Middle literacy

2. Dropping households with mattress deposit and above median value (50 thousand yen for SHF 2010 and SHF Family 2016, and 30 thousand yen SHF Single 2016) 11

f

Mann-Whitney rank-sumtest

Nearestneighbor kernel

ePr

-0.756

0.450

-0.615

0.539

1.003

0.316

1.679

0.093

1.119

0.263

2.817

0.005

2.286

0.022

2.648

0.008

-0.649

0.517

-0.792

0.429

1.575

0.115

3.021

0.003

na l

Jo ur

H0: Middle literacy = Low literacy

Nearestneighbor

TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

SHF 2010 Family household data H0: High literacy = Middle literacy

Propensity score kernel

oo

Propensity score

pr

Imputation methods

SHF 2016 Family household data H0: High literacy = Middle literacy

12

f oo

1.360

0.174

2.919

0.004

e-

H0: Middle literacy = Low literacy

0.070

0.944

H0: High literacy = Middle literacy

1.125

0.261

1.978

0.048

-0.576

0.564

0.371

0.711

H0: Middle literacy = Low literacy

1.575

0.115

1.283

0.200

1.570

0.116

1.301

0.193

0.113

Jo ur

na l

Pr

pr

SHF 2016 Single-person household data

1.584

13

1. All observations

f

pr

Nearestneighbor

Nearestneighbor kernel

TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

H0: High literacy = Middle literacy

0.641

0.522

-0.319

0.750

-6.224

0.000

-17.206

0.000

H0: Middle literacy = Low literacy

-0.655

0.513

-0.361

0.718

-0.610

0.542

-1.396

0.163

-5.492

0.000

-3.689

0.000

-13.428

0.000

-14.282

0.000

Jo ur

na l

SHF 2010 Family household data

Propensity score kernel

e-

Mann-Whitney rank-sumtest

Propensity score

Pr

Imputation methods

oo

Table 4. Cash ratios by group of imputed financial literacy variables

SHF 2016 Family household data H0: High literacy = Middle literacy

14

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-5.054

0.000

-4.941

0.000

-5.416

0.000

-8.546

0.000

-1.929

0.054

-3.977

0.000

-6.298

0.000

0.000 -17.483

0.000

-1.388

0.165

-5.577

0.000

pr

H0: Middle literacy = Low literacy

e-

SHF 2016 Single-person household data

-9.193

H0: Middle literacy = Low literacy

-6.454

Jo ur

na l

Pr

H0: High literacy = Middle literacy

0.000

2. Dropping households with a mattress deposit

Imputation methods

Mann-Whitney rank-sumtest

Propensity score Nearest-neighbor Nearest-neighbor kernel kernel TestPTestPTestPTestPstatistics value statistics value statistics value statistics value

Propensity score

15

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f SHF 2016 Family household data

-0.287

0.774

-6.083

0.000

-17.052

0.000

0.427

-0.466

0.642

-0.877

0.380

-1.317

0.188

-5.593

0.000

-3.744

0.000

-13.304

0.000

-14.314

0.000

na l

H0: High literacy = Middle literacy

-0.794

0.526

e-

H0: Middle literacy = Low literacy

0.635

Pr

H0: High literacy = Middle literacy

pr

SHF 2010 Family household data

-4.924

0.000

-5.002

0.000

-5.574

0.000

-8.452

0.000

-9.092

0.000

-1.990

0.047

-4.011

0.000

-6.265

0.000

-6.555

0.000 -17.430

0.000

-1.328

0.184

-5.491

0.000

H0: Middle literacy = Low literacy

Jo ur

SHF 2016 Single-person household data H0: High literacy = Middle literacy H0: Middle literacy = Low literacy

16

f oo

FHS 0102 ot 0102 SPP ILF detupmI dlohesuoh ylimaF 000.2 005.1 000.1 005.0 000.0 robhgien-tseraeN robhgien-tseraeN lenrek

erocs ytisneporP lenrek

erocs ytisneporP

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ecnarusni tisoped tuoba wonK ecnarusni tisoped tuoba draeH ecnarusni tisoped tuoba wonk ton oD

6102 ot SLF 6102 morf ILF detupmI dlohesuoh ylimaF FHS 000.2 005.1 000.1

e-

005.0 000.0 robhgien-tseraeN robhgien-tseraeN lenrek

erocs ytisneporP lenrek

erocs ytisneporP

ecnarusni tisoped tuoba wonK ecnarusni tisoped tuoba draeH ecnarusni tisoped tuoba wonk ton oD

6102 ot SLF 6102 morf ILF detupmI dlohesuoh nosrep-elgniS FHS

Pr

000.2 005.1 000.1

005.0 000.0 robhgien-tseraeN lenrek

robhgien-tseraeN

erocs ytisneporP lenrek

erocs ytisneporP

na l

ecnarusni tisoped tuoba wonK ecnarusni tisoped tuoba draeH ecnarusni tisoped tuoba wonk ton oD

Jo ur

Figure 1. Average imputed value of Financial Literacy Index by knowledge of deposit insurance

17

f oo

SHF 2010 Family household Amount of cash holdings 20 15

pr

10 5 0

Propensity score kernel

Nearest-neighbor

Nearest-neighbor kernel

High literacy average

Middle literacy average

Low literacy average

High literacy median

Middle literacy median

Low literacy median

e-

Propensity score

SHF 2016 Family household Amount of cash holdings

Pr

25 20 15

10 5 0 Propensity score kernel

Nearest-neighbor

Nearest-neighbor kernel

na l

Propensity score

High literacy average

Middle literacy average

Low literacy average

High literacy median

Middle literacy median

Low literacy median

SHF 2016 Single-person household Amount of cash holdings 35

25 20

15 10 5 0

Jo ur

30

Propensity score

Propensity score kernel

Nearest-neighbor

Nearest-neighbor kernel

High literacy average

Middle literacy average

Low literacy average

High literacy median

Middle literacy median

Low literacy median

Figure 2. Cash holdings and average imputed Financial Literacy Index 18

f oo pr ePr na l Jo ur

Figure 3. Cash holdings by groups of imputed Financial Literacy Index (SHF 2016 Single-Person household data, Propensity score matching)

19

f oo pr ePr na l Jo ur

Figure 4. Cash ratio by groups of imputed Financial Literacy Index (SHF 2016 Single-Person household data, Propensity score matching)

20

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Appendix 1: Data used for the empirical analysis

Appendix Tables 1 through 3 report the means of the variables used for the analysis. First, Appendix Table 1 combines the PPS 2010

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family household and the SHF 2010 family household data. A total of 4,497 observations from the PPS 2010 and 2,579 observations from SHF 2010 were used, as shown in the bottom row labeled N. The mean value of Financial Literacy Index in the PPS 2010 was 1.701, and

e-

the mean value of Deposit Insurance in the SHF 2010 was 1.801, as shown in the second and third rows, respectively. Regarding the common covariates, the means of the dummy variables for the categories of annual household income (before-tax income in the PPS 2010, after-tax income in the SHF 2016) were reported along with the amount of household financial assets. Categories in the PPS 2010 data

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were used to construct the corresponding categories for the SHF 2010 data. Then some of the categories were added so that each contained at least 5% of the sample.

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For example, Income_200_400 had a value of one for a household that responded that its annual household income was greater than 2 million yen and lower than or equal to 4 million yen, and zero otherwise. Asset_250_500 had a value of one for a household that responded that its financial assets were greater than 2.5 million yen and lower than or equal to 5 million yen, and zero otherwise. Income_NA and Asset_NA were used as dummy variables to identify PPS 2010 households and SHF 2010 family households not reporting

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their annual household income or amount of financial assets, respectively. The remainder of Appendix Table 1 reports the means of the dummy variables for respondents’ (in the SHF, household heads) individual characteristics: age, gender, educational attainment, occupation, area of residence, and whether the respondent was in debt or a homeowner. Specifically, the dummy variables for age (Age) were 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, and equal to or

greater than 70 years. Dummy variables were also used to indicate whether a respondent (in the SHF, the household head) was a male (Male). Dummy variables were specified to indicate educational attainment: Senior high, Junior college, University, and Graduate (for 21

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graduate school). Additional dummy variables were used to indicate each respondent’s job situation: self-employed (Self-employed), full(Full-time), or part-time (Part-time) worker. Other dummy variables indicated the industry in which the respondents worked: Agriculture, Construction, Manufacturing, Wholesale and retail sales (Wholesale_retail), Services, and Utilities and education (Gasedu). These

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common covariates were constructed to indicate the respondents’ job situations and the industries in which the respondents worked by

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adding different questions regarding the job categories in the two data sets.

Dummy variables were specified to indicate whether the respondent household had debt (Debt) and owned their home (Homeowner). Finally, dummy variables were used for eight regions of residence (Hokkaido, Tohoku, Hokuriku, Chubu, Kinki, Chugoku,

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Shikoku, and Kyushu), specifying the Kanto region as the base case in the following regressions. A sample was dropped if it did not provide one of the following types of demographic information: age, educational attainment, job situation, debt, and homeownership. Data from

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Appendix Table 1 suggest that most of the means were close in the two data sets, except for Male, Homeowner, and the job categories. Appendix Table 2 shows the results of combining the FLS 2016 and SHF 2016 family household data. A total of 25,000 observations were used from the FLS 2016 and 2,888 were used from the SHF 2016 family household data, as shown in the bottom row labeled N. The sample size reflects the availability and consistency of the common covariates in the two data sets. The mean value of the

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Financial Literacy Index in the FLS 2016 was 1.401, and the mean value of Deposit Insurance in the SHF 2016 family household data was 1.913, as shown in the second and the third rows. Regarding the common covariates, the means of the dummy variables are reported for the categories of annual household income (before-tax income in the FLS 2016, after-tax income in the SHF) and the amount of household financial assets. Categories in the FLS 2016 data were used to construct the categories for the SHF data. Several categories had few observations: Income_0 in the SHF 2016 data and Income_1500_ in both data.

22

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The remainder of the table reports the means of the dummy variables for the respondents’ (in the SHF, household heads) individual characteristics, including age, gender, educational attainment, occupation, area of residence, and debt. The difference in the demographic

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information available in PPS 2010 and in FLS 2016 leads to the following different covariates not available in Appendix Table 1: (1) The dummy variables for age (Age) have the categories 25–29, 70–74, and equal to or more than 75 years;

e-

(2) The dummy variables indicating educational attainment have the category of Vocational college; and

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(3) The dummy variables indicating each respondent’s job situation have the category of student (Student). The difference in the demographic information available in the PPS 2010 and the FLS 2016 leads to two covariates in Appendix Table 1 not being available in Appendix Table 2; these include the dummy variables for job categories and home ownership. Samples were dropped

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if they did not respond to one of the following demographic information requests: age, educational attainment, and debt. The figures in Appendix Table 2 show that most of the means of the covariates were close in the two data sets, except for Income_0, Male, Age25_29, Age75_, Full-time, and Student.

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Appendix Table 3 provides details on the combination of the FLS 2016 and SHF 2016 single-person household data. A total of 16,364 observations from the FLS 2016 and 2,500 from the SHF 2016 single-person household data were used, as shown in the bottom row labeled N. Note that the SHF 2016 single-person data is a web survey that requires all households to provide their household income and the amount of household financial assets. To construct common covariates, then, samples from the FLS 2016 were dropped if respondents did not answer questions about their household income and their outstanding amount of financial assets. That is one reason why the number of observations was reduced to 16,364 for the FLS 2016, rather than the 25,000 available as noted in Appendix Table 2. 23

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The mean value of the Financial Literacy Index in the FLS 2016 was 1.556 and the mean value of Deposit Insurance in the SHF 2016 single-person household data was 2.075, as shown in the second and the third rows.

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Regarding the common covariates, the means of the dummy variables are reported for the categories of annual household income (before-tax income in the FLS 2016, after-tax income in the SHF) and the amount of household financial assets. Categories in the FLS

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2016 data were used to construct the categories for the SHF data. Several categories were limited to few observations: Income750_1000, Income1000_1500, and Income_1500. The remainder of Appendix Table 3 reports the means of the dummy variables indicating a respondent’s individual characteristics: age, gender, educational attainment, occupation, whether the respondent was in debt, and area of

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residence.

The difference between the SHF 2016 single-person data and the SHF 2016 family household data required the use of dummy

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variables: For age (Age), categories were not available for 70 to 74 and equal to or more than 75 years for the SHF, hence samples were dropped from the FLS 2016 if they were equal to or greater than 70 years. Samples were also dropped if respondents did not provide information on their age, educational attainment, and/or debt.

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Appendix Table 3 shows that most of the means were close in the two data sets, except for Asset_0, Income750_1000, and Income1000_1500. This might be because the SHF 2016 single-person data is a web survey of persons whose age is less than or equal to 69 years, and this could potentially include younger and less wealthy respondents than those represented in the FLS 2016 dataset. Appendix 2: Matching Results

The following analysis begins with specification of all of the common covariates as the explanatory variable 𝑋𝑖𝑗𝑡 because there was no hint that any of those were superior in predicting a particular data sample surveyed in one of the two data sets. If some variables had an 24

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absolute value of standardized difference after matching of more than 0.1, the variable with the largest absolute value of the standardized difference was dropped and matched again using the remaining common covariates as explanatory variables. This continued until all of

using the three combinations of the data sets in turn.

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the absolute values of the standardized differences after matching were less than 0.1, as suggested by Austin (2011). Results were reported

First, the second and third columns of Appendix Tables 4 provide the results of the logistic treatment model regressing the indicator

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variable Findata on 𝑋𝑖𝑗𝑡 for the propensity score matching (column labeled PS), and for the propensity score matching with the

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Epanechnikov kernel function (PS-K) taking the SHF 2010 family household data samples as the base group and the PPS 2010 data as the treated group. All estimations used the STATA 14 command logit. To conserve space, the standard errors are not reported, but *, **, and *** show that the estimates are statistically significant at the 10%, 5%, and 1% levels, respectively. The row labeled N provides the sample size of the logistic treatment model. The remaining rows report the goodness-of-fit statistics for the logistic treatment model, including

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the pseudo R2 (labeled PseudoRsq), the log-likelihood ratio (LLR), the percentage of observations correctly classified (% correctly classified), and the area under the ROC curve. The percentages of observations correctly classified were 79.17% and 79.25%, and the areas under the ROC curve were 0.877 and 0.879, which demonstrate the moderate fits of the models to the data. The first panel of

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Appendix Figure 1 depicts the overlap plot of the logistic treatment model for the propensity score matching, indicating an overlap between the two data sets even though the data from the PPS 2010 were concentrated across small values of the propensity score for the SHF 2010 data. The fourth through seventh columns in Appendix Table 4 detail the standardized differences of the covariates to compute the treatment effects or the potential outcome differences after the propensity score matching using the logit treatment model (column labeled PS), propensity score matching with the Epanechnikov kernel function (PS-K), nearest-neighbor matching (NN), and multivariate distance matching (Mahalanobis distance) with the Epanechnikov kernel function (NN-K). The absolute values of the standardized differences after matching were less than 0.1. 25

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Next, the second and third columns in Appendix Table 5 provide the results of the logistic treatment models regressing the indicator variable Findata on 𝑋𝑖𝑗𝑡 for the propensity score matching (column labeled PS) and for the propensity score matching with the Epanechnikov kernel function (PS-K) taking the SHF 2016 family household data samples as the base group and the FLS 2016 data as

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the treated group. The percentages of observations correctly classified were 89.83% and 89.69% and the areas under the ROC curve were 0.851 and 0.848, which again illustrate the moderate fits of the models to the data. The second panel of Appendix Figure 1 depicts the

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overlap plot of the logistic treatment model for the propensity score matching, indicating overlap, despite the data from the FLS 2016 concentrating on small values of the propensity score for the data from the SHF 2016. The fourth through seventh columns in Appendix

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Table 5 provide the standardized difference of covariates used for the computation of treatment effects and potential outcome differences in a manner similar to that of Appendix Table 4.

The second and third columns in Appendix Table 6 provide the results of the logistic treatment models regressing the indicator

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variable Findata on 𝑋𝑖𝑗𝑡 for the propensity score matching (column labeled PS) and for the propensity score matching with the Epanechnikov kernel function (PS-K) taking the SHF 2016 single-person household data samples as the base group and the FLS 2016 data as the treated group. The percentages of observations correctly classified were 86.84% and 87.03%, and the areas under the ROC

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curve were 0.794 and 0.762, which again demonstrate moderate fits of the models to the data. The third panel of Appendix Figure 1 illustrates the overlap plot of the logistic treatment model, again showing overlap, even though the data from the FLS 2016 were concentrated on the small values of the propensity score for data from the SHF 2016 single-person households. The fourth through seventh columns in Appendix Table 6 provide the standardized difference of the covariates in a manner similar to the results in Appendix Table 4.

26

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The SHF 2010 Family household Mean 1.801 0.050 0.260 0.270 0.160 0.078 0.043 0.050 0.089 0.380 0.102 0.091 0.056 0.098 0.049 0.071 0.084 0.070 0.071 0.109

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0.046 0.201 0.211 0.197 0.109 0.058 0.080 0.097 0.237 0.130 0.087 0.081 0.085 0.061 0.062 0.087 0.170 0.061 0.098

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Financial Literacy Index Deposit Insurance Income_200 Income_200_400 Income_400_600 Income_600_800 Income_800_1000 Income_1000_1200 Income_1200_ Income_NA Asset_250 Asset_250_500 Asset_500_750 Asset_750_1000 Asset_1000_1500 Asset_1500_2000 Asset_2000_3000 Asset_3000_ Asset_NA Age30_34 Age35_39

The PPS 2010 Family household Mean 1.701

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Appendix Table 1. Summary statistics for individual characteristics

27

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0.114 0.122 0.137 0.129 0.149 0.063 0.076 0.929 0.430 0.133 0.296 0.033 0.197 0.685 0.088 0.066 0.125 0.214 0.093 0.386 0.105 0.478 0.679 0.064 0.089

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Pr

0.117 0.131 0.121 0.129 0.136 0.092 0.055 0.475 0.492 0.172 0.254 0.023 0.134 0.402 0.138 0.028 0.053 0.121 0.093 0.227 0.061 0.495 0.866 0.040 0.068

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Age40_44 Age45_49 Age50_54 Age55_59 Age60_64 Age65_69 Age70_ Male Senior high Junior college University Graduate Self-employed Full-time Part-time Agriculture Construction Manufacturing Wholesale_retail Services Gasedu Debt Homeowner Hokkaido Tohoku

28

f

0.051 0.149 0.142 0.074 0.035 0.131 2,579

pr

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0.063 0.156 0.172 0.060 0.036 0.115 4,497

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Hokuriku Chubu Kinki Chugoku Shikoku Kyushu N

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Note: Annual disposable income and financial assets in units of 10,000 yen.

29

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0.036 0.157 0.289 0.166 0.097 0.051 0.016 0.188 0.136 0.151 0.099 0.053 0.047 0.070 0.115 0.329 0.085 0.078

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Financial Literacy Index Deposit Insurance Income_0 Income_0_250 Income_250_500 Income_500_750 Income_750_1000 Income_1000_1500 Income_1500_ Income_NA Asset_0 Asset 0_250 Asset_250_500 Asset_500_750 Asset_750_1000 Asset_1000_2000 Asset_2000_ Asset_NA Age25_29 Age30_34

Mean 1.401

The SHF 2016 Family household Mean 1.913 0.006 0.128 0.366 0.230 0.079 0.056 0.014 0.122 0.298 0.108 0.088 0.088 0.046 0.145 0.162 0.064 0.021 0.053

e-

The FLS 2016

oo

Appendix Table 2. Summary statistics for individual characteristics

30

pr

f

oo

0.064 0.105 0.094 0.109 0.108 0.106 0.135 0.085 0.115 0.922 0.104 0.560 0.077 0.003 0.423 0.082 0.049 0.315 0.034 0.401 0.055 0.097 0.050 0.151 0.149

e-

Pr

0.105 0.087 0.083 0.081 0.085 0.088 0.106 0.087 0.039 0.493 0.070 0.358 0.140 0.048 0.324 0.105 0.113 0.386 0.042 0.290 0.044 0.073 0.042 0.140 0.163

na l

Jo ur

Age35_39 Age40_44 Age45_49 Age50_54 Age55_59 Age60_64 Age65_69 Age70_74 Age75_ Male Self-employed Full-time Part-time Student Senior high Vocational college Junior college University Graduate Debt Hokkaido Tohoku Hokuriku Chubu Kinki

31

f

0.073 0.035 0.102 2,888

oo

0.058 0.031 0.112 25,000

Jo ur

na l

Pr

e-

Note: Annual disposable income and financial assets in units of 10,000 yen.

pr

Chugoku Shikoku Kyushu N

32

f

pr

Pr

0.046 0.198 0.358 0.203 0.115 0.059 0.020 0.191 0.227 0.150 0.081 0.072 0.107 0.172 0.080 0.082 0.109 0.091 0.082

na l

Jo ur

Financial Literacy Index Deposit Insurance Income_0 Income_0_250 Income_250_500 Income_500_750 Income_750_1000 Income_1000_1500 Income_1500_ Asset_0 Asset 0_250 Asset_250_500 Asset_500_750 Asset_750_1000 Asset_1000_2000 Asset_2000_ Age25_29 Age30_34 Age35_39 Age40_44 Age45_49

Mean 1.556

The SHF 2016 Single–person household Mean 2.075 0.090 0.426 0.368 0.090 0.014 0.008 0.003 0.486 0.160 0.068 0.063 0.031 0.082 0.111 0.164 0.095 0.111 0.082 0.072

e-

The FLS 2016

oo

Appendix Table 3. Summary statistics for individual characteristics

33

pr

f

oo

0.089 0.061 0.116 0.094 0.583 0.090 0.537 0.118 0.068 0.232 0.101 0.097 0.458 0.085 0.164 0.054 0.051 0.029 0.120 0.162 0.051 0.025 0.106 2,500

e-

Pr

0.083 0.084 0.085 0.102 0.532 0.069 0.392 0.126 0.054 0.312 0.099 0.104 0.411 0.047 0.315 0.046 0.071 0.040 0.137 0.160 0.057 0.030 0.115 16,364

na l

Jo ur

Age50_54 Age55_59 Age60_64 Age65_69 Male Self-employed Full-time Part-time Student Senior high Vocational college Junior college University Graduate Debt Hokkaido Tohoku Hokuriku Chubu Kinki Chugoku Shikoku Kyushu N

34

f

Jo ur

na l

Pr

e-

pr

oo

Note: Annual disposable income and financial assets in units of 10,000 yen.

35

PPS 2010 and SHF 2010 family households

Income_1000_1200 Income_1200_ Income_NA Asset_250_500

0.562***

0.684***

0.815*** 0.546***

-0.295**

Jo ur

Asset_500_750

0.630***

0.561***

Asset_750_1000

Asset_1000_1500

0.110 0.571***

Asset_1500_2000 Asset_2000_3000 Asset_3000_

0.646***

0.697*** 1.012*** 0.745*** 0.548*** -0.130 0.813*** 0.467*** 0.894*** 0.751***

f

Matched standardized differences PS-K NN 0.014 0.005 0.029 -0.076

pr

PS 0.027 0.056

e-

Income_800_1000

0.554***

Pr

Income_600_800

na l

Income_200 Income_400_600

Logit treatment models PS PS-K -0.062 -0.018 0.082 0.123

oo

Appendix Table 4. Logit treatment model and matched standardized differences

0.069

0.070

0.049

0.069

0.087

0.051

0.005

0.004

0.032

0.055

0.054

0.058

NN-K 0.023

0.058

0.003

0.046

0.009

0.037

0.071

0.079

0.018

0.018

0.002

0.047

0.042

0.089

0.078

0.017

-0.001

0.003

0.057

0.011

0.050

-0.042

0.006

0.006

0.059

0.008

0.038

-0.049

36

f

Age40_44

0.145

Age45_49

0.127

Age50_54

-0.098

Age55_59

-0.021

Age60_64

Age70_

0.946*** 2.600***

Jo ur

male

-0.111

na l

Age65_69

0.011 0.082

-0.027

0.007

-0.015

0.056

-0.007

-0.050

pr

0.282**

0.607*** 0.597*** 0.729*** 0.732*** 0.961*** 0.862*** 1.029*** 0.902*** 1.725*** 2.498***

-0.058

0.019

0.011

-0.001

0.015

-0.019

-0.012

0.013

-0.017

0.018

-0.024

-0.028

-0.035

0.000

0.008

-0.018

-0.063

-0.005

-0.046

0.024

-0.042

0.018

0.050

-0.024

0.002

0.018

0.023

0.013 0.022

0.088

0.080

0.093

-0.076 0.000 -0.019

-0.002 -0.067

e-

Age35_39

0.256

Pr

Age30_34

oo

Asset_NA

Senior high

Junior college

University Graduate Self-employed Full-time

0.379*** -0.039 -0.186 -0.209* 0.413***

0.072 0.019 -0.103 -0.180 0.424***

0.082 -0.013 0.032

0.080 0.000 0.044

-0.010

0.029

37

Wholesale retail Services Gasedu Debt Homeowner Hokkaido

Jo ur

Tohoku Hokuriku Chubu Kinki Chugoku Shikoku Kyushu

1.145*** 0.489*** -0.239* 0.151 0.041 0.115 -0.218

Constant

5.421***

0.000

f

oo

0.013

1.228*** 0.483*** -0.233* 0.174 0.059 0.139 -0.189

0.035

-0.022

-0.016

-0.012

-0.061

pr

Manufacture

0.043

0.006

e-

Construction

0.465*** 3.940*** 3.720*** 3.686*** 3.349*** 3.926*** 4.218*** 0.039

Pr

Agriculture

0.536*** 3.983*** 3.741*** 3.693*** 3.374*** 3.926*** 4.230*** 0.130*

na l

Part-time

-0.041

-0.005

0.000

-0.019

0.030

0.027

0.012

0.057

-0.056

-0.034

-0.048

-0.061

-0.016

0.000

0.028

0.001

0.003

0.003

0.052

0.040

0.005

-0.024

-0.074 0.031 -0.004 -0.030 -0.007

-0.071 0.018 0.045 -0.029 -0.028

0.006 0.023 -0.006 0.038 0.003 0.002 0.004

-0.021 0.044 0.009 0.049 -0.009 0.009 -0.030

0.008

0.049

6.233*** 38

f

7,076 0.375 -2901.937 79.25% 0.879

oo

7,076 0.369 -2927.556 79.17% 0.877

pr

N PseudoRsq LLR % correctly classified Area under ROC curve

e-

Notes: Results from PS: Propensity score matching; PS-K: Propensity score matching with the Epanechnikov kernel function; NN: Nearest-neighbor matching using Mahalanobis distance; NN-K: Multivariate-distance matching using Mahalanobis distance with the Epanechnikov kernel function. *, **, and *** show that the

Jo ur

na l

Pr

estimates are statistically significant at the 10%, 5%, and 1% levels, respectively.

39

f oo

Appendix Table 5. Logit treatment model and matched standardized differences

Logit treatment models PS PS-K

Income_750_1000 Income_1000_1500 Income_1500_ Income_NA Asset_0

0.352*

1.157*** -0.114

Jo ur

Asset_250_500

-0.020

Asset_500_750

Asset_750_1000

Asset_1000_2000 Asset_2000_

e-

Income_500_750

0.767*** 0.321*** 1.039*** 0.778***

PS

-0.078

0.187*** 0.230***

Pr

Income_250_500

2.069*** 0.180*** 0.304***

0.139

na l

Income_0

Matched standardized differences PS-K NN

pr

FLS 2016 and SHF 2016 family households

0.490*** 1.784*** 0.553*** 1.198*** 0.686*** 1.432*** 1.173***

NN-K

0.019

0.001

-0.064

0.055

0.067

0.025

-0.048

0.049

0.052

0.069 0.019

0.090

-0.054

0.012

0.000

0.000

0.016 -0.022

-0.041

0.004

0.028

0.039

0.047

0.024

0.016

0.025

0.041

0.042

0.010

0.094

0.046

0.023

-0.004

-0.060

-0.006

0.024

-0.001

-0.061

40

Age40_44

-0.080 0.089 0.489***

-0.233** -0.038 0.630***

Age60_64 Age65_69 Age70_74

1.201*** 2.509***

Jo ur

Age75_ Male

Self-employed Full-time Part-time Student

0.760*** 0.816*** 1.053*** 1.382*** 1.333*** 2.290*** 2.185*** 0.591*** 1.072***

Pr

Age55_59

0.638*** 0.666*** 0.653*** 0.634***

na l

Age50_54

0.104

f

-0.030

e-

Age45_49

0.061 -0.085

oo

Age25_29 Age30_34 Age35_39

0.021

1.076***

0.053 0.033

0.018 0.000 0.012

0.060 0.075

-0.011

-0.007

-0.061

-0.008

-0.026

pr

Asset_NA

0.047

0.013

-0.006

-0.035

-0.035

-0.021

-0.001

-0.031

-0.010

-0.041

-0.002

-0.034

-0.015

-0.008

-0.040

0.029

-0.027

0.015

0.040

-0.019

-0.007

-0.051

-0.041

0.080 0.045

0.028

-0.028 -0.078 -0.029

41

Graduate Debt Hokkaido Tohoku Hokuriku Chubu Kinki

Shikoku Kyushu

Constant

N PseudoRsq LLR % correctly classified

-0.250**

-0.004

1.807*** 0.421*** -0.198* 0.294*** -0.068 -0.011 0.298*** -0.184 0.056

3.852*** 27,888 0.250 -6964.545 89.83%

f

oo

0.047

0.018

-0.043

0.035

1.717***

Jo ur

Chugoku

-0.209**

-0.030

pr

University

1.227***

-0.085

e-

Junior college

1.303***

0.561***

0.269**

0.414*** -0.221** 0.343*** -0.210** -0.151**

Pr

Vocational college

0.918***

na l

Senior high

0.293*** -0.167 -0.012

0.046

-0.013

0.030

0.047

-0.041

-0.073

0.031

-0.001

0.003

0.050

0.092

0.045

0.018

0.008

0.041 -0.033

0.002 0.018 0.007

0.054 -0.039 0.002

0.036

-0.035

0.016

0.051

0.052 -0.075

0.051 -0.030

0.003 0.034

0.033 0.017

-0.090 -0.024

6.610*** 27,888 0.240 -7053.575 89.69% 42

f

0.851

0.848

oo

Area under ROC curve

Note: Results from PS: Propensity score matching; PS-K: Propensity score matching with the Epanechnikov kernel function; NN: Nearest-neighbor matching using

pr

Mahalanobis distance; NN-K: Multivariate-distance matching using Mahalanobis distance with the Epanechnikov kernel function. *, **, and *** show that the estimates are statistically significant at the 10%, 5%, and 1% levels, respectively.

FLS 2016 and SHF 2016 single-person households

Income_500_750 Income_750_1000

0.848***

0.144***

1.750***

0.828***

3.091***

Jo ur

Income_1000_1500

Pr

Income_250_500

Logit treatment models PS PS-K 0.019 -0.205**

na l

Income_0

e-

Appendix Table 6. Logit treatment model and matched standardized differences

3.077***

PS -0.003

Matched standardized differences PS-K NN -0.034 0.004

0.061

0.067

-0.033

-0.013

Asset_250_500 Asset_500_750

1.238***

1.533***

0.378*** 0.317***

0.380*** -0.248**

0.069

-0.083

0.002

0.000

0.029 -0.070

Income_1500_ Asset_0

NN-K 0.029

0.003

0.020

0.086

0.057

0.044

0.039

43

0.091

f

0.460***

-0.137

Asset_2000_

Age50_54 Age55_59 Age60_64 Age65_69

0.492***

1.062***

Jo ur

Male Self-employed

0.411*** -0.069

Full-time Part-time Student

Senior high

Vocational college

0.475***

0.962*** 0.361***

0.536***

0.297***

0.294*** 0.042

0.268*** 0.035

0.092

-0.013

0.004

-0.004

-0.001

0.031 0.005 0.011

0.049 0.061 0.002 -0.044

0.001 0.004 -0.004 -0.002

0.001 0.007 0.052 0.071

-0.008

-0.010

0.000

0.015

0.004

-0.036

-0.001 -0.009

-0.050

-0.006

0.031

-0.094 0.034

0.048

0.033

0.089

0.090

-0.062

-0.056

0.003

0.052

0.011

-0.024

0.043

0.016

0.035

0.045

e-

0.459*** -0.076 -0.069 0.043 0.032 0.281*** 0.077

Pr

Age30_34 Age35_39 Age40_44 Age45_49

0.388*** -0.068 -0.073 0.038

na l

Age25_29

0.042

pr

Asset_1000_2000

-0.032

0.439***

oo

Asset_750_1000

0.021

-0.038

44

-0.027

-0.012

0.091

f 0.678***

0.078

1.025*** -0.042

Hokuriku Chubu Kinki Chugoku Shikoku Kyushu Constant

0.385*** 0.163** 0.054 0.194* 0.236

0.294***

0.213***

2.097*** 18,864 0.170 -6124.364 86.84% 0.794

2.527*** 18,864 0.138 -6363.721 87.03% 0.762

Jo ur

N pseudoRsq LLR % correctly classified Area under ROC curve

0.485*** 0.185** 0.123* 0.230** 0.379**

na l

Hokkaido Tohoku

-0.026 -0.002

0.036

-0.018

-0.095

-0.017

0.017

0.026

pr

Debt

-0.187**

0.015

-0.020

-0.010

0.004 0.019

0.070

-0.009

0.010

0.005

0.087

0.073 0.011 0.047 0.030

-0.004 -0.022 -0.013 0.023

0.047 0.001 0.012 0.004

0.092 0.023

-0.001

0.011

0.042

0.087

e-

Graduate

0.331*** 0.887***

Pr

University

oo

Junior college

45

0.048

f

oo

Note: Results from PS: propensity score matching; PS-K: propensity score matching with the Epanechnikov kernel function; NN: Nearest-neighbor matching using Mahalanobis distance; and NN-K: Multivariate-distance matching using Mahalanobis distance with the Epanechnikov kernel function. *, **, and *** show that the

Jo ur

na l

Pr

e-

pr

estimates are statistically significant at the 10%, 5%, and 1% levels, respectively.

46

f e-

pr

oo

The PPS 2010 family household and the SHF 2010 family household

Jo ur

The FLS 2016 and the SHF 2016 single-person household

na l

Pr

The FLS 2016 and the SHF 2016 family household

47

f oo

Appendix Figure 1. Overlap charts

Jo ur

na l

Pr

e-

pr

Note: Findata = 0: Data belongs to SHF 2016 or SHF 2010; Findata = 1: Data belongs to FLS 2016 or PPS 2010.

48