OSIPP Discussion Paper: DP-2019-E-005

Does Financial Difficulty Damage Cognitive Function?
May 7, 2019
Yumi Ishikawa
PhD student, Osaka School of International Public Policy (OSIPP)【Keywords】aging, cognitive function, elderly, financial difficulty, financial situation, income,Japan, universal health coverage【Abstract】
This study aims to estimate the effect of financial difficulty on cognitive function in a sample of theJapanese elderly population, using a panel dataset which includes randomly selected elderly
Japanese citizens aged 60 and over from the National Survey of the Japanese Elderly. It isappropriate dataset to capture the effect on cognitive impairment in the sense that cognitive function
can gradually degenerate after retirement in many cases. We estimate the effect of household incomeon the probability of the onset of cognitive impairment at a following survey point using
random-effect probit model. There is a significant negative effect from financial difficulty on
cognitive function. When participants’ household income drops by 1%, they are 2.2% more likely todevelop cognitive impairment. Financial support plays an important role in improving recipients’cognitive function. It should be noted that we found the effect of financial difficulty even in Japan
which has a universal health coverage.

Correspondence: y-ishikawa@osipp.osaka-u.ac.jp
The data for this secondary analysis, "National Survey of the Japanese Elderly 1987, 1990, 1993, 1996, 1999, and2002, Tokyo Metropolitan Institute of Gerontology, University of Michigan, and University of Tokyo" was provided
by the Social Science Japan Data Archive, Center for Social Research and Data Archives, Institute of Social Science,
The University of Tokyo. I acknowledge the project’s contributors for making the data available. I acknowledge
Professor Miki Kohara for her elaborate guidance and persistent encouragement. I am also grateful to Professor
Nobuo Akai, Associate Professor Masayuki Kudamatsu, and Associate Professor Tetsuya Matsubayashi for theirinsightful comments and useful discussions. I take sole responsibility for any remaining errors.

1. Introduction
Cognitive function impairment is one of the leading causes of disability among older people
worldwide. It is commonly accompanied by mild neurocognitive disorder or, in the worst case,dementia. In 2015, 45.7 million people, suffered from dementia (Prince et al., 2015). Japan has oneof the largest populations of people with dementia (Prince et al., 2013). The Japanese cost ofdementia, which consists of medical, social, and informal care costs, was estimated to be about
US$ 133 billion in 2015 (Cabinet Office Japan, 2016). The burden of impairment in cognitive
function affects patients, their families, and society.
Previous studies report that financial difficulty damages cognitive function (Al Hazzouri et al., 2017;
Lynch, Kaplan, & Shema, 1997; Marden, Tchetgen, Kawachi, & Glymour, 2017). Anandi et al.
(2013) also find that farmers’ cognitive function declines before harvest, when they are in afinancially difficult situation. However, Carvalho et al. (2016) provide evidence that there is nodifference in cognitive function before and after payday. As indicated above, a growing body ofliterature focuses on the effect of financial difficulty on cognitive function, however there is still a
discrepancy in the results. If there is any relationship between financial difficulty and cognitive
function, it has important policy implications. That is, financial support plays an important role not
only in improving recipients’ financial situations, but also in improving their cognitive function.
In addition, financial difficulty may affect the risk factors for cognitive impairment, which are
well-established in past studies. Livingston et al. (2017) demonstrate that mental illness andunhealthy behaviours such as smoking and physical inactivity are risk factors for cognitive
impairment. These risk factors may be affected by financial difficulty (Grip, Lindeboom, &Montizaan, 2011; M. G. Marmot, Shipley, & Rose, 1984).
This study aims to estimate the effect of financial difficulty on cognitive function in a sample of theJapanese elderly population. The remainder of this paper is structured as follows: Chapter 2
illustrates the empirical strategies, data, and variables; Chapter 3 presents the results of the analyses;
Chapter 4 discusses the reasons for the results; and is followed by conclusions in Chapter 5.

2. Method
2.1

Empirical strategy

To identify the effect of financial difficulty on the probability of the onset of cognitive impairment,
we estimate the following model:

2CIit =βhhincHHINCit-1 + X’ it-1γ+εit
εit = μi + e it
CIit is a dummy variable which equals one if the individual suffers from cognitive impairment,
HHINCit-1 represents household income, Xit-1 is a vector of other controls, μi denotes an unobservable
individual heterogeneity, and e it is an error term. i denotes individuals and t denotes the survey year.
𝛽hhinc and 𝛾 are the parameters to be estimated, where 𝛽hhinc is the parameter of interest.
This study uses household income including pension as a proxy for financial situation. By using
household income instead of individual income, we partially address potential endogeneity, that is,
the possibility that unobservable individual heterogeneity, such as patience, may be associated withboth cognitive functioning and individual income. We also address potential reverse causality, that is,
the possibility that participants who have poor cognitive function retire early, which then leads to anindividual income drop. We show estimation results which use alternative indicators for financial
situation and discuss the appropriateness of using income later in Chapter 3.
OLS regressions yield biased estimates when unobservable individual heterogeneity causes
autocorrelation. In this study, the equation is estimated using the random-effect probit model
considering individual heterogeneity by μi. We do not apply the fixed-effect model. This is because
this survey asks about household income using multiple-choice questions with several income
brackets, treating it as a kind of status, and the fixed-effect model is not suitable for capturing
time-invariant effects. To address reverse causality further, we regress cognitive impairment at time t
on household income at time t-1.
The role of financial difficulty in determining cognitive function is consistent with the social
determinant’s hypothesis. The hypothesis assumes that financial difficulty leads to poor healthoutcomes (Marmot, 2005). Thus, we expect that the smaller the household income, the worse thecognitive function, that is 𝛽hhinc <0. All statistical analyses are conducted using STATA 15 (StataCorp,
2017).
2.2

Data

This study uses a panel dataset which includes randomly selected elderly Japanese citizens aged 60
and over from the National Survey of the Japanese Elderly. It consists of six waves of surveys
conducted in 1987, 1990, 1993, 1996, 1999, and 2002, and contains individual information abouthousehold income, cognitive function, socio-demographics, socioeconomic status, health, and healthbehaviours. The population aged 60 and older was initially selected by two-stage stratified random
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sampling in 1987 and followed up in succeeding surveys (Tokyo Metropolitan Institute ofGerontology & University of Michigan, 1993). Additional younger samples were supplemented in
1990, 1996, and 1999 (Tokyo Metropolitan Institute of Gerontology & University of Michigan,
1993). The sample is nationally representative of Japanese citizens aged 60 and over (Tokyo
Metropolitan Institute of Gerontology & University of Michigan, 1993) . The data are provided by
the Social Science Japan Data Archive, Centre for Social Research and Data Archives, Institute ofSocial Science, The University of Tokyo. Informed consent is gathered for each survey point andethical approval is obtained from Tokyo Metropolitan Institute of Gerontology Ethics Committee.
2.3

Measures

Cognitive function
Cognitive function is measured by a memory-related questionnaire. The questionnaire is similar to ashort portable mental status questionnaire (SPMSQ), which detects the presence of cognitive
impairment and assesses the degree (Pfeiffer, 1975). The questionnaire contains nine questions: (1)‘What is your address?’; (2) ‘What are the date, month, and year today?’; (3) ‘What day of the week
is it?’; (4) ‘What was your mother’s maiden name?’; (5) ‘Who is the current prime minister?’; (6)‘Who was the prime minister before him?’; (7) ‘Can you count backwards from 20 by 3’s?’; (8)‘What is your date of birth?’; and (9) ‘How old are you?’. For each question, a correct answer isallocated one point and an incorrect answer is allocated zero points; these answers form a cognitive
function score ranging from 0 to 9. We use this score to produce a binary measure of cognitive
function. Participants whose cognitive function score is 7 or over are categorised as the base group:‘No cognitive impairment’. Participants whose scores are 6 or less are categorised as ‘Cognitively
impaired’. This measure is based on SPMSQ criteria: zero to two errors signifying normal cognitive
function, three to four errors signifying mild cognitive impairment, five to seven errors signifying
moderate cognitive impairment, and eight or more errors signifying severe cognitive impairment(Pfeiffer, 1975). In this study, ‘Cognitively impaired’ includes mild neurocognitive disorder anddementia. They are both characterised by a decline from a previous level of cognitive function but indementia, in contrast to mild neurocognitive disorder, the decline affects independence in the
person’s performance of daily living activities (WHO, 2018).
Table 1 reports statistics Summary on variables used in the analyses. The mean cognitive function
score is 8.02 out of 9. The estimated population prevalence of scoring 6 or lower, which is thecategory of ‘Cognitively impaired’, is 12%.

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TABLE 1 : Statistics Summary
Variables

Mean

Std. Dev.

Min

Max

Cognitive function score

8.02

1.27

0

9

Cognitive impairment
Household income

0.12
271.660.33
146.00

0
42.43

1
1000

Household income < bottom 20%

0.29

0.45

0

1

Household income < bottom 40%

0.45

0.50

0

1

Household income < bottom 60%

0.65

0.48

0

1

Household income < bottom 80%

0.82

0.39

0

1

Hospital availability
Sex

8.00
0.57

2.56
0.50

5.20
0

14.39
1

Age

72.09

7.11

60

99

City size

2.27

1.49

0

4

Marital status

0.72

0.97

0

3

Family size

3.35

1.90

1

12

Years of schooling

8.99

2.77

0

17

Employment status
0.28
0.45
0
1
The sample sizes are as follows: 3,288 in 1987, 2780 in 1990, 2441 in 1993, 3436 in 1996,
4969 in1999, and 4337 in 2002.

Financial Situation
This study uses household income as a proxy for financial situation. Specifically, we use equivalent
income, which divides household income by the square root of family size (OECD, 2009). In thesurvey, household income is measured by multiple choice questions with several income brackets.
We produce a continuous scale of household income by taking median values of each income
bracket.
Hospital availability
This study uses the number of hospitals per 100,000 population in the area1 where participants live.
The data on the number of hospitals by area and survey year is obtained from surveys of medical
institutions which are conducted by the Health Statistics Office, Director-General for Statistics andInformation Policy, Ministry of Health, Labour and Welfare. The data on population by area andsurvey year is obtained from a demographic survey conducted by the Ministry of Internal Affairs andCommunications.

1

Hokkaido, Tohoku, Kanto, Hokuriku, Tosan, Tokai, Kansai, Chugoku, Shikoku Kita-Kitakyushu andMinami-Kitakyushu were used as areas.

5Socio-demographic indicators
Socio-demographic variables include sex, age, urban scale, marital status, and family size. Urban
scale is grouped into five categories: (1) Principal cities, including the twenty-three wards in central
Tokyo, Osaka city, Yokohama city, Nagoya city, Kyoto city, Kitakyusyu city, Sapporo city, Kawasaki
city, Kobe city, Hiroshima city and Fukuoka city; (2) Cities with a population of 200,000 or higher;(3) Cities with a population of 100,000 or higher; (4) Cities with a population of 100,000 or lower;and (5) Towns / Villages. Marital status was grouped into four categories: (1) married; (2)separated/divorced; (3) widowed; and (4) never married. ‘Do not know’ answers are originallymeasured, but they are later excluded from this analysis.
Socio-economic indicators
Socioeconomic variables include employment status and years of schooling. Employment status isoriginally measured as employed, taking a medical leave of absence, unpaid, unemployed, retired,unable to work due to disability, housework, and other. For this project, employment status wasrecoded as a binary variable (employed vs not employed) from which other was excluded.

3. Result
Table 2 presents the effect of household income on the probability of the onset of cognitive
impairment. Column (1) shows the result of the simple regression only on income. The coefficient ofincome is -0.412, which is negative and statistically significant. The marginal effect of income is-0.051, which is reported in the last row in each column as ϕ(𝐱′β̂) ∙ β̂hhi𝑛𝑐 where β̂hhi𝑛𝑐 is the obtained
estimate on income and ϕ is a density function of bivariate normal distribution. This means thatparticipants are 5.1% more likely to develop cognitive impairment when their income drops by 1%.
Column (2) represents the case with other covariates such as the availability of hospitals andsocio-demographic indicators. Although the magnitude of the estimate and thus its marginal effectdecreases, the effect is still statistically significant at the 1% significance level. Column (3) reports
the case including socioeconomic indicators. Although the estimate decreases further in absolutevalue, the effect is still statistically significant at the 1% significance level. The results indicate that
financial difficulty leads to low cognitive function.

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TABLE 2: Association between household income and cognitive impairment(Random-effects probit model)
Dependent Variable: A binary indicator that equals one if there are positive screens for cognitive
impairment (cognitive function score < 7) and zero otherwise
(1)
(2)
(3)
VARIABLES

CATEGORIES

ln household income-0.412***(0.060)-0.372***(0.067)0.012(0.014)0.059(0.080)0.064***(0.006)-0.066(0.116)-0.120(0.136)-0.019(0.117)0.086(0.111)0.433**(0.210)0.220**(0.089)-0.100(0.296)0.005(0.021)-0.188***(0.068)0.009(0.014)-0.090(0.080)0.054***(0.006)-0.054(0.113)-0.117(0.134)-0.063(0.113)0.035(0.108)0.405**(0.205)0.161*(0.087)-0.109(0.291)-0.015(0.020)-0.128***(0.015)-0.218***(0.078)0.495(0.325)-4.486***(0.604)-3.295***(0.583)

Hospital availability
Sex

Malea
FemaleAge
City sizea※Principal cities
Cities >= 200,000
Cities >= 100,000
Cities < 100,000
Towns / Villages

Marital status

Married a
Separated / Divorced
WidowedNever married

Family size
Years of schooling
Employment status

Not employed a
Employed

Constant

MARGINAL EFFECTS
ln household income-0.051-0.043Observations
5,973
5,952
Number of id
2,976
2,969
Log likelihood-1,719.394 -1,611.763
Wald Statistic
46.81***
172.73***Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
abase category※
Principal cities: the twenty-three wards in central Tokyo, Osaka city, Yokohama city,
Nagoya city, Kyoto city, Kitakyusyu city, Sapporo city, Kawasaki city, Kobe city,
Hiroshima city and Fukuoka city

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-0.022
5,883
2,918-1,543.680
212.33***

As for other explanatory variables, age, marital status, years of schooling, and employment statushave statistically significant effects at least at the 10% significance level. Older participants are more
likely to develop cognitive impairment. Compared with married participants, separated/divorced or
widowed participants are more likely to develop cognitive impairment. Highly educated participantsand employed participants are less likely to develop cognitive impairment.
To verify our finding, we conduct three sets of robustness checks. First, we use alternative indicatorsof financial situation to check whether the finding is sensitive to our choice of income. Althoughincome including pension should be strongly related to financial situation for the elderly people,
income may be insufficient to capture their financial situation. It is often said that consumption is abetter indicator of an individual’s welfare reflecting his/her life-time conventional financial situation.
Because our survey asks questions about monthly living expenses, we use this instead of income.
The result shows that the negative effect of consumption on the cognitive impairment rate remains
statistically significant at the 5% significance level, although information on consumption isavailable only in 1999 and 2002 and the number of the sample is decreased. Thus, we can say thatthe definition of financial situation does not affect the result.
Second, we use a different functional form of income without taking its logarithm. The result showsthat the negative effect of income remains statistically significant at the 1% significance level. We
also investigate at what level of income we can still observe its negative effect. We regress the onsetof cognitive impairment on four income dummy variables separately. The coefficient of the bottom
20% income dummy, the bottom 40% income dummy, and the bottom 60% income dummy arestatistically significant. The only exception is the bottom 80% income dummy, where the coefficientis statistically insignificant. That is, at most income levels, except for the top 20% of income
distribution, financial situation has a negative effect on cognitive function.
Finally, we assess the sensitivity of the measure of cognitive function. In our analyses, we use the
6-point cut-off of memory score to produce a binary measure of cognitive impairment. Although we
replace this cut-off with the 5-point and the 7-point cut-offs, the income effect remained statisticallysignificant at the 5% significance level. Moreover, we attempted changing the definition of cognitive
impairment. In our survey, the memory-related questionnaire includes two questions about the namesof the past prime ministers. There is a possibility that these answers reflect the participants’ interestin politics, instead of cognitive ability. Participants who are apathetic about politics may fail toanswer these questions correctly, which does not necessarily mean that they are cognitively impaired.
Therefore, we re-estimate the income effect with the cognitive function measured by seven questions
excluding those two. The income effect remains statistically significant at the 5% significance level.
8Thus, the level and the definition of cognitive impairment do not alter our obtained results.

4. Discussion: the link between financial difficulty and cognitive function
In the previous section, we find that financial difficulty has a negative effect on cognitive function,and this effect is observed at most income levels. Why do the financially needy have lower cognitive
function? There are three possible pathways from financial difficulty to cognitive function.
First, financial stress could damage an individual’s cognitive function. To see whether this is apossibility for our sample, we estimate the effect of income on the depression rate2 in our sample.
Other confounding variables are the same as Column (3) of Table 2. The coefficient of income isnegative and statistically significant, meaning that low-income participants are more likely todevelop depression. As Grip et al. (2011) suggest, for their Dutch elderly male sample, a reduction in
pension may lead to a higher depression rate. Alternatively, as Livingston et al. (2017) demonstrate,
depression can be a risk factor for cognitive impairment.
Second, financial resources could prevent cognitive impairment through health behaviour. There isan established association between healthy behaviours and adequate financial resources (Marmot etal. 1984). Table 3 confirms this possibility, showing the estimation result of regressing various healthbehaviours on income. The first column shows the effect of income on the frequency of physicalactivity. The coefficient of income is positive and statistically significant, meaning that high-income
participants spend more time in physical activity. The second column represents the income effect on
smoking. The coefficient of income is negative and statistically significant, indicating thathigh-income participants are less likely to smoke. These results are in line with the findings from
Marmot et al. (1984). Livingston et al. (2017) also demonstrate that physical activity is important for
preventing cognitive impairment, whereas smoking is a risk factor for cognitive impairment.
Furthermore, we examine the income effect on frequency of medical check-ups and report the resultsin the third column. The coefficient of income is positive and statistically significant, meaning thathigh-income participants take more frequent medical check-ups, although information on medical
check-ups is available only in 1990 and the number of the sample is decreased. Thus, financial
resources can affect these healthy behaviours, and accordingly improve cognitive function.

2

In our analysis, depression is a binary indicator that equals one if there are positive screens for depression
which are measured using the CES-D.

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TABLE3: Association between household income and health behaviours
DEPENDENT VARIABLESVARIABLESln household income

Random-effects probit model
Physical Activitya
Smokingb
0.187***(0.040)
YES
YES
YES

-0.229**(0.096)
YES
YES
YES

Hospital availability
Socio-demographics indicators
Socio-economic indicators
MARGINAL EFFECTS
ln household income
0.049-0.016
Observations
9,111
9,126
Number of id
3,808
3,810
Log likelihood-5316.077-2911.97
Wald Statistic
163.93***708.16***R-squared*** p<0.01, ** p<0.05, * p<0.1
a: equals one if high-physical activity and zero otherwise
b: equals one if smoking and zero otherwisec: the number of medical check-ups during the past year

OLS
Medical Check-up c
0.281***(0.133 )
YES
YES
YES
0.281
1,333

0.0201

Finally, financial resources could mitigate cognitive decline through treatment even after cognitive
function starts to decline. Although Japan has a national health insurance program, there are some
out-of-pocket medical expenses, which are generally 10%-30% of the insured treatment cost. In
addition to the out-of-pocket fee, patients need to pay travel costs to access treatment. Thus,
participants with adequate financial resources seem more likely to access medical care which thenmakes it easy for them to mitigate cognitive decline. Furthermore, the gap in access to out-of-pocket
treatment, which is not covered by a national health insurance program, seems to be larger.
Eisenberg et al. (1993) indicate that individuals with adequate financial resources are more likely touse unconventional therapy in the US population sample. Although we are not able to examine thispathway because of data limitations, it could be argued that financial resources affect cognitive
impairment through improving access to medical care and broadening treatment options.

5. Conclusion
This study assessed the effect of financial difficulty on cognitive function in a sample of theJapanese elderly population. The results suggest that financial difficulty impedes cognitive function,even after controlling forother confounding variables such as hospital availability,

socio-demographic, and socioeconomic indicators. Income support may play an important role not
only in improving recipients’ financial situations but also in improving their cognitive function.

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This study used longitudinal data on the elderly population in Japan from 1987 to 2002, to capture
the determinants of changes in cognitive impairment. Our panel data targeting the elderly population
is appropriate because cognitive function can gradually degenerate after retirement in many cases. It
is also appropriate because Japan has an interesting social, economic, and medical environment for
the elderly population. There is a mandatory retirement age of sixty-five years old: many people
retire as they turn 65. After that age, most people are covered by universal health coverage andpension. It should be noted that we found the effect of financial difficulty on cognitive function evenin Japan which has a national health insurance program. This suggests that in countries without
universal health coverage, financial difficulty may have a stronger effect on cognitive function.
Future research is needed to clarify the role of medical interventions in cognitive impairment.
Although we controlled for the number of hospitals by area in our study, the individual usage of
medical care and its quality cannot be examined because of lack of information. Future research canbe extended to understanding the role of the quantity and quality of healthcare availability in therelationship between financial difficulty and cognitive function.

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