ISSN (Print) 0473-453X

Discussion Paper No. 1133

ISSN (Online) 2435-0982

TEMPORAL INSTABILITY OF RISKPREFERENCE AMONG THE POOR:
EVIDENCE FROM PAYDAY CYCLES
Mika Akesaka
Peter Eibich
Chie Hanaoka
Hitoshi Shigeoka

May 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanTemporal Instability of Risk Preference among the Poor:
Evidence from Payday Cycles *Mika Akesaka †Osaka University

Peter Eibich ‡Max Planck Institute for
Demographic Research

Chie Hanaoka §Toyo
University

Hitoshi Shigeoka **Simon Fraser
University and NBER

Current Version: May 3, 2021
Abstract
The poor live paycheck to paycheck and are repeatedly exposed to strong
cyclical income fluctuations. We investigate whether such income
fluctuations affect risk preference among the poor. If risk preference
temporarily changes around payday, optimal decisions made before payday
may no longer be optimal afterward, which could reinforce poverty. By
exploiting Social Security payday cycles in the US, we find that risk
preference among the poor relying heavily on Social Security changes
around payday. Rather than cognitive decline before payday, thedeterioration of mental health and relative deprivation may play a role. We
find similar evidence among the Japanese elderly.
Keywords: poverty, risk preference, Social Security, mental health, relative deprivation,elderly, Health and Retirement Survey, Japanese Study of Aging and Retirement
JEL code: D81, D91, I32* The authors thank Keith Marzilli Ericson, David Freeman, Alex Imas, Stephan Meier, Takeshi Murooka,

Krishna Pendakur, and seminar participants at Osaka University (ISER and OSIPP), Simon Fraser University,
NBER Aging Spring meeting, and the Joint Workshop on Behavioral Economics for their comments andsuggestions. The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging and isconducted by the University of Michigan. The Japanese Study of Aging and Retirement (JSTAR) is
conducted by the Research Institute of Economy, Trade, and Industry (RIETI), Hitotsubashi University, andUniversity of Tokyo. The Japanese General Social Surveys (JGSS) are conducted by Osaka University ofCommerce, in collaboration with the Institute of Social Science at the University of Tokyo. Akesaka
acknowledges financial support from JSPS KAKENHI (15H05728, 20H05631, 20K13511, 21H04397) andthe Joint Usage/Research Center for Behavioral Economics at ISER. Hanaoka acknowledges financial
support from JSPS KAKENHI (17K03783, 19H05487, 20K20418). Any errors are our own.
† Institute of Social and Economic Research, Osaka University, 6-1, Mihogaoka, Ibaraki, Osaka 5670047, JAPAN. Email: akesaka@iser.osaka-u.ac.jp‡ Max Planck Institute for Demographic Research, Konrad-Zuse-Str. 1, 18057 Rostock, GERMANY.
Email: eibich@demogr.mpg.de§ Faculty of Economics, Toyo University, Hakusan 5-28-20, Bunkyo-Ku, Tokyo, 112-8606, JAPAN.
Email: hanaoka077@toyo.jp** Corresponding author: Department of Economics, Simon Fraser University, 8888 University Drive,
Burnaby, BC V5A1S6, CANADA, and NBER. Email: hitoshi_shigeoka@sfu.ca
11. Introduction
The poor often live paycheck to paycheck, and heavily rely on government benefits such aspension and social welfare programs. Numerous studies have documented expenditure increasing
sharply after the receipt of such payment, suggesting that these individuals lack financial
resources before payday. 1 Since these payments are usually periodic (e.g., monthly), the poor are
repeatedly exposed to strong and nearly unavoidable cyclical income fluctuations.
This paper asks whether such frequent, temporary and anticipated income fluctuations
induced by periodic payments change individuals’ risk preference. Risk preference is afundamental determinant of everyday economic decision-making (e.g., payday loans andpurchase of lottery tickets) as well as issues with long-term consequences (e.g., saving,
investment, and insurance choices) for individuals. As a result, in many circumstances, thetemporal change in risk preference around payday—if it exists—indicates that the optimal risk
decisions made before payday may no longer be optimal after payday at a different level of risk
preference. If such non-optimal decisions further deepen the poor’s dependence on periodicallypaid welfare programs, such time-varying risk preference induced by periodic payment may be
another source of the feedback loop that reinforces poverty. As periodic payment of pensions,salaries, and social welfare programs is ubiquitous around the world, 2 the answers to thisuniversal question can have substantial policy relevance.
We test this question by exploiting Social Security payday cycles in the United States. In
our sample period, Social Security benefits are paid on the 3rd of every month. Past research has
documented that expenditures and food consumption increase sharply on the 3rd of the month(Stephens 2003; Mastrobuoni and Weinberg 2009), indicating that individuals face scarcefinancial resources before payday. Since the restricted version of the Health and Retirement
Study (HRS)—the primary working data in this study—includes the exact dates on whichinterviews took place, we can compare the risk preference of those who were interviewed before
and after the Social Security payday (i.e., 3rd of the month). We run balanced tests to ensure therandom assignment of interview dates to respondents over the month. This approach allows us toobtain the causal estimate of the effect of income fluctuation around payday on the risk
1 See, for example, studies in the US (Wilde and Ranney 2000; Stephens 2003; Shapiro 2005; Mastrobuoni and

Weinberg 2009; Hastings and Washington 2010) and the UK (Huffman and Barenstein 2005; Stephens 2006).
2 E.g., Supplemental Nutrition Assistance Program (SNAP) and Supplemental Security Income (SSI) in theUS.
2preference of Social Security beneficiaries. 3
We find that risk preference—measured by the hypothetical gambles over lifetime incomein the HRS—changes around payday among Social Security beneficiaries. 4 In particular,
individuals who rely heavily on Social Security income and are indeed poor (average annual
household income is USD 24,688), become more risk-tolerant just before payday—when
financial resources are scarce—than after payday. In terms of magnitude, the risk tolerance
parameter is higher by 0.43 SD before payday than after payday, which cannot be dismissed asnoise. We do not find the same systematic pattern for those who are relatively rich and as a result
do not rely heavily on Social Security benefits, suggesting that dependence on this income plays
a role.
Our results are robust to different ways of defining heavy dependence on Social Security
income, different ways of constructing outcomes, and different specifications. Specifically, ourresults do not need to rely on any particular form of utility function to derive risk preference
parameters. We also carry a battery of placebo tests to support our results. Taken together, theseresults suggest that the risk preference of the elderly poor, who rely heavily on Social Security
benefits, may not be temporally stable in short intervals, at least in our setting.
To probe the mechanisms of temporal change in risk preference around payday, we firstexamine cognitive functioning around payday. Mullainathan and Shafir, among otherresearchers, argue that financial strain impedes cognitive function and worsens the quality ofdecision-making. 5 We do not find any before–after payday difference in cognition measures,
implying that cognitive decline around payday does not explain the temporal shift in risk
preference. Instead, we find suggestive evidence that mental state plays some role. We find that
individuals are more likely to be depressed before than after payday. Since we do not observe
any changes in mental health among individuals who do not heavily rely on Social Security

3 Whereas our across-subjects design does not allow us to compare the level or rank stability of risk preferencein the same individual (within subject), this approach still enables us to compare the aggregate of risk
preference and obtain the causal estimates of temporal financial shocks on risk preference. Mastrobuoni and
Weinberg (2009) take a similar across-subjects approach which exploits random interview dates around Social
Security payday.
4 This gamble is clearly framed as one of long-run risk, not small-to-moderate gambles that can be reimbursed
immediately. This mitigates the concern that liquidity constraints restricting individuals’ choices drive ourresults.
5 See Shah et al. (2012), Mullainathan and Shafir (2013), and Mani et al. (2013). See also Schilbach et al.
(2016) and Dean et al. (2019) for the growing literature on the psychology of the poor.
3

benefits, the deterioration of mental health before payday might be one channel through whichrisk preference changes around payday.
To assess external relevance, we also examine the same question using data for the elderly
in Japan. This exercise is more than a simple replication, since the US and Japan have very
different baseline risk preferences. Falk et al.’s (2018) global survey shows that Japan is one of
the most risk-averse countries whereas the US is the opposite. Furthermore, the public pension in
Japan is paid on the 15th of every even month instead of every month like in the US. This twomonth interval between paydays allows us to include week-of-the-month fixed effects (e.g., theeffect of the first week of the month) for Japan, which is impossible in the case of the US. Thissetting mitigates the concern that the US results are driven by other events at the beginning of thecalendar month, such as housing payment. 6 We find corroborative evidence consistent with theresults found in the HRS data: Japanese individuals also become more risk-tolerant just before
payday.
We also revisit the potential mechanisms using Japanese data. We find that Japanese
respondents, who rely heavily on pension benefits, feel financially anxious before payday, whichis in line with the finding on depression in the US. Interestingly, respondents also feel relatively
deprived before payday, and hence, another potential mechanism could be that such feeling
induces one to take more risks, as one strives to catch up to their comparison standards. Although
we cannot precisely pin down the underlying mechanisms, mental state and the feeling ofrelative deprivation, and their interplays, may influence temporal fluctuations in risk preferences
around payday.
This study is related to at least two strands of the literature. First, it adds new insight to theliterature on poverty and individual preferences (e.g., Carvalho et al. 2016). 7 The literatureargues that the economic environment of the poor shapes their individual preferences, which, inturn, could trap them in a cycle of poverty. We propose a new contextual feature—time-varying
risk preferences induced by periodic payments—that might lead to suboptimal decisions, as
another potential trigger promoting poverty. One potential way to mitigate the temporal shift in

6 For example, Vellekoop (2018) shows that most housing payments (either rent or mortgage payments) areconcentrated at the beginning and the end of the calendar month in the US.
7 None of the existing studies explicitly focus on the elderly. See, for example, Lawrance (1991), Banerjee andMullainathan (2010), Tanaka et al. (2010), Spears (2011), Gloede et al. (2015), Bartos et al. (2018), andHaushofer and Fehr (2019).
4

risk preference may be to increase the frequency of payments in smaller amounts and shortenintervals between the payments. More frequent access to income allows people to smoothen theirconsumption patterns (Stephens and Unayama 2011), and potentially mitigate financial distress. 8
Second, this study is related to the broader literature on the stability of preferences, which
are generally assumed to be static economic primitives (Stigler and Becker 1977). 9 The growing
literature investigates whether large financial shocks, such as the Great Depression, alter risk
preferences (e.g., Necker and Ziegelmeyer 2016; Guiso et al. 2018). 10 Here, we emphasize that
this study is conceptually distinct from these prior studies. Figure 1, which is adapted from
Schildberg-Hörisch (2018, Figure 1), illustrates the conceptual framework of time-varying risk
preference throughout the life course, which allows us to distinguish between permanent andtemporal shifts in risk preference. Unlike previous studies that investigate the impact of a large,
one-time, permanent, and unanticipated shock on baseline risk preferences (the dashed line), we
evaluate the impact of the frequent, temporal, and anticipated variation in financial resources on
the temporal shift around baseline risk preference (the jagged line). 11 This distinction isimportant from a policy perspective: if the shift is permanent, policymakers can still design thepolicy taking the risk preference after the shock as given; however, if the shift is temporary, it is
not apparent which level of risk preference they should address to design an optimal policy. 12
The rest of the paper is organized as follows. Section 2 describes the data, Section 3
presents our identification strategy, Section 4 reports our findings from the HRS, Section 5reports the supplemental findings from Japanese data, and Section 6 concludes.

8 Our results also make the case for consumer protection laws such as the cooling-off period for contracts tocancel a purchase (Rekaiti and Van den Bergh 2000; Schildberg-Hörisch 2018).
9 For a review of risk preferences, see Mata et al. (2018). For time preferences, see Krupka and Stephens(2013), and Meier and Sprenger (2015). For social preferences, see Carlsson et al. (2014) and Volk et al.
(2012).
10 A growing body of literature has suggested that experiencing not only financial crises but other large
negative shocks such as natural catastrophes and conflicts too may alter one’s risk preference. See Chuang andSchechter (2015) and Schildberg-Hörisch (2018) for a review.
11 Alternatively, our results can be interpreted as state-dependent preferences, where preferences are a stablefunction of states of nature that change over time (i.e., income), as long as these states are exogenous. This
would allow choices to vary with various states of nature, keeping the preference to be stable. However, it isempirically challenging to answer what constitutes an appropriate “state of nature” (Andersen et al. 2008;
Dasgupta et al. 2017).
12 For example, Harrison and Ng (2016) show that the welfare of social insurance policy is contingent on therisk preferences of the beneficiaries. If risk preference is unstable over time, what we consider as a welfareenhancing policy today could become a welfare-reducing policy in the future. See also Andersen et al. (2008),
and Harrison et al. (2020).
5

2. Data
2.1. Sample selection
Our dataset comes from the HRS, a nationally representative, biannual survey of theelderly in the US. We use data from 1994, 1998, 2000, 2002, and 2006. 13 The restricted version
contains the exact dates on which interviews took place, which is crucial information for our
empirical strategy.
The HRS is randomly administered by the Survey Research Center at the University ofMichigan to individuals over the month. During the time period covered by our data, Social
Security benefits were paid out on the 3rd of the month if it was a weekday or the first business
day prior to the 3rd in case it was a holiday or the weekend. Between 1994 and 2006, 73.1% ofSocial Security beneficiaries received their checks through direct deposit, and thus had access totheir benefits on pay dates (Social Security Administration 2019).
Using the exact interview dates and the known Social Security payday, we can calculate
the distance between them in days. 14 Then, we use this distance (in days or weeks) as the proxyvariable for the respondent’s financial resources. This approach enables us to compare theindividual preferences of those interviewed before (i.e., those more likely to run out of money)
and after (i.e., those more likely to have sufficient financial resources) the Social Security
payment. In Section 3.2, we show that the interview dates are randomly assigned and thus areuncorrelated with the date on which individuals receive their Social Security paycheck.
We restrict our sample to single or coupled households with at least one member over 62,
who started to receive Social Security payments before May 1997. 15 Following Mastrobuoni and
Weinberg (2010), we drop the widowed and divorced from the sample to ensure that anindividual is not paid based on an ex- or deceased spouse’s payment schedule.
Individuals from low-income households are unavoidably more dependent on Social
Security benefits, and as a result, more likely to be repeatedly exposed to cyclical income
13 Whereas the HRS began in 1992, we did not include data from 1992 because it included only four risk

categories in the hypothetical gamble question, which we use to derive the risk preference parameter. It was
expanded to six categories from 1994 onwards. After 2006, the data no longer includes the hypotheticalgamble question. Note that the gamble question is only asked to new respondents and a random subsample of
returning respondents. After the sample restriction is imposed, no observations are left in the data from 2004.
14 We verified with the HRS team that 92.4% of completed respondents start and end on the same date, so themeasurement errors from the difference in interview dates and reply dates are minimal.
15 For those who started receiving Social Security payments after May 1997, the payday changes to the second,
third, or fourth Wednesday, depending on the beneficiary’s birthday.
6fluctuations. Appendix Figure A1 shows that individuals with high dependency on Social
Security income are indeed poor. The figure plots the relationship between the share of Social
Security income among total household income on the x-axis and the total household income on
the y-axis. It clearly shows that high dependence on Social Security is driven by low total
household income (i.e., the denominator of the share) since the amount of Social Security
income (i.e., the numerator of the share) does not vary much across individuals. For that reason,
we divide the sample into individuals from households in which the share of Social Security
income among total household income is above 50% (more dependent and poor/low-income), or
43% of the full sample, and below 50% (less reliant and rich/high-income). 16 Similar approaches
are taken by Stephens (2003), and Mastrobuoni and Weinberg (2009). Later, we show that our
results are qualitatively robust even when the high/low dependence thresholds are changed to
40%/60%.

2.2. Outcome: Risk tolerance
Here, we describe how we measure and construct risk preference. The HRS uses
hypothetical gambles over lifetime income to elicit risk attitudes. Respondents choose between asafe and a risky job in hypothetical scenarios. With equal chances, a risky job will double theincome or cut monthly income by a specific fraction (1/10, 1/5, 1/3, 1/2, and 3/4). Varying thesize of downside risk on the monthly income in subsequent questions refines the measure of risk
preference (see Appendix Table D1 for the questionnaires).
We follow Barsky et al. (1997) and Kimball et al. (2008) to derive the risk tolerance
parameter 𝜃𝜃 for each individual. Specifically, we assume that constant relative risk aversion
𝑊𝑊 1−1/𝜃𝜃(CRRA), 𝑈𝑈(𝑊𝑊) = (1−1/𝜃𝜃), well approximates individuals’ utility over lifetime income, and thatthe coefficient of relative risk tolerance 𝜃𝜃= −U/wU″may differ across individuals (Pratt 1964).

Appendix Table A1 provides the relationship between the six gamble response categories and the

16 Whereas Mastrobuoni and Weinberg (2009) limit their sample to individuals from households in whichSocial Security income makes up at least 80% of total household income, the share in their sample is 41%,
which is roughly similar to the share of our above-50% sample (43%). The more stringent 80% criteria only
applies to 17.2% of our sample (N=211), which substantially shrinks our sample size. This contrast indicatesthat our sample is relatively less reliant on Social Security benefits than that of Mastrobuoni and Weinberg(2009), who use the Continuing Survey of Food Intake by Individuals, 1994–1996.
7

corresponding downside risks that the individual accepts or rejects. 17 Importantly, we later showthat we do not need to rely on this particular functional form; our results are robust using theordinal measure of risk tolerance in Section 4.2. Panel (a) of Appendix Figure A2 shows thefrequency distributions of gamble responses. The figure reveals substantial heterogeneity in risk
attitudes. The figure also shows that most individuals are unwilling to take risks and roughly halfof the respondents belong to the lowest risk category (category 1).
Even though the gamble response is hypothetical, there are several reasons to justify theuse of this variable to examine the stability of risk preference (Camerer and Hogarth 1999). First,
Barsky et al. (1997) show that this risk measure, obtained by hypothetical gamble questions in
the HRS, is correlated with actual risk-taking behaviors such as smoking, drinking, andownership of financial assets. Second, the stakes of the gambles over lifetime income are large,
which is necessary to properly measure risk preference within the expected-utility framework, as
discussed in Rabin (2000). Third, Dohmen et al. (2011) validate the use of hypothetical choicequestions by comparing them to incentivized lottery questions. Finally, the gambles over lifetime
income are clearly framed as gambles over long-run risks, not small-to-moderate gambles thatcan be reimbursed immediately. This mitigates the concern that our results do not reflect thechange in risk preference per se but reflect, rather, the liquidity constraints that restrict
individuals’ choices (Haushofer and Fehr 2014). 18 For these reasons, this risk tolerance measurein the HRS was widely used in previous studies (e.g., Barsky et al. 1997; Kimball et al. 2008;
Sahm 2012).
Table 1 presents descriptive statistics for the analysis sample. The first row in Table 1
shows that the mean of the risk tolerance parameter in the sample is 0.540 with a standard
deviation of 0.834. The average age is 69.3 years, and 92% of the sample are married. Roughly
half (50.4%) are female.

17 The category numbers are higher as the individual’s willingness to accept income risk increases, and thus thecorresponding risk tolerance parameter is larger. We use the midpoint of the intervals of risk tolerance
parameters between the two adjacent risk categories. As a robustness check, we construct an ordinal risk
tolerance measure that does not rely on the interval of the risk categories as described in Section 4.2.
18 Furthermore, if individuals choose the safe option to alleviate liquidity constraints before payday, it would
go against our finding that individuals become risk-tolerant before payday.
83. Identification strategy
3.1. Econometric model
We first organize the interview dates in relation to the 3rd of each month. For ease of
explanation, we use the 3rd of the month, but this is adjusted by a few days if it is not a weekday.
Then, we construct each synthetic month around the 3rd of the month ranging from –14 to +13
days, limiting the sample to 14 days around payday (Stephens 2003). Importantly, we can exactly
identify before and after payday without errors as we know both the exact payday as well as theinterview date. 19
Given this structure of the data, the econometric model is written as
𝑘𝑘=−1,−2,+2
𝑌𝑌𝑖𝑖 = ∑𝑘𝑘≠+1
𝛽𝛽𝑘𝑘 1(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 = 𝑘𝑘) + 𝛾𝛾𝑋𝑋𝑖𝑖′ + 𝛿𝛿𝐹𝐹𝐹𝐹 + 𝜀𝜀𝑖𝑖 , [1]where 𝑌𝑌𝑖𝑖 is the outcome for individual 𝑖𝑖. 1(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 = 𝑘𝑘) is a dummy that takes one if the weekbelongs to two weeks before (𝑘𝑘 = −2), a week before (𝑘𝑘 = −1), or two weeks after (𝑘𝑘 = +2)

the payday (Stephens 2003, 2006; Evans and Moore 2011, 2012). We later show that our results
are robust by narrowing the sample to just around the payday and using the daily interval insteadof week aggregation, which gives more statistical power. Our reference week is the week

immediately after payday (𝑘𝑘 = +1). Since we are particularly interested in the effect of financial

strain on risk preference, our main coefficient of interest is 𝛽𝛽−1, the coefficient from the week

before the payday. This coefficient captures the difference in outcomes before and after payday.

The estimates on other week dummies help us understand the within-month dynamics of change
in outcomes in the longer term, which is not possible with only one-week interval data (cf.
Carvalho et al. 2016). 𝑋𝑋𝑖𝑖′ denote the individual characteristics, namely age, gender, household

size, self-reported health indicators, highest years of schooling, marital status, race, retirement
status, and Metropolitan statistical areas (MSA) status dummies. We also include a number of
fixed effects (FEs), 𝛿𝛿𝐹𝐹𝐹𝐹 , namely day of the week FEs, year FEs, month FEs, and year-monthFEs. 𝜀𝜀𝑖𝑖 is an error term. We estimate equation [1] by OLS unless specified otherwise.

The change in financial resources around payday could affect one’s risk preference in two

opposite ways. On the one hand, limited financial resources before payday could increase themarginal utility of consumption, and hence reduce one’s willingness to take risks. This
19 This setting allows us to avoid the potential endogeneity of the survey response timing in Carvalho et al.

(2016). See Mani et al. (2020) for further discussion on this point.
9observation is in line with association studies showing that the poor tend to be risk-averse (Guiso
and Paiella 2008; Dohmen et al 2011). On the other hand, the literature on social comparison
argues that the sense of relative deprivation tends to increase one’s willingness to take risks, as
individuals strive to catch up to their comparison standards (Mishra et al. 2015; Payne et al.
2017). For example, Haisley et al. (2008) show that increased feelings of poverty among lowincome individuals induce them to take more risks (buying more lottery tickets) to correct for
their low-income status. 20 If a lack of financial resources before payday increases one’s feelingsof being poorer than others or than one’s desired state, one might be more willing to take risks.
Finally, as we discuss in Section 4.3, the empirical evidence relating to the impact of theassociation between stress/depression and financial worries on risk preference is very mixed.
Therefore, we view that the direction of change in risk preference on payday is ultimately anempirical question.

3.2. Randomization tests
The key to our identification strategy is that the interview dates are randomly assigned toindividuals, and thus are uncorrelated with the date on which individuals receive their Social
Security paycheck. To test that the interview dates are exogenous to financial situations,following Mastrobuoni and Weinberg (2009), we estimate equation [1], where outcomes arepredetermined variables in the dataset, namely age, gender, years of schooling, and indicators forbeing married, retired, in good health, in poor health, and black. We also test for differences inindicators for whether a household is located in the Northeast, Midwest, South, or West.
Table 2 presents the results of the randomization tests for the entire sample in panel (a),and separately for individuals from households in which the share of Social Security income
among total household income is above and below 50% in panels (b) and (c), respectively. The
table presents F-tests of the joint null hypothesis that each of the estimates on the four-weekdummies is equal to zero with corresponding p-values. Table 2 shows that only regionaldummies are ever statistically different at the 5% levels, potentially reflecting that the survey

20 Relatedly, risk-sensitivity theory (RST) suggests that individuals shift from risk-aversion to risk-pronenessin situations of higher perceived need, where need refers to a disparity between an individual’s present anddesired states (Pietras and Hackenberc 2001; Mishra and Lalumière 2010). This theory from biology was
originally developed to explain animal foraging behaviors. See Mishra (2014) for a review.
10was administered at different times for each region. In our regressions, we control for all thesepredetermined variables. 21

4. Results
4.1. Graphical evidence
We first present our graphical evidence. Figure 2 plots the measure of risk tolerance, 𝜃𝜃, forindividuals for whom the share of Social Security income is above 50% in panel (a) and below
50% in panel (b), with 2-day bins around the payday. Panel (a) clearly shows that the risk

tolerance parameter is discontinuously higher just before the payday, indicating that respondents
are more risk-tolerant just before payday. Interestingly, within the course of the month, the risk
tolerance parameter increases monotonically, indicating that respondents gradually become morerisk tolerant as they become more financially distressed. On the contrary, we do not see such asystematic pattern for below-50% respondents in panel (b), suggesting that the results for theabove-50% respondents in panel (a) are indeed driven by the availability of Social Security
income.

4.2. Basic results
Here, we formally present the results from estimating equation [1]. The results are shownin Table 3. Column (1) of Table 3 shows the estimate for the respondents with high dependence
on Social Security income (i.e., above-50% respondents). The before-payday week (𝑘𝑘 = −1) is
associated with a 0.352 increase in risk tolerance. Since the standard deviation of the risk

tolerance parameter is 0.816 in this sample, this represents a 0.43 SD increase in risk tolerance.
Other week dummies are not statistically significant at conventional levels. On the contrary,column (2) of Table 3 shows no such pattern among respondents who rely less on Social Security
income. 22 Taken together, these results indicate that payment of Social Security plays a role in

21 One remaining issue is the sample selection: it could be problematic if individuals with higher or lower risk

tolerance are symmetrically more likely to be selected into the survey in a particular week. Whereas testing theissue of sample selection requires risk tolerance measures on respondents who are not interviewed (which do
not exist), it is reassuring that the observable characteristics among those who are interviewed are balanced.
See Harrison et al. (2020) for discussions on endogenous sample selection and attrition.
22 The p-value of the difference in estimates on a week before payday (week -1) between above-50% sample incolumn (1) and below-50% sample in column (2) is 0.106.
11

elevated risk tolerance before payday.
Robustness—. Our results on risk tolerance are robust to different ways of defining highdependence on Social Security income. Table 4 presents the results where we define highdependence by 40% and 60% shares of Social Security income among total household income,which correspond to 56% and 31% of the overall sample, respectively.
For ease of comparison, column (1) in Table 4 replicates the baseline estimates fromcolumn (1) from Table 3, where we define high dependence by a 50% share of Social Security
income (sample share of 43%, N= 527). When we define high dependence by 40% share (N=700), the estimates on a week before payday stay statistically significant at the 5% level. Once
we change the threshold to 60% (N = 380), statistical significance drops to a 10% level;
however, this estimate is similar to the baseline specification in column (1). Appendix Table A2
reports the results for those below the 40% and 60% thresholds, which reveal no changes in risk
preference before payday, further supporting our null effect for those who rely less on Social
Security income.
Importantly, our results are not driven by the cardinality of our risk preference measure, as
shown in Table 5. First, we construct an ordinal measure, which is one if respondents choose themost or second-most risk-tolerant categories (categories 5 and 6) in the gamble question, andzero otherwise. Column (1) of Table 5 shows that our results are robust using this ordinalmeasure. Next, column (2) presents the estimates of the ordered probit model; our results areagain robust to another ordinal measure. Finally, as a separate note, column (3) shows that our
results are robust to the interval regression, suggesting that the arbitrary choice of midpoints inconstructing a cardinal measure of risk preference plays little role.
We also estimate the regression discontinuity (RD) model as a part of our robustness
check, using the distance from the payday (measured in days) as a running variable. 23 Appendix
Table A3 summarizes the results. Rather surprisingly, we obtain statistically significant results(p<0.01), even with this small sample size. Since we only use the sample in the vicinity of the
payday, the RD estimates are larger than our baseline OLS estimates. We prefer our OLS
approach over the RD design because we are not only interested in single days on either side of
payday, but the entire profile of the risk tolerance parameter throughout the month. Nonetheless,
it is reassuring that our results are robust to this different specification.
23 We follow Calonico et al. (2014) to choose the data-driven optimal bandwidth.

12

Placebo—. We run the same specification for the samples among which we do not expect
to find effects. Appendix Table A4 reports the results. In particular, we examine two samples: (i)people aged 50–61 years who are younger than the eligibility age of 62 for Social Security, and(ii) people aged 62 and over who do not yet receive Social Security benefits. It is reassuring thatnone of the estimates on the week before payday are statistically significant or economicallylarge.

4.3. Possible mechanism
Thus far, we provide robust evidence that individuals from households with a heavy
dependence on Social Security become more risk-tolerant just before payday. To probe themechanism behind this phenomenon, we examine the cognitive function and mental health ofbeneficiaries before and after payday.
Cognitive function—. A number of studies present the link between cognition and risk
preference (e.g., Frederick 2005; Burks et al. 2009; Dohmen et al. 2010; Benjamin et al. 2013).
More recently, Mullanaithan and Shafir (2013), among others, argue that poverty reducescognitive capacity because poverty-related concerns consume mental resources (or bandwidth),
which also degrades the quality of decisions. For example, Mani et al. (2013) find that thecognitive function of farmers in India diminishes before the harvest, when they are poor,
compared with after harvest, when they are wealthier. In contrast, Carvalho et al. (2016) do notfind supportive evidence that financial strain impedes cognitive function and worsens decisionmaking.
Interestingly, Lichand and Mani (2019) argue that the discrepancy between Mani et al.
(2013) and Carvalho et al. (2016) can be explained by (i) the lack of uncertainty in payment
timing and amounts in Carvalho et al. (2016), and/or (ii) the higher income levels of the US
respondents relative to the Indian farmers in Mani et al. (2013). Since the timing and amount ofSocial Security are known to the beneficiaries, the lack of cognitive decline in our case could
also be explained by the lack of uncertainty.
We define cognition status based on a 27-point score composed of the word recall, serial
sevens, and backward counting tests. Panel (b) of Appendix Figure A2 shows the frequency
distributions of these 27-point cognition scores. Following Ayyagari and Frisvold (2016), scores
between 12 and 27 are classified as normal; between 7 and 11 as cognitive impairment, not13dementia (CIND); and between 0 and 6 as dementia. Among the households whose income shareof Social Security is above 50%, the fractions of CIND and dementia are 20.8% and 3.4%
respectively (see Table 6).
Table 6 reports the estimates from equation [1], where the outcome is a dummy for normal,
CIND, and dementia in each column. None of the estimates in Table 6 are statistically significant
at the conventional levels. Appendix Table A5 repeats the same exercises for the sample ofindividuals whose share of Social Security among total household income is above 40% and60%, and we do not find any before-after payday changes in any outcomes. Thus, cognitive load
does not seem to drive our results, at least in our setting and within our measures of cognitive
function (cf., Raven’s Matrices test).
Mental status—. Next, we look at mental status or mood. Mental health could besusceptible to income fluctuation as financial strain is often associated with higher levels ofstress and depressionsee Haushofer and Fehr (2014) and Ridley et al. (2020) for a review. 24
The empirical evidence of the impact of stress/depression on risk preferences is, however, mixed
at best (Kremer et al. 2019). 25 The evidence from these studies is less informative for our setting
because most of them are conducted in the lab, and most importantly, examine the effect of onetime acute stress rather than the moderate, temporary, and repeated stress under study. Note thatcognitive function and mental status are not mutually exclusive. Depressed individuals might bemore likely to be cognitively impaired or vice versa.
The HRS asks a question about depression. Specifically, it asks whether the person felt
depressed much of the time during the past week, and the respondent answers “yes/no.” Wecreate a dummy that takes the value of one if the person chooses “yes.” Since the reference
period of this question is a week from the survey date, we subtract seven days from the original
distance in days and reconstruct the week dummies in equation [1] accordingly.
Table 7 presents the results for depression. Column (1) presents the OLS estimates for
24 The literature from psychology emphasizes that psychological well-being affects individuals’ risk

preferences (see Lerner et al. 2015 for a review).

25 Some studies find that stress increases risk aversion (Mather et al. 2009; Porcelli and Delgado 2009;

Kandasamy et al. 2014; Cahlíková and Cingl 2017), whereas others find it increases risk tolerance (Starcke etal. 2008; Putman et al. 2010; Pabst et al. 2013a, 2013b; Buckert et al. 2014; Cueva et al. 2015; Bayer et al.
2019) or has no significant effect (Delany et al. 2014; Sokol-Hessner et al. 2016). Cobb-Clark et al. (2020) do
not find any differences in behavioral risk preferences between the mentally well and the depressed but theyalso find that depression is related to people’s stated risk preferences and risk-taking behaviors, which arecontext-specific.
14

individuals whose Social Security share is above 50%. Individuals who are heavily reliant on
Social Security are 8.1% more likely to feel depressed a week before, than a week after, payday(p<0.10). Since the mean is only 15.3%, we also report the estimates from the non-linear model,
namely Probit, in column (2), and find similar marginal effects. 26 Columns (3)-(6) repeat thesame exercises for the sample of individuals whose share of Social Security among total
household income is above 40% and 60%, and find that the biggest impact is observed when theincome share is set above the 60% threshold in columns (5) and (6).
To examine whether mental health is the potential driving factor of change in risk
preference, we also examine the mental health of those who should not be much affected: those
whose income share of Social Security is below 50% and whose risk preferences do not changearound payday. Appendix Table A6 shows that those who rely less on Social Security are notdepressed before payday.
Taken together, although they are at best suggestive, these contrasting results between highvs. low dependence imply that one of the many underlying mechanisms for the increased risk
tolerance before payday may be the deterioration of mental status. Whereas the question on
depression in the HRS refers to the week prior to the interview date, risk tolerance is measured
on the interview date; as a result, the respondents who answered each question are off by one
week. This, unfortunately, precludes us from conducting a mediation analysis in our acrosssubjects design to examine whether depression is the underlying mediator for changes in risk
preference.
Our results on risk preference differ from the seminal work by Carvalho et al. (2016),
which also exploits randomness in survey dates and compares preference, cognitive function, anddecision-making shortly before and after payday among US low-income respondents. They findthat while the poor with liquidity constraints behave as if they are present-biased before payday,
they did not find any before-after differences in risk preference and cognitive function
performance. Although the sign of their estimates on the CRRA parameter was the same as ours(see Table 4 in Carvalho et al. 2016), their estimate was far from statistically significant.
The key advantage in our setting is that our nationally representative sample—albeit only
comprising the elderly—includes both rich respondents who rely less on Social Security as well
26 The WHO (2017), in “Mental health of older adults,” states that approximately 15% of adults aged 60 andover suffer from a mental disorder, which is close to the 15.3% in our data.
15

as poor respondents more reliant on it, unlike Carvalho et al. (2016), who exclusively focus on
the poor. 27 The contrasting results on depression between the respondents with high vs. low
dependence on Social Security imply that mental health plays some role. 28 We will revisit thisissue using data from Japan in the next section. Relatedly, another potential reason is that we
exclusively focus on the elderly, 29 who could be more mentally vulnerable, and sensitive to eventemporary and anticipated income fluctuation. 30 In this regard, our results are very close to thefindings of Dalton et al. (2020); the study is rare in that it collects both risk preference measures
as well as mental health outcomes in a field setting. They find that owners of small retailbusinesses in Vietnam exposed to financial worries are more stressed and behave less risk-averse
than those assigned to placebo treatment.

5. Supplemental evidence from JapanTo assess external relevance, we also examine whether the same results hold among theelderly in Japan. The public pension in Japan is paid on the 15th of every even month. 31
Stephens and Unayama (2011) show that benefit receipt of public pension significantly affects
household consumption in Japan.
Risk preference—. We again compare individuals’ risk preferences before and after
payday. This is more than a simple replication exercise, since the US and Japan have very
different baseline risk preferences. Falk et al. (2018) collected individual preferences from80,000 people in 76 countries and showed that Japan is one of the most risk-averse countries(#6), whereas the US is on the other side of the spectrum (#52) 32  see Figure 1 in Falk et al.
(2018).

27 Carvalho et al. (2016) exclusively focus on individuals in low-income households (annual household income

below $40,000) in the US.

28 To our knowledge, Carvalho et al. (2016) did not report the results on mental health.

29 The average age in Carvalho et al.’s (2016) in Study 1 and 2 is 49 and 56, respectively, which is muchyounger than the average of 69 years in our sample.

30 For example, in addition to common stressors, the elderly may experience life stressors particular to later

life, such as significant ongoing loss in capacity and a decline in functional ability. Furthermore, the social
isolation during the COVID-19 pandemic could exacerbate the mental vulnerability of the elderly (Armitage
and Nellums 2020).
31 If the 15th in the month falls on a weekend or holiday, benefits are paid on the first business day before the15th, as in the case of Social Security payments in the US.
32 Authors’ calculation based on the data here: https://www.briq-institute.org/global-preferences/rankings#2-00 (last accessed on 03/10/2020).
16

Our data comes from the Japanese Study of Aging and Retirement (JSTAR) from 2007–2013. JSTAR, beginning in 2007, is modeled after the HRS, and interview surveys are conductedbiennially. Respondents are those aged over 50 from 10 municipalities. 33 The restricted version
contains the exact dates on which the interviews took place.
Risk tolerance in JSTAR is measured by the hypothetical question on the choice of income
between two scenarios, which is conceptually similar to the lifetime income gamble question in
the HRS. The safe option guarantees a 20% increase in income, whereas the risky option
increases income by either 50% or 5% with a specific probability. Varying the chance of thedownside risk on risky options refines the measure of risk preference. The exact wording of thequestionnaire is presented in Appendix Table D2. To be consistent with the HRS, we derive therisk tolerance parameter 𝜃𝜃 in the same way, assuming the CRRA utility function. Again, higher
values indicate more risk tolerance. 34

Appendix Table B2 presents the descriptive statistics of the JSTAR sample. Interestingly,

JSTAR respondents rely more heavily on pension benefits than HRS respondents. The average
share of public pension in total household income in JSTAR (64.2%) is even higher than thethreshold of 50% share in HRS. Thus, we do not divide the sample by the share of the public
pension here to maximize the statistical power (N= 2,728). Following the HRS analysis, to avoid
the influence of other income sources, we restrict the sample to households in which at least one
person receives the public pension and there are no cohabiting children or parents. The averageage is 68.5 years, which is very similar to the HRS sample (69.3 years). Some 80.3% aremarried; roughly half (51.0%) are female.
Since the payment of the public pension in Japan is every two months instead of every
month, we slightly modify equation [1] to include more week dummies as follows:

33 The data is collected by the Research Institute of Economy, Trade, and Industry (RIETI), Hitotsubashi

University, and University of Tokyo. See https://www.rieti.go.jp/en/projects/jstar/ for the details of JSTAR.
These municipalities are Takikawa City in Hokkaido Prefecture, Sendai City in Miyagi Prefecture, Adachi
Ward in the Tokyo metropolis, Kanazawa City in Ishikawa Prefecture, Shirakawa Town in Gifu Prefecture,
Naha City in Okinawa Prefecture, Tosu City in Saga Prefecture, Chofu City in the Tokyo metropolis,
Tondabayashi City in Osaka Prefecture, and Hiroshima City in Hiroshima Prefecture.
34 Appendix Table B1 provides the relationship between the eight income response categories and thedownside risks. The larger category numbers indicate that individuals are willing to accept higher income risk,
and thus the corresponding risk tolerance parameter is larger. Appendix Figure B1 presents the frequency
distributions of risk categories, revealing substantial heterogeneity in risk attitudes.
17′

𝑘𝑘=−1,−2,−3,−4,+2,+3,+4
𝑌𝑌𝑖𝑖 = ∑𝑘𝑘≠+1
𝛽𝛽𝑘𝑘 1(𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 = 𝑘𝑘) + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝛿𝛿𝐹𝐹𝐹𝐹 + 𝜀𝜀𝑖𝑖 ,[1]’Again, our reference week is the week immediately after payday (𝑘𝑘 = +1), and our maincoefficient of interest is β−1, the coefficient for the week before payday. In addition to the FEs

included in equation [1], the two-month intervals between paydays allow us to include week-ofthe-month FEs, because these data include the calendar months when the public pension is not

paid (odd months), which is impossible in the case of the US. This setting partially mitigates theconcern that the results in the US are driven by other events at the beginning of the calendar
month, such as housing payment (Vellekoop 2018).
Appendix Table B3 presents the results from testing the randomness of the interview dates.
Specifically, we run equation [1]’ on the predetermined variables in the dataset. The table reports
the F-tests and corresponding p-values of the null hypothesis that each of the estimates on theeight-week dummies is equal to zero. Except for age, none of the 11 estimates is statisticallysignificant at the 5% level. 35 In our regressions, we control for all these predetermined variables.
We first present our graphical evidence. Appendix Figure B2 plots the measured risk
tolerance with 2-day bins around the payday. As the interval between payments is two months,
our synthetic month around the 15th of the month ranges from –28 to +27. The figure shows thatthe risk tolerance parameter is discontinuously higher just before payday, indicating that
respondents are more risk-tolerant before payday in Japan as in the US.
Table 8 reports the results from estimating equation [1]’ without any individual controls incolumn (1) and with controls in column (2). Column (1) shows that the before-payday week (𝑘𝑘 =−1) is associated with the increase of risk tolerance by 0.056 (p<0.05). This represents a 28.7%

increase in risk tolerance of the standard deviation (0.195). None of the other week dummies are

statistically significant, suggesting that temporal shift in risk preference is concentrated within

one week prior to payday. Adding individual controls in column (2) barely changes the estimate
on a before-payday week dummy, supporting the success of the randomization of interview
dates.
Appendix Table B4 reports the results of other specifications and reassuringly, our results
are fairly robust. Appendix Table B5 presents the results of placebo checks using two samples:(i) people aged 50–59 years who are younger than the eligibility age of 60 for public pension in
35 For privacy reasons, geographical information is not available in JSTAR.

18

Japan, 36 and (ii) people aged 60 and above, who do not yet receive pension benefits. Again, it isreassuring that none of the estimates on the week before payday are statistically significant or
economically large. Whereas the country and the sample period are different in the HRS andJSTAR, we find similar patterns in both data, which strengthens the external validity of ourresults.
Revisiting potential mechanisms—. Ideally, we would want to examine whether we canobserve the same patterns on depression in JSTAR as we find in the HRS. Unfortunately,
JSTAR’s depression measure is collected by self-report questionnaires, instead of face-to-faceinterviews, and as a result, the exact dates of data entry are unknown.
Therefore, we resort to another dataset, the Japanese General Social Surveys (JGSS),
which is modeled after the General Social Surveys in the US. 37 Again, the restricted version
contains the exact dates on which interviews took place. We use the data from 2008, whichcontains the two key variables we examine below (financial anxiety and self-perceived social
status). 38 Appendix Table C1 presents the descriptive statistics of the JGSS sample. To make itcomparable to the JSTAR sample, we restrict the sample to individuals over 60, whose main
source of income is the pension benefit. 39 Appendix Table C2 reports the results from testing therandomness of the interview dates. Except for one, none of the 11 estimates are statisticallysignificant at the 5% level. Again, in our regressions, we control for all these predeterminedvariables.
We examine financial anxiety, as this is most closely related to the mental status, given that
there is no direct question on depression in JGSS. Indeed, financial strain is often associated withhigher levels of stress and depression (Ridley et al. 2020). Specifically, to the question “Do you
feel anxious about your economic situation in the future?”, we create a dummy that takes the
36 Strictly speaking, the Pension Reform Act implemented in 2001 raises the pension eligibility age from 60 to

65 by one year every two years for the fixed part of the pension benefit from 2001 (Kondo and Shigeoka2017). However, for most of the respondents in our data, the pension eligibility age is 60.
37 JGSS is carried out by Osaka University of Commerce, in collaboration with the Institute of Social Scienceat the University of Tokyo since 2000. See https://jgss.daishodai.ac.jp/english/surveys/sur_top.html for thedetails. The subject population consists of men and women aged 20 to 89 as of September 1 of the particularsurvey year; they are selected using a stratified 2-stage sampling method. The stratification divides Japan into
six blocks  Hokkaido/Tohoku, Kanto, Chubu, Kinki, Chugoku/Shikoku, and Kyushu  and further divides
those into four groups: largest cities, cities with populations of 200 thousand or more, cities with populations
under 200 thousand, and towns/villages.
38 Unfortunately, HRS collected financial strain and subjective social status in the leave-behind questionnaires;
thus, the exact dates of data entry are unknown.
39 JGSS does not collect the amount of pension benefit but collects information on the main source of income.
19

value of one if the respondents choose either “I feel very anxious” or “I feel somewhat anxious,”or zero otherwise. 40 See panel (a) in Appendix Figure C1 for the frequency distributions ofresponses.
Panel (a) in Appendix Figure C2 plots this outcome with 2-day bins around the payday.
Note that since the spell of interview dates is short in JGSS, we restrict the sample to windows of
14 days, instead of 28, from payday. 41 The figure demonstrates that the fraction of financiallyanxious individuals is discontinuously higher just before payday, which is similar to the finding
on depression in the US. Columns (1) and (2) report the results from estimating equation [1],where the outcome is the financial anxiety, and confirms the graphical findings; column (1) from
OLS shows that the before-payday week (𝑘𝑘 = −1) is associated with a 20.6 percentage points
increase in financial anxiety (p<0.05), and column (2) from probit presents similar results.

JGSS allows us to explore another potential mechanism discussed in the literature on risky

choice and relative deprivation/social comparison. The idea behind the literature in our context isthat scarce financial resources before payday makes people feel relatively deprived compared toothers; it induces one to take more risks to catch up to the comparison. For example, Haisley etal. (2008) show that enhanced feelings of poverty among low-income individuals induce them totake more risks. Similarly, Mishra et al. (2015) and Payne et al. (2017) show that higher
inequality increases risk-taking due to upward social comparisons.
Specifically, the JGSS asks a question of self-placement on a 10-point social status scale:“If we were to divide the contemporary Japanese society into the following ten strata, which
would you say you belong to?” We create a dummy that takes the value of one if the respondents
choose the lowest and second-lowest categories (categories 9 and 10) and zero otherwise. Panel(b) in Appendix Figure C2 shows that the fraction of individuals who view themselves as
belonging to a low social status, is discretely higher just before payday. Column (3) of Table 9shows that the before-payday week (𝑘𝑘 = −1) is also associated with 7.6 percentage points

increases in low self-perceived social status (p<0.05). This effect is substantial, given that themean of the outcome is 9.9%. Almost identical results are obtained from probit in column (4). 42
Taken individually, each piece of evidence in the US and Japan is not sufficient to pin
40 We obtain similar results if the outcome takes the value of one only for “I feel very anxious” (not shown).
41 The interview dates span from Oct. 1 to Dec. 22 in 2008; pension paydays are Oct. 15 and Dec. 15.

42 See Appendix Table C3 for the other specification, and Appendix Table C4 for the placebo checks. These

tests also support the interpretation of our results.

20

down the underlying mechanism of temporary fluctuation in risk preference around payday,
since we cannot formally run the mediation analysis. However, taken together, the evidence
suggests that the mental state and/or feeling of relative deprivation may play certain roles.
Indeed, there are interplays between mental state and relative deprivation. Relative deprivationis, in fact, shown to negatively affect both physical and mental health (e.g., Eibner et al. 2004;
Jones and Wildman 2008; Daly et al. 2013). We do not, however, claim that these are the only
mechanisms, and we are well aware that many alternative explanations could be consistent with
our findings. While it is beyond the scope of this paper to identify the exact mechanism, it leaves
ample avenues for future research.

6. Conclusion
We evaluate the impact of income fluctuations on measured risk preference among theelderly by exploiting the periodic payday cycles of Social Security benefits in the US and public
pension benefits in Japan. In both countries, we find evidence of temporary but systematic
variation in risk preference around payday, at least among the samples of older adults in the
1990s in the US, and in the 2000s in Japan. To our knowledge, this is the first study to show thatrisk preference may not be temporally stable. This temporal shift in risk preference around
payday indicates that optimal decisions made before payday may turn into non-optimal decisions
afterward.
It is important to stress that these results on risk preference have been found in countries
with very different baseline risk preferences (the US and Japan). This suggests that evidence
from this study may apply more generally to a broad set of countries with different levels of
underlying risk preference. Relatedly, our results indicate that even commonly occurring income
fluctuations of realistic magnitude might temporarily alter individual risk preferences.
This study provides several avenues for future research. First, whereas our across-subjects
design still allows us to investigate the temporal stability of aggregate risk preference, it isimportant from a policy perspective to identify the characteristics of individuals who exhibit
unstable risk preference, using a within-subject design. Second, more research is needed tounderstand the underlying mechanisms for such temporal shifts in preference. Finally, examining
whether our results hold in a different subject pool (e.g., non-elderly), and different magnitudesof temporal financial distress (e.g., intervals) with various levels of uncertainty, would be an
21

interesting extension. For instance, the pension is paid every two weeks in Australia. As periodic
payment of welfare programs, pensions, and salaries is prevalent in many countries, there is
ample opportunity to apply a similar research design, and investigate whether a similar pattern inrisk or other preferences (e.g., time and social preferences) can be observed in other settings.

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26

Figure 1—Conceptual framework of time-varying risk preferences

Source: This figure is adapted from Schildberg-Hörisch (2018, Figure 1). The figure provides the conceptual framework of time-varying
risk preference throughout the life course. The x-axis is the age and the y-axis is the risk preference where the higher value indicatesmore risk tolerance. The solid line shows increasing baseline risk aversion based on the empirical evidence that people become morerisk averse as they get older. The dashed line shows a possible permanent shift from baseline risk preference due to a large, one-time,permanent, and unanticipated shock such as the Great Depression. The jagged line represents a temporal shift around baseline risk
preference due to periodic payments as we investigate in this study.

27

Figure 2—Risk tolerance before and after payday(a) SS/income share above 50 percent(N=527)(b) SS/income share below 50 percent(N=700)Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is individuals
whose share of Social Security (SS) among total household income is above 50% in panel (a) and below 50% in panel (b). Each plot
represents the two-day average of risk tolerance for two weeks before and after payday. The line corresponds to the linear fit. See themain text for the construction of risk tolerance measures.
28

Table 1—Descriptive statistics
Variables
Risk tolerance: θDays since check arrival
Social security benefits/household income
Non-savers
Cognition
NormalCIND
Dementia
Depression
Individual characteristics:
Age (in years)
FemaleYears of schooling
MarriedRetiredHousehold size
Northeast
MidwestSouth
West
Good health
Poor health
African American

Obs

Mean

SD

Min

Max

1227
1227
1227
1227

0.540
0.482
0.481
0.346

0.834
8.59
0.279
0.476

0.13
-14
0
0

3.27
13
1
1

1185
1185
1185
1226

0.831
0.149
0.019
0.115

0.374
0.356
0.138
0.319

0
0
0
0

1
1
1
1

1227
1227
1227
1227
1227
1227
1227
1227
1227
1227
1227
1227
1227

69.251
0.504
12.383
0.924
0.756
1.944
0.174
0.292
0.349
0.183
0.439
0.245
0.069

3.355
0.500
2.950
0.264
0.429
0.228
0.379
0.455
0.477
0.387
0.496
0.430
0.254

62
0
0
0
0
1
0
0
0
0
0
0
0

85
1
17
1
1
2
1
1
1
1
1
1
1

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. Non-savers are defined as
individuals whose overall savings have a net value under $10,000. The measures of cognition are classified as normal, cognitive
impairment, not dementia (CIND), and dementia, which are mutually exclusive. See section 4.3 for the definition of each cognition
variable. Depression is a dummy that takes the value of one if the person felt depressed much of the time during the past week.

29

Table 2—Test for randomization of interview dates
Age

FemaleYrs. ofschooling(a) Full sample
0.642
0.216
0.536
F-stat
0.587
0.885
0.657
p-value
1227
1227
1227
N
(b) SS/income share above 50 percent
0.608
0.145
0.617
F-stat
0.609
0.932
0.603
p-value
527
527
527
N
(c) SS/income share below 50 percent
0.593
0.384
1.503
F-stat
0.619
0.763
0.212
p-value
700
700
700
N

MarriedRetiredHousehold sizeNorthea Midwes
st
t

South

West

Good
healthPoor
healthAfricanAmeric
an

0.572
0.633
1227

0.462
0.708
1227

1.608
0.185
1227

3.316
0.019
1227

0.569
0.634
1227

4.146
0.006
1227

5.765
0.0006
1227

0.646
0.585
1227

0.887
0.446
1227

1.846
0.136
1227

0.658
0.578
527

0.233
0.873
527

1.484
0.217
527

3.421
0.017
527

0.323
0.808
527

2.974
0.031
527

1.347
0.258
527

1.049
0.370
527

1.627
0.182
527

1.054
0.367
527

0.351
0.787
700

1.227
0.298
700

0.371
0.773
700

2.048
0.105
700

1.047
0.370
700

2.203
0.086
700

4.745
0.002
700

2.782
0.040
700

1.565
0.194
700

2.176
0.089
700

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. F-stats and p-values correspond to joint null tests whereby
each of the coefficients on the four-week dummies is zero. The sample is individuals whose share of Social Security (SS) among total household income is above 50%
in panel (b) and below 50% in panel (c).

30

Table 3—Main results: Risk tolerance
SS/income shareabove 50 percent
below 50 percent
(1)
(2)
0.040
-0.025(0.094)(0.096)

Week -2
Week -1

0.352**(0.139)0.068(0.109)Week +2

0.154(0.098)
×
0.514
527
0.134

0.103
(0.087)
×
0.559
700
0.185

Covariates
Mean of outcome
N
R squared

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is individuals
whose share of Social Security (SS) among total household income is above 50% in panel (a) and below 50% in panel (b). The outcome
is risk tolerance. estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses. The omitted category
is week +1. Covariates include age, age squared, gender, household size, self-reported health indicators, highest years of schooling,
marital status, race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-monthFEs. See the main text for the construction of each preference measure. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

Table 4—Different thresholds: Risk tolerance
above 50 percent(1)

SS/income shareabove 40 percent(2)

above 60 percent(3)

Week -2

0.040(0.094)0.013(0.080)0.052(0.123)

Week -1

0.352**(0.139)0.264**(0.118)0.320*(0.165)

Week +2

0.154(0.098)0.122(0.085)0.042(0.111)

Covariates
Mean of outcome
N
R squared
Sample share

×
0.514
527
0.134
43 percent

×
0.511
681
0.125
56 percent

×
0.494
380
0.168
31 percent

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. Column (1) replicates thebaseline results of column (1) in Table 3. The sample is limited to SS/income share above 40% in columns (2), and above 60% in column(3). The outcome is risk tolerance. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses.
The omitted category is week +1. Covariates include age, age squared, gender, household size, self-reported health indicators, highest
years of schooling, marital status, race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs,and year-month FEs. SS stands for Social Security. Significance levels: *** p<0.01, ** p<0.05, * p<0.10
31

Table 5—Other robustness checks: Risk tolerance

Week -2
Week -1
Week +2
Covariates
Mean of outcome
N

Risk tolerance
categories
5 or 6(1)-0.014(0.038)0.118**(0.055)0.041(0.039)
×
0.113
527

Ordered
ProbitInterval
regression

(2)
0.026
(0.149)0.399**(0.188)0.165(0.147)
×
2.144
527

(3)
0.057
(0.208)0.630**(0.280)0.272(0.209)×527

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is limited toindividuals whose share of Social Security (SS) among total household income is above 50%. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) fromequation [1] are reported with robust errors in parentheses. The omitted category is week +1. The outcome in column (1) is a binary
measure of risk preference that takes the value of 1 if the person chose the highest or second-highest risk tolerance categories (categories
5 or 6) and 0 otherwise. The outcome in column (2) is an ordinal measure of risk preference coded from 1 for the lowest risk tolerance
category to 6 for the highest category. Column (3) reports the estimates of the interval regression. Covariates include age, age squared,
gender, household size, self-reported health indicators, highest years of schooling, marital status, race, retirement status, MSA status
dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-month FEs. Significance levels: *** p<0.01, ** p<0.05, *p<0.10

Table 6—Cognition
Outcome:Normal
(1)

CIND
(2)

Dementia(3)

Week -2

0.073(0.052)-0.062(0.053)-0.011(0.022)

Week -1

0.089(0.063)-0.065(0.063)-0.025(0.026)

Week +2

0.071(0.051)-0.037(0.052)-0.034(0.024)

Covariates

×

×

×

0.756
518
0.278

0.208
518
0.207

0.034
518
0.308

Mean of outcome
N
R squared

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is limited toindividuals whose share of Social Security (SS) among total household income is above 50%. The classification is based on a 27-pointscore composed of the word recall, serial sevens, and backward counting tests. Scores between 12 and 27 are classified as normal,
between 7 and 11 as cognitive impairment, not dementia (CIND), and between 0 and 6 as dementia. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2)
from equation [1] are reported with robust errors in parentheses. The omitted category is week +1. Covariates include age, age squared,
gender, household size, self-reported health indicators, highest years of schooling, marital status, race, retirement status, MSA status
dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-month FEs. Significance levels: *** p<0.01, ** p<0.05, *p<0.1032

Table 7—Depression
SS/income shareabove 50 percent

above 40 percent

above 60 percent

OLS

ProbitOLS

ProbitOLS

Probit

(1)

(2)

(3)

(4)

(5)

(6)

Week -2

0.024(0.054)0.023(0.047)0.024(0.048)0.018(0.041)

0.051(0.069)0.057(0.058)

Week -1

0.081*(0.046)

0.076*(0.040)0.062(0.038)0.056*(0.033)0.118**(0.059)0.127**(0.049)Week +2

0.009(0.038)-0.002(0.041)-0.031(0.031)-0.049(0.034)0.039(0.051)

0.035(0.056)

Covariates
Mean of outcome

×
0.153

×
0.163

×
0.143

×
0.148

×
0.176

×
0.180

N

527

478

Sample share

43%

39%

681
56%

638
52%

380
31%

343
28%

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is limited to
SS/income share above 50% in columns (1) and (2), above 40% in columns (3) and (4), and above 60% in columns (5) and (6). The
outcome is a dummy that takes the value of one if the person felt depressed much of the time during the past week. The estimates
𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses. The omitted category is week +1. Note thatsince the reference period of this question is a week from the survey date, we define the week dummies in equation [1] accordingly.
Covariates include age, age squared, gender, household size, self-reported health indicators, highest years of schooling, marital status,
race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-month FEs.
Significance levels: *** p<0.01, ** p<0.05, * p<0.10

33

Table 8—Supplemental analysis from Japan on risk tolerance (JSTAR)
(1)

(2)

Week -4-0.017(0.032)-0.020(0.032)

Week -3-0.028(0.039)-0.026(0.039)Week -2

0.027(0.030)0.030(0.030)Week -1

0.056**(0.027)0.059**(0.027)

Week +2

0.013(0.024)0.009(0.024)

Week +3-0.005(0.029)-0.004(0.029)Week +4

0.022(0.034)0.026(0.034)
×
0.153
2728
0.054

Covariates
Mean of outcome
N
R squared

0.153
2728
0.036

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013 (N = 2728). The
outcome is risk tolerance. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 =−4, −3, −2, −1, +2, +3, +4) from equation [1]’ are reported with robust errors inparentheses. The omitted category is week +1. Covariates include age, age squared, and dummy variables for gender, marital status,
educational background, sampling cohort, working status, and good and bad health status. Time effects are related to the date of thesurvey interview and include dummy variables for the year, month, interactions of year and month, day of the week, number of weeks,
and interactions of the day of week and number of weeks of the interview date. See the main text for the construction of risk preference
measures. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

34

Table 9—Supplemental analysis from Japan on potential mechanisms (JGSS)
Outcomes:

Feel financially anxious
OLS
Probit(1)(2)

Week -20.150*(0.091)0.169**(0.080)0.008(0.039)0.006(0.043)

Week -1

0.206**(0.092)0.222***(0.078)0.076**(0.038)0.076**(0.038)

Week +2

0.098(0.099)
×
0.628
277
0.296

0.110
(0.088)
×
0.649
262
-

0.023
(0.037)
×
0.099
593
0.101

0.026
(0.043)
×
0.117
503
-

Covariates
Mean of outcome
N
R squared

Low self-perceived social status
OLS
Probit(3)(4)

Notes: The data come from the Japanese General Social Surveys (JGSS) in 2008. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from estimating
an equation [1] are reported with robust errors in parentheses. The omitted category is week +1. The outcome in columns (1) and (2) isa dummy that takes the value of one if the respondents choose either “I feel very anxious” or “I feel somewhat anxious” to the question“Do you feel anxious about your economic situation in the future?” or zero otherwise. The outcome in columns (3) and (4) is a dummythat takes the value of one if the respondents choose the lowest and second-lowest categories (categories 9 and 10) to the question “Ifwe were to divide the contemporary Japanese society into the following ten strata, which would you say you belong to?” or zerootherwise. Covariates include dummy variables for age, gender, marital status, educational background, working status, good and badhealth status, region fixed effects (FEs), and month FEs. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

35Online Appendix(Not for Publication)

36

Appendix A: Analysis for the US (HRS)
Figure A1—The relationship between the share of Social Security income
out of total household income and total household income

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The x-axis is the share ofSocial Security income out of total household income, and the y-axis is the total household income. The dotted line corresponds to an
income share of at least 50% or 0.5 (which comprises 43% of the full sample).

37

Figure A2—Frequency distributions(a) Risk preferences (N=1227)(b) Cognition (N=1185)

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. Table A1 provides therelationship between the gamble response category and the downside risks that the individual accepts or rejects. See Appendix Table D1for the questionnaires about risk preference. Cognition is a 27-point score composed of the word recall, serial sevens, and backward
counting tests.
38

Table A1—Categories of risk tolerance responses
Response
category
1
2
3
4
5
6

Downside risk of risky jobs
AcceptReject
None
1/10
1/10
1/5
1/5
1/3
1/3
1/2
1/2
3/4
3/4
None

Bounds on Risk tolerance: θ
Lower
Upper
0
0.13
0.13
0.27
0.27
0.50
0.50
1.00
1.00
3.27
3.27
∞

Fraction (%)
48.98
17.36
11.98
9.54
6.52
5.62

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006 (N = 1,227). Respondents
choose between a safe and a risky job in hypothetical scenarios. With equal chances, a risky job will double the income or cut monthly
income by a specific fraction (downside risk). Varying the downside risk on the monthly income in subsequent questions refines themeasure of risk preference. Panel (a) of Appendix Figure A1 shows the frequency distributions of risk choices. At the lower bound of
the risk tolerance category (#1), an individual rejects a risky job with the smallest downside risk. At the upper bound of the risk
tolerance category (#6), an individual accepts the largest downside risk. The exact wording of the questionnaire is presented inAppendix Table D1.

Table A2—Robustness checks: Risk tolerance (below thresholds)
below 50 percent

SS/income share
below 40 percent

below 60 percent

(1)

(2)

(3)

Week -2-0.025(0.096)-0.073(0.120)-0.029(0.083)

Week -1

0.068(0.109)0.045(0.129)0.146(0.100)Week +2

0.103(0.087)0.089(0.102)0.123(0.079)Covariates
Mean of outcome
N
R squared
Sample share

×
0.559
700
0.185
57 percent

×
0.576
546
0.191
44 percent

×
0.560
846
0.142
69 percent

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. Column (1) replicates thebaseline results of column (2) in Table 3. The sample is limited to SS/income share below 40% in columns (2), and below 60% in column(3). The outcome is risk tolerance. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses.
The omitted category is week +1. Covariates include age, age squared, gender, household size, self-reported health indicators, highest
years of schooling, marital status, race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs,and year-month FEs. SS stands for Social Security. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

39

Table A3—Regression discontinuity analysis: Risk tolerance

Before the pension payment
Bandwidth (days)
Covariates
N

(1)

(2)

1.040***(0.192)

1.243***(0.282)

3.226
×
527

2.358
×
527

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is limited to
SS/income share above 50%. SS stands for Social Security. The outcome is risk tolerance. Robust estimates are reported with robust
standard errors in parentheses. One common mean square error optimal bandwidth selector for the RD treatment effect estimator is usedin column (1), and one common coverage error rate optimal bandwidth selector for the RD treatment effect estimator is used in column(2). The regression is conducted using the "rdrobust" command in Stata software, developed by Calonico et al. (2014). Covariates
include age, age squared, gender, total household income, self-reported health indicators, highest years of schooling, marital status, race,
retirement status, MSA status dummies, and calendar week fixed effects. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

Table A4—Placebo: Risk tolerance
People aged
50 to 61 years

People aged 62+
who do not receive SS

(1)

(2)

Week -2

0.059(0.041)-0.034(0.114)

Week -1-0.008(0.041)

0.081(0.139)Week +2

0.057(0.042)-0.141(0.120)Covariates
Mean of outcome
N
R squared

×
0.588
3,582
0.042

×
0.479
390
0.186

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The outcome is risk tolerance.
The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses. The omitted category is week +1.
Covariates include age, age squared, gender, household size, self-reported health indicators, highest years of schooling, marital status,
race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-month FEs. Column (1)limits the sample to people aged 50-61 years who are younger than the eligibility age of 62 for Social Security. Column (2) limits the
sample to people aged 62 and over who do not yet receive Social Security benefits. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

40

Table A5—Robustness: Cognition
SS/income share
SS/income share
SS/income shareabove 50 percent
above 40 percent
above 60 percent
Normal CIND Dementia
Normal CIND Dementia
Normal CIND Dementia
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
0.073
-0.062-0.0110.079*-0.081*
0.001
0.044-0.040-0.003
Week -2(0.052) (0.053)(0.022)(0.044) (0.044)(0.019)(0.066)(0.067)(0.027)0.089-0.065-0.0250.070-0.049-0.0200.106-0.085-0.020Week -1(0.063) (0.063)(0.026)(0.057) (0.057)(0.022)(0.082)(0.080)(0.032)0.071-0.037-0.0340.059-0.030-0.0290.049-0.019-0.030Week +2(0.051) (0.052)(0.024)(0.042) (0.043)(0.019)(0.064)(0.065)(0.030)
×
×
×
×
×
×
×
×
×
Covariates
0.208
0.034
0.774
0.192
0.032
0.714
0.242
0.042
Mean of outcome 0.756
518
518
518
670
670
670
375
375
375
N
0.278
0.207
0.308
0.247
0.178
0.284
0.381
0.238
0.347
R-squared
Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. Columns (1)-(3) replicate thebaseline results of columns (1)-(3) in Table 6. The sample is limited to SS/income share above 40% in columns (4)-(6), and below 60%
in columns (7)-(9). The classification is based on a 27-point score composed of the word recall, serial sevens, and backward counting
tests. Scores between 12 and 27 are classified as normal, between 7 and 11 as cognitive impairment, not dementia (CIND), and between
0 and 6 as dementia. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses. The omitted
category is week +1. Covariates include age, age squared, gender, household size, self-reported health indicators, highest years ofschooling, marital status, race, retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs, andyear-month FEs. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

Table A6—Robustness: Depression (below thresholds)
below 50 percent
OLS
Probit(1)(2)

SS/income share
below 40 percent
OLS
Probit(3)(4)

below 60 percent
OLS
Probit(5)(6)

Week -2-0.030(0.041)-0.003(0.038)-0.044(0.038)-0.039(0.037)-0.028(0.033)-0.023(0.030)Week -1-0.017(0.032)-0.014(0.032)-0.031(0.030)-0.032(0.028)-0.019(0.027)-0.025(0.025)

Week +2-0.013(0.037)-0.009(0.032)-0.065**(0.031)-0.058**(0.029)-0.062**(0.027)-0.067**(0.028)

Covariates
Mean of outcome
N
Sample share

×
0.078
545
44%

×
0.094
455
37%

×
0.085
699
57%

×
0.096
625
51%

×
0.087
846
69%

×
0.096
765
62%

Notes: The data come from the Health and Retirement Study (HRS) in 1994, 1998, 2000, 2002, and 2006. The sample is limited to
SS/income share above 50% in columns (1) and (2), above 40% in columns (3) and (4), and above 60% in columns (5) and (6). The
estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from equation [1] are reported with robust errors in parentheses. The outcome is a dummy that takes thevalue of one if the person felt depressed much of the time during the past week. The omitted category is week +1. Note that since thereference period of this question is a week from the survey date, we define the week dummies in equation [1] accordingly. Covariates
include age, age squared, gender, household size, self-reported health indicators, highest years of schooling, marital status, race,
retirement status, MSA status dummies, calendar week fixed effects (FEs), year FEs, month FEs, and year-month FEs. Significancelevels: *** p<0.01, ** p<0.05, * p<0.1041Appendix B: Analysis on risk preferences for Japan (JSTAR)
Figure B1—Frequency distributions: Risk preferences (JSTAR)

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013 (N = 2,728). Appendix
Table B1 provides the relationship between the gamble response category and the downside risks that the individual accepts or rejects.
The exact wording of the questionnaire is presented in Appendix Table D2 (N= 2,705).

Figure B2—Risk tolerance before and after payday (JSTAR)

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013 (N = 2728). Each plot
represents the two-day average of risk tolerance residualized by survey-interview date fixed effects (FEs) four weeks before and after
the check arrival. The survey-interview date FEs include dummy variables for the year, month, interactions of year and month, day of
the week, number of weeks, and interactions of the day of week and number of the week. The line corresponds to the linear fit. See themain text for the construction of risk tolerance measures. Note that public pension benefit in Japan is paid on the 15th of every othermonth.
42

Table B1—Categories of risk tolerance responses (JSTAR)
Pay choices
Choice 1

Response
category
1
2
3
4
5
6
7
8

Choice 2

100% chance of an increase by 50%
90% chance of an increase by 50% and
10% chance of an increase by 5%
80% chance of an increase by 50% and
20% chance of an increase by 5%
70% chance of an increase by 50% and100% chance
30% chance of an increase by 5%of an increase
60% chance of an increase by 50% andby 20%
40% chance of an increase by 5%
50% chance of an increase by 50% and
50% chance of an increase by 5%
40% chance of an increase by 50% and
60% chance of an increase by 5%-

Bounds on Risk tolerance: θ
Lower
Upper

Fraction
(%)

-

0.00

12.02

0.00

0.06

30.24

0.06

0.08

9.79

0.08

0.11

11.73

0.11

0.16

9.75

0.16

0.25

9.13

0.25

0.62

6.6

0.62

∞

10.74

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013 (N = 2,728).
Respondents choose income from two scenarios where the safe option guarantees a 20% increase in income, while the risky option
increases the income by either 50% or 5% with a specific probability. Varying the downside risk on risky options refines the measureof risk preference. At the upper bound of the risk tolerance category (#8), an individual accepts the largest downside risk. Appendix
Figure B1 shows the frequency distributions of risk choices. The exact wording of the questionnaire is presented in Appendix Table
D2.

43

Table B2—Descriptive statistics (JSTAR)
Variables
Risk tolerance: θDays since check arrival
Social Security Benefits / Household Income
Individual Characteristics:
Age
FemaleEducation
Elementary/Middle school
High School
Junior College
Vocational School
University or above
MarriedNot working
Good Health
Poor Health

Obs

Mean

SD

Min

Max

2728
2728
2388

0.153
-0.932
0.642

0.195
15.094
0.313

0
-28
0

0.622
27
1

2728
2728

68.515
0.510

4.992
0.500

51
0

81
1

2728
2728
2728
2728
2728
2728
2728
2728
2728

0.310
0.458
0.039
0.059
0.126
0.803
0.615
0.342
0.138

0.463
0.498
0.194
0.235
0.332
0.398
0.487
0.475
0.345

0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013.

Table B3—Test for randomization of interview dates (JSTAR)

F-statp-value

(1)

(2)

Age

Female

2.482
0.016

0.629
0.732

(3)

(4)

Elementary/Middleschool(5)(6)
Education

(7)

(8)

(9)

(10)

(11)

High
SchoolJuniorCollege

Vocational
SchoolUniversity
or above

MarriedNot
Working

Good
HealthPoor
Health

0.992
0.436

0.957
0.462

1.668
0.114

1.653
0.118

1.261
0.267

0.600
0.756

0.770
0.613

1.474
0.173

0.746
0.633

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013 (N = 2,728). F-stats
and p-values correspond to joint null tests whereby each coefficient on the eight-week dummies is zero.

44

Table B4—Robustness checks: Risk tolerance (JSTAR)
Risk tolerance
categories
7 or 8(1)

Ordered
ProbitInterval
regression

(2)

(3)

Week -4-0.057(0.062)0.048(0.173)-0.022(0.034)

Week -3-0.070(0.076)-0.023(0.208)-0.032(0.042)

Week -2

0.047(0.059)-0.012(0.167)0.036(0.033)

Week -1

0.102*(0.054)0.118(0.153)0.064**(0.030)

Week+20.015(0.048)0.068(0.122)0.006(0.027)

Week +3-0.036(0.057)-0.037(0.159)-0.002(0.032)

Week +40.030(0.065)0.043(0.181)

0.031(0.037)

Covariates

×

×

×

Mean of outcome

0.173

3.844

-

N

2728

2728

2728

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013. The outcome is risk
tolerance. The outcome in column (1) is a binary measure of risk preference that takes the value of 1 if the person chose the highest or
second-highest risk tolerance categories (categories 7 or 8) and 0 otherwise. The outcome in column (2) is an ordinal measure of risk
preference coded from 1 for the lowest risk tolerance category to 8 for the highest category. Column (3) reports the results of interval
regression. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 =−4, −3, −2, −1, +2, +3, +4) from equation [1]’ are reported with robust errors in parentheses. The
omitted category is week +1. Covariates include age, age squared, and dummy variables for gender, marital status, educational
background, sampling cohort, working status, and good and bad health status. Time effects are related to the date of the survey interview
and include dummy variables for the year, month, interactions of year and month, day of the week, number of weeks, and interactionsof the day of week and number of weeks of the interview date. See the main text for the construction of risk preference measures.
Significance levels: *** p<0.01, ** p<0.05, * p<0.10

45

Table B5—Placebo: Risk tolerance (JSTAR)
People aged
50 to 59 years

People aged 60+
who do not receive pension

(1)

(2)

Week -4

0.047(0.057)-0.022(0.087)

Week -3-0.035(0.077)0.014(0.115)

Week -2

0.032(0.064)-0.060(0.095)

Week -1-0.004(0.064)-0.038(0.100)Week +2-0.050(0.044)0.056(0.062)

Week +3

0.013(0.060)-0.039(0.078)

Week +40.080(0.066)-0.079(0.097)

Covariates

××

Mean of outcome

0.176

0.151

N

765

336

Notes: The data come from the Japanese Study of Aging and Retirement (JSTAR) in 2007, 2009, 2011, and 2013. The outcome is risk
tolerance. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 =−4, −3, −2, −1, +2, +3, +4) from equation [1]’ are reported with robust errors in parentheses. The
omitted category is week +1. Covariates include age, age squared, and dummy variables for gender, marital status, educational
background, sampling cohort, working status, and good and bad health status. Time effects are related to the date of the survey interview
and include dummy variables for the year, month, interactions of year and month, day of the week, number of weeks, and interactionsof the day of week and number of weeks of the interview date. See the main text for the construction of risk preference measures.
Column (1) limits the sample to people aged 50-59 years who are younger than the eligibility age of 60 for pension. Column (2) limits
the sample to people aged 60 and over who do not yet receive pension benefits. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

46Appendix C: Analysis for Japan (JGSS)
Figure C1—Frequency distributions (JGSS)(a) Financial anxiety (N= 277)
.5

Fraction

.4

.3

.2

.1

0
1

2

3

4

Category

5(b) Self-perceived social status (N= 593)
.4

Fraction

.3

.2

.1

0
1

2

3

4

5

6

Category

7

8

9

10

Notes: The data come from the Japanese General Social Surveys (JGSS) in 2008. The question on financial anxiety is “Do you feelanxious about your economic situation in the future?” The categories 1 to 5 correspond to “I feel very anxious”, “I feel somewhat
anxious”, “I have mixed feelings”, “I don’t feel anxious very much”, and “I don’t feel anxious at all.” The question on self-perceived
social status is “If we were to divide the contemporary Japanese society into the following ten strata, which would you say you belongto?” and categories range from 1 (top) to 10 (bottom).

47

Figure C2—Outcomes before and after payday (JGSS)(a) Feel financialy anxious (N=277)

Feel financially anxious

.8

.7

.6

.5

.4
-14 -12 -10

-8

-6

-4

-2

0

2

4

Days since check arrival

6

8

10

12

14

10

12

14

(b) Low self-perceived social status (N=593)

Low self-perceived social status

.2

.15

.1

.05

0
-14 -12 -10

-8

-6

-4

-2

0

2

4

Days since check arrival

6

8

Notes: The data come from the Japanese General Social Surveys (JGSS) in 2008. Each plot represents the two-day average of outcomes
for two weeks before and after payday. The line corresponds to the linear fit. The outcome in panel (a) is a dummy that takes the valueof one if the respondents choose either “I feel very anxious” or “I feel somewhat anxious” to the question “Do you feel anxious about
your economic situation in the future?” or zero otherwise. The outcome in panel (b) is a dummy that takes the value of one if the
respondents choose the lowest and second-lowest categories (categories 9 and 10) to the question “If we were to divide the contemporary
Japanese society into the following ten strata, which would you say you belong to?” or zero otherwise.

48

Table C1—Descriptive statistics (JGSS)
Variables

Obs

Feel financially anxious
Low self-perceived social status
Days since check arrival
Individual Characteristics:
Age
FemaleEducation
Elementary/Middle school
High School
Junior College
Vocational School
University or above
MarriedNot working
Good Health
Poor Health

Mean

SD

Min

Max

277
593
593

0.628
0.099
-1.501

0.484
0.3
7.985

0
0
-14

1
1
13

593
593

71.321
0.501

6.678
0.5

60
0

89
1

593
593
593
593
593
593
593
593
593

0.456
0.341
0.015
0.053
0.126
0.752
0.839
0.459
0.211

0.498
0.474
0.122
0.225
0.333
0.432
0.368
0.499
0.409

0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1

Notes: The data come from the JGSS in 2008. “Feel financially anxious” is a dummy that takes the value of one if the respondents
choose either “I feel very anxious” or “I feel somewhat anxious” to the question “Do you feel anxious about your economic situation in
the future?” or zero otherwise. “Low position in the society” is a dummy that takes the value of one if the respondents choose the lowest
and second lowest categories (categories 9 and 10) to the question “If we were to divide the contemporary Japanese society into thefollowing ten strata, which would you say you belong to?” or zero otherwise. “Feel financially anxious” is asked to only half of the
respondents.

Table C2—Test for randomization of interview dates (JGSS)

F-statp-value

(1)

(2)

(3)

(4)

(5)
Education

(6)

(7)

(8)

(9)

(10)

(11)

Age

FemaleElementary/
MiddleschoolHigh
SchoolJuniorCollege

Vocational
SchoolUniversity
or above

MarriedNot
Working

Good
HealthPoor
Health

0.596
0.618

0.157
0.925

0.800
0.494

1.144
0.330

0.295
0.829

0.949
0.416

0.826
0.480

1.701
0.166

2.922
0.033

0.740
0.529

2.096
0.100

Notes: The data come from the JGSS in 2008 (N= 593). F-stats and p-values correspond to joint null tests whereby each of thecoefficients on the four-week dummies is zero.

49

Table C3—Robustness checks: Potential mechanisms (JGSS)
Feel financiallyanxiousOrdered
Probit(1)0.521**(0.227)Low self-perceived
social status
Ordered
Probit(2)
0.005(0.132)

Week -1

0.659***(0.228)0.216*(0.130)Week +20.318(0.259)
×
3.560
277

0.134
(0.136)
×
6.000
593

Outcome:Week -2

Covariates
Mean of outcome
N

Notes: The data come from the Japanese General Social Surveys (JGSS) in 2008. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from estimating
an equation [1] are reported with robust errors in parentheses. The omitted category is week +1. The outcome in column (1) is an ordinalmeasure of financial anxiety coded from 1 for “I don’t feel anxious at all” to 5 for “I feel very anxious” to the question “Do you feelanxious about your economic situation in the future?”. The outcome in column (2) is an ordinal measure of position in the society codedfrom 1 for “top” to 10 for “bottom” to the question “If we were to divide the contemporary Japanese society into the following ten strata,
which would you say you belong to?” Covariates include dummy variables for age, gender, marital status, educational background,working status, good and bad health status, region fixed effects (FEs), and month FEs. Significance levels: *** p<0.01, ** p<0.05, *p<0.10

Table C4—Placebo: Potential mechanisms (JGSS)
Outcome:Feel financially anxious
OLS
Probit(1)(2)

Week -2

0.122(0.091)-0.043(0.103)0.084(0.088)
×
0.787
221
0.216

Week -1
Week +2
Covariates
Mean of outcome
N
R squared

0.101(0.082)-0.030(0.071)0.088(0.070)
×
0.789
218
-

Low self-perceived social status
OLS
Probit
(3)
(4)
0.024
(0.044)-0.000(0.039)-0.033(0.028)
×
0.068
428
0.118

0.028
(0.040)0.009(0.038)-0.051(0.036)
×
0.081
358
-

Notes: The data come from the Japanese General Social Surveys (JGSS) in 2008. The sample is limited to people aged 50-59 years who
do not receive a pension benefit. The estimates 𝛽𝛽𝑘𝑘 (𝑘𝑘 = −2, −1, +2) from estimating an equation [1] are reported with robust errors inparentheses. The omitted category is week +1. The outcome in column (1) is a dummy that takes the value of one if the respondents
choose either “I feel very anxious” or “I feel somewhat anxious” to the question “Do you feel anxious about your economic situation in
the future?” or zero otherwise. The outcome in column (2) is a dummy that takes the value of one if the respondents choose the lowest
and second-lowest categories (categories 9 and 10) to the question “If we were to divide the contemporary Japanese society into thefollowing ten strata, which would you say you belong to?” or zero otherwise. Covariates include dummy variables for age, gender,
marital status, educational background, working status, good and bad health status, region fixed effects (FEs), and month FEs.
Significance levels: *** p<0.01, ** p<0.05, * p<0.1050Appendix D: Data Appendix
Table D1—Questionnaire for the HRS
Question 0:
Suppose that you are the only income earner in the family. Your doctor recommends that you move because of allergies, and you haveto choose between two possible jobs.
The first would guarantee your current total family income for life. The second is possibly better paying, but the income is also lesscertain. There is a 50-50 chance the second job would double your total lifetime income and a 50-50 chance that it would cut it by athird. Which job would you take -- the first job or the second job?
1. FIRST JOB → to Question C
2. SECOND JOB → to Question A
8. Don’t know / Not available → End
9. Refuse → End
Question A:
Suppose the chances were 50-50 that the second job would double your lifetime income, and 50-50 that it would
cut it in half. Would you take the first job or the second job?
1. FIRST JOB → End
2. SECOND JOB → to Question B
8. Don’t know / Not available → End
9. Refuse → End
Question B:
Suppose the chances were 50-50 that the second job would double your lifetime income and 50-50 that it would
cut it by seventy-five percent. Would you take the first job or the second job?
1. FIRST JOB → End
2. SECOND JOB → End
8. Don’t know / Not available → End
9. Refuse → End
Question C:
Suppose the chances were 50-50 that the second job would double your lifetime income and 50-50 that it would
cut it by twenty percent. Would you take the first job or the second job?
1. FIRST JOB → to Question D
2. SECOND JOB → End
8. Don’t know / Not available → End
9. Refuse → End
Question D:
Suppose the chances were 50-50 that the second job would double your lifetime income and 50-50 that it would
cut it by 10 percent. Would you take the first job or the second job?
1. FIRST JOB → End
2. SECOND JOB → End
8. Don’t know / Not available → End
9. Refuse → End51

Table D2—Questionnaire for JSTAR
Question: Suppose how you are paid at work were to change next month only, which of the following options would you prefer?
Please assume that the amount of your pay is not related to your ability or effort and that this change will be in effect for the next
month only.
Choice 1

Choice 2

100% chance of an increase by 50%
90% chance of an increase by 50% and
10% chance of an increase by 5%
80% chance of an increase by 50% and
20% chance of an increase by 5%
70% chance of an increase by 50% and
30% chance of an increase by 5%
60% chance of an increase by 50% and
40% chance of an increase by 5%
50% chance of an increase by 50% and
50% chance of an increase by 5%
40% chance of an increase by 50% and
60% chance of an increase by 5%100% chance of an increase by 20%52