NBER WORKING PAPER SERIES

THE RETIREMENT-CONSUMPTION PUZZLE:

NEW EVIDENCE FROM PERSONAL FINANCES

Arna Olafsson

Michaela Pagel

Working Paper 24405

http://www.nber.org/papers/w24405

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

March 2018

We are grateful for the financial financial support of the Pension Research Center (PeRCent) at 

Copenhagen Business School, and we thank Andrew Caplin, Rachel Kranton, Jawad Addoum, 

Jon Parker, Matthew Rabin, Botond Koszegi, David Laibson, Steve Zeldes, Emi Nakamura, Jon 

Zinman, Jennifer La'O, Emir Kamenica and seminar participants at the Federal Reserve Bank of 

Richmond, University of Queensland, PerCent 2017 Conference, University of Minnesota, 

Columbia University, and ECWFC at the WFA 2017 for a range of insightful comments. We are 

also indebted to August Schweitz Eriksson and Meniga for providing the Icelandic data and to 

Steffen Meyer for replicating our results in German bank data. We also thank Belinda Chen for 

outstanding research assistance. The views expressed herein are those of the authors and do not 

necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been 

peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies 

official NBER publications.

© 2018 by Arna Olafsson and Michaela Pagel. All rights reserved. Short sections of text, not to 

exceed two paragraphs, may be quoted without explicit permission provided that full credit, 

including © notice, is given to the source.
The Retirement-Consumption Puzzle: New Evidence from Personal Finances

Arna Olafsson and Michaela Pagel

NBER Working Paper No. 24405

March 2018

JEL No. D12,D14,E21,J26,J32

ABSTRACT

This paper uses a detailed panel of individual spending, income, account balances, and credit 

limits from a personal finance management software provider to investigate how expenditures, 

liquid savings, and consumer debt change around retirement. The longitudinal nature of our data 

allows us to estimate individual fixed-effects regressions and thereby control for all selection on 

time-invariant (un)observables. We provide new evidence on the retirement-consumption puzzle 

and on whether individuals save adequately for retirement. We find that, upon retirement, 

individuals reduce their spending in both work-related and leisure categories. However, we feel 

that it is difficult to tell conclusively whether expenses are work related or not, even with the best 

data. We thus look at household finances and find that individuals delever upon retirement by 

reducing consumer debt and increasing liquid savings. We argue that these findings are difficult 

to rationalize via, for example, work-related expenses. A rational agent would save before 

retirement because of the expected fall in income, and dissave after retirement, rather than the 

exact opposite

Arna Olafsson

Copenhagen Business School

Department of Finance

Solbjerg Plads 3

2000 Frederiksberg

Denmark

Denmark

ao.fi@cbs.dk

Michaela Pagel

Columbia Business School

3022 Broadway

Uris Hall

New York, NY 10027

and NBER

mpagel@columbia.edu
1 Introduction

A large literature analyzes changes in household consumption upon retirement. After all,

household consumption is the most important part of aggregate demand and the increase in

the share of the workforce that will be approaching or past retirement age in the coming years

puts this topic at centerstage for both policy-makers and researchers. This paper uses new

data from a personal finance management software to shed additional light on the effects of

retirement on individual spending, income, and personal finances, that is, liquid savings and

consumer debt.

A central implication of Modigliani’s (1954) life-cycle model, the standard model for analyzing consumption-savings decisions by households, is that consumption should be smoothed

across periods of predictably high and low income. Retirement is arguably among the most

predictable and important income changes individuals encounter in their lives, and consumption should therefore not be affected by its onset. However, a number of empirical studies (e.g.,

Banks et al., 1998; Bernheim et al., 2001; Haider and Stephens, 2007; Schwerdt, 2005) find a

sharp decline in consumption during the first years of retirement, a phenomenon referred to as

the retirement-consumption puzzle.1

But other studies argue that a drop in consumption at retirement is actually not puzzling.

Aguiar and Hurst (2005, 2013) and Hurst (2008) strongly question the claim that there is a

decline in actual consumption utility at retirement. They argue that spending, rather than consumption, decreases on the grounds that individuals reduce their work-related expenses and

overall spending through more efficient shopping and household production, as a result of

changes in the opportunity cost of time after retirement.2 So the mechanism for the drop in

spending upon retirement is still debated, as is the broader question whether individuals save

adequately for retirement and whether Modigliani’s (1954) life-cycle model is applicable.

This paper revisits the retirement-consumption puzzle with new, accurate, and comprehensive data on spending, income, consumer debt, and liquid savings from personal finance management software. The accurate spending data, covering more than six years, offers a unique

opportunity to reevaluate the puzzle. Moreover, to the best of our knowledge, this paper is the

first to document changes in personal finances, that is, liquid savings and consumer debt, around

retirement. The data allow us to analyze changes in individual spending, interest payments, and

financial account balances upon retirement using individual fixed-effects regressions. We find

1The first study to document a clear decline in consumption at the onset of retirement was conducted by Banks

et al. (1998), using a pseudo panel constructed from 25 years of the Family Expenditure Survey (FES) in the UK.

Bernheim et al. (2001) also found a drop in consumption at retirement using longitudinal data from the Panel

Study of Income Dynamics (PSID). The drop in consumption at retirement is theoretically rationalized by Pagel

(2017) and Huang and Caliendo (2011), among others. Moreover, Ameriks et al. (2007) and Haider and Stephens

(2007) provide evidence that the drop is expected.

2While some additional studies provide evidence supporting this hypothesis (see, e.g., Hurd and Rohwedder, 2003; Battistin et al., 2009), others argue against it (such as Stephens Jr and Toohey, 2017). However, all

these studies are based on survey data. Analyzing the puzzle using transaction-level data from personal finance

management software, as introduced by Gelman et al. (2014), is a natural next step.

1
evidence of reductions in both work-related and leisure spending categories. However, we note

that it is difficult to tell conclusively which expenses are work related, even when we consider

very fine categories. Additionally, we look at personal finances and document that individuals

delever around retirement by reducing their consumer debt and increasing their liquid savings.

Our findings about personal finances are very interesting because they are difficult to rationalize via, for example, theories about work-related expenses. Any rational agent who expects a

fall in income at retirement will save before retirement and dissave after retirement, rather than

the other way around.

Unless the reduction in work-related expenses is larger than the fall in income, agents

should always save before retirement and dissave after. If the reduction in work-related expenses is larger than the fall in income, they should retire early. However, the average individual in our sample continues to work voluntarily some time after reaching the age thresholds at

which disincentives to retire no longer apply. Thus, our finding that individuals increase their

savings after retiring is difficult to explain by work-related expenses or any other theory that

reduces consumption needs around retirement, such as by reference to the opportunity costs of

time. Our findings could be explained by individuals liquidating retirement accounts or other

illiquid assets, such as houses, around retirement. To address this concern, we make sure our

findings are robust to controlling for income from all sources. Furthermore, we find that investment transactions and all other sources of income are unaffected by retirement status. Overall,

we carefully discuss a number of theories consistent with rational planning: work-related expenses, opportunity costs of time, wealth shocks and liquidation of assets, health shocks, and

intra-household bargaining. We conclude, however, that they all have difficulty explaining our

findings.

We thus argue that we cannot retire the consumption puzzle just yet. After all, the retirementconsumption puzzle is about individuals not being able to plan for an expected, large, and

salient reduction in income. Our finding that consumption and consumer debt drop while liquid

savings increase can be explained by undersaving before retirement and adjusting consumption

patterns afterward, which requires a time-inconsistent overconsumption problem such as hyperbolic discounting (as in Laibson et al., 1998) that is corrected after retirement, as we show in a

fully-fledged life-cycle model. Alternatively, Pagel (2017) shows that a model of expectationsbased loss aversion, as developed by Koszegi and Rabin  ̋ (2009), predicts that individuals will

correct their overconsumption problem upon retiring. The intuition is the following: when income is uncertain, individuals overconsume in the present and hope for a better realization of

income in the future. But when income is certain, as it is after retirement, overspending results

in a sure reduction in future spending, and because the agent dislikes this sure loss, she corrects

her overconsumption problem after retirement. We thus rationalize a simultaneous decrease

in consumption and increase in savings upon retirement in a realistically calibrated life-cycle

model.

Reassuringly, we find the same empirical pattern in two commonly used US consump2
tion survey data sets, the Consumer Expenditure Survey (CEX) and the Survey of Consumer

Finances (SCF). In the CEX data, we find that retirement results in an increase in savings (measured as income minus spending), checking account balances, and savings account balances.

In the SCF data, we find that retirement results in reductions in leverage and debt and in increases in checking, savings, and call account balances. We control for cohort, age, and year

effects, although one has to keep in mind that these results suffer from selection bias and measurement error. Beyond the CEX and SCF data, we thus replicate our results in individualand transaction-level bank account data from Germany by running fixed-effects regressions.

Finally, we use two more US survey data sets to replicate our results: the PSID and the HRS.

Because these surveys poll households consecutively, we can also include individual fixed effects in our regressions. In all these data sets, we find that consumption and debt holdings

decrease but savings and checking account balances increase along with other measures of

wealth.

In this paper, we explore the retirement-consumption puzzle using a very accurate panel of

individual spending and income. The data were captured in the course of business by Meniga,

a provider of personal finance management software in Iceland. This data set has already

proven useful for studying, among other things, the spending responses of individuals to income arrivals (Olafsson and Pagel, forthcoming) and the drivers of individuals’ attention to

their personal finances (Olafsson and Pagel, 2018). We are aware of only one previous study

to use transaction-level data to investigate the consumption drop upon retirement: Agarwal et

al. (2015) use debit and credit card spending data from Singapore. Our data set has five advantages: (1) In Iceland, consumers use electronic means of payments almost exclusively, which

eliminates one limitation of the data from Singapore, where a significant fraction of expenditures in certain shops, such as fresh markets and small grocery stores, are paid with cash.

(2) We observe the incomes and expenditures of individuals over a long period of time around

retirement. Thus, we have sufficient power to estimate individual fixed-effects regressions,

controlling for all selection on observable and unobservable time-invariant characteristics. (3)

We can identify individual spending and income because all financial accounts are personal in

Iceland. At the same time, we can identify individuals within a household, which allows us

to control for household income. (4) We can see how individuals spend within certain categories of spending, which gives us the opportunity to test whether consumers substitute toward

cheaper but more time consuming goods when they retire. In addition, our categorization is

very accurate and comprehensive. (5) Because we have detailed information on all account

balances, credit limits, and financial fees, we are able to document the changes in personal

finances at the onset of retirement.

With respect to changes in household finances upon retirement, the only related paper we

are aware of is Addoum (2016), which analyzes changes in household portfolios upon retirement using PSID data. The author shows that portfolios become less risky when male spouses

retire as women gain bargaining power and are more risk averse. Moreover, our findings are

3
related to recent work documenting that mean and median cohort wealth, for either singles or

couples, can be stationary or rising for many years after retirement (Poterba et al., 2011a,b)

even though the standard Modigliani’s (1954) life-cycle model predicts that individuals should

decumulate assets. This empirical finding has been termed the ”retirement-savings puzzle” and

is theoretically explained by DeNardi et al. (2010) and Laitner et al. (2017), among others.

The remainder of this paper proceeds as follows. Section 2 briefly reviews the Icelandic

pension system, describes our data, and reports summary statistics. Section 3 presents our

empirical approach and findings. Section 4 discusses how our findings can be theoretically

rationalized. Finally, Section 5 offers some concluding remarks.

2 Background, data, and summary statistics

2.1 Background and Icelandic pension system

A long working life is more common in Iceland than in most other countries. Figure 1 compares the average effective retirement ages of men and women in Iceland, Germany, and the

United States. This data is obtained from the Organisation for Economic Co-operation and Development (OECD). In our data, we label individuals above age 60 as retired when we observe

pension payments above a certain limit for several months and salary income below a certain

limit for several months.3 Figure 2 displays the share of retired individuals at each age and Figure 3 displays the distribution of retirement ages. Our inferred time of retirement is consistent

with the information from the OECD on effective retirement age in Iceland displayed in Figure

1.

The Icelandic pension system consists of three pillars: a tax-financed public pension (i.e.,

social security benefits), compulsory occupational pension funds (i.e., defined benefit/contribution

plans), which are the dominant feature of the system, and voluntary private pensions with tax

incentives (i.e., tax-deductible savings). The age thresholds at which individuals are no longer

punished for retiring early are the following: the public pension, which is need-based, is paid

from age 67 on. The occupational pension is paid from age 67 on, but it is possible to start

withdrawing it as early as 65 with a reduced benefit, or as late as 70 with additional benefits.

This pension is paid as an annuity and depends partly on individual contributions. The occupational pension system is very transparent, and individuals can easily acquire all the necessary

information about their annuitized retirement income. Finally, private pension savings can be

withdrawn from age 60 on. Appendix A contains a more detailed review of the key features of

the pension system in Iceland.

3More specifically, individuals older than 60 are labeled as retired if (1) we see them receiving at least

1,000,000 ISK (9,770 USD) in pension payments over the sample period, (2) the pension payment in the current month is at least 30,000 ISK (293 USD), and (3) we do not see a salary payment higher than 150,000 ISK

(1,466 USD) in the current month or the three months before and after.

4
2.2 Data

In this paper, we exploit new data from Iceland generated by Meniga, a financial aggregation

software provider to European banks and financial institutions. Meniga was founded in 2009

and is the European market leader of white-label Personal Finance Management (PFM) and

next-generation online banking solutions, reaching more than 50 million users in 20 countries.

Meniga’s account-aggregation platform lets users view all their accounts and credit cards from

multiple banks in one place.

In January 2017, Iceland’s population was 338,349 individuals, of whom 262,846 were

older than 16. At that time, Meniga had 50,573 users, or about 20 percent of the population

above age 16. Because their service is marketed through banks, the sample of users is fairly

representative. Each day, the software automatically records all the bank and credit card transactions, including descriptions and the balances, overdrafts, and credit limits. The software also

collects demographic information such as age, gender, marital status, and postal code. Figure 4

displays screenshots of the app’s user interface. The first shows background characteristics that

the user provides, the second shows transactions, and the third shows bank account information.

We use the entire de-identified population of active users in Iceland and the data derived

from their records. The data on spending, income (including interest income from savings

account balances), overdraft interest, and financial fees runs from 2011 to 2017 and the data

on balances and limits spans 2014 to 2017. We restrict the sample to individuals above the

age 60 who receive at least one salary transaction (before receiving their pension payments)

and engage in at least one grocery store transaction per month. We perform the analysis on

user-level data aggregated at a monthly level for different income sources, spending categories,

and measures of household finances. Individuals in the same household can link each other, in

which case we obtain information on both individual and total household spending and income.

All accounts in Iceland are personal; thus, we know each individual’s spending, salary and

pension income, balances, and credit limits.

Spending data

Spending is categorized, and each category is aggregated at both the individual and the household level. The panel thus provides individual- and household-level expenditure information

for disaggregated categories. We consider ten fairly broad spending categories: groceries, fuel,

alcohol, ready-made food, home improvement, transportation, clothing and accessories, sports

and activities, and pharmacies.4 We also have more disaggregated categories. For instance, for

ready-made food, we know the type of restaurant, such as bakery, canteen, or fine dining.

4We can observe expenditures on alcohol that is not bought at bars and restaurants because a state-owned

company, the State Alcohol and Tobacco Company, has a monopoly on the sale of alcoholic beverages in Iceland.

5
Income data

When the data are extracted from the PFM system, they have already been categorized by a

three-tiered approach: system, user, and community rules. The system rules are applied when

codes from the transaction system clearly indicate the type of transaction being categorized.

For example, when transactions in the Icelandic banking system contain the value “04” in a

field named “Text key,” the payer has indicated payment of salary. User rules apply when no

system rules are in place but a user persistently categorizes transactions with certain text or

code attributes to a specific category, so that the system automatically creates a rule that is

applied to all further such transactions. If neither system rules nor user rules apply, the system

detects identical categorization rules from multiple users, which allows for the generation of

a community rule applying the categorization across the whole community. The PFM system

has already detected first-party transactions, such as between two accounts belonging to the

same household, and excluded them. Thus, multiple steps were taken to achieve an accurate

categorization of transactions based on banking system codes, transaction texts, amounts, and

payer profiles.

Payers identity and NACE category (The Statistical Classification of Economic Activities

in the European Community)5are also added to each income transfer whenever possible.6 The

system sorts income into 23 categories. Regular income categories are child support, benefits,

child benefits, interest income, invalidity benefits, parental leave, pension, housing benefits,

rental benefits, rental income, salary, student loans, and unemployment benefits. The irregular

income categories are damages, grant, other income, insurance claims, investment transactions,

reimbursements, tax rebates, and travel allowances.

Bank data

The amount of savings and overdrafts that individuals hold can be inferred from information on

balances, interest income from savings accounts, and interest paid on overdrafts. An overdraft

occurs when withdrawals from a current account exceed its available balance. This means that

the balance is negative and the bank is providing credit to the account holder, with interest being

charged at the agreed rate. Virtually all current accounts in Iceland offer a pre-agreed overdraft

facility, the size of which is based on individual credit scores and histories. Customers can use

this overdraft facility at any time without consulting the bank, and it can be maintained indefinitely. Although an overdraft facility may be authorized, technically the money is repayable on

demand by the bank. In reality, this is a rare occurrence, as the overdrafts are profitable for the

5This is is the industry classification system used in the European Union.

6Payer identity can be hard or impossible to identify because of limited information in transaction data, such

as generic transaction texts. In specific cases where the payer could not be identified, a proxy id was created to

enable the binding of payments from single sources even though the true source id is unknown. In some cases, no

attempts could be made to bind transactions by origin via a proxy id. Some payments without actual payer identity

may have a proxy id, but never a NACE category, as the real id of the payer is unknown. All such transactions are

categorized as other income.

6
bank and expensive for the customer.

In addition, we have information on three types of financial penalties: late-payment interest,

non-sufficient-funds fees, and late fees. Credit card companies charge late-payment interest

daily from the date a payment is due and payable to the date it is paid in full. Non-sufficientfunds fees occur when the overdraft limit is exceeded in a consumer’s current account. In

the event of attempted debit card transactions, the bank charges the account with these fees.

Finally, late fees are fees assessed for paying bills after their due dates.

2.3 Summary statistics

Table 1 displays summary statistics of the user population, including income and spending in

US dollars and demographic statistics. We can see that the average user is 41 years old and 49

percent of users are female. For comparison, Statistics Iceland reports the average age in the

population to be 37 years, with 49.5 percent being female. Thus, our demographic statistics

are very similar to those of the overall population. This is reassuring because of the concern

when using app data that the user population may be young, well-situated, male, and techsavvy relative to the overall population. The representative national household expenditure

survey conducted by Statistics Iceland also reports income and spending statistics. In the table,

parentheses indicate when spending categories did not match perfectly with the data. We can

see that the income and spending figures are very similar for those categories that match well.

Our sample is fairly representative because individuals are offered to sign up for the software

automatically by the bank when they access their accounts online and the internet penetration

is 97% in Iceland.

Table 2 displays summary statistics for individuals who are eligible for retirement, i.e.,

have reached age 60, whether or not they are retired. We can see in the raw data that on average

retired individuals have lower incomes, spend less (in both individual spending and household

spending), have more savings, hold less consumer debt, have more access to credit (liquidity),

and incur fewer bank fees and penalty payments.7

Figure 5 summarizes the salary, pension-payment, and expenditure trajectories of men and

women using binned averages by age. The figure shows how salary payments decrease and

pension payments increase between the ages of 60 and 70. Labor income and spending are

both hump-shaped over the life cycle.

7Liquidity is defined as access to credit and thus current account balance plus savings account balance plus

overdraft limits plus credit card limits minus overdrafts and credit card balances.

7
3 Analyses

3.1 Life-cycle profiles of spending and income

We start our analysis by investigating whether we can replicate the familiar hump-shaped profile of spending over the life cycle in our data. Following Aguiar and Hurst (2013) and Agarwal et al. (2015), we obtain the age profile of consumption by regressing log total spending

on age, controlling for month-by-year and individual fixed effects. We also estimate separate

regressions for log spending in our ten broad consumption categories. Figure 6 plots the age

coefficients for log total monthly spending. The expenditure profiles do indeed exhibit the conventional hump-shaped patterns, in which spending peaks in the late thirties, at a bit less than

40 log points higher than the level for twenty year olds, and spending declines by about the

same amount from the individual’s late fifties until retirement age.

Aguiar and Hurst (2013), Agarwal et al. (2015), and Figure 6 document considerable heterogeneity in life-cycle patterns of spending across different consumption categories. Spending

in grocery stores and supermarkets is the largest part of household total spending (excluding

expenditure commitments like housing and utilities) and it does exhibit the hump-shaped pattern, but other spending categories do not. For instance, ready-made food expenditures rise in

the individual’s twenties and then stay more or less constant until declining in the sixties. Alcohol expenditure is constantly rising, while spending on clothes and accessories is constantly

falling.

As shown in Figure 6, we also find considerable heterogeneity in the life-cycle patterns

of spending in more refined categories. For instance, grocery stores are categorized as either

budget or expensive stores. Spending in budget supermarkets exhibits a much greater hump

than spending in more expensive grocery stores. Furthermore, the share of expenditures in

expensive grocery stores falls during the twenties, reaches its bottom around age 30, and stays

constant until the early forties, when it starts rising again.

We also find different patterns among more refined ready-made food categories. Expenditures on fast food increase in the individual’s twenties and stay more or less constant until they

start falling during the sixties. This is consistent with fast food being a work-related expense

or a substitute for home production that increases as individuals enter the labor market and

decreases as they retire. The same applies to casual dining. Expenditure on fine dining, on

the other hand, increases gradually and then stays pretty much constant. This pattern appears

reasonable for a kind of expense that is unlikely to be a work related or a substitute for home

production and thus to be relatively unaffected by labor-market status.

Taken together, these figures suggest that individuals shift toward more time-consuming

food expenditures as they retire from the labor market, which is perfectly consistent with the

findings in the literature (e.g., Aguiar and Hurst, 2013).

8
3.2 The effects of retirement on spending and personal finances

To examine the effects of retirement on spending and personal finances we run the following

regression:

log(Cit) = αb + βRetired b it + γb log(Yit) + φmy + ηi + it (1)

where Cit is the dependent variable of interest and Yit is income of individual i at time t. φmy

is a month-by-year fixed effect, and ηiis an individual fixed effect. Controlling for individual fixed effects allows us to compare individuals to themselves before and after retirement.

Retiredit is an indicator equal to 1 if individual i has retired at time t and the βb coefficient thus

measures the effect of retirement on the outcome variable under consideration.

Spending by category

Table 3 shows the estimated effect of retirement on spending based on the individual fixed

effects model, Equation (1), with and without controlling for total income.8 We can control

either for individual income or for total household income when individuals link each other.

These results show that spending drops significantly upon retirement. When we control for

income, the drop is about 70% of the reduction without controlling for income, which suggests

that individuals change their spending habits not only because income drops at retirement.

In turn, we find results consistent with a reduction in work-related expenses: spending on

fuel, ready-made food, and clothing drops substantially. Grocery spending falls as well, which

might be attributed to more efficient shopping and home production, as individuals have more

time at their disposal after retirement. However, leisure-related expenses (for instance, sports

and activities) also decrease substantially, suggesting that individuals are correcting an overconsumption problem. Other spending that can hardly be attributed to work, such as alcohol

bought in stores and pharmacy spending, also falls upon retirement.

Nevertheless, we feel that the results on spending in such broad categories do not paint a

conclusive picture of whether or not work-related expenses explain the retirement-consumption

puzzle. We thus turn to a finer categorization and, following the literature, examine spending

on food more closely.

Spending by grocery store and type of restaurant

In the previous section, we showed that consumers substitute across different types of grocery

stores and of ready-made food places over their life cycles. We know the type of grocery

store (budget or expensive) and the types of restaurant (such as bakery or fine dining). We can

8Because individual spending categories include many zeros, we replace the log of all values between zero and

one Icelandic krona with zero to avoid losing these observations. Our estimates are not qualitatively or quantitatively affected by winsorizing to address outliers instead of taking the log of the outcome variables. Moreover, our

results are not affected by controlling for winsorized income as opposed to log income.

9
also distinguish between individual trips and overall daily spending. Previous studies, even

those using transaction-level data, may have failed to capture some patterns. The reason is that

consumers in countries where card payments are not as widely accepted as in Iceland typically

use different payment instruments in different types of stores. For instance, cash payments are

much more common in fresh markets and smaller grocery stores and, as Figure 6 shows, older

cohorts exhaust a larger share of their grocery spending in such stores. This would suggest

that the spending of older cohorts is underestimated in countries where cash is among the most

common payment instruments. These results are consistent with findings by Agarwal et al.

(2015) using the shopping scanner data. However, given that almost all purchases in Iceland

are made with non-cash instruments (and we can control for cash withdrawals), this cannot

be part of the explanation for the drop in consumption at retirement in Iceland. In fact, it

suggests that the numbers on groceries and ready-made food shown in Table 3 underestimate

the reduction in the amount of grocery spending as consumers spend a larger share in more

expensive supermarkets at later ages but still reduce their overall spending.

Figure 7 shows the life-cycle profiles of (1) restaurant spending and visits, (2) expenditure

in different types of restaurants, such as bars or fine dining, and (3) visits to different types of

restaurants. We can clearly see humps for all the life-cycle profiles of both spending and trips

and for all the categories of restaurants.

Moreover, we can look at the effects of retirement on these categories. We can see in Table

4 that individuals exhibit a drop in spending for all categories of restaurants, including bars

and fine dining. This result is more consistent with a reduction in overconsumption around

retirement than with a reduction in work-related expenses.

Nevertheless, we again feel that the results on spending, even with the most accurate and

comprehensive data and a very fine categorization, do not paint a conclusive picture of whether

or not work-related expenses, home production, and efficient shopping explain the retirementconsumption puzzle. After all, one could easily construct a theory of how fine dining or other

activities are work related and thus bound to fall around retirement without affecting consumption utility and without concluding that individuals do not save properly for retirement. Overall, we thus conclude that whether a puzzle exists cannot be decided conclusively even with the

most comprehensive and accurate spending data.

Personal finances

Using the same estimation approach, we can investigate the effects of retirement on bank account balances, overdrafts, credit limits, and bank fees. Table 5 displays the estimated effects

of retirement on household finances based on the individual fixed effects model, Equation (1),

with and without controlling for total income. The most interesting result is that individuals

delever. When we measure consumer debt via interest expenses, we find a 13.2% reduction.

Thus, individuals not only cut their consumption, but they also reduce their consumer debt

10
considerably. This deleveraging cannot easily be rationalized by a reduction in work-related

expenditures, as we discuss in detail below. When rational agents expect a substantial reduction

in income, they will optimally save before and dissave after retirement rather than the other way

around. So instead, it seems sensible to suggest that individuals decide to correct their overconsumption and debt holdings by delevering. Figure 8 shows the life-cycle profile of interest

payments on consumer debt holdings. Consumer debt features a pronounced hump similar to

those of consumption and income. Obviously, if consumer debt were to smooth consumption

and income over the life cycle, it would be U-shaped, not hump-shaped.

We find that households not only delever, they also increase their liquid savings (measured

by interest income on bank account balances) substantially, between 5.5% and 12.1% depending on whether income is controlled for. This again hints at individuals rethinking their financial

plans at the onset of retirement. Figure 9 displays the life-cycle profile of interest income from

savings accounts and shows a clear increase around the time of retirement. Moreover, we find

that individuals increase their current account balances and decrease their overdraft holdings

and credit card balances. Finally, individuals also reduce total and late fee payments.

3.3 Replicating the analysis in other data sets

In this section, we show that we can replicate our results in commonly used survey data sets

from the US and in another individual- and transaction-level data set from Germany. It is

reassuring that we can document the same results in survey consumption data most commonly

used in the literature, the Consumer Expenditure Survey (CEX) and the Survey of Consumer

Finances (SCF). However, these data sets suffer from selection bias and measurement error,

so to bolster the credibility of our findings we replicate our analysis using additional data sets

from the US as well as Germany where we can employ individual fixed effects and conclude

that our results are not specific to Iceland.

CEX data

We use data from the CEX for 1980 to 2002. Following Aguiar and Hurst (2013), we use the

NBER extraction files by John Sabelhaus and Ed Harris of the Congressional Budget Office.

The data set links four quarterly interviews for each respondent household and collapses all

the spending, income, and wealth categories into a consistent set of categories covering all the

years. The CEX is conducted by the Bureau of Labor Statistics and surveys a large sample of

the US population to collect data on consumption expenditures, demographics, income, and assets. Following Harris and Sebelhaus (2000) suggest, consumption expenditures include food,

tobacco, alcohol, amusement, clothing, personal care, housing, house operations (such as furniture and house supplies), personal business, transportation (such as autos and gas), recreational

activities (such as books and sports), and charity expenditures. Alternatively, we could consider

non-durable or discretionary consumption only. Income consists of wages, business income,

11
farm income, rents, dividends, interest, pension, social security, supplemental security, unemployment benefits, worker’s compensation, public assistance, food stamps, and scholarships.

We regress savings, measured simply as income minus overall consumption, on a full set

of age, year, and cohort fixed effects. In addition to the retirement and age effects of interest,

the data are contaminated by potential time and cohort effects, which constitutes an identification problem, as time minus age equals cohort. In the portfolio-choice literature, it is standard

practice to solve the identification problem by acknowledging age and time effects (as tradable

and non-tradable wealth varies with age, and contemporaneous stock market happenings are

likely to affect participation and shares) while omitting cohort effects (Campbell and Viceira,

2002). By contrast, in the consumption literature it is standard to omit time effects but acknowledge cohort effects (Gourinchas and Parker, 2002). By including the full set of fixed effects,

we identify the regression simply by an arbitrary trend assumption (we find the same results

when omitting the year fixed effects while including the region’s unemployment rate, following

Gourinchas and Parker (2002)). Instead of age dummies, we can use a polynomial in age to

the fifth power, and we can control for family size and number of earners in the same way.

In addition to measuring savings as income minus consumption, we use checking and savings

account balances as outcome variables.

Table 7 shows the regression results for savings (measured as income minus consumption),

checking account balances, and savings account balances. The effect of retirement is positive,

significant, and economically large in all specifications. But, given that we cannot control for

household fixed effects, this result may be driven by selection: richer households are retiring

earlier. However, all the results hold even when we control for income.

SCF data

The SCF is a statistical survey of income, balance sheets, pensions, and other demographic

characteristics of families in the United States, sponsored by the Federal Reserve Board in

cooperation with the Treasury Department. We use the data from six waves from 1992 to 2007.

However, as in the case of the CEX data, the SCF does not survey households consecutively

and we can therefore not employ household fixed effects. As before, we estimate the effect of

retirement jointly controlling for age, time, and cohort fixed effects and identify the model with

a random assumption about its trend. We control for family size in the same manner as for the

CEX data.

Table 8 shows the regression results for leverage, debt, and savings account balances. The

effect of retirement is negative, significant, and economically meaningful for the SCF measures

of leverage and debt both when controlling for income or not. For savings account balances, the

effect of retirement is positive, significant, and large once we control for income. We also find

very similar results for related variables, such as overdraft debt, credit card, checking, current,

12
and call account balances.9

The results of both the CEX and SCF data are prone to an obvious selection bias. Clearly,

individuals with large savings and little consumer debt may choose to retire earlier. Therefore,

we also replicate our results in data sets that allow us to control for individual fixed effects

and thus for all selection on time-invariant observable or unobservable characteristics. In such

specifications, we only identify an effect off of households transitioning into retirement.

German bank account data

We replicate our results in a data set that includes information on income, spending, and checking, credit, and portfolio accounts from a German bank covering approximately 5,000 individuals who transitioned into retirement in the period from 1998 to 2010. The results can be found

in Table 9. We find that savings (measured as income minus consumption) increases when

individuals retire, and we see increases in current account balances and portfolio values and

decreases in credit account balances.

PSID data

We also use the Panel Study of Income Dynamics (PSID) data from 1968 to 2015. The PSID is a

nationally representative survey of households in the United States conducted by the University

of Michigan. It was administered annually from 1968 to 1997 and then biennially post 1997.

Furthermore, it included questions that relate to specific consumption and savings measures

post 1997. Following the literature, we use the consumption and savings data post 1997 (Li

et al., 2010) and consumption is measured as expenditure on food, housing, transportation,

education, childcare, and healthcare. Because the PSID surveys households consecutively, we

can also include individual fixed effects in our regressions. The results of the PSID analysis are

consistent with our other results. The coefficients on consumption are negative and significant,

indicating that individuals consume less after retirement. Moreover, retired individuals increase

their balances in savings and checking accounts, which corroborates our previous findings.

Finally, individuals also hold less debt, and their overall wealth increases with and without

accounting for equity.10 These results can be found in Table 10.

HRS data

Finally, we replicate our results using the Health and Retirement survey (HRS) conducted by

the University of Michigan. This survey asks individuals and their spouses who are over 50

9A call deposit account is a bank account for investment funds that provides instant access to funds and interest

accrual.

10Because about one-third of the wealth observations in the PSID data are negative, we winsorize the wealth

variables rather than logging them. If we log the variables, the results are qualitatively similar but only marginally

significant.

13
years old about their health, employment, quality of life, and wealth. It was conducted biennially from 1992 to 2014. In 2001, the HRS sent a consumption-and-activities mail survey

(CAMS) to a subsample of the initial HRS population, and it has tracked consumption for

these households biennially ever since. The RAND Center for the Study of Aging provides

clean versions of each wave of HRS data, which we merged with the CAMS data set to extract

information on individuals’ consumption and wealth. We construct the savings variable as the

sum of the values of CDs, government savings bonds, checking, savings, and money market

accounts, stocks, mutual funds, investment trusts, bonds, bond funds, and all other savings.

The results of the HRS analysis are consistent with our previous results. When they retire,

individuals consume less but increase their savings, IRA assets, and overall wealth. The results

can be found in Table 11.

4 Potential theoretical explanations of our findings

We loosely categorize potential theoretical explanations for our findings as follows: We first

consider explanations that are in one way or another based on rational planning for retirement

and do not feature an obvious irrational component, such as limitations to planning or cognition, or behavioral component, such as non-standard preferences or beliefs. We then move on

to explanations that have an irrational or behavioral component. In turn, we discuss a fullyfledged life-cycle model that features non-standard preferences, either present bias that is corrected at retirement or expectations-based loss aversion, and generates a drop in consumption

at retirement and an increase in savings simultaneously.

4.1 Explanations based on rational planning

Overall, our results on liquid savings and consumer debt suggest that we should not yet retire

the consumption puzzle. Any rational agent will save before retirement, given that she expects

a fall in income, and dissave after. However, we observe that individuals do the opposite:

they dissave before and save after retirement. Thus, the joint observations of a fall in income,

a fall in consumption, a decrease in consumer debt, and an increase in savings are hard to

reconcile with a rational model of consumption smoothing. We next discuss some more specific

potential explanations, but we conclude that work-related expenses, opportunity costs of time,

wealth shocks and liquidation of assets, health shocks, and intra-household bargaining all have

difficulty explaining our findings.

Work-related expenses

In what situation would a sudden reduction in work-related expenses upon retirement cause an

increase in savings? To answer that question, we outline five scenarios for individuals’ income

and pension profiles. (1) A flat income profile before retirement and a lower, flat pension profile

14
after retirement. Any patient agent in this situation would smooth consumption by accumulating liquid savings before retirement and decreasing savings after. (2) A flat income profile

before retirement and a lower but increasing pension profile after retirement. In this case, a

patient agent would again smooth consumption by saving before retirement and potentially

by increasing debt at the start of retirement but decreasing debt thereafter. (3) A flat income

profile before retirement and a lower, decreasing pension profile after retirement. Again, any

patient agent would smooth consumption by accumulating savings before retirement and decreasing savings after. (4) A flat income profile before retirement and a pension profile that is

just marginally lower than income (by less than work-related expenses) after retirement, and

decreasing. In this scenario, a patient agent might increase savings at the start of retirement.

However, we observe a substantial reduction in income at the start of retirement. Furthermore,

the pension profile is increasing after retirement, as can be seen in Figure 5. (5) A flat income

profile before retirement and a pension profile that is higher than income after retirement. A

patient agent in this case might accumulate debt and then decrease it at the start of retirement.

However, we see that pensions are lower than labor income in Figure 5, and we can thus rule out

this scenario. Furthermore, work-related expenses cannot be larger than the difference between

pension and labor income, as individuals would simply retire early in that case.

More systematically, we can think about the following calculations. If income decreases,

then savings must decrease or debt must increase, other things being equal. However, if workrelated expenses decrease, then savings must increase or debt must decrease. Moreover, if the

agent is very impatient then the consumption path may be steeply decreasing, which in any

period will cause savings to increase or debt to decrease. Therefore, to explain our findings,

the decrease in work-related expenses plus the decrease in spending from the period before

retirement to after (the latter decrease can be assumed to be zero for monthly to annual horizons

if individuals are reasonably patient) must exceed the drop in income at retirement. While

this is a theoretical possibility, work-related expenses are unlikely to be as large as the drop

in income upon retirement that we find in Icelandic and US data. Furthermore, as we have

already mentioned, individuals would simply retire early in such a case. We could also look at

individuals with very large drops in income to reject that theory. Moreover, the theory does not

square with a known comparative static (see Bernheim et al., 2001, among others): when the

drop in income is larger, we also see a larger drop in consumption, so the reason here cannot

be work-related expenses being too large or households being too impatient.

Our argument is illustrated in Figure 10. In a rational model, if an agent expects a fall in

income and expenditure then savings will increase as long as the fall in expenditure is smaller

than the fall in income. If the fall in expenditure is larger than the fall in income, then savings

will increase. In that case, however, the agent gains on net by retiring early. It is thus very hard

to explain, in any rational model, the joint observation that savings increase after retirement

and individuals who are eligible do not retire immediately.

As Figure 2 shows, there are no discontinuities in the fraction of individuals retiring at the

15
retirement age thresholds of 60, 65, 67, and 70. A mass of individuals retires at 60, but this is

a mechanical effect, as we start defining individuals as retired from age 60. Since we restrict

the analysis to individuals over 60 and include individual fixed effects, we do not identify our

effects off of this mass. Furthermore, there is no discontinuous increase at age 65 or 67; if

anything, the mass is larger at ages 64 and 69. Finally, there is no discontinuous increase from

age 69 to 70. Overall, the spikes are fairly different for men and women, suggesting a lot of

heterogeneity, but across the whole population the distribution looks fairly normal. We thus

conclude that individuals do not immediately retire at the benefits thresholds. On average,

individuals appear to retire voluntarily at least a couple months after the age thresholds and

sometimes years after.

Opportunity costs of time

Retirement puts additional time at the individual’s disposal that can be spent on leisure, used

to find bargains and otherwise shop less expensively, or used for home production. While it

is unclear whether additional leisure time results in an increase or a decrease of the marginal

utility of consumption, it can be reasonably assumed that home production increases the value

of expenditures, as argued by Aguiar and Hurst (2005). In principle, however, more time spent

on home production can simply be reinterpreted as a reduction in work-related expenses, and

then the above arguments apply directly and cast doubt on this explanation. If expenses drop by

more than income, whether due to additional disposable time or for other reasons, individuals

could gain in life-time resources by retiring early.

Wealth shocks and liquidation of assets

Banks et al. (1998) and Bernheim et al. (2001) argue that the drop in consumption is brought

about by the systematic arrival of adverse information. In this case, however, the drop in consumption would always be accompanied by a decrease rather than an increase in savings, as

retirement comes with a negative wealth shock. The same is true for the model by Blau (2008)

who argues that uncertainty about one’s retirement date can generate a drop in consumption at

retirement. In the author’s model, either individuals retire as planned and smooth their consumption, or retirement is triggered by a negative wealth shock that also causes a drop in

consumption. In principle, however, to generate an increase in savings, one needs a positive

wealth shock.

A positive wealth shock may be brought about by selling one’s house or other assets upon

retirement. However, our findings are robust to controlling for all income which includes other

income (other income contains, e.g., housing transactions and uncategorized investment transactions). Furthermore, we can more directly address the concern that housing transactions and

liquidation of assets are driving our results by estimating the effect of retirement on investment

income and uncategorized income. Investment transactions can be identified because of the

16
NACE categorization, and income that cannot be classified is listed as ”uncategorized” income

and could be due, for example, to the sale of real estate. We therefore estimate the effect of

retirement on these two income categories, and the results can be found in Table 6. The fact that

we do not find an effect on investment related income and a negative effect on uncategorized

income should relieve concerns about liquidation of assets or housing transactions explaining

why individuals increase their savings and delever upon retirement. We also ensure that our

results are not affected when we control for individual as opposed to total household income.

Another concern related to housing is that individuals wait to retire until they have paid off

their mortgages, in which case their increase in savings and decrease in consumer debt is only

a consequence of the reduced debt burden after making the final mortgage payment. However,

we do not see a sharp decline in mortgage debt by individuals who have reached the official

retirement age, as can be seen in Figure 11. A large fraction of mortgage debt is paid off before

individuals reach age 60, and the remainder declines smoothly as individuals reach average

retirement age.

Health shocks

Individuals may choose to retire in response to an adverse health shock. While it is again unclear whether health results in an increase or a decrease in the marginal utility of consumption,

it can reasonably be assumed that an adverse health shock implies an adverse wealth shock,

which should decrease and not increase savings. As we discussed, any negative shocks will

have trouble explaining our findings of an increase in savings and a reduction in debt holdings.

It could be that a health shock makes spending less enjoyable or increases a precautionary

savings motive, which then increases savings by reducing consumption and, at the same time,

explains why the individual retires. However, we find that pharmacy spending falls upon retirement and is about 7.3% lower afterward, which strongly suggests that health shocks are not

the reason the average individual in our sample retires (when individuals buy medical supplies,

they do so in pharmacies and must make a copayment).

Intra-household bargaining

Lundberg et al. (2003), among others, argues that the drop in consumption is caused by the

fact that the retirement of male spouses generates a shift in the distribution of bargaining power

within households. On average, wives gain bargaining power and use this to exert their preferences for increased savings, because women have on average higher life expectancies than

men. Furthermore, Addoum (2016) and Olafsson and Thornqvist  ̈ (2018) provide evidence of

intra-household bargaining affecting household portfolio choice.

Our findings on spending, liquid savings, and consumer debt are broadly consistent with

this mechanism. However, we find very similar results for both men and women when we split

the sample by gender, which seems inconsistent with household bargaining being first-order

17
important. Olafsson and Pagel (2016) shed further light on how spending and debt holdings at

the household level are influenced by differences in preferences over spending and consumer

debt displayed by the members of the household.

4.2 Explanations based on insufficient planning and overconsumption

Insufficient planning

One possible theoretical explanation is the following: the freeing up of cognitive resources allows individuals to reconsider their savings and consumption plans upon retirement. However,

that theory would not predict a systematic reduction in debt and increase in savings unless insufficient time for planning always results in overconsumption. Haider and Stephens (2007)

and Ameriks et al. (2007) argue that insufficient planning and overconsumption are the reason

for the drop in consumption, and such a systematic effect is suggested by work on cognitive

resources and decision making, as in Mullainathan et al. (2007), although their findings are not

confirmed by Carvalho et al. (2016). In fact, we observe reductions in total financial fees paid

as well as late fees paid. Thus, individuals may be better at planning, but they also have more

time to pay all their bills on schedule.

The insufficient-planning life-cycle models, such as those of Huang and Caliendo (2011)

and Caliendo and Aadland (2007), generate a drop in consumption at retirement but not a simultaneous increase in savings. For instance, the model in Huang and Caliendo (2011) generates

a drop in consumption at retirement because individuals consume all their income and never

save. Thus, the moment income drops, consumption must drop. Clearly, however, savings

would not increase in this case: if individuals consume their entire income when it is high, they

will also do so when it is low. The same is true for the models by Reis (2006) and by Campbell

and Mankiw (1989). With respect to poverty and cognitive resources, a widely-applied model

does not exist at this point, which makes writing down a corresponding theory feel reverseengineered.

Overconsumption and present bias

An alternative theoretical explanation for undersaving before retirement is quasi-hyperbolic

discounting preferences, as in Laibson et al. (1998) and Laibson et al. (2007). Hyperbolic

discounting preferences cannot generate a drop in consumption at retirement per se because the

agent is equally impatient before and after retirement and can smooth consumption perfectly. If

a liquidity constraint would bind, a fall in income might cause a fall in consumption. In such a

situation, however, we have to assume that individuals hit their liquidity constraints before and

after retirement, but empirically, we see individuals having substantial liquidity. Furthermore,

the same critique applies as for the insufficient-planning models, if individuals hit their liquidity

constraint, their savings are zero before retirement. If income falls at retirement, their savings

18
should be zero as well.

To rationalize a drop in consumption and an increase in savings after retirement, one needs

to assume that the hyperbolic discount factor changes when the individual retires. Although this

appears plausible, it again feels somewhat reverse-engineered. Nevertheless, in the following

section, we theoretically analyze how far a change in the exponential or hyperbolic discount

factor, and thus in the agent’s degree of present bias, can generate a drop in consumption at

the same time as an increase in savings at retirement in a fully-fledged life-cycle model that is

calibrated realistically in line with the literature.

Expectations-based loss aversion

As we will show, a theoretical explanation for our findings requires a different degree of

present bias before and after retirement. Koszegi and Rabin  ̋ (2009) and Pagel (2017) show

that expectations-based reference-dependent preferences predict that the degree of present bias

depends on the level of income uncertainty, which is arguably lower after retirement. In this

model, individuals reduce their overconsumption after the start of retirement and thus may

simultaneously decrease their consumption and increase their savings.

Expectations-based loss aversion, as developed by Koszegi and Rabin  ̋ (2009) and applied in

a life-cycle model by Pagel (2017), predicts a drop in consumption at retirement. The intuition

is that expectations-based reference dependence introduces an overconsumption problem when

income is uncertain. After retirement, however, income uncertainty is absent in a standard lifecycle model, which ends time-inconsistent overconsumption. The agent stops overconsuming

because he is allocating certain retirement income instead of uncertain labor income. Certainty

means that overconsumption today yields a sure loss in future consumption, and this loss would

hurt more than today’s overconsumption would give pleasure, as the agent is loss averse. Thus,

the agent suddenly begins controlling his time-inconsistent desire to overconsume, and his

consumption drops at retirement. The assumption of no uncertainty during retirement is made

in all standard life-cycle consumption models, as these are abstracted from portfolio choice.

The drop in consumption at retirement is thus a necessary artifact of news-utility preferences in

the standard environment. However, the drop is robust to three alternative assumptions: small

income uncertainty during retirement (for instance, inflation and pension risk), potentially large

discrete income uncertainty (for instance, health shocks), and mortality risk (refer to Pagel,

2017, for a more detailed discussion). This change in the agent’s degree of present bias may

cause a simultaneous increase in savings, which we now examine in a fully-fledged life-cycle

model.

We consider Koszegi and Rabin  ̋ (2009) preferences in a discrete-time life-cycle model with

periods indexed by t ∈ 1, ..., T. The preferences reduce to standard preferences (Carroll, 1997)

or hyperbolic-discounting preferences (Laibson et al., 1998) for certain parameter combinations. We also discuss temptation-disutility preferences (Gul and Pesendorfer, 2004) as another

19
alternative. In period t, the utility function consists of consumption utility, contemporaneous

news utility about current consumption Ct, and prospective news utility about the entire stream

of future consumption {Ct+τ}

T

τ=1. Thus, lifetime utility in each period t ∈ 0, ..., T is

Et[

X

T −t

τ=0

β

τUt+τ ] = u(Ct) + n(Ct

, Ft−1

Ct

) + γ

X

T −t

τ=1

β

τn(F

t,t−1

Ct+τ

) + Et[

X

T −t

τ=1

β

τUt+τ ], (2)

where β ∈ [0, 1) is an exponential discount factor. The first term on the right-hand side of

Equation (2), u(Ct), corresponds to the consumption utility in period t. The other terms depend on consumption and beliefs. The second term, n(Ct

, Ft−1

Ct

), corresponds to news utility

over contemporaneous consumption; here, the agent compares his present consumption Ct

with his beliefs F

t−1

Ct

. The agent’s beliefs, F

t−1

Ct

, correspond to the conditional distribution of

consumption in period t, given the information available in period t−1. Thus, the agent experiences news utility over “news” about contemporaneous consumption by evaluating his current

consumption Ct relative to his previous beliefs F

t−1

Ct

n(Ct, Ft−1

Ct

) = η

Z Ct

−∞

(u(Ct) − u(c))dFt−1

Ct

(c) + ηλ Z ∞

Ct

(u(Ct) − u(c))dFt−1

Ct

(c). (3)

The parameter η > 0 thus weights the news-utility component relative to the consumptionutility component, and the coefficient of loss aversion λ > 1 implies that losses outweigh

gains. The third term in Equation (2), γ

PT −t

τ=1 β

τn(F

t,t−1

Ct+τ

), corresponds to the news utility

experienced in period t over the entire stream of future consumption. Prospective news utility

about period t + τ consumption depends on F

t−1

Ct+τ

, the beliefs with which the agent entered the

period, and on F

t

Ct+τ

, the agent’s updated beliefs about consumption in period t + τ . The agent

experiences news utility over news about future consumption by evaluating his updated beliefs

about future consumption F

t

Ct+τ

relative to his previous beliefs F

t−1

Ct+τ

as follows

n(F

t,t−1

Ct+τ

) = Z ∞

−∞

(η

Z c

−∞

(u(c) − u(r)) + ηλ Z ∞

c

(u(c) − u(r)))dFt,t−1

Ct+τ

(c, r). (4)

As can be seen in Equation (2), the agent discounts exponentially prospective news utility

by β ∈ [0, 1]. Moreover, he discounts prospective news utility relative to contemporaneous

news utility by a factor γ ∈ [0, 1]. Thus, he puts a weight γβτ < 1 on prospective news

utility regarding consumption in period t + τ . For certain parameter combinations, the Koszegi  ̋

and Rabin (2009) preferences reduce to well-known specifications. For η = 0 or λ = 1

and γ = 1, they reduce to standard preferences (Carroll, 2001; Gourinchas and Parker, 2002;

Deaton, 1991). For η > 0, λ = 1, and γ < 1, the preferences correspond to hyperbolicdiscounting preferences, with the hyperbolic-discount factor given by 1+γη

1+η

(Angeletos et al.,

20
2001; O’Donoghue and Rabin, 1999). More specifically, the hyperbolic agent’s lifetime utility

is u(C

b

t

) + bEt[

PT −t

τ=1 β

τu(Cb

t+τ

)] where b ∈ [0, 1] is the hyperbolic-discount factor.

In the following, we describe the news-utility agent’s consumption and time-inconsistency

problems before and after retirement following Koszegi and Rabin  ̋ (2009) and Pagel (2017).

Suppose that in periods t ∈ {T − R, ..., T}, the agent earns income without uncertainty. If

uncertainty is absent, the personal equilibrium of the news-utility agent corresponds to the

standard agent’s equilibrium if the discount factor on prospective versus contemporaneous news

utility is weakly larger than the inverse of the coefficient of loss aversion γ ≥

1

λ

. If γ < 1

λ

,

then the monotone-personal equilibrium of the news-utility agent corresponds to a hyperbolicdiscounting agent’s monotone-personal equilibrium, with the hyperbolic-discount factor given

by 1+γηλ

1+η

.

The news-utility agent is likely to follow the standard agent’s path if uncertainty is absent

and the agent’s prospective news discount factor is high enough. The basic intuition is that

when the agent decides to increase his present consumption, he also considers a certain loss

in future consumption, which is very painful. Thus, unless the agent discounts prospective

news utility significantly, he decides not to increase his present consumption. More formally,

suppose that the agent allocates his deterministic cash-on-hand between present consumption

CT −1 and future consumption CT . Under rational expectations, he cannot fool himself, hence

he cannot experience actual news utility in equilibrium in a deterministic model. Accordingly,

his expected utility maximization problem corresponds to the standard agent’s maximization

problem (determined by setting present and future marginal consumption utilities equal, with

the discount factor and interest rate). Suppose that the agent’s beliefs about consumption in

both periods correspond to this equilibrium path. Taking his beliefs as given, the agent deviates

if the gain from consuming more exceeds the discounted loss from consuming less in the future;

that is,

u

0

(CT −1)(1 + η) > β(1 + r)u

0

(CT )(1 + γηλ).

Thus, he follows the standard agent’s path iff the discount factor on prospective versus contemporaneous news utility is weakly larger than the inverse of the coefficient of loss aversion,

γ ≥

1

λ

. In this case, the pain associated with a certain loss in future consumption is larger than

the pleasure gained from present consumption. However, if γ < 1

λ

, the agent deviates and must

choose a consumption path that just meets the consistency constraint, thereby behaving as a

hyperbolic-discounting agent, with a hyperbolic discount factor of 1+γηλ

1+η

< 1. Thus, during

retirement, the implications of the agent’s prospective news discount factor γ are simple: it

must be high enough to keep the news-utility agent on the standard agent’s track.

After retirement, the agent is less inclined to overconsume. The basic intuition for overconsumption before retirement is that the agent consumes house money–that is, labor income that

he was not certain to receive. Such uncertain income wants to be consumed before his expectations catch up iff the prospective news discount factor is less than one, i.e., γ < 1. In the period

21
just before retirement, the agent finds the loss in future consumption merely as painful as a

slightly less favorable realization of his labor income, YT −1 ∼ FY ; that is, the agent trades off

being somewhere in the gain domain today against being somewhere in the gain domain in the

future. By contrast, after retirement the agent associates a certain loss in future consumption

with an increase in present consumption–that is, he trades off a current gain against a sure loss

in the future. For example, suppose the agent’s retirement period is period T only. The agent’s

first-order condition in period T − 1, absent uncertainty in period T, is given by

u

0

(CT −1)(1+η(λ−(λ−1)FY (YT −1))) = β(1+r)u

0

(CT )(1+γη(λ−(λ−1)FY (YT −1))). (5)

In Equation (5), it can be seen that, iff the prospective news discount factor equals one, i.e.,

γ = 1, contemporaneous and prospective marginal news utility cancel each other out. However, iff γ < 1, the agent reduces the weight on future utility relative to present utility by a

factor of 1+γηλ

1+ηλ <

1+γη

1+η

< 1. After retirement, the news-utility agent follows the standard

agent’s consumption path if the prospective news discount factor γ is sufficiently high, and

otherwise follows a hyperbolic agent’s consumption path with discount factor b =

1+γηλ

1+η

. Because min{

1+γηλ

1+η

, 1} >

1+γη

1+η

iff γ < 1, the agent’s factor for reducing the weight on future

utility is necessarily lower in the period just before retirement than afterward–implying that

consumption drops at retirement. This drop is thus brought about by a change in the agent’s

effective time-inconsistency problem, which is necessary for observing a drop in consumption

at the same time as an increase in savings.

In the following, we assess whether the model’s quantitative predictions about the drop in

consumption at retirement match the empirical evidence. We also assess whether the model can

generate a simultaneous increase in savings. In addition to the standard and news-utility agents,

we show results for quasi-hyperbolic preferences, as in Laibson et al. (2012), and temptationdisutility preferences, as developed by Gul and Pesendorfer (2004) and assumed in Bucciol

(2012). For the standard, hyperbolic, and tempted agents, we assume that the effective discount

factor changes to generate a drop in consumption at retirement and possibly a simultaneous

increase in savings. For hyperbolic-discounting preferences, we assume that the agent is subject

to present bias before retirement but not after; that is, his hyperbolic discount factor is less than

one before retirement but equal to one after. For the temptation-disutility preferences, we

also assume that the agent experiences temptation disutility only before retirement, not after.

In addition, we assume for comparison that the standard agent’s exponential discount factor

increases to one after retirement, so that he becomes more patient as well.

We choose the model environment in line with the life-cycle consumption literature and

present the numerical results of a power-utility model; that is, u(C) = C1−θ

1−θ

, with θ being

the coefficient of constant relative risk aversion.11 We follow Carroll (1997) and Gourinchas

11The model cannot be solved analytically, but it can be solved by numerical backward induction (see Gourinchas and Parker, 2002; Carroll, 2001, among others). The numerical solution is illustrated in greater detail in

Appendix B.

22
and Parker (2002), who specify income Yt as log-normal and characterized by deterministic

permanent income growth Gt, permanent shocks NP

t

, and transitory shocks NT

t

, which allow

for a low probability p of unemployment or illness

Yt = PtN

T

t = Pt−1GtN

P

t N

T

t

N

T

t =

(

e

s

T

t with probability 1 − p and s

T

t ∼ N(μT , σ2

T

)

0 with probability p

)

N

P

t = e

s

P

t s

P

t ∼ N(μP , σ2

P

).

Labor income is stochastic up until period T − R, when the agent enters retirement and his

income is deterministic. The life-cycle literature suggests fairly tight ranges for the parameters

of the log-normal income process, which are approximately μT = μP = 0, σT = σP = 0.1, and

p = 0.01. The deterministic profile Gtis estimated from the CEX data.12 The agent has access

to a simple savings account that pays net interest r = 0.01. For the preference parameters, we

use calibrations that are standard in the literature, as displayed in Table 12 and discussed by

Pagel (2017) among others.

Figure 12 contrasts the four agents’ consumption paths with the empirical consumption and

income profiles from the CEX data. Hyperbolic-discounting preferences push the consumption

profile upward at the beginning and downward at the end of life. Temptation disutility also

causes overconsumption at the beginning of life, which decreases when consumption opportunities are depleted. Standard, hyperbolic-discounting, news-utility, and temptation-disutility

preferences all generate a hump-shaped consumption profile in line with the evidence (refer

to Ameriks and Zeldes, 2004, for instance). Moreover, Figure 12 shows a large drop in consumption at retirement, period T − R, for the news-utility and hyperbolic agents’ consumption

profiles as well as the CEX consumption data. The tempted agent’s profile features a very small

drop, and the standard agent’s profile only features a kink. Thus, one needs a change in the degree of present bias or time inconsistency, not only the discount rate, to generate a sizable drop

in consumption at retirement (temptation-disutility preferences are time-consistent preferences,

as are standard preferences). Note, however, that for the standard, hyperbolic, and temptationdisutility agents, the change in consumption around retirement is only brought about by the

preference parameters and thus the effective discount rate changing. If the preference parameters were constant, the standard, hyperbolic, and temptation-disutility agents would smooth

consumption around retirement and only the news-utility agent’s consumption profile would

feature a drop at retirement.

Let us now demonstrate the drop in a regression using simulated data. For 200 agents,

indexed by i, we simulate four years of consumption and income data points, indexed by t,

12Following Gourinchas and Parker (2002), we choose 25 as the beginning of working life and then Ret ˆ = 11

years of retirement and Tˆ = 78 in accordance with the average retirement age in the US according to the OECD

and the average life expectancy in the US according to the UN.

23
around the retirement date and run the regression

log(Cit) = αb + βRetired b it + γb log(Yit) + εit.

We thus run exactly the equivalent regression in our simulated data as in our empirical analysis.

We look at logged consumption as the outcome variable, control for income, and consider the

coefficient of a retirement dummy Retiredit. The coefficient βb will then determine the percentage drop in consumption at retirement. To theoretically illustrate our statistical power, we

intentionally choose a much lower quantity for the number of simulated agents. Moreover, we

run the same regression but with savings, log(Xit − Cit), on the left-hand side. The results are

shown in Table 13. In the news-utility and hyperbolic-discounting models, we obtain a negative and significant drop in consumption, while the standard and tempted agents’ coefficients

are basically zero. The news-utility agent’s drop in consumption is larger than the hyperbolic

agent’s, even though the preference parameters generating the degree of present bias, γ and b,

are equal before retirement, and both agents become perfectly time-consistent after retirement,

as b is equal to one after retirement and γ > 1

λ

.

Moreover, for the news-utility and hyperbolic agents the coefficient for savings is positive

and significant, but it is negative for the other agents. Clearly, the news-utility and hyperbolic

agents will also decumulate their savings after retirement. However, there is another force, the

change in the degree of present bias, that can temporarily increase their savings upon retirement.

For the news-utility agent, the fall in consumption is large enough to generate a temporary

increase in savings roughly in line with what we see empirically. For the hyperbolic agent,

we can also observe an increase in savings, although it is somewhat smaller. The savings

coefficient of the hyperbolic agent is thus closer to those of the standard and tempted agents.

In turn, we can introduce work-related expenses into the model. We simply assume that

consumption is ten percent higher before retirement because of work-related expenses. We

then run the regressions measuring the drop in consumption at retirement, which now trivially

indicate a ten percent larger drop for all agents. However, the results for savings growth are

unchanged and thus constitute another phenomenon that any model of spending around retirement should be able to rationalize. The results for savings growth are unchanged because

we can simply treat work-related expenses as a reduction in income. A somewhat lower income profile before retirement will not cause an increase in savings after retirement, as long as

income before retirement is higher than income after.

5 Conclusion

We document how spending, liquid savings, and consumer debt change in response to retirement using a large proprietary data set on income, spending, account balances, and credit limits. We show that the overall decline in spending post retirement is only partially attributable to

24
work-related expenses. In addition to lower spending on ready-made food and clothes (which

is consistent with a reduction in work-related expenses), we also observe reductions in leisure

goods, suggesting the presence of an overconsumption problem before retirement and an adjustment after. Nevertheless, we note that even the most accurate and comprehensive spending

data cannot conclusively decide whether spending alone or actual consumption utility drops at

retirement. Thus, whether or not the drop in consumption is puzzling and more broadly whether

or not individuals save adequately for retirement, is a difficult question to answer using spending data alone.

We thus analyze changes in liquid savings and consumer debt around retirement, and we

find that individuals reduce consumer debt substantially and increase liquid savings. The findings about debt and savings are very difficult to reconcile in a rational model and thus imply

that we cannot ”retire the consumption puzzle” just yet. After all, the retirement-consumption

puzzle says that individuals do not plan rationally for an expected reduction in income at retirement. However, any rational plan for a reduction in income would involve saving before

and dissaving after retirement. Our empirical findings that individuals decrease their consumer

debt and increase their liquid savings is thus the opposite of what a rational agent would do.

25
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28
Men Women

Figure 1: Average effective retirement age for men and women in Iceland (solid line)

compared to Germany (dotted line) and the United States (dashed line)

Men Women

Figure 2: Share retired by age

29
Figure 3: Distribution of retirement age: women (white bars) and men (gray bars)

30
Table 1: Summary statistics

Mean Standard Statistics

Deviation Iceland

Monthly regular income 3,547 3,717 3,768

Monthly salary 3,157 3,494 2,867

Monthly spending:

Total 1,535 1,429

Groceries 546 454 572

Fuel 276 302 (419)

Alcohol 72 141 99

ready-made food 198 202 (294)

Home Improvement 175 543 (267)

Transportations 68 817 77

Clothing & accessories 102 211 112

Sports & activities 51 173 (42)

Pharmacies 47 72 49

Age 40.6 11.5 37.2

Female 0.49 0.50 0.48

Eligibible for retirement 0.087 0.282 0.194

Retired 0.039 0.194

Note: All numbers are in US dollars. Parentheses indicate that data categories do not match perfectly.

31
Table 2: Summary statistics

Eligible but not retired Retired

Mean St.dev. Mean St.dev.

Demographics:

Age 64.2 3.6 71.2 5.8

Female 0.41 0.49 0.41 0.49

Monthly income:

Total 4,580 3,973 3,403 2,563

Regular 4,462 3,856 3,293 2,426

Irregular 89 391 79 374

Salary 4,066 3,706 708.695 2,084

Total hh 4,865 4,263 3,727 2,947

Personal finances:

Total bank fees 49 98 37 85

Late fees 18 79 14 44

Overdraft interest 52 81 43 89

Overdraft amount 970 4,326 852 4,196

Savings account balance 6,259 73,649 8,720 27,868

Interest income 248 2,217 337 1,306

Current account balance 2,184 13,033 2,079 9,173

Overdraft limit 2,024 5,578 2,281 6,128

Credit card balance -1,051 6,339 -1,221 14,435

Credit card card limit 3,872 9,640 4,504 15,944

Liquidity 11,581 71,056 13,967 30,179

Daily spending:

Individual:

Total 56.7 38.3 53.4 31.3

Groceries 16.4 12.4 16.6 10.4

Fuel 7.9 7.5 6.5 6.2

Alcohol 2.8 4.4 2.6 4.3

Ready-made food 4.1 4.9 3.4 4.0

Home improvements 6.2 11.6 5.5 10.4

Home security 0.3 0.8 0.3 0.8

Transportation 2.7 5.7 2.2 5.2

Clothing & accessories 3.5 6.3 2.7 5.1

Sports & activities 0.3 1.3 0.2 1.1

Pharmacies 2.0 2.5 2.4 2.6

Household:

Total 60.3 41.8 57.6 35.8

Groceries 17.4 12.9 17.7 11.0

Fuel 8.2 7.7 7.0 6.4

Alcohol 2.9 4.6 2.9 4.6

Ready-made food 4.4 5.2 3.7 4.4

Home improvements 6.7 12.4 6.0 11.3

Home security 0.3 0.8 0.3 0.8

Transportation 2.9 6.2 2.5 5.6

Clothing & accessories 3.7 6.7 3.0 5.5

Sports & activities 0.4 1.4 0.3 1.3

Pharmacies 2.1 2.6 2.6 2.7

Note: All numbers are in US dollars.

32
Figure 4: The financial aggregation app: screenshots

Notes: This figure shows the Meniga app interfaces.

Men Women

Figure 5: Total and labor income, expenditure, and pension payments over age

33
Total spending

Spending by category

Grocery spending by price category (total m

expensive grocery 

Share of expensive groceries (the share 

stores/supermarkets that are spen

Figure 6: Life-cycle profiles of expenditures

The figures plot the estimated age coefficient from the regression of the outcome variables relative to the coefficient for month-by-year fixed effects and individual fixed effects.

34
Table 3: Effects of retirement on spending by category

Total Grocery Fuel Alcohol Ready-made Home Home Transportatio

expenditure -food improvement security 

Without controlling for income:Retired -0.136*** -0.121*** -0.230*** -0.142*** -0.145*** -0.056 -0.046*** -0.239*** 

(0.010) (0.019) (0.025) (0.033) (0.02) (0.037) (0.012) (0.036) 

R-sqr 0.079 0.046 0.013 0.027 0.05 0.056 0.003 0.038 Controlling for income:Retired -0.093*** -0.077*** -0.176*** -0.097*** -0.099*** 0.017 -0.038*** -0.177*** 

(0.010) (0.018) (0.025) (0.033) (0.020) (0.037) (0.012) (0.036) 

R-sqr 0.095 0.051 0.017 0.029 0.055 0.059 0.003 0.041 #obs 781,263 781,263 781,263 781,263 781,263 781,263 781,263 781,263 #individuals 12,143 12,143 12,143 12,143 12,143 12,143 12,143 12,143 Individual FE X X X X X X X X 

Month-by-year FE X X X X X X X X 

Note: a This table shows regression results for log spending by different categories using individual and calendar fixed effects as we

controlling for total income. Standard errors are clustered at the individual level. b Significance levels: * p<0.1 ** p<0.05 *** pc All coefficients represent percentage changes.

35
Restaurant spending (solid) and visits (broken)

Restaurant trips by category per month

Restaurant expenditure by ca

Expenditure by type of 

Figure 7: Life-cycle profiles of restaurant trips and grocery store visits

36
Table 4: Effects of retirement on restaurant spending and visits

Total Casual dining Cold dishes Cafes Bakeries Bars Fas

Spending:Without controlling for income:Retired -0.234*** -0.441*** 0.007 -0.071*** -0.003 -0.247*** -0.3

(0.021) (0.024) (0.014) (0.020) (0.020 ) (0.017) (0

R-sqr 0.233 0.113 0.012 0.046 0.07 0.027 0Controlling for income:Retired -0.136*** -0.382*** 0.014 -0.043** 0.028 -0.238*** -0.3

(0.02 (0.023) (0.014) (0.02 0) (0.020) (0.018) (0

R-sqr 0.278 0.134 0.015 0.055 0.084 0.031 0Number of visits:Without controlling for income:Retired -3.768*** -0.623*** -0.001 -0.116*** 0.027 -0.190*** -1.2

(0.241) (0.042) (0.004) (0.029) (0.030) (0.023) (0

R-sqr 0.056 0.033 0.002 0.01 0.022 0.009 0Controlling for income:Retired -3.448*** -0.588*** 0.001 -0.100*** 0.052* -0.189*** -1.1

(0.245) (0.042) (0.004) (0.03 0) (0.031) (0.024) (0

R-sqr 0.067 0.038 0.003 0.012 0.027 0.009 0Individual FE X X X X X X 

Month-by-year FE X X X X X X #obs. 900,090 900,090 900,090 900,090 900,090 900,090 90

#individuals 14,287 14,287 14,287 14,287 14,287 14,287 14

Note:

a This table shows regression results for the log spending on different restaurant categories and on the number of visits to diffe

controlling for total income. Standard errors are clustered at the individual level.

b Significance levels: * p

<0.1 ** p

<0.05 *** p

c All coefficients represent percentage changes.

37
Table 5: Effects of retirement on personal finances

Total Late Overdraft Interest Current acct Overdraft Credit card Cre

fees fees interest income balance amount balance 

Without controlling for income:Retired -0.208*** -0.144*** -0.132*** 0.054** 0.162* -0.100** -0.116** -

(0.037) (0.035) (0.034) (0.022) (0.093) (0.045) (0.057) (0

R-sqr 0.058 0.028 0.035 0.059 0.024 0.001 0.010 0Controlling for income:Retired -0.035 -0.022 -0.028 0.121*** 0.238** -0.099** -0.103* -

(0.037) (0.035) (0.033) (0.056) (0.022) (0.088) (0.057) (0

R-sqr 0.078 0.041 0.045 0.051 0.026 0.001 0.010 0#obs 885,189 885,189 885,189 885,189 331,668 331,668 331,668 33#individuals 12,143 12,143 12,143 12,143 12,143 12,143 12,143 1Individual FE X X X X X X X Month-by-year FE X X X X X X X 

Note: a This table shows regression results for log interest payments, balances, and limits using individual and mon

fects as well as a dummy for retirement with and without controlling for total income. Standard errors are clustered at b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01 c All coefficients represent percentage changes.

38
Figure 8: Consumer debt

This figure plots the estimated age coefficient from the regression of log total monthly overdraft interest payments on age dummies

relative to the coefficient for age 20. The regressions include month fixed effects, year fixed effects, and individual fixed effects.

Figure 9: Interest income

This figure plots the estimated age coefficient from the regression of log total monthly interest income from bank account balances

on age dummies relative to the coefficient for age 20. The regressions include month-by-year and individual fixed effects.

39
Table 6: Effects of retirement on investment transactions and uncategorized income

Investment Uncategorized Investment Uncategorized

transactions income transactions income

Retired -0.010 -0.071*** -0.014 -0.079***

(0.009) (0.012) (0.009) (0.012)

R-sqr 0.0232 0.0211 0.0247 0.0235

#obs 885,189 885,189 885,189 885,189

#individuals 12,144 12,144 12,144 12,144

Individual FE X X X X

Month-by-year FE X X X X Total HH X X income

Note: a This table shows regression results for log investment related income and uncategorized income. Standard errors are clustered at the individual level. b Significance levels:

* p<0.1 ** p<0.05 *** p< 0.01 c All coefficients represent percentage changes.

Table 7: Effects of retirement on savings (measured as income minus consumption),

checking, and savings account balances in CEX data

Income minus

consumption

Income minus

consumption

Current

account

Current

account

Savings

account

Savings

account

Retired 0.203∗∗∗ 0.0960∗∗∗ 1.024∗∗∗ 0.957∗∗∗ 1.488∗∗∗ 1.391∗∗∗

(6.84) (3.64) (8.80) (8.18) (8.60) (8.06)

Unemployment rate X X X X X X

# earners X X X X X X

# family X X X X X X

Cohort FE X X X X X X

Year FE X X X X X X

Age FE X X X X X X

Total HH

income X X X

#obs 36,505 36,505 26,046 25,813 21,408 21,248

Note: a This table shows regression results for household savings (measured as income minus spending), checking and savings account balances. Standard errors are robust to heteroskedasticity.

b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

40
Table 8: Effects of retirement on leverage, debt, current and savings account balances in

SCF data

Leverage Debt Current

account

Savings

account

Retired -0.293∗∗ -0.349∗∗∗ -0.674∗∗∗ -0.211∗∗∗ 0.006 0.329∗∗∗ 0.076 0.407∗∗∗

(-3.02) (-3.38) (-15.81) (-5.31) (0.15) (9.02) (1.44) (8.02)

# family X X X X X X X X

Cohort FE X X X X X X X X

Year FE X X X X X X X X

Age FE X X X X X X X X

Total HH

income X X X X

#obs 128,805 128,085 99,249 98,734 119,110 118,461 58,612 58,422

Note: a This table shows regression results for leverage, debt, checking and savings account balances.

Standard errors are robust to heteroskedasticity. b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

Table 9: Effects of retirement on income minus spending, current account balances, portfolio,

and credit account balances in German bank data

Income minus

spending

Current

account

Value of

portfolio

Credit account

balance

Retired 0.145∗∗ 0.149∗∗∗ 0.311∗∗∗ 0.0781 0.358∗∗∗ 0.327∗∗∗ -0.152 -0.153

(3.20) (7.20) (6.42) (1.81) (7.44) (5.86) (-1.82) (-1.83)

Indiv FE X X X X X X X X

Year FE X X X X X X X X

Month FE X X X X X X X X

Total HH

income X X X X

#obs 1,407,347 1,407,347 1,407,347 1,407,347 250,664 250,664 158,173 158,173

Note: a This table shows regression results for log household savings (measured as income minus spending), current account balances, portfolio, and credit account balances. Standard errors are clustered at the

individual level. b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

41
Table 10: Effects of retirement on spending, checking and savings account balances, amount

of debt, and wealth in PSID data

Spending Savings Debt Wealth

Retired -0.146∗∗∗ -0.0958∗∗∗ 0.00342 0.143∗∗ -0.310∗∗ -0.334∗∗ 17875.9∗∗ 24945.9∗∗∗

(-9.28) (-6.33) (0.07) (2.81) (-2.98) (-3.11) (3.06) (4.18)

Indiv FE X X X X X X X X

Year FE X X X X X X X X

Total HH

income X X X X

#obs 68,895 68,240 68,895 68,240 44,442 43,989 68,895 68,240

Note: a This table shows regression results for log household spending, checking and savings account

balances, amount of debt, and wealth (winsorized not logged due to many negative observations). Standard

errors are clustered at the individual level. b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

Table 11: Effects of retirement on spending, savings, amount of IRA assets, and wealth

in HRS data

Spending Savings IRA assets Financial wealth

Retired -0.240∗∗∗ -0.229∗∗∗ 0.0814 0.234∗∗∗ 0.271∗∗∗ 0.295∗∗∗ 0.106 0.235∗∗∗

(-2.82) (-2.70) (1.10) (3.17) (3.79) (4.03) (1.45) (3.25)

Indiv FE X X X X X X X X

Year FE X X X X X X X X

Total HH

income X X X X

#obs 1,184 1,184 9,455 9,455 9,455 9,455 9,455 9,455

Note: a This table shows regression results for log household spending, savings, amount of IRA

assets, and wealth. Standard errors are clustered at the individual level.

b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

42
Figure 10: Illustration of the argument

This figure illustrates why a rational model has difficulty explaining our two findings that 1) individual savings increase after retirement and 2) eligible individuals do not retire immediately. A

fall in income and work-related expenses (or any other theory that would decrease consumption

such as a health shock) will only increase savings if work-related expenses (or the fall in consumption more generally) are larger than the fall in income. However, in that case, the individual

gains on net in life-time resources if she retires early.

43
Figure 11: Housing wealth and mortgage debt over age

This figure plots the amount of mortgage debt and housing wealth (source: Statistics Iceland)

over age.

44
Table 12: Environmental and preference parameters

parameter μP σP μT σT p Gt r P0

A0

P0

R T

value 0 0.1 0 0.1 0.01 Gct 0.01 1 0.0096 11 78

parameter β θ η λ γ b τ

value 0.97 2 1 2 0.7 0.7 0.1

This table displays all calibrated parameters.

Figure 12: Life-cycle profiles and CEX consumption and income data

This figure contrasts the five agents’ consumption paths with the average CEX consumption and

income data. The parameter values are μT = μP = 0, σT = σP = 0.1, p = 0.01, , r = 0.01,

and Gtis estimated from the CEX data. The preference parameters are β = 0.97, θ = 2, η = 1,

λ = 2, γ = 0.7, the hyperbolic discounting parameter is b = 0.7, and the temptation-disutility

parameter is τ = 0.1. The standard agent’s exponential discounting parameter β is 1 after retirement, the hyperbolic discounting parameter is 1 after retirement, and the temptation-disutility

parameter is 0. The unit of consumption and income is the log of 1984 dollars controlling for

cohort, family size, and time effects.

45
Table 13: Effects of retirement on spending and savings

in simulated data

Standard

agent

News-utility

agent

Hyperbolic

agent

Tempted

agent

Consumption regressions:

Retired 0.06∗∗∗ -0.38∗∗∗ -0.14∗∗∗ -0.004∗∗∗

(22.67) (-94.40) (-44.76) (-1.42)

Total HH

income X X X X

#obs 8,00 8,00 8,00 8,00

Savings regressions:

Retired -0.006∗∗∗ 0.052∗∗∗ 0.016∗∗∗ -0.0004∗∗∗

(-21.93) (86.56) (44.61) (-1.37)

Total HH

income X X X X

#obs 8,00 8,00 8,00 8,00

Note: a The table displays the regression results for 200 agents

and their simulated data points for four years around the retirement date. The displayed regression coefficients represent the percentage fall or increase in consumption and savings due to retirement. The corresponding t-statistics are displayed in parentheses.

b Significance levels: * p<0.1 ** p<0.05 *** p< 0.01

46
A The Icelandic pension system

The Icelandic pension system consists of three pillars: a tax-financed public pension

(social security benefits), compulsory occupational pension funds which are the dominant feature of the system, and voluntary private pensions with tax incentives.

Pillar one - public pensions. The social security system in Iceland was founded in

1936 with the main purpose of ensuring the livelihood of those unable to work because

of old age or disability. The system provides old age, disability, sickness, maternity,

and survivors pensions. The old age pension is paid from the age of 67. The public

pension is paid as a basic pension and supplementary additions to single or low income

people. The basic pension is low or roughly 10 percent of the average earnings of

unskilled workers and is means-tested by a 30 percent reduction rate after a certain

income threshold. The main transfers are, however, paid through the supplementary

pension which is also means-tested with a 45 percent reduction rate. The maximum

pension per year for an individual without any supplementary income is almost the same

as the minimum wage level. The public pension system in Iceland is fully financed by

taxes. The main financing source is the social security tax which is earmarked to the

social security system. The social security tax rate is currently 5.79 percent and the tax

base is total salaries. The social security tax is paid by the employers.

Pillar two - occupational pensions. Occupational pensions are the cornerstone of

the Icelandic pension system. The compulsory employer and employee-financed pension system provides benefits amounting to 50-60 percent of full time earnings during

employment. The contribution rate must be at least 11 percent with the employer paying

7 percent and the employee 4%. Premiums are fully deductible for tax purposes. The

accumulated pension rights in the occupational pension funds are generally indexed to

the consumer price index. The contribution can be divided into two parts. The first part

goes towards acquiring pension rights which (for a 40 years period of contributions)

should give a lifelong pension amounting to at least 56 percent of wages at the end of

the contribution period. The second part can go towards acquiring additional pension

rights, including defined contribution schemes with individual accounts. The main rule

is that members can begin to withdraw old-age pensions at the age of 67. It is, however,

possible to start withdrawing pension as early as 65, but then with a reduced benefit, or

as late as 70 with additional benefits.

Pillar Three - voluntary individual pension savings. Employees can deduct from

their taxable income a contribution to authorized individual pension schemes. Currently,

the maximum taxable deduction by the employee is 4 percent. In addition, all employers have agreed in wage settlements to contribute 2 percent to those voluntary pension

savings if the employee matched the amount with at least the same percentage. The

total contribution can therefore be 6 percent. The voluntary pension savings cannot be

distributed until the age of 60.13

13After the financial crisis, individuals were given permission to take out private pension savings to

pay down debt. We observe all such one-time withdrawals but exclude individuals younger than 60 in all

analyses.

47
B Derivation of the theoretical framework

B.1 The news-utility model

Before starting with the fully-fledged problem, we outline the second-to-last period for

the case of power utility. In the second-to-last period the agent allocates his cash-onhand XT −1 between contemporaneous consumption CT −1 and future consumption CT ,

knowing that in the last period he will consume whatever he saved in addition to last

period’s income shock CT = XT = (XT −1−CT −1)R+YT . According to the monotonepersonal equilibrium solution concept, in period T − 1 the agent takes the beliefs about

contemporaneous and future consumption he entered the period with {F

T −2

CT −1

, FT −2

CT

} as

given and maximizes

u(CT −1) + n(CT −1, FT −2

CT −1

) + γβn(F

T −1,T −2

CT

) + βET −1[u(CT ) + n(CT , FT −1

CT

)]

which can be rewritten as

u(CT −1)+η

Z CT −1

−∞

(u(CT −1)−u(c))dFT −2

CT −1

(c)+ηλ Z ∞

CT −1

(u(CT −1)−u(c))dFT −2

CT −1

(c)

+γβ Z ∞

−∞

Z ∞

−∞

(u(c)−u(r))dFT −1,T −2

CT

(c, r)+βET −1[u(CT )+η(λ−1) Z ∞

CT

(u(CT )−u(c))dFT −1

CT

(c)].

To gain intuition for the model’s predictions, we explain the derivation of the first-order

condition

u

0

(CT −1)(1+η(λ−(λ−1)F

T −2

CT −1

(CT −1))) = γβRET −1[u

0

(CT )]η(λ−(λ−1)F

T −2

AT −1

(AT −1))

+βRET −1[u

0

(CT ) + η(λ − 1) Z ∞

CT

(u

0

(CT ) − u

0

(c))dFT −1

CT

(c)].

The first two terms in the first-order condition represent marginal consumption utility

and news utility over contemporaneous consumption in period T − 1. As the agent

takes his beliefs {F

T −2

CT −1

, FT −2

CT

} as given in the optimization, we apply Leibniz’s rule

for differentiation under the integral sign. This results in marginal news utility being the

sum of states that would have promised less consumption F

T −2

CT −1

(CT −1), weighted by η,

or more consumption 1 − F

T −2

CT −1

(CT −1), weighted by ηλ,

∂n(CT −1, FT −2

CT −1

)

∂CT −1

= u

0

(CT −1)η(λ − (λ − 1)F

T −2

CT −1

(CT −1)).

Note that, if contemporaneous consumption is increasing in the realization of cash-onhand then we can simplify F

T −2

CT −1

(CT −1) = F

T −2

XT −1

(XT −1). Returning to the maximization problem the third term represents prospective news utility over future consumption

CT experienced in T −1. As before, marginal news utility is given by the weighted sum

of states γβRET −1[u

0

(CT )]η(λ − (λ − 1)F

T −2

AT −1

(AT −1)). Note that F

T −2

CT

(c) is defined

as the probability P r(CT < c|IT −2) and

P r(CT < c|IT −2) = P r(AT −1R + YT < c|IT −2) = P r(AT −1 <

c − YT

R

|IT −2).

48
Thus, if savings and therefore future consumption are increasing in the realization of

cash-on-hand, then we can simplify F

T −2

AT −1

(AT −1) = F

T −2

XT −1

(XT −1).

The last term in the maximization problem represents consumption and news utility

over future consumption CT in the last period T, i.e., the first derivative of the agent’s

continuation value with respect to consumption or the marginal value of savings. Expected marginal news utility η(λ − 1) R ∞

CT

(u

0

(CT ) − u

0

(c))dFT −1

CT

(c) is positive for any

concave utility function such that

Ψ

0

T −1 = βRET −1[u

0

(CT )+η(λ−1) Z ∞

CT

(u

0

(CT )−u

0

(c))dFT −1

CT

(c)] > βRET −1[u

0

(CT )] = Φ0

T −1

.

As expected marginal news disutility is positive, increasing in σY , absent if σY = 0,

and increases the marginal value of savings, we say that news-utility introduces an “additional precautionary-savings motive.” The first-order condition can now be rewritten

as

u

0

(CT −1) =

Ψ

0

T −1 + γΦ

0

T −1

η(λ − (λ − 1)F

T −2

XT −1

(XT −1))

1 + η(λ − (λ − 1)F

T −2

XT −1

(XT −1))

.

Beyond the additional precautionary-savings motive Ψ

0

T −1 > Φ

0

T −1

implies that an increase in F

T −2

XT −1

(XT −1) decreases

Ψ

0

T −1

Φ

0

T −1

+ γη(λ − (λ − 1)F

T −2

XT −1

(XT −1))

1 + η(λ − (λ − 1)F

T −2

XT −1

(XT −1))

,

i.e., the terms in the first-order condition vary with the income realization XT −1 so that

consumption is excessively smooth and sensitive.

The news-utility agent’s maximization problem in any period T − i is given by

u(CT −i) + n(CT −i, FT −i−1

CT −i

) + γ

X

i

τ=1

β

τn(F

T −i,T −i−1

CT −i+τ

) +X

i

τ=1

β

τET −i

[U(CT −i+τ )].

Again, we can normalize maximization problem by P

1−θ

T −i

as all terms are proportional

to consumption utility u(·). In normalized terms, the news-utility agent’s first-order

condition in any period T − i is given by

u

0

(cT −i) =

Ψ

0

T −i + γΦ

0

T −i

η(λ − (λ − 1)F

T −i−1

cT −i

(cT −i))

1 + η(λ − (λ − 1)FT −i−1

aT −i

(aT −i))

We solve for each optimal value of c

∗

T −i

for a grid of savings aT −i, as Ψ

0

T −i

and Φ

0

T −i

are functions of aT −i until we find a fixed point of c

∗

T −i

, aT −i, F

T −i−1

aT −i

(aT −i), and

F

T −i−1

cT −i

(cT −i). We can infer the latter two from the observation that each cT −i +aT −i =

xT −i has a certain probability given the value of savings aT −i−1 we are currently iterating on. However, this probability varies with the realization of permanent income

GT −ie

s

P

T −i ; thus, we cannot fully normalize the problem but have to find the right consumption grid for each value of GT −ie

s

P

T −i rather than just one. The first-order condition

49
can be slightly modified as follows

u

0

(GT −ie

s

P

T −i cT −i) =

(GT −ie

s

P

T −i )

−θΨ

0

T −i + γ(GT −ie

s

P

T −i )

−θΦ

0

T −i

η(λ − (λ − 1)F

T −i−1

cT −i

(cT −i))

1 + η(λ − (λ − 1)FT −i−1

aT −i

(aT −i))

to find each corresponding grid value. Note that, the resulting two-dimensional grid

for cT −i will be the normalized grid for each realization of s

T

t

and s

P

t

, because we

multiply both sides of the first-order conditions with (GT −ie

s

P

T −i )

−θ

. Thus, the agent’s

consumption utility continuation value is

Φ

0

T −i−1 = βRET −i−1[

∂cT −i

∂xT −i

(GT −ie

S

P

T −i )

−θu0

(cT −i)+(1−

∂cT −i

∂xT −i

)(GT −ie

S

P

T −i )

−θΦ

0

T −i

].

The agent’s news-utility continuation value is given by

P

−θ

T −i−1Ψ

0

T −i−1 = βRET −i−1[

dCT −i

dXT −i

u

0

(CT −i)

+η(λ − 1) Z

CT −i<CT −i−1

T −i

(

dCT −i

dXT −i

u

0

(CT −i) − x)dFT −i−1

dCT −i

dXT −i

u0(CT −i)

(x)

+γη(λ−1) Z

AT −i<AT −i−1

T −i

(

dAT −i

dXT −i

P

−θ

T −iΦ

0

T −i−x)dFT −i−1

dAT −i

dXT −i

P

−θ

T −iΦ

0

T −i

(x)+(1−

dCT −i

dXT −i

)P

−θ

T −iΨ

0

T −i

]

(here, R

CT −i<CT −i−1

T −i

means the integral over the loss domain) or in normalized terms

Ψ

0

T −i−1 = βRET −i−1[

dcT −i

dxT −i

u

0

(cT −i)(GT −ie

s

P

T −i )

−θ

+η(λ−1) Z

CT −i<CT −i−1

T −i

(

dcT −i

dxT −i

u

0

(cT −i)(GT −ie

S

P

T −i )

−θ−x)dFT −i−1

dcT −i

dxT −i

u0(cT −i)(GT −ie

SP

T −i )−θ

(x)

+γη(λ−1) Z

AT −i<AT −i−1

T −i

(

daT −i

dxT −i

Φ

0

T −i

(GT −ie

S

P

T −i )

−θ−x)dFT −i−1

daT −i

dxT −i

Φ

0

T −i

(GT −ie

SP

T −i )−θ

(x)+(1−

dcT −i

dxT −i

)(GT −ie

S

P

T −i )

−θΨ

0

T −i

]

B.2 The hyperbolic-discounting model

We consider an agent with hyperbolic-discounting preferences with the hyperbolicdiscounting parameter denoted by γ. The agent’s maximization problem in any period

T − i is

max{u(CT −i) + γ

X

i

τ=1

β

τET −i

[u(CT −i+τ )]}.

We can normalize the maximization problem by P

1−θ

T −i

as for the standard agent. In turn,

we can solve the model by numerical backward induction (as Laibson et al. (2012)) and

50
the first-order condition is

u

0

(cT −i) = γΦ

0

T −i = γβRET −i

[

∂cT −i+τ

∂xT −i+1

(GT −i+1e

s

P

T −i+1 )

−θu0

(cT −i+1)+(1−

∂cT −i+1

∂xT −i+1

)(GT −i+1e

s

P

T −i+1 )

−θΦ

0

T −i+1].

B.3 The temptation-disutility model

Consider an agent with temptation-disutility preferences as developed by Gul and Pesendorfer (2004) following the specification of Bucciol (2012). The “tempted” agent’s

lifetime utility is given by

u(Ct) − λ

td(u(C ̃

t) − u(Ct)) + Et

[

X

T −t

τ=1

β

τ

(u(Ct+τ ) − λ

td(u(C ̃

t+τ ) − u(Ct+τ )))]

with C ̃

t being the most tempting alternative consumption level and λ

td ∈ [0,∞). Note

that, in a life-cycle model context the most tempting alternative is to consume the entire

cash-on-hand. However, not more as borrowing could be infinitely painful with power

utility and a chance of zero income in all future periods. For illustration, in the secondto-last period the agent’s maximization problem is

u(CT −1) − λ

td(u(XT −1) − u(CT −1)) + βET −1[u(R(XT −1 − CT −1) + YT )]

which can be normalized by P

(1−θ)

T −1

(then CT = PT cT for instance) and the maximization problem becomes

(PT −1)

1−θ

(u(cT −1)−λ

td(u(xT −1)−u(cT −1)))+(PT −1)1−θβET −1[(GT es

P

T )

1−θu(

R

GT e

s

P

T

(xT −1−cT −1)+yT )]

which results in the following first-order condition

u

0

(cT −1) = 1

1 + λ

td βET −1[(GT e

s

P

T )

−θRu0

(

R

GT e

s

P

T

(xT −1 − cT −1) + yT )]

with Φ

0

T −1

being a function of savings xT −1 − cT −1. The first-order condition can be

solved very robustly by iterating on a grid of savings aT −1 assuming c

∗

T −1 = (Φ0

T −1

)

− 1

θ =

(f

Φ0

(aT −1))− 1

θ . The normalized agent’s first-order condition in any period T −i is given

by

c

−θ

T −i =

1

1 + λ

td βET −i

[(GT −i+1e

s

P

T −i+1 )

−θR

dcT −i+1

dxT −i+1

u

0

(cT −i+1)]

+(1 −

dcT −i+1

dxT −i+1

)(GT −i+1e

S

P

T −i+1 )

−θΦ

0

T −1

].

51