The impact of retirement on the healthiness of foodpurchases ∗Marit Hinnosaar†
July 30, 2018

Abstract
The paper estimates the causal impact of retirement on the healthiness of foodpurchases. The identification strategy uses early and full retirement ages as instruments
for retirement. Using household-level scanner data, I find that retirement increases fruitand vegetable purchases and overall healthiness of food purchases. I also find indirect
evidence that retirement increases the time spent on shopping and food preparation:
it increases shopping frequency and shifts purchases to fresh and unprepared foodproducts. This suggests that time constraints might play a role in limiting healthy foodconsumption.

JEL codes: I12, L66, D12
Keywords: Health behaviors, retirement, consumer behavior, dietary choices

1

Introduction

Obesity is a public health problem, it is a risk factor for many diseases, including heart disease,diabetes, and cancer, and it increases health care expenditures (Cawley and Meyerhoefer,2012). One of the main causes of obesity is an unhealthy diet (Cutler, Glaeser, and Shapiro,
2003; Bleich, Cutler, Murray, and Adams, 2008; Cawley, 2015). While many factors contribute∗

Researcher(s) own analyses calculated (or derived) based in part on data from The Nielsen Company(US), LLC and marketing databases provided through the Nielsen Datasets at the Kilts Center for Marketing
Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the Nielsen
data are those of the researcher(s) and do not reflect the views of Nielsen. Nielsen is not responsible for, had
no role in, and was not involved in analyzing and preparing the results reported herein.
†
Collegio Carlo Alberto and CEPR, e-mail: marit.hinnosaar@gmail.com.

1

to unhealthy diets, lack of time has been suggested as one of these (Jabs and Devine, 2006;
Monsivais, Aggarwal, and Drewnowski, 2014). A major increase in the amount of free time
takes place with retirement. Indeed, individuals above the retirement age have lower obesity
rates (Flegal, Kruszon-Moran, Carroll, Fryar, and Ogden, 2016). While there exists causal
evidence that retirement leads to increases in physical activity (Kämpfen and Maurer, 2016),
less is known about the impact of retirement on diet.
In this paper, I study the impact of retirement on the healthiness of diet. In particular, I
estimate the causal impact of retirement on fruit and vegetable purchases, more generally,
healthy food purchases, and an overall food healthiness index. Identification is complicatedbecause retirement is a choice which might depend on other events, such as health shocks,
that lead to changes in diet. To overcome the endogeneity problem, my main empiricalstrategy uses early and full retirement ages as instruments.
The dataset used in the paper is the Nielsen Homescan Consumer Panel. The dataset
includes detailed household-level data of food expenditures and purchased quantities in the
U.S. The dataset also includes information about demographic characteristics, including
employment status. I use data from the years 2004–2016. The majority of the households
remain in the panel for several years, which makes it possible to study within householdchanges. In the main part of the analysis, I restrict attention to single-person households.
Single-person households provide a clean setting as food purchases data is available at thehousehold-level. At the end of the paper, I extend the analysis to all households.
I use two approaches to estimate the impact of retirement on the healthiness of foodpurchases. My main empirical strategy is an instrumental variables design in a panel data
model with household fixed effects. Following previous literature on estimating the impact ofretirement on various outcomes, I use early and full retirement benefits eligibility ages as
instrumental variables for retirement.1 As an alternative empirical strategy, I use a panel
event-study design.
I find that retirement leads to large increases in the healthiness of food purchases. The
results are robust to many alternative specifications and hold when calculated based on either
relative product shares or absolute values using either expenditures or quantities. The effectis common for both men and women, low and high education groups, and individuals from
low and high-income regions.
1

Papers using a similar identification strategy include Coe and Zamarro (2011); Kämpfen and Maurer(2016); Godard (2016); Heller-Sahlgren (2017). Previous literature has provided strong evidence that retirement
decisions correspond to the Social Security retirement benefits eligibility ages (French, 2005; Kämpfen andMaurer, 2016; Fitzpatrick and Moore, 2018).

2

Next, to shed light on the mechanism of how retirement might lead to an increase in thehealthiness of food purchases, I analyze other changes in purchases and shopping behavior
that are associated with retirement. The analysis is motivated, on the one hand, by theevidence that healthy food products, especially fresh fruit and vegetables, are not available
in every store, and on the other hand, by the literature showing that store choice is driven
mostly by convenience (travel distance) as opposed to prices or variety (Marshall and Pires,2018). I find indirect evidence that retirement increases the time spent on shopping and foodpreparation: it increases shopping frequency and shifts purchases to fresh and unpreparedfood products. This suggests that time constraints might play a role in limiting healthy foodconsumption as retirement generates a large increase in free time.
The paper contributes to the studies on the impact of retirement on health, and more
specifically, on health behaviors.2 Most closely related are the papers estimating the causal
impact of retirement on obesity and related health behaviors. Kämpfen and Maurer (2016),
using data from the U.S., found that retirement leads to an increase in physical activity.
On the other hand, Godard (2016), using data from many European countries, found that
retirement leads to weight gain for men retiring from physically demanding jobs and those
who were already at risk of obesity, but not for women. Neither of these studies looks atchanges in diet. The impact of retirement on diet quality (among many other outcomes)
was analyzed by Eibich (2015), using data from Germany. He measured diet quality using
a self-reported survey question of whether the respondent follows a health-conscious dietand did not find that retirement has any significant effect on that measure.3 All thesestudies used similar identification strategies to the current paper—early and full retirement
ages as instruments for retirement, and either an instrumental variables estimation strategy(Kämpfen and Maurer, 2016; Godard, 2016) or regression discontinuity design (Eibich, 2015).
The current paper extends these results by analyzing the impact of retirement on diet using
detailed data on purchases.
The paper also relates to the studies on the impact of the opportunity cost of time on
health behaviors. Most closely related is the literature studying the impact of unemployment
on health behaviors.4 Ruhm (2000, 2005) found that recessions and higher unemployment
2

Studies on the impact of retirement on health and health behaviors include Coe and Zamarro (2011);
Insler (2014); Eibich (2015); Celidoni, Dal Bianco, and Weber (2017); Heller-Sahlgren (2017); Mazzonna andPeracchi (2017); Fitzpatrick and Moore (2018); Müller and Shaikh (2018); Shai (2018).
3
There is literature motivated by the permanent income hypothesis analyzing the impact of retirement
on food consumption (most notably, Aguiar and Hurst (2005)). The main findings are that expenditures
decrease, eating out decreases (especially in fast-food restaurants), but quantity and diet quality does not. In
contrast to these studies, I concentrate on the impact on the healthiness of diet.
4
Other studies have found that an increase in the opportunity cost of time affects negatively health3

rates are associated with increases in physical exercise, decreases in obesity, and improvements
in diet. The current paper analyzes how the healthiness of diet is affected by retirement,
which generates an alternative shock to the opportunity cost of time. My findings support
the evidence from time use literature that retirement leads to significant increases in timespent on shopping and cooking (Aguiar and Hurst, 2005; Stancanelli and Van Soest, 2012).
The current paper provides indirect evidence that retirement leads to more time spent on
grocery shopping and food preparation.
The rest of the paper is organized as follows. Section 2 describes the data. Section
3 describes the empirical strategy and presents the main results and robustness analysis,
restricting attention to single-person households. Section 4 analyzes other changes in shopping
behavior and purchases to understand the possible mechanisms behind the main results.
Section 5 extends the analysis to all households. Section 6 concludes.

2

Data

In the paper, the main data source is the Nielsen Homescan Consumer Panel.5 The panel
is representative of the U.S. population. The households in the panel are asked to scan all
their grocery purchases bought from any outlet for personal in-home consumption. The
dataset includes UPC level information of purchases. In addition to purchases, the dataset
has information on household demographic characteristics, including household composition
and income, and household heads’ employment status and age. The dataset covers thirteen
years, 2004–2016.
In the following subsections, I outline the choices made in the construction of the data
and describe the variables used in the analysis. Further details are provided in appendix A.

2.1

SampleMain sample of single-person households. In the main analysis (sections 3 and 4), I
focus on single-person households. Single-person households provide a clean setting because
the food purchases data is available only at the household-level. Moreover, concentrating on
single-person households avoids modeling how retirement of one household member affects
behaviors and children’s health (Dehejia and Lleras-Muney, 2004; Miller and Urdinola, 2010).
5
The dataset is from The Nielsen Company (US), LLC and marketing databases provided by the Kilts
Center for Marketing Data Center at The University of Chicago Booth School of Business. Information on
the access to the data is available at: http://research.chicagobooth.edu/nielsen.

4the others. In section 5, I repeat the analysis including all households. I focus my attention
on individuals who are 55–74 years old. Most individuals retire while being in the middle ofthat age group.
Out of the 23,376 single-person households aged 55–74, I exclude households who work
part-time (less than 30 hours per week) or move back to employment (about 5,000 households).
This is done to focus on the typical straight path from full-time employment to retirement. I
also drop from the main sample households who don’t regularly report food purchases (asdescribed in appendix A). The final sample consists of 17,005 individuals.
Alternative sample of households of all sizes. In section 5, I extend the analysis to
all households that have at least one household head aged 55–74. The analysis concentrates
on household heads because there is more detailed data about employment for household
heads. There are altogether 80,930 such households. As in the previous sections, I exclude
households where either household head works part-time (less than 30 hours per week) ormoves back to employment (about 26,000 households). I also drop households that don’t
regularly report food purchases. The final sample consists of 52,029 households.

2.2

Main explanatory variable of interest: retirement status

Following much of the previous literature that estimates the impact of retirement, I define
retirement as when an individual does “not work for pay”. There are alternative definitions,for example, that individual has retired when he or she receives a pension (social security
benefits) or states that he or she has retired. These alternative measures are not available in
my dataset. Moreover, defining retirement as not working has the benefit of being comparablewith the previous literature. The caveat of using this definition of retirement is that it cannot
distinguish whether an individual is retired or unemployed or out of the labor force. However,
the main results in the paper are obtained using instrumental variables (IV), which increase
the incentives to retire. The IV method identifies the impact of the move to retirement(or more generally, “not working for pay”) that is generated by the instruments. Figure 1apresents age specific employment rates for the individuals in the main sample.6,76Note that in the main sample (single-person households), there are no large gender differences in
employment rates (see figure B.1a in appendix B). However, in the case of larger households, female
employment rates are slightly lower (figure B.1b in appendix B).
7
Note that for each age group the employment rate is a few percentage points lower than in the U.S.
Current Population Survey. The difference comes mainly from the fact that the main sample in the paper
excludes part-time employed (see figure B.2 in appendix B that shows the difference with part-time employed).

5

Food healthiness normalized index
-.05
0
.05
.1

80
% employed
40
60
20
0

55

60

62

65 66
Age

70

55

(a) Percentage employed

60

62

65 66
Age

70

(b) Food healthiness index

Figure 1: Employment and food healthiness across age
Note: Sample includes single-person households aged 55–74 in the main sample, which is constructed as
described in subsection 2.1.

2.3

Instruments: social security retirement ages

Following the previous literature (e.g. Coe and Zamarro (2011); Kämpfen and Maurer (2016);
Godard (2016); Heller-Sahlgren (2017)), I use eligibility for retirement benefits as instruments
for retirement. Specifically, I use the Social Security retirement age cutoffs of early retirementage (62) and full retirement age (65–66 depending on the year of birth). The early retirement
age is the earliest age when an individual may choose to start to receive retirement benefits.
The early retirement age is set to 62 and has remained constant over time. The full retirement
age is the age when an individual may start to receive the full retirement benefits. The earlier
one starts to receive the benefits, the more the amount is reduced, which gives incentives
to wait. The full retirement age has been increased over time. For anyone born in 1937 or
earlier, the full retirement age equals 65. For those born in 1960 or later, the full retirement
age equals 67. For those born in between these years, the full retirement age increases yearlyby two months, except for those born in 1943–1954, for whom it equals 66. Note that for
the individuals currently retiring, their full retirement age has not yet reached 67. Morespecifically, in the sample, for those who have reached their full retirement age, their full
retirement age is no later than at 66 years of age.

6

2.4

Outcome variables

2.4.1

Main outcome variables

In the main analysis, I measure the healthiness of food purchases using mostly the outcomes
constructed based on expenditure shares of food product groups as defined in the USDA
Thrifty Food Plan. A similar approach of measuring the healthiness of diet has been used in
the related literature (e.g. Volpe, Okrent, and Leibtag (2013); Handbury, Rahkovsky, andSchnell (2015); Oster (2018)). For further details of the construction of the measures see
appendix A.
The first outcome measure is the expenditure share of fruits and vegetables. The second
outcome measure is the expenditure share of healthy foods. Healthy foods include fruits,
vegetables, whole grain products, low-fat dairy products, fish, poultry, eggs, and nuts. The
remaining food categories are classified as unhealthy.
The third outcome measure is a food healthiness index, an expenditure score thatmeasures how much the household’s grocery purchases deviate from the expenditure shares
recommended by the USDA Thrifty Food Plan. The measure was constructed by Volpe,
Okrent, and Leibtag (2013) and has been used in subsequent literature (for example, Handbury,
Rahkovsky, and Schnell (2015)). The index is calculated as an inverse of summed up squared
penalties for purchasing relatively too much unhealthy foods or too little healthy foods.
Specifically, for household i in quarter t the food healthiness index equals:
−1Indexit = X2
1[sitj < sTitjF P ] · sitj − sTitjF P

j∈JH

+

X


TFP 2

1[sitj > sTitjF P ] · sitj − sitj(1)j∈JU H

where sitj is the household’s actual expenditure share of product category j and sTitjF P is thecorresponding expenditure share in the USDA Thrifty Food Plan (TFP); JH denotes the setof product categories that are healthy and JU H those that are unhealthy. I normalize theindex so that it is easier to interpret the estimation results.
Figure 1b presents the average food healthiness normalized index across age. The figure
shows an overall increase in food healthiness and highlights large jumps at ages 62, 65, and 66
when individuals reach their early or full retirement ages. Figure B.3 in appendix B presents
similar graphs for fruits and vegetables and healthy foods expenditure shares.
In addition to expenditure shares, I use the absolute values of quantities and expendituresof fruit and vegetables and healthy and unhealthy foods. I also calculate an analogous foodhealthiness index based on quantities as opposed to expenditures. To measure the quantity7

of food purchases I transform the reported quantitities of purchased food products to ounces.
For some products, for example, eggs and some fruits and vegetables, the dataset does notinclude information about their weight. In the case of these products, I make an assumption
about the typical weight of the product.
For all the outcome measures I use information only on products with UPCs (barcodes).
In the Nielsen panel, only a subset of households is asked to record products without UPCs.
These products without UPCs are non-packaged items from categories such as fruit, vegetables,
meats, and baked goods. To take advantage of a bigger sample of households and makepurchases of all households comparable, I don’t analyze products without UPCs. Because
fresh fruit and vegetables are sometimes sold unpackaged without UPCs, the constructedmeasures of fruit and vegetable purchases provide lower bounds on all fruit and vegetablepurchases.
Table 1 presents descriptive statistics of the main sample of single-person households. For
the sample of all households, descriptive statistics are presented in table B.1 in appendix B.
2.4.2

Other characteristics of shopping behavior

To analyze other changes in behavior when the household retires (in section 4), I constructthree types of measues. These characterize shopping frequency, and purchases of fresh versus
storable products and prepared versus unprepared foods.
First, for each household and quarter I calculate various measures of shopping frequency.
The first measure is the average number of days per week when the household was shoppingat any type of store. The second measure is the average number of days per week shoppingin grocery and discount stores. The third measure is the average number of days per weekbuying fresh fruits and vegetables.
Second, for each household I calculate quarterly purchased quantity (in ounces) and
expenditures of fresh versus frozen or canned vegetables. From these measures I exclude
potatoes because there is limited variation in whether potatoes are bought fresh, frozen, or
canned, and I would like to avoid the results being driven by only potatoes. Note that as
above, products without UPCs are excluded. Again, as fresh vegetables are sometimes sold
unpackaged without UPCs, the constructed measure provides a lower bound on all fresh
vegetables purchases. Note that when analyzing fresh versus frozen or canned vegetablepurchases, the sample is slightly smaller, because I restrict attention to households who arepurchasing vegetables. Table 1 shows that in this data, fresh vegetables account for, on
average, about 40% of all vegetables expenditures and quantity.
8

Third, I classify all food products as either unprepared or prepared. I use the classificationof unprepared foods based on Park and Capps (1997) (or what Okrent and Kumcu (2016)
call basic ingredients). The unprepared foods are raw or minimally processed foods usedin producing a meal or snack that are generally composed of a single ingredient, such asflour, rice, grains, dried beans, milk, cream, butter and margarine, shorterning and oil, eggs,
fresh meat, poultry, seafood, fruits, and vegetables. Since other parts of the analysis focused
specifically on fruits and vegetables, I calculate two measures for unprepared foods—eitherwith or without fruits and vegetables.
Table 1: Summary statistics
Variable

MeanSE
Observations
Panel A: Demographic characteristics
Retired0.56
0.50
262916
Age
62.99
5.65
262916
Income40197.50 26850.96
262916
Female0.71
0.45
262916
College
0.38
0.49
262916
Panel B: Purchases, main variables
Expenditure share of fruits and vegetables
0.13
0.09
262916
Expenditure share of healthy foods
0.26
0.13
262916
Food healthiness normalized index
-0.00
1.00
262916
Expenditure of fruits and vegetables
46.21
43.50
262916
Expenditure of healthy foods
90.57
69.17
262916
Expenditure of unhealthy foods
264.21
159.96262916
Quantity of fruits and vegetables
598.28546.44262916
Quantity of healthy foods
1135.40892.04262916
Quantity of unhealthy foods
2762.85 2139.47
262916
Panel C: Other characteristics of shopping behavior
Shop. freq. at any type of store
1.96
1.11
262916
Shop. freq. at grocery and discount stores
1.32
0.77
262916
Shop. freq. of fresh fruits and vegetables
0.32
0.31
262916
Fresh vegetables expenditure share
0.43
0.34
238506
Fresh vegetables quantity share
0.35
0.33
237844
Unprepared food expenditure share
0.14
0.08
262916... excluding fruits and vegetables
0.09
0.06
262916
Note: A unit of observation is a household-quarter pair. Sample includes single-person households aged 55–74
in the main sample, which is constructed as described in subsection 2.1.

9

2.5

Additional data

To analyze household heterogeneity in section 3.3, I use county level median household income
from the U.S. Census Bureau, Small Area Income and Poverty Estimates (SAIPE) Program
for the year 2012. I divide all households in the sample into two groups based on whether
they live in a county with a higher or lower median income.

3

The impact of retirement on the healthiness of foodpurchases

In this section, first, I describe the main empirical strategy which obtains the instrumental
variables estimator in a panel data model with household fixed effects. I will then present
the main results and robustness analysis. Finally, as an alternative empirical strategy, I will
also present results in the event study framework and conduct placebo tests.

3.1

Main empirical strategy

Using household-level quarterly data I estimate the following panel data regression withhousehold fixed effects and instrumental variables:
Yit = βRetiredit + HouseholdF Ei + Y earQuarterF Et + αXit + εit(2)

The outcome variable (Y ) is a measure of healthiness of food purchases. The coefficient ofinterest is β which measures the impact of retirement (Retired). Regressions include household
fixed effects (HouseholdF E), indicator variables for each time period (Y earQuarterF E),
and time-varying household characterists (X). The indicator variables for each time period areincluded to control for aggregate time trends, macroeconomic shocks, and seasonal changes inpurchases. Household fixed effects are included to control for household-specific time-invariant
characteristics. In the main specification, the time-varying household characteristics include
age and age squared. In robustness analysis, I use alternative sets of time-varying household
characteristics. First, I exclude all time-varying household characteristics, second, in addition
to age and age squared, I also include income. Across all specificiations, standard errors are
clustered at the household-level.
I use instruments to address the concern that retirement could be endogenous to foodpurchases. For instruments I use indicator variables of whether the individual is above the10early and full retirement age, respectively. I allow retirement age thresholds to have a different
impact by gender.

3.2

Main results

Table 2 presents the main results with three different outcome variables and in each case withand without instruments. The outcome variables are the logit transformation of expenditure
shares of fruits and vegetables (columns 1 and 2) and healthy foods (columns 3 and 4), andfood healthiness normalized index (columns 5 and 6). Logit transformation of an expenditure
share, ln(S/(1 − S)), rescales the share S from the (0, 1) interval to the real line.
The parameter estimates indicate that retirement leads to an increase in the expenditure
share of fruits and vegetables (column 2) and healthy foods (column 4) and to an increase in
the food healthiness index (column 6). Across the three outcome variables, the estimates with
instruments indicate a much larger effect than without instruments. First-stage estimates arepresented in table 3.
Table 2: Impact of retirement on the healthiness of food purchases: FE and IV estimates

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
0.083*** 0.676*** 0.057***
0.398***
0.089*** 0.489***(0.020)(0.176)(0.012)(0.107)(0.017)(0.147)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.246-2.246-1.187-1.187-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1–2, the outcome variable is thelogit transformation ln(S/(1 − S)) of fruits and vegetables expenditure share S. Analogously, in columns 3–4,
the outcome variable is the logit transformation of healthy foods expenditure share. In columns 5–6, theoutcome variable is the food healthiness normalized index. Sample includes single-person households aged
55–74 in the main sample, which is constructed as described in subsection 2.1. Standard errors are clusteredby household. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

The magnitudes of the estimated effects of retirement on the healthiness of food purchases
are large. To put the size of the effect into context, suppose we compare the impact of11

retirement to that of having a college education. Individuals with college education purchasehealthier food (see pooled OLS estimates in table B.2 in appendix B). While FE estimatesshow that the effect of retirement is less than half the size of having a college education, theIV estimates show effects that are two to four times the size of having a college education.
Table 3: First-stage regression. Dependent variable: indicator for being retired
RetiredFemale above early retirement age
0.044***(0.007)
Female above full retirement age
0.077***(0.008)
Male above early retirement age
0.053***(0.011)
Male above full retirement age
0.046***(0.012)Age
0.017*(0.009)Age squared-0.000**(0.000)Year-quarter FE
YesHousehold FE
Yes
Kleibergen-Paap rk Wald F-statistic 42.954
Households
17005
Households-quarters
262916
Note: A unit of observation is a household-quarter pair. The table presents estimates from a fixed effects
panel data regression. Dependent variable is an indicator variable for being retired. Female above early
retirement age is an indicator variable for being both female and above the early retirement age (at least 62
years old). Female above full retirement age is an indicator variable for being both female and above the full
retirement age (which depends on the year of birth). Analogous definitions of retirement eligibility cutoffs areused for men. Sample includes single-person households aged 55–74 in the main sample, which is constructed
as described in subsection 2.1. Standard errors are clustered by household. *** Indicates significance at the 1
percent level, ** at 5 percent level, * at 10 percent level.

3.3

Robustness

Robustness of the results is analyzed in table 4 and in tables B.3–B.14 in appendix B. The
robustness analysis addresses the following set of possible concerns: (1) control variables, (2)the set of food categories, (3) the sample of households, and (4) outcome variables.
The first concern is about the household-specific time-varying control variables. I startby analyzing whether the results depend on the specific functional form of how age affects
12

food purchases. In panel A of table B.3, I re-estimate regressions from table 2 excluding
age and age squared. The results remain the same, which provides reassurance that thedependence on age is not driving the estimates. In the main regressions, household income
was not included. Hence, while the level of income is taken into account by households’ fixed
effects, the changes are not accounted for. In general, we expect a change in income to affectfood purchases, but mostly when the change is unexpected. In this sample, the main change
in income happens when a person retires. These changes are not unexpected and hence, most
likely, don’t much affect purchases. Panel B of of table B.3 presents estimates from the same
regressions with logarithm of income included. Results remain the same. The first-stage
estimates for both panel A and B are presented in table B.4.
Table B.5 addresses the concern that the results might be driven by a single food group.
In the main analysis, potatoes and juice are both large product categories which are includedin fruits and vegetables purchases and therefore also in healthy foods purchases. On the other
hand, soda is a large category in the unhealthy food group. To check that the results are notdriven by these single categories, table B.5 re-estimates the regressions in table 2 excluding
potatoes (panel A), juice (panel B), and soda (panel C). Results remain the same.
Table B.6 re-estimates the regressions in table 2 using different samples of households. In
panel A, the sample excludes part-time employed households (working 30–34 hours a week).8
Panel B relaxes the assumption of restricting the sample to those reporting food purchases
regularly. In panel C, the sample is restricted to households who remain in the sample for atleast three years. In all these cases, results remain the same. The first-stage estimates arepresented in table B.7.
The final set of estimates addresses the concern about the specific form of outcome
variables. The main analysis used outcome variables constructed from expenditure shares.
There are benefits of analyzing the shares because the dataset doesn’t include all foodpurchases, specifically, it doesn’t include food consumption at restaurants. While foodpurchases for at home consumption are likely to increase with retirement, category-specificexpenditure shares are more likely to remain the same. However, in the following part, I
analyze alternative outcome variables.
Panel A of table 4 presents estimates from regressions where the outcome variables
are the absolute value of expenditure on fruits and vegetables (columns 1 and 2), healthy
foods (columns 3 and 4), and unhealthy foods (columns 5 and 6). The estimates show that8

As subsection 2.1 describes, households that worked less than 30 hours per week were excluded from themain sample.

13

retirement leads to an increase in the expenditures on fruits and vegetables and healthy
foods, while there is no meaningful increase in the purchases of unhealthy foods. Panel B
presents the estimates from regressions where the outcome variables are quantities. If weare concerned that expenditures reflect changes in prices, e.g. retired people buy cheaper
products, then analyzing quantities alleviates the concern. Therefore, it is reassuring thatthe results with quantity (in panel B) are similar. Table B.8 in appendix A re-estimates theregressions in table 4 using logarithm of expenditures and quantities to make sure that theresults are not driven by large values. The results remain similar.
Table 4: Impact of retirement on food expenditures and quantities
Fruits & vegetables
Healthy foods
Unhealty foods
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Expenditures ($ per quarter)
Retirement
2.446*** 20.893*** 3.913*** 28.913*** -1.052
16.194(0.753)(6.480)(1.193) (10.105) (2.486) (20.295)
Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
46.21446.21490.56890.568264.213 264.213
Households
17005
17005
17005
17005
17005
17005
Households-quarters 262916
262916262916262916262916262916
Panel B: Quantity (ounces per quarter)
Retirement45.271*** 279.221*** 74.212*** 299.660** 89.160*** 335.693(8.904)(81.517) (14.673) (124.446) (32.468) (280.301)
Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
598.276598.276 1135.398 1135.398 2762.853 2762.853
Households
17005
17005
17005
17005
17005
17005
Households-quarters 262916
262916262916262916262916262916
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. Outcome variables are either expenditures
measured in dollars (in panel A) or quantities measured in ounces (panel B). Outcome variables measurepurchases of fruits and vegetables (columns 1 and 2), healthy foods (columns 3 and 4), and unhealthy
foods (columns 5 and 6). Sample includes single-person households aged 55–74 in the main sample, whichis constructed as described in subsection 2.1. Standard errors are clustered by household. *** Indicatessignificance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

The main analysis used logit transformation of the expenditure shares. The benefit of
the transformation is to get outcome variables that are rescaled from the (0, 1) interval to
the real line. But it could be more informative to look at the results with non-transformed
14

expenditure shares. The results from these regressions are presented in Table B.9 and these
are in essence similar to the main results.
The main analysis used a food healthiness index constructed with gender-specific recommended expenditure shares that remained constant across age. For robustness, I recalculate
the index using gender and age-group specific recommended expenditure shares. Table B.10
shows that the effect of retirement is now even larger compared to the estimates from themain specification.
Heterogeneous effects by gender, education, and location. The result that retirement leads to healthier food purchases is common across different demographic groups.
Appendix B presents estimates from the same regression as in table 2, but with samples
separated by gender (table B.11), levels of education (college versus no college in table B.12),
and county level median income (table B.13). In all these groups, retirement leads to healthierfood purchases. Only in the case of food healthiness index in the case of men, the IV estimateof the impact of retirement is not statistically significant. However, the magnitude of theestimated coefficient in the sample of men is very similar to that estimated in the sample ofwomen. Note also that the sample of men is small and the first-stage estimation with thissample is rather weak. First-stage estimates with all the samples are presented in table B.14.

3.4

Event study approach

In this part, I present results from event study regressions. The goal is to estimate the impactof retirement separately for each quarter before and after retiring takes place. I use the same
controls as in regression (2), but I don’t use any instruments. I estimate the following event
study regression:
Yit =

17
X

βq 1[QuarterT oRetirementit = q]

q=−16

+ HouseholdF Ei + Y earQuarterF Et + αXit + εit(3)where 1[QuarterT oRetirement = q] is an indicator variable for a given quarter before andafter retirement, such that, q = 0 is the last quarter of employment. The outcome variable(Y ) is a measure of the healthiness of food purchases, controls include household fixed
effects (HouseholdF E), indicator variables for each time period (Y earQuarterF E), andtime-varying household characterists (X).
15

I estimate the regression using the same main sample that was used above. In this sample,
there are three groups of households: some retire while in the sample, others have retired
before entering the sample, and the third group never retires while in the sample. In theevent study framework, identification of the main parameters of interest β-s comes from thehouseholds who retire while in the sample. But to help to identify time period fixed effects I
keep the other two groups also in the sample. I assign them to be more than four years fromretirement either at quarter q = −16 (if employed) or at quarter q = 17 (if retired). In thisway, these households will never be used to estimate the treatment effects presented on thefollowing figure.
Figure 2a presents point estimates and 90-percent confidence intervals of β-s from regression(3) where the outcome variable is the healthiness index. It shows treatment effects four
years before retirement and four years after. Quarter zero (q = 0) is the base group in theestimation, hence, the treatment effects are measured relative to q = 0. The estimates are
somewhat noisy, but show that the healthiness of purchases is larger after retirement than
before.9 As expected, the estimates, in terms of magnitude, are comparable to the resultsfrom the panel data fixed effects regressions without instruments (column 5 in table 2). The
estimates from similar regressions where outcome variables are the logit transformation of
expenditure share of fruits and vegetables and of healthy foods are presented on figure B.4 inappendix B.
Placebo tests. We may be concerned that there is some mechanical reason we observe this
response. To alleviate the concern, I use the sample of individuals who don’t retire during
their period in the sample (that is, they are either employed or already retired, but theirstatus doesn’t change). For these individuals, I randomly generate a retirement date within
their sample period. Figure 2b presents results from the same event study regression withthe randomly generated retirement dates. It is reassuring to see that the results show noincrease in the healthiness of purchases with “fake” retirement. Figure B.5 in appendix B
presents estimates from similar placebo regressions where outcome variables are the logit
transformation of expenditure share of fruits and vegetables and of healthy foods.
9

Note that in the survey, employment is recorded at yearly frequency, therefore, the exact quarter whenretirement took place isn’t known. The dashed lines on figure 2a mark the interval when retirement musthave taken place.

16

.15
.05

.1

.1

0

0

-.1

-.05

-.1
-.2

-15

-11

-7
-3
1
5
9
Quarters from retirement

13

-15

(a) Event study

-11

-7
-3
1
5
9
Quarters from retirement

13(b) Placebo test

Figure 2: Event study and placebo test: normalized healthiness index
Note: The graphs present point estimates and confidence intervals of the effect of retirement in up to four
years before and after retirement estimated from regression (3). All estimates are relative to the base which isquarter 0. In addition to household fixed effects, all regressions include indicators for a given quarter before
or after retirement, year-quarter dummies, age, and age squared. Dependent variable is the normalized foodhealthiness index. Panel (a) presents estimates from a regression using data of real retirement timing, whilepanel (b) using data of randomly drawn “fake” retirement. On panel (a), the sample includes single-personhouseholds aged 55–74 in the main sample, which is constructed as described in subsection 2.1. On panel(b), the sample excludes households that retired while in the sample. 90% confidence intervals are presented
using standard errors clustered by household.

4

Other changes in behavior when retiring

To shed light on the mechanism of how retirement might lead to an increase in the healthinessof food purchases, in this section I discuss other changes in the shopping behavior andpurchases that are associated with retirement. First, I present evidence of increases in
shopping frequency at retirement. Then I analyze switching from storable to fresh products
and from prepared to unprepared products. All these provide indirect evidence that retirement
leads individuals to spend more time on shopping and food preparation.
Shopping frequency. To be able to eat fresh food, one has to buy it rather often, because
fresh food, especially fresh fruits and vegetables, is not easily storable. In this part, I estimate
the same panel data fixed effects model with and without instruments (regression 2), where
outcome variables are various measures of shopping frequency. The first measure is theaverage number of days per week when the household was shopping at any type of store. The
second measure is the average number of days per week shopping in grocery and discount17

stores. I look specifically at grocery and discount stores, because these stores (as opposed toconvenience stores, drug stores, or warehouse clubs) tend to have a larger selection of freshand healthy foods. The third measure is the average number of days per week buying fresh
fruits and vegetables.
Table 5 shows that retirement leads to an increase in shopping frequency measured by
the average number of days per week when the household visited any type of store (columns
1–2), grocery or discount stores (3–4), and bought fresh fruits and vegetables (columns 5–6).
As expected, retirement increases shopping frequency during weekdays, as shown in table
B.15 in appendix B. The estimates on shopping frequency on one hand support the evidence
from the previous section that retired households simply buy more fresh fruits and vegetables.
On the other hand, the estimates show that retirement leads households to spend more timeon shopping for fresh fruits and vegetables.
Table 5: Impact of retirement on the frequency of shopping trips

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Average number of days per week with shopping trips
Any type
Grocery &Fresh fruitsof storediscount stores& vegetables
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
0.161*** 0.362*** 0.107*** 0.208** 0.029*** 0.132***(0.018)(0.135)(0.013) (0.101) (0.006)(0.047)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1.957
1.957
1.320
1.320
0.321
0.321
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. Outcome variable is the average number ofdays per week when household made purchases in any type of store (columns 1 and 2), grocery or discountstores (column 3 and 4), purchased fresh fruits or vegetables (column 5 and 6). Standard errors are clusteredby household. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

Switching from storable products to fresh products. Motivated by the finding ofincreased shopping frequency, I analyze switching from storable to fresh products. I restrict
attention to vegetables, where fresh products compared to storable ones (frozen or canned)
are more time consuming in the following two ways. First, fresh vegetables compared tofrozen and canned vegetables have a considerably shorter shelf life and hence need to be
18

2
1.5
1
0

.5

Percentage change in quantity

2
0
-2
-4

Estim. change in the share4

2.5

bought more often. Second, fresh vegetables typically require more time for preparation (likewashing, peeling, and cutting).
Figure 3 presents point estimates and 90-percent confidence intervals from panel data
fixed effects regressions with instrumental variables from tables B.16 and B.17 in appendix B.
In figure 3a, the outcome variable is the logit transformation of either expenditure or quantity
share of fresh vegetables in all vegetable purchases. In figure 3b, the outcome variable is thelogarithm of the quantity of either fresh vegetables (green bar) or canned/frozen vegetables(yellow bar). Figure 3 shows that retirement leads to an increase in the share of fresh
vegetables purchases. The figure also shows some evidence of gender differences suggestingthat the effect of switching to fresh vegetables is driven by women.

Expend.Quantity

Expend.Quantity

Expend.Quantity

Fresh Not fresh

Fresh Not fresh

Fresh Not fresh

All

Men

Women

All

Men

Women

(a) Change in the share of fresh vegetables(b) Perc. change in the quantity of vegetables

Figure 3: Estimated change in the purchases of fresh versus frozen and canned vegetables
Note: The graphs present point estimates (bar) and 90% confidence intervals (line) from panel data fixed
effects regressions with instrumental variables from tables B.16 and B.17 in appendix B. In addition tohousehold fixed effects, all regressions include year-quarter dummies, age, and age squared. On figure 3a, theoutcome variable is the logit transformation ln(S/(1 − S)) of either expenditure or quantity share, S, of fresh
vegetables in all vegetable purchases. On figure 3b, the outcome variable is the logarithm of the quantity ofeither fresh vegetables (green bar) or canned/frozen vegetables (yellow bar). 90% confidence intervals arecalculated using standard errors clustered by household.

Switching from prepared to unprepared products. Another group of food products
requiring more preparation time is unprepared products. In this part, I analyze whether
retirement leads to a relative increase in purchases of unprepared food products.
Figure 4 presents point estimates and 90-percent confidence intervals from panel data fixed
effects regressions with instrumental variables from tables B.18 and B.19 in appendix B. In
19

.5
0
-.5

-.5

-1

Estim. change in the share

1

Perc. change in expenditures0.5
1

figure 4a, the outcome variable is the logit transformation of expenditure share of unpreparedfood either with or without fruits and vegetables. In section 2, fruits and vegetables were
included in the list of unprepared food products. But we already saw above that retirement
leads to an increase in purchases of fruits and vegetables. Therefore, it is interesting to test
whether retirement increases unprepared food share even without fruits and vegetables. In
figure 4b, the outcome variable is the logarithm of expenditure on either unprepared (greenbar) or prepared (yellow bar) food products. Figure 4 shows that retirement leads to anincrease in the share of unprepared food purchases. The estimates suggest that the effect ofswitching from prepared to unprepared food is driven by women.

Incl. FV Excl. FV

Incl. FV Excl. FV

Incl. FV Excl. FV

All

Men

Women

Unprep.Prep.
All(a) Change in the share of unprepared food

Unprep.Prep.
Men

Unprep.Prep.
Women(b) Perc. change in food expenditures

Figure 4: Estimated change in the purchases of prepared versus unprepared foodNote: The graphs present point estimates (bar) and 90% confidence intervals (line) from panel data fixed
effects regressions with instrumental variables from tables B.18 and B.19 (panel B and C) in appendix B. In
addition to household fixed effects, all regressions include year-quarter dummies, age, and age squared. On
figure 4a, the outcome variable is the logit transformation ln(S/(1−S)) of expenditure share, S, of unpreparedfood either with or without fruits and vegetables. On figure 4b, the outcome variable is the logarithm of theexpenditure on either unprepared, excluding fruits and vegetables, (green bar) or prepared (yellow bar) foodproducts. 90% confidence intervals are calculated using standard errors clustered by household.

5

Generalizability of the findings to larger households

The analysis of single-person households provides clean results, because the outcome variablesof food purchases are measured only at the household-level and the retirement decision is
not affected by other household members’ behavior. However, it is also interesting to see
whether the results generalize to larger households. In this section, I extend the sample to all20

households that have at least one household head in the age group 55–74.
When analyzing the impact of retirement on purchases in larger households, one has tofirst decide how to define retirement. In my preferred specification, I define retirement as thenumber of household heads being retired. I restrict attention to household heads, because forother members there is less information about their labor market status. In this specification,
the variable Retired takes values 0, 1, or 2. Instruments will be the same four variables as in
the previous sections: indicator variables for male or female household head being above theearly or full retirement age. First-stage estimates are presented in column 1 in table B.20 inappendix B.
Table 6: Impact of retirement on the healthiness of food purchases: all households
Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Retired
0.034*** 0.121*** 0.031***0.077***
0.068*** 0.552***(0.006)(0.033)(0.004)(0.022)(0.007)(0.042)Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
-2.122-2.122-1.161-1.1610.000
0.000
Households
52029
52029
52029
52029
52029
52029
Households-quarters 831768
831768831768831768831768831768
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. Retired takes values 0, 1, or 2 depending
on how many household heads have retired. In addition to household fixed effects, all regressions include
year-quarter dummies, average age and age squared of household heads, indicator for children, and indicatorvariables for each household size (except 2-person household, which is the base group). In columns 1–2,
the outcome variable is the logit transformation ln(S/(1 − S)) of fruits and vegetables expenditure share S.
Analogously, in columns 3–4, the outcome variable is the logit transformation of healthy foods expenditure
share. In columns 5–6, the outcome variable is the food healthiness normalized index. Sample consists ofhouseholds with at least one household head aged 55–74 that belong to the alternative sample of householdsof all sizes, which is constructed as described in subsection 2.1. Standard errors are clustered by household.
*** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

Table 6 presents results from the same regressions as in table 2 re-estimated in the sample
of all households aged 55–74. The results are qualitatively similar—retirement leads tosignificantly healthier food purchases. In the case of the food healthiness index, a household
head retirement increases healthiness of food purchases by about half a standard deviation.
This is similar to the result from only single-person households. In the case of fruits andvegetables’ and healthy foods’ expenditure shares, the effects are smaller in magnitude
21

compared to single-person households. A household head retirement leads to an increase in
the expenditure share of fruits and vegetables that is about the same magnitude as having atleast one household head with a college degree (see table B.21 in appendix B). In the caseof expenditure share of healthy foods, the effect is about half the size of having a college
education.
In appendix B, I repeat the analysis with alternative specifications. Table B.22 presents
results from the same regressions when excluding single-person households from the sample.
The effects are qualitatively similar but smaller in magnitude. Table B.23 presents estimateswith an alternative definition of retirement. Specifically, in households with two household
heads, I define being retired as a single variable taking value 0.5 if only one household headis retired and 1 if both household heads are retired. In households with a single household
head, the variable takes value 1 if the household head has retired and zero otherwise. As
expected, the coefficient estimates are larger in magnitude. First-stage estimates of all these
regressions are presented in table B.20 in appendix B.

6Conclusions

This paper analyzes the impact of retirement on the healthiness of diet. I find that retirement
increases fruit and vegetable purchases and overall healthiness of food purchases. As shoppingand cooking healthy food requires time and retirement generates a large increase in available
time, I speculate that time constraints might play a role in limiting healthy food consumption.
I find evidence that retirement increases time spent on shopping and food preparation.
In this paper, I focused on the impact of retirement on broad categories of food purchases,
like fruits and vegetables and broadly defined healthy foods. The same data and methods
can be used to estimate the impact on purchases of more detailed food groups, calories, andnutrients. It could also be used to further understand the role of local supply conditions(types of stores and available food products) in determining the extent of changes in foodpurchases at retirement.
However, the analysis faces several limitations. First, the dataset does not include
information on food consumption in restaurants. If retirement leads households to eat more athome compared to eating out, the impact on the healthiness of overall food consumption may
be different. Indeed, previous research shows that retirement decreases food consumption atrestaurants, but the effect comes almost exclusively from fast-food restaurants (Aguiar and
Hurst, 2005). This implies that the positive impact of retirement on the healthiness of overall

22

food consumption could be even larger because fast-food tends to be unhealthy. Second, thedataset does not include any health measures, nor information on BMI or obesity. It would
be interesting to see whether changes in diet actually translate into corresponding changes inweight. Finally, it should be noted that the instrumental variables estimates measure thelocal average treatment effect on compliers, those who retire because of Social Security earlyand full retirement ages. The results cannot be extrapolated to others who retire becauseof other reasons. For example, if retirement is triggered by a job loss, then it generates an
unexpected decrease in income, which could limit healthy food consumption.

References
Aguiar, M., and E. Hurst (2005): “Consumption versus Expenditure,” Journal of Political
Economy, 113(5), 919–948.
Bleich, S., D. Cutler, C. Murray, and A. Adams (2008): “Why is the developed world
obese?,” Annual Review of Public Health, 29, 273–295.
Cawley, J. (2015): “An economy of scales: A selective review of obesity’s economic causes,
consequences, and solutions,” Journal of Health Economics, 43, 244–268.
Cawley, J., and C. Meyerhoefer (2012): “The medical care costs of obesity: An
instrumental variables approach,” Journal of Health Economics, 31(1), 219–230.
Celidoni, M., C. Dal Bianco, and G. Weber (2017): “Retirement and cognitive decline.
A longitudinal analysis using SHARE data,” Journal of Health Economics, 56, 113–125.
Coe, N. B., and G. Zamarro (2011): “Retirement effects on health in Europe,” Journal of
Health Economics, 30(1), 77–86.
Cutler, D. M., E. L. Glaeser, and J. M. Shapiro (2003): “Why Have Americans
Become More Obese?,” Journal of Economic Perspectives, 17(3), 93–118.
Dehejia, R., and A. Lleras-Muney (2004): “Booms, Busts, and Babies’ Health,” The
Quarterly Journal of Economics, 119(3), 1091–1130.
Eibich, P. (2015): “Understanding the effect of retirement on health: Mechanisms andheterogeneity,” Journal of Health Economics, 43, 1–12.

23

Fitzpatrick, M. D., and T. J. Moore (2018): “The mortality effects of retirement:
Evidence from Social Security eligibility at age 62,” Journal of Public Economics, 157,
121–137.
Flegal, K. M., D. Kruszon-Moran, M. D. Carroll, C. D. Fryar, and C. L. Ogden(2016): “Trends in Obesity Among Adults in the United States, 2005 to 2014,” JAMA,
315(21), 2284–2291.
French, E. (2005): “The Effects of Health, Wealth, and Wages on Labour Supply andRetirement Behaviour,” The Review of Economic Studies, 72(2), 395–427.
Godard, M. (2016): “Gaining weight through retirement? Results from the SHARE survey,”
Journal of Health Economics, 45, 27–46.
Handbury, J., I. Rahkovsky, and M. Schnell (2015): “Is the Focus on Food Deserts
Fruitless? Retail Access and Food Purchases Across the Socioeconomic Spectrum,” Working
Paper 21126, National Bureau of Economic Research.
Heller-Sahlgren, G. (2017): “Retirement blues,” Journal of Health Economics, 54, 66–78.
Insler, M. (2014): “The Health Consequences of Retirement,” Journal of Human Resources,
49(1), 195–233.
Jabs, J., and C. M. Devine (2006): “Time scarcity and food choices: An overview,”
Appetite, 47(2), 196–204.
Kämpfen, F., and J. Maurer (2016): “Time to burn (calories)? The impact of retirementon physical activity among mature Americans,” Journal of Health Economics, 45, 91–102.
Marshall, G., and T. Pires (2018): “Measuring the Impact of Travel Costs on Grocery
Shopping,” The Economic Journal, forthcoming.
Mazzonna, F., and F. Peracchi (2017): “Unhealthy Retirement?,” Journal of Human
Resources, 52(1), 128–151.
Miller, G., and B. Urdinola (2010): “Cyclicality, Mortality, and the Value of Time: The
Case of Coffee Price Fluctuations and Child Survival in Colombia,” Journal of Political
Economy, 118(1), 113–155.

24

Monsivais, P., A. Aggarwal, and A. Drewnowski (2014): “Time Spent on Home Food
Preparation and Indicators of Healthy Eating,” American Journal of Preventive Medicine,47(6), 796–802.
Müller, T., and M. Shaikh (2018): “Your retirement and my health behavior: Evidence
on retirement externalities from a fuzzy regression discontinuity design,” Journal of Health
Economics, 57, 45–59.
Okrent, A. M., and A. Kumcu (2016): “US Households’ Demand for Convenience Foods,”
Discussion Paper ERR-211, U.S. Department of Agriculture, Economic Research Service.
Oster, E. (2018): “Diabetes and Diet: Purchasing Behavior Change in Response to Health
Information,” American Economic Journal: Applied Economics, forthcoming.
Park, J. L., and O. Capps (1997): “Demand for Prepared Meals by U.S. Households,”
American Journal of Agricultural Economics, 79(3), 814–824.
Ruhm, C. J. (2000): “Are Recessions Good for Your Health?,” The Quarterly Journal of
Economics, 115(2), 617–650.
(2005): “Healthy living in hard times,” Journal of Health Economics, 24(2), 341–363.
Shai, O. (2018): “Is retirement good for men’s health? Evidence using a change in theretirement age in Israel,” Journal of Health Economics, 57, 15–30.
Stancanelli, E., and A. Van Soest (2012): “Retirement and Home Production: A
Regression Discontinuity Approach,” American Economic Review: Papers and Proceedings,
102(3), 600–605.
Volpe, R., A. Okrent, and E. Leibtag (2013): “The Effect of Supercenter-format Storeson the Healthfulness of Consumers’ Grocery Purchases,” American Journal of Agricultural
Economics, 95(3), 568–589.

25

A

Online Appendix: Dataset construction

In this Appendix, I describe the construction of the dataset from the Nielsen Homescan
consumer panel from years 2004–2016.
Aggregation of purchases to food product categories. First, I aggregate food products into 52 product categories used by USDA Quarterly Food at Home Price Database(QFAHPD) based on Todd, Mancino, Leibtag, and Tripodo (2010). Second, I further aggregate these categories into 24 broader product categories in the USDA Thrifty Food Plan
(TFP) using the mapping in Volpe and Okrent (2012). I exclude one category – water – fromall the analysis.
Defining healthy and unhealthy food categories. I classify the TFP product categories
into healthy or unhealthy following Volpe, Okrent, and Leibtag (2013) and Handbury,
Rahkovsky, and Schnell (2015). Specifically, in the only two product categories, where theirclassifications differ, I follow Handbury, Rahkovsky, and Schnell (2015) and classify cheeseand meat categories as unhealthy.
Food healthiness index. To construct the food healthiness index defined by equation 1
in section 2, I use recommended individual expenditure shares from the USDA Thrifty Food
Plan in Carlson, Lino, Juan, Hanson, and Basiotis (2007). The recommended individual
expenditure shares are gender and age-group specific.
For multi-person households, I calculate the recommended household-level expenditureP
shares as a weighted average of the individual shares: sTi F P = k wk sTk F P . For each household
member k, the weight wk is based on the OECD equivalence scale:
wAdult =wChild =1+(nAdults −1)×0.5
nAdults

1 + (nAdults − 1) × 0.5 + nChildren × 0.3
0.3
1 + (nAdults − 1) × 0.5 + nChildren × 0.3

(4)

(5)

In the main analysis, for single-person households I use gender-specific recommended
expenditure shares that are kept constant across age. Specifically, for everyone in the sample(individuals aged 55–74), I use shares recommended for the age group 55–70 in order to avoid
generating a discrete jump at age 71. In the robustness analysis, I present estimates where

26the food healthiness index is recalculated using gender and age-group specific recommended
shares. The results are very similar.
Sample of households. I restrict attention to households with food purchases and with atleast one household head aged 55–74 in a given year. I exclude households where a household
head works less than 30 hours per week or moves back to employment. As is common in theliterature using scanner data, I use a minimum purchase requirement. Namely, I exclude
households who don’t report food purchases in at least five of the TFP food groups eachquarter. In robustness analysis, I relax the requirement and results remain similar.
Household demographic characteristics. Information on demographic characteristics,
including employment status, is collected once a year in the Fall prior to the year whenpurchases are recorded. In the quarterly analysis, I use employment status with one quarterlead (one quarter ahead to the quarter of the purchases data). However, the results are
similar if contemporaneous employment status was used.
In the dataset, household income is reported using intervals. In the analysis, I use an
income measure that corresponds to the mid-point of the reported income interval. Except
for the highest income group, for which the mid-point cannot be calculated as no highest
level is reported. For that group I assume that their household annual income equals 115,000
dollars, which is consistent with the current income distribution.
For each household, I construct a variable indicating whether at least one of the household
heads has graduated from college. In the analysis in subsection 3.3, I would like to divide
households into two groups based on graduating from college. In doing that, it makes sense
to keep for each household the education variable constant over time. Therefore, for eachhousehold head, I use the lowest reported education level across years. Note that using thelowest level as opposed to the contemporaneous level does not change much because in theage group 55–74, education level, typically, remains constant over the years.

References
Carlson, A., M. Lino, W. Juan, K. Hanson, and P. P. Basiotis (2007): “Thrifty FoodPlan, 2006,” CNPP-19, United States Department of Agriculture, Center for Nutrition
Policy and Promotion.
Handbury, J., I. Rahkovsky, and M. Schnell (2015): “Is the Focus on Food Deserts
27

Fruitless? Retail Access and Food Purchases Across the Socioeconomic Spectrum,” Working
Paper 21126, National Bureau of Economic Research.
Todd, J. E., L. Mancino, E. S. Leibtag, and C. Tripodo (2010): “Methodology Behind
the Quarterly Food-at-Home Price Database,” Technical Bulletins 97799, United States
Department of Agriculture, Economic Research Service.
Volpe, R., and A. Okrent (2012): “Assessing the Healthfulness of Consumers’ Grocery
Purchases,” EIB-102, U.S. Department of Agriculture, Economic Research Service.
Volpe, R., A. Okrent, and E. Leibtag (2013): “The Effect of Supercenter-format Storeson the Healthfulness of Consumers’ Grocery Purchases,” American Journal of Agricultural
Economics, 95(3), 568–589.

28

Online Appendix: Additional figures and tables

0

0

20

20

% employed40
60% employed
40
60

80

80

B

55

60

62
Men

65 66
Age

70

55

60

Women

62
Men

(a) Single-person households

65 66
Age

70

Women

(b) All

Online Appendix Figure B.1: Percentage employed by age and gender80% employed
40
60
20
0

0

20

% employed
40
60

80

Note: On panel (a), the sample consists of single-person households. On panel (b), the sample consists ofhouseholds of all sizes, the graph describes employment status of household heads.

55

60

62

65 66
Age

70

55

(a) Excluding part-time employed

60

62

65 66
Age

70

(b) Including part-time employed

Online Appendix Figure B.2: Percentage employed across age
Note: On panel (a), sample includes households with at least one of the household head aged 55–74 thatbelong to the alternative sample of households of all sizes, which is constructed as descibed in subsection 2.1.
On panel (b), the same sample is extended to include part-time employed. The graphs describe employment
status of household heads.

29

.28
.24

Share of healthy foods
.25
.26
.27

Share of fruits & vegetables
.125
.13
.135
.14
.12

55

60

62

65 66
Age

70

55

60

(a) Fruits and vegetables

62

65 66
Age

70

(b) Healthy foods

Online Appendix Figure B.3: Expenditure share of fruits and vegetables and healthy foods
across age

-.2

-.1

-.05

-.1

0

0

.05

.1

.1

Note: Sample consists of single-person households.

-15

-11

-7
-3
1
5
9
Quarters from retirement

13

-15

(a) Share of fruits and vegetables

-11

-7
-3
1
5
9
Quarters from retirement

13(b) Share of healthy foods

Online Appendix Figure B.4: Event study
Note: The graphs present point estimates and confidence intervals of the effect of retirement in up to four
years before and after retirement estimated from regression (3). All estimates are relative to the base which isquarter 0. In addition to household fixed effects, all regressions include indicators for a given quarter before
or after retirement, year-quarter dummies, age, and age squared. Dependent variable is logit transformationof the expenditure share of either fruits and vegetables (panel (a)) or healthy foods (panel (b)). The sample
includes single-person households aged 55–74 in the main sample, which is constructed as described insubsection 2.1. 90% confidence intervals are presented using standard errors clustered by household.

30

.05

.2

-.05

0

.1
0

-.1

-.1

-.15

-.2

-15

-11

-7
-3
1
5
9
Quarters from retirement

13

-15

(a) Share of fruits and vegetables

-11

-7
-3
1
5
9
Quarters from retirement

13(b) Share of healthy foods

Online Appendix Figure B.5: Placebo
Note: In the dataset, used in the estimation, retirement dates are randomly drawn. The graphs present
point estimates and confidence intervals of the effect of “fake” retirement in up to four years before and after“fake” retirement estimated from regression (3). All estimates are relative to the base which is quarter 0.
In addition to household fixed effects, all regressions include indicators for a given quarter before or after
retirement, year-quarter dummies, age, and age squared. Dependent variable is logit transformation of theexpenditure share of either fruits and vegetables (panel (a)) or healthy foods (panel (b)). The sample includes
single-person households aged 55–74 in the main sample, except the households that actually retired while inthe sample. 90% confidence intervals are presented using standard errors clustered by household.

31

Online Appendix Table B.1: Summary statistics, all households
Variable

MeanSE
Panel A: Demographic characteristics
Number of household heads retired (0-2)
0.88
0.76
Average age of household heads
61.85
6.48
Income57377.26 32362.74
Female0.90
0.30
College
0.41
0.49
Children
0.07
0.26
Household size
2.04
1.01
Panel B: Purchases, main variables
Expenditure share of fruits and vegetables
0.13
0.08
Expenditure share of healthy foods
0.26
0.11
Food healthiness normalized index
0.00
1.00

Observations
831768831768831768831768831768831768831768831768831768831768

Note: A unit of observation is a household-quarter pair. Sample includes households with at least one of
the household head aged 55–74 that belong to the alternative sample of households of all sizes, which isconstructed as descibed in subsection 2.1.

32

Online Appendix Table B.2: Demographic characteristics and healthiness of food purchases

Log. income
College
FemaleAge
Age squared
Year-quarter FE
Region FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of Food healthinessfruits & vegetables
healthy foods
normalized index
(1)
(2)
(3)
0.151***
0.095***
0.100***(0.012)(0.008)(0.010)0.176***
0.200***
0.178***(0.018)(0.013)(0.018)0.233***
0.116***-0.102***(0.022)(0.016)(0.019)
0.026
0.006
0.002
(0.025)(0.017)(0.021)-0.0000.000
0.000(0.000)(0.000)(0.000)
Yes
Yes
Yes
Yes
Yes
Yes
-2.246-1.187-0.000
17005
17005
17005
262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
pooled OLS model. In column 1, the outcome variable is the logit transformation of fruits and vegetables
expenditure share. In column 2, the outcome variable is the logit transformation of healthy foods expenditure
share. In column 3, the outcome variables is the food healthiness normalized index. Sample includes
single-person households aged 55–74 in the main sample, which is constructed as descibed in subsection 2.1.
Standard errors are clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent
level, * at 10 percent level.

33

Online Appendix Table B.3: Robustness: Impact of retirement on food purchases, alternative
controls

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: No time-varying household-specific controls
0.084*** 0.646*** 0.057***
0.366***
0.089*** 0.415***(0.020)(0.175)(0.012)(0.106)(0.017)(0.144)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.246-2.246-1.187-1.187-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916
Panel B: Age and income included
0.087*** 0.700*** 0.058***
0.411***
0.092*** 0.506***(0.020)(0.182)(0.012)(0.111)(0.017)(0.151)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.246-2.246-1.187-1.187-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects,
all regressions include year-quarter dummies. In panel B, regressions include also age, age squared, andlogarithm of income. In columns 1 and 2, the outcome variable is the logit transformation of fruits andvegetables expenditure share. In columns 3 and 4, the outcome variable is the logit transformation of healthy
foods expenditure share. In columns 5 and 6, the outcome variable is the food healthiness normalized index.
Sample includes single-person households aged 55–74 in the main sample, which is constructed as descibed insubsection 2.1. Standard errors are clustered by household. *** Indicates significance at the 1 percent level,** at 5 percent level, * at 10 percent level.

34

Online Appendix Table B.4: Robustness, first-stage: Alternative controls

Female above early retirement age
Female above full retirement age
Male above early retirement age
Male above full retirement age

Panel A(1)
0.046***(0.007)0.073***(0.008)0.055***(0.011)
0.042***(0.012)Age
Age squared
Log. income
Year-quarter FE
Household FE
Kleibergen-Paap rk Wald F-statistic
Households
Households-quarters

Yes
Yes
44.37517005
262916

Panel B(2)
0.043***(0.006)0.075***(0.007)0.047***(0.010)0.048***(0.011)0.019**(0.009)-0.000**(0.000)-0.161***(0.007)
Yes
Yes
44.51617005
262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a fixed effects
panel data regression. Dependent variable is an indicator variable for being retired. Female above early
retirement age is an indicator variable for being both female and above the early retirement age (at least 62
years old). Female above regular retirement age is an indicator variable for being both female and above theregular retirement age (which depends on the year of birth). Analogous definitions of retirement eligibility
cutoffs are used for men. Sample includes single-person households aged 55–74 in the main sample, whichis constructed as descibed in subsection 2.1. Standard errors are clustered by household. *** Indicatessignificance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

35

Online Appendix Table B.5: Robustness: Impact of retirement on food purchases, dependence
on specific products

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Potatoes excluded
0.079*** 0.611*** 0.058***
0.363***
0.087*** 0.476***(0.022)(0.188)(0.012)(0.111)(0.017)(0.148)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.401-2.401-1.245-1.245-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916
Panel B: Juice excluded
0.109*** 0.706*** 0.060***
0.377***
0.089*** 0.488***(0.021)(0.195)(0.012)(0.110)(0.017)(0.147)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.484-2.484-1.277-1.277-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916
Panel C: Soda excluded
0.079*** 0.674*** 0.051***
0.389***
0.067*** 0.449***(0.020)(0.177)(0.012)(0.107)(0.017)(0.148)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.159-2.159-1.083-1.083-0.000-0.000
17005
17005
17005
17005
17005
17005
262916262916262916262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1 and 2, the outcome variable isthe logit transformation of fruits and vegetables expenditure share. In columns 3 and 4, the outcome variable
is the logit transformation of healthy foods expenditure share. In columns 5 and 6, the outcome variable is thefood healthiness normalized index. Each panel excludes one product category from all purchases: potatoes(panel A), juice (panel B), soda (panel C). Sample includes single-person households aged 55–74 in the main
sample, which is constructed as descibed in subsection 2.1. Standard errors are clustered by household. ***Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

36Online Appendix Table B.6: Robustness: Impact of retirement on food purchases, alternative
samples

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Sample excludes part-time employed
0.083*** 0.701*** 0.055***
0.436***
0.093*** 0.474***(0.021)(0.184)(0.013)(0.112)(0.018)(0.154)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.244-2.244-1.188-1.188-0.000-0.000
16010
16010
16010
16010
16010
16010
244748244748244748244748244748244748Panel B: Sample does not exclude households with few purchases
0.104*** 0.780*** 0.068***
0.426***
0.098*** 0.462***(0.022)(0.201)(0.014)(0.131)(0.017)(0.143)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.343-2.343-1.241-1.241-0.039-0.039
18181
18181
18181
18181
18181
18181
283632283632283632283632283632283632
Panel C: Sample excludes households with less than 3 years
0.080*** 0.737*** 0.057***
0.422***
0.088*** 0.519***(0.020)(0.178)(0.012)(0.108)(0.017)(0.149)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.225-2.225-1.171-1.171
0.012
0.012
9226
9226
9226
9226
9226
9226
219948219948219948219948219948219948

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1 and 2, the outcome variable isthe logit transformation of fruits and vegetables expenditure share. In columns 3 and 4, the outcome variable
is the logit transformation of healthy foods expenditure share. In columns 5 and 6, the outcome variable isthe food healthiness normalized index. The sample includes single-person households aged 55–74 in the main
sample with the following modifications. In panel A, the sample excludes part-time employed households(working 30–34 hours per week). In panel B, the sample relaxes the minimum purchase requirement, suchthat, it includes households who bought in at least two product categories each quarter. In panel C, thesample is restricted to households who remain in the sample for at least three years. Standard errors are
clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent
level.

37

Online Appendix Table B.7: Robustness, first-stage: Alternative samples
Panel A
(1)
Female above early retirement age
0.043***(0.007)
Female above full retirement age
0.077***(0.008)
Male above early retirement age
0.052***(0.011)
Male above full retirement age
0.051***(0.012)Age
0.031***(0.009)Age squared-0.000***(0.000)Year-quarter FE
YesHousehold FE
Yes
Kleibergen-Paap rk Wald F-statistic
41.075
Households
16010
Households-quarters
244748Panel B Panel C(2)(3)
0.044*** 0.044***(0.006)(0.007)0.077*** 0.079***(0.008)(0.008)0.049*** 0.055***(0.010)(0.011)
0.043*** 0.046***(0.011)(0.012)0.015*
0.017*(0.009)(0.009)-0.000** -0.000**(0.000)(0.000)
Yes
Yes
Yes
Yes
45.20841.763
18181
9226
283632219948

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a fixed effects
panel data regression. Dependent variable is an indicator variable for being retired. Female above early
retirement age is an indicator variable for being both female and above the early retirement age (at least 62
years old). Female above regular retirement age is an indicator variable for being both female and above theregular retirement age (which depends on the year of birth). Analogous definitions of retirement eligibility
cutoffs are used for men. The sample includes single-person households aged 55–74 in the main sample withthe following modifications. In column 1, the sample excludes part-time employed households (working 30–34
hours per week). In column 2, the sample relaxes the minimum purchase requirement, such that, it includes
households who bought in at least two product categories each quarter. In column 3, the sample is restricted
to households who remain in the sample for at least three years. Standard errors are clustered by household.
*** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

38Online Appendix Table B.8: Robustness: Impact of retirement on logarithm of food expenditures and quantities

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Fruits & vegetables
Healthy foods
Unhealthy foods
w/o IVIV
w/o IVIV
w/o IV
IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Logarithm of expenditures ($ per quarter)
0.074*** 0.583*** 0.061*** 0.417***
0.006
0.073
(0.016)(0.142)(0.012)(0.109)(0.009) (0.079)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.453
3.453
4.244
4.244
5.406
5.406
17005
17005
17005
17005
17005
17005
262916262916262916262916262916 262916
Panel B: Logarithm of quantity (ounces per quarter)
0.105*** 0.748*** 0.094*** 0.406*** 0.034*** 0.129(0.020)(0.180)(0.014)(0.124)(0.011) (0.094)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5.907
5.907
6.706
6.706
7.680
7.680
17005
17005
17005
17005
17005
17005
262916262916262916262916262916 262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. Outcome variables are the logarithm ofeither expenditures measured in dollars (panel A) or quantities measured in ounces (panel B). Outcome
variables measure purchases of fruits and vegetables (columns 1 and 2), healthy foods (columns 3 and 4), andunhealthy foods (columns 5 and 6). Sample includes single-person households aged 55–74 in the main sample,
which is constructed as descibed in subsection 2.1. Standard errors are clustered by household. *** Indicatessignificance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

39Online Appendix Table B.9: Robustness: Impact of retirement on food purchases, expenditure
sharesRetirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Expenditure share offruits & vegetables
healthy foods
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
0.005*** 0.041*** 0.009*** 0.049***(0.001)(0.012)(0.002)(0.017)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.130
0.130
0.258
0.258
17005
17005
17005
17005
262916262916262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. Outcome variables are the expenditure
shares of fruits and vegetables (columns 1 and 2) or healthy foods (columns 3 and 4). Sample includes
single-person households aged 55–74 in the main sample, which is constructed as descibed in subsection 2.1.
Standard errors are clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent
level, * at 10 percent level.

Online Appendix Table B.10: Robustness: Impact of retirement on food purchases. Dependent
variable: age and gender specific food healthiness normalized index

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Food healthiness normalized index
FE
FE-IV
(1)
(2)
0.119***
2.226***(0.017)(0.221)
Yes
Yes
Yes
Yes
-0.000-0.000
17005
17005
262916262916

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects,
all regressions include year-quarter dummies, age, and age squared. The outcome variable is the foodhealthiness normalized index calculated using gender and age-group specific recommended expenditure shares.
Sample includes single-person households aged 55–74 in the main sample, which is constructed as descibed insubsection 2.1. Standard errors are clustered by household. *** Indicates significance at the 1 percent level,** at 5 percent level, * at 10 percent level.

40

Online Appendix Table B.11: Impact of retirement on food purchases, by gender

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Women
0.077*** 0.501*** 0.062***
0.308***
0.094*** 0.489***(0.022)(0.178)(0.013)(0.111)(0.018)(0.151)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.184-2.184-1.160-1.160-0.036-0.036
12345
12345
12345
12345
12345
12345
187184
187184
187184
187184
187184
187184
Panel B: Men
0.094** 1.392**
0.043*
0.769**
0.075**
0.510(0.042)(0.547)(0.024)(0.312)(0.036)(0.429)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.399-2.399-1.252-1.252
0.090
0.090
4660
4660
4660
4660
4660
4660
75732
75732
75732
75732
75732
75732

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1 and 2, the outcome variable isthe logit transformation of fruits and vegetables expenditure share. In columns 3 and 4, the outcome variable
is the logit transformation of healthy foods expenditure share. In columns 5 and 6, the outcome variable isthe food healthiness normalized index. Sample includes single-person households aged 55–74 in the main
sample, which is constructed as descibed in subsection 2.1. The sample is restricted to either women (panelA) or men (panel B). Standard errors are clustered by household. *** Indicates significance at the 1 percent
level, ** at 5 percent level, * at 10 percent level.

41Online Appendix Table B.12: Impact of retirement on food purchases, by education

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Education: college
0.070** 0.767** 0.043**
0.539***
0.081*** 0.892***(0.031)(0.316)(0.019)(0.193)(0.031)(0.317)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.121-2.121-1.051-1.051
0.140
0.140
6342
6342
6342
6342
6342
6342
101044
101044
101044
101044
101044
101044
Panel B: Education: no college
0.092*** 0.605*** 0.066***
0.323**
0.094*** 0.293*(0.026)(0.210)(0.015)(0.128)(0.019)(0.157)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.324-2.324-1.272-1.272-0.087-0.087
10663
10663
10663
10663
10663
10663
161872161872161872161872161872161872

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1 and 2, the outcome variable isthe logit transformation of fruits and vegetables expenditure share. In columns 3 and 4, the outcome variable
is the logit transformation of healthy foods expenditure share. In columns 5 and 6, the outcome variable isthe food healthiness normalized index. Sample includes single-person households aged 55–74 in the main
sample, which is constructed as descibed in subsection 2.1. The sample is restricted to either those with atleast college education (panel A) or those without (panel B). Standard errors are clustered by household. ***Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

42Online Appendix Table B.13: Impact of retirement on food purchases, by median householdincome of the county

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: County income: above median
0.041
0.452** 0.036**
0.263**
0.081*** 0.395**(0.027)(0.230)(0.016)(0.130)(0.023) (0.181)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.236-2.236-1.152-1.152
0.030
0.030
8495
8495
8495
8495
8495
8495
134880134880134880134880134880134880Panel B: County income: below median
0.134*** 0.888*** 0.082***
0.498***
0.098*** 0.460**(0.029)(0.280)(0.018)(0.175)(0.025) (0.222)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.256-2.256-1.223-1.223-0.032-0.032
8510
8510
8510
8510
8510
8510
128036128036128036128036128036128036

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In addition to household fixed effects, allregressions include year-quarter dummies, age, and age squared. In columns 1 and 2, the outcome variable isthe logit transformation of fruits and vegetables expenditure share. In columns 3 and 4, the outcome variable
is the logit transformation of healthy foods expenditure share. In columns 5 and 6, the outcome variable isthe food healthiness normalized index. Sample includes single-person households aged 55–74 in the main
sample, which is constructed as descibed in subsection 2.1. The sample is restricted to either those fromcounties above median income (panel A) or below median income (panel B). Standard errors are clustered byhousehold. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

43Online Appendix Table B.14: First-stage, by demographic characteristics
GenderCollege
Income
Women
Men
Yes
No
High
Low
(1)
(2)
(3)
(4)
(5)
(6)
Female above early retirement age 0.044***0.041*** 0.046*** 0.044*** 0.041***(0.006)(0.011) (0.008) (0.009) (0.009)
Female above full retirement age
0.078***
0.072*** 0.081*** 0.091*** 0.063***(0.008)(0.014) (0.009) (0.011) (0.011)
Male above early retirement age
0.050*** 0.049*** 0.053*** 0.027** 0.084***(0.010) (0.016) (0.014) (0.013) (0.017)
Male above full retirement age
0.043*** 0.044*** 0.044*** 0.056*** 0.034**(0.011) (0.017) (0.016) (0.017) (0.016)
Age
0.016
0.016
0.012
0.016 0.026** 0.006(0.011) (0.018) (0.016) (0.011) (0.013) (0.013)
Age squared-0.000* -0.000-0.000-0.000 -0.000** -0.000(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Kleibergen-Paap rk Wald F-statistic 68.735 16.729 13.152 30.381 24.195 20.335
Households
12345
4660
6342
10663
8495
8510
Households-quarters
18718475732
101044 161872 134880 128036
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a fixed effects
panel data regression. Dependent variable is an indicator variable for being retired. Female above early
retirement age is an indicator variable for being both female and above the early retirement age (at least 62
years old). Female above regular retirement age is an indicator variable for being both female and above theregular retirement age (which depends on the year of birth). Analogous definitions of retirement eligibility
cutoffs are used for men. Sample includes single-person households aged 55–74 in the main sample, whichis constructed as descibed in subsection 2.1. The sample is restricted to either women (column 1) or men(column 2); those with college degree (column 3) or without (column 4), those from counties with above
median income (column 5) or below median income (column 6). Standard errors are clustered by household.
*** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

44Online Appendix Table B.15: Probability of shopping in grocery or discount stores by day of
week
Mon
(1)

Tue
(2)

Wed
Thu
Fri
Sat
Sun
(3)
(4)
(5)
(6)
(7)
Fixed effects estimates
Retirement
0.033*** 0.052*** 0.050*** 0.046*** 0.032*** -0.053*** -0.052***(0.003) (0.003) (0.004) (0.003) (0.004) (0.004)(0.004)Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
0.173
0.174
0.178
0.174
0.197
0.213
0.210
Households
17005
17005
17005
17005
17005
17005
17005
Households-quarters 262916 262916 262916 262916 262916 262916
262916
Fixed effects, instrumental variables estimates
Retirement
0.049** 0.103*** 0.099*** 0.084*** 0.037-0.041 -0.122***(0.024) (0.026) (0.026) (0.025) (0.029) (0.030)(0.029)Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
0.173
0.174
0.178
0.174
0.197
0.213
0.210
Households
17005
17005
17005
17005
17005
17005
17005
Households-quarters 262916 262916 262916 262916 262916 262916
262916
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with instrumental variables (panel B) or without (panel A). In addition to household
fixed effects, all regressions include year-quarter dummies, age, and age squared. Outcome variable is theprobability of household purchasing in grocery or discount stores in a given day of week from Monday (column
1) to Sunday (column 7). For each day of week and household-quarter pair, the probability is calculated as
the number of days with shopping trips divided by the total number days that quarter. Sample includes
single-person households aged 55–74 in the main sample, which is constructed as descibed in subsection 2.1.
Standard errors are clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent
level, * at 10 percent level.

45Online Appendix Table B.16: Dependent variable: Logit transformation of the share of fresh
vegetables in all vegetables purchases

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

All
Men
Women
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Expenditure share of fresh vegetables
0.019
1.215*
0.116
0.701-0.028 1.263*(0.077) (0.697) (0.173) (1.986) (0.082) (0.727)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-0.991 -0.991 -1.436 -1.436 -0.823 -0.823
15681
15681
4182
4182
11499
11499
238506 238506 65487
65487 173019 173019
Panel B: Quantity share of fresh vegetables-0.005 1.184*
0.047
0.362
-0.033 1.327*(0.077) (0.699) (0.173) (1.980) (0.082) (0.731)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-1.512 -1.512 -1.891 -1.891 -1.369 -1.369
15634
15634
4168
4168
11466
11466
237844 237844 65280
65280 172564 172564

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel
data fixed effects model either with or without instrumental variables. Dependent variable is the logit
transformation of the share of fresh vegetables in all vegetables purchases, calculated using either expenditures(panel A) or quantities (panel B). In addition to household fixed effects, all regressions include year-quarter
dummies, age, and age squared. Sample includes single-person households aged 55–74 in the main sample,
which is constructed as descibed in subsection 2.1. The sample is restricted in columns 3 and 4, to men, andin columns 5 and 6, to women. Standard errors are clustered by household. *** Indicates significance at the
1 percent level, ** at 5 percent level, * at 10 percent level.

46

Online Appendix Table B.17: Dependent variable: Logarithm of quantity of fresh or
frozen/canned vegetable purchases
All

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Men

Women
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Logarithm of fresh vegetables quantity
0.166*** 0.938*** 0.155**
1.179 0.169*** 0.880***(0.033)(0.287)(0.069) (0.774) (0.036)(0.302)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.010
3.010
2.690
2.690
3.137
3.137
15634
15634
4168
4168
11466
11466
25594825594872764
72764
183184
183184
Panel B: Logarithm of frozen and canned vegetables quantity
0.189*** 0.590** 0.180*** 1.319* 0.194***
0.437(0.032)(0.274)(0.066) (0.753) (0.035)(0.291)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.996
3.996
3.837
3.837
4.058
4.058
15634
15634
4168
4168
11466
11466
25594825594872764
72764
183184
183184

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. Dependent variable is the logarithm of
the quantity (measured in ounces) of fresh vegetables (panel A) or frozen and canned vegetables (panel B).
In addition to household fixed effects, all regressions include year-quarter dummies, age, and age squared.
Sample includes single-person households aged 55–74 in the main sample, which is constructed as descibedin subsection 2.1. The sample is restricted in columns 3 and 4, to men, and in columns 5 and 6, to women.
Standard errors are clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent
level, * at 10 percent level.

47

Online Appendix Table B.18: Dependent variable: Logit transformation of the share of
unprepared food in all grocery purchases
All
FE
(1)
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

0.121***(0.016)Yes
Yes-2.09317005
2629160.167***(0.021)
Yes
Yes-2.71417005
262916

Men
Women
FE-IV
FE
FE-IV
FE
FE-IV
(2)
(3)
(4)
(5)
(6)
Panel A: Share of unprepared food0.401*** 0.113*** 0.280 0.123*** 0.399***(0.146)(0.033) (0.440) (0.017)(0.146)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.093-2.242-2.242-2.033-2.033
17005
4660
4660
12345
12345
26291675732
75732
187184
187184
Panel B: Share of unprepared foodwithout fruits and vegetables
0.348* 0.137*** -0.181 0.180*** 0.432**(0.178)(0.044) (0.532) (0.023)(0.181)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.714-2.825-2.825-2.669-2.669
17005
4660
4660
12345
12345
26291675732
75732
187184
187184

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel
data fixed effects model either with or without instrumental variables. Dependent variable is the logit
transformation of the expenditure share of unprepared food, where fruits and vegetables are either included(panel A) or excluded (panel B). In addition to household fixed effects, all regressions include year-quarter
dummies, age, and age squared. Sample includes single-person households aged 55–74 in the main sample,
which is constructed as descibed in subsection 2.1. The sample is restricted in columns 3 and 4, to men, andin columns 5 and 6, to women. Standard errors are clustered by household. *** Indicates significance at the
1 percent level, ** at 5 percent level, * at 10 percent level.

48

Online Appendix Table B.19: Dependent variable: Logarithm of expenditures on preparedand unprepared foodAll

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters
Retirement
Year-quarter FE
Household FE
Mean dep. var.
Households
Households-quarters

Men
Women
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Log. of unprepared food expenditures
0.111*** 0.453*** 0.070** 0.478 0.129*** 0.445***(0.014)(0.126) (0.028) (0.349) (0.016)(0.132)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.626
3.626
3.545
3.545
3.659
3.659
17005
17005
4660
4660
12345
12345
26291626291675732
75732
187184
187184
Panel B: Log. of unprepared food expenditures
without fruits and vegetables
0.135*** 0.368*** 0.076** 0.239 0.160*** 0.383***(0.015)(0.131) (0.030) (0.347) (0.018)(0.138)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
3.095
3.095
3.069
3.069
3.105
3.105
17005
17005
4660
4660
12345
12345
26291626291675732
75732
187184
187184
Panel C: Log. of prepared food expenditures
0.007
0.130*-0.023 0.321*
0.020*
0.108(0.009)(0.077) (0.017) (0.190) (0.011)(0.085)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5.640
5.640
5.675
5.675
5.625
5.625
17005
17005
4660
4660
12345
12345
26291626291675732
75732
187184
187184

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. Dependent variable is the logarithm ofexpenditures (measured in dollars) of unprepared food (panel A), unprepared food excluding fruits andvegetables (panel B), or prepared food (panel C). In addition to household fixed effects, all regressions include
year-quarter dummies, age, and age squared. Sample includes single-person households aged 55–74 in themain sample, which is constructed as descibed in subsection 2.1. The sample is restricted in columns 3 and
4, to men, and in columns 5 and 6, to women. Standard errors are clustered by household. *** Indicatessignificance at the 1 percent level, ** at 5 percent level, * at 10 percent level.

49Online Appendix Table B.20: First-stage regression, all households. Dependent variable:
RetiredFemale above early retirement age
Female above full retirement age
Male above early retirement age
Male above full retirement age
Age
Age squared
Other controls
Year-quarter FE
Household FE
Kleibergen-Paap rk Wald F-statistic
Households
Households-quarters(1)
0.076***(0.005)0.081***(0.005)0.110***(0.006)
0.117***(0.007)0.034***(0.005)-0.000***(0.000)Yes
Yes
Yes
259.31652029
831768(2)
0.078***(0.006)
0.068***(0.007)0.105***(0.006)0.118***(0.007)0.038***(0.005)-0.000***(0.000)
Yes
Yes
Yes
200.84040234
594376(3)
0.038***(0.003)0.046***(0.004)0.031***(0.004)0.033***(0.004)0.031***(0.003)-0.000***(0.000)
Yes
Yes
Yes
124.64452029
831768

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a fixed effects
panel data regression. In columns 1–2, dependent variable takes values 0, 1, or 2 depending on how many
household heads have retired. In column 3, in the case of households with two household heads, dependent
variable takes value 0.5 if only one household head is retired and 1 if both; in households with a single
household head, it takes value 1 if the household head has retired and zero otherwise. Female above early
retirement age is an indicator variable for being both female and above the early retirement age (at least 62
years old). Female above regular retirement age is an indicator variable for being both female and above theregular retirement age (which depends on the year of birth). Analogous definitions of retirement eligibility
cutoffs are used for men. Age is the average age of household heads and analogously for Age squared. Other
controls include an indicator for children and dummy variables for each household size (base group is 2-personhousehold). Sample consists of households with at least one household head aged 55–74 that belong to thealternative sample of households of all sizes, which is constructed as described in subsection 2.1. Standard
errors are clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at
10 percent level.

50Online Appendix Table B.21: Demographic characteristics and healthiness of food purchases,
all households

Log. income
College
FemaleChildrenAge
Age squared
Year-quarter FE
Region FE
Household size FE
Mean dep. var.
Households
Households-quarters

Logit transformation of expenditure share of Food healthinessfruits & vegetables
healthy foods
normalized index
(1)
(2)
(3)
0.147***
0.109***
0.139***(0.006)(0.004)(0.006)0.148***
0.179***
0.207***(0.008)(0.006)(0.009)0.202***
0.108***-0.097***(0.019)(0.014)(0.018)-0.065***-0.084***-0.048***(0.012)(0.010)(0.015)0.004
0.002
0.089***(0.007)(0.005)(0.008)0.000*
0.000**-0.001***(0.000)(0.000)(0.000)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-2.122-1.161
0.000
52029
52029
52029
831768831768831768

Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
pooled OLS model. In column 1, the outcome variable is the logit transformation of fruits and vegetables
expenditure share. In column 2, the outcome variable is the logit transformation of healthy foods expenditure
share. In column 3, the outcome variables is the food healthiness normalized index. Sample consists ofhouseholds with at least one household head aged 55–74 that belong to the alternative sample of householdsof all sizes, which is constructed as described in subsection 2.1. Age is the average age of household heads
and analogously for Age squared. Standard errors are clustered by household. *** Indicates significance atthe 1 percent level, ** at 5 percent level, * at 10 percent level.

51Online Appendix Table B.22: Impact of retirement on the healthiness of food purchases, allhouseholds except single-person households
Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Retired0.024*** 0.069** 0.024***0.052**
0.053*** 0.381***(0.006)(0.033)(0.004)(0.024)(0.008)(0.044)Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
-2.071-2.071-1.151-1.151
0.095
0.095
Households
40234
40234
40234
40234
40234
40234
Households-quarters 594376
594376594376594376594376594376
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. Retired takes values 0, 1, or 2 depending
on how many household heads have retired. In addition to household fixed effects, all regressions include
year-quarter dummies, average age and age squared of household heads, an indicator for children, andindicator variables for each household size (except 2-person household, which is the base group). In columns 1
and 2, the outcome variable is the logit transformation of fruits and vegetables expenditure share. In columns
3 and 4, the outcome variable is the logit transformation of healthy foods expenditure share. In columns 5
and 6, the outcome variable is the food healthiness normalized index. Sample consists of households with atleast one household head aged 55–74 that belong to the alternative sample of households of all sizes except
single-person households. Standard errors are clustered by household. *** Indicates significance at the 1
percent level, ** at 5 percent level, * at 10 percent level.

52Online Appendix Table B.23: Impact of retirement on the healthiness of food purchases, allhouseholds, alternative definition of retirement
Logit transformation of expenditure share of
Food healthinessfruits & vegetables
healthy foods
normalized index
FE
FE-IV
FE
FE-IV
FE
FE-IV
(1)
(2)
(3)
(4)
(5)
(6)
Retired
0.060*** 0.311*** 0.051***0.203***
0.103*** 1.474***(0.011)(0.084)(0.007)(0.058)(0.011)(0.117)Year-quarter FE
Yes
Yes
Yes
Yes
Yes
Yes
Household FE
Yes
Yes
Yes
Yes
Yes
Yes
Mean dep. var.
-2.122-2.122-1.161-1.1610.000
0.000
Households
52029
52029
52029
52029
52029
52029
Households-quarters 831768
831768831768831768831768831768
Note: A unit of observation is a household-quarter pair. Each column presents estimates from a panel data
fixed effects model either with or without instrumental variables. In households with two household heads,
Retired takes value 0.5 if only one household head is retired and 1 if both. In households with a single
household head, Retired takes value 1 if the household head has retired and zero otherwise. In addition tohousehold fixed effects, all regressions include year-quarter dummies, average age and age squared of household
heads, an indicator for children, and indicator variables for each household size (except 2-person household,which is the base group). In columns 1 and 2, the outcome variable is the logit transformation of fruits andvegetables expenditure share. In columns 3 and 4, the outcome variable is the logit transformation of healthy
foods expenditure share. In columns 5 and 6, the outcome variable is the food healthiness normalized index.
Sample consists of households with at least one household head aged 55–74 that belong to the alternative
sample of households of all sizes, which is constructed as described in subsection 2.1. Standard errors are
clustered by household. *** Indicates significance at the 1 percent level, ** at 5 percent level, * at 10 percent
level.

53